Partial Availability and RGBI Methods to Improve System Performance in Different Interval of Time: The Drill Facility System Case Study

Size: px
Start display at page:

Download "Partial Availability and RGBI Methods to Improve System Performance in Different Interval of Time: The Drill Facility System Case Study"

Transcription

1 Open Journal of Modellng and Smulaon, 204, 2, Publshed Onlne Ocober 204 n ScRes. hp:// hp://dx.do.org/0.4236/ojms Paral Avalably and RGBI Mehods o Improve Sysem Performance n Dfferen Inerval of me: he Drll Facly Sysem Case Sudy Eduardo Calxo, Glson Bro de Lma Alves Glson 2, Adelc Menezes de Olvera 3 UFRJ-COPPE, Ro de Janero, Brazl 2 UFF-LAEC, Ro de Janero, Brazl 3 Perobras S.A., Ro Grande do Nore, Brazl Emal: eduardo.calxo@homal.com Receved 29 July 204; revsed 30 Augus 204; acceped 25 Sepember 204 Copyrgh 204 by auhors and Scenfc Research Publshng Inc. hs work s lcensed under he Creave Commons Arbuon Inernaonal Lcense (CC BY). hp://creavecommons.org/lcenses/by/4.0/ Absrac he man objecve of hs sudy s o propose a mehodology o defne he operaonal avalably for a sysem n dfferen nerval of me based on Mone Carlo smulaon. In addon, s also an objecve o denfy crcal equpmen n such nerval of me and defne when carryng ou nspecons o deec and preven falures. Nowadays, many sofware packages whch apply Mone Carlo smulaon based on relably dagram block do no show he operaonal avalably defned by nerval of me. In mos of cases, here s no resul ha shows how sysem performs n specfc nerval of me. Dependng on suaon, s mporan o defne he operaonal avalably by dfferen nerval of me n order o follow up sysem performance along me. In order o solve such problem, s proposed he paral avalably mehodology based on sysem age. Indeed, such mehod regards equpmen age based n dfferen perod of me ha wll resuls n Paral Avalably. ha means, as nsance, n case of wo years of smulaon here wll be he cumulave operaonal avalably and paral operaonal avalably resuls for frs and second years for example. herefore, s also mporan o defne he nspecon me n each nerval of me (year) n order o deec possble equpmen falure and defne prevenve manenance o avod such falures ha wll be performed by RGBI mehod. In order o show such mehodologes, wll be carred ou a drll facly case sudy whch s requred o defne operaonal avalably of he sysem on he frs and second years as well as nspecon me. Keywords Paral Operaonal Avalably, Sysem Age, Probably Densy Funcon (PDF), How o ce hs paper: Calxo, E., de Lma Alves Glson, G.B. and de Olvera, A.M. (204) Paral Avalably and RGBI Mehods o Improve Sysem Performance n Dfferen Inerval of me: he Drll Facly Sysem Case Sudy. Open Journal of Modellng and Smulaon, 2, hp://dx.do.org/0.4236/ojms

