SPATIAL MIXTURE MODELLING FOR THE JOINT DETECTION-ESTIMATION OF BRAIN ACTIVITY IN fMRI
英语
The Neutral Grounding Resistor Sizing Using an Analytical Method Based on Nonlinear Transformer Model for Inrush Current MitigationGholamabas M.H.Hajivar Shahid Chamran University,Ahvaz, Iranhajivar@S.S.MortazaviShahid Chamran University,Ahvaz, IranMortazavi_s@scu.ac.irMohsen SanieiShahid Chamran University,Ahvaz, IranMohsen.saniei@Abstract-It was found that a neutral resistor together with 'simultaneous' switching didn't have any effect on either the magnitudes or the time constant of inrush currents. The pre-insertion resistors were recommended as the most effective means of controlling inrush currents. Through simulations, it was found that the neutral resistor had little effect on reducing the inrush current peak or even the rate of decay as compared to the cases without a neutral resistor. The use of neutral impedances was concluded to be ineffective compared to the use of pre-insertion resistors. This finding was explained by the low neutral current value as compared to that of high phase currents during inrush. The inrush currents could be mitigated by using a neutral resistor when sequential switching is implemented. From the sequential energizing scheme performance, the neutral resistor size plays the significant role in the scheme effectiveness. Through simulation, it was found that a few ohms neutral grounding resistor can effectively achieve inrush currents reduction. If the neutral resistor is directly selected to minimize the peak of the actual inrush current, a much lower resistor value could be found.This paper presents an analytical method to select optimal neutral grounding resistor for mitigation of inrush current. In this method nonlinearity and core loss of the transformer has been modeled and derived analytical equations.Index Terms--Inrush current, neutral grounding resistor, transformerI.I NTRODUCTIONThe energizing of transformers produces high inrush currents. The nature of inrush currents have rich in harmonics coupled with relatively a long duration, leads to adverse effects on the residual life of the transformer, malfunction of the protection system [1] and power quality [2]. In the power-system industry, two different strategies have been implemented to tackle the problem of transformer inrush currents. The first strategy focuses on adapting to the effects of inrush currents by desensitizing the protection elements. Other approaches go further by 'over-sizing' the magnetic core to achieve higher saturation flux levels. These partial countermeasures impose downgrades on the system's operational reliability, considerable increases unit cost, high mechanical stresses on the transformer and lead to a lower power quality. The second strategy focuses on reducing the inrush current magnitude itself during the energizing process. Minimizing the inrush current will extend the transformer's lifetime and increase the reliability of operation and lower maintenance and down-time costs. Meanwhile, the problem of protection-system malfunction is eliminated during transformer energizing. The available inrush current mitigation consist "closing resistor"[3], "control closing of circuit breaker"[4],[5], "reduction of residual flux"[6], "neutral resistor with sequential switching"[7],[8],[9].The sequential energizing technique presents inrush-reduction scheme due to transformer energizing. This scheme involves the sequential energizing of the three phases transformer together with the insertion of a properly sized resistor at the neutral point of the transformer energizing side [7] ,[8],[9] (Fig. 1).The neutral resistor based scheme acts to minimize the induced voltage across the energized windings during sequential switching of each phase and, hence, minimizes the integral of the applied voltage across the windings.The scheme has the main advantage of being a simpler, more reliable and more cost effective than the synchronous switching and pre-insertion resistor schemes. The scheme has no requirements for the speed of the circuit breaker or the determination of the residual flux. Sequential switching of the three phases can be implemented through either introducing a mechanical delay between each pole in the case of three phase breakers or simply through adjusting the breaker trip-coil time delay for single pole breakers.A further study of the scheme revealed that a much lower resistor size is equally effective. The steady-state theory developed for neutral resistor sizing [8] is unable to explain this phenomenon. This phenomenon must be understood using transient analysis.Fig. 1. The sequential phase energizing schemeUPEC201031st Aug - 3rd Sept 2010The rise of neutral voltage is the main limitation of the scheme. Two methods present to control the neutral voltage rise: the use of surge arrestors and saturated reactors connected to the neutral point. The use of surge arresters was found to be more effective in overcoming the neutral voltage rise limitation [9].The main objective of this paper is to derive an analytical relationship between the peak of the inrush current and the size of the resistor. This paper presents a robust analytical study of the transformer energizing phenomenon. The results reveal a good deal of information on inrush currents and the characteristics of the sequential energizing scheme.II. SCHEME PERFORMANCESince the scheme adopts sequential switching, each switching stage can be investigated separately. For first-phase switching, the scheme's performance is straightforward. The neutral resistor is in series with the energized phase and this resistor's effect is similar to a pre-insertion resistor.The second- phase energizing is one of the most difficult to analyze. Fortunately, from simulation studies, it was found that the inrush current due to second-phase energizing is lower than that due to first-phase energizing for the same value of n R [9]. This result is true for the region where the inrush current of the first-phase is decreasing rapidly as n R increases. As a result, when developing a neutral-resistor-sizing criterion, the focus should be directed towards the analysis of the first-phase energizing.III. A NALYSIS OF F IRST -P HASE E NERGIZING The following analysis focuses on deriving an inrush current waveform expression covering both the unsaturatedand saturated modes of operation respectively. The presented analysis is based on a single saturated core element, but is suitable for analytical modelling of the single-phase transformers and for the single-phase switching of three-phase transformers. As shown in Fig. 2, the transformer's energized phase was modeled as a two segmented saturated magnetizing inductance in series with the transformer's winding resistance, leakage inductance and neutral resistance. The iron core non-l inear inductance as function of the operating flux linkages is represented as a linear inductor inunsaturated ‘‘m l ’’ and saturated ‘‘s l ’’ modes of operation respectively. (a)(b)Fig. 2. (a) Transformer electrical equivalent circuit (per-phase) referred to the primary side. (b) Simplified, two slope saturation curve.For the first-phase switching stage, the equivalent circuit represented in Fig. 2(a) can accurately represent behaviour of the transformer for any connection or core type by using only the positive sequence Flux-Current characteristics. Based on the transformer connection and core structure type, the phases are coupled either through the electrical circuit (3 single phase units in Yg-D connection) or through the Magnetic circuit (Core type transformers with Yg-Y connection) or through both, (the condition of Yg-D connection in an E-Core or a multi limb transformer). The coupling introduced between the windings will result in flux flowing through the limbs or magnetic circuits of un-energized phases. For the sequential switching application, the magnetic coupling will result in an increased reluctance (decreased reactance) for zero sequence flux path if present. The approach presented here is based on deriving an analytical expression relating the amount of inrush current reduction directly to the neutral resistor size. Investigation in this field has been done and some formulas were given to predict the general wave shape or the maximum peak current.A. Expression for magnitude of inrush currentIn Fig. 2(a), p r and p l present the total primary side resistance and leakage reactance. c R shows the total transformer core loss. Secondary side resistance sp r and leakage reactance sp l as referred to primary side are also shown. P V and s V represent the primary and secondary phase to ground terminal voltages, respectively.During first phase energizing, the differential equation describing behaviour of the transformer with saturated ironcore can be written as follows:()())sin((2) (1)φω+⋅⋅=⋅+⋅+⋅+=+⋅+⋅+=t V (t)V dtdi di d λdt di l (t)i R r (t)V dt d λdt di l (t)i R r (t)V m P ll p pp n p P p p p n p PAs the rate of change of the flux linkages with magnetizing current dt d /λcan be represented as an inductance equal to the slope of the i −λcurve, (2) can be re-written as follows;()(3) )()()(dtdi L dt di l t i R r t V lcore p p P n p P ⋅+⋅+⋅+=λ (4) )()(L core l p c l i i R dtdi−⋅=⋅λ⎩⎨⎧==sml core L L di d L λλ)(s s λλλλ>≤The general solution of the differential equations (3),(4) has the following form;⎪⎩⎪⎨⎧>−⋅⋅+−⋅+−−⋅+≤−⋅⋅+−⋅+−⋅=(5) )sin(//)()( )sin(//)(s s 22222221211112121111λλψωττλλψωττt B t e A t t e i A t B t e A t e A t i s s pSubscripts 11,12 and 21,22 denote un-saturated and saturated operation respectively. The parameters given in the equation (5) are given by;() )(/12221σ⋅++⎟⎟⎠⎞⎜⎜⎝⎛⋅−++⋅=m p c p m n p c m m x x R x