On Construction of Odd-fractional Factorial Designs

Size: px
Start display at page:

Download "On Construction of Odd-fractional Factorial Designs"

Transcription

1 J. Sa. Appl. Pro., o., -7 SP Journal of Sascs Applcaons & Proaly --- An Inernaonal SP aural Scences Pulsn Cor. On Consrucon of Odd-fraconal Facoral Desns Ike Basl Onukou Deparmen of Sascs, Unversy of Uyo, Akwa Iom Sae, era Emal Address: keaslonukou@yaoo.com Receved: Dec., ; Revsed Fe., ; Acceped Fe. 8, Pulsed onlne: Aprl Asrac: Fraconal desns nvolve selecon from a ven se of expermenal reamens as suse of reamens o makeup a specfed desn measure a as suc sascal properes as alance, relave effcency, D-opmaly ec. For decades sascans ave reled on e use Defnn Conracs DC, and Lan Squares LS o consruc fraconal facoral desns. Bu ese meods are sown o ave very lmed rane of applcaons and somemes produce desns a are snular. Ts paper nroduces e meod of Concenrc Balls for consrucn non-snular fraconal desns. Eac all consss of reamens a are of equal dsance from e cener and usn a se of rules for selecn reamens from a all e meod yelds a small se of admssle desns. Te es memer of s admssle se s e desred desn:{bes n e sense of maxmzn e deermnan of e normalzed nformaon marx or maxmzn e relave effcency of e facoral effecs.}umercal examples sow a e meod covers every rane of expermenal desn condons and can produce fraconal desns a are D-opmal. Keywords: Odd-fracon, concenrc alls, relave effcency Inroducon Consrucon of fraconal facoral desns s a opc a s exensvely reaed n mos sandard exs on desn of expermens; see, e.. Cocran and Cox 957, Anderson and Mclean 974. From n-ndependen, non-socasc varales, were e varae, x appears a s -levels, we e s s s n reamens and consder ree knds of reamen spaces : Te unform or symmerc form; s x, x,, xn; x,,, s, s s sn, Te non-unform or asymmerc ype; A x,, xn; s s. for a leas one par of, Te Irreular ype; ; e.. x, x,, x ; x,,, s, R n Oer eomerc forms can also occur.e. x, x,, xn ; x,,,, n s a produc of connuous nervals; owever, e coverae of s repor does no nclude connuous nervals. As saed earler, e prolem of neres ere s o consruc an -pon desn p,.e. an en e numer of parameers n e response s s s n fraconal facoral desn, p funcon f x. Te fracon s consdered an odd-fracon f s no dvsle y any s, oerwse s a reular fracon. For decades, e pracce as een o consruc fraconal facoral desns usn eer Lan Squares LS or Defnn Conrass DC; see, e.. Anderson and Mclean. Two prolems can arse from s approac: a Te DC and LS meods are napplcale, as n. 7

2 J. Sa. Appl. Pro., o., -7 Te meods can produce snular or near snular desns as sown n ale..., even wen e relave effcency of e desn s consdered ood. Ts paper nroduces e Concenrc Balls meod of consrucon a as a wde rane of applcaons and can produce an admssle se of equvalen desns, leavn e scens o make a coce. Te meod proceeds as follows: Arrane e suppor pons no H roups or alls, so a suppor pons a are of e same dsance from e cener are n one all. Tus e all, x, x,, xn conans n suppor pons, =,,..., H, xk s an n-componen vecor, k =,,...,n, were, d x xk s e dsance from e cener, and d d d. Paron k H no su-roups accordn o e numer of neave sns and zeros appearn. a e suppor pon xk; see secon ree of s paper. Apply e selecon rules; see secon wo o uld up e requred desn. Tese rules yeld a small se of admssle desns wose deermnans and relave effcences can e easly compared. Applcaon of e dea of roupn of reamens owards consrucon of D-opmal exac desns ave een employed y Onukou and Iwundu 7; and for D-opmals desns for -level facoral models and auoreressve error y Ye and Huan5. Consrucon and analyss of fraconal facoral desns on a wder plaform as een consdered y Guns and Mason 9.A rane of ecnques for consrucon of asymmerc fraconal facorals as well as condons for nonexsence of e desns ave een ven y Dey and Raul 999. A way as offered y Oludua and Madukafe 9 for serean fraconal facoral desns on e ass of er D-opmal and loss of nformaon values. As lon as neres n a facoral expermen s resrced o a lmed numer of parameers facoral effecs researc n fraconal desns wll connue o flours. In wa follows, e asc alera for e ecnque s dscussed n secon wo, wle numercal llusraons are ven n secon ree.. Alerac Bass Te expermenal space wll e represened y e rple, F, ; x, x,, x ; x,, s,,, n s a connuous, compac, merc space of rals, x x n Fx f x; x s a se of connuous, dfferenale funcons. x x; x s a se of connuous, non-neave error funcons. Eac se of e rple s consdered fne and oeer ey form a ass for n-dep sudy of e sujec of desn of expermens; see, e.. Pazman 987, Aknson and Donev 99, Onukou 997. Le f x e a frs-order neracve funcon defned y. f x e = x j s an p n n p exended desn marx; e p parameers comprsn e lnear and neracve erms,, s an lock ncdence marx; da k, k,, k ; k j en e sze of e j lock, s a p-parameer vecor of reamen effecs e s an -componen vecor of random error Te deermnan of e nformaon marx n. equals, k j de de I R; R j s e marx of loss of nformaon..

