GROUP REPLACEMENT POLICY FOR A MAINTAINED COHERENT SYSTEM
|
|
- Kelly Booth
- 6 years ago
- Views:
Transcription
1 Jurnal f the Operatins Research Sciety f Japan Vl. 25, N. 3, September The Operatins Research Sciety f Japan GROUP REPLACEMENT POLICY FOR A MAINTAINED COHERENT SYSTEM Mamru Ohashi Anan Technical Cllege (Received June 8,98; Final May 20,982) Abstract In this paper we cnsider a cherent system cnsisting f n cmpnents. Ea..:h cmpnent is repaired upn failure. This maintained cherent system is mnitred cntinuusly, and based upn the results f mnitring a decisin must be made as t whether r nt t replace the system. We shw that the ptimal grup replacement plicy has a mntne prperty withut regard t the values f failure and repair rates. Further we discuss sufficient cnditins fr the replacement f the maintained cherent system.. Intrductin This paper deals with a cherent system cnsisting f n cmpnents. Each cmpnent is subject t randm failure. Upn failure the cmpnent is repaired and recvers its functining perfectly. The maintained cherent system has been investigated by Barlw and Prschan [], Chiang and Niu [2], and Rss [5]. In particular, Rss prved that the distributin f the time t first system failure has NBU (i.e., new better than used) prperty when all cmpnents are initially up at time zer, and have expnential uptime distributins with parameter Ai fr i=,2,...,n and dwntime distributins with parameter ~i fr i=,2,..,n. Thus effrts t replace the maintained cherent system befre system failure may be advantageus. On the ther hand, when cmpared with individual repair upn failed cmpnents, the grup replacement may cause t thrwaway sme cmpnents which are in gd perating cnditin. Hwever, we can expect t btain a large discunt n the purchase price and ther ecnmic attendant t large-scale undertakings. In this paper we cnsider a replacement prblem fr a cherent system with repairable cmpnents. The abve system is mdeled by a cntinuus time Markv decisin prcess. The system is mnitred cntinuusly, and based upn the 228
2 Grup Replacement Plicy 229 results f mnitring a decisin must be made as t whether r nt t replace the maintained cherent system. The bj,~ctive f this paper is t study the structure f the ptimal grup replacement plicy minimizing the expected ttal discunted cst fr the maintained cherl~nt system. We shw that the ptimal grup replacement plicy has a mntne prperty withut regard t the values f failure rate A. and repair rate ~.. Further we discuss sufficient cnditins ~ ~ fr the replacement f the maintained cherent system. Finally t illustrate the ptimal grup replacement plicy, a numerical example is presented. 2. Prblem Frmulatin Cnsider a maintained cherent system. The system cnsists f n cmpnents and n repair facilities. Each f its cmpnents is either up r dwn, indicating whether it is functining r nt, and acts independent f the behaviur f ther cmpnents. When the ith cmpnent ges up [dwn], it remains up [dwn] fr expnentially distributed time with parameter A. [~.] and then ges dwn ~ ~ [up]. Let uptimes and dwntimes be independent. We suppse that the system state at any time depends n cmpnent states thrugh a cherent structure functin ~ (see Barlw and Prschan []). Let l x.(t)j ~ ~O if the ith cmpnent is up at time t, therwise, fr any ien={l,2,...,n}. Then the evlutin f the state f the ith cmpnent is described by the stchastic prcess {X. (t), t~o}. ~ Let and let HX(t) )=~: if the system is up at time t, therwise, then the state f the system is summarized by binary n-vectr X(t) with a state space S and the actual state f the maintained cherent system is described by the stchastic prcess {~(X(t)), t~o}. In the present paper we are interested in a grup replacement prblem fr the abve system. decisin is made t replace t~e At each time epch tet=[o,), bserving the state X(t), a maintained cherent system, r t keep it. We assume that the time needed t replace the system is expnential with parameter ~O. Let n(x)ed={o,l} represent the decisin made fr the system at any time t, Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
3 230 M.Ohashi where n(x)=o means t replace the system and n(x)=l means t keep it. As the cst rate r(x,n(x» assciated with the maintained cherent system, we cnsider the fllwing cst rate. At time t when the state is X and decisin n(x)=l is made n the maintained cherent system, then the cst is incurred at the rate r(x,l)=p(l-~(x»+ Er., where P is the system dwn cst rate, r. (ien) is iec (X).-.- O the repair cst rate f the ith cmpnent and CO(X) dentes the set f currently failed cmpnents. On the ther hand, when decisin n(x)=o is made, then the cst is incurred at the rate r(x,o)=r, where R is the replacement cst rate (i.e., R/~O is the expected replacement cst). The bjective is t investigate the structure f the ptimal grup replacement plicy minimizing the expected ttal discunted cst with discunt factr a>o. Nw let V (x) be the minimum expected ttal discunted cst when a We begin n n fr the state f the system is X(0)=x=(x l,x 2,,x ) at the beginning. by deriving the weak infinitesimal peratr A f 'the prcess {X (t); t~o} T n ned. Fr a functin f in the dmain f An we have A f(x)=lim t-l[e [f(x(t»]-f(x)] n NO x = E ~. (f(l.,x)-f(x»+ EA. (f(o.,x)-f(x» fec(x).