Home

2D FSV Tool User's Guide

1. 45 Dad Riz Poli MR rr 48 592 Grade spread Chalft emstiautisntavi tit RUD a ela acer C lie oder Rt au usss 49 O inputDala SIC I iunii rod u uu ul ulus umasa ERR ERE REN oS Eo HE SE ERES 51 61 MATLAB FSV ASCIH f mnal iiiicicioes uio vu mi ve ii aaa EE nva oE aaa aw vuU E d Cui va nau ECKE ERR 51 O blu MOGUCOM eet 51 6 1 2 Case 1 Set of data computed directly in MATLAB a 52 6 1 3 Case 2 Setof data generated outside MATLAB cccccceceeeeeeeeeeeseeeaneeesseseeeeeeeeeeeeeeeesseaas 53 6 14 Building the proper data format for FSV 2D 54 62 CSTASCII Formation uuu xs usss 56 OZE Bpom CSI Silla uuu uniman uw ha dre ad eon ra dleok Gn th aon C Dci satu ista asss 56 6 3 EZFDIDmovie Omata u Aa aaa aAa ie aana 59 7 Output Data Structure amp Location T T T T T T 60 Jl Data laid 60 72 NON COMPING Gala uuu Sau aaa vo a ana aain daaa 61 Z3 Combined dakk isis usua p xen Gago aa a u FREE EE aaa Va ES 62 8S Output Results uwawan annor acd RC GER FCR RR TR RR ER RD RD ERE RR 63 9 EQmpexSuuuy ia Rer IEEE PRERER MERKEN EEA VEM VAR EV EVA EVEN VEM TANE YEN REHERREE CK ERI FERNER ER 66 10 DOCENSE DR 66 TL C ONAC IS iaaa a a m Swa 67 12 Ack owledgemerntu uuu A aeara aaa aE na VR aaa KU E Eaa V E ag au RR
2. Data z Phaseipeaks axisYmirrared t E Data 1 Magnitude domain Exk Data 2 Phase damain ExE z Data 1 _FPhaserpeaks 2 txt Data 1 Phase domain txt E Data 2 Magnitude peaks Exk Data z Magnitude domain txt Mome file Data _2_Magnitude_domain tst Tipo file tet k Annulla Fig 4 24 Window for domain files selection 2 4 Select the fourth file i e Data 2 Magnitude peaks txt containing the data of component 1 of second combined set 33 Select Combined Magnitude Domain 2 MATLAB FSV ASCII EJE3 Cerca in combined dada fe E E Data 1 _Maqniturdle peaks Exkt z Data 2 Phaselpeaks axisYmirrared E E Data 1 Magnitude domain txt E Data z Phase damain ExE E Data 1 Phaserpeaks 2Y Ext E Data_1_Phase_domain txt E Data 2 Magnikude peaks Exk E Data _z Magnitude dormain txt Hill Home file Data 2 Magnitude peaks txt Tipo file xl T Annulla Fig 4 25 Window for data files selection 2 5 Select the fifth file 1 e Data 1 Phase domain txt containing the domain of component 2 of first combined set Select Combined Phase Domain 1 MATLAB F5V ASCII EJE3 Cerca In 3 combined dada k z Data 1 Magnitudelpeaks txt Data Phase peaks axis mirrored E Data 1 Magnitude domain txt Data z Phase damain ExE E Data 1 Phaserpeaks z Ext pata _1_Phase_domain txt Data 2 Magnikude peaks Exk Data z Magnitude domain tx
3. FSV Message TRIAL PERIOD EXPIRED Fig 10 2 Trial expired GUI Pressing OK is displayed the License GUI dialog and from here is possible to load the license file To obtain a license contact us at orlandi ing univaq it and send the following informations 66 e Your Name e Organization name e The authorization code that Is displayed in the specific text box Use the key combination Ctrl C to copy it in the clipboard When you receive the license file you have to load it from the License GUI dialog Pressing Load license button 1s displayed the following GUI Select the license file Cerca In r UAq licenser libo U4 licenser Sy Carlo_Polisini_1i0 FSW li E uaqproducts txt Nome fle Cano Polisini 1D FSV license t t Tipo file tut gt Annulla Fig 10 3 License loading confirmation dialog From here browse to the license file and select it If the license is valid you get a confirmation message like this License Message Saks THE LICENSE IS SUCCESFULLY LOADED THIS LICENSE IS VALID SINCE 18 Dec 2007 TO 15 Dec 2006 Fig 10 4 License loading confirmation dialog 11 Contacts For any problem or comment please contact Dr A Duffy DMU Applied Electromagnetics Group apd dmu ac uk Prof A Orlandi UAq EMC Laboratory orlandi ing univaq it If you wish to be included in the FSV User List please e mail a short request to Prof A Orlandi UAq EMC Laboratory orlandi ing un
4. e Analysis mode inputpaths contains the complete path of the input files used In the FSV run The fig files are MATLAB figure files e Analysis mode inputdata figure of the input data after synchronization e Analysis mode DC inputdata figure of the DC Data used in the calculation of ADM FDM and GDM e Analysis mode Low inputdata figure of the Low Data used in the calculation of ADM FDM and GDM e Analysis mode High inputdata figure of the High Data used in the calculation of ADM FDM and GDM e Analysis mode xjDMc confidence histogram e Analysis mode xjDMi figure of the point by point amplitude differences e Analysis mode ODMi figure of the point by point offset amplitude differences The mat file is MATLAB data file containing all the relevant FSV variables It is the input file for the Data Display Tool e Analysis mode Analysis contains the analysis data in MAT format The GSthrs x subdirectories have this structure x 1s the threshold value Es Amplitude GradeSpread 85 mat m Amplitude GradeSpreadChart_ 85 Fig GradeSpread_ 85 txt Fig 7 2 Grade Spread folder for Time Domain Amplitude analysis x threshold 85 e Greadspread_ x txt contains the values and the ranges of Grade and Spread for each FSV variable ADM FDM and GDM e Analysis mode GradeSpreadChart x fig figure of the Grade Spread chart e Analysis mode GradeSpread x mat contains the Gra
5. Error using gt Fsv 2 d gt cutoff Can t apply 2D FS on this dataset The Breakpoint is too close to the edge of the domain The HI Filter Array cannot be defined Fig 4 3 Error Message GUI for breakpoint greater than BKmax Only for the combined data In the combined analysis two pair of data sets are compared 1 e 1 Magnitude 1 Magnitude 2 2 Phase 1 Phase 2 For each pair of data sets 1 and 2 the previous above two condition must be valid in addition to 27 be correctly combined the domain of each Magnitude and Phase parts must be identical In the example Magnitude 1 2 domain must be identical to Phase 1 2 domain 4 2 Dimensional units for the domain To compare two sets of data whose domains have different dimensional units it 1s needed to specify the dimensional units for each domain After having selected an input data set domain and data files or only data file depending on the format 2D FSV displays the following GUI Dimensional units for the domain Saks Set dimensional units X AXIS Domain 1 crr Fig 4 4 Dimensional units for the domain GUI set 1 domain The dimensional units can be chosen among per unit use this for not dimensional data m cm mm um nm ft inch mils Dimensional units for the domain akg Set dimensional unita Domain 1 Fig 4 5 Dimensional units for the domain GUI set 1 domain The default settings are cm for both ax
6. The same sequence of windows is proposed for the loading of the second set of data domain and data 4 4 Loading CST ASCII format With this format it s possible to import data from CST EM STUDIO Each set is given by one file see Section 6 2 An example of loading one CST ASCII set 1 Select the CST ASCII data file 1 e cst surface dat containing the set as in Fig 4 10 25 Select Amplitude Data 1 CST ASCII cst_ surface 3 dat cst surface big dat Mome file csl surface 2 dat Tipo file dat Annulla Fig 4 10 Window for data files selection Now it is requested to specify the dimensional units for the X axis and Y axis of the loaded domain by means of the Dimensional units for the domain GUI see Fig 4 11 More info on this window will be given in Section 4 2 Dimensional units for the domain SSE zat dimensional units X Axiz Domain 1 cm Fig 4 11 Dimensional units for the domain GUI The same sequence of windows is proposed for the loading of the second set of data domain and data 4 5 EZ FDTD format 2D FSV 1s capable to load a frame from an EZ FDTD movie files as a surface and to compare it with another EZ FDTD frame or another surface from other formats Each EZ FDTD data set Is given by two files model data file kmf and movie data file csv 1 Select the EZ FDTD model data file 1 e dua microstrip02 KMF containing the parameters of the structure of EZ FDTD m
7. 394 E 03 sGdlEg 03 3G4 E 03 394 E 03 sGdlEg 03s 3G4 E 03 384 E 03 384 E 03 sGdlEg 03 384 E 03 384 E 03 sGdlEg 03 3G4 E 03 3G4 E 03 394 E 03 sGdlEg 0s 3G4 E 03 394 E 03 sGdlEg 0s 3G4 E 03 3G4 E 03 6 3 EZ FDTD movie format EZ FDTD is another 3D full wave commercial electromagnetic CAD by EMS PLUS www ems plus com based on the technique of Finite Difference Time Domain FDTD With FSV is possible to load the output movie data file of a EM simulated field by this CAD This movie contains field values that have been sampled over time in the pre defined movie plane It contains values over the movie plane for each time interval that was sampled The movie file 1s used in conjunction with the kmf kernel movie file file Both the movie file s MVxxx CSV and the kmf file are placed In the output directory EZFDTD modelfilename The kmf file has the following information in it which 1s used by the GUI to read and display the movie data Contents of kmf file KernelModelData FileVersion EZ KERNEL2 0 CompletedSteps completedsteps ModelSteps modelsteps TimeStepSize timestepsize MovieFrames XY XZ YZ loc movieframes MovieHeight XY XZ YZ loc movierows MovieWidth XY XZ YZ loc moviecolumns MovieTemporalDecimation XY XZ YZ loc movietemporaldecimation MovieMaxValue XY XZ YZ loc E H
8. Matlab Code 6 3 Y y nit RAYS The command lines 6 2 and 6 3 generate a row vector x y from Xmin Ymin tO Xmax Ymax step Ax Ay Next calculate the size of the vectors x and y this can be done by the MATLAB built in function length Matlab Code L length x L length y Ou 3 Transform the domain specified by vectors x and y into a format that MATLAB can accept in order to evaluate functions of two variables and to generate mesh surface plots This 1s performed by a specific MATLAB command Matlab Code 6 5 X Y meshgrid x y The output of meshgrid are two matrices X and Y e X isa matrix whose L rows are copies of the x vector whose length is Lx The dimensions of X are then L x Lx e Yisamatrix whose L columns are copies of the transposed y vector whose length is Ly The dimensions of Y are then L x L 52 4 The values of z f x y can be now computed They will give rise to the Z matrix whose dimension are Ly x L An example of computation of z x y is Matlab Code Z X 2 Y 2 ve At this point one can plot or store the surface data given by the matrices X Y Z Matlab Code 6 7 mesh X Y 2 The matrices X Y and Z are now ready to be cast in the proper format for FSV 2D This is described in Section 6 1 4 6 1 3 Case 2 Set of data generated outside MATLAB Given the function z f x y 6 8 Which has been evaluated computed measured
9. domain Ext Data 4 peaks axis mirrored txt Data 4 domain txt Z Data Slpeaks 24 txt Z Data 5 domain txt E readme domain structure txt Mome file Data 1 peaks E t Tipo file tut E Annulla Fig 4 19 Window for data files selection 1 3 Select the third file 1 e Data 2 domain txt containing the second domain set as in Fig 4 20 30 Select Amplitude Domain 2 MATLAB FSV ASCII Cerca In data examples E Data i peaks txt B Data 1 domain Ext E Data z peaks txt Data z domain bt Data S peaks 24 txt Data 3 domain txt Z Data 4 peaks axisvmirrored Exk Data 4 domain txt Data Sr peaks 2 Y Exkt Z Data 5 domain txt Z readme domain structure txt Home File ID ata 2 dornain tet Tipo file bet Annulla Fig 4 20 Window for domain files selection 2 4 Select the fourth file 1 e Data 2 peaks txt containing the second set as in Fig 4 21 4 7 Frequency domain analysis Select Amplitude Data 7 MATLAB FSV ASCII Cerca in 3 data examples E Data_1ipeaksi txt E Data 1 damain ExE Data 2ipeaks txt Data 2 domain txt Data 3 peaks z ExE E Data 3 domain bk Z Data 4 peaks axisvmirrored Exk Data 4 domain txt Data Sr peaks z Y Ext Data 5 domain txt Z readme domain structure txt Home file E ala z peaks Ixt Tipo file bet Annulla Fig 4 21 Window for
