NEURAL DATA ENVELOPMENT ANALYSIS: A SIMULATION

Size: px
Start display at page:

Download "NEURAL DATA ENVELOPMENT ANALYSIS: A SIMULATION"

Transcription

1 Itratoal Joural f Idustral grg v NURAL ATA NVLPMNT ANALYSIS: A SIMULATIN Luz Bod Nto Marcos Prra stllta Ls la Goçalvs Goms João Carlos Corra Batsta Soars d Mllo 3 Fabao S. lvra. d g. ltrôca Tlcomucaçõs Uvrsdad do stado do Ro d Jaro Rua São Fracsco Xavr 54 Bl. A Sala 536 Maracaã 55-9 Ro d Jaro RJ Brazl. mal: {L. Bod Nto lbod@ur.br; F.S. lvra fabao@magl.com.br} Programa d g. d Produção Uvrsdad Fdral do Ro d Jaro Ctro d Tcologa F-5 Ilha do Fudão Ro d Jaro RJ Brazl. mal: {M.P.. Ls ls@.ufr.br;.g. Goms ggoms@.ufr.br} 3. d g. d Produção Uvrsdad Fdral Flums Rua Passo da Pátra 56 São omgos 4-4 Ntró RJ Brazl mal: {gmacsm@vm.uff.br} Ths ar vstgats th crato of ffccy masurmts structurs of dcso-mag uts MUs calld Nuro- A by usg hgh-sd otmzato moduls calld Nuro-LP srd th "hlosohy" of a ucovtoal artfcal ural tor ANN ad umrcal mthods. I addto th lar rogrammg roblm LPP s trasformd to a otmzato roblm thout costrats by usg a sudo-cost fucto hr's addd a trm of alty causg hgh cost all tm that o of th costrats gos volatd. Th roblm s covrtd to a dffrtal quatos systm. A ucovtoal ANN mlmts a umrcal soluto basd gradt mthod. Sgfcac: Th covrgc sd ca b trmly hgh f th roosd moduls as tgratd a ch VLSI Vry Larg Scal Itgrato or CMS - Comlmtary Mtal-d Smcoductor ad coctd to a fr slot a comutr. Kyords: A Mathmatcal Programmg Comutatoal Itllgc VLSI CMS. Rcvd ; Acctd. INTRUCTIN Th ata vlomt Aalyss A s a mathmatcal tchqu that has th obctv of aalyzg th dcso-mag uts MUs rformac. It also allos th valuato of th rlatv oratoal ffccy of orgazatos MUs cotmlatg ach MU rlatvly to all th othrs that comos th vstgatd MUs grou Chars Coor ad Sford 996. Th A tchqu comars th MU ffccs by thr ablts trasformg uts oututs masurg th rachd outut rlato trms of th rovso suld by th ut. I th d of th aalyss th A tchqu s abl to tll hch uts ar rlatvly ffct ad hch uts ar rlatvly ffct Agulo 998. Th A tchqu volvs th us of Lar Programmg LP to solv a grou of tr-rlatd of th lar rogrammg roblms LPPs as may as th MU umbrs fally obctfyg dtrmg th rlatv ffccy of ach MU stllta. tmzato moduls calld Nuro-LP ll b usd th ural modl roosd Nuro- A srd by th artfcal ural tor hlosohy Bod. Th A modls ca b ortd to uts or oututs ad ths ortato must b rvously chos by th aalyst as startg ot th A aalyss. Th ortato to uts dcats that at to rduc th uts g th oututs uaffctd. I th othr had th ortato to oututs dcats that at to cras th oututs thout affctg th uts Coll 998. Th most mortat modls ar th follog: CCR ad BCC A modls. CCR Modl rstd by Chars Coor ad Rhod 978 that bulds a o aramtrcal surfac lar by arts ovr th data ad dtrms th vstgatd MUs tchcal ffccy ovr ths surfac. It as cocvd as a ut ortd modl ad t ors th costat rtur of scal CRS hch mas that ach varato th uts roducs a roortoal varato th oututs. Th roblm cossts dtrmg th u ad v ght valus to mamz th lar combato of th oututs dvdd by th lar combato of th uts stllta 998. Th rocss must b ratd to ach of th MUs ad by ths rocsss dtrm th rlatv valu of ach MU ffccy. But quato f u ad v ar th otmum soluto vctors u ad v ll b otmum soluto vctors too ad cosqutly th roblm ll

2 Itratoal Joural f Idustral grg v rst ft solutos. To solv ths roblm Chars ad Coor 96 troducd a lar trasformato that allos trasformg lar fractoal roblms to LPPs cratg th modl calld Multlrs quato. Ma h subct to : u v hr : h r - r r - MU ffccy total amout of s - total amout of - total amout of Y X amout of amout of s u Y s v X u Y v ght to ut v X uts oututs MU u ght to outut... outut to MU ut to MU Ma h subct to : r s v X u Y u v s r u Y v X... It s ossbl drv th dual modls to multlr rmal. So th dual ll rst a smallr amout of costrats s+r < + bcaus th A modl rqurs that th umbr of MUs b gratr tha th umbr of varabls. By th osd rasos th dual modl calld vlo bg asly solvd s rfrrd comard to th Multlrs modl. I th vlo modl th obctv s to dtrm th valus of mmzg as 3. M subct to : Y θx θ - Y X...s... r