-.- iec l (x).-.- ~O (f(ll) -f(x» n(x)"'l, n(x)=o, where C.(x)={ilx.=j, ien}, (j=o,l), (.,x)=(xl,.,x. l' 'x.+l,,x), and J n ll=(l,.,l). Of great imprtance t us is Dshi's frmula (see Dshi [3]) (2.) av (x)=inf{r(x,n(x»+a V (x)}. a ned n a In the fllwing sectin the structural prperties f the ptimal grup replacement plicy minimizing the expected ttal discunted cst are characterized. It is shwn that a mntnic plicy is ptimal and sufficient cnditins fr replacing the whle system are presented. Here we ntice that the existp.nce f a statinary plicy minimizing the expected ttal discunted cst is guaranteed, since all csts are bunded and the actin space is finite. 3. Structure f Optimal Grup Replacement Plicy In this sectin we discuss an ptimal grup replacement plicy fr a maintained cherent system. We can find the ptimal grup replacement plicy by slving the functinal equatin (2.). We cannt btain, hwever, a slutin as a functin f the parameters in the mdel. S sme prperties n the ptimal Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
4 Grup Replacement Plicy 23 plicy and the crrespnding ptimal cst f the functinal equatin (2.) are discussed. The fllwing lemmas shw the strueture f ptimal expected ttal discunted cst functin, and they are used in the prf f therems which present the structural prperties f the ptimal grup replacement plicy. Lemma 3.. The minimum expected ttal discunted cst V (x) is a nna. increasing functin f its cmpnent variables x., ien. t- Prf: The functinal equatin (2.) can be written as V (x)=min {P(l-Ijl(x»+ E (P.+~.v (l.,x»+ E A.V (.,x) Cl. C () t- t- Cl. t-. C ( ) t- Cl. t-e 0 t- X t-e x (3.) +(A- E ~.- E A.)V (x)}/(a+a.), iec 0 0c) t- iecl(x) t- Cl. {R+~OV () +(\-~0) V (x)} / (\+a.), Cl. (~ where A is any value larger than max{~o,max{ E ~.+ E A.+ max A.}}. XES ieco(x) t- iec l (x) t- ieco(x) t- We can calculate by using the successiv(~ apprximatin technique: V (x;n+)=min {P(l-CP(x»+ E (T'.+~.V (l.,x;n»+ E LV (O.,x;n) Cl. ieco(x) t- t. a. t. iec l (x) t. a. t. (3.2) +(A-:~ ~.- E L)V (x;n)}/(a+a.), ieco(x) t- iec l (x) t- a. {R+~OV 0.,( ;n) +(A-~O) V. (x;n) } / (A+a.), where V (x;o)=o fr all XES. Then prving the mntnicity f V (x) is carried a. a. ut by using the mathematical inductin. Fr n=l the result fllws easily frm the prperties f the structure functin cp and the definitins f CO(x) and Cl (x). Suppse the result is true fr :sme n. At the n+l-th stage, if the ptimal decisin is t keep the maintained cherent system fr (O.,X)ES, ien, t- then V (O.,x;n+)-V (l.,x;n+)~[p(l-ijl(o.,x»+ E (T'.+~.V (O.,l.,x;n» a. t. a. t. - t. C (0 ) J J a. t. J JE 0 i'x + E LV (O.,O.,x;n)+(A- E ~.- E L)V (O.,x;n)] jecl(oi'x) J a. t. J jeco(oi'x) J jecl(oi'x) J a. t. /(A+a.] - [P(l-CP(l.,x»+ E (T'.+~.V (.,.,x;n»+ E A. t. jeco(li'x) J J a. t. J JEC l (li'x) J 'V (l.,o.,x;n)+(\- E ~.- E A.)V (l.,x;n)]/[\+a.] Cl. t. J ( ) J C ( ) J Cl. - JEC O i'x JE i'x Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
5 232 M Ohashi ~[P(<P<.,x)-<P<O.,x»+!'.+ E l.(v (O.,.,x;n)-V (.,.,x;n» C ( ) J a..- J a..- J JE 0 i'x ;:,,0. + E L(V (O.,O.,x;n)-V (.,O.,x;n» C ( ) J a..- J a..- J JE i'x On the ther hand, even if the ptimal decisin is t replace the maintained +(A-A.- E.- E A.)(V (O.,x;n)-V (l.,x;n»]/[a+a.].- jec ( ) J C ( ) J a..- a..- O i'x JE i'x cherent system, then V (O.,x;n+l)-V (l.,x;n+l»o is prved similarly t the = abve. Thus fr each n, V (x;n) is a nnincreasing functin f its cmpnent a. variables x., ien. Then frm the successive apprximatin technique, as.- lim V (x;n)=v (x), a. a. n-- Va.(x) is a nnincreasing functin f each f its cmpnent variables xi' ien. Lemma 3.2. The minimum expected ttal discunted cst V (x) is nt larger a. than RI.. The abve lemma is easily prved by the functinal equatin (3.). the structure prperties f the ptimal grup replacement plicy fr a maintained cherent system are characterized. Therem 3.. Next If all cmpnents are perating, then the ptimal decisin is t keep the maintained cherent system. Prf: Fllws directly frm the functinal equatin (3.) and Lemma 3.2. Therem 3.2. The ptimal grup replacement plicy TI*(x) is a nndecreasing functin f each f lts cmpnent variables x., ien..- The functinal equatin (2.) can be written as Prf: V (x)=min {P(l-<p(x»+ E (!'.+. V (.,x»+ E A. V (0.,x) a..c() c().-a..-.-e 0 X.-E x (3.3) +(A- E.- E A.)V (x)}/(a+a.), ieco(x).- iec l (x).- a. {R+llOVa. () } (0.+ 0 ). Ntice that the latter quantity des nt cntain variable x. Frm Lemma 3. and the abve fact, the re sui t is easily btained. The fllwing Therems 3.3 and 3.4 are cncerned with sufficient cnditins fr the replacement f the maintained cherent system. Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
