Nonlinear Response Functions of Strongly Correlated Boson Fields Bose-Einstein Condensates

合集下载

常微分方程的英文

常微分方程的英文

常微分方程的英文Ordinary Differential EquationsIntroductionOrdinary Differential Equations (ODEs) are mathematical equations that involve derivatives of unknown functions with respect to a single independent variable. They find application in various scientific disciplines, including physics, engineering, economics, and biology. In this article, we will explore the basics of ODEs and their importance in understanding dynamic systems.ODEs and Their TypesAn ordinary differential equation is typically represented in the form:dy/dx = f(x, y)where y represents the unknown function, x is the independent variable, and f(x, y) is a given function. Depending on the nature of f(x, y), ODEs can be classified into different types.1. Linear ODEs:Linear ODEs have the form:a_n(x) * d^n(y)/dx^n + a_(n-1)(x) * d^(n-1)(y)/dx^(n-1) + ... + a_1(x) * dy/dx + a_0(x) * y = g(x)where a_i(x) and g(x) are known functions. These equations can be solved analytically using various techniques, such as integrating factors and characteristic equations.2. Nonlinear ODEs:Nonlinear ODEs do not satisfy the linearity condition. They are generally more challenging to solve analytically and often require the use of numerical methods. Examples of nonlinear ODEs include the famous Lotka-Volterra equations used to model predator-prey interactions in ecology.3. First-order ODEs:First-order ODEs involve only the first derivative of the unknown function. They can be either linear or nonlinear. Many physical phenomena, such as exponential decay or growth, can be described by first-order ODEs.4. Second-order ODEs:Second-order ODEs involve the second derivative of the unknown function. They often arise in mechanical systems, such as oscillators or pendulums. Solving second-order ODEs requires two initial conditions.Applications of ODEsODEs have wide-ranging applications in different scientific and engineering fields. Here are a few notable examples:1. Physics:ODEs are used to describe the motion of particles, fluid flow, and the behavior of physical systems. For instance, Newton's second law of motion can be formulated as a second-order ODE.2. Engineering:ODEs are crucial in engineering disciplines, such as electrical circuits, control systems, and mechanical vibrations. They allow engineers to model and analyze complex systems and predict their behavior.3. Biology:ODEs play a crucial role in the study of biological dynamics, such as population growth, biochemical reactions, and neural networks. They help understand the behavior and interaction of different components in biological systems.4. Economics:ODEs are utilized in economic models to study issues like market equilibrium, economic growth, and resource allocation. They provide valuable insights into the dynamics of economic systems.Numerical Methods for Solving ODEsAnalytical solutions to ODEs are not always possible or practical. In such cases, numerical methods come to the rescue. Some popular numerical techniques for solving ODEs include:1. Euler's method:Euler's method is a simple numerical algorithm that approximates the solution of an ODE by using forward differencing. Although it may not provide highly accurate results, it gives a reasonable approximation when the step size is sufficiently small.2. Runge-Kutta methods:Runge-Kutta methods are higher-order numerical schemes for solving ODEs. They give more accurate results by taking into account multiple intermediate steps. The most commonly used method is the fourth-order Runge-Kutta (RK4) algorithm.ConclusionOrdinary Differential Equations are a fundamental tool for modeling and analyzing dynamic systems in various scientific and engineering disciplines. They allow us to understand the behavior and predict the evolution of complex systems based on mathematical principles. With the help of analytical and numerical techniques, we can solve and interpret different types of ODEs, contributing to advancements in science and technology.。

数显式温度控制仪

数显式温度控制仪

数显式温度控制仪周鹏电子信息工程9911班摘要:本次设计的数字式温度显示调节仪表以热电阻为输入信号源,通过内部配置的信号预处理与前置放大电路、控制电路、显示电路等来实现对温度的控制与调节。

本设计分析了数显式温度控制仪的原理结构、工作方式,且在设计中使用了op07、ICL7107、MC7805集成芯片和LED数码显示器等元器件,具有线路简单,成本低廉,线性化精度高,理论和实验证明,其非线性误差可控制在0.5%以下。

该温度控制器虽结构简单,但控温精度高,且具备超温保护功能。

关键词:传感、温度补偿放大器线性、温度控制Abstract:This adjuster achieves the temperature measuring and adjustment, which use sensors as the inputted signal source, making use of the signal processing circuit, controlling circuit and LED circuit arranged inside, etc.Analyzing the configuration and working modes of a digital display temperature control. it used the OP07, ICL7107、MC7805 integrated electric circuit and LED digital monitor etc, It was having the advantages of simple circuit, cheap cost and the high linear accuracy. Proved by theory and experiment, the nonlinear errors can be controlled under 0.5 percent. The configuration of temperature control is simple, but it can control temperature with great accuracy, and with functions as alert and protection after exceed temperature.Key word:sensors、temperature compensation linearity, temperature control.一、前言科学的发展为测量仪器仪表不断提供新原理、新技术及新型元器件,同时随着科研和生产的高速发展,又对测量技术提出更新、更高的要求。

R语言(介绍)

R语言(介绍)
R-语言/软件
• R免费 • R 资源公开(不是黑盒子,也不是吝啬鬼) • R可以在UNIX, Linux, Windows和OS X运行. • R 有优秀的内在帮助系统. • R有优秀的画图功能 • 学生能够轻松地转到商业支持的 S-Plus程序(如果
需要使用商业软件) • R语言有一个强大的,容易学习的语法,有许多内在
Packages (继续)
• MASSMain Package of Venables and Ripley's MASS • methodsFormal Methods and Classes • mgcvGAMs with GCV smoothness estimation and GAMMs by REML/PQL • multtestResampling-based multiple hypothesis testing • nlmeLinear and nonlinear mixed effects models • nnetFeed-forward Neural Networks and Multinomial Log-Linear Models • nortestTests for Normality • outliersTests for outliers • plsPartial Least Squares Regression (PLSR) and Principal Component Regression (PCR) • pls.pcrPLS and PCR functions • rpartRecursive Partitioning • SAGxStatistical Analysis of the GeneChip • smaStatistical Microarray Analysis • spatialFunctions for Kriging and Point Pattern Analysis • splinesRegression Spline Functions and Classes • statsThe R Stats Package • stats4Statistical Functions using S4 Classes • survivalSurvival analysis, including penalised likelihood. • tcltkTcl/Tk Interface • toolsTools for Package Development • utilsThe R Utils Package

二次谐波理论相关

二次谐波理论相关

Engineering the second harmonic generation pattern from coupled gold nanowiresA.Benedetti,M.Centini,*C.Sibilia,and M.BertolottiDipartimento di Energetica,Universitàdi Roma,La Sapienza,Via A.Scarpa16,00161Roma,Italy*Corresponding author:marco.centini@uniroma1.itReceived November2,2009;accepted December16,2009;posted January5,2010(Doc.ID119439);published February4,2010We numerically investigated second harmonic generation from systems composed of two coupled gold nano-wires.The developed method allows one to arbitrarily change the shape of the wire cross section in order to explore the generated and scatteredfield patterns.Our results suggest that the overall second harmonic gen-eration is related to the electromagnetic energy per unit length stored in the gap area between the wires.These geometrical considerations make further optimization possible.As an example we discuss the possibility to select dipolar emission and/or quadrupolar emission patterns.The selection mechanism responsible for this kind of emission can be traced back to the interaction between nonlinear sources with the surface plasmon resonance of the metallic wires.©2010Optical Society of AmericaOCIS codes:160.4330,190.3970,190.4350,290.5850.1.INTRODUCTIONThe second order nonlinear response fromflat metal screens has been widely investigated,both theoretically and experimentally,from the late1960s–1980s[1–11]. Some of this work was devoted to the study of the second harmonic generation(SHG)in reflection by thin metal layers and/or metallic gratings,with the subsequent re-porting of the enhancement of nonlinear conversion effi-ciencies due to the surface plasmon polariton(SPP)exci-tation at the metal/dielectric interface[12–15].Regarding the SHG in systems composed of many metallic particles and/or percolated systems,several models have been pro-posed that utilize the effective medium approximation and experimental techniques have been developed to characterize the nonlinear optical responses[16–20]. Also,a large number of works have been devoted to the study of materials and single molecule behavior to en-hance and characterize the nonlinear response[21–27].The last few years witnessed renewed interest in the study of nonlinear second order properties of metal/ dielectric composites thanks to the development of nano-technologies and nanoscience.The SHG has been de-tected in metal nanoparticles and the effects of scatterer size on the dipole or multipole contributions have been ex-perimentally investigated[28,29].A strong second har-monic(SH)signal has been found emanating from sharp metal tips[30],thus opening the way to interesting appli-cations for nearfield nonlinear microscopy and spectros-copy.The SHG enhancement from nanoantennas and nanodimers has been observed both in near and farfields [31–34].Other recent works[35,36]deal with the SHG in gold nanowires.In these studies the effect offield polar-ization direction with respect to the direction of the wires is emphasized.Moreover,the SHG from metamaterials composed of split-ring resonators was experimentally re-ported by Klein et al.[37,38].Another experimental pa-per,recently published,reports the SHG from a gold plate having a periodic pattern of rectangular apertures on top [39].In[39]the effect reported is connected to the shape of the apertures and to the propagation constants of the guided electromagnetic(e.m.)modes inside the apertures. This feature has been employed to enhance the nonlinear response of GaAs using it tofill patterned metallic screens with holes,taking advantage of the high trans-mission regime and strongfield localization inside the holes[40].All these aspects,combined with the state of art technology capabilities,make these materials very in-teresting for basic and applied research.From a theoretical point of view,the farfield pattern of the generated SH has been studied in detail for a single metallic cylinder or other two-dimensional(2D)single ob-jects as well as for nanospheres and deformed nano-spheres[41–45].Also,the scattered SH generated by a SPP at a surface nanodefect was theoretically investi-gated[46].Recent works report on numerical results ob-tained by the implementation of afinite difference time domain(FDTD)algorithm[47–50].Reference[48]deals with the SHG from resonant nanoslits on a silver sub-strate,by taking into account the nonlinearity arising in the free electron response from the magnetic Lorentz force.In[47]the quantum jellium model[10]for the non-linear response of a metal was implemented in a FDTD algorithm;numerical results are shown for periodically patterned metallic screens.Limitations of the validity of the approach used in[47]for nanostructured objects are related to the parameterization procedure used for the nonlinear coefficient of the metal,performed assuming an infiniteflat surface.In[49]a semiclassical approach, based on the free electron gas model for the nonlinear op-tical response of a metal,has been adopted to successfully analyze experimental results of[50].Also,in[51]the au-thors showed that for patterned metallic membranes hav-0740-3224/10/030408-9/$15.00©2010Optical Society of Americaing thicknesses of a few tens of nanometers the farfield SH is dominated by dipole contributions of the nonlinear response.In this work we study the enhancement of the SHG due to the interaction between two2D metallic wires with sec-tions of arbitrary shape,focusing on the possibility of tai-loring the emission pattern by properly designing the shape and the position of the wires.Numerical calcula-tions on two different kinds of double-wire systems,rect-angular section(RS)and trapezoidal section(TS)of the wires,are performed.The SHG as a function of the dis-tance(air gap)between the wires is investigated in both the near and farfield regimes.The enhancement of the SH signal is then explained in terms of the pump energy stored inside the gap.Our results reveal a high sensitivity of the emission pattern and the overall power with re-spect to displacements of just a few nanometers.In par-ticular we show that the SHfield with dipole and quad-rupole symmetries can be achieved by changing the geometrical parameters.Although we only consider sys-tems composed of two wires,our approach can be applied for an arbitrary number of objects with different shapes and sizes.Wefinally investigate localization effects of the SHfield at the surface,revealing the existence of hot spots that can be used for nanoillumination and single molecule spectroscopy[52].The paper is divided into four main sections:Following this introduction,in Section2,we describe the used the-oretical model and integration method.In Section3we show the results of our numerical analysis focusing our attention on the different behaviors of emittedfields as functions of the nano-object shape and position.Finally, in Section4the main conclusions and perspectives for fu-ture work are drawn.2.THEORETICAL MODELAt optical frequencies the linear response of a metal is strongly affected by the bound valence electrons.We chose to model the electric permittivity of gold from the far infrared to the ultraviolet range with a Drude–Lorentz model by consideringfive Lorentz-like[53–55] resonators,␧r͑␻͒=␧D͑␻͒+͚k=15G k␻P2␻0,k2−␻2−i␻␥k,␧D͑␻͒=1−G0␻P2␻͑␻+i␥0͒=1−␻02␻͑␻+i␥0͒,͑1͒where␧D is the electric permittivity obtained by consider-ing the Drude model only.␻p is the plasma frequency as-sociated with the total number of electrons per unit vol-ume.G k and␻0,k are,respectively,the oscillator strengths and resonant frequencies and␥k are damping constants related to each oscillator.␻0=ͱG0␻p is the plasma fre-quency associated with intraband transitions(relatedonly to the free electron density)with damping constant ␥0.Indeed we assume that,in the absence of external ex-citation,the free electron density n is constant and equal to its bulk value n0,being␻02=͑e2n0/m␧0͒,where e is the absolute value of the electron charge and m is the effec-tive electron mass.The values of the parameters of the Drude–Lorentz model are reported in[53].We model the optical response of the metal by assum-ing an effective current density,Jជ=JជD+ץPជb.e.ץt,͑2͒where JជD is the current density induced by the e.m.field on the free electrons and Pជb.e.is the polarization vector due to the presence of bound electrons.Each term on the right hand side of Eq.(2)contains both linear and nonlin-ear contributions.At this stage we focus our attention on the nonlinear contributions arising from conduction elec-trons only.We study the SHG process in the undepleted pump ap-proximation from a metallic2D structure that reacts in the presence of a transverse magnetic͑p-͒polarized e.m.field;the system is completely2D.The nonlinear second order response of the conduction electrons is then evalu-ated as in[49]and the dynamic equations for JជD and n areץJជDץt=JជDneٌជ·JជD+͑JជD·ٌជ͒JជDne−␥0JជD+e2mnEជ−e␮0mJជD∧Hជ,͑3aٌ͒ជ·JជD=eץnץt,͑3b͒where␥0is defined in Eqs.(1)and it represents losses due to Coulomb scattering of conduction electrons with the lattice ions.By adopting a perturbative approach as in[8]we move into the frequency domain and write the current density at the SH frequency as a function of the fundamental fre-quency(FF)field by collecting terms up to the second or-der.Thus,the expressions of thefields,the current,and free electron densities areJជ͑rជ,t͒=͚m=1,2Jជm͑rជ͒e−im␻t+c.c.,Eជ͑rជ,t͒=͚m=1,2Eជn͑rជ͒e−im␻t+c.c.,Hជ͑rជ,t͒=͚m=1,2Hជm͑rជ͒e−im␻t+c.c.,n͑rជ,t͒=n0+͚m=1,2n m͑rជ͒e−im␻t+c.c.,͑4͒where the subscript m=1,2refers to the FF͑␻͒and SH ͑2␻͒angular frequencies.Due to the weak nature of the nonlinear interaction,we consider that the dynamics for the FF is completely described by the linear response,Jជ1=−i͑␧r,1−1͒␻␧0Eជ1,͑5͒where␧r,1is calculated by using Eqs.(1).For the SHfield we haveJ 2ជ=−i 2␻␧0␹r ,2E 2ជ−i 2␻␧0␤ͫabE 1ជٌជ·͓␹D ,1E 1ជ͔+b ͑a −1͒␹D ,12͑E 1ជ·ٌជ͒E 1ជ+b␹D ,14ٌជ͓E 1ជ·E 1ជ͔ͬ,͑6͒wherea =␻␻+i ␥0,b =2␻2␻+i ␥0,␤=−e 2m ␻2,͑7͒and ␹r ͑r ជ͒=␧r ͑r ជ͒−1,␹D ͑r ជ͒=␧D ͑r ជ͒−1are the expressionsfor the spatially dependent electric susceptibility accord-ing to the definitions given in Eqs.(1).We note that the coefficients a and b are exactly equal to unity in the case of no losses.The first term on the right hand side of Eq.(6)represents the linear contribution,which depends on both bound and free electrons;the remaining terms are the nonlinear sources.The evaluation of the nonlinear terms of Eq.(6)requires a detailed analysis to separate bulk and surface contributions.In the bulk,the first non-linear term of Eq.(6)vanishes and the other terms,evalu-ated only for the points inside the metallic objects,are re-sponsible for the bulk contribution to the nonlinear current density,J ជNL Bulk =−i 2␻␧0␤␹D ,12b ͫ͑a −1͒͑E 1ជ·ٌជ͒E 1ជ+12ٌជ͓E 1ជ·E 1ជ͔ͬ.͑8͒In order to evaluate surface contributions we assume thatthe transition from metal to surrounding air is achieved by varying the value of the parameter ␻P 2from the value it assumes in the metal to zero in the vacuum.Thus the other parameters,i.e.,damping coefficients and electron effective mass,are considered as constant coefficients.Following the procedure outlined in [56],modified and adapted to our case (losses and linear contribution of bound electrons),we evaluate the nonlinear surface cur-rents acting as sources for the SH field,X ˆ·J ជNL Surface =−i 2␻␧0abE 1,X ͑−͒E 1,Y ͑−͒␤͑1−␧D ,1͒␦͑Y ͒,Y ˆ·J ជNL Surface =−i 2␻␧0ab ͓E 1,Y͑−͔͒2␤4͓͑1−␧D ,1͒͑␧r ,1+3͔͒␦͑Y ͒.͑9͒Calling r ជЈthe vectors defining the coordinates of theboundaries,X ˆ͑r Јជ͒and Yˆ͑r Јជ͒are the local unit vectors tan-gential and normal (outgoing)to the surface,respectively.In order to provide a comparison with previous results wechoose to evaluate the continuous function ␧r ,1͑r Јជ͒E 1,Y ͑r Јជ͒below the surface as ␧r ,1E 1,Y ͑−͒͑r Јជ͒.Equations (9)are equivalent to the results of [56]with the exceptions of themultiplicative coefficients a and b which account forlosses;the presence of ␧D ,1represents the response of con-ducting electrons only and ␧r ,1accounts for the total lin-ear response of both bound and free electrons.Even though we do not use it in the calculations pre-sented in this work,we add the expression for the surface nonlinear terms evaluated at the interface metal/dielectric,when a dielectric ␧B is considered,X ˆ·J ជNL Surface =−i 2␻␧0abE 1,X ͑−͒E 1,Y͑−͒␤␧B͓␧r ,1͑␧B −1͒+␧B ͑1−␧D ,1͔͒␦͑Y ͒,Y ˆ·J ជNL Surface =−i 2␻␧0ab ͓E 1,Y ͑−͔͒2␤4␧B2͓3␧r ,12͑␧B −1͒+␧B ͓␧r ,1͑␧B −␧D ,1͒+3␧B ͑1−␧D ,1͔͔͒␦͑Y ͒.͑10͒This feature will be used in the future to analyze realstructures composed of metal particles on top of a dielec-tric substrate,for example.Adding the expressions of Eqs.(8)and (9)we finally write the current density for the field at the SH frequency,J 2ជ=−i ͑␧r ,2−1͒2␻␧0E 2ជ+J ជNL Bulk +J ជNLSurface .͑11͒The generated field pattern can be calculated by insertingEq.(11)into the equation for the generated SH electric field,ٌͩជ∧ٌជ∧−͑2␻͒2c2I ញͪE ជ2͑r ជ͒=i ͑2␻͒␮0J ជ2͑r ជ͒,͑12͒where Iញis the identity matrix.The formal solution is ob-tained by substituting Eq.(11)into Eq.(12),E ជ2͑r ជ͒=͵⌺ЈG ញ2͑r ជ,r Јជ͒ͩ2␻cͪ2͓␧r ,2͑r Јជ͒−1͔E 2ជ͑r Јជ͒d ⌺Ј+i 2␻␮0͵⌺ЈG ញ2͑r ជ,r Јជ͒J ជNL Bulk ͑r Јជ͒d ⌺Ј+i 2␻␮0͵⌺ЈG ញ2͑r ជ,r Јជ͒J ជNLSurface ͑r Јជ͒d ⌺Ј,͑13͒where the Green’s tensor G is defined asG ញ2͑r ជ,r Јជ͒=ͫI ញ+ٌជٌជk 0,22ͬg 2͑r ជ,rЈជ͒,͑14͒with g 2being the 2D Green’s scalar function [57]in thehomogeneous background medium,corresponding to the Bessel function of the first order for the SH frequency.⌺Јis the section area of the 2D metallic scatterers.Effects due to the substrate can be included by calculating the Green’s tensor for a stratified medium as outlined in [58].The numerical integration of Eq.(13)is performed us-ing a procedure detailed in [58,59]and adapted to the case of the SHG as shown in [55].Once the field inside the scatterers is calculated,it is straightforward to calculate the SH field at every other point in space.In particular,itis possible to depict the near field as well as the far field pattern.Then we numerically evaluate the nonlinear scattering cross section (NSCS)Q ͑␻͒as defined in [41],Q ͑2␻͒=␴P ͑2␻͒sc ͓P ͑␻͒inc ͔2=͵2␲q ͑␪͉2␻͒d ␪,͑15͒where q ͑␪͉2␻͒is the differential scattering cross section.Here we consider P ͑␻͒inc as the power flow per unit length (watt/meter)of the FF field across the segment ␴as shown in Fig.1.P ͑2␻͒sc is the generated SH power flow per unit length calculated across a circumference of ra-dius R ӷ␭.As already mentioned,Eq.(3a)is formally identical to Eq.(7)of [49],as we began from the same model for the nonlinear response of the metal.However,there are some differences between the two models that can be summa-rized as follows:(i)we study the phenomena in the fre-quency domain,describing the SHG from a monochro-matic pump excitation.(ii)We analytically evaluate the surface contribution by considering a hard interface(abrupt change in the dielectric constant).Using this ap-proach improves the convergence of our numerical algo-rithm.Moreover,the integration method can handle both surface and bulk nonlinear sources thus making it pos-sible to directly compare our results with other models of metal response [4,8,10].(iii)We calculate both far and near field emissions by single or multiple 2D scatterers of arbitrary shapes.The periodicity of the system is not re-quired.Our approach is aimed to the studies of nonlinear microscopy and of nonlinear generation from single nano-sized objects.It may be used,for example,to design and optimize the nonlinear response from single molecules at-tached to metallic nanoantennas for detection and sens-ing applications.In our opinion,the method of [49]ap-pears to be more suitable and efficient for the study of the SHG in periodic metamaterials and sub-wavelength pat-terned screens in the time domain.(iv)We consider the linear response of bound electrons in order to obtain a more realistic value of the linear dielectric constant of the metal both at the FF and SH frequencies.Fig.1.Sketch of the scattering geometry for the FF field.Inset:(up)dimensions of the trapezoid section wires;(down)dimen-sions of the RS wires.102030401020304050012345Gap (nm)r e l a t i v e e n e r g y p e r u n i t l e n g t h i n s i d e t h e g a p r e g i o nN o n l i n e a r s c a t t e r i n g c r o s s s e c t i o n (c m 2/W )Fig.2.Relative energy per unit length inside the gap region incases of TS (solid line)and RS (dashed line)wires.NSCS for the TS (triangles)and RS (squares)wires.Fig.3.(Color online)Modulus of the normalized electric field (a),(c)y -and (b),(d)x -components normalized with respect to the incident field amplitude for gap values of (a),(b)18and (c),(d)38nm.3.NUMERICAL ANALYSISWe consider two different configurations composed of coupled gold wires with (a)RS 300nm ϫ145nm and (b)TS “bowtie antenna type”with B =420nm,d =145nm,and h =300nm (inset of Fig.1).We chose these geom-etries because they both exhibit a resonant behavior when a monochromatic p -polarized (electric field direction along the y axis)FF field with a wavelength of 800nm im-pinges along the horizontal ͑x ͒axis.We point out that the sizes of the objects that we consider have wires dimen-sions ͑h ,B ,d ͒that are not negligible with respect to the in-cident wavelength.As a result we are exploring a different regime with respect to [28,29,51].Our analysis is per-formed by comparing the generated SH fields from the two geometries,relating the enhancement of the emission to the energy confinement of the FF field in the gap re-gion.We will also show that SH patterns are extremely sensitive to the shape of the emitters and the distance be-tween wires.As a first step we study the localization prop-erties for the FF field.We considered a p -polarized plane wave,monochromatic FF electric field of amplitude equal to 100MV/m,traveling from left to right along the x axis.Calculations of the linear FF fields were performed under the Comsol Multiphysics simulation environment.We cal-culated the average energy per unit length in the gap re-gion of area=gd (see Fig.1)as a function of the gap.In order to quantify the localization effect we normalize it with respect to the energy per unit length stored intheFig.4.Differential scattering cross section for the TS wires when different values of the distance between wires ͑g ͒areconsidered.Fig.5.Differential scattering cross section for the RS wires when different values of the distance between wires ͑g ͒areconsidered.Fig. 6.(Color online)z -component of the SH magnetic field (ampere/meter)(snapshots in time)for the (a)TS and (b)RS wires.same area for the same incident field propagating in free space by applying the formulaW =͐gap ͓␧0͉Eជ1͑x ,y ͉͒2+␮0͉H ជ1͑x ,y ͉͒2͔d x d y 2␮0͉H 1,0͉2gd,͑16͒where H 1,0is the amplitude of the incident plane wavemagnetic field,Hជ1͑inc ͒͑x ,y ͒=͑H 1,0e i ͑␻/c ͒x +c.c.͒z ˆ.͑17͒The results are shown in Fig.2.In both cases the maxi-mum energy confinement is observed for a value of thegap of 18nm.We note that the TS wires exhibit a higher value of the relative energy inside the gap region.As an example,in Figs.3(a)–3(d)we depict the behavior of themodulus of the FF electric field y -and x -components nor-malized with respect to the input electric field modulus for the TS and gap values of 18and 38nm.We note the strong confinement inside the gap region.We obtain simi-lar behavior if RS wires are considered.Then we use the linear solutions for the FF fields to calculate the nonlin-ear source terms as described in the previous section.Fi-nally we calculate the generated SH field by the numeri-cal integration of Eq.(13)and the NSCS as defined in Eq.(15)for different values of the air gap between wires.Re-sults are shown in Fig.2.We note that the NSCS follows the FF energy inside the gap behavior for both cases of TS and RS wires.This means that,as expected,most of the SHG is due to FF localization effects in the gap region.TS wires provide better performances with respect to the RS[x10-20cm 2/W][x10-20cm 2/W][x10-20cm 2/W]x (µm)x (µm)x (µm)y (µm )y (µm )y (µm )(a)(b)(c)Fig.7.(Color online)Differential scattering cross section (left column)and magnetic field (ampere/meter)(snapshots in time,right column)calculated for different configurations obtained by varying the value of B (see inset of Fig.1),from (a)45to (b)245and (c)345nm.wires according to the higher values of energy stored in-side the gap.Nevertheless,the contrast between resonant ͑g =18nm ͒and non-resonant emissions is higher for the RS wires.This suggests that further optimizations of the TS scheme are possible (for example,by changing the ra-tio between the major and the minor bases B /d ),in order to increase the SHG efficiency.A deeper look at the SH far field emission pattern re-veals interesting features:Figs.4and 5present the polar plots of the differential nonlinear cross section as a func-tion of the angle of emission.We note that emission pat-terns for the TS and RS wires are very different.Also,sig-nificant differences emerge by varying the size of the gap between wires.In particular,we note that for a gap of 18nm the TS wires exhibit the typical quadrupolar emission related to the quadrupole contribution of the nonlinear re-sponse of the metal.On the other hand,for RS wires the emission pattern is reminiscent of an electric dipole oscil-lating along the x axis.Further details can be obtained by analyzing the magnetic field:In Figs.6(a)and 6(b)we plot the generated SH magnetic field,z -component,for both TS and RS wires with an air gap of 18nm.We note that TS and RS wire emissions correspond to quadrupole and dipole emitters,respectively.Moreover,the RS show aresonant behavior due to the fact that the total length is close to 3/2␭2,with ␭2being the SH wavelength.The modification of the characteristics of emitted radia-tion by multipolar sources close to nanostructured metal surfaces was investigated in [60]where interesting appli-cations to single molecule spectroscopy and imaging are envisioned.Here we stress the point that the source of the SH field inside the gap is coupled to the far field through the wires and,depending on their shapes,the surface plasmon resonances can affect the field emission.For this purpose we performed a set of calculations keeping the values of h =300nm,d =145nm,and g =18nm constants and by varying the value of B .This way we can explore the transition regime from the dipolar emission to the quadrupolar regime.Results are shown in Fig.7.Figures 7(a)–7(c)describe the differential scattering cross section (left side)and the real part of the magnetic field (right side)corresponding to values of B equal to 45,245,and 345nm,respectively.We note that,by increasing the value of B ,the symmetry of the emission pattern gradu-ally shifts from dipolar [Figs.7(a)and 6(b)]to quadrupole-like in intensity but still of dipolar symmetry if we con-sider the field wave fronts [Figs.7(b)and 7(c)]to be purely quadrupolar [Fig.6(a)].Looking at the differential NSCS,we note that the efficiency of the SH generation re-mains of the same order of magnitude.These results il-lustrate that the generation process is mainly driven by the field confinement in the space between the two wires.We note that starting from Fig.7(b),the emission at the four corners of the gap area has a quadrupolar pattern.Nevertheless,the resonant behavior of the coupled wires is responsible for the dipolar emission symmetry.This can be ascertained by analyzing the case of Fig.7(c):The gen-erated field is out of resonance so that the building up of the dipolar mode is inhibited.Finally,the emission pat-tern has the typical quadrupolar symmetry for higher val-ues of B as shown in Fig.6(b).We also plot the modulus of the Poynting vector in the space near the wires for gap values of 18and 38nm for TS [Figs.8(a)and 8(b)]and RS [Figs.9(a)and 9(b)]wires corresponding to the cases discussed in Figs.4and 5.The near field emission patterns show very interesting fea-tures.In particular,for the resonant case ͑gap=18nm ͒,a sub-wavelength hot spot forms.These features could be used for nanoillumination of single dots or for molecule detection and analysis.4.CONCLUSIONSWe numerically studied the second harmonic generation (SHG)from coupled gold nanowires and highlighted the relevance of the distance between wires and the sections of the SH intensity and emission patterns.Our results suggest that the overall SHG is related to the electromag-netic (e.m.)energy per unit length stored in the gap area between the wires,although finer optimizations of the shape and size of the wires need a more accurate investi-gation and goes beyond this work.We also emphasized the effect of the shape of the wire sections and the possi-bility to obtain dipolar and/or quadrupolar emission pat-terns.Different emission symmetry properties are not re-lated to the dipolar and quadrupolar nonlinearresponsesFig.8.(Color online)Modulus of the Poynting vector ͑W/m 2͒outside the TS wires:air gaps of (a)18and (b)38nm.of metals but are determined by the interaction of the nonlinear sources with the plasmon resonance of the me-tallic wires.The numerical method we developed provides an accurate,efficient,and reliable tool for tailoring the emission of the SHG from nanoparticles and nanowires and can be applied to the study of the nonlinear response of a single molecule trapped between the two metallic wires.By introducing a periodic Green’s function formal-ism it is possible to consider periodic arrangements of wires to study nonlinear properties of metamaterials and periodic metallo-dielectric structures.This feature,as well as the extension to a full three-dimensional code,will be the subject for future work.ACKNOWLEDGMENTSWe thank M.Scalora for discussions and comparisons of different numerical tools for the evaluation of the SHG.We also thank rciprete,A.Belardini,and M.Braccini for stimulating and fruitful discussions.REFERENCES1. E.Adler,“Nonlinear optical frequency polarization in a dielectric,”Phys.Rev.134,A728–A733(1964).2.S.Jha,“Theory of optical harmonic generation at a metal surface,”Phys.Rev.140,A2020–A2030(1965).3. F.Brown,R. E.Parks,and A.M.Sleeper,“Nonlinear optical reflection from a metallic boundary,”Phys.Rev.Lett.14,1029–1031(1965).4.N.Bloembergen,R.K.Chang,S.S.Jha,and C.H.Lee,“Optical second-harmonic generation in reflection from media with inversion symmetry,”Phys.Rev.174,813–822(1968).5.N.Bloembergen,R.K.Chang,and C.H.Lee,“Second-harmonic generation of light in reflection from media with inversion symmetry,”Phys.Rev.Lett.16,986–989(1966).6. C.K.Chen,A.R.B.de Castro,and Y.R.Shen,“Coherent second-harmonic generation by counterpropagating surface plasmons,”Opt.Lett.4,393–394(1979).7.G.I.Stegeman,J.J.Burke,and D.G.Hall,“Nonlinear optics of long range surface plasmons,”Appl.Phys.Lett.41,906–908(1982).8.J. E.Sipe and G.I.Stegeman,in Nonlinear Optical Response of Metal Surfaces ,Surface Polaritons,V .M.Agranovich and ls,eds.(North-Holland,1982),pp.661–701.9.M.Corvi and L.W.Schaich,“Hydrodynamic-model calculation of second-harmonic generation at a metal surface,”Phys.Rev.B 33,3688–3695(1986).10. A.Liebsch,Electronic Excitations at Metal Surfaces (Plenum,1997),Chap 5.11.T.F.Heinz,in Second-Order Nonlinear Optical Effects at Surfaces and Interfaces ,Review Chapter in Nonlinear Surface Electromagnetic Phenomena,H.Ponath and G.Stegeman,eds.(Elsevier,1991),p.353.12.J.C.Quail and H.J.Simon,“Second harmonic generation from silver and aluminium films in total internal reflection,”Phys.Rev.B.31,4900–4905(1985).13.H.J.Simon,C.Huang,J.C.Quail,and Z.Chen,“Second-harmonic generation with surface plasmons from a silvered quartz grating,”Phys.Rev.B 38,7408–7414(1988).14.G. A.Farias and A. A.Maradudin,“Second harmonic generation in reflection from a metallic grating,”Phys.Rev.B 30,3002–3015(1984).15.R.Reinisch and M.Nevière,“Electromagnetic theory of diffraction in nonlinear optics and surface-enhanced nonlinear optical effects,”Phys.Rev.B 28,1870–1885(1983).16.K.Li,M.I.Stockman,and D.J.Bergman,“Enhanced second harmonic generation in a self-similar chain of metal nanospheres,”Phys.Rev.B 72,153401(2005).17.S.Ducourtieux,S.Grésillon,A.C.Boccara,J.C.Rivoal,X.Quelin,C.Desmarest,P .Gadenne,V .P .Drachev,W.D.Bragg,V .P .Safonov,V . A.Podolskiy,Z. C.Ying,R.L.Armstrong,and V .M.Shalaev,“Percolation and fractal composites:optical studies,”J.Nonlinear Opt.Phys.Mater.9,105–116(2000).18.V .M.Shalaev,ed.,Optical Properties of Nanostructured Random Media (Springer,2002).19.B.K.Canfield,S.Kujala,K.Jefimovs,J.Turunen,and M.Kauranen,“Linear and nonlinear optical responses influenced by broken symmetry in an array of gold nanoparticles,”Opt.Express 12,5418–5423(2004).20.J.I.Dadap,H.B.de Aguiar,and S.Roke,“Nonlinear light scattering from clusters and single particles,”J.Chem.Phys.130,214710(2009).21.H.E.Katz,G.Scheller,T.M.Putvinski,M.L.Schilling,W.Fig.9.(Color online)Modulus of the Poynting vector ͑W/m 2͒outside the RS wires:air gaps of (a)18and (b)38nm.。

