Prediction of Motion Trajectories Based on Markov Chains
|
|
- Clifford Marshall
- 5 years ago
- Views:
Transcription
1 2011 Intenational Confeence on Compute Science and Infomation Technology (ICCSIT 2011) IPCSIT vol. 51 (2012) (2012) IACSIT Pess, Singapoe DOI: /IPCSIT.2012.V51.50 Pediction of Motion Tajectoies Based on Makov Chains Ling Gan and Li na Su College of Compute Science and Technology, Chongqing Univesity of Posts & Telecommunications, Chongqing, China Abstact. As the eseach of object behavios become moe and moe impotant of compute vision in ecent yeas, Tajectoies analysis became a hot topic as it is an basic poblem of object behavios leaning and desciption. In this pape we pesent a pedict object tajectoies model based on MARKOV CHAINS fo the leaning of tajectoy distibution pattens of event ecognition. Due to MARKOV CHAINS pedict tajectoy model s epetition featue, it keeps coecting the pediction by calculated the data fom abnomal behavios, which called automatic leaning. With the two diffeent sets of data used to do the expeimental that appoved pedict object tajectoies model of MARKOV CHAINS has highly accuacy, efficiency and less dimensions compaed with othe leaning of tajectoy distibution pattens. Keywods: MARKOV CHAINS, tajectoy analysis and leaning, anomaly detection, behavio pediction. 1. Intoduction As the temendous potential value of visual suveillance, people pay moe attention on it. Taget detect, object classification, object tacking and event analysis which ae the basic poblems of visual suveillance obtained widely consideation especially event ecognition. In this pape we focus on tajectoy analysis which also is the basic poblem of event ecognition, without discussing othe fields such as object tacking. Most visual suveillance system and event ecognition based on scenes that is aleady known, in which objects moving with a established way. In this case, each scene needs to define a set of object behavios and keep updating since object behavios changed. We can t pedict object behavios even in this way the envionment is fixed, so it is useful and necessay to find a geneal method of event ecognition fo pedicting object behavios based on automatic geneate model. Johnson et al. pesent a statistics object tajectoy model which geneated fom image sequences, in this model object behavios ae descibed as a set of sequence flow volumes, each volume contains 4 elements to expess the object s position and velocity of image plane. The statistical model of object tajectoies is fomed with two two-laye competitive leaning netwoks that ae connected with leaky neuons. Pape 2 descibed a non-adaptive pedict model, which pedicts moving ca diection in time k+1 based on a sequence statuses of the font k times. As without adaptive leaning, pedict accuacy can be low. 2. Makov chains 2.1. Makov pocess and Makov chain Definition 1: a usually discete stochastic pocess (as a andom walk) in which the pobabilities of occuence of vaious futue states depend only on the pesent state of the system o on the immediately peceding state and not on the path by which the pesent state was achieved called also Makov chain. { X ( n ), n 0,1,2,...} Conside a discete state space E and a andom sequence, if any non-negative integes n, 1 n,... (0... ) 2 n m n 1 n 2 n m and natual numbe k, also with any sequence i, i,... i, j E 1 2 m which fom discete state space E can make below fomula, then { X ( n ), n 0,1,2,...} is the Makov chain. addess: Kanling_@163.com 292
2 n n i n i n i n m n m i m P { X ( k ) j X ( ), X ( ),..., X ( ) } m m m P{ X ( k) j X ( ) } In the fomula (1), if n,,... m means the time of now, 1 2 m 1 n n n means the time of past, at time of n k m (1), status j only depends on time n m s status, not depends on status of n, 1 n,... 2 n m 1,this featue called no afte-effects of Makov chain. Homogeneous Makov chain and k steps tansition pobability n n i P{ X ( k) j X ( ) }, k 1 ( n, n k) m m m Called k steps tansition pobability of Makov chain, maked as. Tansition pobability means when time n s status is i, the pobability of j which is k unit time afte ( n, n k) n, if p does not depend on Makov chain, which defined as homogeneous Makov chain. This condition only depends on tansition stat off status--i, tansition steps--k and tansition eached status j, ( k) nothing to do with n. Meanwhile k steps tansition pobability is maked as p, like below p p ( k) ( n, n k) P{ X ( n k) j X ( n) i}, k 0 In the fomula 0 p p j E ( k) 1, ( k) 1. p (2) 2.2. The detemination of multiple steps (1) Suppose k=1, p called one step tansition pobability, also maked as p fo shot. Matix which contains all one step tansition pobability p maked as P(1) means one step tansition matix at time m ( i, j E),as usual, ( n) we called it p fo shot. So all n steps tansition pobability is p.matix p(n) called n steps tansition pobability Makov chain, use C-K equation we can get the ecuence elations Then P( n) PP( n 1) P( n 1) P (3) Pn ( ) P n 3. K Steps Tansition Pobability matix And The Pediction 3.1. Build the k steps tansition pobability matix We successfully changed the model fom complicated path pediction to fok-to-fok connection, each fok coesponds to the status of Makov chain. We suppose thee ae n foks which means the tansition matix is n n, p means the pobability of fok i connects to j in one step tansition pobability matix (1 i, j n).obviously p can be geneated by statistics. Take N to expess the times of fok i connects fok j fom the vast eal statistical data p N n N j 1 (1 i, j n) (5) We can geneate the one step tansition pobability matix, accoding to the fomula (4) we also can geneate any k steps tansition pobability matix. Pk ( ) P k (4) (6) 3.2. The basic theoy and method of pediction The pediction fo choosing fok in the oad based on the statistical of histoy infomation and tajectoy. So the neaest pevious fok which is chosen takes moe affectivity on the pediction, meanwhile the ealy histoy is less impot as we can ignoe them. Then we can geneate the Makov chain and the pedict pobability of each fok by weighting. 2 a1 a 2 a k ( ) ( 1) ( 2)... ( ) k X t S t P S t P S t k P (7) In the fomula, t means the time of next fok, t-1 means the time of pevious fok, the simila as othes. X(t) means the pedict pobability fom above fomula calculates by weight,it is a 1 n matix with the value 293