6 Grup Replacement Plicy 233 Therem 3.3. If ~~ L ~.+ L A. and R<P(l-~(x»+ L r. fr XES, v-ieco(x) - iec (x) - = ieco(x) - then the ptimal decisin is t replace the maintained cherent system. Prf: The functinal equatin (2.. ) can be written as v (x)=min {P(l-Hx»+ L (l?+~.v (l.,x»+ L LV (.,x)) a ieco(x) - ~ a - iec (x) - a ~ (3.4) I{a+ L ~.+ LA.}, ieco(x) - iec (x) ~ {R+~O Va () } I {a+~o}' The result is shwn by cmparing the terms in functinal equatin (3.4). Thus [V (x)]l-[v (x)]o={p(l-~(x»+ L (r.+~.v (l.,x»+ L A.V (O.,x)} a a :EC O (X) - ~ a - iec (x) 'l- a ~ I{a+ L ~.+ L A.}-{R+~OV () }/{a+~o} icco(x) ~ iec (x) ~ a >[l/{a+ L ~.+ L A.}-l/{a+~O}]R = iec O (;C) ~ iec (x) - +[{ z ~.+ L A.}/{a+ L ~.+ LA.} iec O (x) ~ iec (x) ~ iec O (x) - iec (x) ~ -~0/{(+~0} ]V a () >[l/{a+ L ~.+ L A.}-l/{a+~O}]aV () = ieco~c) ~ iec (x) - a =0. +[{ Z ~.+ L A.}/{a+ L ~.+ LA.} ieco(x) ~ iec (x) - ieco(x) ~ iec (x) ~ -~Ol {(+~0} ]V a () The first inequality fllws frm the assumptins and Lemma 3.. inequality is true frm Lemma 3.2. The last Therem 3.4. If ~O< L ~.+ L A. and R/~~[P(l-~(x»+ L r.] ieco(x) ~ iec (x) 'l- ieco(x) - L ~.+ L A.] fr XES, then the ptimal decisin is t replace the ieco(x) ~ iec (x) - maintained cherent system. Prf: The result is prved similarly t Therem 3.3. Remark. The results f this sectin remain valid even when we extend the cst rate P(l-<p(x»+ L r. t the general cst rate r(x,l), where r(x,l) ieco(x) ~ Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
7 234 M.Ohashi is a nnincreasing functin f each f its cmpnent variables xi' i N with r(ll,l)=o. Remark 2. We ntice that the mntne prperty f the ptimal plicy keeps true irrespective f failure and repair rates, but it is f curse that the actual plicy n(x) depends n the values f failure and repair rates. 4. Numerical Example In this sectin we cnsider the s-called bridge structure system shwn in Figure. T illustrate the ptimal grup replacement plicy f the preceding sectin, we give a numerical example. The failure and repair rates f cmpnents are given in Table. The repair cst rates f cmpnents are als given in Table. The system dwn cst rate, replacement cst rate, and replacement rate are P=S.O, R=lO.O, and ~0=, respectively. Then we btain Figure. The bridge structure system. Table. Failure rates, repair rates, and cst rates. A ~ r U l 0..0 U 2 0. U U Us 0. Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
8 Grup Replllcement Plicy 235 Table 2. The ptimal replacement plicy with P=5.0, R=0.0, 0 =, and (X'=0.05. N III c/>(x) (x) l V(x) the ptimal grup replacement plicy fr the maintained cherent system by the value iteratin methd. Als t illustrate the results f Therems 3.3 and 3.4, the values M (X)= E.+ Eh., M (x)=p(-c/>(x»+ Er., and ieco(x) ~ iec (x) ~ 2 ieco(x) ~ M 2 (x)/m (x) are cmputed. The results f these cmputatins are given in Table 2 in the case f discunt factr a=0.05. N. 4, 6,, 3, 8, and 25 satisfy the cnditin f Therem 3.3, and N., 2, 3, 5, 7, 9, 7, and 2 satisfy the cnditin f Therem 3.4. But N. S, 0, 2, 4, 5, 9, 20, 22, 26, 27, and 29 dn't satisfy these cnditins, while the ptimal decisin is t replace the maintained cherent system. This shws that the cnditins f Therems 3.3 and 3.4 are nt necessary fr replacing the system. N. 0, 2, 4, 5, 9, 20, 22, 26, and 27 shw that a preventive replacement is ptimal. Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
9 236 M. Ohashi 5. Cnclusin In this paper we have been examined the structure f the ptimal grup replacement plicy fr the maintained cherent system. We shwed that the ptimal grup replacement plicy minimizing the expected ttal discunted cst is a mntne plicy withut regard t the failure and repair rates. Further we discussed the sufficient cnditins fr the grup replacement f the maintained cherent system. It is a future prblem t find the structure f the ptimal grup replacement plicy in the case where a grup replacement plicy in the presence f fixed csts fr turning n r turning ff repair facilities. Acknwledgment The authr is grateful t the referees fr their valuable cmments n this paper. References [] Barlw, R.E. and Prschan, F.: Statistical Thery f Reliability and Lif Testing: Prbability Mdels. Hlt, Rinehart and Winstn, New Yrk, 975. [2] Chiang, D.T. and Niu, S.C.: On the Distributin f Time t First System Failure. J. Appl. Prb., Vl.7 (980), [3] Dshi, B.T.: Cntinuus Time Cntrl f Markv Prcesses n an Arbitrary State Space: Discunted Rewards. Ann. Statist., Vl.4 (976), [4] Rss, S.M.: Applied Prbability Mdels with Optimizatin Applicatins. Hlden-Day, San Francesc, 970. [5] Rss, S.M.: On the Time t First Failure in Multicmpnent Expnential Reliability Systems. Stchastic Prcesses Appl., Vl.4 (976), [6] Sivazlian, B.D. and Mahney, J.F.: Grup Replacement f a Multicmpnent System which is Subject t Deteriratin nly. Adv. Appl. Prb., Vl.lO (978), Mamru OHASHI: Anan Technical Cllege, Minbayashi, An an, Tkushima, 744, Japan. Cpyright by ORSJ. Unauthrized reprductin f this article is prhibited.