2 E. Calxo e al. Relably Growh Based Inspecon (RGBI). Inroducon Nowadays, dfferen ypes of sofware appled on RAM analyss are based on Mone Carlo Smulaon mehod and he fnal resul s he cumulave operaonal avalably. Indeed, such resul s cumulave along smulaon me; n anoher words, akes no accoun all sysem downme along smulaon perod of me o calculae he operaonal avalably []. Whenever a hgh performance sysem s n conex, such resul wll be accomplshed as expeced, because he defned perod of smulaon s appropraed o operaonal avalably arge. By he oher way round, s no possble o verfy he operaonal avalably n a specfc nerval of me. Acually, for sysem wh hgh operaonal avalably performance, he paral resul s no a problem because when such sysem acheves operaonal avalably arge n cumulave perod of me, hey mosly acheve avalably arge n specfc nerval of mes as well. Even hough, s necessary n some cases o fnd ou he operaonal avalably n dfferen nervals of me o follow up sysem performance or even esablsh operaonal avalably arges for dfferen nerval of me. In some cases, n order o plan resources lke componen sock, servces order as well as plan prevenve manenance, s very mporan o know whch operaonal avalably sysem wll acheve n specfc nerval of me. herefore, such paral operaonal avalably resul s mporan o fnd ou whch equpmen wll fal n such nerval of me. Indeed, ha s usual for sysem ha acheves low operaonal avalably when consderng long cumulave perod of me. herefore, n hese cases, operaonal avalably s defned for he shor perod of me. Neverheless, mos of sofware does no ake no accoun he operaonal avalably n dfferen nerval of me because he smulaon resul shows only he cumulave operaonal avalably along years. Once we face such suaon, a possble soluon s o defne a fxed perod of me lke one year as nsance and smulae he sysem lfe cycle year by year. I means ha all smulaons wll be performed for one calendar year bu wll consder he sysem age. By hs way, on second year for example he sysem s one year older and he smulaon wll descrbe wha happen durng he second year. In addon, more han defne he paral operaonal avalably, s also mporan o defne he crcal equpmen, he bes nspecon and prevenve manenance me n order o avod falures. Unforunaely, n many cases he operaonal avalably s defned for a specfc nerval of me by calculang he average of cumulave operaonal avalably per me. Indeed, ha wll no solve he problem because s also mporan o verfy whch equpmen fals n dfferen nerval of me n order o preven such falures. he proposal paper wll presen he paral avalably and RGBI mehodologes applyng a drll facles case sudy n order o mprove such sysem performance. 2. Paral Avalably he Mone Carlos smulaon n RAM analyss has he man objecve o defne sysem operaonal avalably and crcal equpmens n order o suppor decsons by mplemenng mprovemens acons when s necessary [2]. Such operaonal avalably resul s cumulave along smulaon me and o have paral operaonal avalably values wo approaches are possble. Dscounng me on PDF (Probably Densy Funcon) parameers. Accounng age on smulaon me. On frs case, o regards change n PDF parameers s necessary o dscoun me on poson parameer n order o no modfy PDF characersc so f nended for example o ancpae n equpmen age n one year, such value s dscouned n poson parameer and f necessary o pospone one year s add such value n poson parameer. ha s easy o realze f been consderng a PDF wh Gaussan shape lke normal, lognormal, gumbel, logsc and loglogsc. Fgure shows normal PDF wh poson parameer dscouned n one year n order o smulae he second year of such equpmen lfe and fnd ou operaonal avalably on hs nerval of me. he blue PDF n Fgure s orgnal PDF and he black one s he me dscouned PDF. he equpmen Operaonal avalably s 00% n one year because here s no falure (normal PDF: µ = 2, σ = 0.). In addon, o fnd ou he equpmen operaonal avalably on second operaonal year s dscouned one year n poson parameer (µ = 2 µ = ). hus, he Mone Carlo smulaon s carred ou regard one year of smulaon me. 45

3 E. Calxo e al. RelaSof Webull ,000 3,200 Probably Densy Funcon Pdf Folo3\Daa Normal-2P RRX SRMMED FM F=00/S=0 Pdf Lne f() 2,400 Folo3\Daa 2 Normal-2P RRX SRMMED FM F=00/S=0 Pdf Lne,600 0,800 Eduardo Calxo Perobras 26/8/200 08:55:3 0,000 0,200 0,760,320,880 2,440 3,000 me, () Folo3\Daa : µ=2,0077, σ=0,077, ρ=0,9944 Folo3\Daa 2: µ=,000, σ=0,020, ρ=0,996 Fgure. PDF parameers dscouned me. Acually, when poson parameer s dscouned n one year, wheher furher nerval of me s smulaed he falure wll occur earler han expeced. hereby, such approach s correc only when he poson parameer dscouned by specfc me s hgher or equal han perod of smulaon me. In case of ohers PDF wh no Gaussan characerscs such lmaon s smlar. Wheher such approach s appled o a general PDF lke Welbul 3P for example, s necessary o dscoun me o locaon parameer. As nsance, f he locaon parameer value s fve years and s nended o know abou he second year, so once he locaon parameer s dscouned n one year he nex sep s perform smulaon for one year. Indeed, he lmaon n such approach s ha such smulaon resuls works only for he frs falure. he followng falure wll occur earler han smulaon resul because he real PDF parameer s dscouned. In Webul 3P case for example, once he locaon parameer (γ) s dscouned, he second falure wll earler han expeced. For example f s nended o smulae ffh year and he locaon parameer value s fve, once he locaon parameers (γ) s dscouned he frs falure occur n one year of smulaon as expeced bu he second one would also happen prevously because he poson parameer was dscouned. hereby, under such crcumsance, he locaon or poson parameer mus be updaed consanly o reproduce he paral avalably for he waned nerval of me. In addon, f beng consdered ha afer repars, equpmens s as good as new, such earler falure can no happen. In case of as bad as old, s accepable ha falure occurs n shor perod of me afer repar bu ha s no expeced [3]. Fgure 2 shows an example of Mone Carlo smulaon o descrbe draw work falure behavor on second operaonal year regards one year dscouned n PDF locaon parameer (γ). he draw work falure s represened by he Webull 3P PDF whch parameers are = 2.0, η = 0.29, γ = Regardng ha he locaon parameer (γ = 0.86) s dscouned n one year n order o smulae he second year, he new PDF parameers wll be ( = 2.0, η = 0.29, γ = 0). herefore, when locaon parameer s dscouned and such value s no less han smulaon perod of me, he second falure wll no ake no accoun on he perod of me of 0.86 years as shown Fgure 2. hus he MBF s 3625 when would be 7533 h (γ = 0.86). In order o avod such problem, he second possbly s o ake no accoun sysem age o fnd ou paral operaonal avalably n dfferen nerval of me ha regards only downmes occurred n such nerval of me. 46