3 Pranes Kumar: Sascal Dependence: Copula Funcons A eomerc meann of loss of nformaon as cos. of e anle of nclnaon of a reamen effec on e locks as een reaed y Onukou. ow, for an -pon desn n one lock =, R rr ; r r, r,, r ; r,,,, p.; r s e loss of nformaon on e reamen effec. Hence, e eomerc mean, p. r r p p ves a measure of e overall effcency of e desn relave o a complee lock desn. oce a for r, r. s maxmzed wen e desn s alanced; e. wen r r r. Bu r. does no p ake no accoun e deermnan, de, and erefore can ake non-zero values for snular desns. Bu y ncludn e deermnan, we e e creron for comparn desns:.4 d m r ; m de / To maxmze.4 e follown selecon rules are o e appled wen makn-up e desn measure : max x j mn xj mn xj xj, j,,, p, j j We recall a x s e exended reamen marx. j Realsn a e numer of suppor pons n n s n p n n ; en, for e response funcon.,e opmal -pon desn s consruced from no e case for a complee quadrac funcon, n.5 f x a a x a x x a x e n only. Bu, s s Te sarn pon of e procedure s dependen on e relave values of and p. Wen = p, e procedure oans e desn as follows: a A e nal sep ake p n suppor pons from and e res of p + n + from, y applcaon e rules n.4 aove. Ts yelds a se of r admssle desns S A.,, r m max m ; S A Compue c A e k sep ake p + k n suppor pons from k k m k max m ; SA d Sop, f k k k m m m and e res from, and compue For >> p, e aove sequence can en y akn p or p+ pons from. I seems a y properly relan e numer of pons o e aken from o e rao p/, sould e possle o develop a nonerave procedure for consrucn opmal fraconal facoral desns for quadrac response funcons.. umercal Examples Gven are rvarae frs-order neracve response surface,. f x, x, x a ax ax ax axx axx axx e and a cuc surface, x, x, x ; x,,,,,.

4 4 Pranes Kumar: Sascal Dependence: Copula Funcons Usn eac of e ree meods, we consder e consrucon of wo fraconal facoral desns: 9 a, for a reular fracon, and 7, for an odd-fracon. 7 Te reamen ale s ven y. Tale of Treamens For x,, x,, x,, cuc surface Usn for nsance xx x as defnn conras; see, e.. Anderson and Mclean, for deals, e DC meod produces e desn 9 DC, wereas e Lan square meod ves 9 LS Te roups and suroups requred for e meod are: Applcaon of e rules under.4 ves wo equvalen desns, Te deermnans of ese desns are repored n Tale. 4 9, 9.. Deermnans, De. And Relave Effcences, RE. For Tree Meods of Consrucn Fraconal Desns for Frs-Order Ineracve Funcons

5 Pranes Kumar: Sascal Dependence: Copula Funcons 5 Seral umer Fraconal Desn METHOD OF COSTRUCTIO DC LS De. RE. De. RE. De. RE x E-5 x E-5 x E- A A A A x E- 4 A A x E- x E- 4 7 A A A A x E A means o Applcale. Smlarly, for e odd-fracon e meod produces wo equvalen desns: 7, Bo e DC and LS meods are napplcale n s case of odd-fracon. If e space of rals s non-unform asymmerc r-varae surface, x, x, x; x, x,, x,,,,, We consder e consrucon of wo desns: c 4 5 for reular fracon, and d 7 5 for odd-fracon. Jus as n ale., a correspondn reamen ale s se-up and snce 4 s dvsle y, e Lan- Square meod can e appled. Te frs sep s o se-up a Paral Lan Square PLS, L usn e leers a and. ex, supermpose L on e frs ac of 5 reamens were x, and en on e oer ac of 5 reamen a x, a L a a a a a a a Fnally, e reamens a concde w e leer are rouped oeer o form e desn: 4 PLS, de. =.9 x E - On e conrary e meod produces an opmal desns;