x R r R x V B()2222)(/1σ⋅++⎟⎟⎠⎞⎜⎜⎝⎛⋅−++⋅=s p c p s n p c s m x x R x x R r R x V B⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛⋅−+++=⋅−−⎟⎟⎟⎠⎞⎜⎜⎜⎝⎛−c p m n p m p c m R x x R r x x R x σφψ111tan tan ⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎝⎛⋅−+++=⋅−−⎟⎟⎟⎠⎞⎜⎜⎜⎝⎛−c p s n p s p c m R R r x x R x σφψ112tan tan )sin(111211ψ⋅=+B A A )sin(222221s t B A A ⋅−⋅=+ωψ mp n p m p m p m p c xx R r x x x x x x R ⋅⋅+⋅−⋅+−⋅+⋅⋅⋅=)(4)()(21211σστm p n p m p m p m p c xx R r x x x x x x R ⋅⋅+⋅−⋅++⋅+⋅⋅⋅=)(4)()(21212σστ s p n p s p s p s p xx R r x x x x x x c R ⋅⋅+⋅−⋅+−⋅+⋅⋅⋅=)(4)()(21221σστ sp n p s p s p sp c xx R r x x x x x x R ⋅⋅+⋅−⋅++⋅+⋅⋅⋅=)(4)()(21222σστ ⎟⎟⎠⎞⎜⎜⎝⎛−⋅==s rs s ri i λλλ10 cnp R R r ++=1σ21221112 , ττττ>>>>⇒>>c R , 012≈A , 022≈A According to equation (5), the required inrush waveform assuming two-part segmented i −λcurve can be calculated for two separate un-saturated and saturated regions. For thefirst unsaturated mode, the current can be directly calculated from the first equation for all flux linkage values below the saturation level. After saturation is reached, the current waveform will follow the second given expression for fluxlinkage values above the saturation level. The saturation time s t can be found at the time when the current reaches the saturation current level s i .Where m λ,r λ,m V and ωare the nominal peak flux linkage, residual flux linkage, peak supply voltage and angular frequency, respectivelyThe inrush current waveform peak will essentially exist during saturation mode of operation. The focus should be concentrated on the second current waveform equation describing saturated operation mode, equation (5). The expression of inrush current peak could be directly evaluated when both saturation time s t and peak time of the inrush current waveform peak t t =are known [9].(10))( (9) )(2/)(222222121//)()(2B eA t e i A peak peak t s t s n peak n n peak R I R R t +−⋅+−−⋅+=+=ττωψπThe peak time peak t at which the inrush current will reachits peak can be numerically found through setting the derivative of equation (10) with respect to time equal to zero at peak t t =.()(11) )sin(/)(022222221212221/ψωωττττ−⋅⋅⋅−−−⋅+−=+−⋅peak t s t B A t te A i peak s peakeThe inrush waveform consists of exponentially decaying'DC' term and a sinusoidal 'AC' term. Both DC and AC amplitudes are significantly reduced with the increase of the available series impedance. The inrush waveform, neglecting the relatively small saturating current s i ,12A and 22A when extremely high could be normalized with respect to theamplitude of the sinusoidal term as follows; (12) )sin(/)()(2221221⎥⎦⎤⎢⎣⎡−⋅+−−⋅⋅=ψωτt t t e B A B t i s p(13) )sin(/)()sin()( 22221⎥⎦⎤⎢⎣⎡−⋅+−−⋅⋅−⋅=ψωτωψt t t e t B t i s s p ))(sin()( 2s n n t R R K ⋅−=ωψ (14) ωλλλφλφωλλφωmm m r s s t r m s mV t dt t V dtd t V V s=⎪⎭⎪⎬⎫⎪⎩⎪⎨⎧⎥⎥⎦⎤⎢⎢⎣⎡⎟⎟⎠⎞⎜⎜⎝⎛−−+−⋅=+⋅+⋅⋅==+⋅⋅=−∫(8) 1cos 1(7))sin((6))sin(10The factor )(n R K depends on transformer saturation characteristics (s λand r λ) and other parameters during saturation.Typical saturation and residual flux magnitudes for power transformers are in the range[9]; .).(35.1.).(2.1u p u p s <<λ and .).(9.0.).(7.0u p r u p <<λIt can be easily shown that with increased damping 'resistance' in the circuit, where the circuit phase angle 2ψhas lower values than the saturation angle s t ⋅ω, the exponential term is negative resulting in an inrush magnitude that is lowerthan the sinusoidal term amplitude.B. Neutral Grounding Resistor SizingBased on (10), the inrush current peak expression, it is now possible to select a neutral resistor size that can achieve a specific inrush current reduction ratio )(n R α given by:(15) )0(/)()(==n peak n peak n R I R I R α For the maximum inrush current condition (0=n R ), the total energized phase system impedance ratio X/R is high and accordingly, the damping of the exponential term in equation (10) during the first cycle can be neglected; [][](16))0(1)0()0(2212=⋅++⎥⎦⎤⎢⎣⎡⋅−+===⎟⎟⎠⎞⎜⎜⎝⎛+⋅⋅n s p c p s pR x n m n peak R x x R x x r R K V R I c s σ High n R values leading to considerable inrush current reduction will result in low X / R ratios. It is clear from (14) that X / R ratios equal to or less than 1 ensure negative DC component factor ')(n R K ' and hence the exponential term shown in (10) can be conservatively neglected. Accordingly, (10) can be re-written as follows;()[](17) )()(22122n s p c p s n p R x m n n peak R x x R x x R r V R B R I c s σ⋅++⎥⎦⎤⎢⎣⎡⋅−+=≈⎟⎟⎠⎞⎜⎜⎝⎛+⋅Using (16) and (17) to evaluate (15), the neutral resistorsize which corresponds to a specific reduction ratio can be given by;[][][](18) )0()(1)0( 12222=⋅++⋅−⋅++⋅−+⋅+=⎥⎥⎦⎤⎢⎢⎣⎡⎥⎥⎦⎤⎢⎢⎣⎡=n s p c p s p n s p c p s n p n R x x R x x r R x x R x x R r R K σσα Very high c R values leading to low transformer core loss, it can be re-written equation (18) as follows [9]; [][][][](19) 1)0(12222s p p s p n p n x x r x x R r R K +++++⋅+==α Equations (18) and (19) reveal that transformers require higher neutral resistor value to achieve the desired inrush current reduction rate. IV. A NALYSIS OF SECOND-P HASE E NERGIZING It is obvious that the analysis of the electric and magnetic circuit behavior during second phase switching will be sufficiently more complex than that for first phase switching.Transformer behaviour during second phase switching was served to vary with respect to connection and core structure type. However, a general behaviour trend exists within lowneutral resistor values where the scheme can effectively limitinrush current magnitude. For cases with delta winding or multi-limb core structure, the second phase inrush current is lower than that during first phase switching. Single phase units connected in star/star have a different performance as both first and second stage inrush currents has almost the same magnitude until a maximum reduction rate of about80% is achieved. V. NEUTRAL VOLTAGE RISEThe peak neutral voltage will reach values up to peak phasevoltage where the neutral resistor value is increased. Typicalneutral voltage peak profile against neutral resistor size is shown in Fig. 6- Fig. 8, for the 225 KVA transformer during 1st and 2nd phase switching. A del ay of 40 (ms) between each switching stage has been considered. VI. S IMULATION A 225 KVA, 2400V/600V, 50 Hz three phase transformer connected in star-star are used for the simulation study. The number of turns per phase primary (2400V) winding is 128=P N and )(01.0pu R R s P ==, )(05.0pu X X s P ==,active power losses in iron core=4.5 KW, average length and section of core limbs (L1=1.3462(m), A1=0.01155192)(2m ), average length and section of yokes (L2=0.5334(m),A2=0.01155192)(2m ), average length and section of air pathfor zero sequence flux return (L0=0.0127(m),A0=0.01155192)(2m ), three phase voltage for fluxinitialization=1 (pu) and B-H characteristic of iron core is inaccordance with Fig.3. A MATLAB program was prepared for the simulation study. Simulation results are shown in Fig.4-Fig.8.Fig. 3.B-H characteristic iron coreFig.4. Inrush current )(0Ω=n RFig.5. Inrush current )(5Ω=n RFig.6. Inrush current )(50Ω=n RFig.7. Maximum neutral voltage )(50Ω=n RFig.8. Maximum neutral voltage ).(5Ω=n RFig.9. Maximum inrush current in (pu), Maximum neutral voltage in (pu), Duration of the inrush current in (s)VII. ConclusionsIn this paper, Based on the sequential switching, presents an analytical method to select optimal neutral grounding resistor for transformer inrush current mitigation. In this method, complete transformer model, including core loss and nonlinearity core specification, has been used. It was shown that high reduction in inrush currents among the three phases can be achieved by using a neutral resistor .Other work presented in this paper also addressed the scheme's main practical limitation: the permissible rise of neutral voltage.VIII.R EFERENCES[1] Hanli Weng, Xiangning Lin "Studies on the UnusualMaloperation of Transformer Differential Protection During the Nonlinear Load Switch-In",IEEE Transaction on Power Delivery, vol. 24, no.4, october 2009.[2] Westinghouse Electric Corporation, Electric Transmissionand Distribution Reference Book, 4th ed. East Pittsburgh, PA, 1964.[3] K.P.Basu, Stella Morris"Reduction of Magnetizing inrushcurrent in traction transformer", DRPT2008 6-9 April 2008 Nanjing China.[4] J.H.Brunke, K.J.Frohlich “Elimination of TransformerInrush Currents by Controlled Switching-Part I: Theoretical Considerations” IEEE Trans. On Power Delivery, Vol.16,No.2,2001. [5] R. Apolonio,J.C.de Oliveira,H.S.Bronzeado,A.B.deVasconcellos,"Transformer Controlled Switching:a strategy proposal and laboratory validation",IEEE 2004, 11th International Conference on Harmonics and Quality of Power.[6] E. Andersen, S. Bereneryd and S. Lindahl, "SynchronousEnergizing of Shunt Reactors and Shunt Capacitors," OGRE paper 13-12, pp 1-6, September 1988.[7] Y. Cui, S. G. Abdulsalam, S. Chen, and W. Xu, “Asequential phase energizing method for transformer inrush current reduction—part I: Simulation and experimental results,” IEEE Trans. Power Del., vol. 20, no. 2, pt. 1, pp. 943–949, Apr. 2005.[8] W. Xu, S. G. Abdulsalam, Y. Cui, S. Liu, and X. Liu, “Asequential phase energizing method for transformer inrush current reduction—part II: Theoretical analysis and design guide,” IEEE Trans. Power Del., vol. 20, no. 2, pt. 1, pp. 950–957, Apr. 2005.[9] S.G. Abdulsalam and W. Xu "A Sequential PhaseEnergization Method for Transformer Inrush current Reduction-Transient Performance and Practical considerations", IEEE Transactions on Power Delivery,vol. 22, No.1, pp. 208-216,Jan. 2007.。
Modelling and Assimilation of Atmospheric Chemistry - ecmwf建模与大气化学ECMWF同化
Why Atmospheric Chemistry at NWP centres ?
- or in a NWP Training Course?