微积分介值定理的英文

微积分介值定理的英文

微积分介值定理的英文The Intermediate Value Theorem in CalculusCalculus, a branch of mathematics that has revolutionized the way we understand the world around us, is a vast and intricate subject that encompasses numerous theorems and principles. One such fundamental theorem is the Intermediate Value Theorem, which plays a crucial role in understanding the behavior of continuous functions.The Intermediate Value Theorem, also known as the Bolzano Theorem, states that if a continuous function takes on two different values, then it must also take on all values in between those two values. In other words, if a function is continuous on a closed interval and takes on two different values at the endpoints of that interval, then it must also take on every value in between those two endpoint values.To understand this theorem more clearly, let's consider a simple example. Imagine a function f(x) that represents the height of a mountain as a function of the distance x from the base. If the function f(x) is continuous and the mountain has a peak, then theIntermediate Value Theorem tells us that the function must take on every height value between the base and the peak.Mathematically, the Intermediate Value Theorem can be stated as follows: Let f(x) be a continuous function on a closed interval [a, b]. If f(a) and f(b) have opposite signs, then there exists a point c in the interval (a, b) such that f(c) = 0.The proof of the Intermediate Value Theorem is based on the properties of continuous functions and the completeness of the real number system. The key idea is that if a function changes sign on a closed interval, then it must pass through the value zero somewhere in that interval.One important application of the Intermediate Value Theorem is in the context of finding roots of equations. If a continuous function f(x) changes sign on a closed interval [a, b], then the Intermediate Value Theorem guarantees that there is at least one root (a value of x where f(x) = 0) within that interval. This is a powerful tool in numerical analysis and the study of nonlinear equations.Another application of the Intermediate Value Theorem is in the study of optimization problems. When maximizing or minimizing a continuous function on a closed interval, the Intermediate Value Theorem can be used to establish the existence of a maximum orminimum value within that interval.The Intermediate Value Theorem is also closely related to the concept of connectedness in topology. If a function is continuous on a closed interval, then the image of that interval under the function is a connected set. This means that the function "connects" the values at the endpoints of the interval, without any "gaps" in between.In addition to its theoretical importance, the Intermediate Value Theorem has practical applications in various fields, such as economics, biology, and physics. For example, in economics, the theorem can be used to show the existence of equilibrium prices in a market, where supply and demand curves intersect.In conclusion, the Intermediate Value Theorem is a fundamental result in calculus that has far-reaching implications in both theory and practice. Its ability to guarantee the existence of values between two extremes has made it an indispensable tool in the study of continuous functions and the analysis of complex systems. Understanding and applying this theorem is a crucial step in mastering the powerful concepts of calculus.。

The Sequential Quadratic Programming Method

The Sequential Quadratic Programming Method

The Sequential Quadratic Programming Method
167
2 ewton Methods and Local Optimality
In this and subsequent sections we trace the development of Newton methods from the simplest case of nonlinear equations, through to the general case of nonlinear programming with equations and inequalities.
x∈IR
subject to ci (x) ≥ 0
i = 1 , 2 , . . . , m.
(1.1)
In this formulation, equation constraints must be encoded as two opposed inequality constraints, that is c(x) = 0 is replaced by c(x) ≥ 0 and −c(x) ≥ 0, which is usually not convenient. Thus in practice a more detailed formulation is appropriate, admitting also equations, linear constraints and simple bounds. One way to do this is to add slack variables to the constraints, which
The Sequential Quadratic Programming Method

Functional-coefficient regression models for nonlinear time series

Functional-coefficient regression models for nonlinear time series

from with
the \curse of dimensionality".
Ui taking values in <k and Xi
tLaketinfgUvai;luXesi;inYi<g1ip=.?T1ypbiecajlolyintklyis
strictly small.
stationary Let E(Y12)
transpose of a matrix or vector. The idea to model time series in such a form is not new; see,
for example, Nicholls and Quinn (1982). In fact, many useful time series models may be viewed
This paper adapts the functional-coe cient modeling technique to analyze nonlinear time series
data. The approach allows appreciable exibility on the structure of tted model without su ering
Ui and Xi consist of some lagged values of Yi. The functional-coe cient regression model has the
form
m(u; x) = Xp aj(u) xj;
(1.2)j=1来自where aj( )'s are measurable functions from <k to <1 and x = (x1; : : :; xp)T with T denoting the