6 Pranes Kumar: Sascal Dependence: Copula Funcons 6 4 w de. = x E - Te consrucon of 5 7 also ves wo equvalen desns: 7 and 7 w de. = x E - ; oce a all e desns for e frs-order neracve funcons are consruced from e frs all for all values of. Ts owever s no e case for quadrac response funcon defned n.5. Apparenly, wo concenrc alls are requred o consruc a desn for quadrac funcons. For example, wo equvalen desns are oaned y e meod for a ; 7 namely, and w m x E -4. Smlarly, for e asymmerc surface, e meod ves wo equvalen opmal desns for 5; namely, ; 4 4 and 4 4,, s ven aove. Summary and Concluson Te paper as sown a DC meod can e used o consruc fraconal facoral desns only wen e facor levels are unform and even a s, e relave effcency of s meod s comparavely nferor. On e oer and, e Lan square meod can e appled o for unform and non-unform levels provded only a e fracon s reular. Of e ree meods s only e

7 Pranes Kumar: Sascal Dependence: Copula Funcons 7 meod a can consruc odd-fraconal facoral desns; desns w e es level of effcency; desns a are requred o e alanced n one replcaon as well as desns a are D-opmal. Acknowledemen We are raeful o e Revewer and e Edor-n-Cef for e references numers and 4. References []. Anderson, V.L. and Mclean, R.A. 974: Desn of Expermens: A Realsc Approac, Marcel Dekker. []. Cocran, W.G. and Cox, G.M. 957: Expermenal Desns, nd Edon, J.Wley []. Dey, Aloke and Mukerjee, Raul 999: Fraconal Facoral Plans; Jon Wley and Sons, ew York. [4]. Guns, Rcard, F. and Mason, Roer, L 9: Fraconal Facoral Desn; Wley Inerdsplnary Revews: Compuaonal Sascs, Vol., Issue, Pae 4 44 [5]. Oludua, A.V. and Madukafe, M.S. 9: D-Opmaly and D L -Opmaly crera for Incomplee Block Desns; Gloal Journal of Maemacal Scences, Vol. 8, o. Pae 7 5. [6]. Onukou, I.B. 997: Foundaons of Opmal Exploraon of Response Surfaces, Epraa Press, sukka, era. [7]. Onukou, I.B. and Iwundu, M.P. 7: A Comnaoral Procedure for Consrucn D-Opmal Exac Desns; Sasca Rvsa Vol.67 Pae [8]. Pazman, A. 987: Foundaon of Opmum Expermenal Desn, Redel Pulsn Company. [9]. Ye, Hon-Gwa and Huan, Mon-a Lo 5: On Exac D-Opmal Desns w wo-level facors and n auocorrelaed

Robustness Experiments with Two Variance Components

Robustness Experiments with Two Variance Components Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference

More information

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng

More information

TSS = SST + SSE An orthogonal partition of the total SS

TSS = SST + SSE An orthogonal partition of the total SS ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION

A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION S19 A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION by Xaojun YANG a,b, Yugu YANG a*, Carlo CATTANI c, and Mngzheng ZHU b a Sae Key Laboraory for Geomechancs and Deep Underground Engneerng, Chna Unversy

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

MAXIMIN POWER DESIGNS IN TESTING LACK OF FIT Douglas P. Wiens 1

MAXIMIN POWER DESIGNS IN TESTING LACK OF FIT Douglas P. Wiens 1 MAXIMIN POWER DEIGN IN TETING LACK OF FIT Douglas P. Wens Absrac We nd desgns wc maxmze e mnmum power, over a broad class of alernaves, of e es for Lack of F, n dscree desgn spaces. Ts complemens prevous

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

Tight results for Next Fit and Worst Fit with resource augmentation

Tight results for Next Fit and Worst Fit with resource augmentation Tgh resuls for Nex F and Wors F wh resource augmenaon Joan Boyar Leah Epsen Asaf Levn Asrac I s well known ha he wo smple algorhms for he classc n packng prolem, NF and WF oh have an approxmaon rao of

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

Delay Dependent Robust Stability of T-S Fuzzy. Systems with Additive Time Varying Delays

Delay Dependent Robust Stability of T-S Fuzzy. Systems with Additive Time Varying Delays Appled Maemacal Scences, Vol. 6,, no., - Delay Dependen Robus Sably of -S Fuzzy Sysems w Addve me Varyng Delays Idrss Sad LESSI. Deparmen of Pyscs, Faculy of Scences B.P. 796 Fès-Alas Sad_drss9@yaoo.fr

More information

Relative controllability of nonlinear systems with delays in control

Relative controllability of nonlinear systems with delays in control Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Outline. Energy-Efficient Target Coverage in Wireless Sensor Networks. Sensor Node. Introduction. Characteristics of WSN