4 E. Calxo e al. Fgure 2. Drawwork (second year smulaed). he operaonal avalably mos be defned as oal me whch sysem s avalable o operae (upme) by oal nomnal me as shows equaon below. n = ( ) = n D where: = real me when sysem s avalable; = nomnal me when sysem mus be avalable. As menoned before, mosly, he Mone Carlo smulaon shows cumulave operaonal avalably bu o know paral operaonal avalably n dfferen nerval of mes s necessary o defne such perods of mes along oal perod of me and hen accoun downmes n each perod of me. Fgure 3 shows an example of me lne (0, n) dvded n hree nerval of me. he equaon whch represens Operaonal avalably along (0, n) s: = n L n k n n = = n L = n k = ( ) = = n L n k n n D = = n L = n k = Indeed, regardng hree dfferen nerval of mes, he operaonal avalably along each perod of me wll be: Perod I Perod II n L = ( 0 ) = n L D n L = 47

5 E. Calxo e al. Perod III Fgure 3. me lne (0, n). ( ) D n L n k = ( ) D n k n = n k = n L n k = n L n = n k n where: = real me when sysem s avalable; = nomnal me when sysem mus be avalable. I s possble o consders as many nerval me as necessary depends on requremens and avalable daa. In hs specfc case, when Mone Carlo smulaon s performed, s necessary o defne sar age for sysem and regards one year as smulaon perod of me. hus, age for frs year s zero, for second s one and for hrd s wo years. Once sysem age s consdered n Mone Carlos smulaon, he smulaon resuls shows always wha happen afer aged me ha s he nerval of me ha mus be defned he operaonal avalably and crcal equpmen. Despe a correc approach, whenever s requred o know abou one specfc perod of me wll be necessary o age all equpmen represened on RBD (relably block dagram) or FA (faul ree analyss). Such approach would be ncluded n sofware packages o show he resuls n dfferen nerval of me auomacally. 3. Relably Growh Based Inspecon (RGBI) Indeed, s no always possble o overhaul he crcal equpmen defned by Mone Carlo smulaon by paral Avalably mehodology. In hs case, s necessary o defne nspecon me o check equpmen condon n order o defne prevenve manenance me n order o avod equpmen falures. In hs secon s proposed he relably growh mehod (Crow AMSAA) o predc he nspecon me for dfferen nerval of me. he relably growh approach s appled o produc developmen and suppor decsons o acheve relably arges afer mprovemen have been mplemened [4]. Varous mahemacal equaons models may be appled n relably growh analyss depend on how he es s carred ou as well as he ype of daa. Such mehods are: = n k 48

6 E. Calxo e al. Duanne; Crow Ansaa; Crow Exended; Lloyd Lpow; Gomperz; Logsc; Crow Exended; and Gomperz. he relably growh based nspecon (RGBI) mehod wll regards Crown AMSAA analyss mehodology o esmae fuure nspecons ha s also appled o assess reparable sysems (equpmen). hus, regardng complee daa whch nclude repars, he Non-Homogeneous Posson Process s appled [5], as shown n Equaon () below: Equaon () ( ) = ρ ( ) E N d he expeced cumulave number of falure can be descrbed also by Equaon (2) below: Equaon (2) E N o deermne he nspecon me, s necessary o use he cumulave number of falure funcon and, based on equpmen falure daa, o defne he followng cumulave falure number. Based on hs number, s necessary o reduce from such me he requred me o carry ou nspecon ask regardng he P-F nerval (poenal and funconal falure me). In fac, applyng such mehodology for drllng desel moor s possble o predc when he nex falure me wll occur and f reducng hs me by me requred o perform nspecon we have he sar nspecon me. he cumulave number of falures s en. herefore, applyng he expeced cumulave number of falures and usng he Crown AMSAA funcon parameers (λ =.5 and =.02) n Equaon (), he nex falure wll expeced o occur n 8.32 years as shown n Equaon (3). Equaon (3) E N 0 = = λ.02 λ E N = λ 0 = = he nex em wll apply a case sudy concernng he boh mehods n order o show he advanages o defne sysem performance n dfferen nerval of me as well as nspecon me o keep such performance. 4. Drll Facles Case Sudy 4.. Paral Avalably Case Sudy In order o clarfy paral avalably approach, such mehod wll be appled n drll facly case sudy whch sysem avalably arge s 90% annually. In addon, s necessary o defne sock polcy and manenance polcy for wo years based n RAM analyss resuls. Indeed, he drll facly do no acheve hgh performance for over one year and some equpmen falures happen on frs year and ohers on second year. herefore, wll be carred ou wo smulaon regardng equpmens age n order o defne avalably and crcal equpmens for frs and second year. Before modelng RDB (relably dagram block) was performng equpmen lfeme daa analyss and one of he mos crcal equpmen s he compressor from ar compressor subsysem. able shows an example of compressor falure PDF. 49