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time seri

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time seri

904N.E.Huang and others10.Discussion98711.Conclusions991References993 A new method for analysing has been devel-oped.The key part of the methodany complicated data set can be decomposed intoof‘intrinsic mode functions’Hilbert trans-This decomposition method is adaptive,and,highly efficient.Sinceapplicable to nonlinear and non-stationary processes.With the Hilbert transform,Examplesthe classical nonlinear equation systems and dataare given to demonstrate the power new method.data are especially interesting,for serve to illustrate the roles thenonlinear and non-stationary effects in the energy–frequency–time distribution.Keywords:non-stationary time series;nonlinear differential equations;frequency–time spectrum;Hilbert spectral analysis;intrinsic time scale;empirical mode decomposition1.Introductionsensed by us;data analysis serves two purposes:determine the parameters needed to construct the necessary model,and to confirm the model we constructed to represent the phe-nomenon.Unfortunately,the data,whether from physical measurements or numerical modelling,most likely will have one or more of the following problems:(a)the total data span is too short;(b)the data are non-stationary;and(c)the data represent nonlinear processes.Although each of the above problems can be real by itself,the first two are related,for a data section shorter than the longest time scale of a sta-tionary process can appear to be non-stationary.Facing such data,we have limited options to use in the analysis.Historically,Fourier spectral analysis has provided a general method for examin-the data analysis has been applied to all kinds of data.Although the Fourier transform is valid under extremely general conditions(see,for example,Titchmarsh1948),there are some crucial restrictions of Proc.R.Soc.Lond.A(1998)Nonlinear and non-stationary time series analysis905the Fourier spectral analysis:the system must be linear;and the data must be strict-ly periodic or stationary;otherwise,the resulting spectrum will make little physicalsense.to the Fourier spectral analysis methods.Therefore,behoves us review the definitions of stationarity here.According to the traditional definition,a time series,X (t ),is stationary in the wide sense,if,for all t ,E (|X (t )2|)<∞,E (X (t))=m,C (X (t 1),X (t 2))=C (X (t 1+τ),X (t 2+τ))=C (t 1−t 2),(1.1)in whichE (·)is the expected value defined as the ensemble average of the quantity,and C (·)is the covariance function.Stationarity in the wide sense is also known as weak stationarity,covariance stationarity or second-order stationarity (see,forexample,Brockwell &Davis 1991).A time series,X (t ),is strictly stationary,if the joint distribution of [X (t 1),X (t 2),...,X (t n )]and [X (t 1+τ),X (t 2+τ),...,X (t n +τ)](1.2)are the same for all t i and τ.Thus,a strictly stationaryprocess with finite second moments is alsoweakly stationary,but the inverse is not true.Both definitions arerigorous but idealized.Other less rigorous definitions have also beenused;for example,that is stationary within a limited timespan,asymptotically stationary is for any random variableis stationary when τin equations (1.1)or (1.2)approaches infinity.In practice,we can only have data for finite time spans;these defini-tions,we haveto makeapproximations.Few of the data sets,from either natural phenomena or artificial sources,can satisfy these definitions.It may be argued thatthe difficulty of invoking stationarity as well as ergodicity is not on principlebut on practicality:we just cannot have enough data to cover all possible points in thephase plane;therefore,most of the cases facing us are transient in nature.This is the reality;we are forced to face it.Fourier spectral analysis also requires linearity.can be approximated by linear systems,the tendency tobe nonlinear whenever their variations become finite Compounding these complications is the imperfection of or numerical schemes;theinteractionsof the imperfect probes even with a perfect linear systemcan make the final data nonlinear.For the above the available data are ally of finite duration,non-stationary and from systems that are frequently nonlinear,either intrinsicallyor through interactions with the imperfect probes or numerical schemes.Under these conditions,Fourier spectral analysis is of limited use.For lack of alternatives,however,Fourier spectral analysis is still used to process such data.The uncritical use of Fourier spectral analysis the insouciant adoption of the stationary and linear assumptions may give cy range.a delta function will giveProc.R.Soc.Lond.A (1998)906N.E.Huang and othersa phase-locked wide white Fourier spectrum.Here,added to the data in the time domain,Constrained bythese spurious harmonics the wide frequency spectrum cannot faithfully represent the true energy density in the frequency space.More seri-ously,the Fourier representation also requires the existence of negative light intensity so that the components can cancel out one another to give thefinal delta function. Thus,the Fourier components might make mathematical sense,but do not really make physical sense at all.Although no physical process can be represented exactly by a delta function,some data such as the near-field strong earthquake records areFourier spectra.Second,tions;wave-profiles.Such deformations,later,are the direct consequence of nonlinear effects.Whenever the form of the data deviates from a pure sine or cosine function,the Fourier spectrum will contain harmonics.As explained above, both non-stationarity and nonlinearity can induce spurious harmonic components that cause energy spreading.The consequence is the misleading energy–frequency distribution forIn this paper,modemode functions The decomposition is based on the direct extraction of theevent on the time the frequency The decomposition be viewed as an expansion of the data in terms of the IMFs.Then,based on and derived from the data,can serve as the basis of that expansion linear or nonlinear as dictated by the data,Most important of all,it is adaptive.As will locality and adaptivity are the necessary conditions for the basis for expanding nonlinear and non-stationary time orthogonality is not a necessary criterionselection for a nonlinearon the physical time scaleslocal energy and the instantaneous frequencyHilbert transform can give us a full energy–frequency–time distribution of the data. Such a representation is designated as the Hilbert spectrum;it would be ideal for nonlinear and non-stationary data analysis.We have obtained good results and new insights by applying the combination of the EMD and Hilbert spectral analysis methods to various data:from the numerical results of the classical nonlinear equation systems to data representing natural phe-nomena.The classical nonlinear systems serve to illustrate the roles played by the nonlinear effects in the energy–frequency–time distribution.With the low degrees of freedom,they can train our eyes for more complicated cases.Some limitations of this method will also be discussed and the conclusions presented.Before introducing the new method,we willfirst review the present available data analysis methods for non-stationary processes.Proc.R.Soc.Lond.A(1998)Nonlinear and non-stationary time series analysis9072.Review of non-stationary data processing methodsWe willfirstgivea brief survey of themethodsstationary data.are limited to linear systems any method is almost strictly determined according to the special field in which the application is made.The available methods are reviewed as follows.(a )The spectrogramnothing but a limited time window-width Fourier spectral analysis.the a distribution.Since it relies on the tradition-al Fourier spectral analysis,one has to assume the data to be piecewise stationary.This assumption is not always justified in non-stationary data.Even if the data are piecewise stationary how can we guarantee that the window size adopted always coincides with the stationary time scales?What can we learn about the variations longer than the local stationary time scale?Will the collection of the locally station-ary pieces constitute some longer period phenomena?Furthermore,there are also practical difficulties in applying the method:in order to localize an event in time,the window width must be narrow,but,on the other hand,the frequency resolu-tion requires longer time series.These conflicting requirements render this method of limited usage.It is,however,extremely easy to implement with the fast Fourier transform;thus,ithas attracted a wide following.Most applications of this methodare for qualitative display of speech pattern analysis (see,for example,Oppenheim &Schafer 1989).(b )The wavelet analysisThe wavelet approach is essentially an adjustable window Fourier spectral analysiswith the following general definition:W (a,b ;X,ψ)=|a |−1/2∞−∞X (t )ψ∗ t −b ad t,(2.1)in whichψ∗(·)is the basic wavelet function that satisfies certain very general condi-tions,a is the dilation factor and b is the translationof theorigin.Although time andfrequency do not appear explicitly in the transformed result,the variable 1/a givesthe frequency scale and b ,the temporal location of an event.An intuitive physical explanation of equation (2.1)is very simple:W (a,b ;X,ψ)is the ‘energy’of X ofscale a at t =b .Because of this basic form of at +b involvedin thetransformation,it is also knownas affinewavelet analysis.For specific applications,the basic wavelet function,ψ∗(·),can be modified according to special needs,but the form has to be given before the analysis.In most common applications,however,the Morlet wavelet is defined as Gaussian enveloped sine and cosine wave groups with 5.5waves (see,for example,Chan 1995).Generally,ψ∗(·)is not orthogonalfordifferent a for continuous wavelets.Although one can make the wavelet orthogonal by selecting a discrete set of a ,thisdiscrete wavelet analysis will miss physical signals having scale different from theselected discrete set of a .Continuous or discrete,the wavelet analysis is basically a linear analysis.A very appealing feature of the wavelet analysis is that it provides aProc.R.Soc.Lond.A (1998)908N.E.Huang and othersuniform resolution for all the scales.Limited by the size of thebasic wavelet function,the downside of the uniform resolution is uniformly poor resolution.Although wavelet analysis has been available only in the last ten years or so,it hasbecome extremelypopular.Indeed,it is very useful in analysing data with gradualfrequency changes.Since it has an analytic form for the result,it has attracted extensive attention of the applied mathematicians.Most of its applications have been in edge detection and image compression.Limited applications have also been made to the time–frequency distribution in time series (see,for example,Farge 1992;Long et al .1993)andtwo-dimensionalimages (Spedding et al .1993).Versatile as the wavelet analysis is,the problem with the most commonly usedMorlet wavelet is its leakage generated by the limited length of the basic wavelet function,whichmakesthe quantitativedefinitionof the energy–frequency–time dis-tribution difficult.Sometimes,the interpretation of the wavelet can also be counter-intuitive.For example,to define a change occurring locally,one must look for theresult in the high-frequencyrange,for the higher the frequency the more localized thebasic wavelet will be.If a local event occurs only in the low-frequency range,one willstill be forced to look for its effects inthe high-frequencyrange.Such interpretationwill be difficultif it is possible at all (see,for example,Huang et al .1996).Another difficulty of the wavelet analysis is its non-adaptive nature.Once the basic waveletis selected,one will have to use it to analyse all the data.Since the most commonlyused Morlet wavelet is Fourier based,it also suffers the many shortcomings of Fouri-er spectral analysis:it can only give a physically meaningful interpretation to linear phenomena;it can resolve the interwave frequency modulation provided the frequen-cy variationis gradual,but it cannot resolve the intrawave frequency modulation because the basic wavelet has a length of 5.5waves.Inspite of all these problems,wavelet analysisisstillthe bestavailable non-stationary data analysis method so far;therefore,we will use it in this paper as a reference to establish the validity and thecalibration of the Hilbert spectrum.(c )The Wigner–Ville distributionThe Wigner–Ville distribution is sometimes alsoreferred toas the Heisenberg wavelet.By definition,it is the Fourier transform of the central covariance function.For any time series,X (t ),we can define the central variance as C c (τ,t )=X (t −12τ)X ∗(t +12τ).(2.2)Then the Wigner–Ville distribution is V (ω,t )=∞−∞C c (τ,t )e −i ωτd τ.(2.3)This transform has been treated extensively by Claasen &Mecklenbr¨a uker (1980a ,b,c )and by Cohen (1995).It has been extremely popular with the electrical engi-neering community.The difficulty with this method is the severe cross terms as indicated by the exis-tence of negativepowerfor some frequency ranges.Although this shortcoming canbe eliminated by using the Kernel method (see,for example,Cohen 1995),the resultis,then,basically that of a windowed Fourier analysis;therefore,itsuffers all thelim-itations of the Fourier analysis.An extension of this method has been made by Yen(1994),who used the Wigner–Ville distribution to define wave packets that reduce Proc.R.Soc.Lond.A (1998)Nonlinear and non-stationary time series analysis909 a complicated data set to afinite number of simple components.This extension is very powerful and can be applied to a variety of problems.The applications to complicated data,however,require a great amount of judgement.(d)Evolutionary spectrumThe evolutionary spectrum wasfirst proposed by Priestley(1965).The basic idea is to extend the classic Fourier spectral analysis to a more generalized basis:from sine or cosine to a family of orthogonal functions{φ(ω,t)}indexed by time,t,and defined for all realω,the frequency.Then,any real random variable,X(t),can beexpressed asX(t)= ∞−∞φ(ω,t)d A(ω,t),(2.4)in which d A(ω,t),the Stieltjes function for the amplitude,is related to the spectrum asE(|d A(ω,t)|2)=dµ(ω,t)=S(ω,t)dω,(2.5) whereµ(ω,t)is the spectrum,and S(ω,t)is the spectral density at a specific time t,also designated as the evolutionary spectrum.If for eachfixedω,φ(ω,t)has a Fourier transformφ(ω,t)=a(ω,t)e iΩ(ω)t,(2.6) then the function of a(ω,t)is the envelope ofφ(ω,t),andΩ(ω)is the frequency.If, further,we can treatΩ(ω)as a single valued function ofω,thenφ(ω,t)=α(ω,t)e iωt.(2.7) Thus,the original data can be expanded in a family of amplitude modulated trigono-metric functions.The evolutionary spectral analysis is very popular in the earthquake communi-ty(see,for example,Liu1970,1971,1973;Lin&Cai1995).The difficulty of its application is tofind a method to define the basis,{φ(ω,t)}.In principle,for this method to work,the basis has to be defined a posteriori.So far,no systematic way has been offered;therefore,constructing an evolutionary spectrum from the given data is impossible.As a result,in the earthquake community,the applications of this method have changed the problem from data analysis to data simulation:an evo-lutionary spectrum will be assumed,then the signal will be reconstituted based on the assumed spectrum.Although there is some general resemblance to the simulated earthquake signal with the real data,it is not the data that generated the spectrum. Consequently,evolutionary spectrum analysis has never been very useful.As will be shown,the EMD can replace the evolutionary spectrum with a truly adaptive representation for the non-stationary processes.(e)The empirical orthogonal function expansion(EOF)The empirical orthogonal function expansion(EOF)is also known as the principal component analysis,or singular value decomposition method.The essence of EOF is briefly summarized as follows:for any real z(x,t),the EOF will reduce it toz(x,t)=n1a k(t)f k(x),(2.8)Proc.R.Soc.Lond.A(1998)910N.E.Huang and othersin whichf j·f k=δjk.(2.9)The orthonormal basis,{f k},is the collection of the empirical eigenfunctions defined byC·f k=λk f k,(2.10)where C is the sum of the inner products of the variable.EOF represents a radical departure from all the above methods,for the expansion basis is derived from the data;therefore,it is a posteriori,and highly efficient.The criticalflaw of EOF is that it only gives a distribution of the variance in the modes defined by{f k},but this distribution by itself does not suggest scales or frequency content of the signal.Although it is tempting to interpret each mode as indepen-dent variations,this interpretation should be viewed with great care,for the EOF decomposition is not unique.A single component out of a non-unique decomposition, even if the basis is orthogonal,does not usually contain physical meaning.Recently, Vautard&Ghil(1989)proposed the singular spectral analysis method,which is the Fourier transform of the EOF.Here again,we have to be sure that each EOF com-ponent is stationary,otherwise the Fourier spectral analysis will make little sense on the EOF components.Unfortunately,there is no guarantee that EOF compo-nents from a nonlinear and non-stationary data set will all be linear and stationary. Consequently,singular spectral analysis is not a real improvement.Because of its adaptive nature,however,the EOF method has been very popular,especially in the oceanography and meteorology communities(see,for example,Simpson1991).(f)Other miscellaneous methodsOther than the above methods,there are also some miscellaneous methods such as least square estimation of the trend,smoothing by moving averaging,and differencing to generate stationary data.Methods like these,though useful,are too specialized to be of general use.They will not be discussed any further here.Additional details can be found in many standard data processing books(see,for example,Brockwell &Davis1991).All the above methods are designed to modify the global representation of the Fourier analysis,but they all failed in one way or the other.Having reviewed the methods,we can summarize the necessary conditions for the basis to represent a nonlinear and non-stationary time series:(a)complete;(b)orthogonal;(c)local;and (d)adaptive.Thefirst condition guarantees the degree of precision of the expansion;the second condition guarantees positivity of energy and avoids leakage.They are the standard requirements for all the linear expansion methods.For nonlinear expansions,the orthogonality condition needs to be modified.The details will be discussed later.But even these basic conditions are not satisfied by some of the above mentioned meth-ods.The additional conditions are particular to the nonlinear and non-stationary data.The requirement for locality is the most crucial for non-stationarity,for in such data there is no time scale;therefore,all events have to be identified by the time of their occurences.Consequently,we require both the amplitude(or energy) and the frequency to be functions of time.The requirement for adaptivity is also crucial for both nonlinear and non-stationary data,for only by adapting to the local variations of the data can the decomposition fully account for the underlying physics Proc.R.Soc.Lond.A(1998)Nonlinear and non-stationary time series analysis911of the processes and not just to fulfil the mathematical requirements for fitting the data.This is especially important for the nonlinear phenomena,for a manifestation of nonlinearity is the ‘harmonic distortion’in the Fourier analysis.The degree of distortion depends on the severity of nonlinearity;therefore,one cannot expect a predetermined basis to fit all the phenomena.An easy way to generate the necessary adaptive basis is to derive the basis from the data.In this paper,we will introduce a general method which requires two steps in analysing the data as follows.The first step is to preprocess the data by the empirical mode decomposition method,with which the data are decomposed into a number of intrinsic mode function components.Thus,we will expand the data in a basis derived from the data.The second step is to apply the Hilbert transform to the decomposed IMFs and construct the energy–frequency–time distribution,designated as the Hilbert spectrum,from which the time localities of events will be preserved.In other words,weneed the instantaneous frequency and energy rather than the global frequency and energy defined by the Fourier spectral analysis.Therefore,before goingany further,we have to clarify the definition of the instantaneous frequency.3.Instantaneous frequencyis to accepting it only for special ‘monocomponent’signals 1992;Cohen 1995).Thereare two basicdifficulties with accepting the idea of an instantaneous fre-quency as follows.The first one arises from the influence of theFourier spectral analysis.In the traditional Fourier analysis,the frequency is defined for thesineor cosine function spanning the whole data length with constant ampli-tude.As an extension of this definition,the instantaneous frequencies also have torelate to either a sine or a cosine function.Thus,we need at least one full oscillationof a sineor a cosine wave to define the local frequency value.According to this logic,nothing full wave will do.Such a definition would not make sense forThe secondarises from the non-unique way in defining the instantaneousfrequency.Nevertheless,this difficulty is no longer serious since the introduction ofthe meanstomakethedata analyticalthrough the Hilbert transform.Difficulties,however,still exist as ‘paradoxes’discussed by Cohen (1995).For an arbitrary timeseries,X (t ),we can always have its Hilbert Transform,Y (t ),as Y (t )=1πP∞−∞X (t )t −t d t,(3.1)where P indicates the Cauchy principal value.This transformexists forallfunctionsof class L p(see,for example,Titchmarsh 1948).With this definition,X (t )and Y (t )form the complex conjugate pair,so we can have an analytic signal,Z (t ),as Z (t )=X (t )+i Y (t )=a (t )e i θ(t ),(3.2)in which a (t )=[X 2(t )+Y 2(t )]1/2,θ(t )=arctanY (t )X (t ).(3.3)Proc.R.Soc.Lond.A (1998)912N.E.Huang andothers Theoretically,there are infinitely many ways of defining the imaginary part,but the Hilbert transform provides a unique way of defining the imaginary part so that the result is ananalyticfunction.A brief tutorial on the Hilbert transform with theemphasis on its physical interpretation can be found in Bendat &Piersol is the bestlocal fitan amplitude and phase varying trigonometric function to X (t ).Even with the Hilbert transform,there is still controversy in defining the instantaneous frequency as ω=d θ(t )d t .(3.4)This leads Cohen (1995)to introduce the term,‘monocomponent function’.In prin-ciple,some limitations on the data are necessary,forthe instantaneous frequencygiven in equation (3.4)is a single value function of time.At any given time,thereis only one frequency value;therefore,it can only represent one component,hence ‘monocomponent’.Unfortunately,no cleardefinition of the ‘monocomponent’signalwas given to judge whether a function is or is not ‘monocomponent’.For lack ofa precise definition,‘narrow band’was adopted a on the data for the instantaneous frequency to make sense (Schwartz et al .1966).There are two definitions for bandwidth.The first one is used in the study of the probability properties of the signalsand waves,wherethe processes are assumed tobe stationary and Gaussian.Then,the bandwidth can be defined in spectral moments The expected number of zero crossings per unit time is given byN 0=1π m 2m 0 1/2,(3.5)while the expected number of extrema per unit time is given byN 1=1π m 4m 2 1/2,(3.6)in which m i is the i th moment of the spectrum.Therefore,the parameter,ν,definedas N 21−N 20=1π2m 4m 0−m 22m 2m 0=1π2ν2,(3.7)offers a standard bandwidth measure (see,for example,Rice 1944a,b ,1945a,b ;Longuet-Higgins 1957).For a narrow band signal ν=0,the expected numbers extrema and zero crossings have to equal.the spectrum,but in a different way.coordinates as z (t )=a (t )e i θ(t ),(3.8)with both a (t )and θ(t )being functions of time.If this function has a spectrum,S (ω),then the mean frequency is given byω = ω|S (ω)|2d ω,(3.9)Proc.R.Soc.Lond.A (1998)Nonlinear and non-stationary time series analysis913which can be expressed in another way asω =z ∗(t )1i dd tz (t )d t=˙θ(t )−i ˙a (t )a (t )a 2(t )d t =˙θ(t )a 2(t )d t.(3.10)Based on this expression,Cohen (1995)suggested that ˙θbe treated as the instanta-neous frequency.With these notations,the bandwidth can be defined asν2=(ω− ω )2 ω 2=1 ω 2(ω− ω )2|S (ω)|2d ω=1 ω 2z ∗(t ) 1i d d t− ω 2z (t )d t =1 ω 2 ˙a 2(t )d t +(˙θ(t )− ω )2a 2(t )d t .(3.11)For a narrow band signal,this value has to be small,then both a and θhave to begradually varying functions.Unfortunately,both equations (3.7)and (3.11)defined the bandwidth in the global sense;they are both overly restrictive and lack preci-sion at the same time.Consequently,the bandwidth limitation on the Hilbert trans-form to give a meaningful instantaneous frequency has never been firmly established.For example,Melville (1983)had faithfully filtered the data within the bandwidth requirement,but he still obtained many non-physical negative frequency values.It should be mentioned here that using filtering to obtain a narrow band signal is unsat-isfactory for another reason:the filtered data have already been contaminated by the spurious harmonics caused by the nonlinearity and non-stationarity as discussed in the introduction.In order to obtain meaningful instantaneous frequency,restrictive conditions have to be imposed on the data as discussed by Gabor (1946),Bedrosian (1963)and,more recently,Boashash (1992):for any function to have a meaningful instantaneous frequency,the real part of its Fourier transform has to have only positive frequency.This restriction can be proven mathematically as shown in Titchmarsh (1948)but it is still global.For data analysis,we have to translate this requirement into physically implementable steps to develop a simple method for applications.For this purpose,we have to modify the restriction condition from a global one to a local one,and the basis has to satisfy the necessary conditions listed in the last section.Let us consider some simple examples to illustrate these restrictions physically,by examining the function,x (t )=sin t.(3.12)Its Hilbert transform is simply cos t .The phase plot of x –y is a simple circle of unit radius as in figure 1a .The phase function is a straight line as shown in figure 1b and the instantaneous frequency,shown in figure 1c ,is a constant as expected.If we move the mean offby an amount α,say,then,x (t )=α+sin t.(3.13)Proc.R.Soc.Lond.A (1998)。

PSO算法及其应用本科论文终稿

PSO算法及其应用本科论文终稿

JIU JIANG UNIVERSITY毕业论文题目PSO算法及其应用英文题目Particle Swarm OptimizationAlgorithm and Its Application 院系信息科学与技术学院专业信息管理与信息系统姓名万冬艳班级学号A073126指导教师邓长寿二○一一年五月摘要粒子群优化是一种新兴的基于群体智能的启发式全局搜索算法,粒子群优化算法通过粒子间的竞争和协作以实现在复杂搜索空间中寻找全局最优点。

