This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.
|
|
- Esther Margery Briggs
- 5 years ago
- Views:
Transcription
1 Ths documen s downloaded from DR-NTU, Nanyang Technologcal Unversy Lbrary, Sngapore. Tle A smplfed verb machng algorhm for word paron n vsual speech processng( Acceped verson ) Auhor(s) Foo, Say We; Yong, Lan; Lang, Dong Caon Foo, S. W., Yong, L., & Lang, D. (009). A smplfed verb machng algorhm for word paron n vsual speech processng. Proceedngs of 4h European Workshop on Image Analyss for Mulmeda Ineracve Servces, Dae 009 URL hp://hdl.handle.ne/00/6039 Rghs A smplfed verb machng algorhm for word paron n vsual speech processng copyrgh 009 World Scenfc Publshng Co. Ths conference's webse s locaed a hp://eproceedngs.worldscne.com/ / _0065.hml.
2 A SIMPLIFIED VITERBI MATCHING ALGORITHM FOR WORD PARTITION IN VISUAL SPEECH PROCESSING SAY WEI FOO School of EEE., Nanyang Technologcal Unversy, Sngapore, E-mal: YONG LIAN AND LIANG DONG ECE Dep., Naonal Unversy of Sngapore, Sngapore, 960 E-mal: {elelany, In hs paper, a novel smplfed Verb machng algorhm for sequence paron s presened. Ths mehod connecs he saes of dfferen HMMs o model he observed sequence. The mehod s appled o vsual speech processng o paron he word no vsual speech elemens. Expermenal resul shows ha good accuracy s acheved wh he proposed mehod.. Inroducon In auomac lp readng sysems usng Hdden Markov Models (HMMs), how o paron he word or senence no ndvdual recognon uns s an mporan opc of research. In radonal mehod, exhausve means such as level buldng s commonly appled o acheve hs goal. In hs paper, a novel aemp n solvng hs problem s repored. In he proposed mehod, he HMMs are reaed as deachable sae se. A word s paroned no vsual speech elemens by connecng he saes of dfferen HMMs. A smplfed Verb machng s performed whle rerevng he sae chan. The expermenal resuls ndcae ha he word can be paroned wh good accuracy f he basc vsual speech elemens are confgured properly.. Consrucon of Vseme Models and Sae Daabase The basc vsual speech elemens n Englsh are called vsemes. A vseme s a me seres ndcang he movemen of he lp ha s repeaed n dfferen word arculaon. In MPEG-4 mulmeda sandards, he vsemes are caegorzed no 4 groups wh one vseme correspondng wh ~5 vsually smlar phonemes []. Our nvesgaon on he dynamcal feaures of vsual speech shows ha he
3 a. Inal phase /o/ Arculaon phase Transon // End phase b. Fgure. a.) The hree phases of vseme producon b.) Segmenaon of he ranson sae from he producon of /o/ moon of he lp can be paroned no hree phases durng he producon of an ndependen vseme: The frs phase s called nal phase, sarng from a closed mouh o he begnnng of sound producon. The nex phase s called arculaon phase, whch s he course when sound s produced. The hrd phase s he end phase when he mouh resores o he relaxed sae. Among he above hree phases, he arculaon phase s he mos mporan for recognon because he dfference beween vsemes chefly les here and s relavely ndependen o he conex. The nal phase and he end phase, on he oher hand, are ransonal ones. They may change much under dfferen conexs. HMMs are used o model he vsemes n hs paper. Assume { S, S, L S } s N he sae se and { O, O, L O } s he symbol alphabe, an N-sae-M-symbol M HMM s deermned by: probables of he nal sae π = [ π ], sae ranson marx A = [ a j ] and symbol emsson marx B = [ b j ]. In he proposed sraegy, hree-sae lef-rgh HMMs are adoped o model he vsemes. If he parameers of he HMMs are properly confgured, he saes of he HMM can be physcally assocaed wh he hree phases of vseme producon []. Afer performed Baum-Welch esmaon, a number of vseme models (HMMs) are obaned. The arculaon saes of he HMMs are colleced as he recognon uns. On he oher hand, he ransons beween vsemes, whch are used o connec he recognon uns, are also segmened ou of wo vsemes jon ogeher, e.g. /o/. As llusraed n Fg. (b), he approxmae ranson phase s manually segmened from he mage sequence. The sascs of he ransons beween dfferen pars of vsemes are obaned by counng and averagng he symbols appeared n he process. These probably funcons are colleced o buld he ranson un se. 3. Smplfed Verb Machng Algorhm for Word Paron The purpose of sequence paron s o decode a sae chan ha he recognon uns and ranson uns appear alernaely. Gven he sae daabase Θ and T-
