Modeling Local Uncertainty accounting for Uncertainty in the Data
|
|
- Amice McLaughlin
- 5 years ago
- Views:
Transcription
1 Modelng Local Uncertanty accontng for Uncertanty n the Data Olena Babak and Clayton V Detsch Consder the problem of estmaton at an nsampled locaton sng srrondng samples The standard approach to ths problem s krgng Krgng ses the spatal correlatons provded by the varogram to calclate the weghts of the sample vales srrondng an nsampled locaton The weghts obtaned from the krgng eqatons mnmze the estmaton varance and accont for the spatal correlaton between the srrondng samples and the estmaton locaton (that s, closeness to the estmaton locaton) and between sample themselves (that s, data redndancy) Krgng reslts n optmal estmaton (n the case of a known varogram model) and provdes a model for local condtonal dstrbtons; n the Gassan framework, the krgng estmate and krgng estmaton varance are exactly the mean and varance of the local condtonal Gassan dstrbtons Oftentmes, however, the exact sample data are not known de to measrement errors In ths case smple krgng can not be drectly appled to nfer the local condtonal dstrbtons A theoretcal framework for ncorporatng data ncertanty nto calclaton of the local ncertanty dstrbtons s reqred Smple Krgng The smple krgng estmator predcts the vale of the varable of nterest at the estmaton locaton as a lnear combnaton of nearby observatons ), =,, n (, (Jornel and Hbregts, 978): ) z * ( ) ( ) = λ + λ m, () = = T where m denotes the statonary mean, λ = ( λ,, λn ) denotes the vector of the smple krgng weghts calclated from the normal system of eqatons = λ Cov( ), )) = Cov(, )), =,,, () where Cov( ), )),, =,,, denotes the data-to-data covarance vales and Cov(, )), =,,, s the data-to-estmaton pont covarance vales The covarance fncton s calclated nder statonarty throgh the semvarogram model γ (h) Smple krgng s the best lnear nbased estmator, that s, t provdes estmates wth mnmm error varance n the least sqare sense gven by = C(0) λ Cov(, )), () where C (0) s the statonary varance = In the Gassan framework the local condtonal dstrbtons are derved by smple krgng as follows Uncertanty at the estmaton locaton s z *, ) () Accontng for the Uncertanty n Data Let s assme that each of the observatons ), =,, n (, avalable for analyss was measred wth some measrement error Frther assme that the measrement errors are dstrbted accordng to 7-
2 Gassan (normal) dstrbton; ths, ncertanty n each observaton (random varable) ), =,, n ( can be expressed as follows: ), ), =,, n (, (5) where and denote the mean and varance of the ncertanty dstrbton n -th data For now let s assme that the observatons ), =,, n (, represent ndependent random varables, eg, each data locaton was measred sng dfferent measrement tool When the observatons are no longer assmed to be known, the mean of the local condtonal dstrbtons s a random varable The varance of the local condtonal dstrbtons gven n () s not a random varable becase the smple krgng varance s homoscedastc (see ) Becase the mean of the local condtonal dstrbton s a random varable, the ncertanty at the nsampled locaton s descrbed the followng herarchcal model, ), ], ]), (6) where ) ( ) ( ) = λ + λ m (7) = = Note that dstrbton of s Gassan becase t s a lnear combnaton of Gassan random varables Frthermore, de to (7), the mean and varance of the dstrbton for can be calclated as follows: ] E λ = + λ m = = (8) λ )] λ m λ = + = + λ m = ; = = = = + ] = Var λ ) λ m = = (9) = Var λ ) = λ )] = λ = = = = Ths, t follows that the local condtonal dstrbtons n the case of data ncertanty can be expressed sng the followng herarchcal model:, ), and ), ), (0) where are gven n (8)-(9) Moreover, note that the mean and the varance of local condtonal dstrbtons are gven by: E ] = ]] = ] = ; () [ ] = ]] + = ] + ] = + Z The shape of the local ncertanty n Z ( s Gassan * ]] () 7-
