An Accurate Image Simulation Method for High-Order Laue Zone Effects

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电子动力衍射模拟计算的新方法

电子动力衍射模拟计算的新方法

摘 要当今,电子晶体学由于具有X射线晶体学所无法比拟的独特优势而变得日益活跃。

然而,由于电子与晶体之间强烈的动力学交互作用,使得用电子显微学方法分析结构复杂化。

为了解决其中的动力衍射问题,多年来电子晶体学家们已经发展了一些数值模拟方法以及近似解析方法。

但这些方法都还存在各自的不足之处。

本文中我们首先通过实空间方法和普遍使用的多层法进行计算对照,发现了实空间方法的独特优势;然后发展了几种数值模拟计算的新方法。

具体内容如下:1)通过模拟计算比较实空间方法和传统的多层法。

发现当片层厚度在一定范围内改变时,用实空间方法计算的结果可以保持七位有效数字不变,而用传统多层法计算的结果不仅明显变化,而且表现出伪高阶劳埃带效应。

该现象是由于分层后在每一层中把晶体势投影到几何面上时人为引入了一个沿入射束方向,以片层厚度ε为周期的二维投影势。

从数学的观点看,由于实空间法同时考虑了散射和传播,所以它比传统的多层法的理论基础更严格,有时甚至计算速度也比传统多层法更快。

考虑到实空间法能满足日益增加的对计算精确度的要求,我们建议同行在进行精确计算时采用实空间法。

2)从薛定谔方程出发推导了一种波函数相位模拟方法。

当我们知道晶体的出射面波函数的振幅后,可以通过该方法重构出波函数的相位。

这在晶体结构测定方面具有十分重要的意义。

3)发展了一种进行像模拟计算的方法——快速倒易空间方法。

众所周知,散射矩阵P是由N个不同的结构因子和N个不同的激化矢量构成的具有N N×个元的矩阵。

然而,由于绝大多数结构因子极小,以至于可以忽略不计,因此散射矩阵的阶数可以大大地减少。

另外,由于结构因子具有二维空间对称性,通过把对称相关的结构因子用对称独立的结构因子表示,散射矩阵的阶数可以进一步减小。

散射矩阵简化后,像模拟计算的速度相对于传统方法的计算速度提高了数百倍。

4)发展了两种用来模拟计算高阶劳埃带衍射的方法。

第一种为可以计算单斜或者三斜晶体高阶劳埃带衍射的半解析法,该方法允许相光栅平面垂直带轴方向而克服了相光栅无限大的这一问题。

XFdtd用户手册说明书

XFdtd用户手册说明书

XFdtd:Electromagnetic Simulation SoftwareElectromagnetic Simulation Solutions for Design Engineers andEM ProfessionalsRemcom provides innovative electromagnetic simulation soft ware and consulting services.Our products simplify the analysis of complex EM problems and lead the market in FDTD-based modeling and simulation.Cell phone antenna design, MRI coil analysis, antenna placement on vehicles and airplanes, and placement of wireless communication systems are made easier with Remcom’s EM simulation soft ware and expertise.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Powerful Flexible Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Simplifi ed Workfl ow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Fast, Intelligent Meshing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6XACT Accurate Cell Technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Results & Output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Circuit Element Optimizer (CEO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Parameters Everywhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Custom Scripted Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11High-Performance Computing Options . . . . . . . . . . . . . . . . . . . . . . . . . 12XStream GPU Acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Why Use the FDTD Method? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Specifi cations & Versions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15N T C O E N T S3Save Time and Streamline Your WorkAs an FDTD simulation solver, XFdtd outpaces other methods in effi ciency as the number of unknowns increases. Learn more on page 14.Key bene fi ts of XFdtd 3D EM Simulation Software include:•Circuit Element Optimizer determines optimal values for lumped circuit elements connected directly into the EM simulation mesh.•PrOGrid Project Optimized Gridding® simplifi es grid creation by considering multiple aspects of a project to optimize the grid for both accuracy and runtime.•XStream® GPU Acceleration for CPUs and GPU clusters enables calculations to fi nish in minutes as compared to hours. •Unlimited Memory support for problems exceeding 60 GB and billions of cells.•XACT Accurate Cell Technology® resolves the most intricate designs with fewer computational resources.•CAD Merge seamlessly integrates new versions of CAD and PCB designs into existing projects.•XTend Script Library automates modeling and design with pre-loaded, customizable scripts for creating custom features.• Guided modeling processes, editable modeling history, ability to edit imported CAD fi les.•Intelligent, ultra-fast meshing expedites previewing of fi nished meshes prior to simulation.XFdtd ®: Fast and Accurate Has Never Been So Easy4Powerful Flexible ModelingSpend less time modeling and more time gett ing results.Whether you’re importing CAD databases or building your own models, the sophisticated modeling tools in XF will make your job easier. The modeling engine in XF allows you to build complicated models from the ground up or modify imported CAD fi les. This reduces the amount of time you spend modeling, leaving you more time to focus on your results.Key Features• 2D Sketcher with constraints: Intuitive grid/objectsnapping and a constraint system allow for quick creation of complex shapes.• Feature history for objects: Modeling operations are chained together on each object,creating an editable history for each model in your project.Importable CAD Formats• ODB++• SAT/SAB • DXF• VDA-FS • STEP • IGES• Pro/E • CATIAv4• CATIAv5• Inventor •STL5Simpli fi ed Work fl owXF streamlines your workfl ow by eliminating time-consuming, redundant tasks.XF multiplies your productivity by allowing you to reuse almost anything you create. Any project can be turned into a template, the parts of your project can be stored in a shared library, and your simulations are saved and the results easily accessed for comparison purposes.Key Features• Custom project templates • Simulation history with all results • Shared libraries• Shared component, sensor, and waveformdefi nitions• PCB Merge for importing layered PCBs in ODB++• CAD MergeSeamless Revisions with CAD MergeIf you work with frequently updated CAD fi les, you’ll only have to set up the hierarchy, materialassignments and meshing priority once. XF preserves this information each time a new version of the fi le is imported, keeping your workfl ow as effi cient as possible.XF’s Assistant is a step-by-step guidethat speeds the learning curve for new users.CAD Merge compares the new geometry with the original and automatically refreshes the project treewith the changes.Run SimulationAssignMaterials,Grid, and MeshSettingsI R STI TE R AT I ON6Fast, Intelligent MeshingXF makes it easier to generate more accurate and effi cient meshes with less work.XF allows you to see the fi nished mesh with materials before the simulation ever starts. This provides the confi dence that the simulation will not fail due to a meshing error. XF’s intelligent and ultra-fast mesh updating capabilities make this process even more seamless than before.PrOGrid Project Optimized GriddingAdditionally, XF’s Project Optimized Gridding algorithm, PrOGrid, streamlines the process of generating an effi cient grid. By considering a combination of geometry features, operating frequency, and material parameters, PrOGrid intelligently creates a grid that is optimized for high accuracy and short run times.PrOGrid Logic1. Guarantee cells per wavelengthin free space and in dielectrics where the wavelength is shorter2. Reduce cell size around curvedgeometry3. Apply boundary refi nement atthe edges of conductors where electric fi elds are strongest Identify geometric features likevertices [ ] and snap grid lines to them11237XACT Accurate Cell TechnologyAccurate meshing of curved geometry.With XF, there is no need to choose performance over accuracy. XACT mesh reduces simulation time while improving the accuracy of even the most intricate designs. Using an advanced sub-cellular conformal method, XF reduces computing resources while maintaining the accuracy of a full wave solver. Faster, more accurate simulations improve the throughput of your designs from start to fi nish.Key Features• Represents small gaps and curved surfaces • Increases accuracy of results• Signifi cantly improves simulation time by reducing unknownsComparisons show the dramatic improvement with XACT.Traditional FDTD Mesh XACT MeshResults & OutputComplete result history.XF was designed to support the way you work by keeping track of every simulation youdo for each project. Results from other projects or past simulations can be added to graphs, viewed in 3D, post-processed, or exported to text fi les. The Results Browser in XF is completely customizable, and fi ltering and searching tools make it easy to fi nd exactly whatyou’re looking for with a few clicks.Approximate MR image and transmit effi ciency visual output types.Visual Output• Planes, surfaces and volumes ofoutput shown with input geometry• E/H/B, conduction current,rotating B near fi elds, in additionto dissipated power density• 3D far fi eld patt erns of E, gain,realized gain, axial ratio, radarcross section• Hearing aid compatibility, SAR,MR transmit effi ciency, andapproximate MR image outputs• Biological temperature riseGraphical Output• Near zone fi elds/currents vs. time• Impedance, S-Parameters vs.frequency, VSWR, active VSWR• Polar plot antenna patt erns• Smith chart impedance plots• FFT of transient results• Group Delay output type• Time Domain Refl ectometry (TDR)and Time Domain Transmission (TDT)output types• Dissipated Power Density9Design Flow with CEO1. Set up the XF project including copper traces,component locations, materials, grid, etc.2. Create a Response Matrix which uses FDTDsimulations to characterize fi eld interactions aff ecting the components.3. Perform a Circuit Optimization that usesS-Parameter and/or effi ciency goals to select the optimal set of component values.4. Verify that the matching network or fi lterperforms as desired with the selectedcomponent values.124Circuit Element Optimizer (CEO)Determine component values for Full-Wave Matching Circuit Optimization (FW-MCO).Circuit Element Optimization is a new technology that is only available in XF – no other electromagnetic simulation tool off ers it. It is unique because of its ability to considerelectromagnetic fi eld interactions between the components and the surrounding environment. This makes it easier than ever to fi nd the optimal set of components and achieve the desired performance with a matching network or fi lter.3S11 Threshold GPS: -6 dBBluetooth: -15 dBComponent ValuesL: 1 nH to 10 nH C: 1pF to 10 pFC1L1L20.2 pF 1.7 nH 0.6 nH10Parameters EverywhereXF helps you find the optimal solution.In XF, parameters are part of the DNA of a project. Parts, components, waveforms, materials and just about everything else in your project can leverage the power of parameters. It’s simple enough for anyone to use, but with advanced capabilities that will make any power user happy.Key Features• Defi ne nearly any value as a parameter, such as the length of a part or the frequency ofa simulation• Mathematical expressions using parameters• Interface with scripts for parameter evaluationEntire assemblies based on the same parameter can be modifi ed by changing one value. Since parameters can be used almost anywhere in XF, you can automate more things and gaincomplete control of your projects.11Custom Scripted FeaturesXF allows you to create your own custom features with a powerful scripting API.With XF, the power is in your hands to create time-saving, custom features that allow you to work faster. Nearly everything in the application can be controlled and accessed through a powerful scripting API. Whether you’re writing custom dialogs or designing custom optimization routines, the scripting API in XF breaks down the walls between what you have and what you need.Key Features• Full-featured Script Editor • Custom dialog creation through scripts• Access to Result DataThe XTend Script Library helps adapt XF to your unique processes to extend the functionality of the tool. The scripts packaged with the application are available for you to modify and fi t to your own needs. Remcom’s support team is also available to customize scripts for your specifi c use; contact Remcom for a quote.12High-Performance Computing Options for Every UserI mprove EM simulation performance using the most modern high-performance computing technologies available.Remcom’s industry-leading EM acceleration is a powerful tool to shorten your developmenttime and release your products to market sooner.Message Passing Interface (MPI) Technology for CPU and GPU ClustersDistributing XF calculations among CPU and/or GPU clusters creates limitless potential.Unlimited Memory SupportNo memory limits! Simulate massive problems exceeding billions of cells.Multiprocessor TechnologyXF calculations are parallelized across all available processors within your computer, greatly speeding calculations.ᮣ See examples and learn more at /no-limits13XStream GPU AccelerationBuilt-in EM simulation acceleration via graphics processing units.XStream tremendously improves EM simulation performance by leveraging the powerful NVIDIA graphics processing units (GPUs) available in modern video cards to make ultra-fast FDTD numerical computations. Leveraging NVIDIA’s latest generation GPUs, XStream enables XF calculations to fi nish in minutes as compared to hours or even days using a CPU only.© 2014 Remcom Inc. All rights reserved.(8 cores)Intel Xeon E5-2670(16 cores)Intel Xeon E5-2670(1)NVIDIA M2090(2)NVIDIA M2090(4)NVIDIA M2090(6)NVIDIA M2090(8)NVIDIA M2090CPUs © 2014 Remcom Inc. All rights reserved.(8)NVIDIA M2090(4)NVIDIA M2090(2)NVIDIA M2090(1)NVIDIA M2090(12)NVIDIA M2090(16)NVIDIA M2090(20)NVIDIA M2090(24)NVIDIA M2090S i m u l a t i o n T h r o u g h p u t i n G i g a c e l l s p e r S e c o n dXFdtd ® Simulation Throughput Using MPI + XStream ® GPU Acceleration51015202530Number of GPUsThroughput Plot of XStream.Throughput Plot of MPI + XStream.14Why Use the FDTD Method?While many electromagnetic simulation techniques are applied in the frequency-domain, FDTD solves Maxwell’s equations in the time domain. This means that the calculation of the electromagnetic fi eld values progresses at discrete steps in time. One benefi t of the time domain approach is that it gives broadband output from a single execution of the program; however, the main reason for using the FDTD approach is the excellent scaling performance of the method as the problem size grows. As the number of unknowns increases, the FDTD approach quickly outpaces other methods in effi ciency.FDTD has also been identifi ed as the preferred method for performing electromagnetic simulations for biological eff ects from wireless devices [1]. The FDTD method has been shown to be the most effi cient approach and provides accurate results of the fi eld penetration into biological tissues.[1] C95.3.2002, Recommended Practice for Measurements and Computations with Respect to Human Exposure to Radio Frequency Electromagnetic Fields , 100kHz to 300GHz. IEEE Standards and Coordinating Committee 28 on Non-Ionizing Radiation Hazards, April 2002.Specifi cations & Versions15© 2016 Remcom Inc. All rights reserved.NVIDIA and CUDA are trademarks and/or registered trademarks of NVIDIA Corporation in the United States and other countries.XF7.5.1.5-0216Remcom, Inc.315 S. Allen St., Suite 416State College, PA 16801 USA +1.888.7.REMCOM (US/CAN)+1.814.861.1299 phone +1.814.861.1308 fax****************Visit for more information3MBAE Systems Cobham Dynetics Ericsson GEGeneral Motors Honda HoneywellIBM LGLockheed Martin Mitsubishi Nokia Samsung Siemens SonyTexas InstrumentsToshiba ToyotaU.S. Food and Drug Administration (FDA)United States Air Force United States Army United States Marines United States Navyᮣ See /customers for more.A Sampling of Our CustomersXFdtd ®: 3D EM simulation soft ware package that provides engineers with powerful and innovative tools for modeling and EM soft ware simulation.Wireless InSite ®: A radiopropagation analysis package for analyzing the impact of the physical environment on the performance of wireless communication systems.XGtd ®: A high frequency GTD/UTD based package for the design and analysis of antenna systems on complex objects such as vehicles and aircraft .Remcom has been leading the EM market with innovative simulation and wireless propagation tools for more than 20 years. 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英文翻译