7 E. Calxo e al. able. Falure daa. Equpmen Componen Dsrbuon me o falure (years) Parameers Compressor Webull η γ Ar compressor Elecrc moor Exponecal MF 0.08 Afer carry ou he lfeme daa analyss, he RBD was buld up regardng he sx subsysem whch drll facly sysem comprses as shows Fgure 4. Performng smulaon for he frs year, sysem acheve 85.44% of operaonal avalably n one year and s expeced 23 falures. he operaonal avalably rank s an mporan ndex o suppor mprovemen decson [6]. Indeed, once equpmen n each subsysem are mosly n seres. By hs way, compressor s he avalably bole neck because have he lowes avalably of drll facly sysem. he avalably rank s shown n able 2. Once he compressor s he mos crcal equpmens, as recommendaon was proposed o analyze he ohers compressor relably and compare among han whch s he hghes relably n order o defne hgher relably requremen for compressors supplers companes. Indeed s expeced o compressor acheve a leas 00% of relably n wo year. Unforunaely, n drll facly sysem he compressor acheves 88.58% of avalably n one year. Consequenly, some mprovemen s requred n compressor. herefore, he followng acon s o defne beer relably requremens for desel pump or nsall oher sand by pump o acheve 00% of avalably n a leas one year as requred. Regardng hs addonal recommendaon, he drll facly sysem wll acheve 9.87% n one year, beng a lle hgher han he nal operaonal avalably arge ha was 90% n one year. Applyng he paral avalably mehods o analyze he second year, he drll facly sysem avalably n second year s 68.84% f no mprovemen n compressor be carred ou. Even hough, regardng hgh compressor relably, ha means mplemen mprovemen acon on he frs year, he drll facly sysem wll acheve 8.95% on second year. Acually, on second year oher equpmens ake place as more crcal n erms of mpac n sysem operaonal avalably. able 3 shows avalably rank on second operaonal year. Despe mprovemen n compressor, some oher mprovemen n ransmsson Box s requred o enable he sysem acheve operaonal avalably arge (90% n one year). herefore, relably requremens mus be defned for such equpmen. Indeed, wear ou s usual n such equpmen and even f s possble o have 00% of relably for such equpmen, s advsable o perform nspecons and prevenve manenance whenever s possble n order o keep ransmsson box avalable as long as possble on second year. hereby, f ransmsson box acheve 00% of operaonal avalably on second year, drll facly sysem wll acheve 9.25 % of operaonal avalably on second year Relably Growh Based Inspecon (RGBI) Case Sudy In order o defne he crcal equpmen nspecon me, s necessary o use he cumulave number of falure funcon and, based on equpmen falure daa, o defne he followng cumulave falure number. Based on hs number, s necessary o reduce from such me he requred me o carry ou nspecon ask regardng he P-F nerval (poenal and funconal falure me). In fac, applyng such mehodology for drllng desel moor s possble o predc when he nex falure me wll occur and f reducng hs me by me requred o perform nspecon we have he sar nspecon me. he cumulave number of falures s en. herefore, applyng he expeced cumulave number of falures and usng he Crown AMSAA funcon parameers (λ =.5 and =.02) n Equaon (), he nex falure wll expeced o occur n 8.32 years as shown n Equaon () as descrbed above. Equaon () 50

8 E. Calxo e al. Fgure 4. Drll facly subsysems. able 2. Avalably rank (year I). Paral operaonal avalably (frs year) Crown block 96.93% Desel pump 96.59% Compressor 95.38% able 3. Avalably rank (year II). Paral operaonal avalably (second year) Mud pump 96.8% ransmsson box 86.86% Compressor 85.48% E N = λ E N = λ.02 0 = = he same approach s used o defne he followng falure usng Equaon (2), n whch eleven s used as he expeced cumulave number of falures as shown n Equaon (2). Equaon (2) E N = λ.02 E N = λ = = In Equaon (3) below, he expeced number of falures used s welve. Equaon (3) E N = λ.02 E N = λ 2 = = Afer defnng he expeced me of he nex falure, s possble o defne he approprae nspecons perod 5