10. etc by the user outside MATLAB The user has to build the correct association between the values of x y and z in MATLAB format in order to plot the surface data l First set the domain values by defining the limits for the x axis Xmin Xmax and the y axis ymin Ymax The samples along x and y can be uniformly spaced or non uniformly spaced samples uniformly spaced the step is Ax for the x values and Ay for the y values The x and y vectors that represent the domain are generated as X axis Matlab Code X Xa AX LX 6 9 y axis Matlab Code 6 10 b Vida MAS samples nonuniformly spaced the user has to manually enter the values for x and y axes X axis Matlab Code X x x x 6 11 y axis Matlab Code 6 12 y yu 53 In this case FSV 2D during import procedure sets automatically the Ax Ay for the x y values to the minimum step between the x samples y samples Then recalculate the x y domain values and interpolates the z axis values For further use it 1s required to evaluate the dimensions of the vectors x and y this can be done by the MATLAB built in function length Matlab Code L length x Ly length y 6 13 2 Now one has to transform the domain specified by vectors x and y into a format that MATLAB can use to evaluate functions of two variables this is performed by the following MATLAB command Matlab Code 6 14 X Y meshgrid x y The output
11. FDM uisa CX y 1 l 6 1 2 1 K weighting factor K magnitude Kphase are the weighting factor 0 Kiagnitude KKohase lt 1 representing the relative subjective importance placed on the two terms The selected values of K are also an important factor in communicating the comparison because it relies on the engineers involved in rationalizing their subjective decision In certain applications only the magnitude of the electromagnetic variable is required in which case setting Kmagnitude 1 Kphase 0 reduces these equations to those previously described In other applications both the magnitude and phase are equally as important For example a wrong equivalent circuit could result when there 1s perfect agreement in the magnitude but a small difference in the phase In this case it would be anticipated that Kmagnitude Kphase 0 5 would be appropriate The FDM is treated in the same way using the same weighting factor The GDM will combine the ADM and FDM as before without inclusion of a separate weighting factor this has already been included in the component measures The output variables of the Combined analysis are the same than those illustrated In points 12 to 20 of the previous section Moreover the user can select more than one pair of K weighting factors for each analysis as is described in 3 4 2 1 Based on this algorithm the FSV Tool has been developed as a standalone application for Windows OS 12 2 How to Inst
12. GUI in combined analysis and an expanded version of Scaling of domain s dataset GUI 39 Dimensional units for the domains Sliced Set dimensional units X AXIS Domain 1 Domain 2 Domain 2 Domain 4 All axes are in perunt v Fig 4 36 Scaling of domain s dataset GUI in combined analysis 5 The Data Display Tool 5 1 The Results GUI The Data Display Tool is an application embedded in the FSV Tool that allows the user to visualize the relevant FSV variables associated with the comparison of two datasets The Data Display Tool plots the data contained in the mat files generated by the FSV procedure The Data Display Tool allows the visualization of the two original data sets and of the FSV variables associated at the FSV analysis performed The three buttons on top of the GUI are e Main menu back to the FSV main window analysis Fig 3 1 e Data Display display the main results GUI panel e Grade Spread chart display the Grade Spread chart panel 5 1 1 The Data Display panel 40 Input Dataset 1 Amplitude MatLab FS ASCII Edit Desktop Window Help c EE 5 8 09 9 E ES 1 MatLab FSV ASCII File View Insert Tools Sele Amplitude Data 1 AN i i Fi Im mecs Fi ii lI abae PAN MAN ghi VM M MR am nl TR m ju ie Me AU i AUS MM nis us j TM None ET HIRE Ny EAE SAU dm FM d WO ai i E oe I Wa uy
13. OD 1D 22 30H Evaluate Field orr predefined Curve Evaluate Field or predefined Face Get Capacitance C Get Double Result Entry Get Double Result value Get Inductance L Bet Humber of Meshcells LF Get Coil Voltage LF Get Double Result Entry LF Get Double Result Value LF Get Total Losses i LF aet Vallage Source Impedance b wi aa i IT 1 Fig 6 3 Add PostProcessing step drop down menu 4 From the Evaluate 0D 1D 2D 3D GUI panel 1 In the Calculation Range section select Dim 2D the dimensions of the section Coord System Cartesian WCS global xyz Normal is the versor of cartesian axis normal to the plane used for the section Y in this example Sampling 2 normal level of the spacing between the samples X Y Z max X Y Z min Set the edges for the 2D domain Note that in the example Y is the versor normal to the plane so for this axis it s only needed to specify the distance from the origin ii In the Evaluated Field and Component section set B field 60 Component Abs take the module of the field value Complex Real Part iil The Result Value section set Maximum2D When done press OK button 57 3 Evaluate OD 1D 2D 3D Calculation Range Dim Coord System Select Solids Solids WIES min global xyz Mormal Sampling 2 nomal Evaluated Field and Component Result Value Field 60 Component L amples Fig 6 4 S
14. T J T T T T 21 4 2 Dimensional units for the domain T T 23 4 3 Loading MATLAB FSV ASCII format 24 44 loading CSTASCILTOImaE inii nur u lu uuu sasa sassa 25 45 EZ DID Dima ti u u u u u u SO ER 26 4 6 nedomainanalysis ii u uu uivas uuu u u u u unu Aani 29 4 7 Frequency domain analysss 31 2 M MEN EE 31 NEM P RES 32 44 3 X ODDDI GOvssinssikinatodtitoveH D IRURE DUAE ORE ToC SS SS Xr u uuu ua ERE E EE D 32 4 8 Invalid input data loadingd 36 49 Summary GW aic siue anaana aaa UOU ERE VES NER E RaEE s D deena dents EEUREEUE RE sediuaadeuadsteacsastecusn di 37 5 INe Data Display IOOLunumnanadiuiscecebitekd a qivwvuka d cencu a Ue un ERU eNsa EE nz RE VU gaza VEG ad sawam sus 40 5L M Res ts GU ordi iik eoi Vibo OX xa RR Y E GR ER ER E RO CR RR OR RR RR 40 5l de Data DIS lay pah L uu u a d Qe Pun an nnn ER QI ptu E 40 zu NE Cl io ieizcioEeg iN RT u hoes uy SA Su u m u S aan 43 52 Displaing the results at the end of an FSV analysss 44 5 3 Displaing the results from the initial window
15. X Y Z moviemaxvalue 59 The movie data is stored in ASCII format and is comma delimited The frame size is determined from the kmf file 7 Output Data Structure amp Location The output data files txt fig and mat and the screenshots generated by the user are stored in an automatically generated subdirectory inside the selected directory for output file see Section 4 6 The names of these subdirectories are Frequency Domain Analysis x or Time Domain Analysis x in which x is an incremental number to distinguish the subdirectories 7 1 Data Structure The main level of an output data folder appears like in Fig 7 1 1 Gsthrs 70 e Amplitude Analvsis mat Csthrs 85 J Amplitude_ADMc fig jasthrs 90 4 amplitude_ADMi fig Amplitude ADM average txt 0 Amplitude DC inputdata fig Amplitude ADMc ExE 0 Amplitude_FOMc ig Amplitude ACM txt amplitude_FDMi Fig Z Amplitude DC inpukdatad txt d Amplitude 3DMc fig Amplitude DC inpubdakaz txt 4 Amplitude GDMi fig Amplitude Donmain Ext 4 Amplitude High inputdata fig Amplitude FOM average txt A Amplitude inputdata Fig Amplitude FOMc txt 0 Amplitude_Low_inputdata fig Z Amplitude FDIMi ExE amplitude_ODMi Fig Amplitude GOM_ average txt E Amplitude GDIMc Ext E Amplitude GDMi Ext Z Amplitude High inputdatal txt Amplitude High inputdataz txt Z Amplitude _inputdatal txt E Amplit
16. data files selection 2 Again in the following examples the input data files are in MATLAB FSV ASCII format 4 7 1 Magnitude In the FREQUENCY DOMAIN Magnitude FSV Tool requests two input datasets to compare Basically you can follow the same procedure as seen in TIME DOMAIN Amplitude analysis Duy Select the first file 1 e Data 1 domain txt containing the first magnitude domain set Select the second file 1 e Data 1 peaks txt containing the first magnitude set Select the third file 1 e Data 2 domain txt containing the second magnitude domain set Select the fourth file 1 e Data 2 peaks txt containing the second magnitude set 3l 4 7 2 Phase In the FREQUENCY DOMAIN FSV Tool requests two input datasets to compare Select the first file 1 e Data 1 domain txt containing the first phase domain set Select the second file 1 e Data 1 peaks txt containing the first phase set Select the third file 1 e Data 2 domain txt containing the second phase domain set Select the fourth file 1 e Data 2 peaks txt containing the second phase set pie 4 7 3 Combined In the FREQUENCY DOMAIN Combined analysis FSV Tool requests two combined input sets to compare 1 Select the directory in which your data are located For test purposes the data examples combined data directory that is located in same folder of the main program fsv2d exe can be used Select the first file 1 e Data 1 Magni
17. for output file see Section 3 2 The names of these subdirectories are Frequency Domain Analysis x or Time Domain Analysis x in which x is an incremental number to distinguish the subdirectories 46 Sfoglia per cartelle Select Folder Far analyzed data 1 C Time Domain Analysis 57 i Time Domain Analysis 58 zJ Time Domain Analysis 59 Time Domain Analysis 60 C Time Domain Analysis 61 zJ Time Domain Analysis 62 E Eo MEET Analvsis 63 73 asthrs 50 3 GSthrs 70 r lc gt Crea nuova cartella OK Annulla Fig 5 7 Selecting a Time Domain analysis output folder e combined analysis select the automatically generated subdirectory Frequency Domain Analysis x inside the selected directory for output file see Section 3 2 and then select the subdirectory K factor x named with the value of the Kynagnitude parameter used Sfoglia per cartelle Select Folder Far analyzed data 1 C3 Frequency Domain Analvsi Frequency Domain Analvsi 3 E Frequency Domain Analvsi E Co MU Gsthrs 85 K Factar 5 E Frequency Domain Analvsi k Factor n 4 7 K_Factor 0 5 r _ kS l m Crea nuova cartella OK Annulla Fig 5 8 Selecting a combined Frequency Domain analysis output folder If the mat file 1s not found in the selected directory or an invalid mat file is found a warning 1s issued 47 Warning Mo valid mat file found in selected F