英文翻译

A Facial Aging Simulation Method Using flaccidity deformation criteriaAlexandre Cruz Berg Lutheran University of Brazil.Dept Computer ScienceRua Miguel Tostes, 101. 92420-280 Canoas, RS, Brazil berg@ulbra.tche.br Francisco José Perales LopezUniversitat les Illes Balears.Dept Mathmatics InformaticsCtra Valldemossa, km 7,5E-07071 Palma MallorcaSpainpaco.perales@uib.esManuel GonzálezUniversitat les Illes Balears.Dept Mathmatics InformaticsCtra Valldemossa, km 7,5E-07071 Palma MallorcaSpainmanuel.gonzales@uib.esAbstractDue to the fact that the aging human face encompasses skull bones, facial muscles, and tissues, we render it using the effects of flaccidity through the observation of family groups categorized by sex, race and age. Considering that patterns of aging are consistent, facial ptosis becomes manifest toward the end of the fourth decade. In order to simulate facial aging according to these patterns, we used surfaces with control points so that it was possible to represent the effect of aging through flaccidity. The main use of these surfaces is to simulate flaccidity and aging consequently.1.IntroductionThe synthesis of realistic virtual views remains one of the central research topics in computer graphics. The range of applications encompasses many fields, including: visual interfaces for communications, integrated environments of virtual reality, as well as visual effects commonly used in film production.The ultimate goal of the research on realistic rendering is to display a scene on a screen so that it appears as if the object exists behind the screen. This description, however, is somewhat ambiguous and doesn't provide a quality measure for synthesized images. Certain areas, such as plastic surgery, need this quality evaluation on synthesized faces to make sure how the patient look like and more often how the patient will look like in the future. Instead, in computer graphics and computer vision communities, considerable effort has been put forthto synthesize the virtual view of real or imaginary scenes so that they look like the real scenes.Much work that plastic surgeons put in this fieldis to retard aging process but aging is an inevitable process. Age changes cause major variations in the appearance of human faces [1]. Some aspects of aging are uncontrollable and are based on hereditary factors; others are somewhat controllable, resulting from many social factors including lifestyle, among others [2].1.1.Related WorkMany works about aging human faces have been done. We can list some related work in the simulation of facial skin deformation [3].One approach is based on geometric models, physically based models and biomechanical models using either a particle system or a continuous system.Many geometrical models have been developed, such as parametric model [4] and geometric operators [5]. The finite element method is also employed for more accurate calculation of skin deformation, especially for potential medical applications such as plastic surgery [6]. Overall, those works simulate wrinkles but none of them have used flaccidity as causing creases and aging consequently.In this work is presented this effort in aging virtual human faces, by addressing the synthesis of new facial images of subjects for a given target age.We present a scheme that uses aging function to perform this synthesis thru flaccidity. This scheme enforces perceptually realistic images by preserving the identity of the subject. The main difference between our model and the previous ones is that we simulate increase of fat and muscular mass diminish causing flaccidity as one responsible element for the sprouting of lines and aging human face.In the next section will plan to present the methodology. Also in section 3, we introduce the measurements procedure, defining structural alterations of the face. In section 4, we present a visual facial model. We describe age simulation thrua deformation approach in section 5. In the last section we conclude the main results and future work.2.MethodologyA methodology to model the aging of human face allows us to recover the face aging process. This methodology consists of: 1) defining the variations of certain face regions, where the aging process is perceptible; 2) measuring the variations of those regions for a period of time in a group of people and finally 3) making up a model through the measurements based on personal features.That could be used as a standard to a whole group in order to design aging curves to the facial regions defined.¦njjjpVM2.1Mathematical Background and AnalysisHuman society values beauty and youth. It is well known that the aging process is influenced by several parameters such: feeding, weight, stress level, race, religious factors, genetics, etc. Finding a standard set of characteristics that could possibly emulate and represent the aging process is a difficult proposition.This standard set was obtained through a mathematical analysis of some face measurements in a specific group of people, whose photographs in different ages were available [7]. To each person in the group, there were, at least, four digitized photographs. The oldest of them was taken as a standard to the most recent one. Hence, some face alterations were attained through the passing of time for the same person.The diversity of the generated data has led to the designing of a mathematical model, which enabled the acquiring of a behavior pattern to all persons of the same group, as the form of a curve defined over the domain [0,1] in general, in order to define over any interval [0,Į] for an individual face. The unknown points Įi are found using the blossoming principle [8] to form the control polygon of that face.The first step consisted in the selection of the group to be studied. Proposing the assessment of the face aging characteristics it will be necessary to have a photographic follow-up along time for a group of people, in which their face alterations were measurable.The database used in this work consisted of files of patients who were submitted to plastic surgery at Medical Center Praia do Guaíba, located in Porto Alegre, Brazil.3.MeasurementsAccording to anatomic principles [9] the vectors of aging can be described aswhich alter the position and appearance of key anatomic structures of the face as can be shown in figure 1 which compares a Caucasian mother age 66 (left side) with her Caucasian daughters, ages 37 (right above) and 33 (right below) respectively.Figure 1 - Observation of family groupsTherefore, basic anatomic and surgical principles must be applied when planning rejuvenative facial surgery and treating specific problems concomitantwith the aging process.4.Visual Facial ModelThe fact that human face has an especially irregular format and interior components (bones, muscles and fabrics) to possess a complex structure and deformations of different face characteristics of person to person, becomes the modeling of the face a difficult task. The modeling carried through in the present work was based on the model, where the mesh of polygons corresponds to an elastic mesh, simulating the dermis of the face. The deformations in this mesh, necessary to simulate the aging curves, are obtained through the displacement of the vertexes, considering x(t) as a planar curve, which is located within the (u,v ) unit square. So, we can cover the square with a regular grid of points b i,j =[i/m,j/n]T ; i=0,...,m; j=0,...,n. leading to every point (u,v ) asfrom the linear precision property of Bernstein polynomials. Using comparisons with parents we can distort the grid of b i,j into a grid b'i,j , the point (u,v )will be mapped to a point (u',v') asIn order to construct our 3D mesh we introduce the patch byAs the displacements of the vertexes conform to the certain measures gotten through curves of aging and no type of movement in the face is carried through, the parameters of this modeling had been based on the conformation parameter.4.1Textures mappingIn most cases the result gotten in the modeling of the face becomes a little artificial. Using textures mapping can solve this problem. This technique allows an extraordinary increase in the realism of the shaped images and consists of applying on the shaped object, existing textures of the real images of the object.In this case, to do the mapping of an extracted texture of a real image, it is necessary that the textureaccurately correspond to the model 3D of that is made use [9].The detected feature points are used for automatic texture mapping. The main idea of texture mapping is that we get an image by combining two orthogonal pictures in a proper way and then give correct texture coordinates of every point on a head.To give a proper coordinate on a combined image for every point on a head, we first project an individualized 3D head onto three planes, the front (x, y), the left (y, z) and the right (y, z) planes. With the information of feature lines, which are used for image merging, we decide on which plane a 3D-head point on is projected.The projected points on one of three planes arethen transferred to one of feature points spaces suchas the front and the side in 2D. Then they are transferred to the image space and finally to the combined image space.The result of the texture mapping (figure 2) is excellent when it is desired to simulate some alteration of the face that does not involve a type of expression, as neutral. The picture pose must be the same that the 3D scanned data.¦¦¦ mi nj lk n j m i lk k j i w B v B u B b w v u 000,,)()()(')',','(¦¦ m i nj n jmij i v B u B b v u 00,)()(),(¦¦ m i nj n j m i j i v B u B b v u 00,)()(')','(¦¦¦ mi nj lk n j m i lk k j i w B v B u B b w v u 000,,)()()(')',','(Figure 2 - Image shaped with texturemapping5.Age SimulationThis method involves the deformation of a face starting with control segments that define the edges of the faces, as¦¦¦ mi nj lk n j m i lk k j i w B v B u B b w v u 000,,)()()(')',','(Those segments are defined in the original face and their positions are changed to a target face. From those new positions the new position of each vertex in the face is determined.The definition of edges in the face is a fundamental step, since in that phase the applied aging curves are selected. Hence, the face is divided in influencing regions according to their principal edges and characteristics.Considering the face morphology and the modeling of the face aging developed [10], the face was divided in six basic regions (figure 3).The frontal region (1) is limited by the eyelids and the forehead control lines. The distance between these limits enlarges with forward aging.The orbitary region (2) is one of the most important aging parameters because a great number of wrinkles appears and the palpebral pouch increases [11]. In nasal region (3) is observed an enlargement of its contour.The orolabial region (4) is defined by 2 horizontal control segments bounding the upper and lower lips and other 2 segments that define the nasogenian fold. Figure 3 - Regions considering the agingparametersThe lips become thinner and the nasogenian fold deeper and larger. The mental region (5) have 8 control segments that define the low limit of the face and descend with aging. In ear curve (6) is observed an enlargement of its size. The choice of feature lines was based in the characteristic age points in figure 6.The target face is obtained from the aging curves applied to the source face, i.e., with the new control segment position, each vertex of the new image has its position defined by the corresponding vertex in the target face. This final face corresponds to the face in the new age, which was obtained through the application of the numerical modeling of the frontal face aging.The definition of the straight-line segment will control the aging process, leading to a series of tests until the visual result was adequate to the results obtained from the aging curves. The extremes of the segments are interpolated according to the previously defined curves, obtained by piecewise bilinear interpolation [12].Horizontal and vertical orienting auxiliary lines were defined to characterize the extreme points of the control segments (figure 4). Some points, that delimit the control segments, are marked from the intersection of the auxiliary lines with the contour of the face, eyebrow, superior part of the head and the eyes. Others are directly defined without the use of auxiliary lines, such as: eyelid hollow, eyebrow edges, subnasion, mouth, nasolabial wrinkle andnose sides.Figure 4 - Points of the control segmentsOnce the control segments characterize the target image, the following step of the aging process can be undertaken, corresponding to the transformations of the original points to the new positions in the target image. The transformations applied to the segments are given by the aging curves, presented in section 4.In the present work the target segments are calculated by polynomial interpolations, based on parametric curves [12].5.1Deformation approachThe common goal of deformation models is to regulate deformations of a geometric model by providing smoothness constraints. In our age simulation approach, a mesh-independent deformation model is proposed. First, connected piece-wise 3D parametric volumes are generated automatically from a given face mesh according to facial feature points.These volumes cover most regions of a face that can be deformed. Then, by moving the control pointsof each volume, face mesh is deformed. By using non-parallel volumes [13], irregular 3D manifolds are formed. As a result, smaller number of deformvolumes are necessary and the number of freedom incontrol points are reduced. Moreover, based on facialfeature points, this model is mesh independent,which means that it can be easily adopted to deformany face model.After this mesh is constructed, for each vertex on the mesh, it needs to be determined which particularparametric volume it belongs to and what valueparameters are. Then, moving control points ofparametric volumes in 3D will cause smooth facialdeformations, generating facial aging throughflaccidity, automatically through the use of the agingparameters. This deformation is written in matricesas , where V is the nodal displacements offace mesh, B is the mapping matrix composed ofBernstein polynomials, and E is the displacementvector of parametric volume control nodes.BE V Given a quadrilateral mesh of points m i,j ,, we define acontinuous aged surface via a parametricinterpolation of the discretely sampled similaritiespoints. The aged position is defined via abicubic polynomial interpolation of the form with d m,n chosen to satisfy the known normal and continuity conditions at the sample points x i,j .>@>M N j i ,...,1,...,1),(u @@>@>1,,1,),,( j j v i i u v u x ¦3,,),(n m n m n m v u d v u x An interactive tool is programmed to manipulate control points E to achieve aged expressions making possible to simulate aging through age ranges. Basic aged expression units are orbicularis oculi, cheek, eyebrow, eyelid, region of chin, and neck [14]. In general, for each segment, there is an associated transformation, whose behavior can be observed by curves. The only segments that do not suffer any transformation are the contour of the eyes and the superior side of the head.5.2Deformation approachThe developed program also performs shape transformations according to the created aging curves, not including any quantification over the alterations made in texture and skin and hair color. Firstly, in the input model the subjects are required to perform different ages, as previouslymentioned, the first frame needs to be approximately frontal view and with no expression.Secondly, in the facial model initialization, from the first frame, facial features points are extracted manually. The 3D fitting algorithm [15] is then applied to warp the generic model for the person whose face is used. The warping process and from facial feature points and their norms, parametric volumes are automatically generated.Finally, aging field works to relieve the drifting problem in template matching algorithm, templates from the previous frame and templates from the initial frame are applied in order to combine the aging sequence. Our experiments show that this approach is very effective. Despite interest has been put in presenting a friendly user interface, we have to keep in mind that the software system is research oriented. In this kind of applications an important point is the flexibility to add and remove test facilities. 6.Results The presented results in the following figuresrefer to the emulations made on the frontalphotographs, principal focus of this paper, with theobjective to apply the developed program to otherpersons outside the analyzed group. The comparisonswith other photographs of the tested persons dependon their quality and on the position in which theywere taken. An assessment was made of the new positions, of the control segments. It consisted in: after aging a face, from the first age to the second one, through the use of polynomial interpolation of the control segments in the models in the young age, the new positions are then compared with the ones in the model of a relative of older age (figure 5). The processed faces were qualitatively compared with theperson’s photograph at the same age. Figure 5 - Synthetic young age model,region-marked model and aged modelAlso the eyelid hollow, very subtle falling of the eyebrow, thinning of the lips with the enlarging of the nasion and the superior part of the lip, enlargingof the front and changing in the nasolabial wrinkle.7.ConclusionsModelling biological phenomena is a great deal of work, especially when the biggest part of the information about the subject involves only qualitative data. Thus, this research developed had has a challenge in the designing of a model to represent the face aging from qualitative data.Due to its multi-disciplinary character, the developed methodology to model and emulate the face aging involved the study of several other related fields, such as medicine, computing, statistics and mathematics.The possibilities opened by the presented method and some further research on this field can lead to new proposals of enhancing the current techniques of plastic face surgery. It is possible to suggest the ideal age to perform face lifting. Once the most affected aging regions are known and how this process occurs over time. Also missing persons can be recognized based on old photographs using this technique. AcknowledgementsThe project TIN2004-07926 of Spanish Government have subsidized this work.8. References[1] Burt, D. M. et al., Perc. age in adult Caucasianmale faces, in Proc. R. Soc., 259, pp 137-143,1995.[2] Berg, A C. Aging of Orbicularis Muscle inVirtual Human Faces. IEEE 7th InternationalConference on Information Visualization, London, UK, 2003a.[3] Beier , T., S. Neely, Feature-based imagemetamorphosis, In Computer Graphics (Proc.SIGGRAPH), pp. 35-42, 1992.[4] Parke, F. I. P arametrized Models for FacialAnimation, IEEE Computer & Graphics Applications, Nov. 1982.[5] Waters, K.; A Muscle Model for Animating ThreeDimensional Facial Expression. Proc SIGGRAPH'87,Computer Graphics, Vol. 21, Nº4, United States, 1987. [6] Koch, R.M. et alia.. Simulation Facial SurgeryUsing Finite Element Models, Proceedings of SIGGRAPH'96, Computer Graphics, 1996.[7] Kurihara, Tsuneya; Kiyoshi Arai, ATransformation Method for Modeling and Animation of the Human Face from Photographs, Computer Animatio n, Springer-Verlag Tokyo, pp.45-58, 1991.[8] Kent, J., W. Carlson , R. Parent, ShapeTransformation for Polygon Objects, In Computer Graphics (Proc. SIGGRAPH), pp. 47-54, 1992. [9] Sorensen, P., Morphing Magic, in ComputerGraphics World, January 1992.[10]Pitanguy, I., Quintaes, G. de A., Cavalcanti, M.A., Leite, L. A. de S., Anatomia doEnvelhecimento da Face, in Revista Brasileira deCirurgia, Vol 67, 1977.[11]Pitanguy, I., F. R. Leta, D. Pamplona, H. I.Weber, Defining and measuring ageing parameters, in Applied Mathematics and Computation , 1996.[12]Fisher, J.; Lowther, J.; Ching-Kuang S. Curveand Surface Interpolation and Approximation: Knowledge Unit and Software Tool. ITiCSE’04,Leeds, UK June 28–30, 2004.[13]Lerios, A. et al., Feature-Based VolumeMetamorphosis, in SIGGRAPH 95 - Proceedings,pp 449-456, ACM Press, N.Y, 1995.[14]Berg, A C. Facial Aging in a VirtualEnvironment. Memória de Investigación, UIB, Spain, 2003b.[15]Hall, V., Morphing in 2-D and 3-D, in Dr.Dobb's Journal, July 1993.。

