Predicting Model of Traffic Volume Based on GreyMarkov


 Sophie Charles
 1 years ago
 Views:
Transcription
1 Vo. No. Modern Apped Scence Predctng Mode of Traffc Voume Based on GreyMarov Ynpeng Zhang Zhengzhou Muncpa Engneerng Desgn & Research Insttute Zhengzhou 5005 Chna Abstract Greymarov forecastng mode of traffc voume was founded by appyng the mode of GM () and Marov random process theory. The mode utzes the advantages of Greymarov GM () forecastng mode and Marov random process n order to dscover the deveopng and varyng tendency of the forecastng data sequences of traffc voume. The anayss of an exampe ndcates that the greymarov mode has good forecastng accuracy and exceent appcabty n predctng traffc voume. Keywords: Grey theory Greymarov mode Predcton of traffc voume. Introducton Generay the pannng of a hghway s desgned on the bass of the traffc voume predcton. The socaed defnton of traffc voume predcton s to study and cacuate the nsde ncrease and change of traffc and to obtan a voume n terms of desgn years accordng to the varety of transportaton capacty and the deveopment of economy and socety n the past present and future etc. Athough many earners have processed arge quantty of researches for predctng the traffc voume the resut s st bad.the transportaton engneerng s a compcated system whch ncudes many factors many structura ayers and many targets. The traffc nformaton contans the obvousy ayer compexty of structure the fuzzy reaton of constructon the varety of deveopment and the ndetermnaton of coeffcents and data. Because of the nfuence of some artfca factors unaffectedy envronmenta change and the restrcton of the technque methods at present t eads to the resut that the statstc or forecast data embrace some errors mstaes scarcty or faes. So the compcated system of traffc voume predcton s a representatvey grey probem (Zhang and Luo 00). The grey mode has been apped n the traffc voume predcton and prmary maes use of mode GM () to perform the forecast (Wen et a.006; Xue and Zeng 006). Because the souton of mode GM () s an exponenta curve that s smooth t doesn t match wth those data that are vbraton sequences and ts forecast accuracy s ower. The study object of marov transton mode s a dynamc system whch forecasts the future by anayzng the nsde reguaton of deveopment n tme to come and t refects the nfuence degree and aws whch es n the transton process of factors from one state to the other. The marov transton mode s sutabe for the souton to predct these stochastc data sequences that are steady but n the reastc word these raw sequences are vbratng and changng n a certan varety trend. From the anayss above we now that the mode GM () and the marov mode coud be ntegrated wth each other to forecast by ther advantages. That s: mode GM () can be used to forecast the change trend of data sequences whe the marov mode can be used to decde the vbraton reguaton of ther deveopment and both can be joned together to become a greymarov forecast mode. Snce t maes fu use of the od nformaton gven from these raw data and ncreases the forecast accuracy the appcaton of the greymarov forecast mode whch provdes a new method to predct these greaty stochastc data sequences has been mproved further.. Estabshment of the Mathematcs Mode. Mode of GM() The grey GM () mode can mae use of the dscrete data seres to estabsh a equaton of grey contnuous dfferenta equaton by addng these data from the frst n Accumuatng Generaton Operator (AGO) and the equaton can be soved to perform the forecast (Deng 990). Let x be a raw seres whch s as foow: = { () L } = L n X x x x n Let x accumuated addng once and the accumuated generatng seres s obtaned: x = x x () L x ( n) () { } 6
2 Modern Apped Scence March 00 where By dfferentatng () ( 0) x = x ( ) = L n = x a whtened dfferenta equaton s obtaned dx dt The whtened tmeresponse of Eq. () s as foow (Deng 990): + ax = u () ( 0 ) () u a a xˆ + = x e + Let the souton u a ˆx accumuate subtratng once and the accumuated subtraton seres s obtaned: xˆ + = xˆ + xˆ (5) The curve of ˆx refects the vbraton trend of the raw seres. Fnay we can adopt the method of Deng (Deng 990) to chec the mode accuracy.. Grey marov chan Let { X n n T} be a marov chan where mn T( n ) and j L (L s caed status seres then the expressng (Sha 99) ( + ) p = P X = j X = (6) m n m s caed the n th step transton probabty and the matrx composed by transton matrx of Marov chan whch s expressed as: P ( n) ( n) ( n ) () p s caed the nth step probabty = p (7) If the eements transton probabtes of marov chan are grey t w be caed a grey marov chan and can be made up of a grey transton matrx (He and Bao 99). In the actua appcaton we now that t s dffcut to mae certan the vaues of transton probabty for t acs some nformaton but t s easy to have the nformaton of grey zone p by studyng the transton probabty. When the transton matrx s a grey matrx t s requred that the eements of whtenzaton matrx P % ( ) = p % s provded that p ( ) 0 % = L. () p j= % j L ; When the premnary dstrbuton of a marov mted chan s P = ( p p L p ) and the whtenzaton transton probabty matrx s P % ( ) = p % then we can get the next step dstrbuton of the chan: P = P P% (8) The second step can be expressed as: P = P P% = P 0 P% (9) The rest may be deduced by anaogy and the n th step dstrbuton s shown as: Pn = P P% n From Eq. t can be seen that we can easy forecast any future dstrbuton of the system f we have aready nown the raw dstrbuton and the grey transton probabty matrx.. Greymarov mode Let x { x x () x ( n) } = L be a raw data seres. After we have checed the mode accuracy we get the 7