Outline. Energy-Efficient Target Coverage in Wireless Sensor Networks. Sensor Node. Introduction. Characteristics of WSN Ener-Effcen Tare Coverae n Wreless Sensor Newors Presened b M Trà Tá -4-4 Inroducon Bacround Relaed Wor Our Proosal Oulne Maxmum Se Covers (MSC) Problem MSC Problem s NP-Comlee MSC Heursc Concluson Sensor

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

Single-loop System Reliability-Based Design & Topology Optimization (SRBDO/SRBTO): A Matrix-based System Reliability (MSR) Method

Single-loop System Reliability-Based Design & Topology Optimization (SRBDO/SRBTO): A Matrix-based System Reliability (MSR) Method 10 h US Naonal Congress on Compuaonal Mechancs Columbus, Oho 16-19, 2009 Sngle-loop Sysem Relably-Based Desgn & Topology Opmzaon (SRBDO/SRBTO): A Marx-based Sysem Relably (MSR) Mehod Tam Nguyen, Junho

More information

Stability Analysis of Fuzzy Hopfield Neural Networks with Timevarying

Stability Analysis of Fuzzy Hopfield Neural Networks with Timevarying ISSN 746-7659 England UK Journal of Informaon and Compung Scence Vol. No. 8 pp.- Sably Analyss of Fuzzy Hopfeld Neural Neworks w mevaryng Delays Qfeng Xun Cagen Zou Scool of Informaon Engneerng Yanceng

More information

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study) Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence

More information

An introduction to Support Vector Machine

An introduction to Support Vector Machine An nroducon o Suppor Vecor Machne 報告者 : 黃立德 References: Smon Haykn, "Neural Neworks: a comprehensve foundaon, second edon, 999, Chaper 2,6 Nello Chrsann, John Shawe-Tayer, An Inroducon o Suppor Vecor Machnes,

More information

Midterm Exam. Thursday, April hour, 15 minutes

Midterm Exam. Thursday, April hour, 15 minutes Economcs of Grow, ECO560 San Francsco Sae Unvers Mcael Bar Sprng 04 Mderm Exam Tursda, prl 0 our, 5 mnues ame: Insrucons. Ts s closed boo, closed noes exam.. o calculaors of an nd are allowed. 3. Sow all

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

Review of Numerical Schemes for Two Point Second Order Non-Linear Boundary Value Problems

Review of Numerical Schemes for Two Point Second Order Non-Linear Boundary Value Problems Proceedngs of e Pasan Academ of Scences 5 (: 5-58 (5 Coprg Pasan Academ of Scences ISS: 377-969 (prn, 36-448 (onlne Pasan Academ of Scences Researc Arcle Revew of umercal Scemes for Two Pon Second Order

More information

Robust and Accurate Cancer Classification with Gene Expression Profiling

Robust and Accurate Cancer Classification with Gene Expression Profiling Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem

More information

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL Sco Wsdom, John Hershey 2, Jonahan Le Roux 2, and Shnj Waanabe 2 Deparmen o Elecrcal Engneerng, Unversy o Washngon, Seale, WA, USA

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose

More information

An adaptive approach to small object segmentation

An adaptive approach to small object segmentation An adapve approach o small ojec segmenaon Shen ngzh ang Le Dep. of Elecronc Engneerng ejng Insue of echnology ejng 8 Chna Asrac-An adapve approach o small ojec segmenaon ased on Genec Algorhms s proposed.

More information

MAXIMIN POWER DESIGNS IN TESTING LACK OF FIT Douglas P. Wiens 1. July 30, 2018

MAXIMIN POWER DESIGNS IN TESTING LACK OF FIT Douglas P. Wiens 1. July 30, 2018 MAXIMIN POWER DESIGNS IN TESTING LACK OF FIT Douglas P. Wens July 3, 28 Absrac In a prevous arcle (Wens, 99) we esablshed a maxmn propery, wh respec o he power of he es for Lack of F, of he absoluely connuous

More information

P R = P 0. The system is shown on the next figure:

P R = P 0. The system is shown on the next figure: TPG460 Reservor Smulaon 08 page of INTRODUCTION TO RESERVOIR SIMULATION Analycal and numercal soluons of smple one-dmensonal, one-phase flow equaons As an nroducon o reservor smulaon, we wll revew he smples

More information

Track Properities of Normal Chain

Track Properities of Normal Chain In. J. Conemp. Mah. Scences, Vol. 8, 213, no. 4, 163-171 HIKARI Ld, www.m-har.com rac Propes of Normal Chan L Chen School of Mahemacs and Sascs, Zhengzhou Normal Unversy Zhengzhou Cy, Hennan Provnce, 4544,