4 3 (K+L)*T nodes S + +L S + +L S + +L S + +L Recognon Uns Transon Uns Fgure. The probably rells ha s generaed from he forward process lengh observaon sequence X = { x, x L x }, he ask s o fnd a sae sequence T Q = { q, q Lq } o maxmze P ( Q X, Θ), whch s equvalen o maxmzng T P ( Q, X Θ). A Verb machng s mplemened as follows. Inalzaon: Assume he sequence sars from a ranson sae, e.g. from he mouh s closed, he frs column of he probably rells s obaned from (), 0 K = and ψ ( ) = 0 < K + L () π b ( x ) K < K + L where π s he nal probably of he -h sae. Because a word may sar from any ranson sae, a unform dsrbuon π = / L ( K < K + L) s appled o he ranson uns. Forward Process: A each momen, a sae wll eher repea self or rans o a un n anoher se. If he (-) h sae s a recognon un, he h sae can be he same recognon un as he (-) h sae or a ranson un. Two probables are hen compued. δ ) = a and δ ) max [ ( a )] K () ( ( = K < K + L and he h column of he probably rells s, = max[, ] b ( x ) and ψ ( ) = arg max[, ] b ( x ) (3) If he --h sae s a ranson sae, he followng probables are compued. δ ) = λ and δ ) max[ ( λ)] K < K + (4) ( ( = < K L where λ has he same meanng as sae reeraon probably a. I conrols he duraon of he ranson sae. Also, we have, = max[, ] b ( x ) and ψ ( ) = arg max[, ] b ( x ) (5) Termnaon: The las column of he probably rells s δ = max[ ( )] and q = arg max[ ] K + L (6) end δ T T K The opmal sae sequence s obaned by backrackng he nodes usng (7). q = ψ ( q ) = T, T, L (7) + + In (4), he enry λ s nroduced o buld a sae chan because he ranson un s merely a probably dsrbuon funcon. In applcaons, λ can be se o dfferen values o beer f he process nvesgaed. + L T
5 4 4. Expermens Some expermens are conduced o assess he performance of he proposed mehod for word paron. As lsed n Table, some words are decoded no vseme combnaons wh greaes probables are lsed n Table and he correc paron s marked wh a. I can be observed ha he correc parons always appear n he bes maches. Word Vseme combnaons Probably lp zoo wh black Table. Examples of word paron wh he proposed sraegy /l/ + rans. + /p/ 43% /l/ + rans. + /I/ + rans. + /p/ 3% /ch/ + rans. + /p/ % /U / 7% /z/ + rans. + /U/ 7% /sh/ + rans. 7% /w/ + rans. + /I/ + rans. + /z/ 35% /U/ + rans. + /z/ 30% /f/ + rans. + /s/ 9% /b/+rans.+/l/+rans.+/a/+rans.+/k/ 7% /b/+rans.+/l/+rans.+/a/+rans.+// 4% /b/ + rans.+ /A/ + rans. + /k/ 3% 5. Concluson In hs paper, he smplfed Verb algorhm s appled o lp readng as an example. Ths mehod can also be adoped n sequence paron of he followng condons:.) he saes of he HMM of he basc recognon uns can be classfed as varable saes and relavely consan saes..) I s dffcul o decode he arge sequence no an HMM chan of he basc recognon uns. References. M. Tekalp and J. Osermann, Face and -D mesh anmaon n MPEG-4, Image Communcaon J. (999). Say We Foo and Lang Dong, Recognon of Vsual Speech Elemens Usng Hdden Markov Models, Proc. PCM 00, LNCS53, (00) 3. L. R. Rabner and B. H. Juang, Fundamenals of speech recognon, Prence Hall Sgnal Processng Seres, (993) 4. Lang Dong and Say We Foo, Boosng of Hdden Markov Models and he Applcaon o Vsual Speech Analyss, Proc. IWSSIP, , (00)
Introduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms
Course organzaon Inroducon Wee -2) Course nroducon A bref nroducon o molecular bology A bref nroducon o sequence comparson Par I: Algorhms for Sequence Analyss Wee 3-8) Chaper -3, Models and heores» Probably
More informationLecture Slides for INTRODUCTION TO. Machine Learning. ETHEM ALPAYDIN The MIT Press,
Lecure Sldes for INTRDUCTIN T Machne Learnng ETHEM ALAYDIN The MIT ress, 2004 alpaydn@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/2ml CHATER 3: Hdden Marov Models Inroducon Modelng dependences n npu; no
More informationDiscrete Markov Process. Introduction. Example: Balls and Urns. Stochastic Automaton. INTRODUCTION TO Machine Learning 3rd Edition
EHEM ALPAYDI he MI Press, 04 Lecure Sldes for IRODUCIO O Machne Learnng 3rd Edon alpaydn@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/ml3e Sldes from exboo resource page. Slghly eded and wh addonal examples
More informationOn One Analytic Method of. Constructing Program Controls
Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna
More informationJohn Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany
Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy
More informationSampling Procedure of the Sum of two Binary Markov Process Realizations
Samplng Procedure of he Sum of wo Bnary Markov Process Realzaons YURY GORITSKIY Dep. of Mahemacal Modelng of Moscow Power Insue (Techncal Unversy), Moscow, RUSSIA, E-mal: gorsky@yandex.ru VLADIMIR KAZAKOV
More informationOutline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model
Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon
More informationIn the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!
ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal
More informationApproximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy
Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae
More information( ) () we define the interaction representation by the unitary transformation () = ()
Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More informationHidden Markov Models Following a lecture by Andrew W. Moore Carnegie Mellon University
Hdden Markov Models Followng a lecure by Andrew W. Moore Carnege Mellon Unversy www.cs.cmu.edu/~awm/uorals A Markov Sysem Has N saes, called s, s 2.. s N s 2 There are dscree meseps, 0,, s s 3 N 3 0 Hdden
More informationHidden Markov Models
11-755 Machne Learnng for Sgnal Processng Hdden Markov Models Class 15. 12 Oc 2010 1 Admnsrva HW2 due Tuesday Is everyone on he projecs page? Where are your projec proposals? 2 Recap: Wha s an HMM Probablsc
More informationSolution in semi infinite diffusion couples (error function analysis)
Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of
More informationRobustness Experiments with Two Variance Components
Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference
More informationDynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005
Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc
More informationFoundations of State Estimation Part II
Foundaons of Sae Esmaon Par II Tocs: Hdden Markov Models Parcle Flers Addonal readng: L.R. Rabner, A uoral on hdden Markov models," Proceedngs of he IEEE, vol. 77,. 57-86, 989. Sequenal Mone Carlo Mehods
More informationPerformance Analysis for a Network having Standby Redundant Unit with Waiting in Repair
TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen
More informationVariants of Pegasos. December 11, 2009
Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on
More informationCubic Bezier Homotopy Function for Solving Exponential Equations
Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.
More informationAn introduction to Support Vector Machine
An nroducon o Suppor Vecor Machne 報告者 : 黃立德 References: Smon Haykn, "Neural Neworks: a comprehensve foundaon, second edon, 999, Chaper 2,6 Nello Chrsann, John Shawe-Tayer, An Inroducon o Suppor Vecor Machnes,
More informationVideo-Based Face Recognition Using Adaptive Hidden Markov Models
Vdeo-Based Face Recognon Usng Adapve Hdden Markov Models Xaomng Lu and suhan Chen Elecrcal and Compuer Engneerng, Carnege Mellon Unversy, Psburgh, PA, 523, U.S.A. xaomng@andrew.cmu.edu suhan@cmu.edu Absrac
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4
CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped
More informationRobust and Accurate Cancer Classification with Gene Expression Profiling
Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem
More informationA Deterministic Algorithm for Summarizing Asynchronous Streams over a Sliding Window
A Deermnsc Algorhm for Summarzng Asynchronous Sreams over a Sldng ndow Cosas Busch Rensselaer Polyechnc Insue Srkana Trhapura Iowa Sae Unversy Oulne of Talk Inroducon Algorhm Analyss Tme C Daa sream: 3
More informationDigital Speech Processing Lecture 20. The Hidden Markov Model (HMM)
Dgal Speech Processng Lecure 20 The Hdden Markov Model (HMM) Lecure Oulne Theory of Markov Models dscree Markov processes hdden Markov processes Soluons o he Three Basc Problems of HMM s compuaon of observaon
More informationLi An-Ping. Beijing , P.R.China
A New Type of Cpher: DICING_csb L An-Png Bejng 100085, P.R.Chna apl0001@sna.com Absrac: In hs paper, we wll propose a new ype of cpher named DICING_csb, whch s derved from our prevous sream cpher DICING.
More informationHEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD
Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,
More informationChapter 6: AC Circuits
Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.
More informationFTCS Solution to the Heat Equation
FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence
More informationChapter Lagrangian Interpolation
Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and
More informationClustering (Bishop ch 9)
Cluserng (Bshop ch 9) Reference: Daa Mnng by Margare Dunham (a slde source) 1 Cluserng Cluserng s unsupervsed learnng, here are no class labels Wan o fnd groups of smlar nsances Ofen use a dsance measure
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationImplementation of Quantized State Systems in MATLAB/Simulink
SNE T ECHNICAL N OTE Implemenaon of Quanzed Sae Sysems n MATLAB/Smulnk Parck Grabher, Mahas Rößler 2*, Bernhard Henzl 3 Ins. of Analyss and Scenfc Compung, Venna Unversy of Technology, Wedner Haupsraße
More informationHIDDEN MARKOV MODELS FOR AUTOMATIC SPEECH RECOGNITION: THEORY AND APPLICATION. S J Cox
HIDDEN MARKOV MODELS FOR AUTOMATIC SPEECH RECOGNITION: THEORY AND APPLICATION S J Cox ABSTRACT Hdden Markov modellng s currenly he mos wdely used and successful mehod for auomac recognon of spoken uerances.
More informationTSS = SST + SSE An orthogonal partition of the total SS
ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally
More informationSingle-loop System Reliability-Based Design & Topology Optimization (SRBDO/SRBTO): A Matrix-based System Reliability (MSR) Method
10 h US Naonal Congress on Compuaonal Mechancs Columbus, Oho 16-19, 2009 Sngle-loop Sysem Relably-Based Desgn & Topology Opmzaon (SRBDO/SRBTO): A Marx-based Sysem Relably (MSR) Mehod Tam Nguyen, Junho
More informationNew M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)
Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor
More informationHidden Markov Model for Speech Recognition. Using Modified Forward-Backward Re-estimation Algorithm
IJCSI Inernaonal Journal of Compuer Scence Issues Vol. 9 Issue 4 o 2 July 22 ISS (Onlne): 694-84.IJCSI.org 242 Hdden Markov Model for Speech Recognon Usng Modfed Forard-Backard Re-esmaon Algorhm Balan
More informationGENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim
Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran
More informationLecture 18: The Laplace Transform (See Sections and 14.7 in Boas)
Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on
More information(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function
MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,
More informationSurvival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System
Communcaons n Sascs Theory and Mehods, 34: 475 484, 2005 Copyrgh Taylor & Francs, Inc. ISSN: 0361-0926 prn/1532-415x onlne DOI: 10.1081/STA-200047430 Survval Analyss and Relably A Noe on he Mean Resdual
More informationOn computing differential transform of nonlinear non-autonomous functions and its applications
On compung dfferenal ransform of nonlnear non-auonomous funcons and s applcaons Essam. R. El-Zahar, and Abdelhalm Ebad Deparmen of Mahemacs, Faculy of Scences and Humanes, Prnce Saam Bn Abdulazz Unversy,
More informationConsider processes where state transitions are time independent, i.e., System of distinct states,
Dgal Speech Processng Lecure 0 he Hdden Marov Model (HMM) Lecure Oulne heory of Marov Models dscree Marov processes hdden Marov processes Soluons o he hree Basc Problems of HMM s compuaon of observaon
More informationGORDON AND NEWELL QUEUEING NETWORKS AND COPULAS
Yugoslav Journal of Operaons Research Vol 9 (009) Number 0- DOI:0.98/YUJOR0900C GORDON AND NEWELL QUEUEING NETWORKS AND COPULAS Danel CIUIU Facul of Cvl Indusral and Agrculural Buldngs Techncal Unvers
More informationIntroduction to Boosting
Inroducon o Boosng Cynha Rudn PACM, Prnceon Unversy Advsors Ingrd Daubeches and Rober Schapre Say you have a daabase of news arcles, +, +, -, -, +, +, -, -, +, +, -, -, +, +, -, + where arcles are labeled
More informationExistence and Uniqueness Results for Random Impulsive Integro-Differential Equation
Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal
More informationWiH Wei He
Sysem Idenfcaon of onlnear Sae-Space Space Baery odels WH We He wehe@calce.umd.edu Advsor: Dr. Chaochao Chen Deparmen of echancal Engneerng Unversy of aryland, College Par 1 Unversy of aryland Bacground
More informationImage Morphing Based on Morphological Interpolation Combined with Linear Filtering
Image Morphng Based on Morphologcal Inerpolaon Combned wh Lnear Flerng Marcn Iwanowsk Insue of Conrol and Indusral Elecroncs Warsaw Unversy of Technology ul.koszykowa 75-66 Warszawa POLAND el. +48 66 54
More information( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model
BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng
More informationOff line signatures Recognition System using Discrete Cosine Transform and VQ/HMM
Ausralan Journal of Basc and Appled Scences, 6(12): 423-428, 2012 ISSN 1991-8178 Off lne sgnaures Recognon Sysem usng Dscree Cosne Transform and VQ/HMM 1 Behrouz Vasegh, 2 Somayeh Hashem, 1,2 Deparmen
More informationLet s treat the problem of the response of a system to an applied external force. Again,
Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem
More informationJ i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.
umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal
More informationThe Dynamic Programming Models for Inventory Control System with Time-varying Demand
The Dynamc Programmng Models for Invenory Conrol Sysem wh Tme-varyng Demand Truong Hong Trnh (Correspondng auhor) The Unversy of Danang, Unversy of Economcs, Venam Tel: 84-236-352-5459 E-mal: rnh.h@due.edu.vn
More informationJanuary Examinations 2012
Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons
More informationEEL 6266 Power System Operation and Control. Chapter 5 Unit Commitment
EEL 6266 Power Sysem Operaon and Conrol Chaper 5 Un Commmen Dynamc programmng chef advanage over enumeraon schemes s he reducon n he dmensonaly of he problem n a src prory order scheme, here are only N
More informationPart II CONTINUOUS TIME STOCHASTIC PROCESSES
Par II CONTINUOUS TIME STOCHASTIC PROCESSES 4 Chaper 4 For an advanced analyss of he properes of he Wener process, see: Revus D and Yor M: Connuous marngales and Brownan Moon Karazas I and Shreve S E:
More informationAbstract This paper considers the problem of tracking objects with sparsely located binary sensors. Tracking with a sensor network is a
Trackng on a Graph Songhwa Oh and Shankar Sasry Deparmen of Elecrcal Engneerng and Compuer Scences Unversy of Calforna, Berkeley, CA 9470 {sho,sasry}@eecs.berkeley.edu Absrac Ths paper consders he problem
More informationPhysical Simulation Using FEM, Modal Analysis and the Dynamic Equilibrium Equation
Physcal Smulaon Usng FEM, Modal Analyss and he Dynamc Equlbrum Equaon Paríca C. T. Gonçalves, Raquel R. Pnho, João Manuel R. S. Tavares Opcs and Expermenal Mechancs Laboraory - LOME, Mechancal Engneerng
More informationNATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours
NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all
More informationGraduate Macroeconomics 2 Problem set 5. - Solutions
Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K
More informationCH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC
CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal
More informationFall 2010 Graduate Course on Dynamic Learning
Fall 200 Graduae Course on Dynamc Learnng Chaper 4: Parcle Flers Sepember 27, 200 Byoung-Tak Zhang School of Compuer Scence and Engneerng & Cognve Scence and Bran Scence Programs Seoul aonal Unversy hp://b.snu.ac.kr/~bzhang/
More informationComparison of Differences between Power Means 1
In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,
More informationMath 128b Project. Jude Yuen
Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally
More informationDepartment of Economics University of Toronto
Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of
More informationUS Monetary Policy and the G7 House Business Cycle: FIML Markov Switching Approach
U Monear Polc and he G7 Hoe Bness Ccle: FML Markov wchng Approach Jae-Ho oon h Jun. 7 Absrac n order o deermne he effec of U monear polc o he common bness ccle beween hong prce and GDP n he G7 counres
More informationTHE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS
THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he
More information12d Model. Civil and Surveying Software. Drainage Analysis Module Detention/Retention Basins. Owen Thornton BE (Mech), 12d Model Programmer
d Model Cvl and Surveyng Soware Dranage Analyss Module Deenon/Reenon Basns Owen Thornon BE (Mech), d Model Programmer owen.hornon@d.com 4 January 007 Revsed: 04 Aprl 007 9 February 008 (8Cp) Ths documen
More informationSampling Coordination of Business Surveys Conducted by Insee
Samplng Coordnaon of Busness Surveys Conduced by Insee Faben Guggemos 1, Olver Sauory 1 1 Insee, Busness Sascs Drecorae 18 boulevard Adolphe Pnard, 75675 Pars cedex 14, France Absrac The mehod presenly