3 Vo. No. smuaton sequence as: xˆ { xˆ xˆ () xˆ ( n) } 8 = L by mode GM(). Let ˆ Modern Apped Scence ( 0 ) y xˆ = for a vbraton sequence Yˆ whch s a marov chan we can dvde t nto states accordng to the concrete crcumstance and ts any state can be expressed as: = % % ˆ = y + A % y ( ) % = + B = L () ˆ where A and B are constant whch can be decded by the dfference between the forecast vaue and the raw data. Yˆ s a functon whch s changed n tme and so are the grey whtened eements of % %. If N ( m ) s the data number of the raw seres whch transfer m step from to j and N s the number of data that are n the grey zone then we ca: N p ( m) = j = L () N the m th step transton probabty. The transton matrx R( m ) s as foow: p( m) p( m) L p ( m) p( m) p( m) p ( m) Rm L = () M M M M p( m) p( m) L p( m) R( m ) refects the transton reguaton between dfferent states and s the foundaton of the forecast mode of grey marov. We can predct the future trend of the system by studyng the stochastc transton matrx R( m ). In practca appcaton f the forecast vaues s to be paced n the zone then nvestgate the th ne of the matrx R nsde and f max{ pj } = pr we can concude the next state of the system may transfer ts state from j to r. If R has more than two nes whose probabty vaues are same ae or cose to each other and t s dffcut to decde the next drecton of the system wth certan t s needed to study and chec the matrx R() or R( m )(m ). At the same tme t can decde the transton of the system by checng R or R( m )(m ) and ~ ~ aso be made sure the forecast zone [ ]. Fnay the eventua forecast s n the mdde pont of the grey zone then got: Yˆ = ( % +% ) () whch aso can be expressed as: Yˆ = yˆ + ( A + B) (5). Exampe Anayss The data of a hghway s traffc voume through years are sted n Tab... Estabshment of GM () mode From tabe we get x ={ }. After do them n AGO we obtan x ={ }. Then we can have two constants: a = u = 65.. dx By combnng wth Eq. () we can estabsh the mode GM(): x = 65.. dt After sovng the equaton tmeresponse functon can be obtaned as:
4 Modern Apped Scence March xˆ ( + ) = 97.e From Eq. (5) t can be got: yˆ xˆ ( ) xˆ ( ) xˆ = + = +. The examnaton resut of the predcton accuracy s as foow: x = 96.7 S = 06.0 q =.5 S = 7.. The postexamnaton margn rato s as foow: C = S / S = < 0.5. The probabty of tte error s as foow: P{ q q < 0.675S} = P{ q q < 80.0} = > 0.95 The accuracy grade of the forecast s exceent (Deng 990).. Compartmentazaton of the predcton Accordng to the raw traffc voume and for smpfcaton the predcton vaues can be dvded nto four states by Eq. () as foows: =[ % % ]: % ˆ = y 0.x % ˆ = y 0.05x =[ % =[ % =[ % % ]: % = y x % = ˆ 0.05 ŷ % ]: % = % y ( ) ŷ ˆ = x % ]: % = y + x % y ( ) ˆ 0.05 ˆ = + 0.x =[ % ˆ = y x % ˆ = y + 0.x where y ˆ s the forecast traffc as mode GM () and x s the annua average traffc voume. If we show the vaues of the fact the predcton yˆ and four states through these years we w obtan a dagram sted as Fg. n whch there are four parae and symmetry band dstrcts form the top to the bottom.. Cacuaton of the transton probabty From Fg. we now that the number of raw sequence whch s n the zone of s N = N = N = N = and s the number of raw data from to respectvey by a step. If the rest may be deduced n the same way we can cacuate the number of raw transton data. Fnay we have % ]: % / / / / p whch maes up of the matrx R = 0 / 0 / 0 / / 0 forecast the transton state of the traffc voume n the future.. Decson of the predcton and vbraton zone by Eq. (). Accordng to R we can By studyng R we now that the average predcton of 00w mosty be n the vbraton zone whch s [ ].Then usng formua () or (5) we have ˆ Y (00) = ( )=65. In the same way we can get Y ˆ (00) =58 Y ˆ (00) =6660 Y ˆ (00) =76 Y ˆ (005) =99. Concuson The grey mode GM () refects the macroscopca reguaton the marov mode shows the vbraton deveopment of the mcrocosmc system and both not ony have the mutua advantage but aso can mae fu use of the nformaton whch s ncuded n these raw data. Therefore the forecastng greymarov mode has much hgher accuracy reabty and appcaton n the traffc voume predcton. On the other hand because the predcton accuracy s n ne wth the raw data seres and the dvded states but there s not a gven standard that can reay unfy and sette these probems and the appcaton of the mode st needs a further research and mprovement. 9