AN INTERMITTENTLY USED SYSTEM WITH PREVENTIVE MAINTENANCE
J. Operatins Research Sc. f Japan V!. 15, N. 2, June 1972. 1972 The Operatins Research Sciety f Japan AN INTERMITTENTLY USED SYSTEM WITH PREVENTIVE MAINTENANCE SHUNJI OSAKI University f Suthern Califrnia
More informationON THE (s,s) POLICY WITH FIXED INVENTORY HOLDING AND SHORTAGE COSTS
Jurnal f the Operatins Research Sciety f Japan Vl. 34, N. 1, March 1991 1991 The Operatins Research Sciety f Japan ON THE (s,s) POLICY WITH FIXED INVENTORY HOLDING AND SHORTAGE COSTS Tmnri Ishigaki Nagya
More informationLyapunov Stability Stability of Equilibrium Points
Lyapunv Stability Stability f Equilibrium Pints 1. Stability f Equilibrium Pints - Definitins In this sectin we cnsider n-th rder nnlinear time varying cntinuus time (C) systems f the frm x = f ( t, x),
More informationPure adaptive search for finite global optimization*
Mathematical Prgramming 69 (1995) 443-448 Pure adaptive search fr finite glbal ptimizatin* Z.B. Zabinskya.*, G.R. Wd b, M.A. Steel c, W.P. Baritmpa c a Industrial Engineering Prgram, FU-20. University
More informationBootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) >
Btstrap Methd > # Purpse: understand hw btstrap methd wrks > bs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(bs) > mean(bs) [1] 21.64625 > # estimate f lambda > lambda = 1/mean(bs);
More informationSupport-Vector Machines
Supprt-Vectr Machines Intrductin Supprt vectr machine is a linear machine with sme very nice prperties. Haykin chapter 6. See Alpaydin chapter 13 fr similar cntent. Nte: Part f this lecture drew material
More informationAdmissibility Conditions and Asymptotic Behavior of Strongly Regular Graphs
Admissibility Cnditins and Asympttic Behavir f Strngly Regular Graphs VASCO MOÇO MANO Department f Mathematics University f Prt Oprt PORTUGAL vascmcman@gmailcm LUÍS ANTÓNIO DE ALMEIDA VIEIRA Department
More informationinitially lcated away frm the data set never win the cmpetitin, resulting in a nnptimal nal cdebk, [2] [3] [4] and [5]. Khnen's Self Organizing Featur
Cdewrd Distributin fr Frequency Sensitive Cmpetitive Learning with One Dimensinal Input Data Aristides S. Galanpuls and Stanley C. Ahalt Department f Electrical Engineering The Ohi State University Abstract
More informationHomology groups of disks with holes
Hmlgy grups f disks with hles THEOREM. Let p 1,, p k } be a sequence f distinct pints in the interir unit disk D n where n 2, and suppse that fr all j the sets E j Int D n are clsed, pairwise disjint subdisks.
More informationComputational modeling techniques
Cmputatinal mdeling techniques Lecture 4: Mdel checing fr ODE mdels In Petre Department f IT, Åb Aademi http://www.users.ab.fi/ipetre/cmpmd/ Cntent Stichimetric matrix Calculating the mass cnservatin relatins
More informationThe blessing of dimensionality for kernel methods
fr kernel methds Building classifiers in high dimensinal space Pierre Dupnt Pierre.Dupnt@ucluvain.be Classifiers define decisin surfaces in sme feature space where the data is either initially represented
More informationAdmin. MDP Search Trees. Optimal Quantities. Reinforcement Learning
Admin Reinfrcement Learning Cntent adapted frm Berkeley CS188 MDP Search Trees Each MDP state prjects an expectimax-like search tree Optimal Quantities The value (utility) f a state s: V*(s) = expected
More informationSequential Allocation with Minimal Switching
In Cmputing Science and Statistics 28 (1996), pp. 567 572 Sequential Allcatin with Minimal Switching Quentin F. Stut 1 Janis Hardwick 1 EECS Dept., University f Michigan Statistics Dept., Purdue University
More informationCOMP 551 Applied Machine Learning Lecture 11: Support Vector Machines
COMP 551 Applied Machine Learning Lecture 11: Supprt Vectr Machines Instructr: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/cmp551 Unless therwise nted, all material psted fr this curse
More informationDiscounted semi-markov decision processes : linear programming and policy iteration Wessels, J.; van Nunen, J.A.E.E.
Discunted semi-marv decisin prcesses : linear prgramming and plicy iteratin Wessels,.; van Nunen,.A.E.E. Published: 01/01/1974 Dcument Versin Publisher s PDF, als nwn as Versin f Recrd (includes final
More informationCOMP 551 Applied Machine Learning Lecture 5: Generative models for linear classification
COMP 551 Applied Machine Learning Lecture 5: Generative mdels fr linear classificatin Instructr: Herke van Hf (herke.vanhf@mail.mcgill.ca) Slides mstly by: Jelle Pineau Class web page: www.cs.mcgill.ca/~hvanh2/cmp551
More informationDepartment of Economics, University of California, Davis Ecn 200C Micro Theory Professor Giacomo Bonanno. Insurance Markets
Department f Ecnmics, University f alifrnia, Davis Ecn 200 Micr Thery Prfessr Giacm Bnann Insurance Markets nsider an individual wh has an initial wealth f. ith sme prbability p he faces a lss f x (0
More informationNOTE ON APPELL POLYNOMIALS
NOTE ON APPELL POLYNOMIALS I. M. SHEFFER An interesting characterizatin f Appell plynmials by means f a Stieltjes integral has recently been given by Thrne. 1 We prpse t give a secnd such representatin,
More informationFebruary 28, 2013 COMMENTS ON DIFFUSION, DIFFUSIVITY AND DERIVATION OF HYPERBOLIC EQUATIONS DESCRIBING THE DIFFUSION PHENOMENA
February 28, 2013 COMMENTS ON DIFFUSION, DIFFUSIVITY AND DERIVATION OF HYPERBOLIC EQUATIONS DESCRIBING THE DIFFUSION PHENOMENA Mental Experiment regarding 1D randm walk Cnsider a cntainer f gas in thermal
More informationMATHEMATICS SYLLABUS SECONDARY 5th YEAR
Eurpean Schls Office f the Secretary-General Pedaggical Develpment Unit Ref. : 011-01-D-8-en- Orig. : EN MATHEMATICS SYLLABUS SECONDARY 5th YEAR 6 perid/week curse APPROVED BY THE JOINT TEACHING COMMITTEE
More informationInflow Control on Expressway Considering Traffic Equilibria
Memirs f the Schl f Engineering, Okayama University Vl. 20, N.2, February 1986 Inflw Cntrl n Expressway Cnsidering Traffic Equilibria Hirshi INOUYE* (Received February 14, 1986) SYNOPSIS When expressway
More informationFloating Point Method for Solving Transportation. Problems with Additional Constraints
Internatinal Mathematical Frum, Vl. 6, 20, n. 40, 983-992 Flating Pint Methd fr Slving Transprtatin Prblems with Additinal Cnstraints P. Pandian and D. Anuradha Department f Mathematics, Schl f Advanced
More informationModule 4: General Formulation of Electric Circuit Theory
Mdule 4: General Frmulatin f Electric Circuit Thery 4. General Frmulatin f Electric Circuit Thery All electrmagnetic phenmena are described at a fundamental level by Maxwell's equatins and the assciated
More informationWhat is Statistical Learning?