18. is the machine where you want to run the application The MCRInstaller opens a command window and begins preparation for the installation Then the MCR Installer wizard appears click Next to begin the installation and follow the instructions on the GUI 3 Unzip the content of the zip file 2D FSVxxx zip in the installation directory 1 e 2D FSV and double click on fsv2d exe 4 In the zip package it is also provided an icon file 1co to create a desktop shortcut for the fsv2d exe file according to the standard Windows procedure Note for Windows Users You must have administrative privileges to install the MCR on a target machine since it modifies both the system registry and the system path Running the 2D FSV after the MCR has been set up on the target machine requires only user level privileges 13 3 Run FSV Tool 3 1 Initial Window and analysis Run the FSV Tool from the exe in the installation directory or double click on the desktop icon When FSV Tool 1s started an initial window appears see Fig 3 1 The user may choose any of the major functions desired 2D FSV 2 0 5L FSv PROCEDURE ANALYSIS UAq EMC Laboratory O TIME DOMAIN DMU Applied Electromagnetic Group UMR EMC Laboratory O FREQUENCY DOMAIN NOTE TO THE USER All the figures fig and ASCII files txt are generated in the working directory tabulated format License The figures are not displayed on the screen ASCI files shoul
19. legend is displayed that associates one of the six natural language descriptor categories to each range of values ADMc FDMc GDMc This 1s a graph with six vertical bars one for each six natural language descriptor categories In the top of graph is displayed xDMtot xDMconf xDMpw values x A F G in a yellow textboxes see Section 1 1 5 1 2 Grade Spread chart By selecting the Grade Spread chart button the FSV Grade Spread chart appears see Section 1 1 All variables are displayed on the chart for the default threshold of 8596 43 gt 2D FSV 2 0 4 Results Main menu Data display i Grade Spread Chart Grade Spread Chart 1 threshold 85 Kadm 1 Kfdm 1 Points to plot ADM FDM GDM Grade Spread thereshold Threshold 6 Spread Export Figure Fig 5 4 The GRADE SPREAD coloured chart e The value of threshold currently used and the kapu and krpy used in forming GDM eq 1 11 are shown above the GRADE SPREAD chart e Export Figure button take a screenshot of the currently graph displayed The displayed graph is exported in PNG format The figure is saved in a file located in the subdirectory inside the selected directory for output data named with the value of the threshold e Points to plot checkboxes select the GRADE SPREAD points to plot from ADM FDM and GDM e Grade Spread threshold box from this section the threshold can be modified entering the new v
20. of meshgrid 1s two matrices X and Y e Xisamatrix whose L rows are copies of the x vector whose length is Lx The dimensions of X are then L x Lx e Yisamatrix whose L columns are copies of the transposed y vector whose length is Ly The dimensions of Y are then L x Lx 3 The values of z f x y should be cast in matrix Z L x Lx in a proper order The order of the matrix elements is given in 6 15 fos fo f i fy fO FG y2 es E 6 15 f O35 yr f Q5 yr M f Gi yu e Atthis point one can plot or store the surface data given by the matrices X Y Z by using the command Matlab Code ines v vs 6 16 The matrices X Y and Z are now ready to be cast in the proper format for FSV 2D This 1s described in Section 6 1 4 6 1 4 Building the proper data format for FSV 2D Assume that the matrices X Y and Z have been defined as In the previous sections Now they should be cast for proper FSV 2D input FSV 2D requires for each one of the two surfaces that should be compared two input files The first file 1s the Domain file which contains the x and y values on which the z values are computed The structure of the Domain file 1s described in the following figure 54 Row 1 Col Lx Row Ly Col L Row 1 Col 1 Row 1 Row 1 Col L Coll Row Ly Row L Col L Col 1 X domain Y domain Fig 6 1 Structure of the of the Domain file Row L Col 1 The X and Y domain val
21. 2D FSV Tool User s Guide Version 2 December 2007 TABLES OF CONTENTS 1 Introduction to the 2D Feature Selective Validation FSV Theory 4 l1 The Two Dimensional Feature Selective Validation 2D FSV method 5 12 The Combined Analysis for complex values 11 12 1 K WIG MENG Td C LOTO u y PR a nka bv aii o ra ig 12 2 Howto Installaddsdnisiirs Coe skvERG R DIO PX OOO OGDOQUOR OR ORO EO ER EE KR RR CER UV EROR ERCR EORR RED 13 21 Sysenm Req lfementiSQu uu u u occu avis aa e ui awa ue du ao E RERU cu eY wav Cra vnd ow x ER EVE Ru uvas 13 242 Jisalato Doni u on ua RM RR SRI RD On C ERE ODER RR RO RU D ERR ER 13 3 R unFSsV IOOloinsnge ane ue iE EXER FR EA ERA pasas h a asas 14 3 1 Initial Window and analysis 14 32 CUI Chav tea Pde Eases aes aa iru cca aaah ERG a asss sua a a ERE HUS 16 33 dImnputdata lOadIng uuu Aaa EON dada wc sasa sawas 16 SA ZDFESVCOompUutabloliiu oria kie Ki i dan RO I Ke RR Cue Ri o OR bil i AR TR TR 17 521 iNODcombiled dat sevstenin oda etait n MELLE Durs acepta rre S uuu ads uuu aid 17 BAZ JC OmbDIDed data iiid t s eL OR UO UI UU Ed ford um d oae id d vts d ad Up idle a wa 18 A InpUE Datel Loe GING etr 21 4 1 Domain and data requirements T T T J T
22. RUE 68 13 Selected FSV Validation Bibliography 69 Introduction to the 2D Feature Selective Validation FSV Theory The 2D Feature Selective Validation FSV Tool is a standalone application that implements the two dimensional 2D FSV theory The 2D FSV Tool is as a joint project between Applied Electromagnetics Group De Monfort Univ Leicester UK http www eng dmu ac uk aeg UAq EMC Laboratory Univ of L Aquila L Aquila Italy http www diel univaq it labs emc UMR EMC Laboratory Univ of Missouri Rolla Rolla USA http www emclab umr edu The 2D Feature Selective Validation FSV algorithm has been developed to compare two sets of bidimensional surface data not necessarily in the electrical engineering field and put them in an objective and comprehensible form Several motivations form the basis of FSV e The need to control variations between visual assessment results e The reduction of cost a skilled engineer is an expensive commodity e The desire to reduce ambiguities e The inability of humans to process and cache extremely large volumes of data The FSV theory was conceived as a technique to quantify the comparison of data sets by mirroring engineers visual perceptions Furthermore FSV allows automated comparisons of large volumes of complex data whilst reliably categorising the results into a common set of quality bands The FSV offer
23. _FDMi fig Wi Combined GDMc Fig Combined GOMi Fig Fig 7 4 K factor xj folder for Frequency Domain Combined analysis The data file structure and content is as described in Section 7 1 Note that all the data files relating the input data are absent because they are saved in the top level of output folder Fig 7 3 moreover the data files related to offset calculations ODM are absent because the offset computation is only performed on input components 8 Output Results Consider two Peaks distributions as input data in which Peaks 2 1s the mirror image of Input data 1 with respect the y axis Input Dataset 1 Amplitude MatLab FS ASCII MatLab FSV ASCII File Edit View Insert Tools Desktop Window Help D m b 5 k AANTD XE D EJ m mi Amplitude Data 1 Amplitude Data 2 n Eo P i mm NM 1 if ce m i 2 iu m ji S m SUAM W 2 A Mig Fig 8 1 Input data example The significant FSV outputs are 63 e ADM 1s a figure of merit of the comparison of amplitudes and trends of the two data sets to compare the lower the ADM the better the comparison e FDM is a figure of merit of the comparison of details derivatives of the two datasets to be compared the lower the FDM the better the comparison e GDM isa figure of merit of the combination of ADM and GDM These output can be at the end of the algorithm e xDMi values of x A F G DM for each pair of samples of the two datasets to be c
24. a i PA 4 TUS j 2 j 4 a CIS l A aos za jd WE n j dh x _ Me Ni D E Combined Analysis K 0 5 ADMI O ADMc FDMi FDMc GDMi GDMc Hidelegend Hide synthetic figures Fig 5 10 Data Display Tool after selecting two folder 5 3 2 Grade Spread chart Selecting the Grade Spread chart button the user has the option to select which output data should be considered for the GRADE SPREAD chart from the radio buttons of the Select chart selection box Fig 5 11 49 2D FSV 2 0 3 Results Main menu Data display Grade Spread Chart Select chart Folder 1 chart Folder 2 chart Points to plot ADM FDM GDM Grade Spread thereshold Threshold 0 9 1 Export Figure Fig 5 11 Window of the Grade Spread charts 2D FSV 2 0 4 Results Main menu Data display Grade Spread chart Grade Spread Chart Grade Spread Chart 1 threshold 85 Kadm 1 Kfdm 1 Select chart Folder 2 chart Points to plot ADM FDM GDM Grade Spread thereshold Threshold 85 6 Export Figure Fig 5 12 Selecting Grade Spread chart of Folder 1 data 50 Selecting one chart 1s possible to independently update the threshold or the points to be displayed 2D FSV 2 0 4 Results Main menu Data display Grade Sprea
25. all 2 1 System Requirements The 2D FSV Tool is developed to run under WINDOWS 2000 or Windows XP It has not yet been tested on WINDOWS VISTA 2 2 Installation The 2D FSVxxx zip file xxx indicates the number of the version contains all the needed parts of the 2D FSV Tool Method 1 users that have a version of MATLAB installed 1 Check ifa MATLAB compatible version is installed in your system type in matlab prompt version ifthe output is 7 1 0 246 R14 Service Pack 3 your machine is able to run 2D FSV Otherwise see the Method 2 2 Unzip the content of the zip file 2D FSVxxx zip in the installation directory 1 e 2D FSV and double click on fsv2d exe 3 In the zip package it is also provided an icon file ico to create a desktop shortcut for the fsv2d exe file according to the standard Windows procedure Method 2 users that do not have MATLAB installed or an earlier MATLAB version than from 7 1 0 To run 2D FSV to another development machine that does not have MATLAB 7 1 0 installed including a machine that has MATLAB but it is a different version of MATLAB 7 1 0 the users must install the MCR Matlab Component Runtime library ver 7 3 if it is not already installed on the user machine l Get the package MCRInstaller exe from http ing univaq it uaqemc 2D FSV 2 0 0 that is the MCR ver 7 3 bundled with MATLAB 7 1 0 246 R14 SP3 2 Run MCRInstaller exe once on the target machine that
26. alue in the appropriate text field or moving the slider and pushing the Update button The new values of GRADE and SPREAD are computed and the variables can be displayed on the chart 5 2 Displaing the results at the end of an FSV analysis At the end of an FSV analysis it 1s possible to visualize the results of the last FSV analysis performed by selecting view results in the end analysis GUI Fig 3 8 The Data Display Tool starts and automatically loads the data of last FSV analysis performed For combined analysis it loads the results relating the last K weighting factor used 44 Input Dataset 1 Amplitude MatLab FS ASCII MatLab FSV ASCII File Edit View Insert Tools Desktop Window Help m t h 0 4009 ED E mm Amplitude Data 1 Amplitude Data 2 AT a N i m PN TUN 2D FSV 2 0 4 Results Data Display Excellent 0 0 1 Very Good 0 1 0 2 Good 0 2 0 4 Fair 0 4 0 8 Poor 0 8 1 6 gt 1 Very Poor 1 6 inf Amplitude pee M S NO Ds ie Ps eV Wi oS Ny gt E 4 Edit Figure Export Figure Edit Figure Export Figure Time Domain Analysis Time Domain Analysis ADMi ADMc ADMi ADMc FDMc DM FDMc GDMi GDMc Hidelegend Hide synthetic figures Hidelegend Hide synthetic figures Fig 5 5 The original data window and main Data Display Tool of last analysis performed 5 3 Displaing the results fro