3 of each element in it stands fo the pedict pobability fo the ight fok to be the next. As S(i), t k i t 1means the statuses of pev-i foks, it also is a matix of 1 n,which the value of line one, ow i is 1, the othes ae 0. It is a elative pobability, totally added the value of all elements may ove 1. a,,... 1a 2 a k is sepaately maked as how the impaction takes fom the font 1,2,...,k foks to the next fok. These values can be gained fom expeience, fistly, we set a... 1 a 2 a k. We also need to mak these foks as 0 which did not connect with the fok that is chosen just now, so we can the maximal element fom X(t) and take the coesponding fok as the next fok Desciption of main aithmetic and analysis Aithmetic 1: How to gain the matix of 1 to k steps pobability Input: N { N } n n Output: { N k} K n n // tansition matix of times M //1 to k steps tansition matix //accoding to statistics the one step tansition matix P is geneated FOREACH N N i // cumulate is accumulate pocess Sum=cumulate N N FOREACH N N i P / sum; N M P; 1 // the matix of 1 to k steps tansition pobability can be geneated fom fomula (3) FOR i=2 to k DO //matixmul saves the temp esult of multiply matixs M matixmul (, P ); i M i 1 ENDFOR RETURN M; The whole pocess need 2 n n epeat times, afte statistics we can geneate matix P fom matix N, complexity is O( n n). Thee is no need to use fomula (6) to gain 2 to k steps tansition pobability matix fom one steps tansition pobability matix, just as fomula (3) can be ecusive and also gained 1 to k steps tansition pobability matix. Complexity of each multiply matixs pocess is O( n n n), so the complexity of one to k steps tansition pobability matix is O( K n n n). Aithmetic 2 How to calculate pediction Input: G {G } n n M {M k} { } 1 k //taffic net K n n S Si Now X { X i} 1 n A { Ai} 1 K Output: Result //the most likely next fok //clea X to 0 and calculate the pediction setzeo( X ); FOR i 1 to k DO // matixmul saves the temp esult of multiply matixs Tepmatix matixmul ( Si, Mi) ; X=matixAdd(X,Tepmatix) ; 294
4 ENDFOR // filte out the foks which could be eached though the infomation of G FOR i 1 to n DO IF now, i G THEN X i =0 ; ENDIF ENDFOR //the fok with maximum value in X i esult=selectmax( X1, X 2,..., X n) ; RETURN esult Thee is need k-1 times of addition and multiplication to geneated each pediction, as S i is onedimensional, so the time complexity of the whole pocess is O(k n n+k n), it also needs n times to filte which complexity is O(n), at last, the complexity of filte final esult is O(n), total complexity is O(k n n). 4. Abnomal Tajectoy Detection Above tajectoy pediction system based on histoy data and was geneated by taining MARKOV CHAINS, as we found when emegency happens locally, like taffic was been boken off by natual disastes, o getting busy by taffic accident, then we expect the system esponds as the alet quickly. That is a pedict system which can detect abnomal tajectoy, it tacking cas and use exist MARKOV CHAINS model to pedict and take it is nomal if successfully pedict which fok the ca chosen, when pedict failed the system will keep the ecod into failue list, at the same time maked the fok numbe that ca chosen. Meanwhile the system calculates the times of failue timely, once the ate eaches to a theshold it will tigge a ectification use the ecods which wee kept as abnomal tajectoy happened Tacking ca Each fok of tacking ca system which based on camea need to setup one to ecod the fok that ca chosen and the chaacteistic of the ca so that we can ecognize it at next fok. Fom the exist methods like neual netwok, SVM, template matching we found at the same way many cas shae the same chaacteistic, at this condition eoganization of chaacteistics may not accuate as vehicle detection. Thee ae many stable and accuate methods of plate numbe identify, at each fok we set a camea to captue the plate numbe, then seach with it at the backgound database fo cas tacking Abnomal tajectoy detection Fo cas tacking, it is not only ecods the fok which the ca taken and do the pediction, it also check the esult fom which the system can use to detect the abnomal tajectoy. Suppose that ca V i just passed by fok k, at k, it is need to identify the plate numbe and also checked out the histoy tajectoy 1, 2,..., k 1,then begin to tain MARKOV CHAINS model by input these data, finally get ' k 1 the pediction esult of fok that the ca V i pobably will take. When the ca goes to the next fok ' k 1, the system do the exactly the same pocedue as above, moe it also checks the last pediction ' k 1, If the esult fom cuent ecod is the same with the pedication, shows pedict success, then go on the next one ' k 2, othewise means fail, at this condition it is need to ecod the eal choice, and count the ate of fail, but it is no need to do the pediction. Once the ate of fail eached theshold, MARKOV CHAINS model needs a evise. 5. Expeiment Result And Analysis To veify above method s validity, we setup an expeiment of path pedict. In this expeiment we statistics ecods of histoy tajectoy with 5 foks to choose. Then calculated and saved one to k steps tansition matixes, afte these peconditioning we used the MARKOV CHAINS model to do the pediction fo the kth fok based on the histoy data. Fom 1 shows the majo paametes of the whole expeiment. 295