3 Itratoal Joural f Idustral grg v BCC Modl dvlod by Bar Chars Ad Coor 984 allos varabl rtur of scal VRS avodg stg roblms mrfct comtto stuatos facal costrats tc. I ths cas th VRS frotr cosdrs crasg or dcrasg rturs th ffct frotr. To do ths ob t as troducd th CRS mod a covty costrat mag th sum qual to. Tryg to comar th CRS frotr to th VRS th Fgur that rrsts 5 MUs of o ut ad o outut shos th rlato bt ths frotrs for th to cass. Fgur. CRS ad VRS Frotrs. Y C R S Fgur. - CRS ad VRS Frotr VR S MU3 MU MU5 MU MU4 X I Fgur t s ossbl to vrfy that th MUs 4 ad 5 ar ffct. I th BCC modl that adots VRS ach MU s comard to th ffct MUs that orat th sam scal. So usg th ortato to uts vrfy that th otmum rocto of th MU 4 has a ot that rflcts th cov lar combato of MUs ad. Usg th ortato to uts vrfy that th otmum rocto of th sam MU 4 has a ot that rflcts th cov lar combato of MUs ad 3. Hovr for both ortato cass th lar combato valus ar gv by th s. Th vlo modl ortd to ut ad th rmal drvd modl multlrs ar gv by 4 ad 5. M θ subct to: Y θx - Y X...s... r 4

4 Itratoal Joural f Idustral grg v Ma h subct to : r s v X u Y u ad v s r u Y v X ad... urstrctd I th othr had th Artfcal Nural Ntors ANNs also o as coctost systms ar structurs srd th huma bra fuctoalty Rosblat 96. Th ANNs ar massvly arallld structurs basd o sml rocssg lmts P srd th bologcal uro ad dsly trcoctd. Th ma ANN charactrstcs ar: aralll sarch ad cott addrssg mtatg th bra that thr has mmory addrss or loos for a formato squtally; larg dog th tor th th caacty of larg crta oldg by succssv attr rstatos rc thout th cssty of lctg th algorthms to th cuto of a ob; assocato allog th tor to assocat dffrt attrs; gralzato ablg th tor to dal th os ad dstorto ad corrctly asr a vr s ut by smlarty th othr rvously rstd attrs; abstracto dog th tor th th ablty to abstract th ssc of a ut grou; ad fally th robustss allog thas to th aralllsm that v th th loss of Ps bad orato of th tor ll vr ha.. By scfc algorthms th ANNs lar som oldg trag st by th succssv rstato of amls attrs storg thm th coutlss stg coctos bt th Ps calld ghts. Th oldg s dstrbutd all ovr th tor avalabl ad rady to b usd cuto st may dffrt alcato aras as th ural lar rogrammg Zurada 99. I th cas of th Nuro-LP otmzato moduls art of th Nuro-A modl a structur smlar to th ANN s usd hr th syatc ghts obtad th trag st ar bascally formd by th coffct of th roblm costrat grous Rosblatt 96 ad Wassrma 993. Although thr ar may Ps modls as th Aalogcal lctroc modl ad th Fuushma modl hch s dly usd Fgur s basd o th modl roosd by McCulloch & Ptts. It trs to rroduc a sml ay th bologcal uro orato. Th P uts... or smlarly to th bologcal uro syass rcvg comg sgals of othr uro aos. 5 Fgur. Procssg lmts Modl. W W F W W bas As th bologcal uro th syatc coctos must caus ctato or hbto ractos th ghbor uros. So a good rocdur s to assocat to ach syatc cocto a ostv or a gatv ght valu W W... W dtrmg th ffct that a sourc P has ovr th dstato P. Cosqutly th P ould b abl to trggr h th odrd sum of th uts X ad th ghts W cd th thrshold valu W bas durg th latcy rod. Th P s dfd by a roagato rul that dtrms th ay th uts ll b comutd ad by a actvato fucto F that dtrms th valu of th actvato stat for th dstato P. Th mostly usd formula as roagato rul quato 6 dfs as th odrd sum of th valus rovdd as uts ad th ght valus of th P ut coctos Harvy 994.

5 Itratoal Joural f Idustral grg v Th quato 7 shos th P outut. F Th actvato fucto tys mostly usd ar: th st; th symmtrcal st; th lar; th sudo lar; th sgmod; ad th tasgmod.thr ar bascally to tys of archtcturs usd ANNs: th fdforard tors thout fdbac; ad th fdbacard tors. Th mostly usd tor archtctur s th fdforard. Ths arragmt s comosd of a grou of Ps arragd layrs o or mor that trcoct thmslvs squc. Th most comlt cofgurato rsts o or mor trmdat or hdd layrs bt th ut ad th outut layr ad t s o as mult layr tor. Th fact of havg hdd layrs allo bttr rsults for crta roblms as ll as allog th soluto for mossbl roblms to b solvd th sgl layr tors Wassrma 993 ad Zurada 99. Th ural rocssg s accomlshd to ma hass: Trag ad cuto. Th trag has or larg s th udatg rocss for th cocto ghts. Its goal s to acqur formato ad stor t as a ght matr W Ms 969. I th cas of th Nuro LP ma cll of th Nuro A modl th roblm acoldgmt s rvously o by th LPP costrat coffcts ad lmats th d for ths has. Th cuto has rcall calculats th ANNs outut Y trms of th ctd stmulus th ut X ad th ghts obtad th trag has Y or mosd by th roblm tslf. I th Nuro LP cas th goal for ths has s to rcovr th formato hch mas dtrm th otmum valu for th LPP dcso varabls ad that th cas of th ata Wrag Aalyss may rrst th ffccy valu of a MU Bod. Ths rocss ll b do by th rsoluto of a dffrtal quato systm obtad by th trasformato of th orgal LPP a otmzato roblm thout costrats. Th umrcal mthod usd to solv th dffrtal quato systm ll b th dyamc gradt mthod drvd from th Nto mthod ad that s vry smlar to th ANNs trag mthod. Itally th ANN archtctur usd th Nuro LP modl ll b rstd as ll as th dvlomt of th trag algorthm basd o th mmzato of th sum squard rror th tor outut by th dcrasg gradt mthod ad ts varatos. I th Nuro LP cas th ANN s usd th cuto has ad t alrady has th oldg rfrrd to th LPP rrstd hr by th roblm costrat coffcts. Th mathmatcally rov th LPP trasformato comosd of a obctv fucto ad a costrat collcto a otmzato roblm thout costrats Bazaraa 993. A fucto calld sudo-cost as adotd hr a alty trm as addd causg a hgh cost vry tm a costrat s volatd. Th roblm ca b solvd by th gradt mthod turg t to a dffrtal quato systm hch ca b umrcally solvd. Fally a cas study s rstd. 7. LARNING PARAIGM F TH ARTIFICIAL NURAL WRKS Th rctro crdtd to Rosblatt as th frst larg mach th survsd trag ossblty. As sho Fgur 3 th tor currt outut s comard to ach trato th a dsrd valu T assocatd to th trag attrs gratg a rror sgal. Fally ths sgal suffrs a larg adatv rocss udatg th ghts ach trato Rosblatt 986 Rumlhart 986. Fgur 3. Prctro Archtctur.