What is Statistical Learning? Sales 5 10 15 20 25 Sales 5 10 15 20 25 Sales 5 10 15 20 25 0 50 100 200 300 TV 0 10 20 30 40 50 Radi 0 20 40 60 80 100 Newspaper Shwn are Sales vs TV, Radi and Newspaper,
More informationLeast Squares Optimal Filtering with Multirate Observations
Prc. 36th Asilmar Cnf. n Signals, Systems, and Cmputers, Pacific Grve, CA, Nvember 2002 Least Squares Optimal Filtering with Multirate Observatins Charles W. herrien and Anthny H. Hawes Department f Electrical
More informationA Few Basic Facts About Isothermal Mass Transfer in a Binary Mixture
Few asic Facts but Isthermal Mass Transfer in a inary Miture David Keffer Department f Chemical Engineering University f Tennessee first begun: pril 22, 2004 last updated: January 13, 2006 dkeffer@utk.edu
More informationA Simple Set of Test Matrices for Eigenvalue Programs*
Simple Set f Test Matrices fr Eigenvalue Prgrams* By C. W. Gear** bstract. Sets f simple matrices f rder N are given, tgether with all f their eigenvalues and right eigenvectrs, and simple rules fr generating
More informationSection 6-2: Simplex Method: Maximization with Problem Constraints of the Form ~
Sectin 6-2: Simplex Methd: Maximizatin with Prblem Cnstraints f the Frm ~ Nte: This methd was develped by Gerge B. Dantzig in 1947 while n assignment t the U.S. Department f the Air Frce. Definitin: Standard
More informationOptimization Programming Problems For Control And Management Of Bacterial Disease With Two Stage Growth/Spread Among Plants
Internatinal Jurnal f Engineering Science Inventin ISSN (Online): 9 67, ISSN (Print): 9 676 www.ijesi.rg Vlume 5 Issue 8 ugust 06 PP.0-07 Optimizatin Prgramming Prblems Fr Cntrl nd Management Of Bacterial
More informationComputational modeling techniques
Cmputatinal mdeling techniques Lecture 2: Mdeling change. In Petre Department f IT, Åb Akademi http://users.ab.fi/ipetre/cmpmd/ Cntent f the lecture Basic paradigm f mdeling change Examples Linear dynamical
More informationMath 105: Review for Exam I - Solutions
1. Let f(x) = 3 + x + 5. Math 105: Review fr Exam I - Slutins (a) What is the natural dmain f f? [ 5, ), which means all reals greater than r equal t 5 (b) What is the range f f? [3, ), which means all
More informationSAMPLING DYNAMICAL SYSTEMS
SAMPLING DYNAMICAL SYSTEMS Melvin J. Hinich Applied Research Labratries The University f Texas at Austin Austin, TX 78713-8029, USA (512) 835-3278 (Vice) 835-3259 (Fax) hinich@mail.la.utexas.edu ABSTRACT
More informationRevisiting the Socrates Example
Sectin 1.6 Sectin Summary Valid Arguments Inference Rules fr Prpsitinal Lgic Using Rules f Inference t Build Arguments Rules f Inference fr Quantified Statements Building Arguments fr Quantified Statements
More informationDistributions, spatial statistics and a Bayesian perspective
Distributins, spatial statistics and a Bayesian perspective Dug Nychka Natinal Center fr Atmspheric Research Distributins and densities Cnditinal distributins and Bayes Thm Bivariate nrmal Spatial statistics
More information, which yields. where z1. and z2
The Gaussian r Nrmal PDF, Page 1 The Gaussian r Nrmal Prbability Density Functin Authr: Jhn M Cimbala, Penn State University Latest revisin: 11 September 13 The Gaussian r Nrmal Prbability Density Functin
More informationV. Balakrishnan and S. Boyd. (To Appear in Systems and Control Letters, 1992) Abstract
On Cmputing the WrstCase Peak Gain f Linear Systems V Balakrishnan and S Byd (T Appear in Systems and Cntrl Letters, 99) Abstract Based n the bunds due t Dyle and Byd, we present simple upper and lwer
More informationChE 471: LECTURE 4 Fall 2003
ChE 47: LECTURE 4 Fall 003 IDEL RECTORS One f the key gals f chemical reactin engineering is t quantify the relatinship between prductin rate, reactr size, reactin kinetics and selected perating cnditins.
More informationLHS Mathematics Department Honors Pre-Calculus Final Exam 2002 Answers
LHS Mathematics Department Hnrs Pre-alculus Final Eam nswers Part Shrt Prblems The table at the right gives the ppulatin f Massachusetts ver the past several decades Using an epnential mdel, predict the
More informationChapter Summary. Mathematical Induction Strong Induction Recursive Definitions Structural Induction Recursive Algorithms
Chapter 5 1 Chapter Summary Mathematical Inductin Strng Inductin Recursive Definitins Structural Inductin Recursive Algrithms Sectin 5.1 3 Sectin Summary Mathematical Inductin Examples f Prf by Mathematical
More informationNOTE ON A CASE-STUDY IN BOX-JENKINS SEASONAL FORECASTING OF TIME SERIES BY STEFFEN L. LAURITZEN TECHNICAL REPORT NO. 16 APRIL 1974
NTE N A CASE-STUDY IN B-JENKINS SEASNAL FRECASTING F TIME SERIES BY STEFFEN L. LAURITZEN TECHNICAL REPRT N. 16 APRIL 1974 PREPARED UNDER CNTRACT N00014-67-A-0112-0030 (NR-042-034) FR THE FFICE F NAVAL
More informationPerturbation approach applied to the asymptotic study of random operators.
Perturbatin apprach applied t the asympttic study f rm peratrs. André MAS, udvic MENNETEAU y Abstract We prve that, fr the main mdes f stchastic cnvergence (law f large numbers, CT, deviatins principles,
More informationA proposition is a statement that can be either true (T) or false (F), (but not both).
400 lecture nte #1 [Ch 2, 3] Lgic and Prfs 1.1 Prpsitins (Prpsitinal Lgic) A prpsitin is a statement that can be either true (T) r false (F), (but nt bth). "The earth is flat." -- F "March has 31 days."