改进准静态本征函数法求解反应堆时空中子动力学方程

改进准静态本征函数法求解反应堆时空中子动力学方程

改进准静态本征函数法求解反应堆时空中子动力学方程李明芮;黎浩峰;陈文振;邢晋【摘要】An improved quasi-static method was proposed to solve the space-time neu-tron kinetics equation.Under the improved quasi-static approximation condition,the neutron flux density was decomposed into the range-function and shape-function by the factorization method.Then the approximate analytical solutions in the axial and radial directions were obtained using the eigenfunction method to solve the shape-function.In the solving process,the range-function was simplified as the form of point reactor model,and the analytical solution of three-dimensional space-time neutron kinetic equa-tion for reactor was pared with other analytical method and numerical solution,the improved quasi-static eigenfunction method has wider application scope and faster computation speed.%本文提出一种改进准静态本征函数法并用于求解时空中子动力学方程。

手机屏幕图像缺陷检测方法的研究

手机屏幕图像缺陷检测方法的研究

关键词:缺陷检测;ROI 识别;特征提取;手机液晶屏
I
安徽大学硕士学位论文
手机屏幕图像缺陷检测方法的研究
Abstract
With the rapid progress of technology and industry, LCD screen electronic products have become an integral part of our lives and production. Reality shows a higher and higher request on the quality of LCD screen of these products, while the current level of technology can not avoid sorts of defects. So, the first problem ,which LCD manufactures need to face, is how to identify and solve the defects of the LCD screen rapidly and accurately. Current image defects of LCD products mainly rely on manual inspection that neither meet the accuracy detection of defects in LCD screen, nor guarantee the stability of test results. This thesis is precisely solves this problem as a starting point by using the mobile LCD screen as object of study and combining digital image processing, pattern recognition and computer technology, and in view of the common image defects, referring to the requirements of industrial production detection algorithm on efficiency and accuracy. This thesis have researched and designed an efficient detection algorithm. Simulation experiment results demonstrated my algorithm is efficiency and accurate on the image defects detection. First to standardize design of the mobile transmission system, and equip with acquisition equipments of appropriate models of high-speed image acquisition card, monitoring camera and computer equipment, complete the preparations of the hardware conditions. In the detection process, use surveillance camera to get the mobile image at first. And the images data are captured from the buffer of image acquisition card quickly by directshow technology. Then the weighted-averaged frame of bad frames could reduce the bad effect of the bad frames which are brought about by the harsh environment of image acquisition. In the image preprocessing stage, the noise will be removed by Gaussian pyramid sampling. Dynamic threshold value, getting from each RGB 3-channels by recursive iteration, will be helpful to detecting the screen rectangle by identifying and extracting shape feature of the image, and then using image two-dimensional geometric transformation to auto-correct the mobile