Environmental concern Air pollution Ozone hole Climate change
ppt 1:1012
Atmospheric Chemistry
Transport
Chemical Reactions
Photolysis
catalytical Cycles
Emissions
Atmospheric Reservoir
Training Data assimilation and Satellite Data – Johannes Flemming Dr. Martin Schultz - Max-Planck-Institut für Meteorologie, Hamburg
Rodwell and Jung Published in Quart. J. Roy. Meteorol. Soc., 134, 1479.1497 (2019)
Training Data assimilation and Satellite Data – Johannes Flemming
An other motivation …
Transport
wet & dry Deposition
Modelling atmospheric composition
Mass balance equation for chemical species ( up to 150 in state-of-the-art
GIS软件应用说明
摘自：南京师范大学地理科学学院GIS专业课程http://202.119.109.14/dky/index.htm《GIS软件应用》课程教材：《ArcGIS9地理信息系统空间分析方法》，科学出版社，2006参考教材：《ARCGIS 8 Desktop 地理信息系统应用指南》，清华大学出版社，2002软件：ArcGIS9.0GIS软件应用是地图学与地理信息系统本科专业的选修课程，课程总学时54，计2学分，周学时3，学时分配：讲授28学时，上机实践26学时。
一、课程特点实践性很强的课程，是GIS专业学生必须掌握的基本技能。
通过课堂上和课后的大量实例练习操作，让学生在熟练掌握GIS通用软件的基础上，理解GIS的基本原理和方法，提高解决实际问题的能力。
二、课程教学目标GIS软件应用课程以熟练掌握GIS常用软件位基本目标，通过该门课程的学习，使学生不仅掌握常用GIS软件的操作，加深对GIS基本原理的理解和领会，并能够熟练运用一种GIS软件完成地理空间数据的处理和分析。
三、课程内容以ArcGIS软件为基础，以数据分析处理由浅入深的主线，在介绍ARCGIS的基本操作的基础上着重讲述ArcGIS的空间分析功能模块，培养学生针对问题建模的思想，增加其解决实际问题的能力。
主要内容如下：∙ARCGIS应用基础（ArcMap、ArcCatalog、Geoprocessing等）∙空间数据的采集与组织（Shapefile、Coverage、Geodatabase）∙空间数据的转换与处理（ArcToolbox）∙数据的可视化表达∙矢量、栅格数据的空间分析∙三维分析∙地统计分析∙水文分析∙空间分析建模四、教学方法1.原理介绍：简要讲述GIS的基本原理和方法。
2.课堂演示：在每一个基本原理与方法之后，介绍软件部分相应的功能和方法。
3.屏幕动画：大量的课后练习采用屏幕动画的形式提供给同学，作为作业答案参考资料。
五、教学组织方式课堂讲授与上机实习相结合。
SCM信道模型
4.5.2
Per-path BS angle spread (AS)................................................................................................................... 10
3GPP
Release 11
3
3GPP TR 25.996 V11.0.0 (2012-09)
Contents
Foreword............................................................................................................................................................. 4
3GPP TR 25.996 V11.0.0 (2012-09)
Technical Report
3rd Generation Partnership Project; Technical Specification Group Radio Access Network;
Spatial channel model for Multiple Input Multiple Output (MIMO) simulations
4.4
BS and MS array topologies .............................................................................................................................. 8
cgg模块RAMUR参数
GeneralThe module removes multiples (characterized as events with a slower velocity) or linear events from a trace gather. The Y-flag must be set at the end of every elementarygather. Input traces must be ordered according to the spatial co-ordinate usedfor the Radon transform. Traces are output in the order of the input elementary gathers.Anti-multiple filtering (first option MA)The module computes a model of primary and multiple events. This computation is based on data decomposition into user-defined parabolas and performed using a high-resolution, de-aliased least-squares method in the frequency-space (f-x) domain for each frequency of the pass-band defined by FMIN and FMAX. Events corresponding to parabolas with a greater curvature are considered to be multiples. Events corresponding to parabolas smaller than this threshold are considered to be primary events. The zone limits between primaries and multiples is user defined (DTCUT). The difference between data and the sum of primary and multiple events is considered as linear noise. By default, the module subtracts the model of multiples or the model of multiples plus the linear noise from the input gather (see the LAMBDA parameter).Anti-noise filtering (first option NA)The module builds signal and (linear) noise events. This building is based on a userdefinedlinear model.On input, a gather is modelled as a superimposition of a number of linear events plus the random noise.The computation is performed using a high-resolution, de-aliased least-squares method in the frequency- space (f-x) domain for each frequency of the pass-band defined by FMIN and FMAX.CGG2 RAMUR User’s Manual GeoclusterBy default, the module subtracts the model with organized noise or the model with organized noise plus the random noise from each gather (see LAMBDA parameter). Equalization options (second option PA)Input traces are equalized before filtering. This equalization is similar to that applied by the DYNQU module (blank option) with a sliding operator. The PA option (Preserved Amplitude) reverses equalization after Radon filtering.Geocluster User’s Manual RAMUR 3CGGFUNCTION CALLDescriptionColumn contents1 *3-7 RAMUR9-10 First option:MA: Multiple attenuation using modelling of parabolas.NA: Linear noise attenuation.12-13 Second option:Blank: No equalisation/un-equalisation of input traces.PA: Traces are equalised before processing and un-equalised after processing.15-16 Input buffer: contains the trace to be processed.23-24 Output buffer: contains the processed trace.31-80 ParametersParameters common to all optionsMandatory parametersXRM i i = Far trace offset (in meters or feet). This offset is used as a reference when computing the Radon model. Integer.YMX j j =Maximum coverage of a unit gather (CDP, shotpoint,…). Integer.FMIN f f = Minimum frequency (in Hz) of the spectrum used in the computation of the models. Integer.0 f < F Nyquist.FMAX g g = Maximum frequency (in Hz) of the spectrum used in the computation of the models. Integer.Maximum (0,FMIN) < g < F Nyquist.YB k k = Number of the secondary loop to which output traces are transmitted. Integer.1< k <99The selection of the FMIN and FMAX frequencies is essential in the processing cost. The computation of models is only performed on the user-defined frequency band.CGG4 RAMUR User’s Manual GeoclusterParameters describing the area to scanDTMIN v v = Lower limit (in milliseconds) for scanning parabolas or straight lines relative to the horizontal. Integer.In option MA, it is the minimum residual NMO at offset XRM. Inoption NA, it is the minimum shift at offset XRM with respect tozero.In general, v is negative or null, and always smaller than DTMAX.When v is positive, flat events cannot be modelled.DTMAX w w = Upper limit (in milliseconds) for scanning parabolas or straight lines relative to the horizontal. Integer.In option MA, it is the maximum residual NMO at offset XRM. Inoption NA, it is the maximum shift at offset XRM with respect tozero.DTMIN < w.DDT z z = Increment (in milliseconds) between parabolas or straight linesat offset XRM. Integer.0 < z < (DTMAX-DTMIN)DTCUT eor DTCUT e, DTCUT fe (,f) = Separating threshold(s) in milliseconds used to define thezone of multiples and linear noises. Integer.If DTCUT is coded once only, the lines or parabolas includedbetween DTCUT and DTMAX are not used to build the signalmodel.If DTCUT is coded twice, the lines or parabolas included betweenthe two DTCUTs are not used to build the signal model.Parts which are not used for the signal model are used to model multiplesor linear noises.The values given to e and f must lie within the [DTMIN,DTMAX]interval. If two thresholds are defined e must be smaller than f.orDTKEEP e,or DTKEEP e, DTKEEP fe(,f) = Separating thresholds in milliseconds used to define the zoneof primaries or the signal zone to be preserved. Integer.If DTKEEP is coded only once, only the lines or parabolas included between DTKEEP and DTMAX are used to build the signal model.If DTKEEP is coded twice, only the lines and parabolas includedbetween the two DTKEEPs are used to build the signal model.Parts which are not used for the signal model are used to model theGeocluster User’s Manual RAMUR 5CGGmultiples or linear noises.The values given to e and f must lie within the [DTMIN,DTMAX]interval. If two thresholds are defined, e must be smaller than f.Parameters for windowing managementNCX m, TAPX nThe Radon filtering is performed on spatial sliding windows made ofm traces with an overlap of n traces. Integer.0 < m, 0 n.The size of the sliding spatial window is essential in the quality ofthe results.NCT o, TAPT pThe Radon filtering is performed on o milliseconds time sliding windowswith an overlap of p milliseconds. Integer.0 < o, 0 δ p.Optional parametersProcessing window parametersTIi or KTIr i = Initial time in milliseconds of the processing window after NMO. Integer. By default, i = 0r = Multiplier coefficient of the water bottom values read from theLFD (which must then be defined). Real.LFDd d = Number of the water bottom library. Integer.TAPIi i = Length of the tapering zone in milliseconds between the processed trace and the original data at the beginning of the processingwindow. Real. By default i = 100 msec.TFf f = Final time in milliseconds, after NMO, of the processing window. Integer. By default f = trace length in milliseconds.0 δ TI < TFTAPFg g = Length of the tapering zone in milliseconds between the processed trace and the original data at the end of the processing window.Real. By default g = 100 msec.Parameters for windowing managementXCXh h = Length, in meters, of the spatial sliding windows. If XCX is coded, the spatial sliding windows have a variable number of traces,and NCX is the minimum number of traces in a window.In the MA option, h is indeed the length of the first sliding window(starting at offset zero). The other windows have a constant squaredoffset length (of h2).0 < hCGG6 RAMUR User’s Manual GeoclusterModel and processing parametersPp p = Factor controlling the focusing of the Radon decomposition. Forlarge P values, the Radon spectra will be better focused, sometimesat the expense of the accuracy of the data modelling. The value 0.0results in identical results to MULTP (no weight).By default, p = 1.0.0.0 δ p δ 2.0Fb b = Factor controlling the sparseness of the Radon decomposition.For large b values, the Radon spectra will be sparser, sometimes atthe expense of the accuracy of the data modelling.Real. By default, b = 0.1.0 < bSc c =Whitening factor applied to the weight. The value 0.0 can lead tounstable behavior. The value 1.0 results in identical results toMULTP (no weight). Real.0.0 δ b δ 1.0By default, b = 0.001FWAd d = Between FMIN and FWA, a same weight is applied, which is averaged from a preliminary pass on this frequency band.FMIN δ d < FWBBy default, d = FMIN+(FMAX-FMIN)/10FWBe e = Last frequency where the weight is updated. Between FWB andFMAX the weight is kept constant.FWA < e δ FMAXBy default, e = FMAXLTAPt t = Length, in milliseconds, of the taper centered on the separating threshold curves. This parameter is referenced to XRM. By default,t = 40 ms. Integer.0 < tLTAPMDx x = Length, in milliseconds, of the taper applied to parabolas (Option MA) or straight lines (option NA) from (DTMIN-x) to DTMIN andfrom DTMAX to (DTMAX+x). Integer.By default, x = 100 ms.0 < xComputation of the residual noise modelBy default, the residual noise is assumed to belong to the same frequency range as the multiples. It is then removed simultaneously with the multiples:Output = Input – Multiples – LAMBDA(Input-(Multiples+Primaries))LAMBDAc c = Coefficient between 0 and 1 giving the ratio of the residual noiseto remove. Real.0 = no noise removal.1 = full noise removal.0 δ c δ 1. By default, c = 0Geocluster User’s Manual RAMUR 7CGGInternal selection of algorithmsThe module has several algorithms that perform the same computations. Their relative CPU efficiency depend mainly on the number of traces in the spatial windows (NCX) and the number of p-traces (NP). Most of the so-called ’fast’ algorithms are fast for large enough NCX and/or NP values. By default, the module tries to guess the likely faster algorithm, but with rough criteria: if the CPU is a critical factor, the user should test by himself the relative speeds.Some of the algorithms give approximate results, but with a high enough precision for usual cases. However, in the case of unacceptable results, one should make a test with the exact algorithms, or increase the accuracy of the approximate algorithms. LSId d = Choice of undetermined or overdetermined least-squares inverse.1 = underdetermined case: should be prefered for NP>NCX2 = overdetermined case: should be prefered for NP<NCXSelecting 1 or 2 may slightly change the results. In contrast with theother optimisation parameters, it is adviced to not play with this one,unless you know what you’re doing.Integer. By default, d=1 if NCX NP, d=2 if NCX > NPOPTIMAa a = Tau-p modelling.1 = classical exact2 = fast approximate3 = fast approximate (faster than 2 for very large NCX)Integer. By default, a is set according to LSI, NCX and NPOPTIMBb b = Tau-p stack.1 = classical exact2 = fast approximateInteger. By default, b is set according to NCX and NPOPTIMCc c = System solver choice.1 = direct exact solver2 = fast iterative approximate solverInteger. By default, c is set according to LSI, NCX and NPACCAd d = Accuracy (number of digits) of the approximate algorithm selected by OPTIMA2Integer. By default, d=3ACCCe e = Accuracy (number of digits) of the approximate algorithm selected by OPTIMC2Integer. By default, e=3Parameters specific to the PA optionOptional parametersAVCj j = Length, in milliseconds, of the equalisation operator.Integer. By default, j = 200 ms (equalisation level set to 5000).0 < j.CGG8 RAMUR User’s Manual GeoclusterTRACE HEADERStatus of the output trace headerX = updated word 0 = zeroed word blank = directly transcribed1 2 3 4 5 6 7 8 9 0WORDS 1 to 10WORDS 11 to 20WORDS 21 to 30WORDS 31 to 40WORDS 41 to 50WORDS 51 to 60WORDS 61 to 64Geocluster User’s Manual RAMUR 9CGGRECOMMENDATIONSRemarks• Input traces in RAMUR (MA/NA) must have been NMO-corrected by FANMO. • As the running time is proportional to the FMIN to FMAX range, special atte ntion should be given to these parameters. They should be selected by looking atthe frequency spectrum of the data within the processing window.