5 Vo. No. Modern Apped Scence References Deng J. L. (990). Grey system theory tutora Huazhong Unversty of Scence and Technoogy Press Wuhan Chna. He Y. and BaoY. D. (99). Grey marov chan predcton mode and the mpcaton. Systems Engneerng Theory and Practce 99(): 7. Sha J. Z. (99). Marten decson programmng and ts appcaton n management Natona Defence Industry Press Beng Chna. Wen K. G. Qu S. R. and Wang J. (006). An urban traffc fows predcton mode based on system grey theory. Transactons of Shenyang Lgong Unversty 006 5(): . Xue C. M. and Zeng Y. K. (006). On grey predcton mode for road traffc freght voume. Journa of Kunmng Unversty of Scence and Technoogy ( Scence and Technoogy) 006 (): Zhang X. T. and Luo X. H. (00). The appcaton of grey theory and mode n traffc voume predcton. Hghway 00(8): 7. Tabe. Hstorca Traffc Voume Year AADT (n/d) AADT=Annua Average Day Traffc Fgure. Annua Average Traffc Voume 50
ShortTerm Load Forecasting for Electric Power Systems Using the PSOSVR and FCM Clustering Techniques
Energes 20, 4, 7384; do:0.3390/en40073 Artce OPEN ACCESS energes ISSN 996073 www.mdp.com/journa/energes ShortTerm Load Forecastng for Eectrc Power Systems Usng the PSOSVR and FCM Custerng Technques
More informationON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION
European Journa of Mathematcs and Computer Scence Vo. No. 1, 2017 ON AUTOMATC CONTNUTY OF DERVATONS FOR BANACH ALGEBRAS WTH NVOLUTON Mohamed BELAM & Youssef T DL MATC Laboratory Hassan Unversty MORO CCO
More informationA LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS. Dr. Derald E. Wentzien, Wesley College, (302) ,
A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS Dr. Derald E. Wentzen, Wesley College, (302) 7362574, wentzde@wesley.edu ABSTRACT A lnear programmng model s developed and used to compare
More informationOptimization of JK Flip Flop Layout with Minimal Average Power of Consumption based on ACOR, FuzzyACOR, GA, and FuzzyGA
Journa of mathematcs and computer Scence 4 (05)  5 Optmzaton of JK Fp Fop Layout wth Mnma Average Power of Consumpton based on ACOR, FuzzyACOR, GA, and FuzzyGA Farshd Kevanan *,, A Yekta *,, Nasser
More informationComparison of the Population Variance Estimators. of 2Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method
Appled Mathematcal Scences, Vol. 7, 0, no. 47, 070 HIARI Ltd, www.mhkar.com Comparson of the Populaton Varance Estmators of Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationDesigning of Combined Continuous Lot By Lot Acceptance Sampling Plan
Internatonal Journal o Scentc Research Engneerng & Technology (IJSRET), ISSN 78 02 709 Desgnng o Combned Contnuous Lot By Lot Acceptance Samplng Plan S. Subhalakshm 1 Dr. S. Muthulakshm 2 1 Research Scholar,
More informationDepartment of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6
Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.
More informationTHE CURRENT BALANCE Physics 258/259
DSH 1988, 005 THE CURRENT BALANCE Physcs 58/59 The tme average force between two parallel conductors carryng an alternatng current s measured by balancng ths force aganst the gravtatonal force on a set
More informationNote On Some Identities of New Combinatorial Integers
Apped Mathematcs & Informaton Scences 5(3 (20, 50053 An Internatona Journa c 20 NSP Note On Some Identtes of New Combnatora Integers Adem Kııçman, Cenap Öze 2 and Ero Yımaz 3 Department of Mathematcs
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture  30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
More informationResearch on the Fuzzy Control for Vehicle Semiactive Suspension. Xiaoming Hu 1, a, Wanli Li 1,b
Advanced Materals Research Onlne: 00 ISSN: 9, Vol., pp 9 do:0.0/www.scentfc.net/amr.. 0 Trans Tech Publcatons, Swterland Research on the Fuy Control for Vehcle Semactve Suspenson Xaomng Hu, a, Wanl
More informationNetworked Cooperative Distributed Model Predictive Control Based on State Observer
Apped Mathematcs, 6, 7, 4864 ubshed Onne June 6 n ScRes. http://www.scrp.org/journa/am http://dx.do.org/.436/am.6.73 Networed Cooperatve Dstrbuted Mode redctve Contro Based on State Observer Ba Su, Yanan
More informationDecentralized Adaptive Control for a Class of LargeScale Nonlinear Systems with Unknown Interactions
Decentrazed Adaptve Contro for a Cass of LargeScae onnear Systems wth Unknown Interactons Bahram Karm 1, Fatemeh Jahangr, Mohammad B. Menhaj 3, Iman Saboor 4 1. Center of Advanced Computatona Integence,
More informationNumerical integration in more dimensions part 2. Remo Minero
Numerca ntegraton n more dmensons part Remo Mnero Outne The roe of a mappng functon n mutdmensona ntegraton Gauss approach n more dmensons and quadrature rues Crtca anass of acceptabt of a gven quadrature
More informationStatistical Evaluation of WATFLOOD
tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth
More informationParameter Estimation for Dynamic System using Unscented Kalman filter
Parameter Estmaton for Dynamc System usng Unscented Kalman flter Jhoon Seung 1,a, Amr Atya F. 2,b, Alexander G.Parlos 3,c, and Klto Chong 1,4,d* 1 Dvson of Electroncs Engneerng, Chonbuk Natonal Unversty,
More informationAmusing Properties of Odd Numbers Derived From Valuated Binary Tree
IOSR Journal of Mathematcs (IOSRJM) eiss: 78578, piss: 19765X. Volume 1, Issue 6 Ver. V (ov.  Dec.016), PP 557 www.osrjournals.org Amusng Propertes of Odd umbers Derved From Valuated Bnary Tree
More informationLossy Compression. Compromise accuracy of reconstruction for increased compression.
Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost
More informationA MINMAX REGRET ROBUST OPTIMIZATION APPROACH FOR LARGE SCALE FULL FACTORIAL SCENARIO DESIGN OF DATA UNCERTAINTY
A MINMAX REGRET ROBST OPTIMIZATION APPROACH FOR ARGE SCAE F FACTORIA SCENARIO DESIGN OF DATA NCERTAINTY Travat Assavapokee Department of Industra Engneerng, nversty of Houston, Houston, Texas 77044008,
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More information2 Finite difference basics
Numersche Methoden 1, WS 11/12 B.J.P. Kaus 2 Fnte dfference bascs Consder the one The bascs of the fnte dfference method are best understood wth an example. dmensonal transent heat conducton equaton T
More informationPulse Coded Modulation
Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITUT G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal
More informationGrey prediction model in world women s pentathlon performance prediction applied research
Avalable onlne www.jocpr.com Journal of Chemcal and Pharmaceutcal Research, 4, 6(6):364 Research Artcle ISSN : 9757384 CODEN(USA) : JCPRC5 Grey predcton model n world women s pentathlon performance predcton
More information22.51 Quantum Theory of Radiation Interactions
.51 Quantum Theory of Radaton Interactons Fna Exam  Soutons Tuesday December 15, 009 Probem 1 Harmonc oscator 0 ponts Consder an harmonc oscator descrbed by the Hamtonan H = ω(nˆ + ). Cacuate the evouton
More informationStudy on NonLinear Dynamic Characteristic of Vehicle. Suspension Rubber Component
Study on NonLnear Dynamc Characterstc of Vehcle Suspenson Rubber Component Zhan Wenzhang Ln Y Sh GuobaoJln Unversty of TechnologyChangchun, Chna Wang Lgong (MDI, Chna [Abstract] The dynamc characterstc
More informationScroll Generation with Inductorless Chua s Circuit and Wien Bridge Oscillator
Latest Trends on Crcuts, Systems and Sgnals Scroll Generaton wth Inductorless Chua s Crcut and Wen Brdge Oscllator Watcharn Jantanate, Peter A. Chayasena, and Sarawut Sutorn * Abstract An nductorless Chua
More informationNegative Binomial Regression
STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...
More informationA parametric Linear Programming Model Describing Bandwidth Sharing Policies for ABR Traffic
parametrc Lnear Programmng Mode Descrbng Bandwdth Sharng Poces for BR Traffc I. Moschoos, M. Logothets and G. Kokknaks Wre ommuncatons Laboratory, Dept. of Eectrca & omputer Engneerng, Unversty of Patras,
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationUncertainty Specification and Propagation for Loss Estimation Using FOSM Methods
Uncertanty Specfcaton and Propagaton for Loss Estmaton Usng FOSM Methods J.W. Baer and C.A. Corne Dept. of Cv and Envronmenta Engneerng, Stanford Unversty, Stanford, CA 94305400 Keywords: Sesmc, oss estmaton,
More informationDmitry A. Zaitsev Odessa National Telecommunication Academy Kuznechnaya, 1, Odessa, Ukraine
th Worksho on Agorthms and Toos for Petr Nets, Setember  October, 4, Unversty of Paderborn, Germany, 758 Sovng the fundamenta equaton of Petr net usng the decomoston nto functona subnets Dmtry A Zatsev
More informationOptimal Guaranteed Cost Control of Linear Uncertain Systems with Input Constraints
Internatona Journa Optma of Contro, Guaranteed Automaton, Cost Contro and Systems, of Lnear vo Uncertan 3, no Systems 3, pp 3974, wth Input September Constrants 5 397 Optma Guaranteed Cost Contro of Lnear
More informationOptimum Selection Combining for MQAM on Fading Channels
Optmum Seecton Combnng for MQAM on Fadng Channes M. Surendra Raju, Ramesh Annavajjaa and A. Chockangam Insca Semconductors Inda Pvt. Ltd, Bangaore56000, Inda Department of ECE, Unversty of Caforna, San
More informationWeek3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1s tme nterval. The velocty of the partcle
More informationTimeVarying Systems and Computations Lecture 6
TmeVaryng Systems and Computatons Lecture 6 Klaus Depold 14. Januar 2014 The Kalman Flter The Kalman estmaton flter attempts to estmate the actual state of an unknown dscrete dynamcal system, gven nosy
More information18. SIMPLE LINEAR REGRESSION III
8. SIMPLE LINEAR REGRESSION III US Domestc Beers: Calores vs. % Alcohol Ftted Values and Resduals To each observed x, there corresponds a yvalue on the ftted lne, y ˆ ˆ = α + x. The are called ftted values.