西门子(Siemens) PLM 软件传动工程-挑战与解决方案说明书

西门子(Siemens) PLM 软件传动工程-挑战与解决方案说明书

Predict and reduce gear whine noise 5 times faster Generate transmission gearbox models automatically and boost vibro-acoustic performanceUnrestricted© Siemens AG 2019Realize innovation.Transmission Engineering ChallengesGuarantee Performance and DurabilityReduce Time for SimulationMinimize Vibration and Noise LevelsReduce Weight with Lightweight DesignsAnalysisResultsModellingPrototyping can cost up to 200k$ --per single gear80% of time for manual model creationMicrogeometry modificationscan reduce vibration level with 6dB (=half!)Transmission Error can increase 10x or more!Transmission Engineering ProcessTypical process for NVH analysisMore efficient process in Simcenter 3DTransmission Error or Stiffness, parametersAcoustics, NVH •Gear whine •Gear rattleEnd-to-end integrated process for transmission simulation from CAD to Loads to NoiseTransmission Builder →Motion →Motion-to-Acoustics →Acoustic Analysis•Automatic creation of multi-body simulation models •Accurate 3D simulation of gear forces•Semi-automatic link of gear forces to vibro-acoustics •Efficient and accurate acoustic simulationsPre-processing of loads orsurface vibrationsTransmission layout (stages, dimensions)Multi-body simulation •Simulation of forcesand dynamicsPositioning, dimensions…Gear-centric tool•Analysis of gear pairsMulti-Body Simulation of TransmissionsTransmission Engineering ProcessTypical process for NVH analysisMore efficient process in Simcenter 3DTransmission Error or Stiffness, parametersAcoustics, NVH •Gear whine •Gear rattleEnd-to-end integrated process for transmission simulation from CAD to Loads to NoiseTransmission Builder →Motion →Motion-to-Acoustics →Acoustic Analysis•Automatic creation of multi-body simulation models •Accurate 3D simulation of gear forces•Semi-automatic link of gear forces to vibro-acoustics •Efficient and accurate acoustic simulationsPre-processing of loads orsurface vibrationsTransmission layout (stages, dimensions)Multi-body simulation •Simulation of forcesand dynamicsPositioning, dimensions…Gear-centric tool•Analysis of gear pairs.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsMulti-Body SimulationScopePredicting, Analyzing, Improving the positions, velocities, accelerations and loads of a mechatronic system using an accurate and robust 3D multi-body simulation approachMechatronic Systems Flexible Bodies•Integration with tools for robust design of complex non-linear multi-physics systems:control systems, sensors, electric motors, etc •Predict mechanical system more accurately wrt displacements and loads•Gain insight in frequency response of a mechanism•Enable Noise, Vibration & Harshness (NVH) as well as Durability analysesSimcenter 3D Motion for Transmission Simulation Critical featuresMulti-Body Simulation Industry Modelling Practices•Joints •Constraints •Bearings•Linear Flexible Bodies•Nonlinearity (geometric & materials) by running FEcode•Deformations•Loads•Transmission Error•Time domain •Statics, dynamic,•Mechatronics / controlPost processing•Create gear geometry ✓CAE interface ✓Import CAD•Ext. Forces •Motor•Contacts, FrictionParametric Optimization loop Automation / CustomizationKinematicsDynamicsFlexible bodiesCADSolving1D -modelsControlsTEST dataA manual creation process can consume 80%of time!.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsNew ApproachTransmission Builder Vertical ApplicationProblem: Even experienced 3D-Multi Body Simulation experts can struggle to 1.Model complex parametric transmissions2.Capture all relevant effects correctly and efficiently3.Update and validate their modelsSolution: Transmission Builder Up to 5x faster Model creation processSimcenter TransmissionBuilderGear train specification based on Industry standardsMultibody simulation modelDemonstrationModel Creation and Updating1.Loading of pre-definedTransmission2.Geometry creation3.Creation of rigid bodies forgearwheels and shafts4.Positioning and Joint-definition5.Force element creation.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsNew Solver Methodologies Simulating and ValidatingValidation cases ensure resultsas accurate as non-linear Finite Elements simulationMeasured Transmission ErrorAnalytical MethodSiemens STS Advanced MethodExploiting intrinsic geometric properties of gears + Efficient-Only for gears, not for arbitrary shapes-No deformation includedBut, included as part of the Load CalculationFE based contact detection -“Brute force” Slow+ Any geometry+ Deformation effects includedDedicating Tooth ContactModeling –FE PreprocessorLocal Deformation –Analytic SolutionSlicing –Gear Force Distribution Along Line of Action •Includes Microgeometry Modifications and Misalignments in all DOF•Automatically takes in to account coupling between slices and between teeth•Accounts for actual gear body geometry with advanced stiffness formulation•Evaluates tip contact (approximation)Gear ContactMethodology HighlightsKey Features.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsMulti-Body Simulation of Transmissions SummaryValidated methodologySuperior insight in transmission vibrationsAutomated creation of transmission modelsGear simulation as accurate as FE whileextremely fast•Create CAD + MBD model•Connect and position housing•Add flexible modes (Autoflex)•Set up load casesSimcenter 3D Motion Simulate TransmissionDynamic bearing forcesSimulateAcoustic Simulation of TransmissionsTransmission Engineering ProcessTypical process for NVH analysisMore efficient process in Simcenter 3DTransmission Error or Stiffness, parametersAcoustics, NVH •Gear whine •Gear rattleEnd-to-end integrated process for transmission simulation from CAD to Loads to NoiseTransmission Builder →Motion →Motion-to-Acoustics →Acoustic Analysis•Automatic creation of multi-body simulation models •Accurate 3D simulation of gear forces•Semi-automatic link of gear forces to vibro-acoustics •Efficient and accurate acoustic simulationsPre-processing of loads orsurface vibrationsTransmission layout (stages, dimensions)Multi-body simulation •Simulation of forcesand dynamicsPositioning, dimensions…Gear-centric tool•Analysis of gear pairs.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryAcoustic Process OverviewvvcvMulti-body simulation resultsD a t a p r o c e s s i n g a n d m a p p i n gLoad Recipe Time series Frequency spectraWaterfalls OrdersNoise PredictionMeasured dataORAcoustic Process OverviewFrom Motion to AcousticsInput Loads Time Data to Waterfallof Time DataFFT Post-Processing•Multi-body simulation results•Data selection (forces, vibrations)•Automatic mapping •Multiple RPM•RPM function•Frame size definition•Time range selection•Time segmentation•Fourier transform(windowing, frequencyrange, averaging)•Waterfalls•Functions•Order-cut analysis Benefits•Quick switch between Motion and Acoustics solutions•Efficient data processing (fast pre-solver)•Automatic data mapping•Pre-processing time reductionAcoustic Process Overview Acoustic SimulationGeometry Preparation Meshing andAssemblyStructural/AcousticPre-ProcessingSolver Post-Processing•Holes closing •Blends removal •Parts assembly •Mesh mating•Bolt pre-stress•Structural meshing•Acoustic meshing•Loading frommulti-body analysis•Fluid-StructureInterface•Output requests•Simcenter NastranVibro-Acoustics(FEM AML,FEMAO, ATV)•Structural results•Acoustic results•Contributionanalysis (modes,panels, grids) What-If, Optimization, Feedback to DesignerBenefits•Efficient model set-up•Efficient, accurate solutions•Quick solution update•Deep insight into results.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryAcoustic SimulationModel Preparation –MeshesFrom multi-body analysis•CAD geometry•Structural mesh of body→Used to compute structural modes included in Motion model when accounting for flexibility of body Specific to acoustic analysis•Acoustic mesh around body for exterior noise radiation →Geometry cleaning (ribs removal, holes filling)→Surface and convex meshing →3D elements filling•Microphone mesh for acoustic responseAssembly of structural and acoustic meshesBenefits•Easy, fast, efficient model set-up•Quick switch between CAD and FEM environments •Quick update with associativity of meshes to CAD •Flexible modelling through assemblyAssociativityModel Preparation –Loads and Boundary Conditions Structural constraints and loads•Fixed constraints•Multi-body forces applied at center of bearings→Automatic mapping→Data processing (time to waterfall of time data, FFT) Acoustic boundary conditions•AML (Automatically Matched Layer)→Non-reflecting boundary condition to absorb outgoing acoustic wavesFluid-structure interface•Weak or strong couplingTime dataTo Waterfall of Frequency dataBenefits•Easy, fast, efficient model set-up•Quick switch between FEM and SIM environmentsρc AMLSize ~ 190k nodes ~ 14k nodes Timex s/freq.x/20s/freq.AML (Automatically Matched Layer)•Automatic creation of PML (Perfectly Matched Layer) at solver levelFull absorption of outwards-traveling waves•First, accurate results in “physical” (red) FEM domain •Then, accurate results outside the FEM domain (green), through post-processing •PML layer very close to radiatorBenefits•No manual creation of extra absorbing layer •Optimal absorption •Lean FEM model •Fast computationSolver Technologies –FEM AMLATV (Acoustic Transfer Vector)•Single computation of acoustic transfer vector between vibrating surface and microphones{p ω}=ATV ω×{v n (ω)}•Independence of ATV from load conditions (RPM, order)•For exterior radiation, smooth ATV functions in frequencyBenefits•Large frequency steps for ATV computation, and interpolation for acoustic response •Fast multi-RPM analysisSolver Technologies –ATV=+p ωv n (ω)304050607080901001003005007009001100130015001700S o u n d P r e s s u r e L e v e l (d B )f (Hz)FEMATV Response Frequency100-1700 Hz 100-1700 HzTime22 min3 minNo ATV ATVFEMAO (FEM Adaptive Order)•High-order FEM with adaptive order refinement •Hierarchical high-order shape functions•Auto-adapting fluid element order at each frequency (dependent on f, local c0, local ℎ), to maintain accuracy Benefits•Lean single coarse acoustic mesh •Optimal model size at each frequency •Huge gains vs standard FEM •Faster at lower frequencies•More efficient at higher frequencies • 2 to 10 x fasterAcoustic SimulationSolver Technologies –FEMAOStandard FEM →1 single model for all frequenciesStandard FEM →several modelsfor different frequency rangesFEMAO →1 single model for all frequenciesLess DOF required forFEMAO Optimal DOF size over all frequenciesEdge Shape Functions Face Shape FunctionsFEM FEMAO.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryRigid body vs Flexible body•No significant difference at low frequencies •Above 1400 Hz, more frequency content due to structural modes of flexible housing structurePlain gears vs Lightweight gears (flexible body)•Low harmonic at 200 Hz (6000 RPM), due to gear stiffness variation with holes in lightweight gear •Side band due to tooth stiffness variation (amplitude effect due to coupling with holes)Bearing Forces Frequency Domain Benefits•Deeper insight on input forces•Quick solution update for comparative studies involving design/modelling changesPlain gears vs Lightweight gears (flexible body)•Low RPM•Significant impact of lightweight gears •High RPM•Extra frequency content at low frequenciesRigid body vs Flexible body •Low frequencies•Reduced impact of flexibility •High frequencies•Larger impact of flexibilityRadiated Acoustic Power Functions300 RPM –Plain gears300 RPM –Lightweight gear 5900 RPM –Plain gears5900 RPM –Lightweight gears300 RPM –Rigid body 300 RPM –Flexible body 1500 RPM –Rigid body 1500 RPM –Flexible bodyBenefits•Efficient post-processing for results analysis •Quick solution update for comparative studiesinvolving design/modelling changesRigid Body vs Flexible Body Benefits•Efficient post-processing forresults analysis•Global overview oncorrespondencebetween source(dynamic forces)and receiver(acoustic power)Plain Gears vs Lightweight Gears Benefits•Efficient post-processing forresults analysis•Global overview oncorrespondencebetween source(dynamic forces)and receiver(acoustic power)Contribution AnalysisExamplesMultiple results types: structural displacements and modes, equivalent radiated power, acoustic pressure and power, panel contributions to pressure and power, grid contributions, etcBenefits•Efficient post-processing forresults analysis•Deepunderstanding ofmodel behaviorthrough multipleresults types Structural displacements Acoustic pressure Grid contributionsPanel contributions.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryAcoustic Simulation of Transmissions SummaryEfficient model set-up with CAD associativity for quicksolution updateSuperior insight in vibro-acoustic responseFast and accurate solver technologiesMore efficient link of gear forces from Motion toAcoustics =+p ωv n (ω)Associativity•Transfer bearing forces into frequency domain•Set-up vibro-acoustic model•Map bearing forces onto vibro-acoustic modelSimcenter 3D Acoustics Simulate TransmissionSimulateAcoustic resultsConclusionUnrestricted © Siemens AG 20192019-05-08Page 42Siemens PLM SoftwarePredict and Reduce Gear Whine Noise 5 Times FasterGenerate transmission gearbox models automatically and boost vibro-acoustic performanceSimcenterTransmission Builder Motion Simulation Acoustic SimulationAutomation removes 80% of workload for transmission model generation New gear solver increases efficiencyand accuracy Automatic motion-to-acoustics linksimplifies pre-processing Fast acoustic solver gives superiorinsight to responseUnrestricted © Siemens AG 20192019-05-08Page 43Siemens PLM SoftwareEasy workflow from design specifications NVH gear whine analysisHyundai Motor CompanyGear Whine Analysis of Drivetrains Using Simcenter Simulation & Services•Predictive simulation for system level NVH and gear whine•Bring 3D simulation to the next level of usability, towards an holistic generative approach for drivetrain design and NVH“Simcenter Engineering and Consulting services helped us use the right analysistools to cover the entire gear transmission analysis […] The Simcenter 3D Transmission Builder software tool is well suited for our engineering purposes”Mr. Horim Yang, Senior Research Engineer•Simcenter 3D Motion and Transmission Builder for system level NVH in multibody •Simcenter Engineering and Consulting for solving complex engineering issues AutomaticCAD and multibody creationAccurateFE-based gear elementsMulti-disciplinaryCAD-FEMMultibody-Acoustichttps://youtu.be/bBM5TPP6iBg。

尺度上推 像元聚合方法

尺度上推 像元聚合方法

尺度上推像元聚合方法英文回答:Scaling up and pixel aggregation methods are commonly used techniques in image processing and computer vision. These methods aim to enhance the resolution and quality of images by combining multiple low-resolution images into a single high-resolution image.Scaling up refers to the process of increasing the size of an image while maintaining its aspect ratio. This is often done by interpolating the pixels of the original image to fill in the gaps in the enlarged image. The most commonly used scaling up method is bilinear interpolation, which calculates the values of the new pixels based on the average of the surrounding pixels. This method can produce smooth and visually pleasing results, but it may also introduce blurring and loss of details.On the other hand, pixel aggregation methods involvecombining multiple low-resolution images to create a single high-resolution image. This can be done by aligning the images and averaging the pixel values at each corresponding position. This technique takes advantage of the fact that each low-resolution image captures a slightly different perspective of the scene, which can be used to enhance the overall resolution and reduce noise. Examples of pixel aggregation methods include super-resolution and image stacking.中文回答:尺度上推和像元聚合方法是图像处理和计算机视觉中常用的技术。

图像精度评价方法

图像精度评价方法

图像精度评价方法进行遥感影像分类或进行GIS动态模拟时,需要评价结果的精度,而进行评价精度的方法主要有混淆矩阵、总体分类精度、Kappa 系数、多分误差、漏分误差、每一类的生产者精度(制图精度)和用户精度。