它具有易理解、易实现、全局搜索能力强等特点,倍受科学与工程领域的广泛关注,已经成为发展最快的智能优化算法之一。

论文介绍了粒子群优化算法的基本原理,分析了其特点。

论文中围绕粒子群优化算法的原理、特点与应用等方面进行综述。

然后对粒子群算法在无约束非线性函数极值寻优、线性规划、有约束非线性函数求极值等方面进行了简单的应用。

最后对其未来的研究提出了一些建议及研究方向的展望。

关键词:粒子群优化算法,参数,无约束,有约束,非线性函数,线性规划,最优解AbstractParticle swarm optimization is an emerging global based on swarm intelligence heuristic search algorithm, particle swarm optimization algorithm competition and collaboration between particles to achieve in complex search space to find the global optimum. It has easy to understand, easy to achieve, the characteristics of strong global search ability, and has never wide field of science and engineering concern, has become the fastest growing one of the intelligent optimization algorithms. This paper introduces the particle swarm optimization basic principles, and analyzes its features. It around the particle swarm optimization principles, characteristics settings and applications to conduct a thorough review. Then,foucsing on the application of unconstrained and constrainted optimization extreme nonlinear functions and linear programming. Finally, its future researched and prospects are proposed.Key words:Particle Swarm Optimization, Parameter, Unconstrained, Constraints, Nonlinear Functions, Linear Programming, Optimal Solution目录摘要 (I)Abstract (II)1 绪论1.1研究背景和课题意义 (1)1.2国内外粒子群算法的研究现状 (2)1.3应用领域 (3)1.4论文结构 (4)1.5本章小结 (4)2 基本粒子群算法2.1粒子群算法的起源 (5)2.2算法原理 (6)2.3基本粒子群算法流程 (7)2.4特点 (10)2.5本章小结 (10)3 PSO仿真实验3.1 无约束非线性函数的背景信息 (11)3.2 测试无约束非线性函数 (11)3.3 测试并验证有约束的线性函数 (16)3.4 测试有约束的非线性函数 (18)3.5本章小结 (19)4 粒子群群优化算法的改进策略4.1 粒子群初始化 (21)4.2 领域拓扑 (21)4.3 混合策略 (24)4.4本章小结 (26)总结与展望 (27)致谢 (28)参考文献 (29)1 绪论1.1 研究背景和课题意义粒子群优化算法是一种新兴的基于群体智能的启发式全局搜索算法,背景是人工生命,“人工生命”是来研究具有某些生命基本特征的人工系统。

3GPP TS 36.331 V13.2.0 (2016-06)

3GPP TS 36.331 V13.2.0 (2016-06)

3GPP TS 36.331 V13.2.0 (2016-06)Technical Specification3rd Generation Partnership Project;Technical Specification Group Radio Access Network;Evolved Universal Terrestrial Radio Access (E-UTRA);Radio Resource Control (RRC);Protocol specification(Release 13)The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP. The present document has not been subject to any approval process by the 3GPP Organizational Partners and shall not be implemented.This Specification is provided for future development work within 3GPP only. The Organizational Partners accept no liability for any use of this Specification. Specifications and reports for implementation of the 3GPP TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.KeywordsUMTS, radio3GPPPostal address3GPP support office address650 Route des Lucioles - Sophia AntipolisValbonne - FRANCETel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16InternetCopyright NotificationNo part may be reproduced except as authorized by written permission.The copyright and the foregoing restriction extend to reproduction in all media.© 2016, 3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TSDSI, TTA, TTC).All rights reserved.UMTS™ is a Trade Mark of ETSI registered for the benefit of its members3GPP™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational PartnersLTE™ is a Trade Mark of ETSI currently being registered for the benefit of its Members and of the 3GPP Organizational Partners GSM® and the GSM logo are registered and owned by the GSM AssociationBluetooth® is a Trade Mark of the Bluetooth SIG registered for the benefit of its membersContentsForeword (18)1Scope (19)2References (19)3Definitions, symbols and abbreviations (22)3.1Definitions (22)3.2Abbreviations (24)4General (27)4.1Introduction (27)4.2Architecture (28)4.2.1UE states and state transitions including inter RAT (28)4.2.2Signalling radio bearers (29)4.3Services (30)4.3.1Services provided to upper layers (30)4.3.2Services expected from lower layers (30)4.4Functions (30)5Procedures (32)5.1General (32)5.1.1Introduction (32)5.1.2General requirements (32)5.2System information (33)5.2.1Introduction (33)5.2.1.1General (33)5.2.1.2Scheduling (34)5.2.1.2a Scheduling for NB-IoT (34)5.2.1.3System information validity and notification of changes (35)5.2.1.4Indication of ETWS notification (36)5.2.1.5Indication of CMAS notification (37)5.2.1.6Notification of EAB parameters change (37)5.2.1.7Access Barring parameters change in NB-IoT (37)5.2.2System information acquisition (38)5.2.2.1General (38)5.2.2.2Initiation (38)5.2.2.3System information required by the UE (38)5.2.2.4System information acquisition by the UE (39)5.2.2.5Essential system information missing (42)5.2.2.6Actions upon reception of the MasterInformationBlock message (42)5.2.2.7Actions upon reception of the SystemInformationBlockType1 message (42)5.2.2.8Actions upon reception of SystemInformation messages (44)5.2.2.9Actions upon reception of SystemInformationBlockType2 (44)5.2.2.10Actions upon reception of SystemInformationBlockType3 (45)5.2.2.11Actions upon reception of SystemInformationBlockType4 (45)5.2.2.12Actions upon reception of SystemInformationBlockType5 (45)5.2.2.13Actions upon reception of SystemInformationBlockType6 (45)5.2.2.14Actions upon reception of SystemInformationBlockType7 (45)5.2.2.15Actions upon reception of SystemInformationBlockType8 (45)5.2.2.16Actions upon reception of SystemInformationBlockType9 (46)5.2.2.17Actions upon reception of SystemInformationBlockType10 (46)5.2.2.18Actions upon reception of SystemInformationBlockType11 (46)5.2.2.19Actions upon reception of SystemInformationBlockType12 (47)5.2.2.20Actions upon reception of SystemInformationBlockType13 (48)5.2.2.21Actions upon reception of SystemInformationBlockType14 (48)5.2.2.22Actions upon reception of SystemInformationBlockType15 (48)5.2.2.23Actions upon reception of SystemInformationBlockType16 (48)5.2.2.24Actions upon reception of SystemInformationBlockType17 (48)5.2.2.25Actions upon reception of SystemInformationBlockType18 (48)5.2.2.26Actions upon reception of SystemInformationBlockType19 (49)5.2.3Acquisition of an SI message (49)5.2.3a Acquisition of an SI message by BL UE or UE in CE or a NB-IoT UE (50)5.3Connection control (50)5.3.1Introduction (50)5.3.1.1RRC connection control (50)5.3.1.2Security (52)5.3.1.2a RN security (53)5.3.1.3Connected mode mobility (53)5.3.1.4Connection control in NB-IoT (54)5.3.2Paging (55)5.3.2.1General (55)5.3.2.2Initiation (55)5.3.2.3Reception of the Paging message by the UE (55)5.3.3RRC connection establishment (56)5.3.3.1General (56)5.3.3.1a Conditions for establishing RRC Connection for sidelink communication/ discovery (58)5.3.3.2Initiation (59)5.3.3.3Actions related to transmission of RRCConnectionRequest message (63)5.3.3.3a Actions related to transmission of RRCConnectionResumeRequest message (64)5.3.3.4Reception of the RRCConnectionSetup by the UE (64)5.3.3.4a Reception of the RRCConnectionResume by the UE (66)5.3.3.5Cell re-selection while T300, T302, T303, T305, T306, or T308 is running (68)5.3.3.6T300 expiry (68)5.3.3.7T302, T303, T305, T306, or T308 expiry or stop (69)5.3.3.8Reception of the RRCConnectionReject by the UE (70)5.3.3.9Abortion of RRC connection establishment (71)5.3.3.10Handling of SSAC related parameters (71)5.3.3.11Access barring check (72)5.3.3.12EAB check (73)5.3.3.13Access barring check for ACDC (73)5.3.3.14Access Barring check for NB-IoT (74)5.3.4Initial security activation (75)5.3.4.1General (75)5.3.4.2Initiation (76)5.3.4.3Reception of the SecurityModeCommand by the UE (76)5.3.5RRC connection reconfiguration (77)5.3.5.1General (77)5.3.5.2Initiation (77)5.3.5.3Reception of an RRCConnectionReconfiguration not including the mobilityControlInfo by theUE (77)5.3.5.4Reception of an RRCConnectionReconfiguration including the mobilityControlInfo by the UE(handover) (79)5.3.5.5Reconfiguration failure (83)5.3.5.6T304 expiry (handover failure) (83)5.3.5.7Void (84)5.3.5.7a T307 expiry (SCG change failure) (84)5.3.5.8Radio Configuration involving full configuration option (84)5.3.6Counter check (86)5.3.6.1General (86)5.3.6.2Initiation (86)5.3.6.3Reception of the CounterCheck message by the UE (86)5.3.7RRC connection re-establishment (87)5.3.7.1General (87)5.3.7.2Initiation (87)5.3.7.3Actions following cell selection while T311 is running (88)5.3.7.4Actions related to transmission of RRCConnectionReestablishmentRequest message (89)5.3.7.5Reception of the RRCConnectionReestablishment by the UE (89)5.3.7.6T311 expiry (91)5.3.7.7T301 expiry or selected cell no longer suitable (91)5.3.7.8Reception of RRCConnectionReestablishmentReject by the UE (91)5.3.8RRC connection release (92)5.3.8.1General (92)5.3.8.2Initiation (92)5.3.8.3Reception of the RRCConnectionRelease by the UE (92)5.3.8.4T320 expiry (93)5.3.9RRC connection release requested by upper layers (93)5.3.9.1General (93)5.3.9.2Initiation (93)5.3.10Radio resource configuration (93)5.3.10.0General (93)5.3.10.1SRB addition/ modification (94)5.3.10.2DRB release (95)5.3.10.3DRB addition/ modification (95)5.3.10.3a1DC specific DRB addition or reconfiguration (96)5.3.10.3a2LWA specific DRB addition or reconfiguration (98)5.3.10.3a3LWIP specific DRB addition or reconfiguration (98)5.3.10.3a SCell release (99)5.3.10.3b SCell addition/ modification (99)5.3.10.3c PSCell addition or modification (99)5.3.10.4MAC main reconfiguration (99)5.3.10.5Semi-persistent scheduling reconfiguration (100)5.3.10.6Physical channel reconfiguration (100)5.3.10.7Radio Link Failure Timers and Constants reconfiguration (101)5.3.10.8Time domain measurement resource restriction for serving cell (101)5.3.10.9Other configuration (102)5.3.10.10SCG reconfiguration (103)5.3.10.11SCG dedicated resource configuration (104)5.3.10.12Reconfiguration SCG or split DRB by drb-ToAddModList (105)5.3.10.13Neighbour cell information reconfiguration (105)5.3.10.14Void (105)5.3.10.15Sidelink dedicated configuration (105)5.3.10.16T370 expiry (106)5.3.11Radio link failure related actions (107)5.3.11.1Detection of physical layer problems in RRC_CONNECTED (107)5.3.11.2Recovery of physical layer problems (107)5.3.11.3Detection of radio link failure (107)5.3.12UE actions upon leaving RRC_CONNECTED (109)5.3.13UE actions upon PUCCH/ SRS release request (110)5.3.14Proximity indication (110)5.3.14.1General (110)5.3.14.2Initiation (111)5.3.14.3Actions related to transmission of ProximityIndication message (111)5.3.15Void (111)5.4Inter-RAT mobility (111)5.4.1Introduction (111)5.4.2Handover to E-UTRA (112)5.4.2.1General (112)5.4.2.2Initiation (112)5.4.2.3Reception of the RRCConnectionReconfiguration by the UE (112)5.4.2.4Reconfiguration failure (114)5.4.2.5T304 expiry (handover to E-UTRA failure) (114)5.4.3Mobility from E-UTRA (114)5.4.3.1General (114)5.4.3.2Initiation (115)5.4.3.3Reception of the MobilityFromEUTRACommand by the UE (115)5.4.3.4Successful completion of the mobility from E-UTRA (116)5.4.3.5Mobility from E-UTRA failure (117)5.4.4Handover from E-UTRA preparation request (CDMA2000) (117)5.4.4.1General (117)5.4.4.2Initiation (118)5.4.4.3Reception of the HandoverFromEUTRAPreparationRequest by the UE (118)5.4.5UL handover preparation transfer (CDMA2000) (118)5.4.5.1General (118)5.4.5.2Initiation (118)5.4.5.3Actions related to transmission of the ULHandoverPreparationTransfer message (119)5.4.5.4Failure to deliver the ULHandoverPreparationTransfer message (119)5.4.6Inter-RAT cell change order to E-UTRAN (119)5.4.6.1General (119)5.4.6.2Initiation (119)5.4.6.3UE fails to complete an inter-RAT cell change order (119)5.5Measurements (120)5.5.1Introduction (120)5.5.2Measurement configuration (121)5.5.2.1General (121)5.5.2.2Measurement identity removal (122)5.5.2.2a Measurement identity autonomous removal (122)5.5.2.3Measurement identity addition/ modification (123)5.5.2.4Measurement object removal (124)5.5.2.5Measurement object addition/ modification (124)5.5.2.6Reporting configuration removal (126)5.5.2.7Reporting configuration addition/ modification (127)5.5.2.8Quantity configuration (127)5.5.2.9Measurement gap configuration (127)5.5.2.10Discovery signals measurement timing configuration (128)5.5.2.11RSSI measurement timing configuration (128)5.5.3Performing measurements (128)5.5.3.1General (128)5.5.3.2Layer 3 filtering (131)5.5.4Measurement report triggering (131)5.5.4.1General (131)5.5.4.2Event A1 (Serving becomes better than threshold) (135)5.5.4.3Event A2 (Serving becomes worse than threshold) (136)5.5.4.4Event A3 (Neighbour becomes offset better than PCell/ PSCell) (136)5.5.4.5Event A4 (Neighbour becomes better than threshold) (137)5.5.4.6Event A5 (PCell/ PSCell becomes worse than threshold1 and neighbour becomes better thanthreshold2) (138)5.5.4.6a Event A6 (Neighbour becomes offset better than SCell) (139)5.5.4.7Event B1 (Inter RAT neighbour becomes better than threshold) (139)5.5.4.8Event B2 (PCell becomes worse than threshold1 and inter RAT neighbour becomes better thanthreshold2) (140)5.5.4.9Event C1 (CSI-RS resource becomes better than threshold) (141)5.5.4.10Event C2 (CSI-RS resource becomes offset better than reference CSI-RS resource) (141)5.5.4.11Event W1 (WLAN becomes better than a threshold) (142)5.5.4.12Event W2 (All WLAN inside WLAN mobility set becomes worse than threshold1 and a WLANoutside WLAN mobility set becomes better than threshold2) (142)5.5.4.13Event W3 (All WLAN inside WLAN mobility set becomes worse than a threshold) (143)5.5.5Measurement reporting (144)5.5.6Measurement related actions (148)5.5.6.1Actions upon handover and re-establishment (148)5.5.6.2Speed dependant scaling of measurement related parameters (149)5.5.7Inter-frequency RSTD measurement indication (149)5.5.7.1General (149)5.5.7.2Initiation (150)5.5.7.3Actions related to transmission of InterFreqRSTDMeasurementIndication message (150)5.6Other (150)5.6.0General (150)5.6.1DL information transfer (151)5.6.1.1General (151)5.6.1.2Initiation (151)5.6.1.3Reception of the DLInformationTransfer by the UE (151)5.6.2UL information transfer (151)5.6.2.1General (151)5.6.2.2Initiation (151)5.6.2.3Actions related to transmission of ULInformationTransfer message (152)5.6.2.4Failure to deliver ULInformationTransfer message (152)5.6.3UE capability transfer (152)5.6.3.1General (152)5.6.3.2Initiation (153)5.6.3.3Reception of the UECapabilityEnquiry by the UE (153)5.6.4CSFB to 1x Parameter transfer (157)5.6.4.1General (157)5.6.4.2Initiation (157)5.6.4.3Actions related to transmission of CSFBParametersRequestCDMA2000 message (157)5.6.4.4Reception of the CSFBParametersResponseCDMA2000 message (157)5.6.5UE Information (158)5.6.5.1General (158)5.6.5.2Initiation (158)5.6.5.3Reception of the UEInformationRequest message (158)5.6.6 Logged Measurement Configuration (159)5.6.6.1General (159)5.6.6.2Initiation (160)5.6.6.3Reception of the LoggedMeasurementConfiguration by the UE (160)5.6.6.4T330 expiry (160)5.6.7 Release of Logged Measurement Configuration (160)5.6.7.1General (160)5.6.7.2Initiation (160)5.6.8 Measurements logging (161)5.6.8.1General (161)5.6.8.2Initiation (161)5.6.9In-device coexistence indication (163)5.6.9.1General (163)5.6.9.2Initiation (164)5.6.9.3Actions related to transmission of InDeviceCoexIndication message (164)5.6.10UE Assistance Information (165)5.6.10.1General (165)5.6.10.2Initiation (166)5.6.10.3Actions related to transmission of UEAssistanceInformation message (166)5.6.11 Mobility history information (166)5.6.11.1General (166)5.6.11.2Initiation (166)5.6.12RAN-assisted WLAN interworking (167)5.6.12.1General (167)5.6.12.2Dedicated WLAN offload configuration (167)5.6.12.3WLAN offload RAN evaluation (167)5.6.12.4T350 expiry or stop (167)5.6.12.5Cell selection/ re-selection while T350 is running (168)5.6.13SCG failure information (168)5.6.13.1General (168)5.6.13.2Initiation (168)5.6.13.3Actions related to transmission of SCGFailureInformation message (168)5.6.14LTE-WLAN Aggregation (169)5.6.14.1Introduction (169)5.6.14.2Reception of LWA configuration (169)5.6.14.3Release of LWA configuration (170)5.6.15WLAN connection management (170)5.6.15.1Introduction (170)5.6.15.2WLAN connection status reporting (170)5.6.15.2.1General (170)5.6.15.2.2Initiation (171)5.6.15.2.3Actions related to transmission of WLANConnectionStatusReport message (171)5.6.15.3T351 Expiry (WLAN connection attempt timeout) (171)5.6.15.4WLAN status monitoring (171)5.6.16RAN controlled LTE-WLAN interworking (172)5.6.16.1General (172)5.6.16.2WLAN traffic steering command (172)5.6.17LTE-WLAN aggregation with IPsec tunnel (173)5.6.17.1General (173)5.7Generic error handling (174)5.7.1General (174)5.7.2ASN.1 violation or encoding error (174)5.7.3Field set to a not comprehended value (174)5.7.4Mandatory field missing (174)5.7.5Not comprehended field (176)5.8MBMS (176)5.8.1Introduction (176)5.8.1.1General (176)5.8.1.2Scheduling (176)5.8.1.3MCCH information validity and notification of changes (176)5.8.2MCCH information acquisition (178)5.8.2.1General (178)5.8.2.2Initiation (178)5.8.2.3MCCH information acquisition by the UE (178)5.8.2.4Actions upon reception of the MBSFNAreaConfiguration message (178)5.8.2.5Actions upon reception of the MBMSCountingRequest message (179)5.8.3MBMS PTM radio bearer configuration (179)5.8.3.1General (179)5.8.3.2Initiation (179)5.8.3.3MRB establishment (179)5.8.3.4MRB release (179)5.8.4MBMS Counting Procedure (179)5.8.4.1General (179)5.8.4.2Initiation (180)5.8.4.3Reception of the MBMSCountingRequest message by the UE (180)5.8.5MBMS interest indication (181)5.8.5.1General (181)5.8.5.2Initiation (181)5.8.5.3Determine MBMS frequencies of interest (182)5.8.5.4Actions related to transmission of MBMSInterestIndication message (183)5.8a SC-PTM (183)5.8a.1Introduction (183)5.8a.1.1General (183)5.8a.1.2SC-MCCH scheduling (183)5.8a.1.3SC-MCCH information validity and notification of changes (183)5.8a.1.4Procedures (184)5.8a.2SC-MCCH information acquisition (184)5.8a.2.1General (184)5.8a.2.2Initiation (184)5.8a.2.3SC-MCCH information acquisition by the UE (184)5.8a.2.4Actions upon reception of the SCPTMConfiguration message (185)5.8a.3SC-PTM radio bearer configuration (185)5.8a.3.1General (185)5.8a.3.2Initiation (185)5.8a.3.3SC-MRB establishment (185)5.8a.3.4SC-MRB release (185)5.9RN procedures (186)5.9.1RN reconfiguration (186)5.9.1.1General (186)5.9.1.2Initiation (186)5.9.1.3Reception of the RNReconfiguration by the RN (186)5.10Sidelink (186)5.10.1Introduction (186)5.10.1a Conditions for sidelink communication operation (187)5.10.2Sidelink UE information (188)5.10.2.1General (188)5.10.2.2Initiation (189)5.10.2.3Actions related to transmission of SidelinkUEInformation message (193)5.10.3Sidelink communication monitoring (195)5.10.6Sidelink discovery announcement (198)5.10.6a Sidelink discovery announcement pool selection (201)5.10.6b Sidelink discovery announcement reference carrier selection (201)5.10.7Sidelink synchronisation information transmission (202)5.10.7.1General (202)5.10.7.2Initiation (203)5.10.7.3Transmission of SLSS (204)5.10.7.4Transmission of MasterInformationBlock-SL message (205)5.10.7.5Void (206)5.10.8Sidelink synchronisation reference (206)5.10.8.1General (206)5.10.8.2Selection and reselection of synchronisation reference UE (SyncRef UE) (206)5.10.9Sidelink common control information (207)5.10.9.1General (207)5.10.9.2Actions related to reception of MasterInformationBlock-SL message (207)5.10.10Sidelink relay UE operation (207)5.10.10.1General (207)5.10.10.2AS-conditions for relay related sidelink communication transmission by sidelink relay UE (207)5.10.10.3AS-conditions for relay PS related sidelink discovery transmission by sidelink relay UE (208)5.10.10.4Sidelink relay UE threshold conditions (208)5.10.11Sidelink remote UE operation (208)5.10.11.1General (208)5.10.11.2AS-conditions for relay related sidelink communication transmission by sidelink remote UE (208)5.10.11.3AS-conditions for relay PS related sidelink discovery transmission by sidelink remote UE (209)5.10.11.4Selection and reselection of sidelink relay UE (209)5.10.11.5Sidelink remote UE threshold conditions (210)6Protocol data units, formats and parameters (tabular & ASN.1) (210)6.1General (210)6.2RRC messages (212)6.2.1General message structure (212)–EUTRA-RRC-Definitions (212)–BCCH-BCH-Message (212)–BCCH-DL-SCH-Message (212)–BCCH-DL-SCH-Message-BR (213)–MCCH-Message (213)–PCCH-Message (213)–DL-CCCH-Message (214)–DL-DCCH-Message (214)–UL-CCCH-Message (214)–UL-DCCH-Message (215)–SC-MCCH-Message (215)6.2.2Message definitions (216)–CounterCheck (216)–CounterCheckResponse (217)–CSFBParametersRequestCDMA2000 (217)–CSFBParametersResponseCDMA2000 (218)–DLInformationTransfer (218)–HandoverFromEUTRAPreparationRequest (CDMA2000) (219)–InDeviceCoexIndication (220)–InterFreqRSTDMeasurementIndication (222)–LoggedMeasurementConfiguration (223)–MasterInformationBlock (225)–MBMSCountingRequest (226)–MBMSCountingResponse (226)–MBMSInterestIndication (227)–MBSFNAreaConfiguration (228)–MeasurementReport (228)–MobilityFromEUTRACommand (229)–Paging (232)–ProximityIndication (233)–RNReconfiguration (234)–RNReconfigurationComplete (234)–RRCConnectionReconfiguration (235)–RRCConnectionReconfigurationComplete (240)–RRCConnectionReestablishment (241)–RRCConnectionReestablishmentComplete (241)–RRCConnectionReestablishmentReject (242)–RRCConnectionReestablishmentRequest (243)–RRCConnectionReject (243)–RRCConnectionRelease (244)–RRCConnectionResume (248)–RRCConnectionResumeComplete (249)–RRCConnectionResumeRequest (250)–RRCConnectionRequest (250)–RRCConnectionSetup (251)–RRCConnectionSetupComplete (252)–SCGFailureInformation (253)–SCPTMConfiguration (254)–SecurityModeCommand (255)–SecurityModeComplete (255)–SecurityModeFailure (256)–SidelinkUEInformation (256)–SystemInformation (258)–SystemInformationBlockType1 (259)–UEAssistanceInformation (264)–UECapabilityEnquiry (265)–UECapabilityInformation (266)–UEInformationRequest (267)–UEInformationResponse (267)–ULHandoverPreparationTransfer (CDMA2000) (273)–ULInformationTransfer (274)–WLANConnectionStatusReport (274)6.3RRC information elements (275)6.3.1System information blocks (275)–SystemInformationBlockType2 (275)–SystemInformationBlockType3 (279)–SystemInformationBlockType4 (282)–SystemInformationBlockType5 (283)–SystemInformationBlockType6 (287)–SystemInformationBlockType7 (289)–SystemInformationBlockType8 (290)–SystemInformationBlockType9 (295)–SystemInformationBlockType10 (295)–SystemInformationBlockType11 (296)–SystemInformationBlockType12 (297)–SystemInformationBlockType13 (297)–SystemInformationBlockType14 (298)–SystemInformationBlockType15 (298)–SystemInformationBlockType16 (299)–SystemInformationBlockType17 (300)–SystemInformationBlockType18 (301)–SystemInformationBlockType19 (301)–SystemInformationBlockType20 (304)6.3.2Radio resource control information elements (304)–AntennaInfo (304)–AntennaInfoUL (306)–CQI-ReportConfig (307)–CQI-ReportPeriodicProcExtId (314)–CrossCarrierSchedulingConfig (314)–CSI-IM-Config (315)–CSI-IM-ConfigId (315)–CSI-RS-Config (317)–CSI-RS-ConfigEMIMO (318)–CSI-RS-ConfigNZP (319)–CSI-RS-ConfigNZPId (320)–CSI-RS-ConfigZP (321)–CSI-RS-ConfigZPId (321)–DMRS-Config (321)–DRB-Identity (322)–EPDCCH-Config (322)–EIMTA-MainConfig (324)–LogicalChannelConfig (325)–LWA-Configuration (326)–LWIP-Configuration (326)–RCLWI-Configuration (327)–MAC-MainConfig (327)–P-C-AndCBSR (332)–PDCCH-ConfigSCell (333)–PDCP-Config (334)–PDSCH-Config (337)–PDSCH-RE-MappingQCL-ConfigId (339)–PHICH-Config (339)–PhysicalConfigDedicated (339)–P-Max (344)–PRACH-Config (344)–PresenceAntennaPort1 (346)–PUCCH-Config (347)–PUSCH-Config (351)–RACH-ConfigCommon (355)–RACH-ConfigDedicated (357)–RadioResourceConfigCommon (358)–RadioResourceConfigDedicated (362)–RLC-Config (367)–RLF-TimersAndConstants (369)–RN-SubframeConfig (370)–SchedulingRequestConfig (371)–SoundingRS-UL-Config (372)–SPS-Config (375)–TDD-Config (376)–TimeAlignmentTimer (377)–TPC-PDCCH-Config (377)–TunnelConfigLWIP (378)–UplinkPowerControl (379)–WLAN-Id-List (382)–WLAN-MobilityConfig (382)6.3.3Security control information elements (382)–NextHopChainingCount (382)–SecurityAlgorithmConfig (383)–ShortMAC-I (383)6.3.4Mobility control information elements (383)–AdditionalSpectrumEmission (383)–ARFCN-ValueCDMA2000 (383)–ARFCN-ValueEUTRA (384)–ARFCN-ValueGERAN (384)–ARFCN-ValueUTRA (384)–BandclassCDMA2000 (384)–BandIndicatorGERAN (385)–CarrierFreqCDMA2000 (385)–CarrierFreqGERAN (385)–CellIndexList (387)–CellReselectionPriority (387)–CellSelectionInfoCE (387)–CellReselectionSubPriority (388)–CSFB-RegistrationParam1XRTT (388)–CellGlobalIdEUTRA (389)–CellGlobalIdUTRA (389)–CellGlobalIdGERAN (390)–CellGlobalIdCDMA2000 (390)–CellSelectionInfoNFreq (391)–CSG-Identity (391)–FreqBandIndicator (391)–MobilityControlInfo (391)–MobilityParametersCDMA2000 (1xRTT) (393)–MobilityStateParameters (394)–MultiBandInfoList (394)–NS-PmaxList (394)–PhysCellId (395)–PhysCellIdRange (395)–PhysCellIdRangeUTRA-FDDList (395)–PhysCellIdCDMA2000 (396)–PhysCellIdGERAN (396)–PhysCellIdUTRA-FDD (396)–PhysCellIdUTRA-TDD (396)–PLMN-Identity (397)–PLMN-IdentityList3 (397)–PreRegistrationInfoHRPD (397)–Q-QualMin (398)–Q-RxLevMin (398)–Q-OffsetRange (398)–Q-OffsetRangeInterRAT (399)–ReselectionThreshold (399)–ReselectionThresholdQ (399)–SCellIndex (399)–ServCellIndex (400)–SpeedStateScaleFactors (400)–SystemInfoListGERAN (400)–SystemTimeInfoCDMA2000 (401)–TrackingAreaCode (401)–T-Reselection (402)–T-ReselectionEUTRA-CE (402)6.3.5Measurement information elements (402)–AllowedMeasBandwidth (402)–CSI-RSRP-Range (402)–Hysteresis (402)–LocationInfo (403)–MBSFN-RSRQ-Range (403)–MeasConfig (404)–MeasDS-Config (405)–MeasGapConfig (406)–MeasId (407)–MeasIdToAddModList (407)–MeasObjectCDMA2000 (408)–MeasObjectEUTRA (408)–MeasObjectGERAN (412)–MeasObjectId (412)–MeasObjectToAddModList (412)–MeasObjectUTRA (413)–ReportConfigEUTRA (422)–ReportConfigId (425)–ReportConfigInterRAT (425)–ReportConfigToAddModList (428)–ReportInterval (429)–RSRP-Range (429)–RSRQ-Range (430)–RSRQ-Type (430)–RS-SINR-Range (430)–RSSI-Range-r13 (431)–TimeToTrigger (431)–UL-DelayConfig (431)–WLAN-CarrierInfo (431)–WLAN-RSSI-Range (432)–WLAN-Status (432)6.3.6Other information elements (433)–AbsoluteTimeInfo (433)–AreaConfiguration (433)–C-RNTI (433)–DedicatedInfoCDMA2000 (434)–DedicatedInfoNAS (434)–FilterCoefficient (434)–LoggingDuration (434)–LoggingInterval (435)–MeasSubframePattern (435)–MMEC (435)–NeighCellConfig (435)–OtherConfig (436)–RAND-CDMA2000 (1xRTT) (437)–RAT-Type (437)–ResumeIdentity (437)–RRC-TransactionIdentifier (438)–S-TMSI (438)–TraceReference (438)–UE-CapabilityRAT-ContainerList (438)–UE-EUTRA-Capability (439)–UE-RadioPagingInfo (469)–UE-TimersAndConstants (469)–VisitedCellInfoList (470)–WLAN-OffloadConfig (470)6.3.7MBMS information elements (472)–MBMS-NotificationConfig (472)–MBMS-ServiceList (473)–MBSFN-AreaId (473)–MBSFN-AreaInfoList (473)–MBSFN-SubframeConfig (474)–PMCH-InfoList (475)6.3.7a SC-PTM information elements (476)–SC-MTCH-InfoList (476)–SCPTM-NeighbourCellList (478)6.3.8Sidelink information elements (478)–SL-CommConfig (478)–SL-CommResourcePool (479)–SL-CP-Len (480)–SL-DiscConfig (481)–SL-DiscResourcePool (483)–SL-DiscTxPowerInfo (485)–SL-GapConfig (485)。