3 It worth notng that when the observatons ), =,, n (, do not represent ndependent random varables, bt are correlated wth a prescrbed correlaton strctre, the mean and varance of the local condtonal dstrbtons can be calclated followng the same steps as before expect varance of needs to be calclated as ] = Var = λ + = λ m = Var λ ) = λ λ Cov[ ), )] = = = = Moreover note that the above dervatons heavly rely on the assmpton that the varogram model for the stdy doman s known; ncertanty n the data does not mpact the assmpton of the statonary varogram model Small Examples Example : Consder the data confgraton shown n Fgre In total, there are condtonng data avalable for nference of the local condtonal dstrbton at the nsampled locaton All condtonng data are known sbect to measrement errors; the dstrbtons of the condtonng data are Gassan wth dfferent means and varances, =,,, () see Table below Stdy doman of sze 0 by 0 nts s assmed to be statonary; statonary mean and varance are 0 and, respectvely The varogram of the data s a sngle strctred sphercal wth ngget effect of 0 and range of correlaton of 0 nts Table : Data locatons and vales Data Data Data Data Unsampled Locaton X poston Y poston Vale N (, ) N (, ) N (, ) N (, )? We wll vary the means and varances of the condtonal data dstrbtons to assess the mpact of data ncertanty on the resltng local ncertanty dstrbton nferred from smple krgng Frst, let s fx s as follows = 08; = 0; = 0; = 0; and examne the effect of s Table show reslts for fve dfferent scenaros for s Note that Table shows only reslts for the varance of the local dstrbton of ncertanty accontng for data ncertanty, that s, Var [ ] ; ths s becase the mean of the local condtonal dstrbton s ndependent of s and eqal to 0088 Table : Effect of s on the local ncertanty dstrbton Case Case Case Case Case Var [ ] 009 ( )
4 It can be clearly noted from Table that wth an ncrease n the data ncertanty (that s, ncrease n the varance of the condtonal data dstrbtons), the varance of the local condtonal dstrbton at the nsampled locaton ncreases Moreover, when there s no ncertanty n the condtonng data; the varance of the local condtonal dstrbton at the nsampled locaton s eqal to smple krgng varance On the other hand, f we fx s as: = 08; = 0; = 0; = 0; we can observe that wth ncrease n the mean of the condtonal data dstrbtons, the mean of the local condtonal dstrbton at the nsampled locaton ncreases, see Table Table : Effect of s on the local ncertanty dstrbton Case Case Case Case E [ ] Note that Table shows only reslts for the mean of the local dstrbton of ncertanty accontng for data ncertanty, that s, E [ ] ; ths s becase the mean of the local condtonal dstrbton s ndependent of s and eqal to0576 It s worth notng that the reslts shown n Tables - were theoretcally calclated from Eqatons ()- () There s, however, another mch more comptatonally ntensve approach based on Monte Carlo smlaton to obtan the same reslt Specfcally, n order to calclate the mean and varance of the local ncertanty dstrbton accontng for parameter ncertanty va Monte Carlo smlaton approach the followng steps need to be ndertaken: At each of the condtonng data locatons draw a vale from the condtonng data dstrbton sng Monte Carlo smlaton approach; Apply smple krgng to calclate the mean and varance of the local condtonal dstrbton sng the condtonal data generated n ; Draw a vale from the local condtonal dstrbton obtaned n Add to the database; Repeat steps - many tmes, say 0000 To show the eqvalence of the theoretcally derved local condtonal dstrbtons of ncertanty and the ones obtaned sng Monte Carlo smlaton, let s repeat analyss of Table Reslts are shown n Tables Table : Theoretcally-derved approach vs Monte-Carlo smlaton: Varance of the local ncertanty dstrbton Var [ ] Case Case Case Case Case 5 Theory Smlaton The reslts of theoretcally-derved approach vs Monte-Carlo smlaton approach match perfectly; the dfference between reslts of both approaches cold have been even smaller f nstead of 0000 drawngs n Monte-Carlo approach or more were sed Example : To frther nderstand the nflence of the data ncertanty on the local condtonal dstrbtons at the nsampled locatons, let s asses the change n the varance of the local condtonal dstrbtons 7-