More information

Fall 2010 Graduate Course on Dynamic Learning

Fall 2010 Graduate Course on Dynamic Learning Fall 200 Graduae Course on Dynamc Learnng Chaper 4: Parcle Flers Sepember 27, 200 Byoung-Tak Zhang School of Compuer Scence and Engneerng & Cognve Scence and Bran Scence Programs Seoul aonal Unversy hp://b.snu.ac.kr/~bzhang/

More information

Dual Approximate Dynamic Programming for Large Scale Hydro Valleys

Dual Approximate Dynamic Programming for Large Scale Hydro Valleys Dual Approxmae Dynamc Programmng for Large Scale Hydro Valleys Perre Carpener and Jean-Phlppe Chanceler 1 ENSTA ParsTech and ENPC ParsTech CMM Workshop, January 2016 1 Jon work wh J.-C. Alas, suppored

More information

Chapter 6: AC Circuits

Chapter 6: AC Circuits Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.

More information

Comparison between the Discrete and Continuous Time Models

Comparison between the Discrete and Continuous Time Models Comparison beween e Discree and Coninuous Time Models D. Sulsky June 21, 2012 1 Discree o Coninuous Recall e discree ime model Î = AIS Ŝ = S Î. Tese equaions ell us ow e populaion canges from one day o

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g

More information

Comparison of Differences between Power Means 1

Comparison of Differences between Power Means 1 In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,

More information

Lecture 11 SVM cont

Lecture 11 SVM cont Lecure SVM con. 0 008 Wha we have done so far We have esalshed ha we wan o fnd a lnear decson oundary whose margn s he larges We know how o measure he margn of a lnear decson oundary Tha s: he mnmum geomerc

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The

More information

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field Submed o: Suden Essay Awards n Magnecs Bernoull process wh 8 ky perodcy s deeced n he R-N reversals of he earh s magnec feld Jozsef Gara Deparmen of Earh Scences Florda Inernaonal Unversy Unversy Park,

More information

Supporting Information: The integrated Global Temperature change Potential (igtp) and relationships between emission metrics

Supporting Information: The integrated Global Temperature change Potential (igtp) and relationships between emission metrics 2 3 4 5 6 7 8 9 Supporng Informaon: Te negraed Global Temperaure cange Poenal (GTP) and relaonsps beween emsson mercs Glen P. Peers *, Borgar Aamaas, Tere Bernsen,2, Jan S. Fuglesved Cener for Inernaonal

More information

T q (heat generation) Figure 0-1: Slab heated with constant source 2 = q k

T q (heat generation) Figure 0-1: Slab heated with constant source 2 = q k IFFERETIL EQUTIOS, PROBLE BOURY VLUE 5. ITROUCTIO s as been noed n e prevous caper, boundary value problems BVP for ordnary dfferenal equaons ave boundary condons specfed a more an one pon of e ndependen

More information

This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.

This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore. Ths documen s downloaded from DR-NTU, Nanyang Technologcal Unversy Lbrary, Sngapore. Tle A smplfed verb machng algorhm for word paron n vsual speech processng( Acceped verson ) Auhor(s) Foo, Say We; Yong,

More information

û s L u t 0 s a ; i.e., û s 0

û s L u t 0 s a ; i.e., û s 0 Te Hille-Yosida Teorem We ave seen a wen e absrac IVP is uniquely solvable en e soluion operaor defines a semigroup of bounded operaors. We ave no ye discussed e condiions under wic e IVP is uniquely solvable.

More information

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria IOSR Journal of Mahemacs (IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume 0, Issue 4 Ver. IV (Jul-Aug. 04, PP 40-44 Mulple SolonSoluons for a (+-dmensonalhroa-sasuma shallow waer wave equaon UsngPanlevé-Bӓclund

More information

ABSTRACT KEYWORDS. Bonus-malus systems, frequency component, severity component. 1. INTRODUCTION

ABSTRACT KEYWORDS. Bonus-malus systems, frequency component, severity component. 1. INTRODUCTION EERAIED BU-MAU YTEM ITH A FREQUECY AD A EVERITY CMET A IDIVIDUA BAI I AUTMBIE IURACE* BY RAHIM MAHMUDVAD AD HEI HAAI ABTRACT Frangos and Vronos (2001) proposed an opmal bonus-malus sysems wh a frequency

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary

More information

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal

More information

The Analysis of the Thickness-predictive Model Based on the SVM Xiu-ming Zhao1,a,Yan Wang2,band Zhimin Bi3,c

The Analysis of the Thickness-predictive Model Based on the SVM Xiu-ming Zhao1,a,Yan Wang2,band Zhimin Bi3,c h Naonal Conference on Elecrcal, Elecroncs and Compuer Engneerng (NCEECE The Analyss of he Thcknesspredcve Model Based on he SVM Xumng Zhao,a,Yan Wang,band Zhmn B,c School of Conrol Scence and Engneerng,

More information

Cubic Bezier Homotopy Function for Solving Exponential Equations

Cubic Bezier Homotopy Function for Solving Exponential Equations Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.