5 Fom 1: Paametes Paametes value Meanings N 5 The numbe of foks K 3 Pediction of k steps histoy tajectoy a [4,1,0.25] Weight aay 1,...,k This expeiment takes monito of cas at 5 foks, and identifies plate numbe also pedicts moving tajectoy, below shows the esult: Pictue 1: Result of detect abnomal tajectoy Pictue 1 shows that compaison of ca tacking befoe and afte the taffic accident, obviously afte the time 4 when the accident happened, the pedict accuacy of tajectoy keeps falling down in those model who can not evise fom abnomal tajectoy. Since taffic police diected othe cas to go by a oundabout oute afte the accident, between the time 6 and 8, models without the ability to detect abnomal tajectoy failed to pedict until at time 8, taffic became nomal again. As ou model which was been impoved kept failing to pedict at the begging just like the othe models, but once the ate eached the theshold (it was been set as 0.4 in the expeiment), it tiggeed a ectification of MARKOV CHAINS, at time 6 afte the e-taining, the pedict system become effective. At time 8 the pocedue taken as the same way until time 10 it ecoveed. 6. Summay This pape pesent a pedict tajectoy method based on Makov chain, this model can pedict the cas tajectoy effectively and can evise once thee is abnomal behavios, with expeiment that poved this model has a highe accuacy and significance. 7. Refeences [1] N. Johnson and D. Hogg, Leaning the distibution of object tajectoies fo event ecognition, Image and Vision Computing, vol. 14, no. 8, pp , [2] PENG Qu,DING Zhi-ming,GUO Li-min.Pediction of Tajectoy Based on Makov Chains. Computing Technology.vol,37.no.8. [3] Weiming Hu, Dan Xie. A Hieachical Self-Oganizing Appoach fo Leaning the Pattens of Motion Tajectoies. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 1, JANUARY [4] T. Collins, A. J. Lipton, and T. Kanade, Intoduction to the special sectionon video suveillance, IEEE Tans. Patten Anal. Machine Intell.,vol. 22, pp , [5] R. J. Howath and H. Buxton, Conceptual desciptions fom monitoing and watching image sequences, Image and Vision Computing, vol. 18, no. 9, pp , [6] R. J. Howath and B. Hilay, An analogical epesentation of space and time, Image and Vision Computing, vol. 10, no. 7, pp , [7] E. Ande, G. Hezog, and T. Rist, On the simultaneous intepetation of eal wold image sequences and thei natual language desciption: The System Socce, in Poc. ECAI-88, Munich, 1988, pp
6 [8] K. Schaefe, M. Haag, W. Theilmann, and H. Nagel, Integation of image sequence evaluation and fuzzy metic tempoal logic pogamming, in KI-97: Advances in Atificial Intelligence, Lectue Notes in Compute Science, 1303, C. Habel, G. Bewka, and B. Nebel, Eds. New Yok: Spinge, 1997, pp [9] M. Band and V. Kettnake, Discovey and segmentation of activities in video, IEEE Tans. Patten Anal. Machine Intell., vol. 22, pp ,
MULTILAYER PERCEPTRONS
Last updated: Nov 26, 2012 MULTILAYER PERCEPTRONS Outline 2 Combining Linea Classifies Leaning Paametes Outline 3 Combining Linea Classifies Leaning Paametes Implementing Logical Relations 4 AND and OR
More informationNew problems in universal algebraic geometry illustrated by boolean equations
New poblems in univesal algebaic geomety illustated by boolean equations axiv:1611.00152v2 [math.ra] 25 Nov 2016 Atem N. Shevlyakov Novembe 28, 2016 Abstact We discuss new poblems in univesal algebaic
More informationCentral Coverage Bayes Prediction Intervals for the Generalized Pareto Distribution
Statistics Reseach Lettes Vol. Iss., Novembe Cental Coveage Bayes Pediction Intevals fo the Genealized Paeto Distibution Gyan Pakash Depatment of Community Medicine S. N. Medical College, Aga, U. P., India
More informationInformation Retrieval Advanced IR models. Luca Bondi
Advanced IR models Luca Bondi Advanced IR models 2 (LSI) Pobabilistic Latent Semantic Analysis (plsa) Vecto Space Model 3 Stating point: Vecto Space Model Documents and queies epesented as vectos in the
More informationBayesian Analysis of Topp-Leone Distribution under Different Loss Functions and Different Priors
J. tat. Appl. Po. Lett. 3, No. 3, 9-8 (6) 9 http://dx.doi.og/.8576/jsapl/33 Bayesian Analysis of Topp-Leone Distibution unde Diffeent Loss Functions and Diffeent Pios Hummaa ultan * and. P. Ahmad Depatment
More informationMultiple Criteria Secretary Problem: A New Approach
J. Stat. Appl. Po. 3, o., 9-38 (04 9 Jounal of Statistics Applications & Pobability An Intenational Jounal http://dx.doi.og/0.785/jsap/0303 Multiple Citeia Secetay Poblem: A ew Appoach Alaka Padhye, and
More informationA Deep Convolutional Neural Network Based on Nested Residue Number System
A Deep Convolutional Neual Netwok Based on Nested Residue Numbe System Hioki Nakahaa Ehime Univesity, Japan Tsutomu Sasao Meiji Univesity, Japan Abstact A pe-tained deep convolutional neual netwok (DCNN)
More informationHOW TO TEACH THE FUNDAMENTALS OF INFORMATION SCIENCE, CODING, DECODING AND NUMBER SYSTEMS?
6th INTERNATIONAL MULTIDISCIPLINARY CONFERENCE HOW TO TEACH THE FUNDAMENTALS OF INFORMATION SCIENCE, CODING, DECODING AND NUMBER SYSTEMS? Cecília Sitkuné Göömbei College of Nyíegyháza Hungay Abstact: The
More informationIdentification of the degradation of railway ballast under a concrete sleeper
Identification of the degadation of ailway ballast unde a concete sleepe Qin Hu 1) and Heung Fai Lam ) 1), ) Depatment of Civil and Achitectual Engineeing, City Univesity of Hong Kong, Hong Kong SAR, China.
More informationGoodness-of-fit for composite hypotheses.