5 (accontng for data ncertanty) over the stdy doman Let s consder the same data confgraton as before; set the means of the condtonng data dstrbtons at: = 8; = 0; = 0; = 0; 0 and consder three dfferent cases, that s, case, case and case 5, for s, see Table In present stdy let s also consder two dfferent varogram models, both sngle strctred sphercal wth ngget effect of 0, bt one wth range of correlaton eqal to 0 nts and the other one wth a range of 5 nts and let s compare reslts Fgre shows reslts obtaned n each case It can be clearly noted from Fgre that wth ncrease n the range of contnty, the varance of the local condtonal dstrbtons decreases The varance of the local condtonal dstrbtons s sally les n the nterval from 0 to However, t can be also hgher than, see Table 5 Table 5: Maxmm varance f the local condtonal dstrbtons over the stdy doman Maxmm Var [ ] Case Case Case 5 Range 5 00 Range Conclsons In ths paper a new nterestng framework for ncorporaton of the data ncertanty nto geostatstcal estmaton s presented The theory behnd the methodology was developed n detal; theoretcal reslts were compared wth practcal reslts obtaned va drect Monte Carlo smlaton Two small examples llstratng the change n the local ncertanty when ncorporatng data ncertanty were presented Fgre : Data confgraton for Examples 7-5
6 Fgre : Varance of the local condtonal dstrbtons accontng for the ncertanty n the data obtaned based on a sngle strctred sphercal varogram wth ngget effect of 0 and range of contnty 5 (left) and 0 (rght) : case (top), case (mddle) and case 5 (bottom) 7-6
An Improved Isogeometric Analysis Using the Lagrange Multiplier Method
An Improved Isogeometrc Analyss Usng the Lagrange Mltpler Method N. Valzadeh 1, S. Sh. Ghorash 2, S. Mohammad 3, S. Shojaee 1, H. Ghasemzadeh 2 1 Department of Cvl Engneerng, Unversty of Kerman, Kerman,
More informationEvaluation of spatial heterogeneity of watershed through HRU concept using SWAT. Xuesong Zhang
Evalaton of spatal heterogenety of watershed throgh HRU concept sng SWAT Xesong Zhang Abstract The accrate smlaton of SWAT can assst the government n makng correct decsons abot water management practces,
More informationy and the total sum of
Lnear regresson Testng for non-lnearty In analytcal chemstry, lnear regresson s commonly used n the constructon of calbraton functons requred for analytcal technques such as gas chromatography, atomc absorpton
More informationBoundary layer and mesh refinement effects on aerodynamic performances of horizontal axis wind turbine (HAWT)
Bondary layer and mesh refnement effects on aerodynamc performances of horzontal axs wnd trbne (HAWT) YOUNES EL KHCHINE, MOHAMMED SRITI Engneerng Scences Laboratory, Polydscplnary Faclty Sd Mohamed Ben
More informationNumerical Solution of Deformation Equations. in Homotopy Analysis Method
Appled Mathematcal Scences, Vol. 6, 2012, no. 8, 357 367 Nmercal Solton of Deformaton Eqatons n Homotopy Analyss Method J. Izadan and M. MohammadzadeAttar Department of Mathematcs, Faclty of Scences, Mashhad
More informationA General Algorithm for Computing Distance Transforms in Linear Time
Ths chapter has been pblshed as: A. Mejster, J. B. T. M. Roerdnk and W. H. Hesselnk, A general algorthm for comptng dstance transforms n lnear tme. In: Mathematcal Morphology and ts Applcatons to Image
More informationA simple piecewise cubic spline method for approximation of highly nonlinear data
Vol., No., 9 () http://d.do.org/./ns.. Natral Scence A sple pecewse cbc splne ethod for approaton of hghly nonlnear Mehd Zaan Cvl Engneerng Departent, Faclty of Techncal and Engneerng, Yaso Unversty, Yaso,
More informationTHE THEORY OF REGIONALIZED VARIABLES
CHAPTER 4 THE THEORY OF REGIONALIZED VARIABLES 4.1 Introducton It s ponted out by Armstrong (1998 : 16) that Matheron (1963b), realzng the sgnfcance of the spatal aspect of geostatstcal data, coned the
More informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More informationGSA Training Notes Raft and Piled-raft Analysis
GSA Tranng Notes Rat and Pled-rat Analyss 1 Introdcton Rat analyss n GSA provdes a means o lnkng GSA statc analyss and sol settlement analyss, so the sol-strctre nteractons can be consdered n the analyss.
More informationHybrid Method of Biomedical Image Segmentation
Hybrd Method of Bomedcal Image Segmentaton Mng Hng Hng Department of Electrcal Engneerng and Compter Scence, Case Western Reserve Unversty, Cleveland, OH, Emal: mxh8@case.ed Abstract In ths paper we present
More informationSome Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.
Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationSimulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010
Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement
More informationSUPPLEMENTARY INFORMATION
The crrent I NS as a fncton of the bas oltage V throgh a N/S pont contact nterface can be descrbed by the BTK theory [3] n whch the nterfacal barrer s represented by a fncton wth a dmensonless strength
More informationA large-alphabet oriented scheme for Chinese and English text compression
A large-alphabet orented scheme for Chnese and Englsh text compresson Hng-Yan G * Department of Compter Scence and Informaton Engneerng atonal Tawan Unersty of Scence and Technology Tape Tawan SUMMARY
More informationUsing BESO method to optimize the shape and reinforcement of the underground openings
Usng BS method to optmze the shape and renforcement of the ndergrond openngs. Ghabrae, Y.M. Xe & X. Hang School of Cvl, nvronmental and Chemcal ngneerng, MI Unversty, Melborne, Astrala ABSAC: In excavaton
More informationOBJECT TRACKING BY ADAPTIVE MEAN SHIFT WITH KERNEL BASED CENTROID METHOD
ISSN : 0973-739 Vol. 3, No., Janary-Jne 202, pp. 39-42 OBJECT TRACKING BY ADAPTIVE MEAN SHIFT WITH KERNEL BASED CENTROID METHOD Rahl Mshra, Mahesh K. Chohan 2, and Dhraj Ntnawwre 3,2,3 Department of Electroncs,
More informationScheduling with Integer Time Budgeting for Low-Power Optimization
Schedlng wth Integer Tme Bdgetng for Low-Power Optmzaton We Jang, Zhr Zhang, Modrag Potkonjak and Jason Cong Compter Scence Department Unversty of Calforna, Los Angeles Spported by NSF, SRC. Otlne Introdcton
More informationJ1.8 APPLICATION OF CFD SIMULATIONS FOR SHORT-RANGE ATMOSPHERIC DISPERSION OVER OPEN FIELDS AND WITHIN ARRAYS OF BUILDINGS
AMS th Jont Conference on the Applcatons of Ar Pollton Meteorology wth the A&WMA, Atlanta, GA, Jan - Feb, 6. J.8 APPLICATION OF CFD SIMULATIONS FOR SHORT-RANGE ATMOSPHERIC DISPERSION OVER OPEN FIELDS AND
More informationRestaurants Review Star Prediction for Yelp Dataset
Restarants Revew Star Predcton for Yelp Dataset Mengq Y UC San Dego A53077101 mey004@eng.csd.ed Meng Xe UC San Dego A53070417 m6xe@eng.csd.ed Wenja Oyang UC San Dego A11069530 weoyang@eng.csd.ed ABSTRACT
More informationSynthesizer 1.0. User s Guide. A Varying Coefficient Meta. nalytic Tool. Z. Krizan Employing Microsoft Excel 2007
Syntheszer 1.0 A Varyng Coeffcent Meta Meta-Analytc nalytc Tool Employng Mcrosoft Excel 007.38.17.5 User s Gude Z. Krzan 009 Table of Contents 1. Introducton and Acknowledgments 3. Operatonal Functons
More informationA combined test for randomness of spatial distribution of composite microstructures
ISSN 57-7076 Revsta Matéra, v., n. 4, pp. 597 60, 007 http://www.matera.coppe.frj.br/sarra/artgos/artgo0886 A combned test for randomness of spatal dstrbton of composte mcrostrctres ABSTRACT João Domngos
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationR s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes
SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges
More informationA. General Type- Fzzy Clsterng There are two knds of type- fzzy sets whch are often sed n clsterng algorthms: 1) nterval and ) general. In nterval typ
014 IEEE Internatonal Conference on Fzzy Systems (FUZZ-IEEE) Jly 6-11, 014, Beng, Chna A Hybrd Type- Fzzy Clsterng Technqe for Inpt Data Preprocessng of Classfcaton Algorthms Vahd Nor, Mohammad-. Akbarzadeh-T.
More informationLecture 5: Probability Distributions. Random Variables
Lecture 5: Probablty Dstrbutons Random Varables Probablty Dstrbutons Dscrete Random Varables Contnuous Random Varables and ther Dstrbutons Dscrete Jont Dstrbutons Contnuous Jont Dstrbutons Independent
More informationFusion Performance Model for Distributed Tracking and Classification
Fuson Performance Model for Dstrbuted rackng and Classfcaton K.C. Chang and Yng Song Dept. of SEOR, School of I&E George Mason Unversty FAIRFAX, VA kchang@gmu.edu Martn Lggns Verdan Systems Dvson, Inc.