1、混淆矩阵(Confusion Matrix): 主要用于比较分类结果和地表真实信息,可以把分类结果的精度显示在一个混淆矩阵里面。

混淆矩阵是通过将每个地表真实像元的位置和分类与分类图象中的相应位置和分类像比较计算的。

混淆矩阵的每一列代表了地面参考验证信息,每一列中的数值等于地表真实像元在分类图象中对应于相应类别的数量;混淆矩阵的每一行代表了遥感数据的分类信息,每一行中的数值等于遥感分类像元在地表真实像元相应类别中的数量。

如有50个样本数据,这些数据分成3类,每类50个。

分类结束后得到的混淆矩阵为:43 5 22 45 30 1 49则第1行的数据说明有43个样本正确分类,有5样本本应该属于第1类,却错误分到了第二类,有2个样本本应属于第一类,而错误的分到第三类。

2、总体分类精度(Overall Accuracy): 等于被正确分类的像元总和除以总像元数,地表真实图像或地表真实感兴趣区限定了像元的真实分类。

被正确分类的像元沿着混淆矩阵的对角线分布,它显示出被分类到正确地表真实分类中的像元数。

像元总数等于所有地表真实分类中的像元总和。

3、Kappa系数:The Kappa Index of Agreement (K): this is an important index that the crossclassification outputs. It measures the association between the two input images and helps to evaluate the output image. Its values range from -1 to +1 after adjustment for chance agreement. If the two input images are in perfect agreement (no change has occurred), K equals 1. If the two images are completely different, K takes a value of -1. If the change between the two dates occurred by chance, then Kappa equals 0. Kappa is an index of agreement between the two input images as a whole. However, it alsoevaluates a per-category agreement by indicating the degree to which a particular category agrees between two dates. The per-category K can be calculated using the following formula (Rosenfield and Fitzpatrick-Lins,1986):K = (Pii - (Pi.*P.i )/ (Pi. - Pi.*P.i )where:P ii = Proportion of entire image in which category i agrees for both datesP i. = Proportion of entire image in class i in reference imageP.i = Proportion of entire image in class i non-reference imageAs a per-category agreement index, it indicates how much a category have changed between the two dates. In the evaluation, each of the two images can be used as reference and the other as non-reference.Kappa系数是另外一种计算分类精度的方法。

镜像法解混响

镜像法解混响
form
We model the rooms of interest as simple rectangular enclosures. This choice of geometry is made for several reasons-
P(co,X,X') =exp[{o•(R/c 4•.R- t)]'
Image method for efficiently simulating small-room acoustics
Jont B. Allen and David A. Berkley
Acoustics Research Department, Bell Laboratories, Murray Hill, New Jersey 07974
basic studies of room acoustics.
(2) This model can be most easily realized in an efficient computer program. (3) The image solution of a rectangular enclosure
The room model assumed is a rectangular enclosure with a source-to-receiver impulse response, or transfer function, calculated using a time-domain image expansion method. Frequent applications have been made of the image method in the past as in deriving the re-

医学影像三维重建方法研究

医学影像三维重建方法研究

摘要医学图像三维重建是目前医学图像处理领域的研究热点,属于多学科交叉的研究课题,涉及到计算机图形学、图像处理、生物医学工程等多种技术,在诊断医学、手术规划及模拟仿真等方面有广泛应用。

本文主要研究了医学影像三维重建中的算法和应用,综述了医学三维重建技术的发展现状,详细讨论了表面三维重建方法和体绘制方法。

为获得更精确的重建结果,提出了一种改进的交互式医学图像分割算法;针对临床应用的需求,提出了一种基于大规模数据集的快速分组算法,可以用于器官(组织)选择、剥离等手术模拟;基于提出的漫游路径自动生成算法,介绍了一种基于物理模型的虚拟内窥镜实现技术。

仿真实验结果表明,本文提出的图像分割算法、数据集快速分组算法及漫游路径自动生成算法具有较高的鲁棒性和实用性。

此外,在理论算法研究的基础上丌发了一个三维图像处理软件包。

关键词:医学图像处理、三维表面重建、体绘制、虚拟内窥镜、Livewire分割算法、多边形分组ABSTRACT3Dreconstructionformedicalimagesisahotsubjectofmedicalimagesprocessing,belongingtomulti-disciplinarysubject,involvedincomputergraphicsandimageprocessinginbiomedicineengineering.Thealgorithmsandapplicationofmedicalimages3Dreconstructionaremainlystudied.Themethodsarediscussedof3Dsurfacereconstructionandvolumerendering.Toobtainthemoreaccurateresults,aninteractiveimagesegmentationalgorithmispresented.Thispaperprovidesafastmassdata—groupingalgorithmtomeettheclinicalrequirements,suchassurgerysimulation,organselectingandseparating.Basedonthealgorithmoffly-·pathgenerationautomatically,thephysicalmodel··basedvirtualendoscopytechniqueispresented.Theexperimentsdemonstratethealgorithmsofimagesegmentation,massdatagroupingandfly—pathgenerationalemorerobustandpractical.Inadditional,asoftwaretoolkitisdevelopedfor3Dmedicalimageprocessing.Keywords:medicalimageprocessing,3Dsurfacereconstruction,volumerendering,virtualendoscopy,segmentationalgorithm,andmassdatagrouping独创性(或创新性)声明本人声明所呈交的论文是我个人在导师指导下进行的研究工作及取得的研究成果。

22264180_基于逆向解算的领航AUV_导航数据后处理方法

22264180_基于逆向解算的领航AUV_导航数据后处理方法

2020年8月徐振烊, 等: 水下地形辅助导航适宜地图分辨率的选取第4期[3]Wadhams P. The Use of Autonomous Underwater Vehiclesto Map the Variability of Under-ice Topography[J]. OceanDynamics, 2012, 62(3): 439-447.[4]张静远, 谌剑, 李恒, 等. 水下地形辅助导航技术的研究与应用进展[J]. 国防科技大学学报, 2015, 37(3): 128-135.Zhang Jing-yuan, Shen Jian, Li Heng, et al. Research andApplication Progress on Underwater Terrain-aided Navi-gation Technology[J]. Journal of National University ofDefense Technology, 2015, 37(3): 128-135.[5]Ramesh R, Jyothi V B N, Vedachalam N, et al. Develop-ment and Performance Validation of a Navigation Systemfor an Underwater Vehicle[J]. Journal of Navigation, 2016, 69: 1097-1113.[6]邹炜, 孙玉臣. 水下地形匹配辅助导航技术研究[J]. 舰船电子工程, 2017, 37(8): 5-10.Zou Wei, Sun Yu-chen. Summary of Underwater TerrainMatching Aided Navigation Technology[J]. Ship Elec-tronic Engineering, 2017, 37(8): 5-10.[7]高永琪, 刘洪, 张毅. 测量误差对水下地形匹配性能的影响研究[J]. 弹箭与制导学报, 2014, 34(1):180-183.Gao Yong-qi, Liu Hong, Zhang Yi. The Study on Meas-urement Errors about Underwater Terrain Matching Per-formance[J]. Journal of Projectiles, Rockets, Missiles andGuidance, 2014, 34(1): 180-183.[8]Allotta B, Caiti A, Costanzi R, et al. A New AUV Naviga-tion System Exploiting Unscented Kalman Filter[J].Ocean Engineering, 2016, 113: 121-132.[9]Wang K, Zhu T, Qin Y, et al. Matching Error of the Itera-tive Closest Contour Point Algorithm for Terrain-aided Navigation[J]. Aerospace Science & Technology, 2018, 73: 210-222.[10]王汝鹏, 李晔, 马腾, 等. AUV地形匹配导航快速收敛滤波[J]. 华中科技大学学报(自然科学版), 2018, 46(7): 99-102.Wang Ru-peng, Li Ye, Ma Teng, et al. Terrain ReferenceNavigation Fast Convergence Filtering of AUV[J]. Jour-nal of Huazhong University of Science and Technolo-gy(Nature Science Edition), 2018, 46(7) : 99-102.[11]Bishop G C. Gravitational Field Maps and NavigationalErrors[J]. IEEE Journal of Oceanic Engineering, 2002, 27(3): 726-737.[12]谌剑, 张静远, 查峰. 变分辨率质点滤波水下地形匹配算法[J]. 中国惯性技术学报, 2012, 20(6): 694-699.Shen Jian, Zhang Jing-yuan, Zha Feng. Alterable Resolu-tion Point-mass Filter Algorithm for Underwater TerrainMatching Method[J]. Journal of Chinese Inertial Tech-nology, 2012, 20(6): 694-699.[13]张亚南, 朱长青, 杜福光. 一种基于信息盒维数的DEM适宜分辨率确定方法[J]. 地理与地理信息科学,2014, 30(6): 17-20.Zhang Ya-nan, Zhu Chang-qing, Du Fu-guang. OptimizingDEM Resolution with Information Box Dimension[J].Geography and Geo-Information Science, 2014, 30(6):17-20.[14]朱伟, 王东华, 周晓光. 基于信息熵的DEM最佳分辨率确定方法研究[J]. GIS技术, 2008(5): 79-82.Zhu Wei, Wang Dong-hua, Zhou Xiao-guang. The Research of Optimizing DEM Resolution Based on Information Entropy[J]. Remote Sensing Information, 2008(5): 79-82.[15]王英钧. 地形辅助导航综述[J]. 航空电子技术, 1998(1):24-29.[16]吕文涛, 王宏伦, 刘畅, 等. 无人机地形匹配辅助导航系统设计与仿真[J]. 电光与控制, 2014, 21(5): 63-68.Lü Wen-tao, Wang Hong-lun, Liu Chang, et al. Designand Simulation of Terrain Matching Aided NavigationSystem for UAVs[J]. Electronics Optics & Control, 2014,21(5): 63-68.[17]蒋秉川, 万刚, 李锋, 等. 机器人超高分辨率立体网格导航地图建模研究[J]. 系统仿真学报, 2017, 29(11):112-119.Jiang Bing-chuan, Wan Gang, Li Feng, et al. RoboticNavigation Map Construction with Ultra-High ResolutionVolumetric Grid[J]. Journal of System Simulation, 2017,29(11): 112-119.[18]Liu F, Balazadegan Y, Gao Y. Tight Integration ofINS/Stereo VO/Digital Map for Land Vehicle Naviga-tion[J]. Photogrammetric Engineering & Remote Sensing,2018, 84(1): 15-23.[19]Zhang T, Xu X, Xu S. Method of Establishing an Under-water Digital Elevation Terrain Based on Kriging Inter-polation[J]. Measurement, 2015, 63: 287-298.[20]Karakasis E, Papakostas G, Koulouriotis D, et al. A Uni-fied Methodology for Computing Accurate QuaternionColor Moments and Moment Invariant[J]. IEEE Transac-tions on Image Processing, 2014, 23(2): 596-611.[21]Fan L, Smethurst J A, Atkinson P M, et al. Propagation ofVertical and Horizontal Source Data Errors into a TINwith Linear Interpolaton[J]. International Journal of Geo-graphical Information Science, 2014, 28(7): 1378-1400. [22]宁永成, 侯代文. 递推的贝叶斯估计方法[J]. 兵器装备工程学报, 2013, 34(10): 130-136.Ning Yong-cheng, Hou Dai-wen. A Survey of RecursiveBayesian Estimation Methods[J]. Sichuan Ordnance Journal, 2013, 34(10): 130-136.(下转第445页)第28卷第4期 水下无人系统学报 Vol.28No.42020年8月JOURNAL OF UNMANNED UNDERSEA SYSTEMS Aug. 2020收稿日期: 2019-12-11; 修回日期: 2020-01-05.作者简介: 董权威(1991-), 男, 硕士, 工程师, 主要从事水下航行器导航、控制技术等研究.[引用格式] 董权威, 岳才谦, 王奥博, 等. 基于逆向解算的领航AUV 导航数据后处理方法[J]. 水下无人系统学报, 2020, 28(4):420-427.基于逆向解算的领航AUV 导航数据后处理方法董权威, 岳才谦, 王奥博, 王亭亭(中国航天空气动力技术研究院, 北京, 100074)摘 要: 由于多自主水下航行器(AUV)协同导航系统在水下长时间工作时, 领航AUV 无法接收外界信息,其定位误差会逐渐发散进而影响整个系统的定位性能。

SPWM变频调速矢量控制系统的建模与仿真

SPWM变频调速矢量控制系统的建模与仿真

SPWM变频调速矢量控制系统的建模与仿真一、本文概述Overview of this article随着电力电子技术和计算机技术的飞速发展,变频调速技术已成为现代工业控制领域中的一项重要技术。