More informationAn Integrated and Interactive Video Retrieval Framework with Hierarchical Learning Models and Semantic Clustering Strategy
An Inegraed and Ineracve Vdeo Rereval Framewor wh Herarchcal Learnng Models and Semanc Cluserng Sraegy Na Zhao, Shu-Chng Chen, Me-Lng Shyu 2, Suar H. Rubn 3 Dsrbued Mulmeda Informaon Sysem Laboraory School
More informationAdvanced Macroeconomics II: Exchange economy
Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence
More informationP R = P 0. The system is shown on the next figure:
TPG460 Reservor Smulaon 08 page of INTRODUCTION TO RESERVOIR SIMULATION Analycal and numercal soluons of smple one-dmensonal, one-phase flow equaons As an nroducon o reservor smulaon, we wll revew he smples
More informationOnline Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading
Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng
More informationAttributed Graph Matching Based Engineering Drawings Retrieval
Arbued Graph Machng Based Engneerng Drawngs Rereval Rue Lu, Takayuk Baba, and Dak Masumoo Fusu Research and Developmen Cener Co LTD, Beng, PRChna Informaon Technology Meda Labs Fusu Laboraores LTD, Kawasak,
More information5th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2015)
5h Inernaonal onference on Advanced Desgn and Manufacurng Engneerng (IADME 5 The Falure Rae Expermenal Sudy of Specal N Machne Tool hunshan He, a, *, La Pan,b and Bng Hu 3,c,,3 ollege of Mechancal and
More informationPolymerization Technology Laboratory Course
Prakkum Polymer Scence/Polymersaonsechnk Versuch Resdence Tme Dsrbuon Polymerzaon Technology Laboraory Course Resdence Tme Dsrbuon of Chemcal Reacors If molecules or elemens of a flud are akng dfferen
More informationTime-interval analysis of β decay. V. Horvat and J. C. Hardy
Tme-nerval analyss of β decay V. Horva and J. C. Hardy Work on he even analyss of β decay [1] connued and resuled n he developmen of a novel mehod of bea-decay me-nerval analyss ha produces hghly accurae
More informationAlgorithm Research on Moving Object Detection of Surveillance Video Sequence *
Opcs and Phooncs Journal 03 3 308-3 do:0.436/opj.03.3b07 Publshed Onlne June 03 (hp://www.scrp.org/journal/opj) Algorhm Research on Movng Objec Deecon of Survellance Vdeo Sequence * Kuhe Yang Zhmng Ca
More informationA Novel Object Detection Method Using Gaussian Mixture Codebook Model of RGB-D Information
A Novel Objec Deecon Mehod Usng Gaussan Mxure Codebook Model of RGB-D Informaon Lujang LIU 1, Gaopeng ZHAO *,1, Yumng BO 1 1 School of Auomaon, Nanjng Unversy of Scence and Technology, Nanjng, Jangsu 10094,
More informationSOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β
SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose
More informationParametric Estimation in MMPP(2) using Time Discretization. Cláudia Nunes, António Pacheco
Paramerc Esmaon n MMPP(2) usng Tme Dscrezaon Cláuda Nunes, Anóno Pacheco Deparameno de Maemáca and Cenro de Maemáca Aplcada 1 Insuo Superor Técnco, Av. Rovsco Pas, 1096 Lsboa Codex, PORTUGAL In: J. Janssen
More informationM. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria
IOSR Journal of Mahemacs (IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume 0, Issue 4 Ver. IV (Jul-Aug. 04, PP 40-44 Mulple SolonSoluons for a (+-dmensonalhroa-sasuma shallow waer wave equaon UsngPanlevé-Bӓclund
More informationResponse of MDOF systems
Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss
More information3. OVERVIEW OF NUMERICAL METHODS
3 OVERVIEW OF NUMERICAL METHODS 3 Inroducory remarks Ths chaper summarzes hose numercal echnques whose knowledge s ndspensable for he undersandng of he dfferen dscree elemen mehods: he Newon-Raphson-mehod,
More informationJournal of Theoretical and Applied Information Technology.