6 Itratoal Joural f Idustral grg v Th Prctro s a tor that ca b rstd ust th a P sgl layr or th multl layrs MLP. Its orato s basd o th follog: th ut sgal X ut attr s multld by a ght grou W adustabl by a larg rul to grat a tral ottal calld that rrsts th odrd sum of th uts by th corrsodg ghts. Th sgal s th rocssd by a lmtr calld actvato fucto F sho fgur.4 rsultg a outut sgal {} or {-}. Th outut sgal s comard to th targt or dsrd valu T roducg th rror sgal usd th tractv adustmt of th ghts by a larg rul gv by hr th aramtr s a ostv valu calld larg rat rlatd to th sd ad th stablty of th ANN covrgc rocss. Th tractv rocss of attr rstato causs th rror dcras ad h ths rror rachs a stablshd valu ts sad that th ANN absorbd th dsrd oldg ad that th rocss covrgd Saura 996. I th frst rmts do by Rosblatt th Prctro trag algorthm as trly basd th tchqu dvlod by Wdro Hoff hr th rror sgal as obtad bfor th actvato fucto ad thrfor lar. v th mag of lally sarabl fuctos th tchqu fald. So Rosblatt dvlod a algorthm adustg th ghts by th mmzato of th sum-squard rror usg th dcrasg gradt mthod Rosblatt 986. Th th rror = T - s obtad by th dffrc bt ths valus tag aftr th actvato fucto. I ths cas du to th drvd mosd by th mthod; th actvato fucto must b drvabl through th hol doma as t has th th tasgmod. So = F = tah hr s a ostv scalar that rrsts th fucto clato Hay 994. P. rrst th P muts ad fo amout a sa.. hr. hr ma rror s Th squard th F T. so As. Alaygth cha rul. rlatd th ghts s gv by thdffrtal gradt Th stataos T T 4 tah

7 Itratoal Joural f Idustral grg v ] - [ : Fally ] [- Thrfor 4 - So As Fgur 4 shos th mrovmt of th Prctro trag mthod roosd by Rosblatt Hay th Kogthat. th Kogthat. ] [ Thrfor Th rror mmzato rocss rqur that th ght varato occurs th drcto of th gradt gatv. So ] [. Aftr th trag has hr th valus of th syatc ghts ar dtrmd th ANN ll gt rady to b cutd. I ths has cuto th ANN rcvs sgals th ut hch dd ot ta art th trag has ad rsts th rsult th outut accordg to th oldg acqurd durg th trag has ad stord th ght matr. I th Nuro LP cas th ANN hr th ghts ar alrady o ad that rrst th roblm costrat coffcts th dtrmato of th outut s th t st hch dcats th valu of th LPP dcso varabls Bod. To th mult layr Prctro a algorthm smlar to th o dvlod ad calld bac-roagato s usd th trag has. Th oly dffrc s th calculato of th rror sgal th trmdat layrs of th Ps. Ths sgal must allo th rror roagato to th rvous layrs bac-roagato utl t rachs th frst o. Although du to th fact that ths subct s ot drctly rlatd to th dvlomt of th Nuro-LP t ll ot b vstgatd Wassrma 993. Fgur 4. Imrovmt of th Prctro Trag. 3. MATHMATICAL BASICS