Section 11 Goodness-of-fit fo composite hypotheses. Example. Let us conside a Matlab example. Let us geneate 50 obsevations fom N(1, 2): X=nomnd(1,2,50,1); Then, unning a chi-squaed goodness-of-fit test
More informationResearch on the Method of Identifying the Indicator Diagram of the Sucker Rod Pumping Unit
Intenational Coe Jounal of Engineeing Vol. No.3 05 ISSN: 44-895 eseach on the Method of Identifying the Indicato Diagam of the Sucke od Pumping Unit Cun Lu, enze Luo, Peng Jiang College of Mechanical and
More informationComputers and Mathematics with Applications
Computes and Mathematics with Applications 58 (009) 9 7 Contents lists available at ScienceDiect Computes and Mathematics with Applications jounal homepage: www.elsevie.com/locate/camwa Bi-citeia single
More informationDetermining solar characteristics using planetary data
Detemining sola chaacteistics using planetay data Intoduction The Sun is a G-type main sequence sta at the cente of the Sola System aound which the planets, including ou Eath, obit. In this investigation
More informationSurveillance Points in High Dimensional Spaces
Société de Calcul Mathématique SA Tools fo decision help since 995 Suveillance Points in High Dimensional Spaces by Benad Beauzamy Januay 06 Abstact Let us conside any compute softwae, elying upon a lage
More information4/18/2005. Statistical Learning Theory
Statistical Leaning Theoy Statistical Leaning Theoy A model of supevised leaning consists of: a Envionment - Supplying a vecto x with a fixed but unknown pdf F x (x b Teache. It povides a desied esponse
More informationGradient-based Neural Network for Online Solution of Lyapunov Matrix Equation with Li Activation Function
Intenational Confeence on Infomation echnology and Management Innovation (ICIMI 05) Gadient-based Neual Netwok fo Online Solution of Lyapunov Matix Equation with Li Activation unction Shiheng Wang, Shidong
More informationPearson s Chi-Square Test Modifications for Comparison of Unweighted and Weighted Histograms and Two Weighted Histograms
Peason s Chi-Squae Test Modifications fo Compaison of Unweighted and Weighted Histogams and Two Weighted Histogams Univesity of Akueyi, Bogi, v/noduslód, IS-6 Akueyi, Iceland E-mail: nikolai@unak.is Two
More informationITI Introduction to Computing II
ITI 1121. Intoduction to Computing II Macel Tucotte School of Electical Engineeing and Compute Science Abstact data type: Stack Stack-based algoithms Vesion of Febuay 2, 2013 Abstact These lectue notes
More informationPulse Neutron Neutron (PNN) tool logging for porosity Some theoretical aspects
Pulse Neuton Neuton (PNN) tool logging fo poosity Some theoetical aspects Intoduction Pehaps the most citicism of Pulse Neuton Neuon (PNN) logging methods has been chage that PNN is to sensitive to the
More informationUsing Laplace Transform to Evaluate Improper Integrals Chii-Huei Yu
Available at https://edupediapublicationsog/jounals Volume 3 Issue 4 Febuay 216 Using Laplace Tansfom to Evaluate Impope Integals Chii-Huei Yu Depatment of Infomation Technology, Nan Jeon Univesity of
More informationA Bijective Approach to the Permutational Power of a Priority Queue
A Bijective Appoach to the Pemutational Powe of a Pioity Queue Ia M. Gessel Kuang-Yeh Wang Depatment of Mathematics Bandeis Univesity Waltham, MA 02254-9110 Abstact A pioity queue tansfoms an input pemutation
More informationCALCULATING THE NUMBER OF TWIN PRIMES WITH SPECIFIED DISTANCE BETWEEN THEM BASED ON THE SIMPLEST PROBABILISTIC MODEL
U.P.B. Sci. Bull. Seies A, Vol. 80, Iss.3, 018 ISSN 13-707 CALCULATING THE NUMBER OF TWIN PRIMES WITH SPECIFIED DISTANCE BETWEEN THEM BASED ON THE SIMPLEST PROBABILISTIC MODEL Sasengali ABDYMANAPOV 1,
More informationInteraction of Feedforward and Feedback Streams in Visual Cortex in a Firing-Rate Model of Columnar Computations. ( r)
Supplementay mateial fo Inteaction of Feedfowad and Feedback Steams in Visual Cotex in a Fiing-Rate Model of Columna Computations Tobias Bosch and Heiko Neumann Institute fo Neual Infomation Pocessing
More informationTHE INFLUENCE OF THE MAGNETIC NON-LINEARITY ON THE MAGNETOSTATIC SHIELDS DESIGN
THE INFLUENCE OF THE MAGNETIC NON-LINEARITY ON THE MAGNETOSTATIC SHIELDS DESIGN LIVIU NEAMŢ 1, ALINA NEAMŢ, MIRCEA HORGOŞ 1 Key wods: Magnetostatic shields, Magnetic non-lineaity, Finite element method.
More informationFailure Probability of 2-within-Consecutive-(2, 2)-out-of-(n, m): F System for Special Values of m
Jounal of Mathematics and Statistics 5 (): 0-4, 009 ISSN 549-3644 009 Science Publications Failue Pobability of -within-consecutive-(, )-out-of-(n, m): F System fo Special Values of m E.M.E.. Sayed Depatment
More informationStanford University CS259Q: Quantum Computing Handout 8 Luca Trevisan October 18, 2012
Stanfod Univesity CS59Q: Quantum Computing Handout 8 Luca Tevisan Octobe 8, 0 Lectue 8 In which we use the quantum Fouie tansfom to solve the peiod-finding poblem. The Peiod Finding Poblem Let f : {0,...,
More informationPAPER 39 STOCHASTIC NETWORKS
MATHEMATICAL TRIPOS Pat III Tuesday, 2 June, 2015 1:30 pm to 4:30 pm PAPER 39 STOCHASTIC NETWORKS Attempt no moe than FOUR questions. Thee ae FIVE questions in total. The questions cay equal weight. STATIONERY
More informationON INDEPENDENT SETS IN PURELY ATOMIC PROBABILITY SPACES WITH GEOMETRIC DISTRIBUTION. 1. Introduction. 1 r r. r k for every set E A, E \ {0},
ON INDEPENDENT SETS IN PURELY ATOMIC PROBABILITY SPACES WITH GEOMETRIC DISTRIBUTION E. J. IONASCU and A. A. STANCU Abstact. We ae inteested in constucting concete independent events in puely atomic pobability
More informationF-IF Logistic Growth Model, Abstract Version
F-IF Logistic Gowth Model, Abstact Vesion Alignments to Content Standads: F-IFB4 Task An impotant example of a model often used in biology o ecology to model population gowth is called the logistic gowth
More informationThree-dimensional Quantum Cellular Neural Network and Its Application to Image Processing *
Thee-dimensional Quantum Cellula Neual Netwok and Its Application to Image Pocessing * Sen Wang, Li Cai, Huanqing Cui, Chaowen Feng, Xiaokuo Yang Science College, Ai Foce Engineeing Univesity Xi an 701,
More information10/04/18. P [P(x)] 1 negl(n).