9 E. Calxo e al. of me. Wheher s beng consdered one monh (0.083 year) as an adequae me o sar each nspecon he followng nspecon me afer nnh, enh and elevenh falure are: Inspecon 8.23 year ( ); 2 Inspecon 9.07 year ( ); 3 Inspecon 9.87 year ( ); he remarkable pon s ha such mehodology regards relably growh or degrades o predc he followng falures along me. In RGBI mehod, whenever new falures occur, s possble o updae he model and ge more accurae values of cumulave expeced number of falure. he example of cumulave falure ploed agans me for a desel moor s presened n Fgure 5, usng cumulave falure funcon parameers =.02 and λ =.5. Based on such analyss, s possble o graphcally observe ha he nex falures (falures 0, and 2 ) wll occur on 8.32; 9.5 and 9.96 years, respecvely. ha means 0.92;.75 and 2.56 year afer las falure (7.4 years). Despe smple applcaon, RGBI analyss requres frs o have Crown AMSAA parameers model o have cumulave expeced number of falure. Such parameers can be esmae by Max lkelhood mehod by usng sofware applcaon. In dong so, whenever s possble, s advsable o use sofware o plo drecly he expeced number of falures graphs. In hs case, s possble o updae hsorcal daa wh new daa and plo expeced fuure falures drecly on graph. Applyng such mehodology for oher drll facly equpmens s possble o defne nspecon perod of me and depend o nspecon resuls prevenve manenance may be plan o ancpae equpmen fal. able 4 shows nspecon polcy defned for Compressor, Desel Moor, Crown Block and ransmsson Fgure 5. Inspecon Based n relably growh. able 4. Inspecon based n relably growh. Inspecons mes (years) Equpmen nspecon 2 nspecon 3 nspecon Compressor Desel moor Crown block ransmsson box

10 E. Calxo e al. Box. Acually, despe Inspecon Based n Growh relably defne an exacly me o nspecon, addon nformaon s mus o be consdered lke logsc me o perform nspecon. Indeed, such me mus be dscouned of nspecon me n order o defne a range of me o carry ou nspecon n each equpmen. In Drll Facly Sysem equpmens was defned one monh (0.083) o perform nspecon and such me s dscouned by expeced falure me. Once agan s mporan o be aware abou he P-F nerval of me. 5. Conclusons he paral avalably mehodology has demonsraed how o perform RAM analyss consderng dfferen nerval of me for sysem whch has no hgh performance for long perod of me. herefore, s possble o assess such sysem performance along me bu n each nended perod of me n order o ake beer decsons relaed o operaonal avalably mprovemen. hereby, s possble o denfy crcal equpmen on he frs and second year and also denfy whch equpmen mpacs on sysem operaonal avalably n dfferen nerval of me. In addon, relably growh based nspecon mehod was carred ou o defne nspecon me for each crcal equpmen defned by paral avalably mehod n order o follow up her performance n dfferen nerval of me. Indeed, such mehod s very mporan because n many cases wll no possble o ake place crcal equpmen and a prevenve manenance polcy wll be requred based on nspecon polcy. he paral avalably mehod would be npu n some sofware o make easer such analyss s very mporan o verfy sysem s performance for each defned perod of me (yearly). he remarkable pon n paral avalably mehodology s o know whch equpmens wll be aged for a specfc perod of me and whch one wll no. Once such mehod s esablshed n a sofware model such analyss s performed auomacally. References [] Calxo, E. and Schm, W. (2006) Análse Ram do Projeo Cenpes II. ESREL, Esorl. [2] Calxo, E. (2006) he Enhancemen Avalably Mehodology: A Refnery Case Sudy. ESREL, Esorl. [3] Calxo, E. (203) Gas and Ol Relably Engneerng: Modelng and Smulaon. Elsever, USA. [4] Crow, L.H. (2008) A Mehodology for Managng Relably Growh durng Operaonal Msson Profle esng. Proceedngs of he 2008 Annual RAM Symposum, Las Vegas, 28-3 January 2008, [5] O Connor, P.D.. (200) Praccal Relably Engneerng. 4h Edon. John Wley & Sons Ld., Hoboken. [6] Marzal, E.M. and Scharpf, E. (2002) Safey Inegraon Level Selecon. Sysemacs Mehods ncludng Layer of Proecon Analyss. he Insrumenaon, Sysems and Auomaon Socey. 53

11

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

5th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2015)