More information7/12/2016. GROUP ANALYSIS Martin M. Monti UCLA Psychology AGGREGATING MULTIPLE SUBJECTS VARIANCE AT THE GROUP LEVEL
GROUP ANALYSIS Martn M. Mont UCLA Psychology NITP AGGREGATING MULTIPLE SUBJECTS When we conduct mult-subject analyss we are tryng to understand whether an effect s sgnfcant across a group of people. Whether
More informationOn Covariance Estimation when Nonrespondents are Subsampled
Metodološk zvezk, Vol. 5, No., 008, 95- On Covarance Estmaton when Nonrespondents are Sbsampled Wocech Gamrot Abstract The phenomenon of nonresponse n a sample srve redces the precson of parameter estmates
More informationFusion of Static and Dynamic Body Biometrics for Gait Recognition
Fson of Statc and Dynamc Body Bometrcs for Gat Recognton Lang Wang, Hazhong Nng, Ten Tan, Wemng H Natonal Laboratory of Pattern Recognton (NLPR) Insttte of Atomaton, Chnese Academy of Scences, Bejng, P.
More informationSteps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices
Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between
More informationPerformance Modeling of Web-based Software Systems with Subspace Identification
Acta Poltechnca Hngarca Vol. 13, No. 7, 2016 Performance Modelng of Web-based Software Sstems wth Sbspace Identfcaton Ágnes Bogárd-Mészöl, András Rövd, Shohe Yokoama Department of Atomaton and Appled Informatcs,
More informationEconometrics 2. Panel Data Methods. Advanced Panel Data Methods I
Panel Data Methods Econometrcs 2 Advanced Panel Data Methods I Last tme: Panel data concepts and the two-perod case (13.3-4) Unobserved effects model: Tme-nvarant and dosyncratc effects Omted varables
More informationLecture 08 Multiple View Geometry 2
Insttte of Informatcs Insttte of Neronformatcs Lectre 8 Mltple Vew Geometry Dade Scaramzza http://rpg.f.zh.ch/ Lab Exercse 6 - Today afternoon Room ETH HG E. from 3:5 to 5: Work descrpton: 8-pont algorthm
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationConstrained Robust Model Predictive Control Based on Polyhedral Invariant Sets by Off-line Optimization
A pblcaton of 47 CHEMICA ENGINEERING TRANSACTIONS VO 3, 3 Cef Edtors: Saro Percc, Jří J Klemeš Copyrgt 3, AIDIC Servz Srl, ISBN 978-88-9568-3-5; ISSN 974-979 Te Italan Assocaton of Cemcal Engneerng Onlne
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More information2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements
Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.
More informationParameter estimation for incomplete bivariate longitudinal data in clinical trials
Parameter estmaton for ncomplete bvarate longtudnal data n clncal trals Naum M. Khutoryansky Novo Nordsk Pharmaceutcals, Inc., Prnceton, NJ ABSTRACT Bvarate models are useful when analyzng longtudnal data
More informationAnalog amplifier card Type VT-VSPA2-1-2X/V0/T1 Type VT-VSPA2-1-2X/V0/T5
Analog amplfer card Type VT-VPA2--2X/V0/T Type VT-VPA2--2X/V0/T5 RE 300-Z/0. Replace: 02. /0 Addtonal nfmaton Infmaton regardng the converson of dfferent amplfer cards to amplfer card type VT-VPA2--2X/V0/T
More information6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour
6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the
More informationLife Tables (Times) Summary. Sample StatFolio: lifetable times.sgp
Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables
More informationApproximating MAP using Local Search
UAI21 PARK & DARWICHE 43 Approxmatng MAP sng Local Search James D Park and Adnan Darwche Compter Scence Department Unversty of Calforna Los Angeles, CA 995 {jd,darwche }@csclaed Abstract MAP s the problem
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationACTIVE SPECKLE PHOTOGRAPHY METHOD USING FOURIER TRANSFORM FOR MEASURING THE THICKNESS OF A TRANSPARENT PLATE
Internatonal Jornal of Physcs and Research IJPR ISSN 50-0030 Vol 3 Isse 3 Ag 03 6-7 TJPRC Pvt Ltd ACTIVE SPECKLE PHOTOGRAPHY METHOD SING FORIER TRANSFORM FOR MEASRING THE THICKNESS OF A TRANSPARENT PLATE
More informationAdaptive Transfer Learning
Adaptve Transfer Learnng Bn Cao, Snno Jaln Pan, Yu Zhang, Dt-Yan Yeung, Qang Yang Hong Kong Unversty of Scence and Technology Clear Water Bay, Kowloon, Hong Kong {caobn,snnopan,zhangyu,dyyeung,qyang}@cse.ust.hk
More informationC I R E D 18 th International Conference on Electricity Distribution Turin, 6-9 June 2005 ABSTRACT
C I R E D 8 th Internatonal Conference on Electrcty Dstrbton Trn, 6-9 Jne 2005 FREQUENCY OF OCCURRENCE OF VOLTAGE OF PECIFIC IZE TO OCCUR AT THE NODE OF DITRIBUTION AND TRANMIION POWER YTEM IN CAE OF A