• The DDT parameter controls the number of functions that are built and the computationtime. An increment of 20 to 32 ms is often sufficient. Tests should bemade to determine the most suitable value for DDT and the DTMIN to DTMAX range.• The F and S parameters control the focalization of the seismic events into the Radon spectra. Large values of F and small values of S lead to sparse spectra atthe expense of the data modelling accuracy.• The use of sliding temporal and spatial windows enables you to simplify the number of seismic events to handle. With a smaller number of events, the high resolution, de-aliased Radon transform is better focused and avoids the artefacts commonly related to aperture and spatial sampling limitations.• RAMUR always outputs the same number of traces with the same offset as input. Invalid traces on input will be invalid traces on output.• Too small spatial windows may lead to poor separation between signal and noise. Typical values are:• NCX: 20,TAPX: 5,NCT: 400, TAPT: 100.• Smaller spatial windows may be used with the NA option• To correctly model the primaries, the DTMIN parame ter should always be negative .• If some algorithmic optimizations are enabled, S0 (the default) can some times lead to instabilities. To avoid this problem, set the S slightly positive value(0.01). However, it has been observed that strongly aliased multiples are better attenuated with S0.• NCT is automatically raised to the nearest power of 2 (in number of samples) of the FFT length.• Decreasing the accuracy of the approximate algorithms (ACCA & ACCC)speeds them up.• Invalid traces are considered as valid traces containing null values. They should therefore be removed before using RAMUR.CGGLimits• The size of the spatial windows (NCX) must be less than 512 traces• The number of parabolas must be less than 1024• The length of temporal windows NCT must be less than 16384*SIGeocluster User’s Manual RAMUR 11CGGEXAMPLESExample 1Tableau 1:Anti-multiple processing(MA option )Anti-noise processing(NA option )Principle: Create a multiple model to besubtracted to the data to be processed.Principle: Create a model of linear noisesto be subtracted to the data to be processedMultiples are assumed to be parabolas. Multiples are assumed to be lines.Step 1: RAMUR works on NMOcorrectedCDP gathers. It is advisable tofirst playback some gathers which areNMO corrected in order to distinguish theresidual curvatures of the multiples.Step 1: RAMUR works on shotpoint orCDP gathers. It is advisable to firstplayback some gathers which are NMOcorrected.Step 2: The previous step enables you to define essential RAMUR parameters (DTMIN, DTMAX, DDT). The values of DTMIN and DTMAX are given at the far-trace offset XRM defined by the user. The DDT parameter is given according to the number of traces. DTMIN and DTMAX must be selected in such a way that they include the totality of the events present. In particular, they must contain the steepest events.Traces to be processed are ordered inWORD4, WORD20.Traces to be processed are ordered inWORD4 or WORD2, WORD20 withreverse and direct traces possiblyseparated (by concatenation WORD2,WORD3 for example).CGG* LIBRI HB 01 1 CGG2 RAMUR3 CALLAS4 RAMURDG48 OPTION MA* LIBRI FD 1 WORD19=LINE2742,WORD4,( 1)= 1680,( 192)= 1680( 516)= 1733,( 764)= 1772,( 844)= 1788,( 904)= 1802,( 964)= 1821,( 976)= 1823,( 1076)= 1867,( 1088)= 1869,( 1364)= 2020,( 1516)= 2103, ( 1600)= 2141,( 1668)= 2165,( 1748)= 2182,( 1860)= 2199,( 1964)= 2207, ( 2104)= 2213,( 2264)= 2213,( 2340)= 2215,( 2352)= 2221,( 2364)= 2235, ( 2372)= 2236,( 2384)= 2224,( 2404)= 2222,( 2428)= 2221,( 2444)= 2225, ( 2456)= 2230,( 2468)= 2234,( 2480)= 2233,( 2500)= 2239,( 2528)= 2248, ( 2552)= 2260,( 2568)= 2263,( 2600)= 2277,( 2712)= 2326,( 2728)= 2330, ( 2752)= 2340,( 2768)= 2351,( 2780)= 2350,( 2824)= 2365,( 2936)= 2394, ( 3004)= 2407,( 3060)= 2415,( 3112)= 2421,( 3192)= 2423,( 3392)= 2428, * BOUCL 1* RUNET 01FILE=local:/discar/proj/1006400/DATA/RAMURDG4.cst* PLOTX 01 ECH70,PAS8,AG,LS0,DG,G30,PLOTTER=BW24,W1900-W9000,NOSIMP,NOCRD,MOT4,MOT20,TOP,(RAMUR INPUT),LHB1,* RAMUR MA 01 02 YB2,XRM4880,YMX192,FMIN5,FMAX80,DTMIN-300,DTMAX1200,DDT12,DTCUT300,NCX192,TAPX5,NCT500,TAPT50,F0.1,KTI1.8,LFD1,TAPI100.,TAPF100.,TF9000.,LSI2,* FINBO* DLOOP 2* PLOTX 02 ECH70,PAS8,AG,LS0,DG,G30,PLOTTER=BW24,W1900-W9000,NOSIMP,NOCRD,MOT4,MOT20,TOP,(RAMUR OUTPUT),LHB1,HISTORY* FINBO* PROCS X(YB1)。
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simulation modelling practice
simulation modelling practiceSimulation modelling is a crucial tool in the field of science and engineering. It allows us to investigate complex systems and predict their behaviour in response to various inputs and conditions. This article will guide you through the process of simulation modelling, from its basicprinciples to practical applications.1. Introduction to Simulation ModellingSimulation modelling is the process of representing real-world systems using mathematical models. These models allow us to investigate systems that are too complex or expensiveto be fully studied using traditional methods. Simulation models are created using mathematical equations, functions, and algorithms that represent the interactions and relationships between the system's components.2. Building a Basic Simulation ModelTo begin, you will need to identify the key elements that make up your system and define their interactions. Next, you will need to create mathematical equations that represent these interactions. These equations should be as simple as possible while still capturing the essential aspects of the system's behaviour.Once you have your equations, you can use simulation software to create a model. Popular simulation softwareincludes MATLAB, Simulink, and Arena. These software packages allow you to input your equations and see how the system will respond to different inputs and conditions.3. Choosing a Simulation Software PackageWhen choosing a simulation software package, consider your specific needs and resources. Each package has its own strengths and limitations, so it's important to select one that best fits your project. Some packages are more suitable for simulating large-scale systems, while others may bebetter for quickly prototyping small-scale systems.4. Practical Applications of Simulation ModellingSimulation modelling is used in a wide range of fields, including engineering, finance, healthcare, and more. Here are some practical applications:* Engineering: Simulation modelling is commonly used in the automotive, aerospace, and manufacturing industries to design and test systems such as engines, vehicles, and manufacturing processes.* Finance: Simulation modelling is used by financial institutions to assess the impact of market conditions on investment portfolios and interest rates.* Healthcare: Simulation modelling is used to plan and manage healthcare resources, predict disease trends, and evaluate the effectiveness of treatment methods.* Education: Simulation modelling is an excellent toolfor teaching students about complex systems and how they interact with each other. It helps students develop critical thinking skills and problem-solving techniques.5. Case Studies and ExamplesTo illustrate the practical use of simulation modelling, we will take a look at two case studies: an aircraft engine simulation and a healthcare resource management simulation.Aircraft Engine Simulation: In this scenario, a simulation model is used to assess the performance ofdifferent engine designs under various flight conditions. The model helps engineers identify design flaws and improve efficiency.Healthcare Resource Management Simulation: This simulation model helps healthcare providers plan their resources based on anticipated patient demand. The model can also be used to evaluate different treatment methods and identify optimal resource allocation strategies.6. ConclusionSimulation modelling is a powerful tool that allows us to investigate complex systems and make informed decisions about how to best manage them. By following these steps, you can create your own simulation models and apply them to real-world problems. Remember, it's always important to keep anopen mind and be willing to adapt your approach based on the specific needs of your project.。
纹理物体缺陷的视觉检测算法研究--优秀毕业论文
摘 要
在竞争激烈的工业自动化生产过程中，机器视觉对产品质量的把关起着举足 轻重的作用，机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检 测技术相比，自动化的视觉检测系统更加经济、快捷、高效与 安全。纹理物体在 工业生产中广泛存在，像用于半导体装配和封装底板和发光二极管，现代 化电子 系统中的印制电路板，以及纺织行业中的布匹和织物等都可认为是含有纹理特征 的物体。本论文主要致力于纹理物体的缺陷检测技术研究，为纹理物体的自动化 检测提供高效而可靠的检测算法。 纹理是描述图像内容的重要特征，纹理分析也已经被成功的应用与纹理分割 和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检 测算法。这种算法能容忍物体变形引起的图像配准误差，对纹理的影响也具有鲁 棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义，如缺陷区域 的大小、形状、亮度对比度及空间分布等。同时，在参考图像可行的情况下，本 算法可用于同质纹理物体和非同质纹理物体的检测，对非纹理物体 的检测也可取 得不错的效果。 在整个检测过程中，我们采用了可调控金字塔的纹理分析和重构技术。与传 统的小波纹理分析技术不同，我们在小波域中加入处理物体变形和纹理影响的容 忍度控制算法，来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字 塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段，我们检测了一系列 具有实际应用价值的图像。实验结果表明 本文提出的纹理物体缺陷检测算法具有 高效性和易于实现性。 关键字: 缺陷检测；纹理；物体变形；可调控金字塔；重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II
Cost Models for Join Queries in Spatial Databases
机械工程及自动化专业外文翻译--应用坐标测量机的机器人运动学姿态的标定翻译
外文原文：Full-Pose Calibration of a Robot Manipulator Using a Coordinate-Measuring MachineThe work reported in this article addresses the kinematic calibration of a robot manipulator using a coordinate measuring m a c h i n e(C M M)w h i c h i s a b l e t o o b t a i n t h e f u l l p o s e o f t h e e n d-e f f e c t o r.A k i n e m a t i c m o d e l i s d e v e l o p e d f o r t h e manipulator, its relationship to the world coordinate frame and the tool. The derivation of the tool pose from experimental measurements is discussed, as is the identification methodolo gy.A complete simulation of the experiment is performed, allowing the observation strategy to be defined. The experimental work is described together with the parameter identification and accuracy verification. The principal conclusion is that the m et ho d is a ble t o calibrate the robot succes sful ly, with a resulting accuracy approaching that of its repeatability.Keywords: Robot calibration; Coordinate measurement; Parameter identification; Simulation study; Accuracy enhancement 1. IntroductionIt is wel l known tha t robo t manip ula tors t ypical ly ha ve reasonable repeatability (0.3 ram), yet exhibit poor accuracy(10.0m m).T h e p r o c e s s b y w h i c h r o b o t s m a y b e c a l i b r a t e di n o r d e r t o a c h i e v e a c c u r a c i e s a p p r o a c h i n g t h a t o f t h e m a n i p u l a t o r i s a l s o w e l l u n d e r s t o o d .I n t h e c a l i b r a t i o n process, several sequential steps enable the precise kinematic p ar am et er s o f th e m an ip u l ato r to b e ide nti fi ed,l ead ing t o improved accuracy. These steps may be described as follows: 1. A kinematic model of the manipulator and the calibration process itself is developed and is usually accomplished with s t a n d a r d k i n e m a t i c m o d e l l i n g t o o l s.T h e r e s u l t i n g m o d e l i s u s e d t o d e f i n e a n e r r o r q u a n t i t y b a s e d o n a n o m i n a l (m a n u f a c t u r e r's)k i n e m a t i c p a r a m e t e r s e t,a n d a n u n k n o w n, actual parameter set which is to be identified.2. Ex pe ri me n ta l m ea su re m e nts o f th e rob ot po se (p art ial o r complete) are taken in order to obtain data relating to the actual parameter set for the robot.3.The actual kinematic parameters are identified by systematicallyc h a n g i n g t h e n o m i n a l p a r a m e t e r s e t s o a s t o r ed u ce t h e e r r o r q u a n t i t y d ef i n e d i n t h e m o d e l l i ng ph a s e.O n e a p p r o a c h t o a c hi e v i n g t h i s i d e n t i f i c a t i o n i s d e t e r m i n i n g t h e a n a l y t i c a l d i f f e r e n t i a l r e l a t i o n s h i p b e t w e e n t h e p o s e v a r i a b l e s P a n d t h e k