5th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2015) 5h Inernaonal onference on Advanced Desgn and Manufacurng Engneerng (IADME 5 The Falure Rae Expermenal Sudy of Specal N Machne Tool hunshan He, a, *, La Pan,b and Bng Hu 3,c,,3 ollege of Mechancal and

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More 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

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More 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

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

Time-interval analysis of β decay. V. Horvat and J. C. Hardy

Time-interval analysis of β decay. V. Horvat and J. C. Hardy Tme-nerval analyss of β decay V. Horva and J. C. Hardy Work on he even analyss of β decay [1] connued and resuled n he developmen of a novel mehod of bea-decay me-nerval analyss ha produces hghly accurae

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More 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

Reactive Methods to Solve the Berth AllocationProblem with Stochastic Arrival and Handling Times

Reactive Methods to Solve the Berth AllocationProblem with Stochastic Arrival and Handling Times Reacve Mehods o Solve he Berh AllocaonProblem wh Sochasc Arrval and Handlng Tmes Nsh Umang* Mchel Berlare* * TRANSP-OR, Ecole Polyechnque Fédérale de Lausanne Frs Workshop on Large Scale Opmzaon November

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

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen

More information

Stochastic Repair and Replacement with a single repair channel

Stochastic Repair and Replacement with a single repair channel Sochasc Repar and Replacemen wh a sngle repar channel MOHAMMED A. HAJEEH Techno-Economcs Dvson Kuwa Insue for Scenfc Research P.O. Box 4885; Safa-309, KUWAIT mhajeeh@s.edu.w hp://www.sr.edu.w Absrac: Sysems

More information

Department of Economics University of Toronto

Department of Economics University of Toronto Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of

More 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

II. Light is a Ray (Geometrical Optics)

II. Light is a Ray (Geometrical Optics) II Lgh s a Ray (Geomercal Opcs) IIB Reflecon and Refracon Hero s Prncple of Leas Dsance Law of Reflecon Hero of Aleandra, who lved n he 2 nd cenury BC, posulaed he followng prncple: Prncple of Leas Dsance:

More information

Tools for Analysis of Accelerated Life and Degradation Test Data

Tools for Analysis of Accelerated Life and Degradation Test Data Acceleraed Sress Tesng and Relably Tools for Analyss of Acceleraed Lfe and Degradaon Tes Daa Presened by: Reuel Smh Unversy of Maryland College Park smhrc@umd.edu Sepember-5-6 Sepember 28-30 206, Pensacola

More information

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

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

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

ESTIMATIONS OF RESIDUAL LIFETIME OF ALTERNATING PROCESS. COMMON APPROACH TO ESTIMATIONS OF RESIDUAL LIFETIME

ESTIMATIONS OF RESIDUAL LIFETIME OF ALTERNATING PROCESS. COMMON APPROACH TO ESTIMATIONS OF RESIDUAL LIFETIME Srucural relably. The heory and pracce Chumakov I.A., Chepurko V.A., Anonov A.V. ESTIMATIONS OF RESIDUAL LIFETIME OF ALTERNATING PROCESS. COMMON APPROACH TO ESTIMATIONS OF RESIDUAL LIFETIME The paper descrbes

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

The Dynamic Programming Models for Inventory Control System with Time-varying Demand

The Dynamic Programming Models for Inventory Control System with Time-varying Demand The Dynamc Programmng Models for Invenory Conrol Sysem wh Tme-varyng Demand Truong Hong Trnh (Correspondng auhor) The Unversy of Danang, Unversy of Economcs, Venam Tel: 84-236-352-5459 E-mal: rnh.h@due.edu.vn

More 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

12d Model. Civil and Surveying Software. Drainage Analysis Module Detention/Retention Basins. Owen Thornton BE (Mech), 12d Model Programmer

12d Model. Civil and Surveying Software. Drainage Analysis Module Detention/Retention Basins. Owen Thornton BE (Mech), 12d Model Programmer d Model Cvl and Surveyng Soware Dranage Analyss Module Deenon/Reenon Basns Owen Thornon BE (Mech), d Model Programmer owen.hornon@d.com 4 January 007 Revsed: 04 Aprl 007 9 February 008 (8Cp) Ths documen

More information

Increasing the Probablility of Timely and Correct Message Delivery in Road Side Unit Based Vehicular Communcation

Increasing the Probablility of Timely and Correct Message Delivery in Road Side Unit Based Vehicular Communcation Halmsad Unversy For he Developmen of Organsaons Producs and Qualy of Lfe. Increasng he Probablly of Tmely and Correc Message Delvery n Road Sde Un Based Vehcular Communcaon Magnus Jonsson Krsna Kuner and

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

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

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

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

Chapter Lagrangian Interpolation

Chapter Lagrangian Interpolation Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and