Mastemath, Sping 208 Into to Lattice lgs & Cypto Lectue 0 0/04/8 Lectues: D. Dadush, L. Ducas Scibe: K. de Boe Intoduction In this lectue, we will teat two main pats. Duing the fist pat we continue the
More informationKunming, , R.P. China. Kunming, , R.P. China. *Corresponding author: Jianing He
Applied Mechanics and Mateials Online: 2014-04-28 ISSN: 1662-7482, Vol. 540, pp 92-95 doi:10.4028/www.scientific.net/amm.540.92 2014 Tans Tech Publications, Switzeland Reseach on Involute Gea Undecutting
More informationASTR415: Problem Set #6
ASTR45: Poblem Set #6 Cuan D. Muhlbege Univesity of Mayland (Dated: May 7, 27) Using existing implementations of the leapfog and Runge-Kutta methods fo solving coupled odinay diffeential equations, seveal
More informationDuality between Statical and Kinematical Engineering Systems
Pape 00, Civil-Comp Ltd., Stiling, Scotland Poceedings of the Sixth Intenational Confeence on Computational Stuctues Technology, B.H.V. Topping and Z. Bittna (Editos), Civil-Comp Pess, Stiling, Scotland.
More informationAppraisal of Logistics Enterprise Competitiveness on the Basis of Fuzzy Analysis Algorithm
Appaisal of Logistics Entepise Competitiveness on the Basis of Fuzzy Analysis Algoithm Yan Zhao, Fengge Yao, Minming She Habin Univesity of Commece, Habin, Heilongjiang 150028, China, zhaoyan2000@yahoo.com.cn
More informationLikelihood vs. Information in Aligning Biopolymer Sequences. UCSD Technical Report CS Timothy L. Bailey
Likelihood vs. Infomation in Aligning Biopolyme Sequences UCSD Technical Repot CS93-318 Timothy L. Bailey Depatment of Compute Science and Engineeing Univesity of Califonia, San Diego 1 Febuay, 1993 ABSTRACT:
More informationPARTICLE TRANSPORT AND DEPOSITION IN POROUS STRUCTURES: EFFECTS OF PARTICLE PROPERTIES, POROSITY AND REYNOLDS NUMBER
ISTP-16, 2005, PRAGUE 16 TH INTERNATIONAL SYMPOSIUM ON TRANSPORT PHENOMENA PARTICLE TRANSPORT AND DEPOSITION IN POROUS STRUCTURES: EFFECTS OF PARTICLE PROPERTIES, POROSITY AND REYNOLDS NUMBER S. Kuz*,
More informationRegularization. Stephen Scott and Vinod Variyam. Introduction. Outline. Machine. Learning. Problems. Measuring. Performance.
leaning can geneally be distilled to an optimization poblem Choose a classifie (function, hypothesis) fom a set of functions that minimizes an objective function Clealy we want pat of this function to
More informationDiffusion and Transport. 10. Friction and the Langevin Equation. Langevin Equation. f d. f ext. f () t f () t. Then Newton s second law is ma f f f t.
Diffusion and Tanspot 10. Fiction and the Langevin Equation Now let s elate the phenomena of ownian motion and diffusion to the concept of fiction, i.e., the esistance to movement that the paticle in the
More informationSafety variations in steel designed using Eurocode 3
JCSS Wokshop on eliability Based Code Calibation Safety vaiations in steel designed using Euocode 3 Mike Byfield Canfield Univesity Swindon, SN6 8LA, UK David Nethecot Impeial College London SW7 2BU, UK
More informationMAGNETIC FIELD AROUND TWO SEPARATED MAGNETIZING COILS
The 8 th Intenational Confeence of the Slovenian Society fo Non-Destuctive Testing»pplication of Contempoay Non-Destuctive Testing in Engineeing«Septembe 1-3, 5, Potoož, Slovenia, pp. 17-1 MGNETIC FIELD
More informationA new approach in classical electrodynamics to protect principle of causality
A new appoach in classical electodynamics to potect pinciple of causality Biswaanjan Dikshit * Lase and Plasma Technology Division Bhabha Atomic Reseach Cente, Mumbai-400085 INDIA * Coesponding autho E-mail:
More informationExploration of the three-person duel
Exploation of the thee-peson duel Andy Paish 15 August 2006 1 The duel Pictue a duel: two shootes facing one anothe, taking tuns fiing at one anothe, each with a fixed pobability of hitting his opponent.
More informationA Comparison and Contrast of Some Methods for Sample Quartiles
A Compaison and Contast of Some Methods fo Sample Quatiles Anwa H. Joade and aja M. Latif King Fahd Univesity of Petoleum & Mineals ABSTACT A emainde epesentation of the sample size n = 4m ( =, 1, 2, 3)
More informationSTUDY ON 2-D SHOCK WAVE PRESSURE MODEL IN MICRO SCALE LASER SHOCK PEENING
Study Rev. Adv. on -D Mate. shock Sci. wave 33 (13) pessue 111-118 model in mico scale lase shock peening 111 STUDY ON -D SHOCK WAVE PRESSURE MODEL IN MICRO SCALE LASER SHOCK PEENING Y.J. Fan 1, J.Z. Zhou,
More informationLinear Program for Partially Observable Markov Decision Processes. MS&E 339B June 9th, 2004 Erick Delage
Linea Pogam fo Patiall Obsevable Makov Decision Pocesses MS&E 339B June 9th 2004 Eick Delage Intoduction Patiall Obsevable Makov Decision Pocesses Etension of the Makov Decision Pocess to a wold with uncetaint
More informationOn the Comparison of Stability Analysis with Phase Portrait for a Discrete Prey-Predator System
Intenational Jounal of Applied Engineeing Reseach ISSN 0973-4562 Volume 12, Nume 24 (2017) pp. 15273-15277 Reseach India Pulications. http://www.ipulication.com On the Compaison of Staility Analysis with
More informationPhysics 211: Newton s Second Law
Physics 211: Newton s Second Law Reading Assignment: Chapte 5, Sections 5-9 Chapte 6, Section 2-3 Si Isaac Newton Bon: Januay 4, 1643 Died: Mach 31, 1727 Intoduction: Kinematics is the study of how objects
More informationThe Substring Search Problem
The Substing Seach Poblem One algoithm which is used in a vaiety of applications is the family of substing seach algoithms. These algoithms allow a use to detemine if, given two chaacte stings, one is
More informationThis is a very simple sampling mode, and this article propose an algorithm about how to recover x from y in this condition.