27. among a list per unit use this for not dimensional data m cm mm um nm ft inch mils EZ FDTD data import Co Input the following information The number of frames in the movie file ig 102 Enter the number of the frame to load 1 nr 102 Enter the dimensions Ax and Au of the elementary cell they can be read in the input file Fig 4 15 EZ FDTD data import GUI menu open If another EZ FDTD frame is chosen as second dataset for comparison the User 1s invited to repeat the steps illustrated by Fig 4 12 and Fig 4 13 Then the EZ FDTD data import GUI is displayed In this case the settings of Ax and Ay chosen for the first dataset Fig 4 14 blue circle are automatically used Fig 4 16 blue circle The In this window only the number of the new frame should be indicated Fig 4 16 red circle 28 EZ FD ID data import Input the following information The number of trames in the movie file iz 1072 ber of the frame to load 1 nr 102 sions Ax and Au of the elementary cell ad in the input file Fig 4 16 EZ FDTD data import GUI set 2 4 6 Time domain analysis 2D FSV 2 0 5L FSY PROCEDURE ANALYSIS Uq EMC Laboratory DMU Applied Electromagnetic Group UMR EMC Laboratory FREQUENCY DOMAIN HOTE TO THE USER Run FSv All the figures 11g and ASCII files txt are generated in the working directory The figures are not displayed on the screen ASCII file
28. as input files Domain txt and Data txt for the first surface and Domain 2 txt and Data 2 txt for the second surface 6 2 CST ASCII Format In CST EM STUDIO is possible to define an arbitrary section of an EM simulated field and get the field value In each point of that surface This surface has to be exported in ASCII format to be used as input for 2D FSV 6 2 1 Exporting CST Surface This procedure is tested on CST STUDIO SUITE 2006B The 2D FSV import procedure works with section of EM field obtained from a plane normal to one of the axis versor 1 Load a simulation obtained by CST STUDIO SUITE 2006B 2 In top bar go under Results Template PS and Post Processing the GUI of Fig 6 2 appears Template Based Postprocessing 1D Results OD Results Add Hew postprocessing step w Result name Template name Value 4 B Field BO Normal 2D Evaluate Field in arbitrary Coon 0 000856239 ERG ee aluate Fie Fig 6 2 Template Based PostProcessing window 56 3 Select 0D Results tab then from drop down menu Fig 6 3 select Evaluate fields in arbitrary coordinate 0D 1D 2D 3D Clicking on this item appears the preferences panel Evaluate 0D 1D 2D 3D of Fig 6 4 Template Based Postprocessing 10 Res te DD Results momo mcm eo m cc m OD Value trom 10 Result DD Value from 20 3D Plat Mix OD Results Calculate Force and Torque Exvaluate Fisld irt arbitrary Coordinates
29. ata loading procedure it is displayed a summary of the loaded data and domain s settings 2D FSV Inputs Data Summary Input Data Summary SET 1 FORMAT CST ASTI DOMAIN FILE cst surface 2 dat DATA FILE cst zurface 2 dat X AXIS mm Y AXIS mm SET 2 FORMAT EZ FDTD DOMAIN FILE MWI ZEZ CSW DATA FILE Ms x3 12bEZ cS X AXIS mim Y AXIS mm Edit Dimensional Units If the data are correct press Continue otherwise press Main Menu to abort the analysis and qo back to the initial window Fig 4 32 Summary GUI For each data set it Is specified FORMAT the format of input data DOMAIN FILE name of the file that containing the domain DATA FILE name of the file that containing the data X AXIS dimensional unit set for the X axis Y AXIS dimensional unit set for the Y axis At this point the user can start the analysis pressing Continue to go back to the initial window pressing Main Menu or to adjust the dimensional units pressing Edit Dimensional Units button Choosing Edit Dimensional Units it is displayed the following GUI 37 Dimensional units for the domains Sliced Set dimensional units X Axis Y Axis All axes are in eruit Fig 4 33 Scaling of domain s dataset GUI summary In the top four popup menus it can be specified the dimensional unit for each axis of the two domains The dimensional units can be chosen among per unit use this for not dimensional data m cm
30. ataset 27 Fig 3 10 Windows for selecting the GDM weighting in combined analysis 3 4 2 1 K weighting factor After the GDM weighting procedures FSV requests to insert one or more Kmagnitude Kphase Weighting factors see section 1 2 The user can choose the value of the K factors by means of a GUI dialog K weighting factor m uj gt x Enter a value beetwen O and 1 in the windows or move the slider I maanitude K phase K values added Add F Continue Analysis Fig 3 11 K weighting factor choice GUI it can be directly inserted the value of Kmagnitude Or Kpnase and automatically the other one is computed according to 1 15 The default value for Kmagnitude and Kphase 18 0 5 Press Add K button to add a new pair of K weighting factors 19 K weighting factor E Jn Ed Enter a value beetvven O and 1 in the windows or move the slider I maanitude I K phase A 1 Kvalues added Add Continue Analysis Fig 3 12 One value of K weighting factor added At least one pair of K weighting factors must be entered in order to continue analysis then the Continue Analysis button is enabled The counter in the bottom left orange box gives the number of K weighting factors added K weighting factor m s Enter a value beetven and 1 in the windows or move the slider K magnitude K phase J C C C 2 K values added Add Continue Analysis Fig 3 13 Two value
31. component 2 of second combined set 35 Select Combined Phase Domain 2 MATLAB F5V ASCII E Lj Cerca In 4 combined dada ka Data 1 _Maqniturle peaks Exkt E Data 2 Phaseripeaks axisYmirrared t E Data_1 Magnitude domain txt E Data z Phase damain ExE E Data 1 Phaserpeaks z Ext z Data 1 Phase domain txt z Data 2 _Maqgniturle peaks Exkt E Data _z Magnitude domain Exk lt lil Home file ID ala 2 Phase peaks axigrmirored txt Tipo file xl T Annulla Fig 4 29 Window for data files selection 4 4 8 Invalid input data loading e When input data files are not entered in the correct order in the case of Domain data file structure or the file selected contain invalid data or structure an error dialog 1s displayed 2D FSV 2 0 4 ERROR m fx INVALID INPUT DATASET Error using gt D atab aad svascil IH VALID DOMAIN FILE SELECTED GK i Fig 4 30 Error window for invalid input data loading e When in the combined analysis the domain of each Magnitude and Phase parts of a given combined set is not identical in every sample this error dialog 1s displayed 2D FSV 2 0 3 ERROR SiS INVALID INPUT DATASET Error using gt Interpolation Combined domain mismatch Fig 4 31 Error window for Combined domain mismatch After an error data loading occurs FSV stop the current task and returns back to the Main menu 3 1 36 4 9 Summary GUI At the end of the d
32. d be read by spreadsheet Plot Results Read Me programs i e WordPad to not altering the Fig 3 1 Initial window Main menu The major functions are Select the analysis mode in the ANALYSIS selection box Run FSV run an 2D FSV analysis see Chapter 3 Quit exit the program Plot Results display results of previous analysis without running a new one see Section 5 3 Read Me display this document License display the license dialog GUI Chapter poA sl s m To run the first analysis the user has to select an analysis mode by using the radio button in the ANALYSIS selection box 14 2D FSV 2 0 5L FSv PROCEDURE ANALYSIS Uq EMC Laboratory DMU Applied Electromagnetic Group UMR EMC Laboratory O FREQUENCY DOMAIN HOTE TO THE USER Run FSv All the figures fig and ASCII files txt are generated in the working directory The figures are not displayed on the screen ASCII files should be read by spreadsheet Plot Results Read Me programs i e WordPad to not altering the tabulated format License Fig 3 2 Selecting the Time Domain analysis e TIME DOMAIN time domain analysis is performed on two set of data that are generic Amplitude values e FREQUENCY DOMAIN frequency domain analysis is performed In this case FSV requests the type of frequency analysis to perform by the following dialog window 2D FSV 2 0 2 c fX Frequency domain analysis Magnitude Fig 3 3 Frequency analysis
33. d chart Grade Spread Chart 2 threshold 85 Kadm 1 Kfdm 1 r Grade Spread Chart Select chart Folder 1 chart Points to plot ADM FDM GDM Grade Spread thereshold Threshold B Spread Export Figure Fig 5 13 Selecting Grade Spread chart of Folder 2 data 6 Input Data Structure 6 1 MATLAB FSV ASCII format 6 1 1 Introduction The aim of this section 1s to describe the MATLAB FSV ASCII format of the input data for the use of FSV 2D from ver 2 0 2 on and to show how to generate this format by using MATLAB Two cases are considered e Case 1 the function z f x y is computed directly in MATLAB e Case 2 function z f x y is generated computed measured etc outside MATLAB In both cases MATLAB is used to recast the data In a proper format for input to FSV 2D as described in Section 6 1 4 5 6 1 2 Case 1 Set of data computed directly in MATLAB Using MATLAB generate a surface z z f x y 6 1 The first task 1s to define the range of the domain along the x and y directions and then to compute f x y at these points Three steps are required 1 Set the limit values for the x axis Xmin Xmax and the y axis Ymin Ymax Then define the incremental steps for x and y as Ax and Ay respectively 2 Generate the x and y vectors that represent the domain of the z f x y function X axis Matlab Code X Xa AX XS 94 y axis
34. de Spread data in MAT format 7 3 Non combined data 61 In this case the output folder appears like in Fig 7 1 and the Grade Spread subdirectories like in Fig TZ 7 3 Combined data For the Combined analysis the output data are saved in subdirectories of the Frequency Domain Analysis directory named with the value of the Kinagnitude parameter used At the top level of the output folder are saved the data files of the two components forming the combined set as described in Section 7 1 except the Grade Spread subdirectories that are saved in relative K subdirectories Cik Factor 0 1 EK Factor 0 5 EK Factor 0 8 Z Combined Domain Exk E Combined InputPaths Ext Magnitude ADM average txt Magnitude ADMc Ext Magnitude ADMi Ext Z Magnitude OC inpukdatal txt Magnitude DC inputdataz txt Magnitude FOM average txt Magnitude_FDMc txt E Magnitude FDMi Ext Magnitude GDM average txt Magnitude_GDMc txt Magnitude_GDMi txt E Magnitude High inputdata1 Ext Magnitude High inpukdabaz txt Magnitude inputdatal txt Z Magnitude inputdataz txt Magnitude Low inputdatal txt Magnitude Low inpubtdata txt Magnitude_ODMi txt Phase ADM average Ext E Phase ADIMc Ext E Phase ADMi Ext Z Phase DC _inputdatal Ext Z Phase DC inpukdabaz txt Phase FDM average txt Phase FDMc Ext Phase FDMi Ext E Phase GDM average Ext E P