More informationDirect Analysis of Pre-Adjusted Loss Cost, Frequency or Severity in Tweedie Models
Drect Analyss of Pre-Adsted Loss Cost, Freqency or Severty n Tweede Models Sheng G. Sh, Ph.D. Abstract Resonse data (loss cost, clam freqency or clam severty are often re-adsted wth known factors and drectly
More informationIntra-Parametric Analysis of a Fuzzy MOLP
Intra-Parametrc Analyss of a Fuzzy MOLP a MIAO-LING WANG a Department of Industral Engneerng and Management a Mnghsn Insttute of Technology and Hsnchu Tawan, ROC b HSIAO-FAN WANG b Insttute of Industral
More informationHermite Splines in Lie Groups as Products of Geodesics
Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the
More informationA New MPS Simulation Algorithm Based on Gibbs Sampling
A New MPS Smulaton Algorthm Based on Gbbs Samplng Steven Lyster, Clayton V. Deutsch, and Thes Dose 2 Centre for Computatonal Geostatstcs Edmonton, Alberta 2 RWE Dea Aktengsellschaft Hamburg, Germany The
More information3D vector computer graphics
3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationChapter 9. Model Calibration. John Hourdakis Center for Transportation Studies, U of Mn
Chapter 9 Model Calbraton John Hourdaks Center for Transportaton Studes, U of Mn Hourd00@tc.umn.edu Why Calbrate? Computers Cannot Magcally Replcate Realty! Smulaton Models Are Desgned to be General Drver
More informationPID_REG3. 1) The C interface version. Type: Target Independent, Application Dependent. C Version File Names: pid_reg3.c, pid_reg3.
ID_REG3 Descrton Dgtal ID Controller wth Ant-wn hs mole mlements a 32-bt gtal ID controller wth ant-wn correcton. It can be se for I or D controller as well. In ths gtal ID controller, the fferental eqaton
More informationELEC 377 Operating Systems. Week 6 Class 3
ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems
More informationEmpirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap
Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*
More informationParametric Study on Pile-Soil Interaction Analyses By Overlaying Mesh Method
Parametrc Stdy on Pe-So Interacton nayses y Overayng Mesh Method. Ohta & F. Mra Yamagch Unversty, Japan SUMMRY: The overayng mesh method (OMM) s an anaytca approach that overaps two or more ndependent
More informationFEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur
FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents
More informationTN348: Openlab Module - Colocalization
TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages
More informationLecture #15 Lecture Notes
Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal
More informationDynamic wetting property investigation of AFM tips in micro/nanoscale
Dynamc wettng property nvestgaton of AFM tps n mcro/nanoscale The wettng propertes of AFM probe tps are of concern n AFM tp related force measurement, fabrcaton, and manpulaton technques, such as dp-pen
More informationElectrical analysis of light-weight, triangular weave reflector antennas
Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna
More informationSpatial Extreme Value Using Bayesian Hierarchical Model For Precipitation Return Levels Prediction
PROCEEDING OF 3 RD INTERNATIONAL CONFERENCE ON RESEARCH, IMPLEMENTATION AND EDUCATION OF MATHEMATICS AND SCIENCE YOGYAKARTA, 6 7 MAY 06 Spatal Extreme Value Usng Bayesan Herarchcal Model For Precptaton
More informationPerformance Evaluation of Information Retrieval Systems
Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence
More informationCompiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz
Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster
More informationNAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics
Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationC2 Training: June 8 9, Combining effect sizes across studies. Create a set of independent effect sizes. Introduction to meta-analysis
C2 Tranng: June 8 9, 2010 Introducton to meta-analyss The Campbell Collaboraton www.campbellcollaboraton.org Combnng effect szes across studes Compute effect szes wthn each study Create a set of ndependent
More informationNetwork Coding as a Dynamical System
Network Codng as a Dynamcal System Narayan B. Mandayam IEEE Dstngushed Lecture (jont work wth Dan Zhang and a Su) Department of Electrcal and Computer Engneerng Rutgers Unversty Outlne. Introducton 2.