More informationRECHERCHES Womcodes constructed with projective geometries «Womcodes» construits à partir de géométries projectives Frans MERKX (') École Nationale Su
Wmcdes cnstructed with prjective gemetries «Wmcdes» cnstruits à partir de gémétries prjectives Frans MERKX (') Écle Natinale Supérieure de Télécmmunicatins (ENST), 46, rue Barrault, 75013 PARIS Étudiant
More informationCONVERGENCE RATES FOR EMPICAL BAYES TWO-ACTION PROBLEMS- THE UNIFORM br(0,#) DISTRIBUTION
Jurnal fapplied Mathematics and Stchastic Analysis 5, Numer 2, Summer 1992, 167-176 CONVERGENCE RATES FOR EMPICAL BAYES TWO-ACTION PROBLEMS- THE UNIFORM r(0,#) DISTRIBUTION MOHAMED TAHIR Temple University
More informationA new Type of Fuzzy Functions in Fuzzy Topological Spaces
IOSR Jurnal f Mathematics (IOSR-JM e-issn: 78-578, p-issn: 39-765X Vlume, Issue 5 Ver I (Sep - Oct06, PP 8-4 wwwisrjurnalsrg A new Type f Fuzzy Functins in Fuzzy Tplgical Spaces Assist Prf Dr Munir Abdul
More informationRevision: August 19, E Main Suite D Pullman, WA (509) Voice and Fax
.7.4: Direct frequency dmain circuit analysis Revisin: August 9, 00 5 E Main Suite D Pullman, WA 9963 (509) 334 6306 ice and Fax Overview n chapter.7., we determined the steadystate respnse f electrical
More informationReinforcement Learning" CMPSCI 383 Nov 29, 2011!
Reinfrcement Learning" CMPSCI 383 Nv 29, 2011! 1 Tdayʼs lecture" Review f Chapter 17: Making Cmple Decisins! Sequential decisin prblems! The mtivatin and advantages f reinfrcement learning.! Passive learning!
More informationSlide04 (supplemental) Haykin Chapter 4 (both 2nd and 3rd ed): Multi-Layer Perceptrons
Slide04 supplemental) Haykin Chapter 4 bth 2nd and 3rd ed): Multi-Layer Perceptrns CPSC 636-600 Instructr: Ynsuck Che Heuristic fr Making Backprp Perfrm Better 1. Sequential vs. batch update: fr large
More information3D FE Modeling Simulation of Cold Rotary Forging with Double Symmetry Rolls X. H. Han 1, a, L. Hua 1, b, Y. M. Zhao 1, c
Materials Science Frum Online: 2009-08-31 ISSN: 1662-9752, Vls. 628-629, pp 623-628 di:10.4028/www.scientific.net/msf.628-629.623 2009 Trans Tech Publicatins, Switzerland 3D FE Mdeling Simulatin f Cld
More informationx x
Mdeling the Dynamics f Life: Calculus and Prbability fr Life Scientists Frederick R. Adler cfrederick R. Adler, Department f Mathematics and Department f Bilgy, University f Utah, Salt Lake City, Utah
More informationNOTE ON THE ANALYSIS OF A RANDOMIZED BLOCK DESIGN. Junjiro Ogawa University of North Carolina
NOTE ON THE ANALYSIS OF A RANDOMIZED BLOCK DESIGN by Junjir Ogawa University f Nrth Carlina This research was supprted by the Office f Naval Research under Cntract N. Nnr-855(06) fr research in prbability
More informationBOUNDED UNCERTAINTY AND CLIMATE CHANGE ECONOMICS. Christopher Costello, Andrew Solow, Michael Neubert, and Stephen Polasky
BOUNDED UNCERTAINTY AND CLIMATE CHANGE ECONOMICS Christpher Cstell, Andrew Slw, Michael Neubert, and Stephen Plasky Intrductin The central questin in the ecnmic analysis f climate change plicy cncerns
More informationFiltering of Nonlinear Systems with Sampled Noisy Data by Output Injection
Filtering f Nnlinear Systems with Sampled Nisy Data by Output Injectin Filipp Cacace, Francesc Cnte, Alfred Germani, Givanni Palmb Abstract This paper cncerns with the state estimatin prblem f nnlinear
More informationDead-beat controller design
J. Hetthéssy, A. Barta, R. Bars: Dead beat cntrller design Nvember, 4 Dead-beat cntrller design In sampled data cntrl systems the cntrller is realised by an intelligent device, typically by a PLC (Prgrammable
More informationYou need to be able to define the following terms and answer basic questions about them:
CS440/ECE448 Sectin Q Fall 2017 Midterm Review Yu need t be able t define the fllwing terms and answer basic questins abut them: Intr t AI, agents and envirnments Pssible definitins f AI, prs and cns f
More informationCAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank
CAUSAL INFERENCE Technical Track Sessin I Phillippe Leite The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Phillippe Leite fr the purpse f this wrkshp Plicy questins are causal
More informationECE 2100 Circuit Analysis
ECE 2100 Circuit Analysis Lessn 25 Chapter 9 & App B: Passive circuit elements in the phasr representatin Daniel M. Litynski, Ph.D. http://hmepages.wmich.edu/~dlitynsk/ ECE 2100 Circuit Analysis Lessn
More informationCOMP 551 Applied Machine Learning Lecture 4: Linear classification
COMP 551 Applied Machine Learning Lecture 4: Linear classificatin Instructr: Jelle Pineau (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/cmp551 Unless therwise nted, all material psted
More informationDataflow Analysis and Abstract Interpretation
Dataflw Analysis and Abstract Interpretatin Cmputer Science and Artificial Intelligence Labratry MIT Nvember 9, 2015 Recap Last time we develped frm first principles an algrithm t derive invariants. Key
More informationPattern Recognition 2014 Support Vector Machines
Pattern Recgnitin 2014 Supprt Vectr Machines Ad Feelders Universiteit Utrecht Ad Feelders ( Universiteit Utrecht ) Pattern Recgnitin 1 / 55 Overview 1 Separable Case 2 Kernel Functins 3 Allwing Errrs (Sft
More informationEmphases in Common Core Standards for Mathematical Content Kindergarten High School
Emphases in Cmmn Cre Standards fr Mathematical Cntent Kindergarten High Schl Cntent Emphases by Cluster March 12, 2012 Describes cntent emphases in the standards at the cluster level fr each grade. These
More informationT Algorithmic methods for data mining. Slide set 6: dimensionality reduction
T-61.5060 Algrithmic methds fr data mining Slide set 6: dimensinality reductin reading assignment LRU bk: 11.1 11.3 PCA tutrial in mycurses (ptinal) ptinal: An Elementary Prf f a Therem f Jhnsn and Lindenstrauss,
More informationFall 2013 Physics 172 Recitation 3 Momentum and Springs
Fall 03 Physics 7 Recitatin 3 Mmentum and Springs Purpse: The purpse f this recitatin is t give yu experience wrking with mmentum and the mmentum update frmula. Readings: Chapter.3-.5 Learning Objectives:.3.