i n e m a t i c p a r a m e t e r s K i n t h e f o r m of a Jacobian,and then inverting the equation to calculate the deviation of t h e k i n e m a t i c p a r a m e t e r s f r o m t h e i r n o m i n a l v a l u e sAlternatively, the problem can be viewed as a multidimensional o p t i m i s a t i o n t a s k,i n w h i c h t h e k i n e m a t i c p a r a m e t e r set is changed in order to reduce some defined error function t o z e r o.T h i s i s a s t a n d a r d o p t i m i s a t i o n p r o b l e m a n d m a y be solved using well-known methods.4. The final step involves the incorporation of the identified k i n e m a t i c p a r a m e t e r s i n t h e c o n t r o l l e r o f t h e r o b o t a r m, the details of which are rather specific to the hardware of the system under study.This paper addresses the issue of gathering the experimental d a t a u s e d i n t h e c a l i b r a t i o n p r o c e s s.S e v e r a l m e t h o d s a r e available to perform this task, although they vary in complexity, c o s t a n d t h e t i m e t a k e n t o a c q u i r e t h e d a t a.E x a m p l e s o f s u c h t e c h n i q u e s i n c l u d e t h e u s e o f v i s u a l a n d a u t o m a t i c t h e o d o l i t e s,s e r v o c o n t r o l l e d l a s e r i n t e r f e r o m e t e r s, a c o u s t i c s e n s o r s a n d v i d u a l s e n s o r s .A n i d e a l m e a s u r i n g system would acquire the full pose of the manipulator (position and orientation), because this would incorporate the maximum information for each position of the arm. All of the methods m e n t i o n e d a b o v e u s e o n l y t h e p a r t i a l p o s e,r e q u i r i n g m o r ed a t a t o be t a k e nf o r t h e c a l i b r a t i o n p r o c e s s t o p r o c e e d.2. TheoryIn the method described in this paper, for each position in which the manipulator is placed, the full pose is measured, although several intermediate measurements have to be taken in order to arrive at the pose. The device used for the pose m e a s u r e m e n t i s a c o o r d i n a t e-m e a s u r i n g m a c h i n e(C M M), w h i c h i s a t h r e e-a x i s,p r i s m a t i c m e a s u r i n g s y s t e m w i t h a q u o t e d a c c u r a c y o f0.01r a m.T h e r o b o t m a n i p u l a t o r t o b e c a l i b r a t e d,a P U M A560,i s p l a c e d c l o s e t o t h e C M M,a n d a special end-effector is attached to the flange. Fig. 1 shows the arrangement of the various parts of the system. In this s e c t i o n t h e k i n e m a t i c m o d e l w i l l b e d e v e l o p e d,t h e p o s e estimation algorithms explained, and the parameter identification methodology outlined.2.1 Kinematic ParametersIn this section, the basic kinematic structure of the manipulator will be specified, its relation to a user-defined world coordinatesystem discussed, and the end-point toil modelled. From these m o d e l s,t h e k i n e m a t i c p a r a m e t e r s w h i c h m a y b e i d e n t i f i e d using the proposed technique will be specified, and a method f o r d e t e r m i n i n g t h o s e p a r a m e t e r s d e s c r i b e d. The fundamental modelling tool used to describe the spatial relationship between the various objects and locations in the m a n i p u l a t o r w o r k s p a c e i s t h e D e n a v i t-H a r t e n b e r g m e t h o d ,w i t h m o d i f i c a t i o n s p r o p o s e d b y H a y a t i,M o o r i n g a n d W u t o a c c o u n t f o r d i s p r o p o r t i o n a l m o d e l s w he n tw o co n se cu t iv e jo i n t a x e s ar e nom ina ll y p a r all el. A s s h o w n i n F i g.2,t h i s m e t h o d p l a c e s a c o o r d i n a t e f r a m e o neach object or manipulator link of interest, and the kinematics a r e d e f i n e d b y t h e h o m o g e n e o u s t r a n s f o r m a t i o n r e q u i r e d t o change one coordinate frame into the next. This transformation takes the familiar formT h e a b o v e e q u a t i o n m a y b e i n t e r p r e t e d a s a m e a n s t o t r a n s f o r m f r a m e n-1i n t o f r a m e n b y m e a n s o f f o u r o u t o f t h e f i v e o p e r a t i o n s i n d i c a t e d.I t i s k n o w n t h a t o n l y f o u r transformations are needed to locate a coordinate frame with r es pect to the p revious one. Whe n consecutiv e ax es are not parallel, the value of/3. is defined to be zero, while for the case when consecutive axes are parallel, d. is the variable chosen to be zero.W h e n c o o r d i n a t e f r a m e s a r e p l a c e d i n c o n f o r m a n c e w i t h the modified Denavit-Hartenberg method, the transformations given in the above equation will apply to all transforms of o n e f r a m e i n t o t h e n e x t,a n d t h e s e m a y b e w r i t t e n i n a g e n e r i c m a t r i x f o r m,w h e r e t h e e l e m e n t s o f t h e m a t r i x a r e functions of the kinematic parameters. These parameters are simply the variables of the transformations: the joint angle 0., the common normal offset d., the link length a., the angle o f tw is t a., a nd th e an g l e /3.. Th e mat rix f orm i s u suall y expressed as follows:For a serial linkage, such as a robot manipulator, a coordinate frame is attached to each consecutive link so that both the instantaneous position together with the invariant geometry a r e d e s c r i b e d b y t h e p r e v i o u s m a t r i x t r a n s f o r m a t i o n.'T h etransformation from the base link to the nth link will therefore be given byF i g.3s h o w s t h e P U M A m a n i p u l a t o r w i t h t h e D e n a v i t-H a r t e n b e r g f r a m e s a t t a c h e d t o e a c h l i n k,t o g e t h e r with world coordinate frame and a tool frame. The transformation f r o m t h e w o r l d f r a m e t o t h e b a s e f r a m e o f t h e manipulator needs to be considered carefully, since there are potential parameter dependencies if certain types of transforms a r e c h o s e n.C o n s i d e r F i g.4,w h i c h s h o w s t h e w o r l d f r a m e x w,y,,z,,t h e f r a m e X o,Y o,z0w h i c h i s d e f i n e d b y a D H t r a n s f o r m f r o m t h e w o r l d f r a m e t o t h e f i r s t j o i n t a x i s o f t h e m a n i p u l a t o r,f r a m e X b,Y b,Z b,w h i c h i s t h e P U M Amanufacturer's defined base frame, and frame xl, Yl, zl which is the second DH frame of the manipulator. We are interested i n d e t e r m i n i n g t h e m i n i m u m n u m b e r o f p a r a m e t e r s r e q u i r e d to move from the world frame to the frame x~, Yl, z~. There are two transformation paths that will accomplish this goal: P a t h1:A D H t r a n s f o r m f r o m x,,y,,z,,t o x0,Y o,z o i n v o l v i n g f o u r p a r a m e t e r s,f o l l o w e d b y a n o t h e r t r a n s f o r m f r o m x o,Y o,z0t o X b,Y b,Z b w h i c h w i l l i n v o l v e o n l y t w o parameters ~b' and d' in the transformFinally, another DH transform from xb, Yb, Zb to Xt, y~, Z~ w hi ch i nv ol v es f o ur p ar a m ete r s e xc ept t hat A01 a n d 4~' ar e b o t h a b o u t t h e a x i s z o a n d c a n n o t t h e r e f o r e b e i d e n t i f i e d independently, and Adl and d' are both along the axis zo and also cannot be identified independently. It requires, therefore, o nl y ei gh t i nd ep e nd en t k i nem a t ic p arame ter s to g o fr om th e world frame to the first frame of the PUMA using this path. Path 2: As an alternative, a transform may be defined directly from the world frame to the base frame Xb, Yb, Zb. Since this is a frame-to-frame transform it requires six parameters, such as the Euler form:T h e f o l l o w i n g D H t r a n s f o r m f r o m x b,Y b,z b t O X l,Y l,z l would involve four parameters, but A0~ may be resolved into 4~,, 0b, ~, and Ad~ resolved into Pxb, Pyb, Pzb, reducing theparameter count to two. It is seen that this path also requires e i g h t p a r a m e t e r s a s i n p a t h i,b u t a d i f f e r e n t s e t.E i t h e r o f t h e a b o v e m e t h o d s m a y b e u s e d t o m o v e f r o m t h e w o r l d f r a m e t o t h e s e c o n d f r a m e o f t h e P U M A.I n t h i s w o r k,t h e s e c o n d p a t h i s c h o s e n.T h e t o o l t r a n s f o r m i s a n E u l e r t r a n s f o r m w h i c h r e q u i r e s t h e s p e c i f i c a t i o n o f s i x parameters:T he total n umber of paramete rs u sed in the k inem atic model becomes 30, and their nominal values are defined in Table 1.2.2 Identification MethodologyThe kinematic parameter identification will be performed as a multidimensional minimisation process, since this avoids the calculation of the system Jacobian. The process is as follows: 1. Be gi n wi t h a g ue ss s e t of k in em atic par am ete r s, s uch a s the nominal set.2. Select an arbitrary set of joint angles for the PUMA.3. Calculate the pose of the PUMA end-effector.4.M e a s u r e t h e a c t u a l p o s e o f t h e P U M A e n d-e f f e c t o r f o r t he s am e se t o f j oi nt a n g les.In g enera l, th e m e a sur ed an d predicted pose will be different.5. Mo di fy t h e ki n em at ic p ara m e te rs in a n o rd erl y man ner i n o r d e r t o b e s t f i t(i n a l e a s t-s q u a r e s s e n s e)t h e m e a s u r e d pose to the predicted pose.The process is applied not to a single set of joint angles but to a number of joint angles. The total number of joint angles e t s r e q u i r e d,w h i c h a l s o e q u a l s t h e n u m b e r o f p h y s i c a l measurement made, must satisfyK p i s t h e n u m b e r o f k i n e m a t i c p a r a m e t e r s t o b e i d e n t i f i e d N i s t h e n u m b e r o f m e a s u r e m e n t s(p o s e s)t a k e n D r r e p r e s e n t s t h e n u m b e r o f d e g r e e s o f f r e e d o m p r e s e n t i n each measurement.In the system described in this paper, the number of degrees of freedom is given bysince full pose is measured. In practice, many more measurements s h o u l d b e t a k e n t o o f f s e t t h e e f f e c t o f n o i s e i n t h e e xp er im en ta l m ea s ur em en t s. T h e o pt imisa tio n pro c e dur e use d is known as ZXSSO, and is a standard library function in the IMSL package .2.3 Pose MeasurementIt is apparent from the above that a means to determine the f u l l p o s e o f t h e P U M A i s r e q u i r e d i n o r d e r t o p e r f o r m t h e calibration. This method will now be described in detail. The end-effector consists of an arrangement of five precisiontooling b a l l s a s s h o w n i n F i g. 5.C o n s i d e r t h e c o o r d i n a t e s o f the centre of each ball expressed in terms of the tool frame (Fig. 5) and the world coordinate frame, as shown in Fig. 6. T h e r e l a t i o n s h i p b e t w e e n t h e s e c o o r d i n a t e s m a y b e w r i t t e n as:w he re P i' i s t he 4 x 1 c o lum n ve ct or of th e coo r d ina tes o f the ith ball expressed with respect to the world frame, P~ is t he 4 x 1 c o lu mn ve ct or o f t h e c oo rdina tes o f t h e it h bal l expressed with respect to the tool frame, and T is the 4 • 4 h o m o g e n i o u s t r a n s f o r m f r o m t h e w o r l d f r a m e t o t h e t o o l frame.The n may be foun d, a n d use d as th e m easure d pose in t he calibration process. It is not quite that simple, however, since it is not possible to invert equation (11) to obtain T. The a bo ve proce ss is performed f or t he four ball s, A, B, C and D, and the positions ordered as:or in the form:S i n c e P',T a n d P a r e a l l n o w s q u a r e,t h e p o s e m a t r i x m a y be obtained by inversion:I n pr ac ti ce it m a y be d i f fic u l t fo r the CM M to a c ces s fou r b a i l s t o d e t e r m i n e P~w h e n t h e P U M A i s p l a c e d i n c e r t a i n configurations. Three balls are actually measured and a fourth ball is fictitiously located according to the vector cross product:R e g a r d i n g t h e d e t e r m i n a t i o n o f t h e c o o r d i n a t e s o f t h ec e n t r e o f a b a l l b a s ed o n me a s u r e d p o i n t s o n i t s s u rf a c e, n o a n a l y t i c a l p r o c e d u r e s a r e a v a i l a b l e.A n o t h e r n u m e r i c a l optimisation scheme was used for this purpose such that the penalty function:w a s m i n i m i s e d,w h e r e(u,v,w)a r e t h e c o o r d i n a t e s o f t h e c e n t r e o f t h e b a l l t o h e d e t e r m i n e d,(x/,y~,z~)a r e t h e coordinates of the ith point on the surface of the ball and r i s th e ba ll di am e te r. I n the t es ts perf orm ed, i t was foun d sufficient to measure only four points (i = 4) on the surface to determine the ball centre.中文译文：应用坐标测量机的机器人运动学姿态的标定这篇文章报到的是用于机器人运动学标定中能获得全部姿态的操作装置——坐标测量机（CMM）。
贝叶斯空间统计模型
Huiyan Sang
Hierarchical Modeling for Spatial Data
Bayesian Spatial Modelling
Spatial model specifications: P(y|X, θ). P(y|X, θ) = MVN(Xβ, σ2H(φ, ν) + τ 2I). where Hi,j = Matérn(si , sj ; φ, ν) Prior specifications for θ = (β, σ2, τ 2, φ, ν).