More informationImprovement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration
Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,
More informationComputer Animation and Visualisation. Lecture 4. Rigging / Skinning
Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume
More informationFor instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)
Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A
More informationSolutions to Programming Assignment Five Interpolation and Numerical Differentiation
College of Engneerng and Coputer Scence Mechancal Engneerng Departent Mechancal Engneerng 309 Nuercal Analyss of Engneerng Systes Sprng 04 Nuber: 537 Instructor: Larry Caretto Solutons to Prograng Assgnent
More informationA Generalized Methodology for Data Analysis
> < A eneralzed Methodology for ata Analyss Plamen Angelov, Fellow, IEEE, Xaowe, and Jose Prncpe, Fellow, IEEE Abstract Based on a crtcal analyss of data analytcs and ts fondatons, we propose a fnctonal
More informationAPPLICATION OF A COMPUTATIONALLY EFFICIENT GEOSTATISTICAL APPROACH TO CHARACTERIZING VARIABLY SPACED WATER-TABLE DATA
RFr"W/FZD JAN 2 4 1995 OST control # 1385 John J Q U ~ M Argonne Natonal Laboratory Argonne, L 60439 Tel: 708-252-5357, Fax: 708-252-3 611 APPLCATON OF A COMPUTATONALLY EFFCENT GEOSTATSTCAL APPROACH TO
More informationWhy visualisation? IRDS: Visualization. Univariate data. Visualisations that we won t be interested in. Graphics provide little additional information
Why vsualsaton? IRDS: Vsualzaton Charles Sutton Unversty of Ednburgh Goal : Have a data set that I want to understand. Ths s called exploratory data analyss. Today s lecture. Goal II: Want to dsplay data
More informationFuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches
Proceedngs of the Internatonal Conference on Cognton and Recognton Fuzzy Flterng Algorthms for Image Processng: Performance Evaluaton of Varous Approaches Rajoo Pandey and Umesh Ghanekar Department of
More informationKent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming
CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems
More informationMECHANICAL ANALYSIS OF 2D-BRAZED JOINT USING A NEW HYBRID MAX- FEM MODEL
MECHANICAL ANALYSIS OF D-BRAZED JOINT USING A NEW HYBRID MAX- MODEL A. IFIS a, F. Blteryst b, M. Noar c a Laboratore d Energétqe et de Mécanqe Théorqe et Applqée, LEMTA CNRS-UMR 756, GIP-InSIC, 7 re d
More informationRisk Assessment on Railway Signal System Based on Fuzzy-FMECA Method
Sensors & Transdcers, Vol. 56, Isse 9, September 0, pp. 0-0 Sensors & Transdcers 0 by IFSA http://www.sensorsportal.com Rsk Assessment on Ralway Sgnal System Based on Fzzy-FMECA Method Yo-Peng Zhang, Zheng-Je
More informationA Semi-parametric Regression Model to Estimate Variability of NO 2
Envronment and Polluton; Vol. 2, No. 1; 2013 ISSN 1927-0909 E-ISSN 1927-0917 Publshed by Canadan Center of Scence and Educaton A Sem-parametrc Regresson Model to Estmate Varablty of NO 2 Meczysław Szyszkowcz
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationMODELING USER INTERESTS USING TOPIC MODEL
Jornal of heoretcal and Appled Informaton echnology 0 th Febrary 203. Vol. 48 No. 2005-203 JAI & LLS. All rghts reserved. ISSN: 992-8645 www.att.org E-ISSN: 87-395 MODELING USER INERESS USING OPIC MODEL
More informationA New Token Allocation Algorithm for TCP Traffic in Diffserv Network
A New Token Allocaton Algorthm for TCP Traffc n Dffserv Network A New Token Allocaton Algorthm for TCP Traffc n Dffserv Network S. Sudha and N. Ammasagounden Natonal Insttute of Technology, Truchrappall,