8 Itratoal Joural f Idustral grg v Cosdr a otmzato roblm thout costrats hr sh to fd th valu  that mmzs a scalar fucto calld Psudo-cost rgy or bctv fucto. Accordg to Cchoc 996 th ot * ll b th global mmum of f * <= for all  ad a local mmum f th rlatosh * <= s t for a crta trval >.If th frst ad th scod drvatvs st th ot * ll b a local mmum f th gradt * = ad th Hssa matr * >. Th cssary ad suffct codtos for th stc of a local mmum ar: For o sgular for th ot * so * ll b <= for all < - * < > f th gradt * = ad th Hssa matr s symmtrc ad ostv * >. Th yamc Gradt Mthod s th mostly srad mthod from th os srd th Stst sct ad th Nto Mthod Bazaraa 993. Its basd th trasformato of th otmzato roblm thout costrats a frst ordr ordary dffrtal quato systm rrstd by 8. d dt hr ar a talcodtos ad s a larg matr. 8 So to fd th valu * that tas to a mmum ts cssary to solv or smulat th soluto of a dffrtal quato systm subctd to tal codtos. Its ossbl to coclud that * ca b dtrmd by th soluto ath or tractory curv of th roosd systm 9. * lm t t 9 Accordg to TAHA 99 gv a cost fucto hch must b mmzd hr = rrsts th ot hr th rocdur starts ad; bg * th gradt of for th th ot th ath th; th da s to dtrm a artcular ath hr d/d s mmzd ach ot of th ath. Th umrcal soluto ca b obtad cosdrg th follog: If succssv ots + ar dtrmd obyg th follog soluto ath: + = - hr <= <= ma s calld larg rat or tgrato st. Th valu of s dtrmd a ay that + alays rsults th mrovmt of th obctv fucto + <. Th rocdur ds h to coscutv ots + ad ar aromatly th sam. As so =. 4. NUR-LP MLING A LPP s classfd as a otmzato roblm th costrats. To solv t usg th ANN hlosohy ts cssary to buld a fucto calld sudo-cost or rgy fucto hch global mmum s th otmum soluto of th LPP. To buld th fucto ts cororatd a fucto or alty trm P [R ] to th orgal obctv fucto Kdy 994 Ch 99 Bargla 996 Zhu 99. Th alty trm must caus a hgh cost alty to th fucto vry tm a costrat s volatd R < ad zro cost f th costrat s satsfd R >. I ths ay th LPP s trasformd to a otmzato roblm thout costrat hr ts dsrabl to fd *  that mmzs th fucto. Th alty trm must alz bg for th cass of o fasbl solutos ad hbt for vabl solutos of th LPP s 996 Pa 995 ad Wrr 998. Th otmzato roblm thout costrat th alty trm ca b solvd smlarly to th ANN trag has alyg th dcrasg gradt mthod. So t must b rtt as a ordary dffrtal quato systm ad solvd umrcally. I ths cas th soluto ath quato + = + hr =... rrsts th umbr of varabls dcats th covrgc th valu of th roblm dcso varabls ad th roduct of th cost fucto gradt rlato to by a costat factor rlatd to th covrgc sd ad th stablty of th mthod. To sur accuracy of th mthod th alty aramtr must b vry hgh. Th ractc shos that valus trmly hgh ar ot covt from th comutg ot of v. I ths cas hghr valus of ar ot cssary for th corrct covrgc of th rocss. So th rasoabl valus th mmum of th sudo-cost fucto s quvalt to th otmum soluto of th orgal LPP. Accordg to Cchoc 996 a grat choc s to cosdr th sudo-cost fucto.

9 Itratoal Joural f Idustral grg v hr } R m{ C m m... If If curvs ar Th soluto gatvst a s ad ushmt If hbt If... ad As b a b a a C R R a C a R m m 5. NUR-LP ML IMPLMNTATIN Th roosd modlg 4. suggsts th mlmtd archtctur usg th Smul tool cororatd Matlab 5.3 vrso ad rrstd Fgur 5. Fgur 5. Imlmtato utl 5 varabls.

10 Itratoal Joural f Idustral grg v NUR-A ML IMPLMNTATIN Cosdrg th data stc for P MUs th R uts ad S oututs ad that th th MU s rrstd by a colum vctor X uts ad a Y oututs. Th rlatosh bt all uts ad oututs s obtad to ach MU: u Y / v X hr u ad v ar outut ght vctors ad ut ght vctors rsctvly. Th otmum valus for ths ghts ar obtad solvg a LPP for ach MU Norma 99 ad Kallrath 997. Th Nuro-A archtctur modl s totally basd th Nuro-LP modl rstd ths ar Bod. So for P MUs ll hav P Nuro-LP moduls. ach LPP of th Nuro-A modl ll rrst a LPP th Nuro-LP modl ad ll b abl to dtrm th rlatv ffccy of o MU of P MUs that comos th systm. Fgur 6 a shos roosd bloc dagram of th Nuro-A modul ad 6 b th adotd A modl to ach MU. Th mlmtato as do usg th CRS vlo modl ut ortd. Th raso for ths choc s du to th fact that ca rduc th umbr of costrats bcaus th vlo modl hav a costrat for ach ut/outut. Som data of a cas volvg 5MUs th to uts ad o outut ar sho Fgur 7 a. Fgur 7 b shos a CRS dagram that s rfrrg to th roblm hr ts ossbl to vrfy that MUs 3 ad 4 ar ot ffct ad MUs ad 5 ar ffct. Fgur 7 c shos th 3 frotr ad fally Fgur 7 d thr s a tabl that comars th obtad rsults usg to commrcal ad coscratd softars Ldo ad Frotr Aalyst th th rsults of th modl roosd ths ar ad calculatg th rror rctag. Fgur 6. Bloc agram ad Adotd Modl.