More 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

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

RELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA

RELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA RELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA Mchaela Chocholaá Unversy of Economcs Braslava, Slovaka Inroducon (1) one of he characersc feaures of sock reurns

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

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

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

January Examinations 2012

January Examinations 2012 Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons

More 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

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

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6) Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen

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

Standard Error of Technical Cost Incorporating Parameter Uncertainty

Standard Error of Technical Cost Incorporating Parameter Uncertainty Sandard rror of echncal Cos Incorporang Parameer Uncerany Chrsopher Moron Insurance Ausrala Group Presened o he Acuares Insue General Insurance Semnar 3 ovember 0 Sydney hs paper has been prepared for

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

Chapters 2 Kinematics. Position, Distance, Displacement

Chapters 2 Kinematics. Position, Distance, Displacement Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen

More information

Modeling and Solving of Multi-Product Inventory Lot-Sizing with Supplier Selection under Quantity Discounts

Modeling and Solving of Multi-Product Inventory Lot-Sizing with Supplier Selection under Quantity Discounts nernaonal ournal of Appled Engneerng Research SSN 0973-4562 Volume 13, Number 10 (2018) pp. 8708-8713 Modelng and Solvng of Mul-Produc nvenory Lo-Szng wh Suppler Selecon under Quany Dscouns Naapa anchanaruangrong

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

Anisotropic Behaviors and Its Application on Sheet Metal Stamping Processes

Anisotropic Behaviors and Its Application on Sheet Metal Stamping Processes Ansoropc Behavors and Is Applcaon on Shee Meal Sampng Processes Welong Hu ETA-Engneerng Technology Assocaes, Inc. 33 E. Maple oad, Sue 00 Troy, MI 48083 USA 48-79-300 whu@ea.com Jeanne He ETA-Engneerng

More information

e-journal Reliability: Theory& Applications No 2 (Vol.2) Vyacheslav Abramov

e-journal Reliability: Theory& Applications No 2 (Vol.2) Vyacheslav Abramov June 7 e-ournal Relably: Theory& Applcaons No (Vol. CONFIDENCE INTERVALS ASSOCIATED WITH PERFORMANCE ANALYSIS OF SYMMETRIC LARGE CLOSED CLIENT/SERVER COMPUTER NETWORKS Absrac Vyacheslav Abramov School

More 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

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

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

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More 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

A Simple Discrete Approximation for the Renewal Function

A Simple Discrete Approximation for the Renewal Function Busness Sysems Research Vol. 4 No. 1 / March 2013 A Smple Dscree Approxmaon for he Renewal Funcon Alenka Brezavšček Unversy of Marbor, Faculy of Organzaonal Scences, Kranj, Slovena Absrac Background: The

More information

On computing differential transform of nonlinear non-autonomous functions and its applications

On computing differential transform of nonlinear non-autonomous functions and its applications On compung dfferenal ransform of nonlnear non-auonomous funcons and s applcaons Essam. R. El-Zahar, and Abdelhalm Ebad Deparmen of Mahemacs, Faculy of Scences and Humanes, Prnce Saam Bn Abdulazz Unversy,

More information

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or

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

Lecture 2 M/G/1 queues. M/G/1-queue

Lecture 2 M/G/1 queues. M/G/1-queue Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly

More information

ELASTIC MODULUS ESTIMATION OF CHOPPED CARBON FIBER TAPE REINFORCED THERMOPLASTICS USING THE MONTE CARLO SIMULATION

ELASTIC MODULUS ESTIMATION OF CHOPPED CARBON FIBER TAPE REINFORCED THERMOPLASTICS USING THE MONTE CARLO SIMULATION THE 19 TH INTERNATIONAL ONFERENE ON OMPOSITE MATERIALS ELASTI MODULUS ESTIMATION OF HOPPED ARBON FIBER TAPE REINFORED THERMOPLASTIS USING THE MONTE ARLO SIMULATION Y. Sao 1*, J. Takahash 1, T. Masuo 1,

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

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

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

Including the ordinary differential of distance with time as velocity makes a system of ordinary differential equations.