More informationx yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting.
The Practce of Statstcs, nd ed. Chapter 14 Inference for Regresson Introducton In chapter 3 we used a leastsquares regresson lne (LSRL) to represent a lnear relatonshp etween two quanttatve explanator
More informationABSTRACT
RELIABILITY AND SENSITIVITY ANALYSIS OF THE KOUTOFN:G WARM STANDBY PARALLEL REPAIRABLE SYSTEM WITH REPLACEMENT AT COMMONCAUSE FAILURE USING MARKOV MODEL M. A. ElDamcese 1 and N. H. ElSodany 2 1 Mathematcs
More informationJAB Chain. Longtail claims development. ASTIN  September 2005 B.Verdier A. Klinger
JAB Chan Longtal clams development ASTIN  September 2005 B.Verder A. Klnger Outlne Chan Ladder : comments A frst soluton: Munch Chan Ladder JAB Chan Chan Ladder: Comments Black lne: average pad to ncurred
More informationIII. Econometric Methodology Regression Analysis
Page Econ07 Appled Econometrcs Topc : An Overvew of Regresson Analyss (Studenmund, Chapter ) I. The Nature and Scope of Econometrcs. Lot s of defntons of econometrcs. Nobel Prze Commttee Paul Samuelson,
More informationStudy on Active Microvibration Isolation System with Linear Motor Actuator. Gongyu PAN, Wenyan GU and Dong LI
2017 2nd Internatonal Conference on Electrcal and Electroncs: echnques and Applcatons (EEA 2017) ISBN: 9781605954165 Study on Actve Mcrovbraton Isolaton System wth Lnear Motor Actuator Gongyu PAN,
More informationCS 468 Lecture 16: Isometry Invariance and Spectral Techniques
CS 468 Lecture 16: Isometry Invarance and Spectral Technques Justn Solomon Scrbe: Evan Gawlk Introducton. In geometry processng, t s often desrable to characterze the shape of an object n a manner that
More informationPsychology 282 Lecture #24 Outline Regression Diagnostics: Outliers
Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.
More informationPrediction Error of the Multivariate Additive Loss Reserving Method for Dependent Lines of Business
Predcton Error of the Mutvarate Addtve Loss Reservng Method for Dependent Lnes of Busness by Mchae Merz and Maro V Wüthrch ABSTRACT Often n nonfe nsurance, cams reserves are the argest poston on the abty
More informationIndeterminate pinjointed frames (trusses)
Indetermnate pnjonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all
More informationSolving Fractional Nonlinear Fredholm Integrodifferential Equations via Hybrid of Rationalized Haar Functions
ISSN 7467659 England UK Journal of Informaton and Computng Scence Vol. 9 No. 3 4 pp. 698 Solvng Fractonal Nonlnear Fredholm Integrodfferental Equatons va Hybrd of Ratonalzed Haar Functons Yadollah Ordokhan
More informationAssignment 5. Simulation for Logistics. Monti, N.E. Yunita, T.