11 Itratoal Joural f Idustral grg v Fgur 7. a ata b CRS agram c 3 Frotr ad d MUs ffccy. 7. CNCLUSINS Th rstd cas as slctd amog hudrds of accomlshd tsts shog that th rstd roosal s cosstt. Th valus obtad th our rototy r valdatd comarg thmslvs th rsults obtad by coscratd softars as LIN to saratly solv th LPPs rfrrg to th MUs ad th FRNTIR to drctly solv th A. I ths cas th obsrvd rror dd ot surass.5% th th ossblty of dcrasg th som mrovmts th accuracy of th mthod ad th umrcal tolrac. Actually a study usg Lagrag Multlrs s bg dvlod hch bascally ll otmz th st fucto sho fgur 5. ad dtfd th otmzator by. Th soluto mthod for th ordary dffrtal quato systm usd th Nuro-LP modl s smlar to th tchqu usd ANN trag has bcaus thy us th dcrasg gradt mthod. Th voluto of th soluto mthod for th dffrtal quato systm rrstd by th soluto ath curv dcats th covrgc th valu of th dcso varabls of th roblm. Fally ts mortat to hghlght that th covrgc sd ca gt trmly hgh f th roosd moduls ar tgratd a ch ad coctd to a fr slot a comutr.

12 Itratoal Joural f Idustral grg v RFRNCS. Abraham Chars Wlla W. Coor Ar Y. L ad Larc M. Sford 996. ata vlomt Aalyss: Thory Mthodology ad Alcato Bosto Klur Acadmc Publshrs.. Agulo Mza L. 998 ata vlomt Aalyss A a trmação da fcêca dos Programas d Pós- Graduação do CPP/UFRJ ssrtação d Mstrado Uvrsdad Fdral o Ro d Jaro CPP RJ Brasl. 3. Bar R. Chars A. Coor W. W.984. Som Modls for stmatg Tchcal a Scal Iffccs ata vlomt Aalyss Maagmt Scc v Bazaraa M. S. Shral H.. Shtty C. M. 993 Nolar Programmg Thory ad Algorthms N Yor USA Joh Wly & Sos Ic. 5. Bod L. N. stllta Ls t al. Nuro-A: Novo Paradgma ara trmação da fcêca Rlatva d Udads Tomadoras d csão 9 º Cogrsso da Assocação Portugusa d Ivstgação racoal º Chars A. Coor W. W. 96 Programmg th lar fractoal fuctoal Naval Rsarch Logstcs Quartrly v.9 3/ Chars A. Coor W. W. Rhods Masurg th ffccy of cso-mag Uts uroa Joural of ratoal Rsarch v Ch J. Shablatt M. Ad Maa C. 99. Imrovd Nural Ntor for Lar ad Nolar Programmg Itratoal Joural of Nural Systms o Cchoc A. Ad Bargla A Nural Ntors for Solvg Lar Iqualty Systms Joural of Paralll Comutg dsoívl m htt://.b.r.go./absl.. Cchoc A. Ad Ubhau R Nural Ntors for tmzato ad Sgal Procssg N Yor USA Joh Wly & Sos Ic.. avd M. Saura 996. Buldg Nural Ntors N Yor Addso-Wsly Publshg Comay.. s J.. Ad Schabl R.B.996. Numrcal Mthods for Ucostrad tmzato ad Nolar quatos glood Clffs N.J. USA Prtc Hall Ic. 3. stllta L. M. Lída Agulo t Al. Aáls d voltóra d ados Prsctvas d Ambt d Aoo à csão Ro d Jaro Brasl CPP/UFRJ. 4. stllta L. M.. Morra 998. Modlo I Sts ara slção d varávs m Modlos d Aáls d voltóra d ados Rvsta Psqusa racoalv.9 o Hamdy A. Taha 99. ratos Rsarch: A Itroducto sth dto N Jrsy Prtc Hall. 6. Htor Pa 995. Métodos Numércos Lsboa Portugal McGra-Hll. 7. Jac M. Zurada 99. Itroducto to Artfcal Nural Systms Lodo Wst Publshg Comay. 8. J.Moscs Zbg goos 995. Advacd Cotrol th MatLab Smul Lodo lls Horood Lmtd. 9. Kallrath J. ad W. J Busss tmzato usg Mathmatcal Programmg Lodo Macmlla Prss Ltd.. Kdy M. P. S Chua L Nural Ntors for Nolar Programmg I Trasactos o Crcuts ad Systms o M. Ms Ad S. Part 969. Prctros Cambrdg MIT Prss.

13 Itratoal Joural f Idustral grg v Math Wors 995. Smul yamc Systm Smulato Usr s Gud Massachustts USA Math Wors Ic. 3. Phl. Wassrma 993. Advacd Mthods Nural Comutg Lodo Va Nostrad Rhold. 4. Robrt L. Harvy 994. Nural Ntor Prcls N Yor Prtc Hall Itratoal dtos. 5. Rosblatt F. 96. Prcls of Nurodyamcs N Yor Sarta dtos. 6. Rumlhart.. Hto G.. Ad Wlla R. J Larg Itral Rrstato by rror Proagato. I Paralll strbutd Procssg Vol Cambrdg M. A... Rumlhart ad J. L. McClllad ds MIT Prss. 7. Smo Hay 994. Nural Ntors a Comrhsv Foudato Lodo Macmlla Collg Publshg Co.. 8. Tm Coll. S. Prasada Rao ad Gorg. Batts 998. A Itroducto to ffccy ad Productvty Aalyss Bosto Klur Acadmc Publshrs. 9. Wrr C. Rhboldt 998. Mthods for Solvg Systms of Nolar quatos Phladlha SIAM dtors. 3. Zhu X. Zhag S. ad Costatds A. G. 99. Lagrag ural Ntors to lar rogrammg Joural of Paralll strbutd Comutg o

Finite Dimensional Vector Spaces.