其中,正弦脉宽调制(SPWM)作为一种高效、精确的变频调速方法,被广泛应用于电机驱动、风电、电力系统等领域。

本文旨在探讨SPWM变频调速矢量控制系统的建模与仿真,通过深入分析和研究,为实际应用提供理论支持和技术指导。

With the rapid development of power electronics and computer technology, variable frequency speed regulation technology has become an important technology in the field of modern industrial control. Among them, sine pulse width modulation (SPWM), as an efficient and accurate variable frequency speed regulation method, is widely used in fields such as motor drive, wind power, and power systems. This article aims to explore the modeling and simulation of SPWM variable frequency speed vector control system, and provide theoretical support and technical guidance for practical applicationsthrough in-depth analysis and research.本文将详细介绍SPWM变频调速矢量控制系统的基本原理和组成结构,包括正弦脉宽调制的原理、矢量控制的基本原理、系统的硬件组成和软件设计等方面。

一种针对超大口径凸非球面的面形检测方法

一种针对超大口径凸非球面的面形检测方法
(1.中国科学院 长春光学精密机械与物理研究所 中国科学院光学系统先进制造技术重点实验室,吉林 长春 130033;
2.中国科学院大学,北京 100049)
摘要:本文提出了一种改良的检测方法用于实现对超大口径凸非球面反射镜进行高精度的面形检测。该方法利用计算 机再现全息和照明透镜混合补偿,实现对超大口径凸非球面的高精度检测。首先,对该方法的基本原理进行了分析和研 究;然后,以一块口径为 800mm的超大口径凸非球面为例,进行了子孔径规划和检测光路中相关光学元件的设计;最后, 以中心子孔径为例,系统分析了该检测装置的敏感度。仿真实验结果表明:计算全息补偿器的设计残差均方根值小于 00013nm,该检测系统的综合检测精度可以优于 6nmRMS。结果表明该检测系统满足超大口径凸非球面反射镜高精 度面形检测的要求。 关 键 词:面形检测;非球面;像差补偿;衍射光学元件 中图分类号:TQ171.6;O435.2 文献标识码:A doi:10.3788/CO.20191205.1147
。需要注意的是,照明透镜的凸面指向 CGH,并 以其凸面作为参考面进行 CGH对准区域的设计。 这样在光路调整时,照明透镜的倾斜,偏心及轴向 失调量都能通过对准区域的干涉条纹体现出,进 而指导光路的调节。此外,由于照明透镜的主要 功能 是 汇 聚 光 束,而 检 测 光 路 中 像 差 补 偿 是 由 CGH完成的,所以针对不同环带的子孔径进行面 形检测时,仅需对 CGH进行单独设计,而照明透 镜是通用的。这不仅降低了检测成本,也极大地 降低了检测时光路调节的难度和时间。光路中的 小孔光栏放置在干涉仪标准镜的焦平面,其作用 是对反射光路中的由 CGH产生的干扰级次进行 隔离,只让目标级次(1,1)级通过小孔。
2 CGH结合照明透镜检测方案

SIMULATION APPARATUS, SIMULATION METHOD AND SIMULA

SIMULATION APPARATUS, SIMULATION METHOD AND SIMULA

专利名称:SIMULATION APPARATUS, SIMULATIONMETHOD AND SIMULATION PROGRAM FORIMAGE READING APPARATUS发明人:NAKASHIGE FUMIHIRO,中重 文宏,NOMOTOMITSUMASA,野本 光正申请号:JP2003013415申请日:20030122公开号:JP2004227225A公开日:20040812专利内容由知识产权出版社提供专利附图:摘要:PROBLEM TO BE SOLVED: To provide the simulation apparatus, simulation method and simulation program for an image reading apparatus which enable characteristics affecting image quality such as a flare phenomenon to be predicted in the design stage of the image reading apparatus thereby enable an image with higher quality to be acquired.SOLUTION: The simulation apparatus is provided with: a setting means 31 for settingan original pattern equivalent to an original, primary illuminating light distribution observed when an original is directly irradiated by an illuminator, secondary illuminating light distribution observed when the primary illuminating lights are reflected on the original surface and the original is illuminated again and the reading position of the original image in the image reading apparatus; an arithmetic means 32 for calculating the primary illuminating light intensity from the primary illuminating light distribution and the reading position and calculating secondary illuminating light intensity from the secondary illuminating light distribution and the original pattern and the reading position; and a display means 33 for displaying the value of the primary illuminating light intensity and the value of the secondary illuminating light intensity.COPYRIGHT: (C)2004,JPO&NCIPI申请人:RICOH CO LTD,株式会社リコー地址:東京都大田区中馬込1丁目3番6号国籍:JP更多信息请下载全文后查看。

stabble diffusion的采样方法

stabble diffusion的采样方法

stabble diffusion的采样方法English:To sample stable diffusion processes, one common method is to use the Euler-Maruyama method. This method involves discretizing the diffusion process and simulating sample paths using the discretized equations. Another approach is to use the Milstein method, which is an extension of the Euler-Maruyama method and provides a more accurate approximation of the stochastic differential equation. In addition, one can employ the exact simulation method, which involves directly simulating from the conditional distribution of the process given its past. This method can be computationally intensive, but it provides exact samples from the diffusion process. Lastly, one can also use the Monte Carlo method, which involves generating random samples from a specified probability distribution. This method can be coupled with other techniques such as importance sampling to improve the efficiency of the sampling process.Translated content:采样稳定扩散过程的一种常用方法是使用欧拉-马鲁雅马方法。

catia零部件内腔容积计算

catia零部件内腔容积计算

catia零部件内腔容积计算Calculating the volume of internal cavities in CATIA is a crucial requirement for various engineering and design tasks. Accurate volume calculations are essential for evaluating the capacity, fluid flow, and other physical properties of the components. In this essay, we will explore the problem of calculating the internal cavity volume in CATIA from multiple perspectives, including the importance of accurate volume calculations, the challenges faced in the process, the available methods for volume calculation, and the potential benefits of using CATIA for this task.Accurate volume calculations are vital in engineering and design as they provide essential information for a wide range of applications. For instance, in fluid dynamics, knowing the volume of internal cavities helps determine the flow rate and pressure within a component. This information is crucial for optimizing the performance of hydraulic systems, cooling mechanisms, and even aerodynamic designs.Moreover, accurate volume calculations are essential for estimating material requirements, cost analysis, and ensuring the structural integrity of the component.However, calculating the volume of internal cavities in CATIA can be challenging due to the complex geometries involved. CATIA, a powerful computer-aided design software, allows engineers to create intricate 3D models with various shapes and sizes. These models often consist of multiple intersecting surfaces, curved edges, and irregular shapes, making it difficult to determine the exact volume manually. Additionally, the presence of internal features, such as ribs, fillets, and chamfers, further complicates the volume calculation process.To overcome these challenges, CATIA offers several methods for volume calculation. One commonly used approach is the "Mass Properties" tool, which provides accurate volume calculations for solid models. By selecting the desired component and using the "Mass Properties" command, CATIA can automatically calculate the volume based on the model's geometry. This method is particularly useful forcomponents with simple shapes and well-defined boundaries.For more complex components, CATIA offers the "Part Design" module, which allows engineers to create internal cavities using features like pockets, slots, and holes. By defining the dimensions and positions of these features, CATIA can accurately calculate the volume of the resulting cavity. This method is highly versatile and enables engineers to create complex internal structures while ensuring accurate volume calculations.Using CATIA for volume calculations offers several benefits. Firstly, the software provides a user-friendly interface that enables engineers to visualize the component in 3D and make precise adjustments to the model if necessary. This helps ensure accurate volume calculations and reduces the chances of errors. Additionally, CATIA's advanced algorithms and mathematical models ensure high precision and reliability in volume calculations, providing engineers with confidence in their results. Moreover, CATIA's integration with other engineering tools and simulation software allows for seamless transfer of volumedata, enabling further analysis and optimization of the component's performance.In conclusion, calculating the volume of internal cavities in CATIA is an important task in engineering and design. Accurate volume calculations are essential for various applications, including fluid dynamics, material requirements, and cost analysis. Despite the challenges posed by complex geometries, CATIA offers effective methods for volume calculation, such as the "Mass Properties" tool and the "Part Design" module. Using CATIA for volume calculations provides several benefits, including a user-friendly interface, high precision, and seamlessintegration with other engineering tools. By leveraging the capabilities of CATIA, engineers can accurately calculate the volume of internal cavities, leading to improved designs and optimized performance.。