More informationComparing Several Means: ANOVA. Group Means and Grand Mean
STAT 511 ANOVA and Regressin 1 Cmparing Several Means: ANOVA Slide 1 Blue Lake snap beans were grwn in 12 pen-tp chambers which are subject t 4 treatments 3 each with O 3 and SO 2 present/absent. The ttal
More informationPreparation work for A2 Mathematics [2017]
Preparatin wrk fr A2 Mathematics [2017] The wrk studied in Y12 after the return frm study leave is frm the Cre 3 mdule f the A2 Mathematics curse. This wrk will nly be reviewed during Year 13, it will
More informationChapter 3: Cluster Analysis
Chapter 3: Cluster Analysis } 3.1 Basic Cncepts f Clustering 3.1.1 Cluster Analysis 3.1. Clustering Categries } 3. Partitining Methds 3..1 The principle 3.. K-Means Methd 3..3 K-Medids Methd 3..4 CLARA
More informationThe Law of Total Probability, Bayes Rule, and Random Variables (Oh My!)
The Law f Ttal Prbability, Bayes Rule, and Randm Variables (Oh My!) Administrivia Hmewrk 2 is psted and is due tw Friday s frm nw If yu didn t start early last time, please d s this time. Gd Milestnes:
More information^YawataR&D Laboratory, Nippon Steel Corporation, Tobata, Kitakyushu, Japan
Detectin f fatigue crack initiatin frm a ntch under a randm lad C. Makabe," S. Nishida^C. Urashima,' H. Kaneshir* "Department f Mechanical Systems Engineering, University f the Ryukyus, Nishihara, kinawa,
More informationDEDICATED TO THE MEMORY OF R.J. WEINSHENK 1. INTRODUCTION
CONVOLUTION TRIANGLES FOR GENERALIZED FIBONACCI NUMBERS VERNER E. HOGGATT, JR. San Jse State Cllege, San Jse, Califrnia DEDICATED TO THE MEMORY OF R.J. WEINSHENK. INTRODUCTION The sequence f integers Fj
More informationCOMP 551 Applied Machine Learning Lecture 9: Support Vector Machines (cont d)
COMP 551 Applied Machine Learning Lecture 9: Supprt Vectr Machines (cnt d) Instructr: Herke van Hf (herke.vanhf@mail.mcgill.ca) Slides mstly by: Class web page: www.cs.mcgill.ca/~hvanh2/cmp551 Unless therwise
More informationIN a recent article, Geary [1972] discussed the merit of taking first differences
The Efficiency f Taking First Differences in Regressin Analysis: A Nte J. A. TILLMAN IN a recent article, Geary [1972] discussed the merit f taking first differences t deal with the prblems that trends
More informationThis section is primarily focused on tools to aid us in finding roots/zeros/ -intercepts of polynomials. Essentially, our focus turns to solving.
Sectin 3.2: Many f yu WILL need t watch the crrespnding vides fr this sectin n MyOpenMath! This sectin is primarily fcused n tls t aid us in finding rts/zers/ -intercepts f plynmials. Essentially, ur fcus
More informationENSC Discrete Time Systems. Project Outline. Semester
ENSC 49 - iscrete Time Systems Prject Outline Semester 006-1. Objectives The gal f the prject is t design a channel fading simulatr. Upn successful cmpletin f the prject, yu will reinfrce yur understanding
More informationMODULAR DECOMPOSITION OF THE NOR-TSUM MULTIPLE-VALUED PLA
MODUAR DECOMPOSITION OF THE NOR-TSUM MUTIPE-AUED PA T. KAGANOA, N. IPNITSKAYA, G. HOOWINSKI k Belarusian State University f Infrmatics and Radielectrnics, abratry f Image Prcessing and Pattern Recgnitin.
More informationCHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India
CHAPTER 3 INEQUALITIES Cpyright -The Institute f Chartered Accuntants f India INEQUALITIES LEARNING OBJECTIVES One f the widely used decisin making prblems, nwadays, is t decide n the ptimal mix f scarce
More informationQuantum Harmonic Oscillator, a computational approach
IOSR Jurnal f Applied Physics (IOSR-JAP) e-issn: 78-4861.Vlume 7, Issue 5 Ver. II (Sep. - Oct. 015), PP 33-38 www.isrjurnals Quantum Harmnic Oscillatr, a cmputatinal apprach Sarmistha Sahu, Maharani Lakshmi
More informationModeling the Nonlinear Rheological Behavior of Materials with a Hyper-Exponential Type Function
www.ccsenet.rg/mer Mechanical Engineering Research Vl. 1, N. 1; December 011 Mdeling the Nnlinear Rhelgical Behavir f Materials with a Hyper-Expnential Type Functin Marc Delphin Mnsia Département de Physique,
More informationOnline Model Racing based on Extreme Performance
Online Mdel Racing based n Extreme Perfrmance Tiantian Zhang, Michael Gergipuls, Gergis Anagnstpuls Electrical & Cmputer Engineering University f Central Flrida Overview Racing Algrithm Offline vs Online
More informationEquilibrium of Stress
Equilibrium f Stress Cnsider tw perpendicular planes passing thrugh a pint p. The stress cmpnents acting n these planes are as shwn in ig. 3.4.1a. These stresses are usuall shwn tgether acting n a small
More informationProbability, Random Variables, and Processes. Probability
Prbability, Randm Variables, and Prcesses Prbability Prbability Prbability thery: branch f mathematics fr descriptin and mdelling f randm events Mdern prbability thery - the aximatic definitin f prbability
More informationMAKING DOUGHNUTS OF COHEN REALS
MAKING DUGHNUTS F CHEN REALS Lrenz Halbeisen Department f Pure Mathematics Queen s University Belfast Belfast BT7 1NN, Nrthern Ireland Email: halbeis@qub.ac.uk Abstract Fr a b ω with b \ a infinite, the
More information1996 Engineering Systems Design and Analysis Conference, Montpellier, France, July 1-4, 1996, Vol. 7, pp