计量经济学Econometrics专业词汇中英文对照

计量经济学Econometrics专业词汇中英文对照

Econometrics 专业词汇中英文对照(按课件顺序)Ch1-3Causal effects:因果影响,指的是当x变化时,会引起y的变化;Elasticity:弹性;correlation (coefficient) 相关(系数),相关系数没有单位,unit free;estimation:估计;hypothesis testing:假设检验;confidence interval:置信区间;difference-in-means test:均值差异检验,即检验两个样本的均值是否相同;standard error:标准差;statistical inference:统计推断;Moments of distribution:分布的矩函数;conditional distribution (means):条件分布(均值);variance:方差;standard deviation:标准差(指总体方差的平方根);standard error:标准误差,指样本方差的平方根;skewness:偏度,度量分布的对称性;kurtosis:峰度,度量厚尾性,即度量离散程度;joint distribution:联合分布;conditional expectation:条件期望(指总体);randomness:随机性i.i.d., independently and identically distributed:独立同分布的;sampling distribution:抽样分布,指的是当抽取不同的随机样本时,统计量的取值会有所不同,而当取遍所有的样本量为n的样本时,统计量有一个取值规律,即抽样分布,即统计量的随机性来自样本的随机性consistent (consistency):相合的(相合性),指当样本量趋于无穷大时,估计量依概率收敛到真实值;此外,在统计的语言中,还有一个叫模型选择的相合性,指的是能依概率选取到正确的模型Central limit theory:中心极限定理;unbiased estimator:无偏估计量;uncertainty:不确定性;approximation:逼近;least squares estimator:最小二乘估计量;provisional decision:临时的决定,用于假设检验,指的是,我们现在下的结论是基于现在的数据的,如果数据变化,我们的结论可能会发生变化significance level:显著性水平,一般取0.05或者0.01,0.1,是一个预先给定的数值,指的是在原假设成立的假设下,我们可能犯的错误的概率,即拒绝原假设的概率;p-value:p-值,指的是观测到比现在观测到的统计量更极端的概率,一般p-值很小的时候要拒绝原假设,因为这说明要观测到比现在观测到的统计量更极端的情况的概率很小,进而说明现在的统计量很极端。

Inverse kinematics positioning using nonlinear programming for highly articulated figures

Inverse kinematics positioning using nonlinear programming for highly articulated figures