Assgnment 5 Smulaton for Logstcs Mont, N.E. Yunta, T. November 26, 2007 1. Smulaton Desgn The frst objectve of ths assgnment s to derve a 90% twosded Confdence Interval (CI) for the average watng tme
More informationPolite Waterfilling for Weighted Sumrate Maximization in MIMO BMAC Networks under. Multiple Linear Constraints
2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Pote Waterfng for Weghted Sumrate Maxmzaton n MIMO BMAC Networks under Mutpe near Constrants An u 1, Youjan u 2, Vncent K. N. au 3, Hage
More informationIrregular vibrations in multimass discretecontinuous systems torsionally deformed
(2) 4 48 Irregular vbratons n multmass dscretecontnuous systems torsonally deformed Abstract In the paper rregular vbratons of dscretecontnuous systems consstng of an arbtrary number rgd bodes connected
More informationKey words. corner singularities, energycorrected finite element methods, optimal convergence rates, pollution effect, reentrant corners
NESTED NEWTON STRATEGIES FOR ENERGYCORRECTED FINITE ELEMENT METHODS U. RÜDE1, C. WALUGA 2, AND B. WOHLMUTH 2 Abstract. Energycorrected fnte eement methods provde an attractve technque to dea wth eptc
More informationComparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy
Comparatve Studes of Law of Conservaton of Energy and Law Clusters of Conservaton of Generalzed Energy No.3 of Comparatve Physcs Seres Papers Fu Yuhua (CNOOC Research Insttute, Emal:fuyh1945@sna.com)
More informationPhysics 5153 Classical Mechanics. Principle of Virtual Work1
P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal
More informationStatistics for Business and Economics
Statstcs for Busness and Economcs Chapter 11 Smple Regresson Copyrght 010 Pearson Educaton, Inc. Publshng as Prentce Hall Ch. 111 11.1 Overvew of Lnear Models n An equaton can be ft to show the best lnear
More informationB and H sensors for 3D magnetic property testing
B and H sensors for 3D magnetc property testng Zh We Ln, Jan Guo Zhu, You Guang Guo, Jn Jang Zhong, and Ha We Lu Faculty of Engneerng, Unversty of Technology, Sydney, PO Bo 123, Broadway, SW 2007, Australa
More informationOverTemperature protection for IGBT modules
OverTemperature protecton for IGBT modules Ke Wang 1, Yongjun Lao 2, Gaosheng Song 1, Xanku Ma 1 1 Mtsubsh Electrc & Electroncs (Shangha) Co., Ltd., Chna Room2202, Tower 3, Kerry Plaza, No.11 Zhongxns
More informationGeometric drawings of K n with few crossings
Geometrc drawngs of K n wth few crossngs Bernardo M. Ábrego, Slva FernándezMerchant Calforna State Unversty Northrdge {bernardo.abrego,slva.fernandez}@csun.edu ver 9 Abstract We gve a new upper bound
More information( ) r! t. Equation (1.1) is the result of the following two definitions. First, the bracket is by definition a scalar product.
Chapter. Quantum Mechancs Notes: Most of the matera presented n ths chapter s taken from CohenTannoudj, Du, and Laoë, Chap. 3, and from Bunker and Jensen 5), Chap... The Postuates of Quantum Mechancs..
More informationUncertainty in measurements of power and energy on power networks
Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:
More informationA DerivativeFree Algorithm for Bound Constrained Optimization
Computatona Optmzaton and Appcatons, 21, 119 142, 2002 c 2002 Kuwer Academc Pubshers. Manufactured n The Netherands. A DervatveFree Agorthm for Bound Constraned Optmzaton STEFANO LUCIDI ucd@ds.unroma.t
More informationA Hybrid Forecast of Exchange Rate based on Discrete GreyMarkov and Grey Neural Network Model
A Hybrd Forecast of Exchange Rate based on Dscrete GreyMarov and Grey eural etwor Model Gol Km a, R Su Yun b ( a Center of atural Scence, Unversty of Scences, Pyongyang, DPR Korea, Emal: golm4@yahoocom
More informationItem calibration in incomplete testing designs
Pscoógca (20), 32, 0732 Item cabraton n ncompete testng desgns Theo JHM Eggen * & Norman D Verhest** *Cto/Unversty of Twente, The etherands **Cto, The etherands Ths study dscusses the justfabty of tem
More informationA principal component analysis and entropy value calculate method in SPSS for MDLAP model
A prncpa component anayss and entropy vaue cacuate method n SPSS for MDLAP mode ZIPENG ZHANG Schoo of Management scence and Engneerng, Shandong Norma Unversty, Jnan, Chna, HONGGUO WANG Schoo of nformaton
More informationA Short Term Forecasting Method for Wind Power Generation System based on BP Neural Networks
Advanced Scence and Technology Letters Vol.83 (ISA 05), pp.775 http://dx.do.org/0.457/astl.05.83.4 A Short Term Forecastng Method for Wnd Power Generaton System based on BP Neural Networks Shenghu Wang,
More informationA Network Intrusion Detection Method Based on Improved Kmeans Algorithm
Advanced Scence and Technology Letters, pp.429433 http://dx.do.org/10.14257/astl.2014.53.89 A Network Intruson Detecton Method Based on Improved Kmeans Algorthm Meng Gao 1,1, Nhong Wang 1, 1 Informaton
More informationCalculus of Variations Basics
Chapter 1 Calculus of Varatons Bascs 1.1 Varaton of a General Functonal In ths chapter, we derve the general formula for the varaton of a functonal of the form J [y 1,y 2,,y n ] F x,y 1,y 2,,y n,y 1,y
More informationXII.3 The EM (ExpectationMaximization) Algorithm
XII.3 The EM (ExpectatonMaxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles
More informationPopClick Noise Detection Using InterFrame Correlation for Improved Portable Auditory Sensing
Advanced Scence and Technology Letters, pp.164168 http://dx.do.org/10.14257/astl.2013 PopClc Nose Detecton Usng InterFrame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,