Finite Dimensional Vector Spaces. Lctur 5. Ft Dmsoal Vctor Spacs. To b rad to th musc of th group Spac by D.Maruay DEFINITION OF A LINEAR SPACE Dfto: a vctor spac s a st R togthr wth a oprato calld vctor addto ad aothr oprato calld scalar

More information

ENGINEERING COMPUTATION BY ARTIFICIAL NEURAL NETWORKS. Explaining Neural Networks

ENGINEERING COMPUTATION BY ARTIFICIAL NEURAL NETWORKS. Explaining Neural Networks SRK oaz Poltcha Pozaa Ittut Mcha Stooa ul. Potroo 3, 6-965 Poza EGIEERIG COMPUAIO BY ARIFICIA EURA EWORKS Eplag ural tor ural tor ar copod o pl lt opratg paralll. h lt ar prd b bologcal rvou t. A atur,

More information

REVISTA INVESTIGACIÓN OPERACIONAL VOL., 32, NO. 2, 93-106, 2011

REVISTA INVESTIGACIÓN OPERACIONAL VOL., 32, NO. 2, 93-106, 2011 REVISA IVESIGACIÓ OPERACIOAL VOL., 3, O., 93-6, A IEGRAED IVEORY POLICY WIH DEERIORAIO FOR A SIGLE VEDOR AD MULIPLE BUYERS I SUPPLY CHAI WHE DEMAD IS QUADRAIC ta H. Shah,Ajay S. Gor ad Chta Jhavr Dpartmt

More information

DEVELOPMENT OF MODEL FOR RUNNING DIESEL ENGINE ON RAPESEED OIL FUEL AND ITS BLENDS WITH FOSSIL DIESEL FUEL

DEVELOPMENT OF MODEL FOR RUNNING DIESEL ENGINE ON RAPESEED OIL FUEL AND ITS BLENDS WITH FOSSIL DIESEL FUEL ENGINEERING FOR RURAL DEVELOPMENT Jlgava, 3.-4.5.3. DEVELOPMENT OF MODEL FOR RUNNING DIESEL ENGINE ON RAPESEED OIL FUEL AND ITS BLENDS WITH FOSSIL DIESEL FUEL Ilmars Dukuls, Avars Brkavs Latva Uvrsty of

More information

Online Insurance Consumer Targeting and Lifetime Value Evaluation - A Mathematics and Data Mining Approach

Online Insurance Consumer Targeting and Lifetime Value Evaluation - A Mathematics and Data Mining Approach Ol Isurac Cosumr Targtg ad Lftm Valu Evaluato - A Mathmatcs ad Data Mg Approach Yuaya L,2, Gal Cook 3 ad Olvr Wrford 3 Rvr ad Harbor Dpartmt, Najg Hydraulc Rsarch Isttut, Najg, 224, 2 Ky Laboratory of

More information

Evaluating Direct Marketing Practices On the Internet via the Fuzzy Cognitive Mapping Method

Evaluating Direct Marketing Practices On the Internet via the Fuzzy Cognitive Mapping Method Itratoal Joural of Busss ad Maagmt Dcmbr, 28 Evaluatg Drct Marktg Practcs O th Itrt va th Fuzzy Cogtv Mappg Mthod Slcuk Burak Hasloglu (Corrspodg author) Dpartmt of Marktg, Faculty of Ecoomc ad Admstratv

More information

REFINED CALCULATION AND SIMULATION SYSTEM OF LOCAL LARGE DEFORMATION FOR ACCIDENT VEHICLE

REFINED CALCULATION AND SIMULATION SYSTEM OF LOCAL LARGE DEFORMATION FOR ACCIDENT VEHICLE 2 3 4 5 6 7 8 9 0 2 3 4 5 6 7 8 9 20 2 22 23 24 25 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 4 42 43 44 REFINED CALCULATION AND SIMULATION SYSTEM OF LOCAL LARGE DEFORMATION FOR ACCIDENT VEHICLE WagFag

More information

Coordination, Cooperation, Contagion and Currency Crises 1

Coordination, Cooperation, Contagion and Currency Crises 1 oorato, oorato, otago a urrc rss Olvr Losl * a Phl Mart ** Jauar 999, ths vrso Jauar 000 bstract W aalz th ffct of tra comtto, tratoal coorato a coorato o currc crss. To o ths, w rst a mcro-fou mol whr

More information

Initial inventory levels for a book publishing firm

Initial inventory levels for a book publishing firm Mőhlytaulmáy Vállalatgazdaságta Itézt 93 Budapst, Fıvám tér 8. (+36 ) 482-5566, Fax: 482-5567 www.u-crvus.hu/vallgazd Ital vtry lvls fr a b publshg frm Imr Dbs Ágs Wmmr 23. sz. Mőhlytaulmáy HU ISSN 786-33

More information

Evaluating Microsoft Hyper-V Live Migration Performance Using IBM System x3650 M3 and IBM N series N5600

Evaluating Microsoft Hyper-V Live Migration Performance Using IBM System x3650 M3 and IBM N series N5600 Lv Mgrato of Workloads o 10GbE vs. 1GbE Ntworks Ju 2011 Evaluatg Mcrosoft Hypr-V Lv Mgrato Prformac Usg IBM Systm x3650 M3 ad IBM N srs N5600 Kt R. Swal IBM Systms ad Tchology Group W Lu NtApp Bra Johso

More information

Problem Set 6 Solutions

Problem Set 6 Solutions 6.04/18.06J Mathmatics for Computr Scic March 15, 005 Srii Dvadas ad Eric Lhma Problm St 6 Solutios Du: Moday, March 8 at 9 PM Problm 1. Sammy th Shar is a fiacial srvic providr who offrs loas o th followig