35. dow the transformed data to separate out the lower and higher portions The high and low portions are then inverse transformed back into the original domain Combinations of these filtered data sets and their derivatives are used to compute the Amplitude Difference Measure ADM and the Feature Difference Measure FDM which can be combined into the Global Difference Measure GDM More specifically the procedure 1s l Read data obtain the overlap surface window and interpolate the data if necessary in the overlap region to ensure coincident pairs of data points This ensures that the two data sets to be compared have the same number of data points located at the same positions on the independent x y axes 2 2D Fourier Transform both data sets Depending on the number of samples a Fast or Discrete two dimensional transformation 1s used 3 Calculate the low data sets using the transformed data a Ignore the circle with a radius of four data points in the transformed set in order to avoid DC and very low frequency components and sum the intensities of the remaining data This assumes that the transform has resulted in DC at the centre point b Obtain a 40 location by summing the data from the annulus with an initial radius of five points 1 e ignoring the first four near DC data as in 3a until the total reaches 40 of the total value calculated in step 3a The 40 location used by the FSV is 2 the lowest of the t
36. e Structure Determination 1977 Surface Science 62 pp 61 80 van Hove MA Tong SY and Elconin MH Surface Structure Refinements of 2H MoS 2H NbSe and W 100 p 2x1 O via new Reliability Factors for Surface Crystallography 1977 Surface Science 64 1 pp 85 95 A Martin 1999 Feature Selective Validation Thesis for Doctor of Philosophy De Montfort University Leicester G Antonini A Ciccomancini Scogna A Orlandi C Ritota A Duffy Applications of FSV to EMC and SI data IEEE Int Symp on EMC Chicago 2005 B Archambeault S Connor Comparing FSV and Human Responses to Data Comparisons IEEE Int Symp on EMC Chicago 2005 AJM Martin A Ruddle A Duffy Comparison of Measured and Computed Local Electric Field Distributions due to Vehicle Mounted Antennas using 2D FSV A Duffy A Martin G Antonini A Orlandi and C Ritota The feature selective validation FSV method in Proc of IEEE 2005 EMC Int Symp Chicago USA 8 12 August 2005 B Archambeault and S Connor Comparing FSV and human responses to data comparisons in Proc of IEEE 2005 EMC Int Symp Chicago USA 8 12 August 2005 69
37. e and range The SPREAD is computed by taking the number of classes starting from the most populated to the lowest one which includes a user defined amount named threshold and set at 85 by default of the total samples of the data sets to be compared SPREAD ranges from 1 best quality to 6 worst quality The SPREAD range is given by considering the range of the classes included in the SPREAD value computation Plot the GRADE SPREAD chart Each figure of merit ADM FDM GDM has associated with it a GRADE SPREAD pair This pair is plotted on the GRADE SPREAD plane named GRADE SPREAD chart Fig 1 2 The chart has colored regions different colors indicate different quality of the comparisons 10 Grade Spread Chart 1 threshold 85 Kkadm 1 HKfdm 1 Grade Spread Chart Points to plot ADM FEM GDM Grade Grade Spread thereshold Threshold 1 2 3 4 5 B Spread Export Figure Fig 1 2 The GRADE SPREAD coloured chart 20 Compute kApy cm and krpm cm In forming GDM in 1 11 the relative weight of ADM and FDM depends on their level of confidence or reliability Higher is the reliability that 1s to say smaller is the value of SPREAD greater is the relative weight of one of them in GDM The following algorithm is used to compute the weighting factor kapu and krpy in 1 11 If Spread Spread then E ow n m Spread Else if Spread gt Spread Then i ow B 1 K S
38. ed by weighting the number of samples of the point by point corresponding variables falling in the six classes of Table 1 1 with the associated weight of the visual six points scale in the same Table xDM V amp EX 2 VG 3 G 4 F 5 P 6 EP withx AF G 1 12 conf where is the number of elements belonging to a class The total value 1s then normalized to the length of the Low High array 17 Determine the equivalent visual scale values ADM FDM and GDM The FSV values can be scaled to the Visual six point scale in Table 1 1 The piece wise visual conversion for this is given in Table 1 2 where y is ADM n FDM t or GDM ot Table 1 2 Piece wise visual conversion If y gt 1 6 Then V 6 The piece wise conversion approach is represented by the graph in Fig 1 1 Visual Rating 17 18 19 Visual Rating EP Range of the classes X A F G D Mtot Fig 1 1 Piece wise visual conversion graph Determine the GRADE value and range The GRADE value is computed by taking the number of classes starting from the best Excellent to the worst Extremely Poor which include a user defined amount named threshold and set at 85 by default of the total samples of the data sets to be compared GRADE value ranges from 1 best quality to 6 worst quality The GRADE range 1s given by considering the range of the classes included in the GRADE value computation Determine the SPREAD valu
39. elegend Hide synthetic figures Hidelegend Hide synthetic figures Fig 8 3 An example of ADMc and FDMc The values of xDMc are used to compute the GRADE and SPREAD values and displayed in form of the GRADE SPREAD chart 2D FSV 2 0 4 Results Grade Spread Chart 1 threshold 85 Kadm 1 Kfdm 1 Grade Spread Chart Points to plot ADM FDM GDM Grade Spread thereshold Threshold 4 85 3 B Spread Export Figure Fig 8 4 Grade Spread chart 65 9 Examples In the directory data examples there are sets of data two files for each set that can be used for the FSV algorithm by means of the FSV Tool 10 License This software 1s distributed with a trial period of 30 days at the end of this the user can request a license with a time limited or unlimited validity to continue the use of the software By clicking on License button in Main menu or when the trial license period is expired 1s displayed the GUI of Fig 10 1 FSV license FSW License Ta obtain an FSV license please send Tour Hame Organization The Authorization Code that are displayed below to orlandiging univag it Authorization Code Remaining trial days 27 Fig 10 1 License GUI dialog In this GUI 1s shown a remaining trial days and the instructions to obtain an valid license When the trial time is expired is displayed this error message at FSV start
40. ettings of Template Based PostProcessing Now from Template Based PostProcessing GUI run the process pressing Evaluate button When the calculation ends back to Settings GUI and press Datafile button to get the CST ASCII data file Fig 6 5 Save this with dat or txt extensions Now the data is ready to be used in FSV 2D tool 58 4659E 02 4459E 02 4459E 02 4659E 02 4459E 02 4459E 02 4459E 02 4659E 02 44659E 0 2 4459E 02 4659E 02 4459E 0 4459E 02 44659E 02 4659E 02 4459E 02 446359E 02 465 9E 02 4459E 02 4459E 02 4459E 02 4659E 02 4459E 02 4459E 02 4659E 02 44359E 02 4859E 02 4559E 02 401 E 02 3694 EF OZ 33 72E 2 13 0 5 2 28E 02 2405E 02 208S5E 02 L f6LE 02 1435E r 0 111 E 02 Of94E 02 04 2E 02 101 5 42 8E 01 3205 5E UT l1833E 01 S611E 01 338BE UT 216 7E 01 393434 E 01 3722 E 1l1 2300ETUT 92 78E 1 6056E 01 2455E 01 9611E 01 E Prova em 20070703121812 PM dat Blocco note el33E U6 234 0E 06 232 5E 06 2 llE u06 2496E 06 305 8E 5 32l14E 5 3388E U6 3822E 5 3853E 05 3 55E U6 3879E 06 3992E 06 Al 4E 05 4195E 06 4252E 5 4512E 06 45 71E 06 4450E 06 4489E 06 4502E 06 4505E 06 4509E 06 4512E 05 4516E 06 4519E 06 4523E 06 PERPRBBRBBBBBBEBBBBBBBBEBEBEEBEBEEFR sGdlEg 0s 3G4 E 03 384 E 03 sGdlEg 0s 3G4 E 03 3G4 E 03
41. hase GDIMc Ext E Phase GDMi Ext Phase High _inpuktdakal txt s Phase High inpukdataz Ext Phase inputdatal txt Phase inputdataz txt E Phase Low inputdatal Ext s Phase Low inputdataz txt E Phase ODMi Ext Magnitude Analysis mat Phase Analysis mat 4 Magnitude_ADMc fig 0 Magnitude_ADMi fig 0 Magnitude DC inputdata fig Wi Magnitude FDIMc Fig 4 Magnitude _FOMi fig 0 Magnitude_GDMc fig d Magnitude GDMi fig 4 Maanitude High inputdata fig 4 Magnitude inputdata Fig 0 Magnitude_Low_inputdata fig 0 Magnitude_ODMi fig 0 Phase_ADMCc fig 4 Phase_ADMi Fig Phase_DC_inputdata fig 0 Phase_FDMc fig 4 Phase_FDMi fig 0 Phase_GDMc fig 0 Phase_GDMi Fig A Phase High inputdata Fig A Phase inputdata fig Phase_Low_inputdata Fig 0 Phase_ODMi Fig Fig 7 3 Output data folder for Frequency Domain Combined analysis Combined_domain contains the input domain after synchronization between all the four sets The K_factor x subdirectories have this structure x is the Kmagnitude Value 62 jGsthrs 85 Combined ADM _ average txt E Combined ADMc Ext E Combined ADMi Ext z Combined FOM average txt E Combined _FDMc txt Combined FDMi Ext z Combined GDM average txt E Combined GDMc Ext E Combined GDMi Ext l Combined Analysis mat Wi Combined ADMc Fig Combined ADMi Fig a Combined FOMc Fig Combined
42. is Selecting per unit on one axis causes the other to be automatically set on per unit Deselecting per unit on one axis causes the other to be automatically set on the default settings cm Moreover selecting per unit for set 1 the set 2 is automatically set as per unit because one cannot logically compare a not dimensional domain with one with the axes in some units in this case the Dimensional units for the domain GUI is not displayed for the second set FSV 2D does not accept the comparison between a not dimensional domain and a dimensional one 23 Dimensional units for the domain Eok Set dimensional units X AXIS Y AXIS Domain 1 fan Dimensional units of Domain 2 are automatically set as per unit Fig 4 6 Dimensional units for the domain GUI set per unit Note that for EZ FDTD data loading the dimensional units of the domain are directly set in the EZ FDTD data import GUI 4 3 Loading MATLAB FSV ASCII format This format can be used when data coming from different sources have to be compared Both data are converted in this MATLAB format and then they are imported into FSV In this format each set 1s given by two files data file and domain file see Section 6 1 Then for each set of data it must be loaded two file first domain file and then data file An example of loading one MATLAB FSV ASCII set is 1 Select the first file 1 e Data 1 domain txt c
43. ivaq it 67 For downloading the most recent version please frequently check the UAq EMC Laboratory FTP site at http ing univaq it uaqemc More information on the FSV project can be found at http www eng cse dmu ac uk FS Vweb 12 Acknowledgement A special thanks to Ing Carlo Polisini Ing Carmine Ritota Ing Franco Campitelli and of the UAq EMC Laboratory for their substantial contribution to this project 68 13 Selected FSV Validation Bibliography 10 11 12 13 14 Johnson J and Picton P Concepts in Artificial Intelligence Vol II Butterworth Heinneman London Hilsenrath OA and Zeevi YY 1990 Feature Extraction and Sensitivity Matching in Visual search in Man and Machine in Brogan D ed Visual Search Taylor and Francis London Koffa K 1935 Principles of Gestalt Psychology Kegan Paul London Cook WM 1931 Ability of Children in Colour Discrimination Child Development 2 pp 303 Duffy AP Woolfson MS and Benson TM 1994 The use of Correlation Functions to Assist the Experimental Validation of Numerical Modelling Techniques Microwave and Optical Technology Letters 7 8 pp 361 364 Woolfson MS Benson TM Christopoulos C and Duffy AP 1995 Quantitative Assessment of the Comparison of Electromagnetic Calculations with Experimental Data Applied Computational Electromagnetics Society Newsletter 1 1 pp 34 39 Zanazzi E and Jona F Reliability Factor for Surfac