Bayesian Spatial Modelling
Spatial model specifications: P(y|X, θ). P(y|X, θ) = MVN(Xβ, σ2H(φ, ν) + τ 2I). where Hi,j = Matérn(si , sj ; φ, ν) Prior specifications for θ = (β, σ2, τ 2, φ, ν).
Step 0: give initial values for θ. Step t + 1:
Sample P(β|θ(t), y, x) from a multivariate normal. Sample P(ν|θ(t), y, x) using MH. Sample P(σ2|θ(t), y, x) using MH. Sample P(τ 2|θ(t), y, x) using MH Sample P(φ|θ(t), y, x) using MH This is called Gibbs+Metropolis Hastings. Repeat until MCMC converges.
Huiyan Sang
Geometric Modeling
Geometric ModelingGeometric modeling is a crucial aspect of computer graphics and design,playing a significant role in various industries such as architecture, engineering, and animation. It involves creating digital representations of objects and environments using mathematical and computational techniques. This process allows for the visualization, analysis, and manipulation of complex geometric shapes, ultimately contributing to the development of innovative products and designs. However, like any technological field, geometric modeling presents its own set of challenges and limitations that need to be addressed. One of the primary challenges in geometric modeling is the accurate representation of real-world objects and environments. Achieving precise and realistic depictions requires a deep understanding of mathematical concepts such as curves, surfaces, and solids. Additionally, the integration of texture, lighting, and shading furthercomplicates the process, as these elements contribute to the overall visual appeal and authenticity of the model. As a result, geometric modelers often face the daunting task of balancing mathematical precision with aesthetic quality, striving to create visually appealing representations that accurately reflect the physical world. Moreover, the scalability of geometric modeling presents anothersignificant challenge. As the complexity and size of models increase, so does the computational demand required for their creation and manipulation. This can leadto performance issues, particularly in real-time applications such as video games and virtual simulations. To address this challenge, geometric modelers must constantly innovate and optimize their techniques to ensure that large-scale models can be efficiently handled and rendered without compromising quality. In addition to technical challenges, geometric modeling also raises ethical considerations, particularly in the context of virtual reality and simulation. The ability to create highly realistic and immersive environments has the potential to blur the lines between the virtual and physical worlds, raising questions aboutthe ethical use of such technology. For instance, the creation of lifelike simulations for training or entertainment purposes may have unintended psychological effects on users, blurring their perception of reality. As such, itis crucial for geometric modelers to consider the ethical implications of theirwork and strive to use their skills responsibly. Despite these challenges, the field of geometric modeling continues to evolve, driven by advancements in technology and the increasing demand for realistic digital representations. Innovations such as 3D scanning and printing have revolutionized the way geometric models are created, allowing for the direct conversion of physical objects into digital form. Additionally, the integration of artificial intelligence and machine learning has the potential to streamline the modeling process, automating repetitive tasks and enabling more efficient creation of complex geometries. Ultimately, the future of geometric modeling holds great promise, as it continues to push the boundaries of what is possible in the digital realm. By addressing the challenges and ethical considerations inherent to the field, geometric modelers can harness the full potential of their craft, contributing to the creation of captivating virtual worlds, groundbreaking designs, and innovative technological solutions. As technology continues to advance, the role of geometric modeling will only become more prominent, shaping the way we interact with and perceive the world around us.。
infectious disease modelling分区
infectious disease modelling分区Infectious disease modelling can be divided into several key areas or partitions:1. Epidemiological modelling: This partition focuses on studying the transmission and spread of infectious diseases within a population. It involves analyzing factors such as the rate of infection, disease progression, and the impact of interventions like vaccinations or social distancing.2. Mathematical modelling: This partition involves using mathematical equations and statistical analysis to understand the dynamics of infectious diseases. It includes developing models such as compartmental models (e.g., SIR models) or agent-based models to simulate disease spread and predict outcomes.3. Spatial modelling: This partition focuses on incorporating geographical or spatial information into infectious disease models. It considers factors such as population density, travel patterns, and the spatial distribution of disease cases to analyze how diseases spread over geographical areas.4. Network modelling: This partition involves studying how infectious diseases spread through social or contact networks. It utilizes network theory and methods to map out connections between individuals and simulate the transmission of diseases within these networks.5. Healthcare systems modelling: This partition focuses on modeling the impact of infectious diseases on healthcare systems.It analyzes variables such as hospital capacities, resource allocation, and the effectiveness of health interventions to understand the strain on healthcare systems during outbreaks.6. Intervention modelling: This partition involves modeling the effects of different interventions or strategies on controlling or mitigating the spread of infectious diseases. It assesses the effectiveness of interventions like quarantine measures, contact tracing, or mass vaccination campaigns to inform public health decision-making.These partitions within infectious disease modelling are interconnected and often overlap, as they collectively aim to provide insights into disease spread, inform public health strategies, and guide decision-making during disease outbreaks.。
Simplifiedwavemodelling
Simplified wave modellingJohn C. BancroftABSTRACTWave motion is modelled using the acoustic wave equation and implemented using MATLAB. This method requires two initial conditions that are introduced using a simple wavelet on a one dimensional propagator such as a string, spring, or wire. This model is expanded to two dimensions that illustrate plane-wave propagation, boundary effects, and Huygen’s wavelets.WAVE ON A STRING.Assume we want to model transverse wave motion on a string (spring, wire, etc). We will compute the motion of a wavelet at equal time increments that simulate photos to evaluate the motion.We start with a Gaussian-shaped wavelet illustrated below in Figure 1 that represents a photograph of the transverse displacement. We have chosen the width of the wavelet to be large, relative to the sample interval so that linear interpolation provides an adequate description, i.e. the sample rate is approximately ten times the maximum frequency to minimize grid dispersion. Given this information:•What is the direction of motion?•What will be the shape in the next photograph?FIG 1: A representation of a Gaussian shaped wavelet on a string.Is the wavelet in Figure 1 going to move to the left, the right or bounce up and down? We simply don’t know because we don’t have enough information. How do we provide that additional information? Let’s examine the wave equation to find out.The string has one distance dimension, x, and we define the displacement p(x), at different time intervals such as p(x, t=1), p(x, t=2), etc. It therefore becomes convenient to define a 2D array, p(x, t), that defines the amplitude of the strings displacement at agiven location, x , and time, t . The movement of energy on the string is governed by the one dimensional acoustic wave-equation()()22222,,1p x t p x t x v t∂∂=∂∂. (1) We will use the finite difference method to approximate the wave-equation. A second derivative of a function, f(x), is approximated in a discrete form of f at position, n , i.e. f n , and is approximated by()211222n n n f x f f f x x δ−+∂−+=∂, (2) where the increments of f n are d x . The finite difference equation for the wave equation becomes: 1,,1,,1,,1222221i j i j i j i j i j i j p p p p p p x v tδδ−+−+−+−+=. (3) This equation can be represented in graphical form in the following figure, with the increment, i , representing distance to the left, and j , the time increment that increases vertically.FIG 2: Finite difference model of full wave-equation.We wish to compute the position of the string at a new time, given some initial condition. We will choose p i, j+1 as the unknown value in the finite-difference equation because it is a single value at a new time, giving()22,1,,11,,1,222i j i j i j i j i j i j v t p p p p p p x δδ+−−+=−−+. (4) According to this equation, we need to know the position of the string at the two previous times, j , and j-1; .i.e. the two initial conditions of the string. Shown below is thefinite-difference operator positioned at the at i th spatial location to calculate the amplitude on the string for the third time, at j=3.i-1 i+1iFIG 3: The first two initial conditions at j =1 and 2 to compute the first line of samples at j=3. If we desire to model a wave travelling down the string, then we need to start with a wavelet on the string at time j=1, and the same wavelet repeated on the string at time j=2 but moved to a new spatial location. The location on the j=2 string is critical and must have a spatial increment defined by the wave velocity, v , and the time increment, d t . These two initial conditions would also match any two sequential photos of the wave moving down the string. They are shown above, along with the finite-difference operator, illustrating the computation of the amplitudes on the string at time, j=3. (The above figure has used a very large d x on the operator for the purposes of illustration only).Computer simulations are illustrated in Figure 4, with (a) showing the amplitude of a Gaussian wavelet on the string at the first two times and the resulting calculation of the wavelet at the third time, k=3. The amplitudes at succeeding times may be calculated from the previous two times as illustrated in (b) that shows the wavelet at a time increment, k=50. The initial two wavelets are also shown to demonstrate how well the amplitude and shape of the wavelet has been preserved after 48 iterations.The complete MATLAB code for producing Figures 4 and 5 is listed in Appendix A. The part of the code that propagates the wavefield is six lines that are encircled to illustrate the simplicity. The coding method is similar to FORTRAN and does not take advantage of vector math that is available to a programmer.a)b)FIG 4: The first two initial conditions and the computation of a) the next third time increment, and b) at 50 time increments later. Note the preservation of the shape and amplitude.The full set of iterations are shown below in a three-dimensional view that shows the amplitude of the wavelet at the incremental times. This is a trivial task in MATLAB that simply displays 2D array, p(t, x), in Figure 5 with the call, “figure (3); mesh(p);”, highlighted in the program listing in appendix A. This figure has zoom androtation features that allow the programmer to view the image from any position. Thesimple coding of this program, along with the powerful MATLAB graphics, provide anexcellent analysis tool.FIG 5: A full set of time images of the displacementTo illustrate the importance of the initial conditions, various configurations of the first two wavelets are shown in the following figures. Note the effect on the subsequent propagations.