Inverse Kinematics Positioning Using Nonlinear Programmingfor Highly Articulated FiguresJianmin Zhao and Norman I.BadlerDepartment of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphia,PA19104-6389AbstractAn articulatedfigure is often modeled as a set of rigid segments connected with joints.Its configuration can be altered by varying the joint angles.Although it is straightforward to computefigure configurations given joint angles(forward kinematics),it is not so tofind the joint angles for a desired configuration (inverse kinematics).Since the inverse kinematics problem is of special importance to an animator wishing to set afigure to a posture satisfying a set of positioning constraints,researchers have proposed many approaches.But when we try to follow these approaches in an interactive animation system where the object to operate on is as highly articulated as a realistic humanfigure,they fail in either generality or performance,and so a new approach is fostered.Our approach is based on nonlinear programming techniques.It has been used for several years in the spatial constraint system in the Jack TM humanfigure simulation software developed at the Computer Graphics Research Lab of the University of Pennsylvania,and proves to be satisfactorily efficient,controllable,and robust.A spatial constraint in our system involves two parts:one on thefigure,called the end-effector,and the other one on the spatial environment,called the goal.These two parts are dealt with separately,so that a neat modular implementation is achieved.Constraints can be added one at a time with appropriate weights designating the importance of this constraint relative to the others,and the system solves them and retains them whenever a constraint is violated because either thefigure or the goal is moved.In case it is impossible to satisfy all the constraints thanks to physical limits,the system stops with the optimal solution for the given weights.In addition,the rigidity of each joint angle can be controlled,which is useful when degrees of freedom are redundant.Categories and Subject Descriptors:I.3.7[Computer Graphics]:Three-Dimensional Graphics and Realism–animationGeneral Terms:Algorithms,PerformanceAdditional Key Words and Phrases:Inverse kinematics,highly articulatedfigures,nonlinear pro-gramming1IntroductionIn computer animation,an articulatedfigure is often modeled as a set of rigid segments connected by joints.A joint is,abstractly,a constraint on the geometric relationship between two adjacent segments.This “relationship”is expressed by a number of parameters called joint angles.With judicious selection of joints, so that,e.g.,segments are connected to form a tree structure,a collection of the joint angles of all the joints corresponds one-on-one to a configuration of thefigure.While this correspondence provides an immediate computer representation of articulatedfigure configurations in the sense that given a set of joint angles it is straightforward to compute the corresponding configuration,the problem offinding a set of joint angles that corresponds to a given configuration,the inverse kinematics problem,persists in practice.The inverse kinematics problem,however,is extremely important in computer animation,since it is often the spatial appearance,rather than the joint angles,that an animator is interested in.Naturally,the problem has received attention of many researchers in computer animation,as well as in robotics(see the next section),but the various algorithms reflect particular aspects of the problem and fail to provide a general,efficient,and robust solution for positioning highly articulatedfigures in an interactive animation system.In interactive manipulation of articulatedfigures,where an animator poses afigure in the spatial context whereas joint angles are merely internal(and possibly hidden)representations of postures(configurations) [18],the joint angles that define the target configuration is much more interesting than the process that the joint angles take in arriving at the target.It is the responsiveness that is essential.Quick response is also essential for practical control of articulatedfigures where the mapping from spatial configurations to joint angles has to be done repeatedly.For example,in path planning with strength constraints,the predictionof the next configuration is transformed to joint angles iteratively[14].Workspace computation is another example[1].In the former example,the time sequence is handled by some other level of control;and in the latter example,the process that the joint angles take in arriving at target postures is not pertinent.It is in this context that we offer a new approach to the inverse kinematics problem.In the following section,we shall talk about our motivation in more detail.Our approach is based on nonlinear programming, a numerical method for solving the minimum of a nonlinear function.It searches for the solution in the high-dimensional joint angle space based on computational economy rather than physical meanings.It deals with joint limits intrinsically rather than as a special case.It is successfully implemented and has found wide uses,as noted above.Because of the complex nature of nonlinear functions,many efficient nonlinear programming algorithms terminate when theyfind local minima.The algorithm we picked has this limitation,too.In practice, however,this is not an unacceptably serious problem.Local minima are less likely when the target configuration is not too distant from the starting one.If they do occur during interactive manipulation,users can easily perturb thefigure configuration slightly to get around the local minima.2BackgroundInverse kinematics for determining mechanism motion is a common technique in mechanical engineering, particularly in robot research[16].In robotics,however,people are mostly concerned about the functionality of manipulators;overly redundant degrees of freedom are usually not desired except for special purposes. Moreover,the computation is usually carried out on particular linkage geometries.In contrast,many interesting objects in the computer animation domain,the humanfigure,for example,have many redundant degrees of freedom when viewed as a tree-structured kinematic mechanism.So it was necessary to look for effective means for solving this problem under various circumstances peculiar to computer animation.Korein and Badler began to study and implement methods for kinematic chain positioning,especially in the context of joint limits and redundant degrees of freedom[12,13].In[3],Badler et al used position constraints to specify spatial configurations of articulatedfigures.They recursively solved for joint angles to satisfy multiple position constraints.But,owing to their simple solver,the constraints handled were limited to the type of point-to-point position constraints only.Girard and Maciejewski adopted a method from robotics.In[11],they calculated the pseudo-inverse of the Jacobian matrix which relates the increment of the joint angles to the displacement of the end-effector in space.The main formula is∆∆where∆is the increment of the joint angle vector,∆is the displacement of the vector representing the position and/or orientation of the end-effector in space,and is the pseudo-inverse of the Jacobian. To understand this,we can think of as a3-D column vector denoting the position of the hand,and as a n-D column vector consisting of all joint angles which may contribute to the motion of the hand—e.g.,all the joint angles from the shoulder to the wrist.This is a differential equality;in other words,the equality holds only if we ignore the displacement of higher order∆2.It was developed to drive the robot, where the increment is small because actual motion has to be carried out physically in continuous way.To simply position a humanfigure in a computer simulated environment,however,it would not be economical to move the end-effector by“small”steps;in making a computer animation sequence,it would not be optimal either to take a step size smaller than necessary.Moreover,the pseudo-inverse calculation required for each step in this formula is normally quite expensive and they did not deal with joint limits.Witkin et al used energy constraints for positioning purposes[24].Constraints can be positional or orientational.They are satisfied if and only if the energy function is zero.The way they solved constraints is to integrate the differential equation:where is the parameter(e.g.,joint angle)vector which defines the configuration of the system,is the energy function of,and is the gradient operator.Clearly,if is the integral with some initial condition,monotonically decreases with time,because2In the joint angle space,constantdefines a line,called the iso-energy line,on which the energy function takes an identical value.For any number(energy level),there is such a line.Under this physical meaning of the energy function,Witkin et al’s method searches the path from the initial configuration to the target configuration which is,at any point, perpendicular to the iso-energy lines.Instead of associating energy functions with constraints,Barzel and Barr introduced deviation functions which measure the deviation of two constrained parts[5].They discussed a variety of constraints in[5],such as point-to-point,point-to-nail,etc.,and their associated deviation functions.A segment in their system of rigid bodies is subjected to both external forces,such as the gravity,and constraint forces,which bring the deviations to zero whenever they are greater.Constraint forces are solved from a set of dynamic differential equations which requires that all deviations go to zero exponentially in a certain amount of time.It is worth noting that an approach based on physical modeling and interpretation is also used by Witkin and Welch on nonrigid bodies whose deformations are controlled by a number of parameters[25].To apply this kind of methods to articulatedfigures,a joint would be considered as a point-to-point constraint and added to the system as an algebraic equation.This poses some practical problems that render such solutions inappropriate to highly articulatedfigures.First,it is not unusual to have several dozen joints in a highly articulatedfigure,which would add to the number of constraint equations substantially.Second,a joint of an articulatedfigure is meant to be an absolute constraint.In other words,it should not compete with any constraint that relates a point on a segment of thefigure to a point in space.This competition often gives rise to numerical instability.We notice that all those methods have a property in common:the target configuration is the result of a process from a starting one.This process bears some physical meaning.In Girard and Maciejewski’s method [11],the process is determined by the end-effector path;in Witkin et al’s method[24],it is determined by the energy function(the path in space is perpendicular to the family of iso-energy lines);in Barzel and Barr’s method[5]or other dynamic methods([25]),the process is determined by the physical interpretations of each segment,and external and constraint forces exerted on it.Not only can these methods solve the constraints,but also offer a smooth process in which the constraints are satisfied in certain contexts.The achieved target configuration is,therefore,natural in the sense that it results from a process that the user is more or less able to comprehend and control.But this property is not free.If we are only concernedabout the target configuration defined by the spatial constraints,rather than the physical realization,which is true in many circumstances,physical methods could be computationally inefficient,because they add extra burdens to the original geometric problem.For example,in searching for a(local)minimum along a line,one mayfirst choose a small step size and then compute the function value until it rises.Another way tofind the solution could be like this.First locate an interval in which the minimum lies,and then use the golden ratio method,a method similar to binary search,tofind the minimum.Thefirst method shows a vivid picture of how the function changes to the minimum gradually,whereas the second method is statistically much faster.Therefore,since a target configuration can be defined by the minimum of an energy function(see [24]),why don’t we look for the minimum directly?As for naturalness of the target configuration,we may give the user more immediate control by allowing the user to specify more constraints,if it remains affordable.Nonlinear programming is a numerical technique to solve for(local)minima of nonlinear functions. The solution search maintains numerical efficiency and robustness;the intermediate values from the starting state to thefinal one could be in general fairly“irregular”.There are two classes of nonlinear programming problems.One is the unconstrained nonlinear programming,where the variables are free to take any values; the other one is the constrained nonlinear programming,where the variables can only take values in a certain range.The constraints on the variablesfit exactly to joint limits of articulatedfigures.Although the latter problem can be theoretically reduced to the former one,both unconstrained and constrained nonlinear programming problems have been studied extensively,because simple reduction may cause numerical instability.So we propose a new approach to the inverse kinematics problem based on nonlinear programming methods.Our target application is interactive manipulation of highly articulatedfigures,such as human figures,where joints and joint limits must not be violated.3Spatial ConstraintsThe basic geometric entity considered here is the articulatedfigure.The data structure of an articulated figure we used is defined by the Peabody language developed at the Computer Graphics Research Lab atcnstr.........joint angle index table weight,joint chain,θGoalG, gAssemblerMG, mgNon-linearObjective FunctionGenerator Programminggoal type,parametersEnd-effector Figure 1:Multiple Spatial Constraint Systemthe University of Pennsylvania [17].A Peabody figure is composed of rigid segments connected together by joints.Each joint has several rotational and translational degrees of freedom subject to joint limits.The data structure can be viewed as a tree,where nodes represent segments and edges represent joints.Having decided on the data structure,we need to address the problem of setting a figure to a desired posture.As discussed in the introduction,we wish to be able to adjust the posture directly in the spatial domain.Our spatial constraints are designed for this purpose.A spatial constraint is simply a demand that the end-effector on a segment of a figure be placed at and/or aligned with the goal in space.To say that a constraint is satisfied is equivalent to saying that the goal is reached.The end-effector’s propensity to hold on to the goal persists until the constraint is disabled or deleted.Figure 1is a diagram of the multiple spatial constraint system in Jack .The system consists of threemajor components:Objective Function Generator,Assembler,and Nonlinear Programming solver.They are described in the following sections.4End-effectors4.1End-effector MappingsFormally,we can view an end-effector as a mapping::Θ1ΘwhereΘis the joint angle space,the set consisting of all joint angle vectors,and3222 where3denotes the set of3-D vectors,and2the set of3-D unit vectors.Accordingly,is a9-D vector,whosefirst three components form a positional vector,designating the spatial position of a point on the end-effector segment,the second and the third three components form two unit vectors,designating directions of two independent unit vectors on the end-effector segment.Given an instance of the joint angles of all the joints,,the end-effector associates a9-D vector according to thefigure definition.Since segments of afigure are rigid,the angle expanded by the last two unit vectors should remain unchanged.A convenient choice is to set it to90degrees.These nine numbers uniquely determine the position and orientation of the end-effector segment in space.Thefirst three numbers are independent, but the next six numbers are not.They must satisfy two unity equations and one expanded angle equation. These three equations take away three degrees of freedom from,so that has only six independent quantities,which are exactly needed to determine the position and orientation of a rigid body in space.Let’s take an example.Let the end-effector segment be the right hand,and the pelvis befixed temporarily, serving as the root of thefigure tree definition.Given joint angles of all the joints from the waist to the right wrist present in vector,the location and orientation of the right hand can be computed and the result is put in,provided that a point and two orthonormal vectors attached on the hand have been selected for reference.21 1solve the constraint requires the derivative quantities.1The matrix is the Jacobian matrix.Its use will be explained later.Naturally,it is this module’s responsibility to compute it.The vector is composed of some combination of a point vector and two unit vectors on the end-effector segment.Referring to Figure2,let be a point vector and be a unit vector on the end-effector segment.It is clear that in order to compute and,it is sufficient to know how to compute, ,,and.Because all the joints in our current humanfigure model are rotational joints,we discuss only rotational joints here.1Let the th joint angle along the chain be,and the rotation axis of this joint be unit vector. It turns out that and can be easily computed with cascaded multiplications of4by4homogeneous matrices.The derivatives can be easily computed,too(see[26]):.(4) 5Goals5.1Goal Potential FunctionsA goal can also be viewed as a mapping::5 where the domain is the same as the range of the end-effector mapping defined in(2),and is the set of non-negative real numbers.Since the function assigns a scalar to a combination of position and directions in space,we call it a potential function.When the end-effector vector is plugged into the potential function as the argument,it produces a non-negative real number,,which is to be understood asthe distance from the current end-effector location(position and/or orientation)to the associated goal.For a pair of an end-effector and a goal,the range of the end-effector must be the same as the domain of the potential function.5.2Goal Computational ModuleThe Goal module is the other part of the Objective Function Generator(Figure1).It is to compute the potential and its gradient,the column vector formed by all its partial derivatives.Let2In practice,however,it may not be adequate,because this potential function,when combined witha position goal,would in effect make one unit difference in length as important as about one radiandifference in angle,which is not always intended.To make one length unit commensurate with degrees in angle,we need to multiply the above by a factor such that1360or,explicitly3602.8 To be moreflexible,the potential function is chosen to be2222.9 The gradient is then22(10)22.(11)A goal direction,such as,could be unconstrained by setting to0.This is useful,for example,to orientationally constrain the normal to the palm of a person holding a cup of water.Position/Orientation Goals.The position and orientation goal can be treated as two goals,but some-times it is more convenient to combine them together as one goal.The potential function for the position/orientation goal is chosen to be a weighted sum of the position and orientation components:2222212 where and are weights assigned to position and orientation,respectively,such that1.The domain322and the gradients,and can be calculated from(7),(10),and(11)above.Aiming-at Goals.The goal is defined by a point in space;the end-effector is defined by a position vector and a unit vector on the end-effector segment.The goal is reached if and only if the ray emanating from in the direction passes through.The domain of the potential function32This type of goal is useful,for example,in posing a humanfigure facing toward a certain point.The potential function2.(15)Line Goals.The goal is defined by a line which passes through points and,where is a unit vector.This line is meant for a point on the end-effector segment to lie on.The potential function2;16its domain3and gradient2.17Plane Goals.The goal is defined by a plane with the unit normal to and a point on it.Similar to the Line Goal,the plane is meant for a point on the end-effector segment to lie on.The potential function2;18its domain3and the gradient2.19 Half-space Goals.The goal is defined by a plane specified the same way as in the Plane Goal.The plane is used to divide the space into two halves.A point on the end-effector segment“reaches”the goal if and only if it is in the same half-space as the point is.The potential function0if0;202otherwiseits domain3and the gradient0if0212otherwise.6Spatial Constraint as a Nonlinear Programming ProblemA spatial constraint constrains an end-effector to a goal.From Section4and5,with the current joint angles being,the“distance”from the end-effector to the goal is simply22 This quantity can be computed byfirst invoking the end-effector module to compute,and then invoking the goal module with as the input argument of the potential function.This process is illustrated in Figure1.Ideally,we want to solve the algebraic equation,In reality,however,this equation is not always satisfiable,for the goal is not always reachable.Thus the problem would be naturally tofind in a feasible region that minimizes the function.Most of the joint angles in ourfigure definition have lower limits and upper limits.The joint angles for the shoulderare confined in a polygon.They can all be expressed in linear inequalities.Therefore,we formulate the problem as a problem of nonlinear programming subject to linear constraints on variables,that is,formally,minimize23subject to1212where12are column vectors whose dimensions are the same as that of’s.The equalities allow for linear relationships among the joint angles,and the inequalities admit of the lower limit and upper limit on,the th joint angle,as do the inequalities.The polygonal region for the shoulder joint angles(elevation,abduction,and twist)can be similarly expressed as a set of inequalities.7Solving the Nonlinear Programming ProblemThe problem posed in(23)tofind the minimum of the objective function is intractable without knowledge of the regularity of the objective function.Properties such as linearity or convexity that regulate the global behavior of a function may help tofind the global minimum.Otherwise,research in nonlinear programming area is mostly done to solve for local minima.It is worthwhile because,in practice,functions are moderate:the local minimum is often what one wants,or if it fails to be,some other local minimum found by another attempt with a new initial point would quite likely be.In order to have quick response,we chose to compromise for local minima.From years of observation, we have not seen many serious problems.The algorithm we used to solve the problem(23)is described in the Appendix.It iterates to approach the solution.At each iteration,it searches for a minimum along a certain direction.In order for the search direction to point to the solution more accurately so that fewer iterations will be needed,the direction is determined based on not only the gradient at the current point,but also the gradients at the previous steps of iteration.Our method is monotonic,namely that after any iterations the value that the objective function takes never increases,and globally convergent,namely that it converges to a(local)minimum regardless of the initial point.These two properties are very attractive to us because the configuration could otherwise diverge arbitrarily,which could cause disaster had the previous posture resulted from substantial effort.To carry out the computation,we need to compute and its gradient.It becomes easy now after preparation in Sections4and5.The function value can be computed as in(22),and the gradient can be computed as follows:defTherefore,our system handles multiple constraints.Since the objective function defined in(22)is non-negative,the multiple constraints are solved by minimizingthe sum of the objective functions associated with all the goalsall251where is the number of constraints,subscript denotes the association with the th constraint,is a non-negative weight assigned to the th constraint to reflect the relative importance of the constraint,and26 Thus,the multiple constraints can be solved as the problem(23)with replaced by all defined in (25).Note that’s can be computed independently,and only a number of additions are needed to compute all.This is also true for the gradient,for the gradient operator is additive,too.Constraints may also be tied together disjunctively,that is,they are considered satisfied if any one of them is satisfied.To solve this problem,we define the objective function asall min271It is useful,for example,to constrain an end-effector outside a convex polyhedron,because the outside space can be viewed as the disjunction of the outward half-spaces defined by the polygonal faces.9Assembler of Multiple ConstraintsAs stated in the previous sections,the overall objective function of multiple constraints can be found by computing separately and independently the objective functions of individual constraints and then adding them together.In this section,we shall explain how the Assembler works.The module Objective Function Generator takes a joint chain,an array of corresponding joint angles, goal type,and other parameters of a constraint as its input and computes the objective function value and its gradient.Since the partial derivatives with respect to the joint angles other than those on the joint chain are zero,the gradient determined by this module has to include only the derivatives with respect to the joint angles on the chain.This property lends itself to a clean modular implementation.However,two gradientvectors so structured for different constraints do not add directly—the th joint angle in one chain may not be the same as the th joint angle in another chain.The difference is resolved by the Assembler module.Suppose there are constraints.LetΘbe the ordered set of joint angles on the joint chain of the th constraint,and be the number of joint angles inΘ.LetΘ1Θ28 the union of allΘ’s with the order defined in certain way,and be the number of joint angles inΘ.In general,1,because of possible overlap amongΘ’s.Let’s define the index table as a mapping:121229 such that the th joint angle inΘcorresponds to the th joint angle in the overall index systemΘ.This index table,along with the weight of the constraint,are passed to the Assembler so that the effect of the th constraint to the gradient of the overall objective function all can be correctly accounted.Once the’s, the derivative of the objective function of the th constraint with regard to the th joint angle inΘ,are available,the Assembler does:For1to do,for12,where stands for the partial derivative of all with regard to the th joint angle inΘ.They are initially set to zero.10Reconciliation of Joint ChainsIt was suggested in Expression(28)that only a union was needed to combine all the joint chains.In fact,it is slightly more complicated,because we allow the user to specify the set of joints in the joint chain as the resource for the constraint satisfaction.The joint chain does not have to go from the end-effector segment back to the root in thefigure definition,and is specified by the user when he or she defines the constraint. Since the constraints may be input one by one,a joint which may affect the end-effector of one constraint but is not picked for the joint chain could well be picked for the joint chain of another constraint.For。

PeakFit介绍

PeakFit介绍

各位虫友大家好,我是虫友rabbit_he. 今天,我做的语音教程是关于软件PeakFit的入门教程---因为我是学催化专业的,之后会带来它实战XRD、XPS、IR峰的应用语音教程,就没在深究它在其它方面的应用了。

具体大家看我的操作(语音可能不太清楚,又是新手做教程,敬请大家原谅,海涵啊^-^),我先给大家介绍一下peakfit到底能干什么?Peakfit -------- 最佳自动分离、拟合与分析非线性数据软件第一名的非线性尖峰曲线套配软件Peakfit 色层分析及光谱研究软件使用非线性曲线的适当峰值可让您侦测、测定及分析隐藏在未解决峰值数据中的峰值,使用可在屏幕上管控的图形分析方式,省略复杂的数学积分技巧。

在光谱(XRD XPS IR=其它一些光谱数据,据我所知现在有很多人用xpspeak分析xps数据,大家学了这个,你不觉得没有必要单独学它了,嘿嘿),层析,电泳分析时,我们常需要非线性曲线套配,以找出合理的峰值.以前要花许多时间的资料分析工作,现在只要几分钟即可,不管是复杂或噪声多的资料,都有办法帮您处理。

--------------------------------------------------------------------------------。

欢迎访问SYSTAT美国网站:/ ,您可以获得更详尽的信息。

点击下载详细手册:SYSTA T_PDF文件-----它主页的pdf没啥用,就四页,我上传的附件中带了一个从程序中提取出来后的帮助文档,pdf格式,295页,有兴趣的虫友多研究研究。

安装时先正常安装peakfit.exe 安完后,启动程序时会有一界面,上有三个按钮update -----continue-----exit,点击update,之后启动keygen.exe输入注册码就ok啦!!!不注册的话,它有三十天的使用期限,期限一到,你就不能使用了。

----------------------------------------------------------------------------------------------------------------------大家没兴趣的话都不用看这个英文介绍,到实战的时候,我会具体而详细的都讲一下,下面简要的挑几个概念讲一下Peakfit's state - of -the - art nonlinear curve fitting is essential for accurate peak analysis and conclusive findings. PeakFit separates and analyzes nonlinear peak data better, more accurately and more conveniently than your lab instrument. Nonlinear curve fitting is by far the most accurate way to reduce noise and quantify peaks.PeakFit uses three procedures to automatically place hidden peaks, while each is a strong solution, one method may work better with some data sets than the others. The three Procedures are... Residuals - initially places peaks by finding local maxima in a smoothed data streamSecond Derivative - searches for local minima within a smoothed second derivative data stream Deconvolution - uses a Gaussian response function with a Fourier deconvolution/ filtering algorithm.PeakFit helps you separate overlapping peaks by statistically fitting numerous peak functions to one data set, which can help you find even the most obscure patterns in your data. The background can be fit as a separate polynomial(多项式), exponential, logarithmic, hyperbolic(双曲线)or power model. This fitted baseline is then subtracted before peak characterization data (such as areas) is calculated, which gives much more accurate results.PeakFit is the automatic choice for Spectroscopy, Chromatography or Electrophoresis. AI Experts throughout the smoothing options and other parts of the program automatically help you to set many adjustments.PeakFit can even deconvolve your spectral instrument response so that you can analyze your data without the smearing that your instrument introduces. By Using 82 nonlinear peak models to choose from, you're almost guaranteed to find the best equation for your data.PeakFit includes 18 different nonlinear spectral application line shapes, including the Gaussian, the Lorentzian, and the V oigt, and even a Gaussian plus Compton Edge model for fitting Gamma Ray peaks. As a product of the curve fitting process, PeakFit reports amplitude (intensity), area, center and width data for each peak. Overall area is determined by integrating the peak equations in the entire model.The V oigt function is a convolution of both the Gaussian and Lorentzian functions. Most analysis packages that offer a V oigt function use an approximation with very limited precision. PeakFit actually uses a closed-form solution to precisely calculate the function analytically. PeakFit has four different Voigt functions, so you can fit the parameters you're most interested in, including the individual widths of both the Gaussian and Lorentzian components, and also the amplitude and area of the Voigt function. PeakFit's precise calculation of the V oigt function is crucial to the accuracy of your analysis.FeaturesNonlinear Curve FittingFull Graphical Placement of PeaksHighly Advanced Baseline SubtractionPeakFit Saves You Precious Research TimePublication-Quality Graphs and Data OutputPeakFit Offers Sophisticated Data ManipulationPeakFit Automatically Places Peaks in Three WaysData InputData PreparationPeak AutoplacementNon - Linear Curve FittingOutput and Export OptionsFitting Multiple Simultaneous Gaussian FunctionsSYSTEM REQUIREMENTS486 Processor or higherWindows 95 and above8 MB RAM required (12 MB or more recommended)5MB hard disk spaceTOPICSWhy Should You Use Nonlinear Curve Fitting?PeakFit Offers Sophisticated Data ManipulationHighly Advanced Baseline SubtractionFull Graphical Placement of Peaks and...Publication-Quality Graphs and Data OutputPeakFit Saves You Precious Research TimePeakFit Automatically Places Peaks in Three WaysWhy Should You Use Nonlinear Curve Fitting?Nonlinear curve fitting is by far the most accurate way to reduce noise and quantify peaks. Many instruments come with software that only approximates the fitting process by simply integrating the raw data numerically. When there are shouldered, or hidden peaks, a lot of noise, or a significant background signal, this can lead to the wrong results. (For example, a spectroscopy data set may appear to have a peak with a 'raw' amplitude of 4,000 units -- but may have a shoulder peak that distorts the amplitude by 1,500 units! This would be a significant error.)PeakFit helps you separate overlapping peaks by statistically fitting numerous peak functions to one data set, which can help you find even the most obscure patterns in your data. The background can be fit as a separate polynomial, exponential, logarithmic, hyperbolic or power model. This fitted baseline is then subtracted before peak characterization data (such as areas) is calculated, which gives much more accurate results. And any noise (like you get with electrophoretic gels orRaman spectra) that might bias raw data calculations is filtered simply by the nonlinear curve fitting process. Nonlinear curve fitting is essential for accurate peak analysis and accurate research.PeakFit Offers Sophisticated Data ManipulationWith PeakFit's visual FFT filter, you can inspect your data stream in the Fourier domain and zero higher frequency points -- and see your results immediately in the time-domain. This smoothing technique allows for superb noise reduction while maintaining the integrity of the original data stream. PeakFit also includes an automated FFT method as well as Gaussian convolution, the Savitzky-Golay method, and the Loess algorithm for smoothing. AI Experts throughout the smoothing options and other parts of the program automatically help you to set many adjustments. And, PeakFit even has a digital data enhancer, which helps to analyze your sparse data. Only PeakFit offers so many different methods of data manipulation.Click to View Larger ImageHighly Advanced Baseline SubtractionPeakFit's non-parametric baseline fitting routine easily removes the complex background of a DNA electrophoresis sample. PeakFit can also subtract eight other built-in baseline equations, or it can subtract any baseline you've developed and stored in a file.Click to View Larger ImageFull Graphical Placement of PeaksIf PeakFit's auto-placement features fail on extremely complicated or noisy data, you can place and fit peaks graphically with only a few mouse clicks. Each placed function has "anchors" that adjust even the most highly complex functions, automatically changing that function's specific numeric parameters. PeakFit's graphical placement options handle even the most complex peaks as smoothly as Gaussians.Publication-Quality Graphs and Data OutputEvery publication-quality graph (see above) was created using PeakFit's built-in graphic engine -- which now includes print preview and extensive file and clipboard export options. The numerical output is customizable so that you see only the content you want.PeakFit Saves You Precious Research TimeFor most data sets, PeakFit does all the work for you. What once took hours now takes minutes –with only a few clicks of the mouse! It’s so easy that novices can learn how to use PeakFit in no time. And if you have extremely complex or noisy data sets, the sophistication and depth ofPeakFit’s data manipulation techniques is unequaled.PeakFit Automatically Places Peaks in Three WaysPeakFit uses three procedures to automatically place hidden peaks; while each is a strong solution, one method may work better with some data sets than the others.The Residuals procedure initially places peaks by finding local maxima in a smoothed data stream. Hidden peaks are then optionally added where peaks in the residuals occur.The Second Derivative procedure searches for local minima within a smoothed second derivative data stream. These local minima often reveal hidden peaks.The Deconvolution procedure uses a Gaussian response function with a Fourier deconvolution/ filtering algorithm. A successfully deconvolved spec-trum will consist of “sharpened” peaks of equivalent area. The goal is to enhance the hidden peaks so that each represents a local maximum. Built-In Nonlinear Functions (83 total)Spectroscopy (18): Gauss Amp(高斯), Gauss Area, Lorentz Amp, Lorentz Area, V oigt Amp, V oigt Area, Voigt Amp Approx, V oigt Amp G/L, Voigt Area G/l, Gauss Cnstr Amp, Gauss Cnstr Area, Pearson VII Amp, Pearson VII Area, Gauss+Lor Area, Gauss*Lor, Gamma Ray, Compton Edge.Chromatography (8): HVL, NLC, Giddings, EMG, GMG, EMG+GMG, GEMG, GEMG 5-parm.Statistical (31): Log Normal Amp, Log Normal Area, Logistic Amp, Logistic Area, Laplace Amp, Laplace Area, Extr V alue Amp, Extr Value Area, Log Normal-4 Amp, Log Normal-4 Area, Eval4 Amp Tailed, Eval4 Area Tailed, Eval4 Amp Frtd, Eval4 Area Frtd, Gamma Amp, Gamma Area, Inv Gamma Amp, Inv Gamma Area, Weibull Amp, Weibull Area, Error Amp, Error Area, Chi-Sq Amp, Chi-Sq Area, Student t Amp, Student t Area, Beta Amp, Beta Area, F Variance Amp, F Variance Area, Pearson IV.General Peak (12): Erfc Pk, Pulse Pk, LDR Pk, Asym Lgstc Pk, Lgstc pow Pk, Pulse pow Pk, Pulse Wid2 Pk, Intermediate Pk, Sym Dbl Sigmoid, Sym Dbl GaussCum, Asym Dbl Sigmoid, Asym Dbl GaussCum.Transition (14): Sigmoid Asc, Sigmoid Desc, GaussCum Asc, GaussCum Desc, LorentzCum Asc, LorentzCum Desc, LgstcDose Rsp Asc, LgstcDoseRsp Desc, LogNormCum Asc, LogNormCum Desc, ExtrValCum Asc, ExtrValCum Desc, PulseCum Asc, PulseCum Desc.User Defined FunctionsUp to 10 Parameters per function.Up to 15 UDF's active during fit.Estimates can contain formulas and constraints.Extensive mathematical, statistical, Bessel, and logic functions.Baseline Fit and SubtractAutomatic detection of baseline points by constant second derivatives.Real-time Fitting in conjunction with data point selection, deselection.Background Functions (10): Constant, Linear, Progressive Linear, Quadratic, Cubic, Logarithmic, Exponential, Power, Hyperbolic and Non-Parametric.Graphical ReviewComponent and Sum Curve graphs.Residuals Graphs, including Distribution and Stabilized Normal Probability Plots.Confidence and Prediction Intervals.Peak Labels (Amplitude, Center, or Area).Numerical ReviewPeak Characterization data: Center, Amplitude, Integrated Area, Analytical Area, FWHM, FW10, FWBASE, Asymmetry at HM, Asymmetry at 10 percent, First and Second Moments, Column Efficiency and Resolution, Percentage Areas, Overlap Areas.Parameter Statistics: Parameter Values, Confidence Limits (90, 95, 99 percent), t-values, Standard Errors.Fit Statistics: Analysis of Variance, F-statistic, Overall Standard Error, r2 value(回归系数平方和---以后我们使用时重点关注的参数,它值在0-1之间,越接近于1,你的拟合效果越好).Data Statistics: Residual Values, Predicted Y-values, Confidence/Prediction Intervals.PeakFit Offers Sophisticated Data ManipulationHighly Advanced Baseline SubtractionPeakFit Automatically Places Peaks in Three WaysPeakFit Automatically Places Peaks in Three WaysSingle Channel Analysis Fitting Multiple Simultaneous Gaussian FunctionsSingle Channel Analysis Fitting Multiple Simultaneous Gaussian FunctionsSingle Channel Analysis Fitting Multiple Simultaneous Gaussian FunctionsSingle Channel Analysis Fitting Multiple Simultaneous Gaussian Functions下面,我们着重看一下程序界面。