More informationAnalytical Chemistry Calibration Curve Handout
I. Quckand Drty Excel Tutoral Analytcal Chemstry Calbraton Curve Handout For those of you wth lttle experence wth Excel, I ve provded some key technques that should help you use the program both for problem
More informationNote 10. Modeling and Simulation of Dynamic Systems
Lecture Notes of ME 475: Introducton to Mechatroncs Note 0 Modelng and Smulaton of Dynamc Systems Department of Mechancal Engneerng, Unversty Of Saskatchewan, 57 Campus Drve, Saskatoon, SK S7N 5A9, Canada
More informationarxiv: v1 [cs.gt] 28 Mar 2017
A Dstrbuted Nash qubrum Seekng n Networked Graphca Games Farzad Saehsadaghan, and Lacra Pave arxv:7009765v csgt 8 Mar 07 Abstract Ths paper consders a dstrbuted gossp approach for fndng a Nash equbrum
More informationThermodynamics II. Department of Chemical Engineering. Prof. Kim, Jong Hak
Thermodynamcs II Department o Chemca ngneerng ro. Km, Jong Hak .5 Fugacty & Fugacty Coecent : ure Speces µ > provdes undamenta crteron or phase equbrum not easy to appy to sove probem Lmtaton o gn (.9
More informationLecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES
COMPUTATIONAL FLUID DYNAMICS: FDM: Appromaton of Second Order Dervatves Lecture APPROXIMATION OF SECOMD ORDER DERIVATIVES. APPROXIMATION OF SECOND ORDER DERIVATIVES Second order dervatves appear n dffusve
More informationLecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding
Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an
More informationAchieving Optimal Throughput Utility and Low Delay with CSMAlike Algorithms: A Virtual MultiChannel Approach
IEEE/AM TRANSATIONS ON NETWORKING, VOL. X, NO. XX, XXXXXXX 20X Achevng Optma Throughput Utty and Low Deay wth SMAke Agorthms: A Vrtua Muthanne Approach PoKa Huang, Student Member, IEEE, and Xaojun Ln,
More informationTesting for seasonal unit roots in heterogeneous panels
Testng for seasonal unt roots n heterogeneous panels Jesus Otero * Facultad de Economía Unversdad del Rosaro, Colomba Jeremy Smth Department of Economcs Unversty of arwck Monca Gulett Aston Busness School
More informationDETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM
Ganj, Z. Z., et al.: Determnaton of Temperature Dstrbuton for S111 DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM by Davood Domr GANJI
More informationCorrespondence. Performance Evaluation for MAP State Estimate Fusion I. INTRODUCTION
Correspondence Performance Evauaton for MAP State Estmate Fuson Ths paper presents a quanttatve performance evauaton method for the maxmum a posteror (MAP) state estmate fuson agorthm. Under dea condtons
More informationLecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management
Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 1 Chapters 14, 15 & 16 Professor Ahmad, Ph.D. Department of Management Revsed August 005 Chapter 14 Formulas Smple Lnear Regresson Model: y =
More informationAndreas C. Drichoutis Agriculural University of Athens. Abstract
Heteroskedastcty, the sngle crossng property and ordered response models Andreas C. Drchouts Agrculural Unversty of Athens Panagots Lazards Agrculural Unversty of Athens Rodolfo M. Nayga, Jr. Texas AMUnversty
More informationUNR Joint Economics Working Paper Series Working Paper No Further Analysis of the Zipf Law: Does the RankSize Rule Really Exist?
UNR Jont Economcs Workng Paper Seres Workng Paper No. 08005 Further Analyss of the Zpf Law: Does the RankSze Rule Really Exst? Fungsa Nota and Shunfeng Song Department of Economcs /030 Unversty of Nevada,
More informationPROPERTIES I. INTRODUCTION. Finite element (FE) models are widely used to predict the dynamic characteristics of aerospace
FINITE ELEMENT MODEL UPDATING USING BAYESIAN FRAMEWORK AND MODAL PROPERTIES Tshldz Marwala 1 and Sbusso Sbs I. INTRODUCTION Fnte element (FE) models are wdely used to predct the dynamc characterstcs of
More informationStatistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation
Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 PrentceHall, Inc. Chap. 131 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear
More informationMarkov chains. Definition of a CTMC: [2, page 381] is a continuous time, discrete value random process such that for an infinitesimal
Markov chans M. Veeraraghavan; March 17, 2004 [Tp: Study the MC, QT, and Lttle s law lectures together: CTMC (MC lecture), M/M/1 queue (QT lecture), Lttle s law lecture (when dervng the mean response tme
More informationExperience with Automatic Generation Control (AGC) Dynamic Simulation in PSS E
Semens Industry, Inc. Power Technology Issue 113 Experence wth Automatc Generaton Control (AGC) Dynamc Smulaton n PSS E Lu Wang, Ph.D. Staff Software Engneer lu_wang@semens.com Dngguo Chen, Ph.D. Staff
More informationThe Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD
he Gaussan classfer Nuno Vasconcelos ECE Department, UCSD Bayesan decson theory recall that we have state of the world X observatons g decson functon L[g,y] loss of predctng y wth g Bayes decson rule s
More informationScheduling problem with uncertain parameters
Bożejo W., Rajba P., Wodec M. Schedung probem wth uncertan parameters Schedung probem wth uncertan parameters by Wojcech Bożejo 1,3, Paweł Rajba 2, Meczysław Wodec 2,3 1 Wrocław Unversty of Technoogy,
More informationPlease review the following statement: I certify that I have not given unauthorized aid nor have I received aid in the completion of this exam.