More information

by John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia

by John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia Studnt Nots Cost Volum Profit Analysis by John Donald, Lcturr, School of Accounting, Economics and Financ, Dakin Univrsity, Australia As mntiond in th last st of Studnt Nots, th ability to catgoris costs

More information

Parallel and Distributed Programming. Performance Metrics

Parallel and Distributed Programming. Performance Metrics Paralll and Distributd Programming Prformanc! wo main goals to b achivd with th dsign of aralll alications ar:! Prformanc: th caacity to rduc th tim to solv th roblm whn th comuting rsourcs incras;! Scalability:

More information

CSSE463: Image Recognition Day 27

CSSE463: Image Recognition Day 27 CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)

More information

Victims Compensation Claim Status of All Pending Claims and Claims Decided Within the Last Three Years

Victims Compensation Claim Status of All Pending Claims and Claims Decided Within the Last Three Years Claim#:021914-174 Initials: J.T. Last4SSN: 6996 DOB: 5/3/1970 Crime Date: 4/30/2013 Status: Claim is currently under review. Decision expected within 7 days Claim#:041715-334 Initials: M.S. Last4SSN: 2957

More information

DYNAMIC PROGRAMMING APPROACH TO TESTING RESOURCE ALLOCATION PROBLEM FOR MODULAR SOFTWARE

DYNAMIC PROGRAMMING APPROACH TO TESTING RESOURCE ALLOCATION PROBLEM FOR MODULAR SOFTWARE DYAMIC PROGRAMMIG APPROACH TO TESTIG RESOURCE ALLOCATIO PROBLEM FOR MODULAR SOFTWARE P.K. Kpur P.C. Jh A.K. Brdh Astrct Tstg phs of softwr gs wth modul tstg. Durg ths prod moduls r tstd dpdtly to rmov

More information

http://www.wwnorton.com/chemistry/tutorials/ch14.htm Repulsive Force

http://www.wwnorton.com/chemistry/tutorials/ch14.htm Repulsive Force ctivation nrgis http://www.wwnorton.com/chmistry/tutorials/ch14.htm (back to collision thory...) Potntial and Kintic nrgy during a collision + + ngativly chargd lctron cloud Rpulsiv Forc ngativly chargd

More information

TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS

TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS MPRA Muich Prsoal RPEc Archiv TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS Mahbub Parvz Daffodil Itratioal Uivrsy 6. Dcmbr 26 Oli at

More information

Study on prediction of network security situation based on fuzzy neutral network

Study on prediction of network security situation based on fuzzy neutral network Avalable ole www.ocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(6):00-06 Research Artcle ISS : 0975-7384 CODE(USA) : JCPRC5 Study o predcto of etwork securty stuato based o fuzzy eutral etwork

More information

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,

More information

A Note on Approximating. the Normal Distribution Function

A Note on Approximating. the Normal Distribution Function Applid Mathmatical Scincs, Vol, 00, no 9, 45-49 A Not on Approimating th Normal Distribution Function K M Aludaat and M T Alodat Dpartmnt of Statistics Yarmouk Univrsity, Jordan Aludaatkm@hotmailcom and

More information

New Basis Functions. Section 8. Complex Fourier Series

New Basis Functions. Section 8. Complex Fourier Series Nw Basis Functions Sction 8 Complx Fourir Sris Th complx Fourir sris is prsntd first with priod 2, thn with gnral priod. Th connction with th ral-valud Fourir sris is xplaind and formula ar givn for convrting

More information

5 2 index. e e. Prime numbers. Prime factors and factor trees. Powers. worked example 10. base. power

5 2 index. e e. Prime numbers. Prime factors and factor trees. Powers. worked example 10. base. power Prim numbrs W giv spcial nams to numbrs dpnding on how many factors thy hav. A prim numbr has xactly two factors: itslf and 1. A composit numbr has mor than two factors. 1 is a spcial numbr nithr prim

More information

CPS 220 Theory of Computation REGULAR LANGUAGES. Regular expressions

CPS 220 Theory of Computation REGULAR LANGUAGES. Regular expressions CPS 22 Thory of Computation REGULAR LANGUAGES Rgular xprssions Lik mathmatical xprssion (5+3) * 4. Rgular xprssion ar built using rgular oprations. (By th way, rgular xprssions show up in various languags:

More information

Exponential Generating Functions

Exponential Generating Functions Epotl Grtg Fuctos COS 3 Dscrt Mthmtcs Epotl Grtg Fuctos (,,, ) : squc of rl umbrs Epotl Grtg fucto of ths squc s th powr srs ( )! 3 Ordry Grtg Fuctos (,,, ) : squc of rl umbrs Ordry Grtg Fucto of ths squc

More information

5.4 Exponential Functions: Differentiation and Integration TOOTLIFTST:

5.4 Exponential Functions: Differentiation and Integration TOOTLIFTST: .4 Eponntial Functions: Diffrntiation an Intgration TOOTLIFTST: Eponntial functions ar of th form f ( ) Ab. W will, in this sction, look at a spcific typ of ponntial function whr th bas, b, is.78.... This

More information

Lecture 20: Emitter Follower and Differential Amplifiers

Lecture 20: Emitter Follower and Differential Amplifiers Whits, EE 3 Lctur 0 Pag of 8 Lctur 0: Emittr Followr and Diffrntial Amplifirs Th nxt two amplifir circuits w will discuss ar ry important to lctrical nginring in gnral, and to th NorCal 40A spcifically.