数学de一些英文名称

数学de一些英文名称

Applied Partial Differential Equations Computer Graphics Computer Aided Geometric Design Selections from System Modeling Biology Mathematics Mathematical Finance Multimedia Techniques Applied Geometry Principle of Computer Network Dynamical Systems The Theory of Interest Actuarial Mathematics Coding Theory Numerical Analysis(Computing Method) Nonlinear Programming Combinatorial Optimization Optimal Control Theory Fractal Geometry Functions of Several Complex Variables Theory of Integral Equations and Its Applications Elementary Number Theory Stochastic Processes Applied software of Mathematics and Practice Introduction to Actuarial Science Advanced course on Mathematical Physics Mathematics of Demography Financial Economics Combinational Analysis Life Insurance Fourier Analysis Introduction to Insurance Introduction to Insurance and Actuarial Science Nonlife Actuarial Mathematics Complex Analysis Introduction to Control Theory Life Actuarial Mathematics Data Structures Statistics Digital Signal Processing Linear Programming Basic Information Theory Element of Database System

计量经济学中英文词汇对照

计量经济学中英文词汇对照

Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covaห้องสมุดไป่ตู้iance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Asymmetric distribution Asymptotic bias Asymptotic efficiency Asymptotic variance Attributable risk Attribute data Attribution Autocorrelation Autocorrelation of residuals Average Average confidence interval length Average growth rate BBB Bar chart Bar graph Base period Bayes' theorem Bell-shaped curve Bernoulli distribution Best-trim estimator Bias Binary logistic regression Binomial distribution Bisquare Bivariate Correlate Bivariate normal distribution Bivariate normal population Biweight interval Biweight M-estimator Block BMDP(Biomedical computer programs) Boxplots Breakdown bound CCC Canonical correlation Caption Case-control study Categorical variable Catenary Cauchy distribution Cause-and-effect relationship Cell Censoring

On the asymptotic expansion of the solutions of the separated nonlinear Schroedinger equati

On the asymptotic expansion of the solutions of the separated nonlinear Schroedinger equati

a r X i v :n l i n /0012025v 3 [n l i n .S I ] 10 M a y 2001On the Asymptotic Expansion of the Solutions of the Separated Nonlinear Schr¨o dinger EquationA.A.Kapaev,St Petersburg Department of Steklov Mathematical Institute,Fontanka 27,St Petersburg 191011,Russia,V.E.Korepin,C.N.Yang Institute for Theoretical Physics,State University of New York at Stony Brook,Stony Brook,NY 11794-3840,USAAbstractNonlinear Schr¨o dinger equation with the Schwarzian initial data is important in nonlinear optics,Bose condensation and in the theory of strongly correlated electrons.The asymptotic solutions in the region x/t =O (1),t →∞,can be represented as a double series in t −1and ln t .Our current purpose is the description of the asymptotics of the coefficients of the series.MSC 35A20,35C20,35G20Keywords:integrable PDE,long time asymptotics,asymptotic expansion1IntroductionA coupled nonlinear dispersive partial differential equation in (1+1)dimension for the functions g +and g −,−i∂t g +=12∂2x g −+4g 2−g +,(1)called the separated Nonlinear Schr¨o dinger equation (sNLS),contains the con-ventional NLS equation in both the focusing and defocusing forms as g +=¯g −or g +=−¯g −,respectively.For certain physical applications,e.g.in nonlin-ear optics,Bose condensation,theory of strongly correlated electrons,see [1]–[9],the detailed information on the long time asymptotics of solutions with initial conditions rapidly decaying as x →±∞is quite useful for qualitative explanation of the experimental phenomena.Our interest to the long time asymptotics for the sNLS equation is inspired by its application to the Hubbard model for one-dimensional gas of strongly correlated electrons.The model explains a remarkable effect of charge and spin separation,discovered experimentally by C.Kim,Z.-X.M.Shen,N.Motoyama,H.Eisaki,hida,T.Tohyama and S.Maekawa [19].Theoretical justification1of the charge and spin separation include the study of temperature dependent correlation functions in the Hubbard model.In the papers[1]–[3],it was proven that time and temperature dependent correlations in Hubbard model can be described by the sNLS equation(1).For the systems completely integrable in the sense of the Lax representa-tion[10,11],the necessary asymptotic information can be extracted from the Riemann-Hilbert problem analysis[12].Often,the fact of integrability implies the existence of a long time expansion of the generic solution in a formal series, the successive terms of which satisfy some recurrence relation,and the leading order coefficients can be expressed in terms of the spectral data for the associ-ated linear system.For equation(1),the Lax pair was discovered in[13],while the formulation of the Riemann-Hilbert problem can be found in[8].As t→∞for x/t bounded,system(1)admits the formal solution given byg+=e i x22+iν)ln4t u0+∞ n=12n k=0(ln4t)k2t −(1t nv nk ,(2)where the quantitiesν,u0,v0,u nk and v nk are some functions ofλ0=−x/2t.For the NLS equation(g+=±¯g−),the asymptotic expansion was suggested by M.Ablowitz and H.Segur[6].For the defocusing NLS(g+=−¯g−),the existence of the asymptotic series(2)is proven by P.Deift and X.Zhou[9] using the Riemann-Hilbert problem analysis,and there is no principal obstacle to extend their approach for the case of the separated NLS equation.Thus we refer to(2)as the Ablowitz-Segur-Deift-Zhou expansion.Expressions for the leading coefficients for the asymptotic expansion of the conventional NLS equation in terms of the spectral data were found by S.Manakov,V.Zakharov, H.Segur and M.Ablowitz,see[14]–[16].The general sNLS case was studied by A.Its,A.Izergin,V.Korepin and G.Varzugin[17],who have expressed the leading order coefficients u0,v0andν=−u0v0in(2)in terms of the spectral data.The generic solution of the focusing NLS equation contains solitons and radiation.The interaction of the single soliton with the radiation was described by Segur[18].It can be shown that,for the generic Schwarzian initial data and generic bounded ratio x/t,|c−xthese coefficients as well as for u n,2n−1,v n,2n−1,wefind simple exact formulaeu n,2n=u0i n(ν′)2n8n n!,(3)and(20)below.We describe coefficients at other powers of ln t using the gener-ating functions which can be reduced to a system of polynomials satisfying the recursion relations,see(24),(23).As a by-product,we modify the Ablowitz-Segur-Deift-Zhou expansion(2),g+=exp i x22+iν)ln4t+i(ν′)2ln24t2] k=0(ln4t)k2t −(18t∞n=02n−[n+1t n˜v n,k.(4)2Recurrence relations and generating functions Substituting(2)into(1),and equating coefficients of t−1,wefindν=−u0v0.(5) In the order t−n,n≥2,equating coefficients of ln j4t,0≤j≤2n,we obtain the recursion−i(j+1)u n,j+1+inu n,j=νu n,j−iν′′8u n−1,j−2−−iν′8u′′n−1,j+nl,k,m=0l+k+m=nα=0, (2)β=0, (2)γ=0, (2)α+β+γ=ju l,αu k,βv m,γ,(6) i(j+1)v n,j+1−inv n,j=νv n,j+iν′′8v n−1,j−2++iν′8v′′n−1,j+nl,k,m=0l+k+m=nα=0, (2)β=0, (2)γ=0, (2)α+β+γ=ju l,αv k,βv m,γ,(7)where the prime means differentiation with respect toλ0=−x/(2t).Master generating functions F(z,ζ),G(z,ζ)for the coefficients u n,k,v n,k are defined by the formal seriesF(z,ζ)= n,k u n,k z nζk,G(z,ζ)= n,k v n,k z nζk,(8)3where the coefficients u n,k,v n,k vanish for n<0,k<0and k>2n.It is straightforward to check that the master generating functions satisfy the nonstationary separated Nonlinear Schr¨o dinger equation in(1+2)dimensions,−iFζ+izF z= ν−iν′′8zζ2 F−iν′8zF′′+F2G,iGζ−izG z= ν+iν′′8zζ2 G+iν′8zG′′+F G2.(9) We also consider the sectional generating functions f j(z),g j(z),j≥0,f j(z)=∞n=0u n,2n−j z n,g j(z)=∞n=0v n,2n−j z n.(10)Note,f j(z)≡g j(z)≡0for j<0because u n,k=v n,k=0for k>2n.The master generating functions F,G and the sectional generating functions f j,g j are related by the equationsF(zζ−2,ζ)=∞j=0ζ−j f j(z),G(zζ−2,ζ)=∞j=0ζ−j g j(z).(11)Using(11)in(9)and equating coefficients ofζ−j,we obtain the differential system for the sectional generating functions f j(z),g j(z),−2iz∂z f j−1+i(j−1)f j−1+iz∂z f j==νf j−z iν′′8f j−ziν′8f′′j−2+jk,l,m=0k+l+m=jf k f lg m,2iz∂z g j−1−i(j−1)g j−1−iz∂z g j=(12)=νg j+z iν′′8g j+ziν′8g′′j−2+jk,l,m=0k+l+m=jf kg l g m.Thus,the generating functions f0(z),g0(z)for u n,2n,v n,2n solve the systemiz∂z f0=νf0−z (ν′)28g0+f0g20.(13)The system implies that the product f0(z)g0(z)≡const.Since f0(0)=u0and g0(0)=v0,we obtain the identityf0g0(z)=−ν.(14) Using(14)in(13),we easilyfindf0(z)=u0e i(ν′)28n n!z n,4g0(z)=v0e−i(ν′)28n n!z n,(15)which yield the explicit expressions(3)for the coefficients u n,2n,v n,2n.Generating functions f1(z),g1(z)for u n,2n−1,v n,2n−1,satisfy the differential system−2iz∂z f0+iz∂z f1=νf1−z iν′′8f1−ziν′8g0−z(ν′)24g′0+f1g20+2f0g0g1.(16)We will show that the differential system(16)for f1(z)and g1(z)is solvable in terms of elementary functions.First,let us introduce the auxiliary functionsp1(z)=f1(z)g0(z).These functions satisfy the non-homogeneous system of linear ODEs∂z p1=iν4−ν′′4f′0z(p1+q1)−i(ν′)28−ν′g0,(17)so that∂z(q1+p1)=−(ν2)′′8z,p1(z)= −iνν′′8−ν′u′032z2,g1(z)=q1(z)g0(z),g0(z)=v0e−i(ν′)24−ν′′4v0 z+i(ν′)2ν′′4−ν′′4u0 ,v1,1=v0 iνν′′8−ν′v′0u n,2n −1=−2u 0i n −1(ν′)2(n −1)n −1ν′′u 0,n ≥2,v n,2n −1=−2v 0(−i )n −1(ν′)2(n −1)n −1ν′′v 0,n ≥2.Generating functions f j (z ),g j (z )for u n,2n −j ,v n,2n −j ,j ≥2,satisfy the differential system (12).Similarly to the case j =1above,let us introduce the auxiliary functions p j and q j ,p j =f jg 0.(21)In the terms of these functions,the system (12)reads,∂z p j =iνz(p j +q j )+b j ,(22)wherea j =2∂z p j −1+i (ν′)28−j −14(p j −1f 0)′8f 0+iν4−ν′′zq j −1−−ν′g 0+i(q j −2g 0)′′zj −1 k,l,m =0k +l +m =jp k q l q m .(23)With the initial condition p j (0)=q j (0)=0,the system is easily integrated and uniquely determines the functions p j (z ),q j (z ),p j (z )= z 0a j (ζ)dζ+iνzdζζζdξ(a j (ξ)+b j (ξ)).(24)These equations with expressions (23)together establish the recursion relationfor the functions p j (z ),q j (z ).In terms of p j (z )and q j (z ),expansion (2)readsg +=ei x22+iν)ln 4t +i(ν′)2ln 24tt2t−(18tv 0∞ j =0q j ln 24tln j 4t.(25)6Let a j (z )and b j (z )be polynomials of degree M with the zero z =0of multiplicity m ,a j (z )=M k =ma jk z k,b j (z )=Mk =mb jk z k .Then the functions p j (z )and q j (z )(24)arepolynomials of degree M +1witha zero at z =0of multiplicity m +1,p j (z )=M +1k =m +11k(a j,k −1+b j,k −1)z k ,q j (z )=M +1k =m +11k(a j,k −1+b j,k −1) z k.(26)On the other hand,a j (z )and b j (z )are described in (23)as the actions of the differential operators applied to the functions p j ′,q j ′with j ′<j .Because p 0(z )=q 0(z )≡1and p 1(z ),q 1(z )are polynomials of the second degree and a single zero at z =0,cf.(19),it easy to check that a 2(z )and b 2(z )are non-homogeneous polynomials of the third degree such thata 2,3=−(ν′)4(ν′′)2210(2+iν),(27)a 2,0=−iνν′′8−ν′u ′08u 0,b 2,0=iνν′′8−ν′v ′08v 0.Thus p 2(z )and q 2(z )are polynomials of the fourth degree with a single zero at z =0.Some of their coefficients arep 2,4=q 2,4=−(ν′)4(ν′′)24−(1+2iν)ν′′8u 0−ν(u ′0)24−(1−2iν)ν′′8v 0−ν(v ′0)22.Proof .The assertion holds true for j =0,1,2.Let it be correct for ∀j <j ′.Then a j ′(z )and b j ′(z )are defined as the sum of polynomials.The maximal de-grees of such polynomials are deg (p j ′−1f 0)′/f 0 =2j ′−1,deg (q j ′−1g 0)′/g 0 =72j′−1,anddeg 1z j′−1 α,β,γ=0α+β+γ=j′pαqβqγ =2j′−1. Thus deg a j′(z)=deg b j′(z)≤2j′−1,and deg p j′(z)=deg q j′(z)≤2j′.Multiplicity of the zero at z=0of a j′(z)and b j′(z)is no less than the min-imal multiplicity of the summed polynomials in(23),but the minor coefficients of the polynomials2∂z p j′−1and−(j−1)p j′−1/z,as well as of2∂z q j′−1and −(j−1)q j′−1/z may cancel each other.Let j′=2k be even.Thenm j′=min m j′−1;m j′−2+1;minα,β,γ=0,...,j′−1α+β+γ=j′mα+mβ+mγ =j′2 . Let j′=2k−1be odd.Then2m j′−1−(j′−1)=0,andm j′=min m j′−1+1;m j′−2+1;minα,β,γ=0,...,j′−1α+β+γ=j′mα+mβ+mγ =j′+12]p j,k z k,q j(z)=2jk=[j+12]z nn−[j+18k k!,g j(z)=v0∞n=[j+12]k=max{0;n−2j}q j,n−k(−i)k(ν′)2k2]k=max{0;n−2j}p j,n−ki k(ν′)2k2]k=max{0;n−2j}q j,n−k(−i)k(ν′)2kIn particular,the leading asymptotic term of these coefficients as n→∞and j fixed is given byu n,2n−j=u0p j,2j i n−2j(ν′)2(n−2j)n) ,v n,2n−j=v0q j,2j (−i)n−2j(ν′)2(n−2j)n) .(32)Thus we have reduced the problem of the evaluation of the asymptotics of the coefficients u n,2n−j v n,2n−j for large n to the computation of the leading coefficients of the polynomials p j(z),q j(z).In fact,using(24)or(26)and(23), it can be shown that the coefficients p j,2j,q j,2j satisfy the recurrence relationsp j,2j=−i (ν′)2ν′′2jj−1k,l,m=0k+l+m=jp k,2k p l,2l q m,2m++ν(ν′)2ν′′4j2j−1k,l,m=0k+l+m=jp k,2k(p l,2l−q l,2l)q m,2m,q j,2j=i (ν′)2ν′′2jj−1k,l,m=0k+l+m=jp k,2k q l,2l q m,2m−(33)−ν(ν′)2ν′′4j2j−1k,l,m=0k+l+m=jp k,2k(p l,2l−q l,2l)q m,2m.Similarly,the coefficients u n,0,v n,0for the non-logarithmic terms appears from(31)for j=2n,and are given simply byu n,0=u0p2n,n,v n,0=v0q2n,n.(34) Thus the problem of evaluation of the asymptotics of the coefficients u n,0,v n,0 for n large is equivalent to computation of the asymptotics of the minor coeffi-cients in the polynomials p j(z),q j(z).However,the last problem does not allow a straightforward solution because,according to(8),the sectional generating functions for the coefficients u n,0,v n,0are given byF(z,0)=∞n=0u n,0z n,G(z,0)=∞n=0v n,0z n,and solve the separated Nonlinear Schr¨o dinger equation−iFζ+izF z=νF+18zG′′+F G2.(35)93DiscussionOur consideration based on the use of generating functions of different types reveals the asymptotic behavior of the coefficients u n,2n−j,v n,2n−j as n→∞and jfixed for the long time asymptotic expansion(2)of the generic solution of the sNLS equation(1).The leading order dependence of these coefficients on n is described by the ratio a n2+d).The investigation of theRiemann-Hilbert problem for the sNLS equation yielding this estimate will be published elsewhere.Acknowledgments.We are grateful to the support of NSF Grant PHY-9988566.We also express our gratitude to P.Deift,A.Its and X.Zhou for discussions.A.K.was partially supported by the Russian Foundation for Basic Research under grant99-01-00687.He is also grateful to the staffof C.N.Yang Institute for Theoretical Physics of the State University of New York at Stony Brook for hospitality during his visit when this work was done. References[1]F.G¨o hmann,V.E.Korepin,Phys.Lett.A260(1999)516.[2]F.G¨o hmann,A.R.Its,V.E.Korepin,Phys.Lett.A249(1998)117.[3]F.G¨o hmann,A.G.Izergin,V.E.Korepin,A.G.Pronko,Int.J.Modern Phys.B12no.23(1998)2409.[4]V.E.Zakharov,S.V.Manakov,S.P.Novikov,L.P.Pitaevskiy,Soli-ton theory.Inverse scattering transform method,Moscow,Nauka,1980.[5]F.Calogero,A.Degasperis,Spectral transforms and solitons:toolsto solve and investigate nonlinear evolution equations,Amsterdam-New York-Oxford,1980.[6]M.J.Ablowitz,H.Segur,Solitons and the inverse scattering trans-form,SIAM,Philadelphia,1981.10[7]R.K.Dodd,J.C.Eilbeck,J.D.Gibbon,H.C.Morris,Solitons andnonlinear wave equations,Academic Press,London-Orlando-San Diego-New York-Toronto-Montreal-Sydney-Tokyo,1982.[8]L.D.Faddeev,L.A.Takhtajan,Hamiltonian Approach to the Soli-ton Theory,Nauka,Moscow,1986.[9]P.Deift,X.Zhou,Comm.Math.Phys.165(1995)175.[10]C.S.Gardner,J.M.Greene,M.D.Kruskal,R.M.Miura,Phys.Rev.Lett.19(1967)1095.[11]x,Comm.Pure Appl.Math.21(1968)467.[12]V.E.Zakharov,A.B.Shabat,Funkts.Analiz Prilozh.13(1979)13.[13]V.E.Zakharov,A.B.Shabat,JETP61(1971)118.[14]S.V.Manakov,JETP65(1973)505.[15]V.E.Zakharov,S.V.Manakov,JETP71(1973)203.[16]H.Segur,M.J.Ablowitz,J.Math.Phys.17(1976)710.[17]A.R.Its,A.G.Izergin,V.E.Korepin,G.G.Varzugin,Physica D54(1992)351.[18]H.Segur,J.Math.Phys.17(1976)714.[19]C.Kim,Z.-X.M.Shen,N.Motoyama,H.Eisaki,hida,T.To-hyama and S.Maekawa Phys Rev Lett.82(1999)802[20]A.R.Its,SR Izvestiya26(1986)497.11。