Including the ordinary differential of distance with time as velocity makes a system of ordinary differential equations. Soluons o Ordnary Derenal Equaons An ordnary derenal equaon has only one ndependen varable. A sysem o ordnary derenal equaons consss o several derenal equaons each wh he same ndependen varable. An eample

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

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

Optimal environmental charges under imperfect compliance

Optimal environmental charges under imperfect compliance ISSN 1 746-7233, England, UK World Journal of Modellng and Smulaon Vol. 4 (28) No. 2, pp. 131-139 Opmal envronmenal charges under mperfec complance Dajn Lu 1, Ya Wang 2 Tazhou Insue of Scence and Technology,

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

National Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration

National Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration Naonal Exams December 205 04-BS-3 Bology 3 hours duraon NOTES: f doub exss as o he nerpreaon of any queson he canddae s urged o subm wh he answer paper a clear saemen of any assumpons made 2 Ths s a CLOSED

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

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

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

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue. Mah E-b Lecure #0 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 are

More information

Lecture VI Regression

Lecture VI Regression Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M

More information

Analysis And Evaluation of Econometric Time Series Models: Dynamic Transfer Function Approach

Analysis And Evaluation of Econometric Time Series Models: Dynamic Transfer Function Approach 1 Appeared n Proceedng of he 62 h Annual Sesson of he SLAAS (2006) pp 96. Analyss And Evaluaon of Economerc Tme Seres Models: Dynamc Transfer Funcon Approach T.M.J.A.COORAY Deparmen of Mahemacs Unversy

More information

Sampling Coordination of Business Surveys Conducted by Insee

Sampling Coordination of Business Surveys Conducted by Insee Samplng Coordnaon of Busness Surveys Conduced by Insee Faben Guggemos 1, Olver Sauory 1 1 Insee, Busness Sascs Drecorae 18 boulevard Adolphe Pnard, 75675 Pars cedex 14, France Absrac The mehod presenly

More 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

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

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

Let s treat the problem of the response of a system to an applied external force. Again,

Let s treat the problem of the response of a system to an applied external force. Again, Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem

More information

Efficient Asynchronous Channel Hopping Design for Cognitive Radio Networks

Efficient Asynchronous Channel Hopping Design for Cognitive Radio Networks Effcen Asynchronous Channel Hoppng Desgn for Cognve Rado Neworks Chh-Mn Chao, Chen-Yu Hsu, and Yun-ng Lng Absrac In a cognve rado nework (CRN), a necessary condon for nodes o communcae wh each oher s ha

More information

NPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management

NPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management P age NPTEL Proec Economerc Modellng Vnod Gua School of Managemen Module23: Granger Causaly Tes Lecure35: Granger Causaly Tes Rudra P. Pradhan Vnod Gua School of Managemen Indan Insue of Technology Kharagur,

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

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

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates Biol. 356 Lab 8. Moraliy, Recruimen, and Migraion Raes (modified from Cox, 00, General Ecology Lab Manual, McGraw Hill) Las week we esimaed populaion size hrough several mehods. One assumpion of all hese

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

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

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

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

[Link to MIT-Lab 6P.1 goes here.] After completing the lab, fill in the following blanks: Numerical. Simulation s Calculations

[Link to MIT-Lab 6P.1 goes here.] After completing the lab, fill in the following blanks: Numerical. Simulation s Calculations Chaper 6: Ordnary Leas Squares Esmaon Procedure he Properes Chaper 6 Oulne Cln s Assgnmen: Assess he Effec of Sudyng on Quz Scores Revew o Regresson Model o Ordnary Leas Squares () Esmaon Procedure o he

More information

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because

More information

Double system parts optimization: static and dynamic model

Double system parts optimization: static and dynamic model Double sysem pars opmizaon: sac and dynamic model 1 Inroducon Jan Pelikán 1, Jiří Henzler 2 Absrac. A proposed opmizaon model deals wih he problem of reserves for he funconal componens-pars of mechanism

More information

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal

More information

Fall 2009 Social Sciences 7418 University of Wisconsin-Madison. Problem Set 2 Answers (4) (6) di = D (10)

Fall 2009 Social Sciences 7418 University of Wisconsin-Madison. Problem Set 2 Answers (4) (6) di = D (10) Publc Affars 974 Menze D. Chnn Fall 2009 Socal Scences 7418 Unversy of Wsconsn-Madson Problem Se 2 Answers Due n lecure on Thursday, November 12. " Box n" your answers o he algebrac quesons. 1. Consder

More information

Lecture 2 L n i e n a e r a M od o e d l e s

Lecture 2 L n i e n a e r a M od o e d l e s Lecure Lnear Models Las lecure You have learned abou ha s machne learnng Supervsed learnng Unsupervsed learnng Renforcemen learnng You have seen an eample learnng problem and he general process ha one

More information

Clustering (Bishop ch 9)

Clustering (Bishop ch 9) Cluserng (Bshop ch 9) Reference: Daa Mnng by Margare Dunham (a slde source) 1 Cluserng Cluserng s unsupervsed learnng, here are no class labels Wan o fnd groups of smlar nsances Ofen use a dsance measure

More information

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

FI 3103 Quantum Physics

FI 3103 Quantum Physics /9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon

More information