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationDynamic Camera Assignment and Handoff
12 Dynamc Camera Assgnment and Handoff Br Bhanu and Ymng L 12.1 Introducton...338 12.2 Techncal Approach...339 12.2.1 Motvaton and Problem Formulaton...339 12.2.2 Game Theoretc Framework...339 12.2.2.1
More informationThe calculation of real-time PCR ratios by means of Monte Carlo Simulation or high-order Taylor expansion
The calculaton o real-tme PCR ratos by means o Monte Carlo Smulaton or hgh-order Taylor expanson Andrej-Nkola Spess Department o Andrology, Unversty Hosptal Hamburg-Eppendor Do we need error propagaton
More informationThe Grouping Methods and Rank Estimator, Based on Ranked Set sampling, for the linear Error in Variable Models
P Internatonal Journal of Scentfc Engneerng and Appled Scence (IJSEAS) Volume-, Issue-7,Jul 06 The Groupng Methods and Rank Estmator, Based on Ranked Set samplng, for the lnear Error n Varable Models Ahmed
More informationResearch Article Quasi-Bézier Curves with Shape Parameters
Hndaw Publshng Corporaton Appled Mathematcs Volume 3, Artcle ID 739, 9 pages http://dxdoorg/55/3/739 Research Artcle Quas-Bézer Curves wth Shape Parameters Jun Chen Faculty of Scence, Nngbo Unversty of
More informationAn Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 97-735 Volume Issue 9 BoTechnology An Indan Journal FULL PAPER BTAIJ, (9), [333-3] Matlab mult-dmensonal model-based - 3 Chnese football assocaton super league
More informationPage 0 of 0 SPATIAL INTERPOLATION METHODS
Page 0 of 0 SPATIAL INTERPOLATION METHODS 2018 1. Introducton Spatal nterpolaton s the procedure to predct the value of attrbutes at unobserved ponts wthn a study regon usng exstng observatons (Waters,
More informationAn Optimal Algorithm to Find a Minimum 2-neighbourhood Covering Set on Cactus Graphs
Annals of Pre Appled Mathematcs Vol 2 No 1 212 45-59 ISSN: 2279-87X (P) 2279-888(onlne) Pblshed on 18 December 212 wwwresearchmathscorg Annals of An Optmal Algorthm to Fnd a Mnmm 2-neghborhood overng Set
More informationMultilevel Iterative Methods
Mltleel Iterate Methods Erc Yn Adsor: Professor Randy Bank, UCSD Contents Introdcton.. 3 Basc Mltgrd Algorthm. Mltgrd fondatons 3. Mltgrd algorthm 4 3 Epermentaton 5 4 Algebrac Mltgrd (AMG) 4. Introdcton
More informationReport #1 Example. Semester
Report # Eample Parallel FEM Class n Wnter Semester Kengo aajma Informaton Technology Center Techncal & Scentfc Comptng I (480-07) Semnar on Compter Scence I (480-04) Report-0 D Statc Lnear Elastc Problem
More informationMonte Carlo Integration
Introducton Monte Carlo Integraton Dgtal Image Synthess Yung-Yu Chuang 11/9/005 The ntegral equatons generally don t have analytc solutons, so we must turn to numercal methods. L ( o p,ωo) = L e ( p,ωo)
More informationA Comparative Study of Constraint-Handling Techniques in Evolutionary Constrained Multiobjective Optimization
A omparatve Stdy of onstrant-handlng Technqes n Evoltonary onstraned Mltobectve Optmzaton Ja-Peng L, Yong Wang, Member, IEEE, Shengxang Yang, Senor Member, IEEE, and Zxng a, Senor Member, IEEE Abstract
More informationAnonymisation of Public Use Data Sets
Anonymsaton of Publc Use Data Sets Methods for Reducng Dsclosure Rsk and the Analyss of Perturbed Data Harvey Goldsten Unversty of Brstol and Unversty College London and Natale Shlomo Unversty of Manchester
More informationDesign for Reliability: Case Studies in Manufacturing Process Synthesis
Desgn for Relablty: Case Studes n Manufacturng Process Synthess Y. Lawrence Yao*, and Chao Lu Department of Mechancal Engneerng, Columba Unversty, Mudd Bldg., MC 473, New York, NY 7, USA * Correspondng
More information