【doc】非零初始值非线性系统的求解和传递函数的递推计算

【doc】非零初始值非线性系统的求解和传递函数的递推计算

非零初始值非线性系统的求解和传递函数的递推计算第15卷第3期武汉工业大学1993年9月JOURNALOFWUHANUNIVERSITYOFTECHNOLOGYv01.15N3Sept.I993零初始值非线性系统的求解和传递函数的递推计算杨惟高(电气自动化系)弋巧摘要:本文培出一种非常简使的轫始值全零和初姑值非零的非线性系皖V oltcrra 响应和待递函数的递推求法,且所得传递函数是一十耐称化的频城核.关键词:!!塑兰苎!塞丝垒丝LV oRerra响应一兰墨塾!递推计算V oltcrra泛函级数是分析非线性系统,特别是弱非线性系统的一种有效方法.但是目前利用V oltcrra级数研究系统哲态的文献还不多见,特别是非零初始值系统的暂态分析.少数研究这一问题的频域分析方法,已经十分繁冗.这些方法或是将V olterra级数的积分限取成一o.到+oo,从而将初始值("…)的值归化为零,而后采用双边多维正,反Laplace变换} 或是采用辅助变量,伪强制函数以及比较系数法去引出一组相应阶次的联立偏微分方程,以得到系统相应阶次的自然特性(或时域核),求解出响应.后一方法虽可处理=0时的非零初始值,但计算十分繁冗,得到的还不是一个对称化的频域核,且不易编制计算机程序予以数值求解,特别是系统阶次越高,辅助求解的线性偏微分方程组的阶次也越高,求解变得十分复杂.本文提出一种求解非零初始值非线性系统时域解的V olterra级数方法,克服了以上方法的种种缺点,使非线性系统在时域内的分析变得简易可行.当非线性系统中的非线性元,器件的特性关系采用幂级数或Taylor级数表征时,非线性系统具有如下规范形:0L(D)x(O+-(1)+∑(0%(1))'j=(£)(1)I一2j-1●一2或∞_一1∞L∽)(1)=(1)一-(c)一∑(∑j(1))j(2)●一2j-1●一2J式中(D)为算子D△的m阶线性多项式I.(f)△(f)=(f)I()为外加激励函数,或实际激励函数e(f)的D的线性多项式.(2)式右边的第二,第三项称为非线性电源,前者称为代数型,后者称为微分型.本文1999年11月30日收到第15卷第3期杨惟高非零初始值非线性系统的求解和传递函数的递推计算l零初始值系统的V oRerra响应及其递推分析当非线性系统初始值全为零时,且仅含有代致型非线性电源,则系统响应x(t)可采用V olterra泛函级致表征为:z(O=0(')●.l其中各阶V olterra响应{x-(t),n=1,2,…)有如下之递推计算公式:.l(')J."一)(棚l:")=一垫J:"一r)")dr式中h(t)为线性子系统L(D)x(t)=g(t)的单位冲激响应,或线性子系统的自然特性. (.]△]--rl+l**'Z.--一+t|_I…I.1(3)(4)而(5)当系统另含微分型非线性电源时,先将其按相应之代致型电源处理,而后根据卷积积分的导致性质,在相应阶响应上添加该电源之贡献.2非零初始值系统的V olterra响应及其递推分析当系统的初始值非零时,设系统模型为:口.z'+口一】z一'+….+atX+口=g(O一戤一(6)■一2及z'(0)=z5,』=0,1,2….(埘一1)采用下述方法将其蜕化成初始全零情酿:step1:作辅助变量"(1);z(O一(7)将其代回原式有:口.'+一l"一-t-….+QJ+口,o==(f)一.(8)●_2且有u0;0,将其零阶初始值零化.step2:引入伪强制函致f(t)使之满足口.,叫'4-口一tf卅4-….+口2+a,f==(})(9)及'=z",j=0,1,2,(m一2)注意:在传毵方法中,(9)式左边的各系数均为1,从而带来解算的繁冗而这里取骧方程的相应系致,这正是此法简捷的基础.step3:对方程进行普通Lapl~ce变换,即L{口.+Qm--IU一'+….口I"+口}=L{口.,抽'十Qm--Jf+….+口2,+ⅡIf}一L{.}考虑到及,则上式经变换后,式中的非零初始值全部对消,有+一I-.+…'+口l8+口0)(s)一(口.+Qm--!+….+az$+口I)(0)100武汉工业大学1993年9月则(s)=一L(R,.(t))!!::±!!=::±::::±口.+~lm--1一+…'+口l口28十418+.口.s一+"一_一.+….+a15+口.)一L(-lfI(10)或(s)一1(8)(8)一HJ(s)L(TY,W?(t))(11)式中日,.及.对应其各自的定义.且YO)一L{,()},可由(9)式的非零初始值线性微分方程解出.(1O)或(11)式所描述的系统已成为一种初始值全零的情况,其各阶V olterra响应‰()可参看(d)式的递推公式求得.即:rr'I()J.-0一),()df1.r_【.一()一一?J.(一f)?+-()df(12)及式中"的定义同式(5)z(t):z.+H(t):z.+(f)-.J(13)下面讨论系统含微分型电源的情况.当系统中另含(2;C~x)时,仍取辅助变量u(t) 一x(t)一xo.代入方程,并将有关线性导数项合并到线性子系统中去.饲如,设1l(量一)c】=面d,Cz+Czlxz]+dz[口tt+Cs2xz]代入()=x(t)--xo后,并加以展开,有一)cj一(2m+3C|Iz;)害+(2+3Caz端)等+(%+‰++36".+△.害+蠹+n警+"+筝+n一.警+tdZu+錾誊一∥)cj上式中,前两项是线性项,将其归化到线性子系统中去.又经上述处理后,微分型电源中的初始电源对系统的影响,由于uo=O,而'一z护,J=l,2,…m—l若设:∞()△(t),及re(O)=∞0=0则L{∞)一s(s)一曲=sW(s)L{∞)一(8)一抛一(∞)口一矿(8)一()因I≥2,故(∞).一().一0L{∞}一(s)一般情况下,由第l5卷第3期杨惟高:非零初始值非线性系统的求解和传遵函数的递推计算[()]n=0'(t)]当f一0时,().=[(t)],只有当j&lt;k时,才有;j)=0.而当J≥t时,有初始值存在.例如:((t)]=口(+)[()]iz)=8;a(zo)在此情况下,由于引入辅助变量后,相应于(2)式方程变形为:‰"㈤+…+al+嘞"一z()一∑6,一()一=(∑y|J(t))(14)●一2』一II一2及=,=1,2…'m一1}t'0=0对上式进行普通].AL'llace变换后,有L{㈤+…十m+a)=z(s)一L{.tII}一二{()}(15)l_..l上式左边因变换所引入的初始值暂时保留,等式右边第二项在变换中不会出现初始值,而将第三项中出现的初始值合并到z(s)中击,并用z(s)表示.现在引入伪强制函数f(t),它应满足的方程的普通Lanlac~变换为:L{口-,+一I一+…'+a2,+口I,}=0)(16)其中,初始值关系为,i』)=",J=0,l,2,…'一2(16)将(16)式代入(15)式,其中z(s)已用z(s)取代,及L{'》)?)变换中已不再含初始值影响,可得:L{"+…+d+嘶")=L{a-,叫+…'a,f+口I,}一二{功J)m-!∞一L{∑(BtII)}(17)其中,经变换后,等式左边与右边第一项内的初始值全部彼此对消l而右边第二,三项变换中已不再出现初始值故有(s)=打,I(s)(s)一H10)L{蜀,I)一矾(s)L{∑(∑)(18)式中一cs△;兰菩c-.)为系统的一阶传递函数.及-()△'_:币:(0)而F(s)可由式(16)的线性微分方程的全响应求得.这样,(18)式方程中已不再含非零初始值.系统经如是处理后,已蜕化成零初始值情况,因而可套用零初始值系统的一系列结果和处理方法.此处不再重复非零初始值系统的n阶传递函数定义为:…&amp;)△(2D式中,V.(?)为U-(t)的n重Laplace变换式IF()为伪强制函数的一维La山∞变换式武汉工业大学lgg~,年9月3传递函数的递推计算传递函数是定义于复频域的.非线性系统的n阶传递函数定义为:8182o.o~.,△铵或.(sl…)△.){jI-(tl岛…))式中,x为n阶V olterra响应的n重Laplace变换式,而k(?)为n个人为变量的n阶自然特性.根据(4)或(12)式的递推关系和组合系统的响应特性可以方便地求出非线性系统的各阶传递函数以(4)式关系为倒.一阶传递函数上面已经给出,咯.二阶传递函数.这时£2(f)=一62fhl"一f)l(f)(下)d下其框图如图1所示.图1求二阶响应和二阶传函图1左端为一乘法系统,各子框都是线性的.相乘后,该乘法系统的二阶传递函数A,(s?s2)=H?(s?)H(sz).后面级联一个线性系统,故非线性系统的二阶传递函数为:2(如)=一62Hl(乱+岛)l()I(如)三阶传函:由如(1)=一函JI:~)拦.+.(T)d=一J.l(1一f)卧(f)df—b.J.^l(t—f)£i.'(f)df=一2J.?0~f)(f)(下)df—Jl(t—f)(f)l(f)(f)d下其对应框图如图2所示.图中上方,平方律组合系统的传函为;(s2岛)=Hi(岛)l(砘)图中下方,立方律组合系统的传函为;(s2sa)=H-(南)I()l()它们均后链一个线性子系统h?(t),而后相加,故整个系统的三阶传递函数为:3(曲)=一l(++)[2如2(岛).(岛)+H.(s)(趣)(岛))第l5卷第3期扬惟高:非零初始值非线性系统的求解和传递函数的递推计算?103. 同理,由(d)或(12)式递推关系和组合系统传递函数的结果,可易于导出H.(?),H'(?)," "从略.圉2袁三阶响应和三阶信函当系统中舍微分型电源时,在一阶传递函数中需叠加电源的贡献.相对于代数型电源贡献而言,只是后面再级联一个线性微分算子而已.4举例倒:初始非零的Duff~n8方程'1]鲁+a等++"a一,(1),帅一a,=6求系统的各阶V olterra响应及传递函数解:s~epl:引入辅助变量()=()一=(c)一口,并将其代回原方程,整理后得.+口等+"+6十ha==(c)十.面十.十6十h=【c)式中:一+3tl口.,b2=3Ⅵ,=H,,(t)一I(})一(口+M)stcp2:引入伪强制函数IQ),使之满足+口,一()及,0=6并从中解出,():由零输入和零状态响应求得:step3:将,()方程代回,并求各阶向应及传函嚣+a害+∥.=d面l+口,一b一b故一阶V oltcrra响应,由(4)式知一‰)△())故一阶传递函数:I(sI)二阶V olt~rra响应{由Q2)式104武议工业大学1993年9月(f)=一Jhi(f—f)?+,()dr'=一62i^L0一f)L(f)H_)f)dfJ从中解出ut(t)式中hi()r'(Hl()}式vi(sL)=--b2H_(s_+gi)HL(sL)Hl()F(墨_)F()故=阶传递函数为($L如)皇一日J(s_+赴)日_(s1)日L(赴)一3"乍看起来,二阶传函H(s)与文献(1]有所不同,但将F(s)数值代入后所得=阶响应是相同的.由后可知,文献(1]所求不是一个对称化传函.求三阶响应及传函:由sr'(f)一一1(f—f)Hj(f)dfti2JⅡrlrl一一2b!I^L0一f)啦(f)L(f)df一6II.^L0一f)H}(f)dfJ●J从中可解出ua(t).或(sL$!)一一2bah_(sJ+赴+唧)(818,)n(m)一61日L(+却+乱)VJ(却)Vl(矗I)n(m)及三阶传函为三h(岛03)=一日L(+如+乱)[2bz日{(sl却)日l(80+61日L(却)日I(却)日(曲)]将H(?),H.,(?),Hz(?)有关结果代^,即得.这一结果与文献(1]表征上有所不同,但本文所求的是一个对称化拔.此外,利用V30_曲)=日l(0ls203),()(赴),(岛)可求得与文献[13相同的解答.同理,可求四阶及更主阶响应和传函,此处从略.采用本文介绍的方法递推求解V olterra响应和各阶传递函数是很方便简捷的,且所求是一个对称化频域拔.它既不需要去解一组辅助的线性偏微分方程,也不要从多维Laplace反变换去求u.(t).参考文献1胡卦.Dulling方程V olterra棱的辑析计算.武议工业大学,1991,13(4).992R.H.[=lake.V~tezraSe比RepresentationofNonline~System.AIEEETrans.App1.1rid.1963.81(7)}3363曾广达.一类非线性系统V柚坩埔响应的递推分析算法.电路与系统,1990,2(1)一56~654曾广达.求解非线性系玩传递函数的递推组台法,电子,1992,20(8);915n舢mem1.OnTheUseofV olterraSeriesReprcsentaUonandm妇ImpulseResponseshNonlinearSystems.RevueA.1994,I(1):l98I.W.S毫Lr-曲tE_V口1t埔一likeExpansionsfoz"Sol~iozmofNonlinearIntegralE'a【jo借andI~fferentialEquaUons.1EEETram,1983,cAs一30(2)lI'092第l5卷第3嘏杨惟高;非零韧始值非线性系统的求解和传递萄救的递推计算?105? TheRecursiveCalculationMethodofSystemSolutionandTransfer FunctionsforNonlinearSystemswRhNon—zeroInRialV aluesY ang胁∞(一舶蛐^"阳如4由-,W6T)Abstract:Averysimpleandconvenientmethodforthecomputationofvolterrarcgpo~ andtransferfunctionsofnonlinearsyst~11~ofboth0fzeroinitialandnon-zeroiIlmalconditio nsisintroducedespecially,thetransferfunctionshereobtainedarethesymmetricalkerneisinfreq uencydomain.Keywords:non-zeroinitialvalues}volterraresponse~;transferfunctions;nonlinear systerp.slfursivecalculation。

相机响应函数定标的正则化方法

相机响应函数定标的正则化方法

相机响应函数定标的正则化方法王安定;谢蓄芬;邹念育【摘要】相机响应函数是真实场景辐射量与图像灰度值之间的映射关系,对相机响应函数的标定具有重要的研究价值.根据相机的成像原理给出了相机响应函数的数学模型,分析了最小二乘法标定相机响应函数存在的病态问题.为了获得稳定、精确的相机响应函数,提出了一种正则化方法对相机响应函数进行标定,该方法在索波列夫空间中设置不同的正则算子.通过实验分别论证索波列夫参数不同时相机响应函数的稳定性,确定了索伯列夫空间参数,应用L曲线法确定了正则化参数.验证结果表明,正则化方法能够显著提高相机响应函数标定的稳定性和精确性.【期刊名称】《大连工业大学学报》【年(卷),期】2018(037)004【总页数】5页(P301-305)【关键词】相机响应函数;正则化方法;L曲线法【作者】王安定;谢蓄芬;邹念育【作者单位】大连工业大学信息科学与工程学院 ,辽宁大连 116034;大连工业大学信息科学与工程学院 ,辽宁大连 116034;大连工业大学信息科学与工程学院 ,辽宁大连 116034【正文语种】中文【中图分类】TP3910 引言相机响应函数(Camera response function,CRF)表达了图像灰度与真实场景辐射量之间的映射关系[1-3],是系统成像过程中各个链路环节效应的总和。

相机响应函数的标定是图像动态范围延展的基础[4-6],受随机噪声、系统响应非均匀性和各种随机扰动的影响,相机响应函数的标定结果偏差较大,难以获得精确的结果,因此相机响应函数的标定受到广泛关注。

从20世纪90年代高动态范围图像的提出到近些年来不断发展,越来越多的研究者开始关注高动态范围成像技术,因此出现了很多对相机响应函数进行标定的方法[7-9]。

Mann等[10]提出了通过不同曝光图像延展系统动态范围的方法,指出相机响应函数的标定受噪声影响。

Mitsunaga等[11]提出用N次多项式近似描述相机响应函数,降低了算法对成像设备的要求,但是算法稳定性欠佳。

  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。
II. NONLINEAR RESPONSE FUNCTION OF EXTERNAation of Ref. [1] throughout the paper. The Hamiltonian describing the system of an externally driven, trapped atomic BEC is given by: ˆ =H ˆ0 + H ˆ ′ (t), H (1)
PACS numbers: 03.75.Fi
I.
INTRODUCTION
We have recently studied an externally driven, finite temperature Bose-Einstein condensates (BEC) described by the time-dependent Hartree-Fock-Bogoliubov (TDHFB) equations [1]. A systematic procedure was outlined for solving these equations perturbatively in the applied external field, and position-dependent linear response functions and susceptibilities were calculated. In this paper we extend this formalism to calculate the second order response function for the condensate, noncondensate density and non-condensate correlation. The linear response provides an adequate description of the system only for weak external perturbations. Otherwise, nonlinear effects contained in the higher order terms in the perturbation series may not be ignored. There are strong similarities between nonlinear optics and the dynamics of BEC owing to the interatomic interactions in Bose condensates. Previous work done in this area includes the demonstration of four wave mixing in zero temperature BEC using the Gross-Pitaevskii Equation (GPE) [2]. In the present article we study nonlinear properties of BEC using the TDHFB framework. Numerous dynamical theories exist for finite temperature BEC that takes into account higher order collision processes, such as the time-dependent Bogoliubov-de Gennes equations [3], the Hartree-Fock-Bogoliubov (HFB) theory [4, 5, 6, 7], Quantum Kinetic Theory [8, 9, 10, 11, 12, 13], and Stochastic methods [14, 15, 16, 17, 18]. The TDHFB theory is a self-consistent theory of BEC in the collisionless regime that progresses logically from the GrossPitaevskii Equation by taking into account higher order correlations of noncondensate operators. Although TDHFB neglects higher order correlations included in the various quantum kinetic theories, the TDHFB equations are valid at very low temperatures near zero, even down to the zero temperature limit, and are far simpler than the kinetic equations which can only be solved using approximations such as ZNG. Another attractive feature of TDHFB from a purely pragmatic point of view is that the Fermionic version of the theory has already been well-developed in Nuclear Physics [4]. We therefore work at the TDHFB level in this paper and our approach draws upon the analogy with the time-dependent Hartree-Fock (TDHF) formalism developed for nonlinear optical response of many electron systems [19]. In Section II we introduce the second order time and frequency domain response functions for an externally driven BEC. Numerical results are discussed in Sections III and IV for a condensate of 2000 atoms in a one dimensional harmonic trap. In Section V we show how this formalism may be applied for computing the second order response for a Fractional Quantum Hall system. In Section VI we conclude. Details of the derivation of the second order response functions are given in the Appendix.
∗ ′ ′ ′ d3 r d3 r′ φ∗ i (r)φj (r )V (r − r )φk (r )φl (r),
(5)
describe the collision between the atoms, with V (r − r′ ) being a general interatomic potential. H ′ (t) describes the effect of a general external force Vf (r, t) on the condensate that mimics the mechanical force applied experimentally such as shaking of the trap [29, 30]. The dynamics of the system is calculated by solving the time-dependent Hartree-Fock-Bogoliubov (TDHFB) equations for the condensate mean field, zi = a ˆi , the non- condensate density ρij = a ˆ† ˆj − a ˆ† ˆj , and the nonia i a condensate correlations κij = a ˆi a ˆj − a ˆi a ˆj . These are presented in Appendix A. These nonlinear coupled equations are solved by an order by order expansion of the variables zi , ρij and κij ; at each order, the resulting equations to be solved become linear [1]. It is found that the sequence of linear equations to be solved has the general form: i¯ h dψ (n) (t) = L(n) ψ (n) (t) + λ(n) (t), dt
1
arXiv:cond-mat/0307718v1 [cond-mat.mes-hall] 30 Jul 2003
Department of Physics and Astronomy, University of Rochester, Box 270216, Rochester, New York 14627-0216 2 Department of Chemistry, University of Rochester, Box 270216, Rochester, New York 14627-0216 3 Corning Incorporated, Process Engineering and Modeling, Corning, New York 14831 (Dated: February 2, 2008)
相关文档
最新文档