数字水印算法基于图像相似度计算说明书

数字水印算法基于图像相似度计算说明书

A Digital Watermarking Algorithm Based on ImageSimilarity CalculationJ. YangInformation Technology DepartmentHubei University of PoliceWuhan, ChinaJ. Qian, T.L. Ma, H.X. ZhangSchool of Printing and PackingWuhan University Wuhan, ChinaAbstract —Normalized correlation algorithms have been used indigital watermarking evaluation system at home and abroad which still exists some shortcomings. In this paper, severalcommon image similarity algorithms in image retrieval technology are selected and applied to watermarking technology. This paper chooses an adaptive embedding and extraction algorithm based on discrete cosine transform (DCT) and usessome different methods to calculate the similarity between the extracted watermark images and the original watermark image. Before embedding, the watermark image has been scrambled.The entire process of image watermarking system is conducted byMatlab, which demonstrates that applying the similaritycalculating methods used in image retrieval into digitalwatermarking technology can improve the accuracy when evaluate the robustness of a watermark system.Keywords-digital watermarking; image retrial; image feature; the similarity calculatingI. I NTRODUCTIONIn digital watermarking technology, how to calculate the similarity between the extracted watermark information with the original watermark information is a very important problem. Because it determines whether the watermark information can be detected properly or not, and whether the evaluation of the robustness of a watermarking algorithm is really reasonable. Presently normalized correlation has been widely used in digital watermarking evaluation system. Although traditional normalized correlation formula can shows the similarity between original watermark and extracted watermark, it also exists some shortcomings. It is necessary to seek a more appropriate similarity calculating method, and apply it to the digital watermarking technology. The objective of this paper is to research the digital watermarking technology based on image retrieval similarity calculation.This paper is organized as follows. The theories analysis arepresented in Section 1. The experimental methods are illustrated in Section 2.The experiment results and discussion are shown in Section 3. Finally, the concluding remarks are given in Section 4. II. T HEORIES A NALYSIS A. Analysis of the Common Watermark Similarity Algorithm Assume that )~,(w w ρrepresents the similarity, w and w ~represent the original watermark of M ×N and the extracted watermark.The watermark similarity is calculated in Eq.(1) proposed by Lin et al[1] : ϖρT TT w w w w w w w ~~~1),(=(1) The similarity proposed by [Luo et al ][2]is defined as w w w w w w TT ~~2),(=ρ (2)[Sun et al][3] proposes to determine the similarity with Eq.(3):~~~~3),(w w ww w w T T =ρ (3)If the watermark information is the length of R of binary sequences, the similarity algorithm defined by [Lv W Y et al][4]is described asIw w w w Ii i i ∑=⊗=1~~4)(),(ρ (4)Eq.(1) and Eq.(4) are generally known as the "normalized correlation". In the Eq.(1), the consideration of similarity calculation is the angle between the two-dimensional vectors. Due to the length of the vector does not affect the size of the angle between them, there will be as long as the angles between the extracted watermark and the original watermark are equal, their NC value is equal to 1. As is showed in Figure 1 left, The pixel values of the original watermark (a )is two times as much as extracted watermark (b ). The similarity NC = 1calculated by Eq.(1) represents the consistency of two imageshowever they are visually very different.As is showed in Figure 1 right, the similar problem causedby the Eq.(2) and the Eq.(3) is that the results of the calculation represent the consistency of two images however they arevisually very different. International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2015)FIGURE I. THE ORIGINAL WATERMARK (A) AND THE EXTRACTEDWATERMARK (B)B. Similarity Algorithms Used in Image RetrialTo solve the above shortcomings of normalized correlation, some researches had been done in the field of image retrieval. In this field, image features are extracted based on the extraction of color feature, texture feature and shape feature, which can be used to calculate the similarity[5].1) Geometric distance: At present there are many distance functions several commonly used in the calculation of image similarity, such as Euclidean distance, Manhattan distance and Chebyshev distance etc.Euclidean distance is also called L2 distance, in n-dimensional space the shortest length of the line between two points is the Euclidean distance[6]. The Euclidean distance is described as:2112),(⎥⎦⎤⎢⎣⎡-=∑=n i i i y x y x ρ, ι=1,2,.....,ν (5)Manhattan distance derives from the city block distance and is also referred to as blocks distance or L1 distance. It is the sum of the calculated absolute wheelbase two points on the coordinate system of the standard. The Manhattan distance formula is given by:∑=-=ni ii y x y x 1),(ρ (6)Chebyshev distance is also called chessboard distance or L ∞distance. The chebyshev distance between two points is equal to the maximum value of the coordinate difference[7]. The actual calculation formula is as follows:{}i i ni y x y x -==,..,2,1max ),(ρ (7)2) Mahalanobis distance: In calculating the distance between image feature vector, if each characteristic component affects each other or the characteristic components play different roles in distinguishing images, a new method to calculate the metric between feature vectors differences can be used which named Mahalanobis distance [8]. Its actual calculation formula is as below:∑=-=Ni ii is C X X D 1)2()1()( (8)3) Histogram distance: In measuring the diversities between different histograms, the most commonly used method is to calculate the histogram intersection. The same number of pixels in each cell between two histograms is the two histograms’ histogram intersection[9]. If the two color histogram X and Y contains N cells, in order to make the range of histogram intersection fall between [0, 1], the formula can be standardized as:{}∑∑===ni in i i i y y x y x 11/,min ),(ρ (9)4) Bhattacharyya distance: In statistics, Bhattacharyya distance is mainly used to measure the two discrete probability distribution. In calculating the histogram similarity, Bhattacharyya has the best effect, but its calculation is the most complex. At the same domain Z, the Bhattacharyya distance formula of the probability distribution P and Q is as follows[10]:∑∈=Xx x q x p q p BC )()(),( (10)III.E XPERIMENTAL M ETHODSA. Experimental ConsiderationsThe experiment idea is as shown in Figure 2.FIGURE II. EXPERIMENTAL CONSIDERATIONSVector image used in the experiment is 512 * 512 grayscale lena. Meanwhile the watermark image is 128*128 "CS" binary image whose format is Bitmap. Watermarking algorithm tested in the experiment is based on the discrete cosine (DCT).The operating system is windows 7. It is easy to observe and compare through a very intuitive GUI(Graphic User Interface) interface which is generated in matlab 7.0 simulation environment. Part of the attack algorithms are also carried out by Matlab, the final calculation of similarity between the original watermark information and extracted watermark information are completed by Matlab 7.0.IV.D ISCUSSION AND R ESULTSTo verify the effects of diffirent methods used to calculate the similarity in digital watermarking technology, the experiment watermarked image has been tested with attacks such as image cropping, rotation, Gauss fuzzy, Guassian noise addition.A. Image CroppingThe four corners of the watermarked image is cropped in experiment 1.As shown in Figure 4, followed by the sheared image, the extracted watermark image after shearing and anti scrambling the watermark image. In the case of cropping, although the extracted watermark here has been a lot of noise, it can still identify original watermark image information "CS ". B. RotationThe watermark is rotate with 30o in experiment 2. As shown in Figure 5, the extracted watermark image after rotation and anti scrambling the watermark image are followed by the rotated image. As we can see, after rotation the extracted watermark image has been unable to identify the original watermark image information.C. Gauss Fuzzy ExperimentExperiment 3 is a Gauss fuzzy operation on watermarked image, which is known as Gaussian low-pass filtering. As shown in Figure 6, the extracted watermark image after rotation and anti scrambling the watermark image are followed by the image after Gauss fuzzy. It is clear to see that after Gauss fuzzy operation the extracted watermark image has been unable to identify the original watermark image information.D. Guassian Noise AdditionExperiment 4 adds Gauss white noise to embedded watermarking image in Matlab, the white Gauss noise density is 0.02. As shown in Figure 7, the extracted watermark image after adding Gaussian noise and anti scrambling the watermark image are followed by the image with additive Gaussian noise. The result is that after shear attack the extracted watermark although there has been a lot of noise, it can still identifyoriginal watermark image information "CS "FIGURE III. IMAGE CROPPING EXPERIMENTFIGURE IV. ROTATION ATTACK EXPERIMENTFIGURE V. GAUSSIAN BLURFIGURE VI. ADD GAUSSIAN NOISEThe normalized correlation values of two images of NC1, NC2, HD correlation value histogram intersection, Euclidean distance OD and Bhattacharyya distance BD are calculated, the results are given in table 1.TABLE I. THE CALCULATION OF WATERMARKING IMAGESIMILARITY Processing mode Attack parameters NC1 NC2 HD OD BD Cropping Four corners shear 0.8596 0.7412 0.9451 0.0777 0.9500 rotation 10 deg 0.2897 0.4014 0.8032 0.2783 0.8484 Gauss fuzzy0.2521 0.3626 0.7710 0.3239 0.8256 Guassian noisedensity 0.020.78670.91560.94210.10740.9380The experimental results shows that the resistance of selected watermarking algorithm to five kinds of attacks was ordered as: Cut >Gaussian noise >Rotate >Gaussian Blur. This result is consistent with the results of evaluation of OD and BD and more accurate than the results of NC1 and NC2.V.C ONCLUSIONDiscussing the contrast problems of extracted watermark images and the original watermark images in the field of digital watermark, the paper proposes a method to calculate the similarity of extracted watermark images and original watermark images with similarity calculation algorithm in image retrieval, and emulates and simulates the total process of embedding, attacking and detection and extraction by means of GUI. The results verifies that applying the method of similarity calculation in image retrieval to the digital watermarking technology can improve the accuracy of the digital watermarking robustness evaluation.A CKNOWLEDGMENTThe authors thank the financial support of the Technology R&D Program of Hubei Province: the project of digital copyright protection and trade service cloud platform (No. YJG0264) and the Hubei Provincial Service Industry Guidance of Capital Construction Program: the project of digital newspaper publishing and copyright trading technology. (Development and Reform Commission issued a notice to guide capital No.[2013]765, Special Account Funds Direct Financial Payment Application No.[2013]206)R EFERENCES[1] Lin C, Wu M, Bloom J A, et al. Rotation, scale, and translation resilientwatermarking for images[J]. Image Processing , IEEE Transactions on, 2001,10(5):767-782.[2] Luo W, Heileman G L, Pizano C E. Fast and robust watermarking ofJPEG files[C]// In Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation ,. IEEE, 2002: 158-162.[3] Sun J F, Wen Q, Wang S X.Based on chaotic array image watermarkingalgorithm[J]. ACTA ELETRONICA SINICA ,2003,32(1):149-153.[4] Li X Y , Zhuang Y T, Pan Y H. The technique and system of content-based image retrieval [J]. Journal of Computer Research & Development , 2001, 38(3):344-354.[5] Li M L. Research of image retrieval technology & design andimplementation of system based on color and shape feature[D]. Xi'an: Northwest University, 2010.[6]Yang W X. Composite Model research based on shape for imagematching[D]. Beijing:Signal and Information Processing North China Electric Power University, 2010.[7]Fu X J. Research on Algorithm of content-based image retrievalintegrated based on color and texture features[D]. South China Normal University,2011.[8]Di Q, Feng X. Survey on content-based image retrieval techniques[J].Computer Engineering, 2005,31(z1):223-225.[9]Yong H. Watermarking algorithm research and application based ontransform domain[D]. Yingchuan: Ningxia University circuit and system, 2010.[10]Chen J B.Research and implementation of image features extraction andits similarity[D]. Xi'an: Software Engineering Xi’an Electronic Sience &Technology University, 2012.。

一种腔体滤波器全腔仿真的方法

一种腔体滤波器全腔仿真的方法

Telecom Power Technology设计应用技术一种腔体滤波器全腔仿真的方法孟弼慧,孙雷,刘志军(京信射频技术(广州)有限公司,广东腔体滤波器的应用十分广泛,然而目前的腔体滤波器的仿真设计与实物加工往往存在较大偏差。

该背景下,介绍了一种腔体滤波器仿真方法,采用导纳矩阵提取滤波器的耦合矩阵及谐振频率,通过优化和迭代从而达到腔体滤波器全腔精确仿真的目的。

通过实例分析与样品制作,验证该方法可行,仿真效率耦合矩阵;谐振频率;腔体滤波器A Full Cavity Simulation Method for Cavity FilterMENG Bihui, SUN Lei, LIU Zhijun(Jingxin Radio Frequency Technology (Guangzhou) Co., Ltd., GuangzhouAbstract: During the construction of mobile communication networks, cavity filters are widely used, but theresimulation design and physicalthis context, the article introduces a simulation method for cavity filters, which uses admittance matrix to extract the 2023年7月10日第40卷第13期· 29 ·Telecom Power TechnologyJul. 10, 2023, Vol.40 No.13孟弼慧,等:一种腔体滤波器 全腔仿真的方法(1)多端口导纳矩阵谐振频率提取的方法。

根据滤波器设计理论,对于一个无功率损耗的谐振器,其谐振时实部与虚部为0。

各谐振器的谐振频率计算公式为()(){}n3n,3n ωIm 0y ω =(2)式中:ω表示角频率,ω=2πf ;f 表示谐振频率;ωn 表示第n 个谐振器的角频率;y (3n,3n)(ω)表示第n 个谐振器对应的导纳矩阵中的Y 参数。

多时相遥感影像阴影角度精确校正仿真

多时相遥感影像阴影角度精确校正仿真

第36卷第3期计算机仿真2019年3月文章编号:1006-9348 (2019 )03-0410-04多时相遥感影像阴影角度精确校正仿真张维、陈报章2,赵亮3(1.中国矿业大学环境与测绘学院,江苏徐州221116;2.中国科学院地理科学与资源研究所,北京100101;3.中国矿业大学力学与土木工程学院,江苏徐州221116)摘要:为了增强遥感影像对实际拍摄区域的还原效果,提高合成影像信息的利用价值,针对当前影像阴影角度校正方法中存 在的阴影区域提取不准确、补偿效果较差、角度校正过程所需时间长等问题,提出基于灰度补偿的多时相遥感影像阴影角度 精确校正方法。

分别计算遥感影像阴影区域的色调差值、蓝色通道与绿色通道的差值及亮度与饱和度差值,结合D-S证据 理论将各差值结果融合作为颜色特征提取影像中的阴影区域。

采用灰度线性变换算法对得到的阴影区域进行灰度补偿,并进行高灰度噪点滤除,实现影像阴影区域的校正与边缘平滑处理。

计算阴影校正后的图像间差值与初始图像间差值,结合 遥感影像设备的轨道运行参数构建阴影角度校正模型,利用模型完成影像阴影角度的精确校正。

实验结果表明,所提方法 阴影角度校正结果更接近真实值,校正耗时更短,具有较好的适用性。

关键词:多时相;遥感影像;阴影区域提取;阴影角度校正中图分类号:TF79 文献标识码:BMulti-Temporal Remote Sensing Image Shadow AngleAccurate Correction SimulationZHANG Wei1,CHEN Bao-zhang2, ZHAO Liang3(1. School of Environment Science and Spatial Informatics, China University of Mining and Technology,Xuzhou Jiangsu 221116, China ;2. I n s t i t u t e of Geographic Science and Natural Resource Research, C A S, Beijing 100101, China;3. School of Mechanics & Civil Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China)A B S T R A C T:This a r t i c l e presents an accurate correction method f o r shadow angle of multi-temporal remote sensingimage based on gray compensation.Respectively, we calculated the hue difference, the difference between the blue channel and the green channel, and the difference between the brightness and the saturation degree i n the shadow re­gion of remote sensing bined with D-S evidence theory, a l l difference r e s ults were mixed together as the color feature t o extract the shadow region in image.Then, we used grayscale linear transformation algorithm t o per­form grayscale compensation on the obtained shadow area and conduct high grayscale noise f i l t e r i n g t o achieve the cor­rection of shadow area and edge smoothing processing.In addition, we calculated the difference between images a f t e r shadow correction and the difference between i n i t i a l images.In combination with the o r b i t a l motion parameter of re­mote sensing imaging equipment, we b u i l t the shadow angle correction model, and then used the model t o complete the accurate correction of shadow shading angle.According t o simulation results, we can see t hat the shadow angle correction of proposed method i s closer t o actual value.Meanwhile, the correction time i s shorter, which has better applicability.K E Y W O R D S:Multi-temporal; Remote sensing image; Shadow region extraction ;Shadow angle correction基金项目:徐州市科技计划项目(KC16SQ187);2017年度江苏省建设 系统科技项目(2017ZD222);徐州科技情报研究计划课题(XKQ2017014);江苏省高等教育教改研究立项课题(2017JSJG284)收稿日期:2018-05-21修回日期:2018-06-281引言遥感技术是当前应用较为广泛的远程测控技术,具有监 测范围广、信息获取速度快、信息更新周期短等特点。