NME (Last, Frst): Please revew the followng statement: I certfy that I have not gven unauthorzed ad nor have I receved ad n the completon of ths exam. Sgnature: INSTRUCTIONS Begn each problem n the space
More informationTrees and Order Conditions
Trees and Order Condtons Constructon of RungeKutta order condtons usng Butcher trees and seres. Paul Tranqull 1 1 Computatonal Scence Laboratory CSL) Department of Computer Scence Vrgna Tech. Trees and
More informationDifferentiating Gaussian Processes
Dfferentatng Gaussan Processes Andrew McHutchon Aprl 17, 013 1 Frst Order Dervatve of the Posteror Mean The posteror mean of a GP s gven by, f = x, X KX, X 1 y x, X α 1 Only the x, X term depends on the
More informationDigital PI Controller Equations
Ver. 4, 9 th March 7 Dgtal PI Controller Equatons Probably the most common tye of controller n ndustral ower electroncs s the PI (Proortonal  Integral) controller. In feld orented motor control, PI controllers
More informationIntegrals and Invariants of EulerLagrange Equations
Lecture 16 Integrals and Invarants of EulerLagrange Equatons ME 256 at the Indan Insttute of Scence, Bengaluru Varatonal Methods and Structural Optmzaton G. K. Ananthasuresh Professor, Mechancal Engneerng,
More informationGlobally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications
Sensors 2008, 8, 80868103; DOI: 10.3390/s8128086 OPEN ACCESS sensors ISSN 14248220 www.mdp.com/journa/sensors Artce Gobay Optma Mutsensor Dstrbuted Random Parameter Matrces Kaman Fterng Fuson wth Appcatons
More informationWorkshop: Approximating energies and wave functions Quantum aspects of physical chemistry
Workshop: Approxmatng energes and wave functons Quantum aspects of physcal chemstry http://quantum.bu.edu/pltl/6/6.pdf Last updated Thursday, November 7, 25 7:9:55: Copyrght 25 Dan Dll (dan@bu.edu) Department
More informationAssignment 4. Adsorption Isotherms
Insttute of Process Engneerng Assgnment 4. Adsorpton Isotherms Part A: Compettve adsorpton of methane and ethane In large scale adsorpton processes, more than one compound from a mxture of gases get adsorbed,
More informationΔ x. u(x,t) Fig. Schematic view of elastic bar undergoing axial motions
ME67  Handout 4 Vbratons of Contnuous Systems Axal vbratons of elastc bars The fgure shows a unform elastc bar of length and cross secton A. The bar materal propertes are ts densty ρ and elastc modulus
More informationAchieving Optimal Throughput Utility and Low Delay with CSMAlike Algorithms: A Virtual MultiChannel Approach
Achevng Optma Throughput Utty and Low Deay wth SMAke Agorthms: A Vrtua Muthanne Approach PoKa Huang, Student Member, IEEE, and Xaojun Ln, Senor Member, IEEE Abstract SMA agorthms have recenty receved
More informationDetermine the Optimal Order Quantity in Multiitems&s EOQ Model with Backorder
Australan Journal of Basc and Appled Scences, 5(7): 863873, 0 ISSN 99878 Determne the Optmal Order Quantty n Multtems&s EOQ Model wth Backorder Babak Khabr, Had Nasser, 3 Ehsan Ehsan and Nma Kazem Department
More informationKey Words: Hamiltonian systems, canonical integrators, symplectic integrators, RungeKuttaNyström methods.
CANONICAL RUNGEKUTTANYSTRÖM METHODS OF ORDERS 5 AND 6 DANIEL I. OKUNBOR AND ROBERT D. SKEEL DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANACHAMPAIGN 304 W. SPRINGFIELD AVE. URBANA, ILLINOIS
More informationMA 323 Geometric Modelling Course Notes: Day 13 Bezier Curves & Bernstein Polynomials
MA 323 Geometrc Modellng Course Notes: Day 13 Bezer Curves & Bernsten Polynomals Davd L. Fnn Over the past few days, we have looked at de Casteljau s algorthm for generatng a polynomal curve, and we have
More information