Journal of heorecal and Appled Informaon echnology 5-9 JAI. All rghs reserved. www.ja.org NEW APPROXIMAION FOR ANDOFF RAE AND NUMBER OF ANDOFF PROBABILIY IN CELLULAR SYSEMS UNDER GENERAL DISRIBUIONS OF
More informationImprovement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling
Rajesh ngh Deparmen of ascs, Banaras Hndu Unvers(U.P.), Inda Pankaj Chauhan, Nrmala awan chool of ascs, DAVV, Indore (M.P.), Inda Florenn marandache Deparmen of Mahemacs, Unvers of New Meco, Gallup, UA
More informationTesting a new idea to solve the P = NP problem with mathematical induction
Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he
More informatione-journal Reliability: Theory& Applications No 2 (Vol.2) Vyacheslav Abramov
June 7 e-ournal Relably: Theory& Applcaons No (Vol. CONFIDENCE INTERVALS ASSOCIATED WITH PERFORMANCE ANALYSIS OF SYMMETRIC LARGE CLOSED CLIENT/SERVER COMPUTER NETWORKS Absrac Vyacheslav Abramov School
More informationRobustness of DEWMA versus EWMA Control Charts to Non-Normal Processes
Journal of Modern Appled Sascal Mehods Volume Issue Arcle 8 5--3 Robusness of D versus Conrol Chars o Non- Processes Saad Saeed Alkahan Performance Measuremen Cener of Governmen Agences, Insue of Publc
More informationVolatility Interpolation
Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local
More informationUNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION
INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he
More informationNeural Networks-Based Time Series Prediction Using Long and Short Term Dependence in the Learning Process
Neural Neworks-Based Tme Seres Predcon Usng Long and Shor Term Dependence n he Learnng Process J. Puchea, D. Paño and B. Kuchen, Absrac In hs work a feedforward neural neworksbased nonlnear auoregresson
More informationOnline Signature Verification Using Vector Quantization and Hidden Markov Model
IOSR Journal of Elecroncs and Communcaon Engneerng (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver. IV (Mar - Apr.2015), PP 48-53 www.osrjournals.org Onlne Sgnaure Verfcaon Usng
More informationHybrid of Chaos Optimization and Baum-Welch Algorithms for HMM Training in Continuous Speech Recognition
Inernaonal Conference on Inellgen Conrol and Informaon Processng Augus 3-5, - Dalan, Chna Hybrd of Chaos Opmzaon and Baum-Welch Algorhms for HMM ranng n Connuous Speech Recognon Somayeh Cheshom, Saeed
More informationMachine Vision based Micro-crack Inspection in Thin-film Solar Cell Panel
Sensors & Transducers Vol. 179 ssue 9 Sepember 2014 pp. 157-161 Sensors & Transducers 2014 by FSA Publshng S. L. hp://www.sensorsporal.com Machne Vson based Mcro-crack nspecon n Thn-flm Solar Cell Panel
More informationMulti-Fuel and Mixed-Mode IC Engine Combustion Simulation with a Detailed Chemistry Based Progress Variable Library Approach
Mul-Fuel and Med-Mode IC Engne Combuson Smulaon wh a Dealed Chemsry Based Progress Varable Lbrary Approach Conens Inroducon Approach Resuls Conclusons 2 Inroducon New Combuson Model- PVM-MF New Legslaons
More informationDetection of Waving Hands from Images Using Time Series of Intensity Values
Deecon of Wavng Hands from Images Usng Tme eres of Inensy Values Koa IRIE, Kazunor UMEDA Chuo Unversy, Tokyo, Japan re@sensor.mech.chuo-u.ac.jp, umeda@mech.chuo-u.ac.jp Absrac Ths paper proposes a mehod
More informationPlanar truss bridge optimization by dynamic programming and linear programming
IABSE-JSCE Jon Conference on Advances n Brdge Engneerng-III, Augus 1-, 015, Dhaka, Bangladesh. ISBN: 978-984-33-9313-5 Amn, Oku, Bhuyan, Ueda (eds.) www.abse-bd.org Planar russ brdge opmzaon by dynamc
More information