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CHIN.PHYS.LETT.Vol.25,No.5(2008)1772 An Accurate Image Simulation Method for High-Order Laue Zone Effects∗CAI Can-Ying(蔡灿英)1,2,ZENG Song-Jun(曾松军)1,2,LIU Hong-Rong(刘红荣)1,2,YANG Qi-Bin(杨奇斌)1,2∗∗1Institute of Modern Physics,Xiangtan University,Xiangtan411105 2Key Laboratory of Low-Dimensional Materials and Application Technology of Ministry of Education,XiangtanUniversity,Xiangtan411105(Received16October2007)A completely different formulation for simulation of the high order Laue zone(HOLZ)diffractions is derived.It refers to the new method,i.e.the Taylor series(TS)method.To check the validity and accuracy of the TS method,we take polyvinglidenefluoride(PVDF)crystal as an example to calculate the exit wavefunction by the conventional multi-slice(CMS)method and the TS method.The calculated results show that the TS method is much more accurate than the CMS method and is independent of the slice thicknesses.Moreover,the purefirst order Laue zone wavefunction by the TS method can reflect the major potential distribution of thefirst reciprocal plane.PACS:61.05.Jd,61.50.AhIt is well known that the most crystal structure information can be extracted from the exit wavefunc-tion contributed by the zero order Laue zone(ZOLZ) diffraction.The ZOLZ diffraction is caused by the projection of the crystal potential along the incident beam direction.Thus,the potential variation along the incident beam direction is lost if only the ZOLZ diffraction is considered.In order to obtain the poten-tial variation along the incident beam direction or the distribution of plane defects,for example,GP zone and stacking fault etc,high-order Laue zone(HOLZ) diffractions must be considered.[1−4]Nowadays,the most widely used image simulation method is the multi-slice(MS)theory[5]which was originally proposed by Cowley and Moodie[6]based on the physical–optical principles.The most important reason for the MS method gaining extensive applica-tion is its relatively fast calculation speed especially after Ishizuka and Uyeda introduced the fast Fourier transformation(FFT)[7]in this method.Twentyfive years later,Van Dyck[8]successfully tackled a new method,i.e.the real space(RS)method,which is directly based on the solution of the Schr¨o dinger equa-tion.In order to carry out the simulation by both the MS and the RS methods,a crystal has to be divided into a series of thin slices perpendicular to the incident beam direction.For the HOLZ diffraction calculation, the slices should be thin enough to be able to dis-play the potential variation along the incident beam direction.[9,10]The crystal potential was described as a series of steps in the RS scheme as shown in Fig.1(a), which is the approximation of the real potential as shown in Fig.1(b).Mathematically,any function can be expanded as Taylor series,which includes many high-order differential terms.Unexceptionally,the exit wavefunction can also be expanded as Taylor se-ries,implying that the exit wavefunction is influenced by not only the magnitude of the potential itself,but also every order differential of the potential.To ig-nore the potential differential effects on the wave func-tion in the RS scheme,the slice thickness should be infinitely thin,which is impossible for the practical application.To solve this problem,we adapt a com-pletely different way to derive the exit wavefunction from the Schr¨o dinger equation.In this study,we as-sume the exit wavefunction to be a Taylor series,then substitute it into the Schr¨o dinger equation and derive the coefficients of the Taylor series,which will be de-scribed in the following in detail.As usual,we start with the Schr¨o dinger equation[11]∆Ψ(r)+4π2k2Ψ(r)+V(r)Ψ(r)=0,(1) where∆is the Laplacian operator in the x−y plane, and V(r)=iσU(r)(λ,σand U(r)are the electron wavelength,the interaction constant and the crystal potential,respectively).For high-energy electrons(≥100keV),the in-fluence of the specimen potential can be considered as perturbation.Thus,the exit wavefunctionΨ(r) can be written as a modulated plane waveΨ(r)=φ(r)exp(2πikr).Substituting the above equation into Eq.(1),one hasdφdz=iλ4π(∆+V)φ,(2)∗Supported by the National Natural Science Foundation of China under Grant No10374077,and Key Foundation of Education Department of Hunan Province under Grant No05C097.∗∗To whom correspondence should be addressed.Email:yqb@ or Yangqibin02002@c 2008Chinese Physical Society and IOP Publishing LtdNo.5CAI Can-Ying et al.1773 where V can be divided into two parts,i.e.V=V P+V1.Fig.1.(a)Step potential used in the RS method(b)realpotential,versus distance along the a-axis in a unit cellwith unit a being the lattice constant.LetV P=kkF(hk0)exp[−2πi(hx+ky)],(3)V1=kkl=0F(hkl)exp[−2πi(hx+ky+lz)],(4)where V P is the projection of the crystal potential and is used to calculate the ZOLZ diffraction,V P+V1are used to calculate the ZOLZ and the HOLZ diffpared with the ZOLZ diffraction,the HOLZ diffractions are so weak that they can be con-sidered as perturbation.Among all the HOLZ diffrac-tions,thefirst one is the strongest;and the others can be neglected in the most practical cases.Thus one just needs to consider l=1in Eq.(4),V1=kkF(hk1)exp[−2πi(hx+ky+z)]=V2exp(−2πiz),(5)whereV2=kkF(hk1)exp[−2πi(hx+ky)].(6)Then Eq.(2)becomesdφdz =iλ4π[∆+V P+V2exp(−2πiz)]φ.(7)To solve Eq.(7),we assume thatφ(R,z)=φ(R,0)+dφdzz+12d2φdz2z2+13!d3φdz3z3+···+1n!d nφdz nz n+···=∞n=0z nn!d nφdz n,(8)orφ(R,(z+∆z))=φ(R,z)+dφdz∆z+12d2φdz∆z2+13!d3φdz3∆z3+···+1n!d nφdz n∆z n+···=∞n=0∆z nn!d nφdz,(9)where R is a plane vector.From Eq.(7),one mayfindthatdφdz=[∆+V P+V2exp(−2πiz/c)]φ,d2φdz2=[∆+V P+V2exp(−2πiz/c)]dφdz+−2πicV2exp(−2πiz/c)φ,d3φdz3=[∆+V P+V2exp(−2πiz/c)]d2φdz2+2−2πicV2exp(−2πiz/c)dφdz+−2πic2V2exp(−2πiz/c)φ,d4φdz=[∆+V P+V2exp(−2πiz/c)]d3φdz+3−2πicV2exp(−2πiz/c)d2φdz2+3−2πic2V2exp(−2πiz/c)dφdz+−2πic3V2exp(−2πiz/c)φ,······d nφdz n=[∆+V P+V2exp(−2πiz/c)]d n−1φdz n−1+(n−1)−2πicV2exp(−2πiz/c)d n−2φdz+(n−1)(n−2)2−2πic2V2·exp(−2πiz/c)d n−3φdz+(n−1)(n−2)(n−3)2×3−2πic3V2·exp(−2πiz/c)d n−4φdz n−4+···+(−2πic)n−1V2exp(−2πiz/c)φ.1774CAI Can-Ying et al.Vol.25Letηk =(∆z )k k !d k φdz k,(10)then we haveφ(R ,z +∆z )=∞ n =0ηn ,(11)whereηn =(∆+V p )∆z n ηn −1+V 2exp(−2πiz/c )∆zn· ηn −1+ −2πi ∆z cηn −2+12! −2πi ∆z c2ηn −3+···+1k ! −2πi ∆z ckηn −k −1+···+ .(12)Let∆zc=γ,the above equation becomes a recur-sive formula as follows:ηn =∆z n(∆+V p )ηn −1+V 2exp(−2πiz/c )n −1 k =0αk ηn −k −1,(13)whereαk =(−2πiγ)kk !.(14)To check the validity and the accuracy of expres-sions (11)–(14),we calculate the exit wavefunction of the ZOLZ and the HOLZ diffractions by the CMS method and the new expressions which we referred as the TS method using polyvinglidene fluoride (PVDF)crystal ((CH 2CF 2)2)as an example;then we compare the results calculated by those two different methods to find out which one is more accurate.PVDF belongs to an orthogonal crystal and the space group is P m 2m with the lattice constants a =0.858nm,b =0.491nmFig.2.(a)Amplitude and (b)phase of exit wavefunction calculated by the TS method,versus distance along the b -axis in a unit cell with unit b being the lattice constant,for z =0.5c .Fig.3.Phase map of two-dimensional exit wavefunctions:(a)calculated by the TS method with slice thickness a/32;(b)calculated by the TS method with slice thickness a/128;(c)calculated by the CMS method with slice thickness a/32;(d)calculated by the CMS method with slice thickness a/128.No.5CAI Can-Ying et al.1775 Fig.4.Maps of the potential and the purefirst order Laue zone wavefunction:(a)map of the potential of the zeroth reciprocal plane;(b)map the potential of thefirst reciprocal plane;(c)map of the purefirst order Laue zone wavefunction (amplitude)calculated by the TS method.and c=0.256nm.Thefitting constants for calcu-lating the structure factors of(PVDF)are taken from Ref.[12].The accelerate voltage is200kV correspond-ing toλ=0.00251nm.The incident beam is along the[100]direction.The y−z plane is divided into 64×32pixels.The thickness of the crystal along the incident beam direction(the a axis)is3.432nm corre-sponding to4times of the lattice constant a.A series of slice thicknesses a/128,a/64,a/32,a/16,a/8,a/4 and a/2are designed for comparing the effects of slice thicknesses on the results calculated by two different methods.The calculated results are shown in Figs.2–4.The calculated results along the[010]direction with z=0.5are schematically shown in Fig.2.From thesefigures we can see that both the amplitude and the phase almost keep constant for the different slice thicknesses(a/128,a/8,a/4and a/2).Figure3shows the phase of wavefunction in the exit plane.The results shown in Figs.3(a)and3(b) are calculated by the TS method with the different slice thicknesses(a/32and a/128).Figures3(c)and 3(d)show the calculated results by the CMS method with the different slice thicknesses.From those maps it can also be seen that the results calculated by the TS method are independent of the slice thickness.How-ever,those calculated by the CMS method are sensi-tive to the slice thickness especially in the peak area. However,when the slice thickness is very thin(here a/128),the results calculated by the CMS is in agree-ment with those calculated by the TS method.It can be concluded that the TS method is a more accurate method than the CMS method.Figure4(a)is the potential map of the zeroth re-ciprocal plane,from which the atomic positions pro-jected along the[100]direction can be obtained.Fig-ure4(b)is the potential map of thefirst reciprocal plane,i.e.V2in Eq.(6),which carries3D potential in-formation and is different from Fig.4(a).Figure4(c)is the map of the purefirst order Laue zone wavefunction (i.e.whole wave function subtracting ZOLZ wavefunc-tion)calculated by the TS pared with Fig.4(b),one mayfind that purefirst order Laue zone wavefunction can reflect major potential distribution of thefirst reciprocal plane although there are some weak differences between Figs.4(b)and4(c),which are probably caused by the interaction between ZOLZ and HOLZ diffraction.In conclusion,the CMS neglects the effect of the high-order differentials of the crystal potential on the HOLZ diffractions so that the slice thickness should be very thin.The TS method takes all the high-order differentials of the potential into account.The calcu-lated results are independent of the slice thicknesses.The purefirst order Laue zone wavefunction by the TS method can reflect the major potential distri-bution of thefirst reciprocal plane which is the3D potential information of the crystal.The TS method is suitable to the simulation requiring very accurate results.References[1]Brown J F and Clark D1952Acta Cryst.5615[2]Munson D and Wheeler M J1968J.Inst.Metal.96252[3]Gard J A1956Brit.J.Appl.Phys.7361[4]Gard J A and Taylor H F W1958Amer.Min.431[5]Spence J C H and Zuo J M1992Electron Microdiffraction(New York:Plenum)[6]Cowley J M and Moodie A F1957Acta Cryst.10609[7]Ishizuka K and Uyeda N1977Acta Cryst.A33740[8]Dyck D Van1980J.Microsc.119141[9]Chen J H,Beek M Op de,Dyck D Van1996Microsc.Mi-croanal.Microstruct.727[10]Self P G,O’Keefe M A,Buseck P R and Spargo A E C1983Ultramicroscopy1135[11]Dyck D Van1983J.Microsc.13231[12]Peng L.M,Ren G,Dudarev S and Whelan M1996ActaCryst.A52257。

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