More information

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System Communcaons n Sascs Theory and Mehods, 34: 475 484, 2005 Copyrgh Taylor & Francs, Inc. ISSN: 0361-0926 prn/1532-415x onlne DOI: 10.1081/STA-200047430 Survval Analyss and Relably A Noe on he Mean Resdual

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information

Asymmetry and Leverage in Conditional Volatility Models*

Asymmetry and Leverage in Conditional Volatility Models* Asymmery and Leverage in Condiional Volailiy Models* Micael McAleer Deparmen of Quaniaive Finance Naional Tsing Hua Universiy Taiwan and Economeric Insiue Erasmus Scool of Economics Erasmus Universiy Roerdam

More information

Math 128b Project. Jude Yuen

Math 128b Project. Jude Yuen Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally

More information

Variance Stabilizing Power Transformation for Time Series

Variance Stabilizing Power Transformation for Time Series Journal of Modern Appled Sascal Meods Volume 3 Issue Arcle 9 --004 Varance Sablzng Power Transformaon for Tme Seres Vcor M. Guerrero Insuo Tecnológco Auónomo de Méxco, guerrero@am.mx Rafael Perera Insuo

More information

Stochastic Maxwell Equations in Photonic Crystal Modeling and Simulations

Stochastic Maxwell Equations in Photonic Crystal Modeling and Simulations Sochasc Maxwell Equaons n Phoonc Crsal Modelng and Smulaons Hao-Mn Zhou School of Mah Georga Insue of Technolog Jon work wh: Al Adb ECE Majd Bade ECE Shu-Nee Chow Mah IPAM UCLA Aprl 14-18 2008 Parall suppored

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

Solving Parabolic Partial Delay Differential. Equations Using The Explicit Method And Higher. Order Differences

Solving Parabolic Partial Delay Differential. Equations Using The Explicit Method And Higher. Order Differences Jornal of Kfa for Maemacs and Compe Vol. No.7 Dec pp 77-5 Solvng Parabolc Paral Delay Dfferenal Eqaons Usng e Eplc Meod And Hger Order Dfferences Asss. Prof. Amal Kalaf Haydar Kfa Unversy College of Edcaon

More information

Machine Learning 2nd Edition

Machine Learning 2nd Edition INTRODUCTION TO Lecure Sldes for Machne Learnng nd Edon ETHEM ALPAYDIN, modfed by Leonardo Bobadlla and some pars from hp://www.cs.au.ac.l/~aparzn/machnelearnng/ The MIT Press, 00 alpaydn@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/mle

More information

Bayesian Inference of the GARCH model with Rational Errors

Bayesian Inference of the GARCH model with Rational Errors 0 Inernaonal Conference on Economcs, Busness and Markeng Managemen IPEDR vol.9 (0) (0) IACSIT Press, Sngapore Bayesan Inference of he GARCH model wh Raonal Errors Tesuya Takash + and Tng Tng Chen Hroshma

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

Epistemic Game Theory: Online Appendix

Epistemic Game Theory: Online Appendix Epsemc Game Theory: Onlne Appendx Edde Dekel Lucano Pomao Marcano Snscalch July 18, 2014 Prelmnares Fx a fne ype srucure T I, S, T, β I and a probably µ S T. Le T µ I, S, T µ, βµ I be a ype srucure ha

More information

On the numerical treatment ofthenonlinear partial differentialequation of fractional order

On the numerical treatment ofthenonlinear partial differentialequation of fractional order IOSR Journal of Mahemacs (IOSR-JM) e-iss: 2278-5728, p-iss: 239-765X. Volume 2, Issue 6 Ver. I (ov. - Dec.26), PP 28-37 www.osrjournals.org On he numercal reamen ofhenonlnear paral dfferenalequaon of fraconal

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

Data Collection Definitions of Variables - Conceptualize vs Operationalize Sample Selection Criteria Source of Data Consistency of Data

Data Collection Definitions of Variables - Conceptualize vs Operationalize Sample Selection Criteria Source of Data Consistency of Data Apply Sascs and Economercs n Fnancal Research Obj. of Sudy & Hypoheses Tesng From framework objecves of sudy are needed o clarfy, hen, n research mehodology he hypoheses esng are saed, ncludng esng mehods.