3d Intenational Confeence on Multimedia echnology(icm 03) A Simple Compessive Sampling Mode and the Recovey of Natue Images Based on Pixel Value Substitution Wenping Shao, Lin Ni Abstact: Compessive Sampling
More informationChaos and bifurcation of discontinuous dynamical systems with piecewise constant arguments
Malaya Jounal of Matematik ()(22) 4 8 Chaos and bifucation of discontinuous dynamical systems with piecewise constant aguments A.M.A. El-Sayed, a, and S. M. Salman b a Faculty of Science, Aleandia Univesity,
More informationA Short Combinatorial Proof of Derangement Identity arxiv: v1 [math.co] 13 Nov Introduction
A Shot Combinatoial Poof of Deangement Identity axiv:1711.04537v1 [math.co] 13 Nov 2017 Ivica Matinjak Faculty of Science, Univesity of Zageb Bijenička cesta 32, HR-10000 Zageb, Coatia and Dajana Stanić
More informationarxiv: v1 [math.co] 1 Apr 2011
Weight enumeation of codes fom finite spaces Relinde Juius Octobe 23, 2018 axiv:1104.0172v1 [math.co] 1 Ap 2011 Abstact We study the genealized and extended weight enumeato of the - ay Simplex code and
More informationMATH 415, WEEK 3: Parameter-Dependence and Bifurcations
MATH 415, WEEK 3: Paamete-Dependence and Bifucations 1 A Note on Paamete Dependence We should pause to make a bief note about the ole played in the study of dynamical systems by the system s paametes.
More informationCSCE 478/878 Lecture 4: Experimental Design and Analysis. Stephen Scott. 3 Building a tree on the training set Introduction. Outline.
In Homewok, you ae (supposedly) Choosing a data set 2 Extacting a test set of size > 3 3 Building a tee on the taining set 4 Testing on the test set 5 Repoting the accuacy (Adapted fom Ethem Alpaydin and
More informationI. Introduction to ecological populations, life tables, and population growth models
3-1 Population ecology Lab 3: Population life tables I. Intoduction to ecological populations, life tables, and population gowth models This week we begin a new unit on population ecology. A population
More informationLab #4: Newton s Second Law
Lab #4: Newton s Second Law Si Isaac Newton Reading Assignment: bon: Januay 4, 1643 Chapte 5 died: Mach 31, 1727 Chapte 9, Section 9-7 Intoduction: Potait of Isaac Newton by Si Godfey Knelle http://www.newton.cam.ac.uk/at/potait.html
More informationSEARCH-BASED DYNAMIC IDENTIFICATION OF INDUCTION MOTORS
SEARCH-BASED DYNAMIC IDENTIFICATION OF INDUCTION MOTORS Alexande Vladimiovich Nesteovskiy Veniamin Geogievich ashiskikh Valey Mihailovich Zavyalov and Iina Yuyevna Semykina uzbass State Technical Univesity
More informationA Simple Model of Communication APIs Application to Dynamic Partial-order Reduction
Simple Model of Communication PIs pplication to Dynamic Patial-ode Reduction Cistian Rosa Stephan Mez Matin Quinson VOCS 2010 22/09/2010 1 / 18 Motivation Distibuted lgoithms ae had to get ight: lack of
More informationOn the exact transient solution of fluid queue driven by a birth death process with specific rational rates and absorption
OPSEARCH Oct Dec 25 524:746 755 DOI.7/s2597-5-99-4 THEORETICAL ARTICLE On the exact tansient solution of fluid queue diven by a bith death pocess with specific ational ates and absoption Shuti Kapoo Dhamaaja
More information763620SS STATISTICAL PHYSICS Solutions 2 Autumn 2012
763620SS STATISTICAL PHYSICS Solutions 2 Autumn 2012 1. Continuous Random Walk Conside a continuous one-dimensional andom walk. Let w(s i ds i be the pobability that the length of the i th displacement
More informationSolving Some Definite Integrals Using Parseval s Theorem
Ameican Jounal of Numeical Analysis 4 Vol. No. 6-64 Available online at http://pubs.sciepub.com/ajna///5 Science and Education Publishing DOI:.69/ajna---5 Solving Some Definite Integals Using Paseval s
More informationChapter 5 Force and Motion
Chapte 5 Foce and Motion In Chaptes 2 and 4 we have studied kinematics, i.e., we descibed the motion of objects using paametes such as the position vecto, velocity, and acceleation without any insights
More informationA Three-Dimensional Magnetic Force Solution Between Axially-Polarized Permanent-Magnet Cylinders for Different Magnetic Arrangements
Poceedings of the 213 Intenational Confeence on echanics, Fluids, Heat, Elasticity Electomagnetic Fields A Thee-Dimensional agnetic Foce Solution Between Axially-Polaied Pemanent-agnet Cylindes fo Diffeent
More informationChapter 5 Force and Motion
Chapte 5 Foce and Motion In chaptes 2 and 4 we have studied kinematics i.e. descibed the motion of objects using paametes such as the position vecto, velocity and acceleation without any insights as to
More informationRecent Advances in Chemical Engineering, Biochemistry and Computational Chemistry
Themal Conductivity of Oganic Liquids: a New Equation DI NICOLA GIOVANNI*, CIARROCCHI ELEONORA, PIERANTOZZI ARIANO, STRYJEK ROAN 1 DIIS, Univesità Politecnica delle ache, 60131 Ancona, ITALY *coesponding