44. m the initial window The Data Display Tool is an application embedded in the FSV Tool that allows the user to visualize the relevant FSV variables associated to the comparison of two datasets The Data Display Tool 45 plots the data contained in the mat files generated by the FSV procedure It can be started from the initial window by selecting the Plot Results Fig 3 1 option without running an FSV analysis 2D FSV 2 0 4 Results Data Display 0 6 0 8 1 0 6 0 8 1 Edit Fiqure Export Figure Edit Figure Export Figure Time Domain Analysis Time Domain Analysis Load data Folder 1 Folder 2 Hidelegend Hide synthetic figures Hidelegend Hide synthetic figures Fig 5 6 The Data Display Tool window before selecting the directory in which the mat files are contained After the initial window of the Data Display Tool appears on the screen select the folders directories containing the FSV Tool output data to be displayed They are contained in mat files The User can select a directory for Folder 1 and a different directory for Folder 2 by pressing the corresponding button The selected plots will appear in the corresponding windows left side for Folder 1 right side for Folder 2 In this way it 1s possible to load and visualize two different FSV e non combined analysis select the automatically generated subdirectory inside the selected directory
45. menu o Magnitude the data input are magnitude values Phase the data input are phase values o Combined the 2D FSV combined analysis performs two FSV analysis magnitude and phase and combines them in a unique result O Note though in the TIME DOMAIN Amplitude analysis and in the FREQUENCY DOMAIN magnitude and phase analysis 2D FSV applies an identical algorithm the tool requests to specify the 15 nature of the input data to be specified in order to use an appropriate terminology and graphic presentation 3 2 Output data folder Push the button Run FSV Now FSV Tool requests the directory to select where the output data files will be saved Sfoglia per cartelle Select Folder For OLITPLIT Data Desktop zJ El Documenti E 4 Risorse del computer w Disco locale C E3 a Unita DVD RAM D 5 Disco locale E 4 Set Disco locale F E GA Unit DVD lt Documenti condivisi C Documenti carlo re s LES hl Crea nuova cartella Annulla Fig 3 4 Window for selecting the destination directory In the selected destination directory the FSV Tool will create a subdirectory named Time Domain Analysis x or Frequency Domain Analysis x In which the output files will be stored see Chapter 7 x is an incremental number to distinguish the subdirectories 3 3 Input data loading After that FSV requests the input data files to analyze according the analysis m
46. mm um nm ft inch mils Selecting per unit on one axis causes the other three are automatically set on per unit Deselecting per unit on one axis causes the other three are automatically set on default settings ee 99 cm Checking the option All axis are In the above menus are disabled and the bottom popup menu is enabled Here it can be set the same dimensional unit for all the two domains Dimensional units for the domains L3 Set dimensional units X AXIS Y Axis Allaxes arein ID Fig 4 34 Scaling of domain s dataset GUI All axes When done click Apply to save the changes or Discard to ignore the changes Combined analysis summary For the combined data analysis is displayed an expanded version of summary GUI 38 0 FSV Inputs Data Summary Input Data Summary SET 1 FORMAT CST ASCII DOMAIN FILE cst surface 2 dat DATA FILE cst surface 2 dat X AXIS cem Y AXIS om SET 2 FORMAT MatLab FS ASC DOMAIN FILE Data 1 domain txt DATA FILE Data 1 peaks txt X AIS cm Y AXIS cm SET 3 FORMAT CST ASTI DOMAIN FILE catsurface_1 dat DATA FILE cstzurface 1 dat X AXIS em Y AXIS om SET 4 FORMAT Matlab FS ASEII DOMAIN FILE Data 2 domain txt DATA FILE Data 2 peaks txt X ANIS cm Y AMIS cm Edit Dimensional Units lf the data are correct press Continue otherwise press Main Menu to abort the analysis and qo back to the initial window Fig 4 35 Summary
47. n the following equation 1 1 This point by point variable 1s also abbreviated as ADM ADM i j ADM i j o ODM i j e OMG 1 1 where CAORACH ADM sa i J NM d bs 1 2 MN gt gt i j Lo i pJ DC G j DC G j ooma p Potes aa PEETRE Cm and cmi are weighting coefficients set equal to 1 1 4 ODM 1 J represents the contribution to ADM 1 of the difference of offset between the two original signals 7 Calculate the single value of ADM A mean value of the ADM xi y ADM 1 gives an overall single figure goodness of fit It is obtained from the following equation ADM i j Ms N j l i 1 5 _ I ADM MN Note a median value rather than a mean value has demonstrated some improvements in agreement with visual interpretations for 1D data although this has yet to be determined for 2D Calculate the ADM confidence histogram The range of values for the ADM and in fact the FDM and GDM can be divided into six categories each with a natural language descriptor Excellent Very Good Good Fair Poor And Very Poor These are the terms that are most often used in descriptions of the quality of comparisons The confidence histogram like a probability density function provides some intelligence as to how much emphasis can be placed on the single figure of merit There is some evidence to show that this mirrors the overall group assessment of any data pai
48. ned data The FSV Tool performs its calculations in the background 40 2D FSV 2 0 3 Elaborating data 1 of 1 40 0 00 18 Po Started at 17 18 18 cpu time used 0 00 07 Fig 3 6 2D FSV progress bar Once this has been completed the user is prompted to use or decline the GDM weighting procedure outlined in point 20 of the Section 1 1 or not 17 Compute k ADM amp k FDM x Do you want GDM weighting Fig 3 7 Window for selecting the GDM weighting At the end of the calculation a new question 1s issued as in the next figure 2D FSV Analysis Ended ej Do you want Fig 3 8 Window at the end of a complete 2D FSV analysis of a pair of data sets If it is answered Load New Data then the 2D FSV Tool back to Initial Window Fig 3 1 and is prepared for a new analysis If it is answered View Results the figures of the results are shown see Section 5 2 3 4 2 Combined data The FSV Tool performs its calculations in background 35 2D FSV 2 0 3 Elaborating data 1 of 2 35 0 01 04 EN w Started at 8 18 43 cpu time used 0 00 05 Fig 3 9 Progress bar of combined analysis In the case of combined analysis for each component comparison FSV calculates ADM FDM and GDM separately and for each one Is issued to use the GDM weighting procedure 18 Compute K ADM amp k_FDM aR Compute K ADM amp k_FDM Saks Do vou want GDM weighting for Dataset 17 Do you want GDM weighting for D
49. ode chosen n initial window FSV analyzes two set of data at a time in a normal computation and four set of combined data 1 e a pair of Magnitude Phase or real imaginary set of data in a combined analysis TIME DOMAIN e Amplitude Two set of data at a time are analyzed Amplitude 1 Amplitude 2 FREQUENCY DOMAIN e Magnitude two set of data at a time are analyzed Magnitude 1 Magnitude 2 e Phase two set of data at a time are analyzed Phase I Phase 2 e Combined four set of data at time are analyzed at a time Each dataset is given by two components Magnitude and Phase Magnitude 1 and Phase 1 Magnitude 2 and Phase 2 16 As described in section 1 2 2D FSV performs the comparison between the corresponding component 1 Magnitude 1 Magnitude 2 2 Phase 1 Phase 2 For each set of data FSV requests to specify the input format of the datasets by the GUI of Fig 3 5 At the present time they are three MATLAB FSV ASCII CST ASCII from CST SUITE 2008 in www cst com and EZ FDTD a 3D full wave FDTD code from www ems plus com see Chapter 6 2D FSV 2 0 4 2D FSV 2 0 4 Select format far set 1 Select format Tor set 2 Matlab FSW ASCII Matlab FSW ASCII CST ASCII CST ASCII EZ FDTD EZ FDTD Fig 3 5 GUIs select input format for set 1 left and set 2 right See Chapter 4 for detailed data loading procedure to follow for each analysis mode 3 4 2D FSV Computation 3 4 1 Non combi
50. of K weighting factor added When all K are inserted 1s possible to run the analysis for each K by pressing Continue Analysis button 62 2D FSV 2 0 3 K weighting 1 of 2 bo 0 00 14 F _ Started at 10 02 40 cpu time used 0 00 06 Fig 3 14 Progress bar of K weighting procedure The GUI prevents the insertion of a pair of K weighting factors that have already been added or where invalid values of K are being entered 0 Kimagnitude Kphase lt 1 and displays these warnings 20 Warning ej Warning A K value already present A K must be K 21 Fig 3 15 K Warning Dialogs At the end of the calculations a new prompt is issued as in the next figure 2D FSV Analysis Ended EIL Do you want Fig 3 16 Window at the end of a complete 2D FSV analysis of a pair of data sets If it is answered Load New Data then the 2D FSV Tool back to Initial Window Fig 3 1 and 1s prepared for a new analysis If it is answered View Results the figures of the results are shown see Section 5 2 4 Input Data Loading The current 2D FSV release take can be chosen from MATLAB FSV ASCII CST ASCII and EZ FDTD formats For the data structure of the input files see Chapter 6 In this examples we assume that input data are stored in FSV Matlab 3D structure Then each set is given by two files data file and domain file 4 1 Domain and data requirements Consider a dataset with a domain on x and y a
51. older Fig 5 9 Data Display Tool warning 5 3 1 Data Display In the following example a Time Domain analysis In Folder 1 and a combined Frequency Domain analysis in Folder 2 are shown Fig 5 10 Input Dataset 1 Folder 1 File Edit View Insert Tools Desktop Window Help zS h 8Qre9 4 08 ao Amplitude Data 1 Amplitude Data 2 J JAWI n m m Pai d A ie E jum o Dre a ua n a gt AWA d MAE Me k m Input Dataset 1 Folder 2 Input Dataset 2 Folder 2 File Edit View Insert Tools Desktop Window Help File Edit View Insert Tools Desktop Window Help cde aeu ao Das heana 2 08 s I Magnitude Data 1 s Magnitude Data 2 Phase Data 1 Phase Data 2 45 2D FSV 2 0 4 Results Data display Grade Spread chart Data Display Amplitude ADMi Excellent 0 0 1 Very Good 0 1 0 2 Good 0 2 0 4 1 Fair 0 4 0 8 Poor 0 8 1 6 Very Poor 1 6 inf Magnitude Phase iss RR es Hous VR M D E Edit Figure Export Figure Time Domain Analysis ADMI FDMi C GDMi Hide legend ADMc Load data O FDMc O Folder 1 Folder 2 GDMc Hide synthetic figures Excellent 0 0 1 Very Good 0 1 0 2 1 Good 0 2 0 4 Fair 0 4 0 8 gt Poor 0 8 1 6 i Very Poor 1 6 inf V S 1 he EE LP li MEUS jM ow prd e r at t TR i rs ei JR pi M i 2e E wn b N u d ME ia een i x E MUNA