More information

Degrees of Freedom. Spherical (ball & socket) 3 (3 rotation) Two-Angle (universal) 2 (2 rotation)

Degrees of Freedom. Spherical (ball & socket) 3 (3 rotation) Two-Angle (universal) 2 (2 rotation) ME 6590 Multbody Dynamcs Connectn Jonts Part I o Connectn jonts constran te relatve moton between adjonn bodes n a multbody system. Jonts rane rom allown no relatve moton (a rd jont) to allown all motons

More information

Implementation of Quantized State Systems in MATLAB/Simulink

Implementation of Quantized State Systems in MATLAB/Simulink SNE T ECHNICAL N OTE Implemenaon of Quanzed Sae Sysems n MATLAB/Smulnk Parck Grabher, Mahas Rößler 2*, Bernhard Henzl 3 Ins. of Analyss and Scenfc Compung, Venna Unversy of Technology, Wedner Haupsraße

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu

More information

Robustness of DEWMA versus EWMA Control Charts to Non-Normal Processes

Robustness of DEWMA versus EWMA Control Charts to Non-Normal Processes Journal of Modern Appled Sascal Mehods Volume Issue Arcle 8 5--3 Robusness of D versus Conrol Chars o Non- Processes Saad Saeed Alkahan Performance Measuremen Cener of Governmen Agences, Insue of Publc

More information

Motion of Wavepackets in Non-Hermitian. Quantum Mechanics

Motion of Wavepackets in Non-Hermitian. Quantum Mechanics Moon of Wavepaces n Non-Herman Quanum Mechancs Nmrod Moseyev Deparmen of Chemsry and Mnerva Cener for Non-lnear Physcs of Complex Sysems, Technon-Israel Insue of Technology www.echnon echnon.ac..ac.l\~nmrod

More information

On Pfaff s solution of the Pfaff problem

On Pfaff s solution of the Pfaff problem Zur Pfaff scen Lösung des Pfaff scen Probles Mat. Ann. 7 (880) 53-530. On Pfaff s soluton of te Pfaff proble By A. MAYER n Lepzg Translated by D. H. Delpenc Te way tat Pfaff adopted for te ntegraton of

More information

On Convergence Rate of Concave-Convex Procedure

On Convergence Rate of Concave-Convex Procedure On Converence Rae o Concave-Conve Proceure Ian E.H. Yen Nanun Pen Po-We Wan an Shou-De Ln Naonal awan Unvers OP 202 Oulne Derence o Conve Funcons.c. Prora Applcaons n SVM leraure Concave-Conve Proceure

More information

Sensor Scheduling for Multiple Parameters Estimation Under Energy Constraint

Sensor Scheduling for Multiple Parameters Estimation Under Energy Constraint Sensor Scheduln for Mulple Parameers Esmaon Under Enery Consran Y Wan, Mnyan Lu and Demoshens Tenekezs Deparmen of Elecrcal Enneern and Compuer Scence Unversy of Mchan, Ann Arbor, MI {yws,mnyan,eneke}@eecs.umch.edu

More information

The Performance of Optimum Response Surface Methodology Based on MM-Estimator

The Performance of Optimum Response Surface Methodology Based on MM-Estimator The Performance of Opmum Response Surface Mehodology Based on MM-Esmaor Habshah Md, Mohd Shafe Musafa, Anwar Frano Absrac The Ordnary Leas Squares (OLS) mehod s ofen used o esmae he parameers of a second-order

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

A Paper presentation on. Department of Hydrology, Indian Institute of Technology, Roorkee

A Paper presentation on. Department of Hydrology, Indian Institute of Technology, Roorkee A Paper presenaon on EXPERIMENTAL INVESTIGATION OF RAINFALL RUNOFF PROCESS by Ank Cakravar M.K.Jan Kapl Rola Deparmen of Hydrology, Indan Insue of Tecnology, Roorkee-247667 Inroducon Ranfall-runoff processes

More information

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

Stochastic Reliability Analysis of Two Identical Cold Standby Units with Geometric Failure & Repair Rates

Stochastic Reliability Analysis of Two Identical Cold Standby Units with Geometric Failure & Repair Rates DOI: 0.545/mjis.07.500 Socasic Reliabiliy Analysis of Two Idenical Cold Sandby Unis wi Geomeric Failure & Repair Raes NITIN BHARDWAJ AND BHUPENDER PARASHAR Email: niinbardwaj@jssaen.ac.in; parasar_b@jssaen.ac.in

More information

Solution for singularly perturbed problems via cubic spline in tension

Solution for singularly perturbed problems via cubic spline in tension ISSN 76-769 England UK Journal of Informaton and Computng Scence Vol. No. 06 pp.6-69 Soluton for sngularly perturbed problems va cubc splne n tenson K. Aruna A. S. V. Rav Kant Flud Dynamcs Dvson Scool

More information

The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems

The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems Swss Federal Insue of Page 1 The Fne Elemen Mehod for he Analyss of Non-Lnear and Dynamc Sysems Prof. Dr. Mchael Havbro Faber Dr. Nebojsa Mojslovc Swss Federal Insue of ETH Zurch, Swzerland Mehod of Fne