More informationMiskolc Mathematical Notes HU e-issn Tribonacci numbers with indices in arithmetic progression and their sums. Nurettin Irmak and Murat Alp
Miskolc Mathematical Notes HU e-issn 8- Vol. (0), No, pp. 5- DOI 0.85/MMN.0.5 Tibonacci numbes with indices in aithmetic pogession and thei sums Nuettin Imak and Muat Alp Miskolc Mathematical Notes HU
More informationA Markov Decision Approach for the Computation of Testability of RTL Constructs
A Makov Decision Appoach fo the Computation of Testability of RTL Constucts José Miguel Fenandes Abstact In the analysis of digital cicuits, to study testability estimation measues, dissipated powe and
More informationAnalytical Solutions for Confined Aquifers with non constant Pumping using Computer Algebra
Poceedings of the 006 IASME/SEAS Int. Conf. on ate Resouces, Hydaulics & Hydology, Chalkida, Geece, May -3, 006 (pp7-) Analytical Solutions fo Confined Aquifes with non constant Pumping using Compute Algeba
More informationThe Congestion of n-cube Layout on a Rectangular Grid S.L. Bezrukov J.D. Chavez y L.H. Harper z M. Rottger U.-P. Schroeder Abstract We consider the pr
The Congestion of n-cube Layout on a Rectangula Gid S.L. Bezukov J.D. Chavez y L.H. Hape z M. Rottge U.-P. Schoede Abstact We conside the poblem of embedding the n-dimensional cube into a ectangula gid
More informationNo. 6 Cell image ecognition with adial hamonic Fouie moments 611 ecognition expeiment is pefomed. Results show that as good image featues, RHFMs ae su
Vol 12 No 6, June 23 cfl 23 Chin. Phys. Soc. 19-1963/23/12(6)/61-5 Chinese Physics and IOP Publishing Ltd Cell image ecognition with adial hamonic Fouie moments * Ren Hai-Ping(Φ ±) a)y, Ping Zi-Liang(
More informationState tracking control for Takagi-Sugeno models
State tacing contol fo Taagi-Sugeno models Souad Bezzaoucha, Benoît Max,3,DidieMaquin,3 and José Ragot,3 Abstact This wo addesses the model efeence tacing contol poblem It aims to highlight the encouteed
More informationCOMPUTATIONS OF ELECTROMAGNETIC FIELDS RADIATED FROM COMPLEX LIGHTNING CHANNELS
Pogess In Electomagnetics Reseach, PIER 73, 93 105, 2007 COMPUTATIONS OF ELECTROMAGNETIC FIELDS RADIATED FROM COMPLEX LIGHTNING CHANNELS T.-X. Song, Y.-H. Liu, and J.-M. Xiong School of Mechanical Engineeing
More informationFunctions Defined on Fuzzy Real Numbers According to Zadeh s Extension
Intenational Mathematical Foum, 3, 2008, no. 16, 763-776 Functions Defined on Fuzzy Real Numbes Accoding to Zadeh s Extension Oma A. AbuAaqob, Nabil T. Shawagfeh and Oma A. AbuGhneim 1 Mathematics Depatment,
More informationA Probabilistic Approach to Susceptibility Measurement in a Reverberation Chamber
A Pobabilistic Appoach to Susceptibility Measuement in a Revebeation Chambe Emmanuel Amado, Chistophe Lemoine, Philippe Besnie To cite this vesion: Emmanuel Amado, Chistophe Lemoine, Philippe Besnie. A
More informationApplication of Parseval s Theorem on Evaluating Some Definite Integrals
Tukish Jounal of Analysis and Numbe Theoy, 4, Vol., No., -5 Available online at http://pubs.sciepub.com/tjant/// Science and Education Publishing DOI:.69/tjant--- Application of Paseval s Theoem on Evaluating
More informationDIMENSIONALITY LOSS IN MIMO COMMUNICATION SYSTEMS
DIMENSIONALITY LOSS IN MIMO COMMUNICATION SYSTEMS Segey Loya, Amma Koui School of Infomation Technology and Engineeing (SITE) Univesity of Ottawa, 6 Louis Pasteu, Ottawa, Ontaio, Canada, KN 6N5 Email:
More informationA Comparative Study of Exponential Time between Events Charts
Quality Technology & Quantitative Management Vol. 3, No. 3, pp. 347-359, 26 QTQM ICAQM 26 A Compaative Study of Exponential Time between Events Chats J. Y. Liu 1, M. Xie 1, T. N. Goh 1 and P. R. Shama
More informationAbsorption Rate into a Small Sphere for a Diffusing Particle Confined in a Large Sphere
Applied Mathematics, 06, 7, 709-70 Published Online Apil 06 in SciRes. http://www.scip.og/jounal/am http://dx.doi.og/0.46/am.06.77065 Absoption Rate into a Small Sphee fo a Diffusing Paticle Confined in
More information6 PROBABILITY GENERATING FUNCTIONS
6 PROBABILITY GENERATING FUNCTIONS Cetain deivations pesented in this couse have been somewhat heavy on algeba. Fo example, detemining the expectation of the Binomial distibution (page 5.1 tuned out to
More informationMarkscheme May 2017 Calculus Higher level Paper 3
M7/5/MATHL/HP3/ENG/TZ0/SE/M Makscheme May 07 Calculus Highe level Pape 3 pages M7/5/MATHL/HP3/ENG/TZ0/SE/M This makscheme is the popety of the Intenational Baccalaueate and must not be epoduced o distibuted
More information16 Modeling a Language by a Markov Process
K. Pommeening, Language Statistics 80 16 Modeling a Language by a Makov Pocess Fo deiving theoetical esults a common model of language is the intepetation of texts as esults of Makov pocesses. This model
More informationCentripetal Force OBJECTIVE INTRODUCTION APPARATUS THEORY
Centipetal Foce OBJECTIVE To veify that a mass moving in cicula motion expeiences a foce diected towad the cente of its cicula path. To detemine how the mass, velocity, and adius affect a paticle's centipetal