52. ompared An example of ADMi and FDMi is shown in Fig 8 2 2D FSV 2 0 4 Results Data display Grade Spread chart Excellent 0 0 1 B Excellent 0 0 1 Very Good 0 1 0 2 io Very Good 0 1 0 2 1 Good 0 2 0 4 Pe Good 0 2 0 4 Fair 0 4 0 8 f PEU tees air 0 4 0 8 i Poor 0 8 1 6 15 Aa IE i J Poor 0 8 1 6 Hery Poor 1 6 inf ke CAE s TIN 7 1 Wery Poor 1 6 inf 7 EN PI CX NS icm 9 r Data Display Amplitude Amplitude 43 l W AMT M yl Y wl MI M ras K Lies e E M y ji p we SUN 2 2 X X Edit Figure Export Figure Edit Figure Export Figure Time Domain Analysis Time Domain Analysis ADMi ADMc DM O ADMcC FDMc O FDMc C GDMi C GDMc i C GDMc Hidelegend Hide synthetic figures Hidelegend Hide synthetic figures Fig 8 2 An example of ADMi and FDMi e xDMc percentage of points in x A F G DM1 that fall in each of the six classes of Table 1 1 described in Chapter 1 see Fig 8 3 64 2D FSV 2 0 4 Results Main menu Data display Grade Spread chart ADMc Data Display m At Ae or ADMtot 0 70116 ADMconf 3 60891 ADMpw 4 75540 FDMtot 0 20505 FDMconf 2 20033 FDMpw 3 03026 Edit Figure Export Figure Time Domain Analysis Time Domain Analysis ADMI ADMc ADMI C FDMi Oro QGEDWG O GDMi GDMc C GDMi Hid
53. on of the variables described in the previous points They are described in the next Chapter Based on these figures of merit the comparison of two data sets can be ranked The ranking useful for making a selection between multiple comparisons 1s given by considering two quality factors for each figure of merit The GRADE and the SPREAD e The GRADE is a direct indication of the quality of the comparison The smaller it 1s the better the comparison e The SPREAD indicates the level of reliability of the outputs The smaller it 1s the higher 1s the reliability of the results e GRADE and SPREAD are computed for each figure of merit ADM FDM GDM and reported on a GRADE SPREAD chart 1 1 The Two Dimensional Feature Selective Validation 2D FSV method The structure of the 2D FSV involves reading the two data files to be compared and interpolating them over common domain so that the data points to be compared are coincident This approach ensures that like is being compared with like and will not affect the overall results unless the data are severely under sampled It must be remembered that the purpose of the FSV is to mimic a visual comparison and so long as any interpolation does not produce visually different results this approach is perfectly acceptable The actual comparison is based on decomposing the original data into trend and feature information This Is done by applying 2D Fourier Transform to the data and to win
54. ontaining the domain set as in Fig 4 7 Select Amplitude Domain 1 MATLAB FSV ASCII Cerca In data examples amp t E Data a_lipedks txt Data 4 peaks axis mirrored txt 2 are E Data 4 domain txt Data METERS txt Data Sr peaks z Y txt Data _2 domain bck Z Data 5 domain txt B Data 3ipeaks z Ext readme domain structure txt z Data 3 domain txt Mome file ID ata 1 domain tl Tipo file wl Annulla Fig 4 7 Window for domain files selection 2 Select the second file 1 e Data 1 peaks txt containing the data set as in Fig 4 8 24 Select Amplitude Data 1 MATLAB FSV ASCII Cerca in 3 data examples d Data l peaks ExE Z Data 4 peaks axisvmirrored Exk Data 1 domain txt s Data 4 domain txt E Data zipeaks ExE E Data _Sipeaks zi txt Data z domain bk Data 5 domain txt Data _aipeaks z txt Z readme domain structure txt E Data 3 domain bk Marne file ID ala 1 peaks bet Tipo file bet Annulla Fig 4 8 Window for data files selection Now it is requested to specify the dimensional units for the X axis and Y axis of the loaded domain by means of the Dimensional units for the domain GUI see Fig 4 9 More info on this window will be given in Section 4 2 Dimensional units for the domain Si 3 Set dimensional units X Axis Domain 1 cm Fig 4 9 Dimensional units for the domain GUI
55. ovie file 26 Select Amplitude Data Model 1 EZ FDTD Document recenti 4 Desktop Document Risorse del computer Risorse direte Mome file dual micrastiip 2 KMF Tipo file kmf m Annulla Fig 4 12 EZ FDTD model file loading 2 Select the movie file 1 e MVXY12EZ CSV containing the sequence of frames coming from the EZ FDTD analysis Select Amplitude Data 1 EZ FDTD Cerca in B EZ FDTD data Documenti recenti Desktop Documenti Risorse del computer Hisorse direte Mame file IMVATT2EZ CSV Tipa File ony Annulla Fig 4 13 EZ FDTD movie data file loading 3 Then it 1s requested to specify which frame has to be loaded among the total number of frames that is automatically evaluated Fig 4 14 green box and displayed to the User from the movie file and the dimensions of each elementary cell Ax and Ay of the frame The following GUI 1s displayed 27 EZ FDTD data import E mE Input the following information The number af frames in the movie file iz Enter the number af the frame to load 1 nr 102 Enter the dimensions Ax and Ay of the elementary cell they can be read in the input file Fig 4 14 EZ FDTD data import GUI In this GUI the User should specify the number of the frame that want to import and the dimensions Ax and Ay As in the Dimensional units for the domain GUI see Section 4 2 the dimensional units can be chosen
56. p 2D FSV 2 0 4 Results Data display Grade Spread chart Data Display Amplitude Data 2 a x ho ia od i ah 1 v 1 0 9 0 8 0 7 0 6 0 5 0 4 0 3 0 2 0 6 0 8 1 Edit Figure Export Figure Time Domain Analysis Time Domain Analysis C ADMi O ADMcC FDMi FDMc O 3 O GDMi O GDMc Hidelegend Hide synthetic figures Hide legend Hide synthetic figures 0 6 0 8 Edit Figure Export Figure Fig 5 1 The original data window and main Data Display Tool window In the main window of the Results GUI there are two drawing areas 4 1 e Drawing box area in which the graph are displayed e Export Figure button take a screenshot of the currently graph displayed The displayed graph is exported in PNG format The figure is saved in a file located in the subdirectory inside the selected directory for output data with the name of the displayed FSV variable e Edit Figure button open the current graph displayed In drawing box in a new window Fig 5 2 with the full figure toolbar to inspect and analyze the figure e Selection box select the graph to display in corresponding drawing box from ADMi FDMi GDMi ADMc FDMc and GDM c radio buttons Hide legend checkbox hide the legend textbox In the ADMi FDMi and GDMi graphs Hide synthetic figures checkbox hide the annotation textboxes in the ADMc FDMc and GDMc graphs eles File Edit View Insert Tool
57. pread Spread Else Kni K 1 1 2 The Combined Analysis for complex values In the FSV 2D analysis the results of the comparisons of Magnitude and Phase can be combined and weighted This is the so called combined analysis FSV treats magnitude and phase parts of the data to be compared separately throughout and recombines them at the end This way is as close a similarity as possible to the manner in which engineers would approach the analysis of the magnitude phase data In a similar manner to the way 11 visual decomposition into amplitude and feature comparisons are combined into an overall conclusion the magnitude and phase parts are considered separately and then weighted in the process of forming an overall opinion The FSV analysis 1s performed as in points 1 to 11 of the previous section on the magnitude and phase parts separately and recombined on a point by point x y base by using ADM and FDM functions through the K weighting factors according to ADM barbie s y T ADM nina y E IS ase ADM ase x y 1 l 3 FDM combined X gt y K FDM ona X y K FDM pase x y 1 14 magnitude phase The K factors ranges from 0 to 1 they are related by the following constrain K I nognie 1 l 5 phase The GDM will combine the ADM and FDM without the inclusion of a separate weighting factor this has already been included in 1 13 and 1 14 GDM mw ez y V ADM une y dr
58. r by a number of engineers The determination of the histogram is simply a case of counting the proportion of points that fall into one of the categories according to the rule base in Table 1 1 Table 1 1 FSV INTERPRETATION SCALE quantitative qualitative point scale I Calculate derivatives in preparation for the FDM calculation The following components need to be calculated The first derivatives of the Lo x y and Hi x y data sets and the second derivatives of the Hi x y data sets The derivatives accentuate the high rate of change features In the original data and differences based on the derivatives are combined in the determination of the FDM In 2D case the first and second derivatives are implemented in the following way ios ane aan 9 Hi 6 Hi x y 1 6 ne eS 4 Pm Ox Oy iij We apply the Forward FW and Backward BW Euler schemes for the computation of the derivatives at the edges of the domain and the centered one for the computation of the derivatives inside the domain The FW Euler scheme is dLo Lo i j 1 Lo i j i J p J dLo Lo i l j Lo i j gros i Lj Lol j dy A where i represents the rows of the matrix j represents the columns and A is equal to 1 The BW Euler scheme is dLo Lo i j Lo i j 1 La i j Lo i j 1 dx A dLo Lo i j Lo i 1 j C upa i j Lo i l j dy A The centered Euler scheme is ao ae
59. s Desktop Window Help SHS hb e e 2 E DE sm A DMI m I ie i Ihe es jd ES lq Mo si MU um a a d aes d x t Fi ad sese d he NW a A AES PUE M is y 2 E zu NEST zb nm M xem pe anm p rae Fig 5 2 ADM in the Edit Figure GUI Select the radio buttons on the bottom left frame to display one variable ADMi ADMc etc After this selection the radio buttons on the opposite bottom right frame are enabled for selection of another variable to be displayed 42 2D FSV 2 0 4 Results Data display Grade Spread chart Excellent 0 0 1 Very Good 0 1 0 2 Good 0 2 0 4 4 Fair 0 4 0 8 s Poor 0 8 1 6 Very Poor 1 6 inf Data Display o 25 E x 4 Edit Fiqure Export Figure Edit Figure Export Figure Time Domain Analysis Time Domain Analysis ADMI M IN ADMc C FDMi FDMc C FDMi FDMc C GDMi C GDMc C GDMi GDMc Hidelegend Hide synthetic figures Hidelegend Hide synthetic figures Fig 5 3 Data Display Tool with graphs displayed ADMi FDMi GDMi At the left of each 3D graph ADMi FDMi GDMI a colorbar is displayed The bottom of the color scale Is associated with 0 Excellent values and the top with 1 6 Extremely poor values Then the labels of the colorbar are 0 Excellent 0 1 Very Good 0 2 Good 0 4 Fair 0 8 Poor and 1 6 Extremely Poor Moreover inside the graph a
60. s Lo i j 1 Lo i J 1 dx 2 A d a Lo i 1 j Lo i 1 j dy 2 A 10 Calculate the point by point FDM The FDM is formed from three parts based on the derivatives calculated in step 9 Lo x y Lo x y FD x y P 2 max s 1 7 vy gt Lo x y is Lo x y FD x y JHi Gsy Ho Gs JI Nto 6 E UU 1 8 N M 2 Hi x y Hi x y FD x ys AIA L2 Ng 1 9 x ap DlH 9 His Gs y 1 9 FDMi 2 FD FD FD 1 10 Being min and max the lowest and highest components x y 1n the data sets 11 Calculate the single value of FDM This 1s done in exactly the same way as for the ADM 8 12 Calculate the FDM confidence histogram This 1s done In exactly the same way as was done for the ADM 13 Obtain the point by point GDM value The GDM is premised on the ADM and FDM being largely independent which means that Jus GDM 2 K api cm nin ADM x k FDMi om FDM x 1 11 the weighting coefficients kapm cm and krpy cm as at point 20 14 Calculate the overall GDM value and the GDM confidence histogram This follows the same procedure as the ADM and FDM 15 Determine the synthetic figures of merit ADM FDM GDMm These variables are the average of the point by point values of ADM FDM and GDM respectively 16 Determine the synthetic figures of merit ADM FDM cong GDM o These values are comput