•Figure 6a and b show the first two wavelets at the same amplitude and location at the centre of the string. The result is two wavelets moving away from eachother. (These two images demonstrate two different perspective views of thesame figure).•Figure 6c shows the two initiating wavelets at the same location, but the second is twice the amplitude, producing an interesting deformation of thestring. A similar type of deformation is obtained by zeroing the amplitude ofthe second wavelet as viewed in Figure 6d.We refer to the setting of the two initial conditions or the defining the deformation ofthe string at the first two time intervals as “exciting” the string.a) b)c) d) FIG 6: Various wave propagations that result when the initial conditions are varied, a) when the location and amplitude are the same, b) the same as (a) but a different view, c) with the same location but the amplitude of the second initial condition has twice the amplitude, and d) when the second initial condition is zero.2D WAVE PROPAGATIONThe propagation of energy on a 2D plane is also quite simple to program by extending the concepts of the 1D program. The two-dimensional wave equation,()()()2222222,,,,,,1p x z t p x z t p x z t x z v t∂∂∂+=∂∂∂, (5) becomes the finite difference equation, 1,,,,1,,,1,,,,1,,,1,,,,122222221i j k i j k i j k i j k i j k i j k i j k i j k i j k p p p p p p p p p x z v t δδδ−+−+−+−+−+−++=. (6) Solving for the single time sample at k+1 we get:1,,,,1,,,1,,,,1,22,,1,,,,122222i j k i j k i j k i j k i j k i j k i j k i j k i j k p p p p p p p p p v t x z δδδ−+−++−−+−+ =−++. (7) This difference equation is illustrated in Figure 7. A plane through the central points represents time at the k th level and the one sample point below this plane is a sample on the previous time layer at k-1. The single point to be computed (the circle) lies on the upper plane at time k+1.FIG 7: Schematic representation of the three-dimensional (x, z, t ) operator.We therefore need two planes of the wavefield (at k-1, and k ) to start the propagation. As in the 1D case, it is critical that the first two planes represent the desired initial conditions. In Figure 8a below, the initial wavefield starts at the centre of the plane and then, after 80 time iteration of computing the wavefield, we get the wavefield shifted to the left as evident in part (b). Note that the amplitude and shape of the wave field has been preserved.a) b) FIG 8: A plane wave designed to propagate to the left, a) at time zero, and b) at after 80 time increments.Figure 9a show the an initialization of a plane-wave that only extend part way across the surface. The resulting wavefield after 80 time iterations is shown in various perspectiveviews in panels b), c), and d). Note the unusual effect of this result as energy has propagated opposite to the direction to the main part of the wavefront.a) b)c) d)FIG 9: A portion of a plane-wave is propagated 40 time increments with: a) the initial location with a rounded edge, b) a perspective view after 40 time lags, c) side view, and d) a plan view.Figure 10 contains two images that result from a 3-D Gaussian-shaped wavelet that is circular in (x, z) as displayed in Figure 11a. The circular wavefront in part (a) in Figure 10 was excited by keeping the two excitation wavelets at the same location, while part (b) was excited by moving the second wavelet to the left with the propagation velocity. Note that even though the wavelet was initially propagated to the left, some energy also moves in the opposite direction.Figure 11 displays three additional wavelets in (b), (c), and (d) that are truncated to widths of five, three, and one sample. The intent of these wavelets is to represent some form of decomposition of a plane wave: i.e. part (d) is just one slice from the wavefront. This slice is definitely aliased in the direction parallel to the wavefront and we should expect some form of dispersion during propagation.Each wavelet is propagated in the direction of the wavefront and creates the circular images on the left side of the corresponding parts of Figure 12. These circular imagesrepresent some form of Huygen’s wavelets that are used to propagate wavefronts. The right side of these figures are the wavefront that are reconstructed from the Huygen’s wavelets. This was achieved by assuming source points were positioned at the original location of the wavefront (i.e. a continuous line of samples) and the resulting Huygen’s wavelets summed.a) b) FIG 10: Two wavefront responses from a Gaussian shaped wavelet at the centre with different initial conditions; with a) the initial wavelets at the same location, and b) when the second initial wavelet is shifted to the left with the medium’s velocity.a) b) c) d) FIG 11: Wavelet sources with a) the full circular wavelet, b) truncated to five points wide, c) three points wide and d) one point wide.a)b)c)d)FIG 12: Huygen’s wavefronts on the left side of each image when the source wavelet is: a) circular; b) 5 points wide; c) 3 points wide; and d) 1 sample wide. The right side is the corresponding reconstruction of the wavefront from an array of source points.FIG 13: Reconstructed wavefront and Huygen’s wavelet approximation when a single sample spike is propagated as part of a wavefront.The results of using a single sample for excitation is illustrated in Figure 13. Aliasing in the direction of wavefield propagation causes excessive dispersion of the energy.The non-aliased circular/Gaussian wavelet of Figure 11a was used to demonstrate the formation of the Huygen’s wavelet. This is illustrated in Figure 14, which shows the propagating wavefields at time intervals of 5, 10, 20, 30, 40, 50, 60, and 80. Note the formation of the phase-shift as the amplitudes become negative, and that some energy is propagated in the reverse direction.The Huygen’s wavelet energy lies on a circular path that extends in all directions around the excitation point. The amplitude tapers to zero in the direction opposite the excitation direction, and then to a maximum in the direction of excitation or propagation. Figure 15 is a side view of the Huygen’s wavelet that shows the peak amplitude has a linear slope that rises in the direction of propagation. This amplitude is described in theory as 1+cosine(q), where q is the angle from the direction of propagation. Also note that the phase-shift appears to be 45 degrees.COMMENTSThe images in Figure 10 illustrate the difference in the energy radiated from an isotropic point source and that of an element on a wavefront.The small portion of MATLAB code that propagates the 2D energy is included asAppendix B.FIG 14: Formation of a Huygen’s wavelet at 5,10, 20, 30, 40, 50, 60, 80 time units.FIG 15: Side view of Huygen’s wavelet displaying the amplitude.CONCLUSIONSWave propagation can be illustrated using simple programs written and displayed in MATLAB.ACKNOWLEDGEMENTSWe acknowledge NSERC and the CREWES sponsors for their continued support.APPENDIX AMATLAB code for modelling 2D data. The two parts encircled contain the wave propagation code and the function call to plot the view of the full movement of the waveform.% Wave on stringclearv = 1000.0; % Velocitydx = 1.0; % x incrementdt = 0.001; % time incrementnx = 100; %Number of x samplesnt = 100; %Number of z samplesp = zeros(nt,nx); % Matrix for strings1 = zeros(nx); % String at time 1s2 = zeros(nx); % String at time 2s40= zeros(nx); % String at time 50xary=zeros(nx); % Plot axis% Define the position of the string at the first two times for ix = -10:10p(1, ix+20 ) = 100*exp(-(ix^2 )/16.0 );p(2, ix+21 ) = 100*exp(-(ix^2 )/16.0 );end% Loop for each time increment: limit time to prevent distortion due to boundary reflectionfor it = 3: nt-55for ix = 2:99p(it,ix) = 2*p(it-1,ix) - p( it-2, ix) +((v*dt/dx)^2)*(p(it-1,ix-1)-2*p(it-1,ix)+p(it-1,ix+1)); endend% Get singel copies of the two excitation arrays and one latter array.for ix = 1:nxxary(ix)=ix;s1(ix) = p(1, ix);s2(ix) = p(2, ix);s40(ix) = p(40, ix);end% Plot the datafigure (1); plot(xary,s2,'r--',xary,s1,'b','LineWidth',3); xlabel('x','FontSize',20), ylabel('Amp','FontSize',20)title(' \it{ Wave on string}', 'FontSize',20)figure (2); plot(xary,s40,'g:',xary,s2,'r--',xary,s1,'b','LineWidth',3);xlabel('x','FontSize',20), ylabel('Amp','FontSize',20)title(' \it{ Wave on string}', 'FontSize',20)figure(3); mesh( p);xlabel('x','FontSize',20), ylabel('t','FontSize',20),zlabel('Amp','FontSize',20)title(' \it{ Wave on string}', 'FontSize',20)a) b)c)FIG A1: MATLAB windows with results of the 1D modelling code showing: a) the initial conditions on a string; b) initial conditions on a string and the wave after 40 time increments; and c) all 40 time increments on the string.APPENDIX BPortion of MATLAB code for modelling 3D data that propagates the wave.%********************************************************* % Compute each time layerfor it = 3:ntdit% Compute each x tracefor ix = 2:nx-1%Compute each sample in tracefor iz = 2:nz-1p = vol(ix, iz, it-1);ptm1 = vol(ix, iz, it-2);pzm1 = vol(ix, iz-1, it-1);pzp1 = vol(ix, iz+1, it-1);pxm1 = vol(ix-1, iz, it-1);pxp1 = vol(ix+1, iz, it-1);%solve wave-equationptp1 = 2.0*p - ptm1 + V^2*dt^2*( (pxm1 -2*p+pxp1)/dx^2 + (pzm1 -2*p + pzp1)/dz^2 );vol(ix, iz, it) = vol(ix, iz, it) + ptp1;endend%********************************************************。
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空间位阻效应英语