More informationAdaptive Checkpointing in Dynamic Grids for Uncertain Job Durations
Adaptive Checkpointing in Dynamic Gids fo Uncetain Job Duations Maia Chtepen, Bat Dhoedt, Filip De Tuck, Piet Demeeste NTEC-BBT, Ghent Univesity, Sint-Pietesnieuwstaat 41, Ghent, Belgium {maia.chtepen,
More informationControl Chart Analysis of E k /M/1 Queueing Model
Intenational OPEN ACCESS Jounal Of Moden Engineeing Reseach (IJMER Contol Chat Analysis of E /M/1 Queueing Model T.Poongodi 1, D. (Ms. S. Muthulashmi 1, (Assistant Pofesso, Faculty of Engineeing, Pofesso,
More informationInternet Appendix for A Bayesian Approach to Real Options: The Case of Distinguishing Between Temporary and Permanent Shocks
Intenet Appendix fo A Bayesian Appoach to Real Options: The Case of Distinguishing Between Tempoay and Pemanent Shocks Steven R. Genadie Gaduate School of Business, Stanfod Univesity Andey Malenko Gaduate
More informationFUSE Fusion Utility Sequence Estimator
FUSE Fusion Utility Sequence Estimato Belu V. Dasaathy Dynetics, Inc. P. O. Box 5500 Huntsville, AL 3584-5500 belu.d@dynetics.com Sean D. Townsend Dynetics, Inc. P. O. Box 5500 Huntsville, AL 3584-5500
More informationAlignment of the ZEUS Micro- Vertex Detector Using Cosmic Tracks
Alignment of the ZEUS Mico- Vetex etecto Using Cosmic acks akanoi Kohno (Univesity of Oxfod), ZEUS MV Goup Intenational Wokshop on Advanced Computing and Analysis echniques in Physics Reseach (ACA5) ESY,
More informationModeling of the fermentation in an internal loop airlift reactor
17 th Euopean Symposium on Compute Aided Pocess Engineeing ESCAPE17 V. Plesu and P.S. Agachi (Editos) 7 Elsevie B.V. All ights eseved. 1 Modeling of the fementation in an intenal loop ailift eacto Ivan
More informationarxiv:physics/ v2 [physics.soc-ph] 11 Mar 2005
Heide Balance in Human Netwoks P. Gawoński and K. Ku lakowski axiv:physics/5385v2 [physics.soc-ph] 11 Ma 25 Depatment of Applied Compute Science, Faculty of Physics and Applied Compute Science, AGH Univesity
More informationA FURTHER SUBSPACE METHOD OPTIMIZATION FOR TRACKING REAL VALUED SINUSOIDS IN NOISE
Électonique et tansmission de l infomation A FURHER SUBSPACE MEHOD OPIMIZAION FOR RACKING REAL VALUED SINUSOIDS IN NOISE ŞEFAN SLAVNICU, SILVIU CIOCHINĂ Key wods: Subspace tacking, Fequency estimation,
More informationRelating Branching Program Size and. Formula Size over the Full Binary Basis. FB Informatik, LS II, Univ. Dortmund, Dortmund, Germany
Relating Banching Pogam Size and omula Size ove the ull Binay Basis Matin Saueho y Ingo Wegene y Ralph Wechne z y B Infomatik, LS II, Univ. Dotmund, 44 Dotmund, Gemany z ankfut, Gemany sauehof/wegene@ls.cs.uni-dotmund.de
More informationCircular Orbits. and g =
using analyse planetay and satellite motion modelled as unifom cicula motion in a univesal gavitation field, a = v = 4π and g = T GM1 GM and F = 1M SATELLITES IN OBIT A satellite is any object that is
More informationOn the integration of the equations of hydrodynamics
Uebe die Integation de hydodynamischen Gleichungen J f eine u angew Math 56 (859) -0 On the integation of the equations of hydodynamics (By A Clebsch at Calsuhe) Tanslated by D H Delphenich In a pevious
More informationQIP Course 10: Quantum Factorization Algorithm (Part 3)
QIP Couse 10: Quantum Factoization Algoithm (Pat 3 Ryutaoh Matsumoto Nagoya Univesity, Japan Send you comments to yutaoh.matsumoto@nagoya-u.jp Septembe 2018 @ Tokyo Tech. Matsumoto (Nagoya U. QIP Couse
More informationMEASURING CHINESE RISK AVERSION
MEASURING CHINESE RISK AVERSION --Based on Insuance Data Li Diao (Cental Univesity of Finance and Economics) Hua Chen (Cental Univesity of Finance and Economics) Jingzhen Liu (Cental Univesity of Finance
More informationAn extended target tracking method with random finite set observations
4th Intenational Confeence on Infomation Fusion Chicago Illinois USA July 5-8 0 An extended taget tacing method with andom finite set obsevations Hongyan Zhu Chongzhao Han Chen Li Dept. of Electonic &
More informationThe r-bell Numbers. 1 Introduction
3 47 6 3 Jounal of Intege Sequences, Vol. 4 (, Aticle.. The -Bell Numbes István Meő Depatment of Applied Mathematics and Pobability Theoy Faculty of Infomatics Univesity of Debecen P. O. Box H-4 Debecen
More information[2007] IEEE. Reprinted, with permission, from [Jiaxin Chen, Jianguo Zhu, Youguang Guo, A 2-D nonlinear FEA tool embedded in Matlab/Simulink
[2007] IEEE. Repinted, with pemission, fom [Jiaxin Chen, Jianguo Zhu, Youguang Guo, A 2-D nonlinea FEA tool embedded in Matlab/Simulin suounding fo application of electomagnetic field analysis in powe
More informationJIEMS Journal of Industrial Engineering and Management Studies
JIEMS Jounal of Industial Engineeing and Management Studies Vol. 4, No. 2, 2017, pp. 19-34 DOI: 10.22116/JIEMS.2017.54603 www.jiems.icms.ac.i Pefomance of CCC- contol chat with vaiable sampling intevals
More information