THE POWER AND LIMIT OF NEURAL NETWORKS T. Y. Lin Department f Mathematics and Cmputer Science San Jse State University San Jse, Califrnia 959-003 tylin@cs.ssu.edu and Bereley Initiative in Sft Cmputing*
More informationFree Vibrations of Catenary Risers with Internal Fluid
Prceeding Series f the Brazilian Sciety f Applied and Cmputatinal Mathematics, Vl. 4, N. 1, 216. Trabalh apresentad n DINCON, Natal - RN, 215. Prceeding Series f the Brazilian Sciety f Cmputatinal and
More informationCollocation Map for Overcoming Data Sparseness
Cllcatin Map fr Overcming Data Sparseness Mnj Kim, Yung S. Han, and Key-Sun Chi Department f Cmputer Science Krea Advanced Institute f Science and Technlgy Taejn, 305-701, Krea mj0712~eve.kaist.ac.kr,
More informationLead/Lag Compensator Frequency Domain Properties and Design Methods
Lectures 6 and 7 Lead/Lag Cmpensatr Frequency Dmain Prperties and Design Methds Definitin Cnsider the cmpensatr (ie cntrller Fr, it is called a lag cmpensatr s K Fr s, it is called a lead cmpensatr Ntatin
More informationOn Huntsberger Type Shrinkage Estimator for the Mean of Normal Distribution ABSTRACT INTRODUCTION
Malaysian Jurnal f Mathematical Sciences 4(): 7-4 () On Huntsberger Type Shrinkage Estimatr fr the Mean f Nrmal Distributin Department f Mathematical and Physical Sciences, University f Nizwa, Sultanate
More informationthe results to larger systems due to prop'erties of the projection algorithm. First, the number of hidden nodes must
M.E. Aggune, M.J. Dambrg, M.A. El-Sharkawi, R.J. Marks II and L.E. Atlas, "Dynamic and static security assessment f pwer systems using artificial neural netwrks", Prceedings f the NSF Wrkshp n Applicatins
More informationA New Evaluation Measure. J. Joiner and L. Werner. The problems of evaluation and the needed criteria of evaluation
III-l III. A New Evaluatin Measure J. Jiner and L. Werner Abstract The prblems f evaluatin and the needed criteria f evaluatin measures in the SMART system f infrmatin retrieval are reviewed and discussed.
More informationA Matrix Representation of Panel Data
web Extensin 6 Appendix 6.A A Matrix Representatin f Panel Data Panel data mdels cme in tw brad varieties, distinct intercept DGPs and errr cmpnent DGPs. his appendix presents matrix algebra representatins
More informationGraduate AI Lecture 16: Planning 2. Teachers: Martial Hebert Ariel Procaccia (this time)
Graduate AI Lecture 16: Planning 2 Teachers: Martial Hebert Ariel Prcaccia (this time) Reminder State is a cnjunctin f cnditins, e.g., at(truck 1,Shadyside) at(truck 2,Oakland) States are transfrmed via
More information2.161 Signal Processing: Continuous and Discrete Fall 2008
MIT OpenCurseWare http://cw.mit.edu 2.161 Signal Prcessing: Cntinuus and Discrete Fall 2008 Fr infrmatin abut citing these materials r ur Terms f Use, visit: http://cw.mit.edu/terms. Massachusetts Institute
More informationTechnical Bulletin. Generation Interconnection Procedures. Revisions to Cluster 4, Phase 1 Study Methodology
Technical Bulletin Generatin Intercnnectin Prcedures Revisins t Cluster 4, Phase 1 Study Methdlgy Release Date: Octber 20, 2011 (Finalizatin f the Draft Technical Bulletin released n September 19, 2011)
More informationOn Boussinesq's problem
Internatinal Jurnal f Engineering Science 39 (2001) 317±322 www.elsevier.cm/lcate/ijengsci On Bussinesq's prblem A.P.S. Selvadurai * Department f Civil Engineering and Applied Mechanics, McGill University,
More informationMath Foundations 20 Work Plan
Math Fundatins 20 Wrk Plan Units / Tpics 20.8 Demnstrate understanding f systems f linear inequalities in tw variables. Time Frame December 1-3 weeks 6-10 Majr Learning Indicatrs Identify situatins relevant
More informationCHAPTER 2 Algebraic Expressions and Fundamental Operations
CHAPTER Algebraic Expressins and Fundamental Operatins OBJECTIVES: 1. Algebraic Expressins. Terms. Degree. Gruping 5. Additin 6. Subtractin 7. Multiplicatin 8. Divisin Algebraic Expressin An algebraic
More informationInterference is when two (or more) sets of waves meet and combine to produce a new pattern.
Interference Interference is when tw (r mre) sets f waves meet and cmbine t prduce a new pattern. This pattern can vary depending n the riginal wave directin, wavelength, amplitude, etc. The tw mst extreme
More informationELE Final Exam - Dec. 2018
ELE 509 Final Exam Dec 2018 1 Cnsider tw Gaussian randm sequences X[n] and Y[n] Assume that they are independent f each ther with means and autcvariances μ ' 3 μ * 4 C ' [m] 1 2 1 3 and C * [m] 3 1 10
More informationDetermining the Accuracy of Modal Parameter Estimation Methods
Determining the Accuracy f Mdal Parameter Estimatin Methds by Michael Lee Ph.D., P.E. & Mar Richardsn Ph.D. Structural Measurement Systems Milpitas, CA Abstract The mst cmmn type f mdal testing system
More informationfor Shipboard EquipIllent
R. J. Hunt Quantitative evaluatin f the usefulness and effectiveness f fleet shipbard guided-missile def ense systems is essential fr assessment f present and f uture fleet defense capability. The measure
More informationSource Coding and Compression
Surce Cding and Cmpressin Heik Schwarz Cntact: Dr.-Ing. Heik Schwarz heik.schwarz@hhi.fraunhfer.de Heik Schwarz Surce Cding and Cmpressin September 22, 2013 1 / 60 PartI: Surce Cding Fundamentals Heik
More information