空间位阻效应英语The Steric Hindrance Effect in SpaceThe concept of steric hindrance, also known as steric inhibition or steric crowding, is a fundamental principle in organic chemistry and has significant implications in the field of space exploration. This phenomenon occurs when the spatial arrangement of atoms or molecules within a chemical structure impedes or restricts the desired reaction or interaction, often due to the bulkiness or size of the substituents involved.In the context of space exploration, the steric hindrance effect plays a crucial role in the design and development of various spacecraft components, materials, and systems. The unique challenges posed by the harsh environment of space, such as extreme temperatures, radiation, and the absence of gravity, require a deep understanding of how steric effects can influence the performance and stability of these systems.One of the primary areas where steric hindrance becomes a significant consideration is in the selection and engineering of spacecraft materials. The materials used in spacecraft constructionmust be able to withstand the rigors of launch, the vacuum of space, and the various stresses encountered during mission operations. The spatial arrangement of atoms and molecules within these materials can greatly impact their mechanical properties, thermal stability, and resistance to degradation.For instance, the choice of polymers used in spacecraft insulation or structural components must take into account the steric effects that can influence their thermal expansion, flexibility, and resistance to radiation damage. The selection of lubricants and sealants for moving parts, such as hinges or joints, must also consider the steric hindrance that could affect their performance and longevity in the space environment.Another crucial application of the steric hindrance effect in space exploration is the design of spacecraft propulsion systems. The efficient and reliable operation of rocket engines, ion thrusters, or other propulsion technologies often depends on the careful management of the spatial arrangement of reactants, catalysts, or propellants within the system. Steric effects can influence the kinetics of chemical reactions, the flow dynamics of propellants, and the overall efficiency of the propulsion system.Furthermore, the steric hindrance effect plays a significant role in the development of space-based sensors and instrumentation. Thedesign of optical systems, such as telescopes or spectrometers, must account for the spatial constraints imposed by the instrument's components, including lenses, mirrors, and detectors. The arrangement of these elements can impact the system's resolution, sensitivity, and overall performance.In the field of astrochemistry, the steric hindrance effect is also relevant in the study of complex organic molecules and their formation in the interstellar medium. The spatial arrangement of atoms within these molecules can influence their stability, reactivity, and the pathways by which they are synthesized in the harsh conditions of space.To address the challenges posed by steric hindrance in space exploration, researchers and engineers employ various strategies, such as molecular modelling, computational chemistry, and advanced materials science. These tools help them to predict, analyze, and mitigate the effects of steric crowding, enabling the development of more robust and efficient spacecraft systems.In conclusion, the steric hindrance effect is a critical consideration in the design and development of spacecraft, systems, and materials for space exploration. By understanding and leveraging this fundamental principle of organic chemistry, scientists and engineers can create innovative solutions that push the boundaries of what ispossible in the exploration and utilization of the final frontier – the vast expanse of space.。
stablediffusion流程解析
英文回答：The stabilization of proliferation processes is an important task for modelling and studying the proliferation of substances under different conditions. The process includes three key steps of preparation, modelling and analysis of results. Preparatory work is needed to determine the nature of the substance, the nature of the medium and the time and spatial scope of the simulation. Modelling is done usingputer simulation methods to obtain numerical simulations of the diffusion process and to produce relevant proliferation patterns and distribution maps. An in—depth analysis of the simulation results,parison of proliferation patterns under different conditions, assessment of proliferation stability and trends, and an important reference for further research routes and policies. This work is important for better understanding and understanding of patterns of proliferation behaviour and for promoting development in related areas.稳定扩散流程是一项用于模拟和研究物质在不同条件下扩散行为的重要工作。
广义空间双重差分模型的构建及其在环境政策评估中的应用_概述
广义空间双重差分模型的构建及其在环境政策评估中的应用概述1. 引言1.1 概述在当今社会，环境保护和可持续发展已成为各国政府以及国际组织关注的重要议题。
实施环境政策评估是确保环境政策的有效性和科学性的重要手段之一。
然而，由于环境问题具有复杂性和空间特征，传统的评估方法在解决这些问题时面临着一定的挑战。
因此，本文将介绍一种新兴的评估方法——广义空间双重差分模型，并探讨其在环境政策评估中的应用。
1.2 文章结构本文共分为五个部分进行论述。
首先，在引言部分将介绍本文的研究背景、目的和结构。
其次，在第二部分将详细阐述广义空间双重差分模型的构建方法，并探讨该模型相对于传统方法的优势和局限性。
第三部分将重点关注广义空间双重差分模型在环境政策评估中的应用，包括概述环境政策评估、探讨该模型在其中所扮演的角色以及通过实际案例展示其效果评估结果。
在第四部分，将对前文进行总结归纳，并展望广义空间双重差分模型应用领域的未来发展趋势。
最后，在参考文献中将列出本文所引用的相关学术著作和研究成果。
1.3 目的本文旨在介绍广义空间双重差分模型的构建方法，并探讨其在环境政策评估中的应用。
通过对该模型进行详细解读和实际案例展示，旨在提供一种新颖而有效的评估方法，促进环境政策制定与实施工作的科学性、准确性和可持续性。
同时，希望通过本文的撰写和阐述，能为相关领域研究人员提供有益启示，并为今后相关研究工作提供参考依据。
2. 广义空间双重差分模型的构建2.1 空间双重差分模型简介空间双重差分模型（Spatial Difference-in-Difference Model）是一种常用于处理观察数据中存在的时间和空间相关性的统计方法。
该模型结合了传统的差分设计和空间延迟影响，能够更准确地评估政策变化对于特定地区或区域的影响。
传统的差分设计将观察对象分为处理组和对照组，在政策干预前后对两组的差异进行比较。
然而，在环境政策评估中，由于地理位置存在着明显的相关性，仅使用传统差分设计可能无法消除环境因素对结果产生的影响。
汽车差速器
author. Email: Geoffrey.Virlez@ulg.ac.be.
˙ and q ¨ are the generalized displacements, vewhere q , q locities and acceleration coordinates, M (q ) is the mass ˙ , t) = g gyr (q , q ˙ ) + g int (q , q ˙ ) − g ext (t), with matrix, g (q , q gyr g the vector of the complementary inertia forces, ˙ ) the vector of the internal forces, e.g., elasg int (q , q tic and dissipations forces, and g ext (t) the vector of the external forces. According to the augmented Lagrangian method, the constraint forces are formulated by ΦT q (pΦ + k λ), where λ is the vector of Lagrange multipliers related to the algebraic bilateral constraints (Φ = 0); k and p are respectively a scaling and a penalty factor to improve the numerical conditioning. Equations (1) and (2) form a system of nonlinear differential-algebraic equations. The solution is evaluated step by step using a second order accurate time
旋量键合图在并联机构动力学建模中的应用
Screw Bond Graph in the Application of the Parallel Mechanism Dynamics Modeling
GUO Fei1, 2 LI Yongquan2, 3 SONG Zhaojing1, 2 ZHANG Lijie1, 2
(1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004; 2. Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao 066004; 3. Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004)
图1 表1
势变量 传统键合图 旋量键合图 机械平动 机械转动 e W F (力) τ (扭矩)
功率流符号
广义变量符号表示
流变量 f T v (速度) ω (角速度) 广义动量 p P pt (动量) pr (角动量) 广义位移 q Q X (位移) θ (角位移)
1.2
基本通口元件
键合图中，通口元件通常用来表示真实系统中 构件的理想数学模型。旋量键合图中，常用的几种 基本通口元件的符号及特性如表 2 所示。
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ቤተ መጻሕፍቲ ባይዱ
ABSTRACT
Within-subject analysis in event-related functional Magnetic Resonance Imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and then on (ii) an estimation step to recover the temporal dynamics of the brain response. Recently, we have proposed a Bayesian detection-estimation approach that jointly addresses (i)-(ii) [1]. This approach provides both a spatial activity map and an estimate of brain dynamics. Here, we consider an extension that accounts for spatial correlation using a spatial mixture model (SMM) based on a binary Markov random field. It allows us to avoid any spatial smoothing of the data prior to the statistical analysis. Our simulation results support that SMM gives a better control of false positive (specificity) and false negative (sensitivity) rates than independent mixtures.
to [5] but more general in the sense that the HRF is estimated in the same time. The parameter controlling the strength of the spatial correlation is set by hand, as the smoothing level used when spatially filtering the data. The combination of these prior distributions with the likelihood allows us to derive the target posterior distribution using Bayes’ rule. We then resort to Gibbs sampling to draw realizations from this posterior law. The posterior mean (PM) estimates of the HRF, the Neural Response Levels (NRLs) and the corresponding labels are directly computed from the generated samples in the Markov Chain Monte Carlo (MCMC) procedure. Compared to [1], a significant gain is achieved in terms of sensitivity and specificity on artificial fMRI data.
In this paper, we introduce an extension based on spatial mixture models (SMM) that accounts for spatial correlation between neighboring voxels in the brain volume (regular lattice in 3D) or on the cortical surface (irregular lattice in 2D) [4–6]. The ensued goal is to favor the detection of activating clusters rather than isolated voxels. This extension consists in modelling a priori the state of a given voxel (non-activating, activating) depending on its neighbors using a symmetric Ising random field. In this regards, our work is close
(a)
(b)
Fig. 1. (a): Slice of the color-coded parcellation at z = −4mm. (b): Parcel-based model of the BOLD signal.
SPATIAL MIXTURE MODELLING FOR THE JOINT DETECTION-ESTIMATION OF BRAIN ACTIVITY IN fMRI
Thomas Vincent1,2, Philippe Ciuciu1,2 and Jérôme Idier3
1 Service Hospitalier Frédéric Joliot (CEA) 4, Place du Général Leclerc, 91406 Orsay, France 2 IFR 49, Institut d’Imagerie Neurofonctionnelle, Paris, France
Index Terms— Bayes procedures, Biomedical signal detection, Magnetic resonance imaging.
1. INTRODUCTION
Since the first report of the BOLD effect in human [2], functional MRI (fMRI) has represented a powerful tool to non-invasively study the relation between cognitive task and the hemodynamic (BOLD) response. Within-subject analysis in fMRI essentially addresses two problems. The first one is about the detection or localization of activated brain areas in response to a given stimulus type or experimental tasks, while the second one concerns the estimation of the temporal dynamic of activated voxels, also known as the Hemodynamic Response Function (HRF). In [1], a novel detection estimation approach has been proposed to address both issues in a region-based analysis, that is on a set of prespecified regions of interest (ROI). Within the Bayesian framework, we first integrated physiological prior information to obtain a slow-varying time course as an estimate of the HRF in every ROI. We have also considered different two-class independent mixture models (IMM) as prior distribution on the response magnitude to accomodate the voxel and stimulusdependent signal fluctuations within the ROI [3].
2. REGION-BASED MODELLING OF fMRI DATA
2.1. Motivations
Hypothesis-driven approaches postulate a model of the HRF response and enable local inference at the voxel level. Such methods take place in the General Linear Model (GLM) framework. They have been popularized by the Statistical Parametric Mapping software (SPM, http://www.fi/spm). In this formulation, the model chosen for the BOLD response is a crucial issue. SPM uses the same temporal model for the whole brain for simplicity and computational reasons. To help cognitive interpretations, we rather advocate for the necessity of a spatially adaptive GLM in which local estimation of the HRF would be performed. The latter does not need to be done at the voxel level, but rather at a coarser regional scale. To define this scale, we use a segregation of the brain volume constrained to the grey matter mask into a few hundreds of connected ROIs, called parcels. Any parcellation procedure can be used, as long as functional homogeinity is guaranteed within each parcel. In this respect, the assumption of a shape-invariant HRF is maintained.