61. s should be read by spreadsheet Plot Results Read Me programs i e WordPad to not altering the tabulated format License Fig 4 17 Selecting the Time Domain analysis In the TIME DOMAIN Amplitude analysis after the Output data folder Is set FSV Tool requests the input data In this example the input data files are in MATLAB FSV ASCII format l Fig 4 18Select the directory in which your data are located For test purposes it can be used the data examples directory that is located in same folder of the main program fsv2d exe Select the first file i e Data 1 domain txt containing the first domain set as in Fig 4 18 29 Select Amplitude Domain 1 MATLAB FSV ASCII Cerca In data examples amp t Data 1ipeaks ExE E Data 1 daomain ExE Data zipeaks ExE Data 2 domain Ext Data 3r peaks z txt Data 3 domain txt Z Data 4 peaks axisvmirrored Exk s Data 4 domain txt E Data _Sipeaks zi txt Data 5 domain txt Z readme domain structure txt Mome file Data 1 domain tet Tipo file tut B Annulla Fig 4 18 Window for domain files selection 1 2 Select the second file 1 e Data 1 peaks txt containing the first set as in Fig 4 19 Select Amplitude Data 1 MATLAB FSV ASCII Cerca In data examples SiData_l peaks txt Data 1 damain ExE Data zipeaks ExE Data 2 domain Ext Data _3 peaks 23 txt Data 3
62. s three figures of merit of the comparison of two data sets e ADM Amplitude Difference Measure and FDM Feature Difference Measure These are available as numerical values and can be converted to a natural language descriptor in a six level scale excellent very good good fair poor Very Poor These combine to give the GDM e GDM Global Difference Measure An overall single figure goodness of fit between the two data sets being compared This allows a simple decision to be made about the quality of a comparison This may be numerical or converted to a natural language descriptor These figures of merit can be further represented in three different ways in order to quantify the quality of the comparison performed e GDMi ADM and FDM These are point by point comparisons of the amplitude differences the feature differences and the global differences This allows a user to analyze the resulting data in some detail probably with the aim of understanding the origin of the contributors to poor comparisons e GDM ADM FDM These give probability density functions which show the proportion of the point by point analyses of each of the components that falls into the six natural language descriptor categories This provides a measure of confidence in the single figure comparisons e GDMi ADMtot FDMis GDM r ADM r FOMcont GDMpw ADMpw FDM pw These are more synthetic figures of merits of the comparison and stem from an elaborati
63. t Hil Mome file ID ata 1 Phase domain tst Tipo file wl w Annulla Fig 4 26 Window for domain files selection 3 6 Select the sixth file i e Data 1 Phase peaks 2 txt containing the data of component 2 of first combined set 34 Select Combined Phase Data 1 MATLAB FSV ASCII Cerca In 4 combined dada ka Data 1 _Maqniturle peaks Exkt Data 2 Phaseipeaks axisYmirrared E Data 1 Magnitude domain Exk E Data z Phase damain ExE E Data 1 Phaserpeaks z Ext z Data 1 Phase damain ExE z Data 2 _Maqgniturdle peaks Exkt E Data _z Magnitude domain txt Hill Mome file Data 1 Phase peaksez tst Tipo file wl gt Annulla Fig 4 27 Window for data files selection 3 7 Select the seventh file 1 e Data 2 Phase domain txt containing the domain of component 2 of second combined set Select Combined Phase Domain 2 MATLAB F5V ASCII EJE3 Cerca In combined dada z Data 1 _Maqniturle peaks Exkt z Data 2 Phaseipeaks axisYmirrared E Data 1 Magnitude domain txt Data z Phase domain txt Data 1 _Phaserpeaks 2 Y Ext Data 1 Phase domain txt Data 2 _Maqgniturle peaks Exkt Data z Magnitude domain txt 4 Home file Data 2 Phase domain tst Tipo file wl Annulla Fig 4 28 Window for domain files selection 4 8 Select the eighth file i e Data 2 peaks axisYmirrored txt containing the data of
64. tude domain txt containing the domain of component of first combined set Select Combined Magnitude Domain 1 MATLAB FSV ASCII EJE3 Cerca In combined dada amp t Data 1 Magnikude peaks Exk Data 2 Phaserpeaks axisYmirrared E El Data 1 Magnitude domain txt E Data z Phase damain ExE Data 1 Phaserpeaks z Ext Data 1 Phase domain txt z Data 2 _Maqgniturdle peaks Exkt E Data _z Magnitude domain txt kS TIT Mome file ID ata 1 Magnitude domain tl Tipo file td Annulla Fig 4 22 Window for domain files selection 1 2 Select the second file 1 e Data 1 Magnitude peaks txt containing the data of component 1 of first combined set 32 Select Combined Magnitude Data 1 MATLAB FSV ASCII EJE3 Cerca In 4 combined dada ka Data 1 Magnikude peaks Exk z Data 2 Phaseipeaks axisYmirrared E E Data _1_Magnitude domain txt E Data z Phase damain ExE E Data 1 Phaserpeaks z Ext z Data 1 Phase damain ExE Data 2 Magnitudel peaks txt B Data z Magnitude domain txt kS TIT gt Home file Data 1_bagnitudelpeak s tst Tipo file td gt Annulla Fig 4 23 Window for data files selection 1 3 Select the third file 1 e Data 2 Magnitude domain txt containing the domain of component 1 of second combined set Select Combined Magnitude Domain 2 MATLAB FSV ASCII EJE3 Cerca In 4 combined dada ka E Data 1 Magnitude peaks Exk
65. ude inputdataz tet E Amplitude InputPaths txt Z Amplitude Low inputdatal text E Amplitude Low inputdataz txt Amplitude _ODMi Ext Fig 7 1 Output data folder for Time Domain Amplitude analysis All data files are saved in the main level of the output folder except in the combined analysis mode Grade Spread data files are saved in appropriate subdirectories their name is given by GSthrs_ x x suffix is the threshold value used The txt files are ASCII files e Analysis mode domain contains the input domain after synchronization e Analysis mode inputdata contains the input data after synchronization Analysis mode expands in Amplitude Magnitude Phase Combined x expands in A F G 60 e Analysis mode DC inputdata contains the DC Data used in the calculation of ADM FDM and GDM e Analysis mode Low inputdata contains the Low Data used in the calculation of ADM FDM and GDM e Analysis mode High inputdata contains the High Data used in the calculation of ADM FDM and GDM e Analysis mode x DMi contains the point by point values of FSV output e Analysis mode ODMi contains the point by point values of FSV output of offset components e Analysis mode x DMc contains the confidence levels of the FSV output e Analysis mode x DM average contains the A F G DMtot A F G Dmconf A F G DMpw conv values and when available the GDM weighting factors Kadm Kfdm
66. ues are placed side by side and then stored in ASCII format This file is created by the following MATLAB command that writes data into an ASCII file Matlab Code XYDomain X Y place X Y values side by side 6 17 dimwrite Domain txt XYDomain delimiter t The first argument of dlmwrite 1s the output filename the second argument is the data to be stored in the ASCII file and last argument specifies the delimiter character in this case the tabular character The second file required by FSV 2D for each one of the two surfaces to be compared 1s the data file that contains the values of the previously defined matrix Z This 1s done by the following MATLAB command line Matlab Code dimwrite Data txt Z delimiter Nt precision 6 18 a et l 55 The first argument of dlmwrite is the output filename the second argument is the data to be stored in the ASCII file the third argument specifies the delimiter character and the last argument specify the number of decimal digits The pair of ASCII files Domain txt and Data txt their names can be defined by the user define completely one surface in 3D space then for each surface to be compared FSV 2D needs a pair of ASCII files Domain txt and Data txt Repeating entire procedure for the second surface to be compared you obtain others two files Domain 2 txt and Data 2 txt At this point you can run FSV 2D and make a comparison of the two surfaces using
67. wo resulting numbers from the two original data sets A break point five data points above this is returned a value that allows a comfortable transition window between the low and the high results c Window the transformed data for both data sets by taking a linearly decreasing envelope from two points below the break point to two points above it Essentially low pass filtering the transformed data d 2D inverse transform the windowed data to give the low region data for both original data sets they are named Lo x y and Lo2 x y 4 Calculate the high data sets using the transformed data Repeat the process from 3c by applying the opposite envelope to the transformed data essentially high pass filtering it These data 1s then inverse transformed to give the high region data for both of the original data sets They are named Hi x y and Hi x y 5 Calculate the DC data sets using the transformed data The 1gnored circle with a radius of four data points in the transformed set In step 3a 1s 2D inverse transformed and give the DC region data for both of the original data sets They are named DC and DC 6 Calculate the ADM on a point by point basis Each data set has N x M points identified by the Cartesian coordinates xi yj 1 1 M and J 1 N For sake of simplicity in the next it will be used the notation xi yj ij At an arbitrary data point i j the ADM is evaluated as i
68. xes of respectively NxM points In the non combined analysis two data sets are compared first of all 2D FSV calculates the overlay window of the input data by finding lower and upper bound of the two domains If the intersection of two domains isn t empty the resulting domain values are interpolated using the minimum of the punctual steps of the two original domains But there are two checks to perform 1 Condition 1 Absolute minimum size In order to correctly build the Low and High filter the data size for each dimension is lower limited by a minimum value that depends by the DC cut off radiusThe total minimum size 1s given by 21 2 DC 12 1 If DC 4 the minimum data size for each dimension is 31 points The error messages of the GUI are JD FSV 2 0 4 ERROR Error using gt Interpolation Can t apply 2D FS an this dataset The synchronized domain has not enough points on Asie gt to define the LOHI Filter Array Fig 4 1 Error Message GUI for insufficient points on x axis 2D FSV 2 0 4 ERROR aR Error using Interpolation Can t apply 2D FS on this dataset The synchronized domain has not enough points on Asie r to define the LOHI Filter Array Fig 4 2 Error Message GUI for insufficient points on y axis 2 Condition 2 High filter realizability The breakpoint value cannot be greater than a certain value to allow the building of high filter The error message of the GUI is JD FSV 2 0 4 ERROR

2D FSV Tool User's Guide

Contents

Download Pdf Manuals

Related Search

Related Contents