More information

Bayes rule for a classification problem INF Discriminant functions for the normal density. Euclidean distance. Mahalanobis distance

Bayes rule for a classification problem INF Discriminant functions for the normal density. Euclidean distance. Mahalanobis distance INF 43 3.. Repeon Anne Solberg (anne@f.uo.no Bayes rule for a classfcaon problem Suppose we have J, =,...J classes. s he class label for a pxel, and x s he observed feaure vecor. We can use Bayes rule

More information

Multivariate Ratio Estimator of the Population Total under Stratified Random Sampling

Multivariate Ratio Estimator of the Population Total under Stratified Random Sampling Open Journal of Statstcs, 0,, 300-304 ttp://dx.do.org/0.436/ojs.0.3036 Publsed Onlne July 0 (ttp://www.scrp.org/journal/ojs) Multvarate Rato Estmator of te Populaton Total under Stratfed Random Samplng

More information

CHAPTER 5: MULTIVARIATE METHODS

CHAPTER 5: MULTIVARIATE METHODS CHAPER 5: MULIVARIAE MEHODS Mulvarae Daa 3 Mulple measuremens (sensors) npus/feaures/arbues: -varae N nsances/observaons/eamples Each row s an eample Each column represens a feaure X a b correspons o he

More information

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue. Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons

More information

EEL 6266 Power System Operation and Control. Chapter 5 Unit Commitment

EEL 6266 Power System Operation and Control. Chapter 5 Unit Commitment EEL 6266 Power Sysem Operaon and Conrol Chaper 5 Un Commmen Dynamc programmng chef advanage over enumeraon schemes s he reducon n he dmensonaly of he problem n a src prory order scheme, here are only N

More information

EFFICIENCY IMPROVEMENTS FOR PRICING AMERICAN OPTIONS WITH A STOCHASTIC MESH: PARALLEL IMPLEMENTATION 1

EFFICIENCY IMPROVEMENTS FOR PRICING AMERICAN OPTIONS WITH A STOCHASTIC MESH: PARALLEL IMPLEMENTATION 1 EFFICIENCY IMPROVEMENTS FOR PRICING AMERICAN OPTIONS WITH A STOCHASTIC MESH: PARAE IMPEMENTATION Absrac Thanos Avramds 2, Yury Znchenko 3, Thomas F. Coleman 4, Arun Verma 5 We dscuss a parallel mplemenaon

More information

Motion in Two Dimensions

Motion in Two Dimensions Phys 1 Chaper 4 Moon n Two Dmensons adzyubenko@csub.edu hp://www.csub.edu/~adzyubenko 005, 014 A. Dzyubenko 004 Brooks/Cole 1 Dsplacemen as a Vecor The poson of an objec s descrbed by s poson ecor, r The

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

Li An-Ping. Beijing , P.R.China

Li An-Ping. Beijing , P.R.China A New Type of Cpher: DICING_csb L An-Png Bejng 100085, P.R.Chna apl0001@sna.com Absrac: In hs paper, we wll propose a new ype of cpher named DICING_csb, whch s derved from our prevous sream cpher DICING.

More information

Sampling Procedure of the Sum of two Binary Markov Process Realizations

Sampling Procedure of the Sum of two Binary Markov Process Realizations Samplng Procedure of he Sum of wo Bnary Markov Process Realzaons YURY GORITSKIY Dep. of Mahemacal Modelng of Moscow Power Insue (Techncal Unversy), Moscow, RUSSIA, E-mal: gorsky@yandex.ru VLADIMIR KAZAKOV

More information

Method of upper lower solutions for nonlinear system of fractional differential equations and applications

Method of upper lower solutions for nonlinear system of fractional differential equations and applications Malaya Journal of Maemak, Vol. 6, No. 3, 467-472, 218 hps://do.org/1.26637/mjm63/1 Mehod of upper lower soluons for nonlnear sysem of fraconal dfferenal equaons and applcaons D.B. Dhagude1 *, N.B. Jadhav2

More information

Scattering at an Interface: Oblique Incidence

Scattering at an Interface: Oblique Incidence Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may

More information

WiH Wei He

WiH Wei He Sysem Idenfcaon of onlnear Sae-Space Space Baery odels WH We He wehe@calce.umd.edu Advsor: Dr. Chaochao Chen Deparmen of echancal Engneerng Unversy of aryland, College Par 1 Unversy of aryland Bacground

More information

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling Rajesh ngh Deparmen of ascs, Banaras Hndu Unvers(U.P.), Inda Pankaj Chauhan, Nrmala awan chool of ascs, DAVV, Indore (M.P.), Inda Florenn marandache Deparmen of Mahemacs, Unvers of New Meco, Gallup, UA

More information

Volatility Interpolation

Volatility Interpolation Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local

More information