More information

Traffic Flow Analysis (2)

Traffic Flow Analysis (2) Traffic Flow Analysis () Statistical Proprtis. Flow rat distributions. Hadway distributions. Spd distributions by Dr. Gang-Ln Chang, Profssor Dirctor of Traffic safty and Oprations Lab. Univrsity of Maryland,

More information

Mathematics. Mathematics 3. hsn.uk.net. Higher HSN23000

Mathematics. Mathematics 3. hsn.uk.net. Higher HSN23000 hsn uknt Highr Mathmatics UNIT Mathmatics HSN000 This documnt was producd spcially for th HSNuknt wbsit, and w rquir that any copis or drivativ works attribut th work to Highr Still Nots For mor dtails

More information

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis 6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces

More information

Non-Linear and Unbalanced Three-Phase Load Static Compensation with Asymmetrical and Non Sinusoidal Supply

Non-Linear and Unbalanced Three-Phase Load Static Compensation with Asymmetrical and Non Sinusoidal Supply Non-Lnar and nbalancd Thr-Phas Load Statc Comnsaton wth Asymmtrcal and Non Snusodal Suly Rys S. Hrrra and P. Salmrón Elctrcal Engnrng Dartmnt Escula Poltécnca Suror, nvrsty of Hulva Ctra. Palos d la Frontra,

More information

RUSSIAN ROULETTE AND PARTICLE SPLITTING

RUSSIAN ROULETTE AND PARTICLE SPLITTING RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate

More information

CIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning

CIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning CIS63 - Artfcal Itellgece Logstc regresso Vasleos Megalookoomou some materal adopted from otes b M. Hauskrecht Supervsed learg Data: D { d d.. d} a set of eamples d < > s put vector ad s desred output

More information

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX III THE ENVELOPE PROPERTY Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

More information

Question 3: How do you find the relative extrema of a function?

Question 3: How do you find the relative extrema of a function? ustion 3: How do you find th rlativ trma of a function? Th stratgy for tracking th sign of th drivativ is usful for mor than dtrmining whr a function is incrasing or dcrasing. It is also usful for locating

More information

A Statistical Approach to Classify and Identify DDoS Attacks using UCLA Dataset

A Statistical Approach to Classify and Identify DDoS Attacks using UCLA Dataset ISSN: 78 133 Itratoal Joural Advad Rarh Comutr Egrg & Thology (IJARCET) Volum, No 5, May 013 A Stattal Aroah to Clafy ad Idtfy DDoS Attak ug UCLA Datat Thw Thw Oo, Thadar Phyu Abtrat Nowaday, Itrt th mot

More information

Reliability-Driven Reputation Based Scheduling for Public-Resource Computing Using GA

Reliability-Driven Reputation Based Scheduling for Public-Resource Computing Using GA 2009 Intrnatonal Confrnc on Advancd Informaton Ntworkng and Applcatons Rlablty-Drvn Rputaton Basd Schdulng for Publc-Rsourc Computng Usng GA Xaofng Wang #, Ch Shn Yo*, Rakumar Buyya* 2, Jnshu Su # 2 #Collg

More information

Foreign Exchange Markets and Exchange Rates

Foreign Exchange Markets and Exchange Rates Microconomics Topic 1: Explain why xchang rats indicat th pric of intrnational currncis and how xchang rats ar dtrmind by supply and dmand for currncis in intrnational markts. Rfrnc: Grgory Mankiw s Principls

More information

How To Value An Annuity

How To Value An Annuity Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Long run: Law of one price Purchasing Power Parity. Short run: Market for foreign exchange Factors affecting the market for foreign exchange

Long run: Law of one price Purchasing Power Parity. Short run: Market for foreign exchange Factors affecting the market for foreign exchange Lctur 6: Th Forign xchang Markt xchang Rats in th long run CON 34 Mony and Banking Profssor Yamin Ahmad xchang Rats in th Short Run Intrst Parity Big Concpts Long run: Law of on pric Purchasing Powr Parity

More information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

More information

Econ 371: Answer Key for Problem Set 1 (Chapter 12-13)

Econ 371: Answer Key for Problem Set 1 (Chapter 12-13) con 37: Answr Ky for Problm St (Chaptr 2-3) Instructor: Kanda Naknoi Sptmbr 4, 2005. (2 points) Is it possibl for a country to hav a currnt account dficit at th sam tim and has a surplus in its balanc

More information

OPTIMAL KNOWLEDGE FLOW ON THE INTERNET

OPTIMAL KNOWLEDGE FLOW ON THE INTERNET İstabul Tcaret Üverstes Fe Blmler Dergs Yıl: 5 Sayı:0 Güz 006/ s. - OPTIMAL KNOWLEDGE FLOW ON THE INTERNET Bura ORDİN *, Urfat NURİYEV ** ABSTRACT The flow roblem ad the mmum sag tree roblem are both fudametal

More information

Advantageous Selection versus Adverse Selection in Life Insurance Market

Advantageous Selection versus Adverse Selection in Life Insurance Market Covr Pag Advantagous Slcton vrsus Advrs Slcton n f Insuranc Markt Ghadr Mahdav mahdav@conomcs.mbo.mda.kyoto-u.ac.j Post Doctoral Rsarch Assocat: Jaan Socty for th Promoton of Scnc JSPS, Graduat School

More information

Approximate Counters for Flash Memory

Approximate Counters for Flash Memory Approximat Coutrs for Flash Mmory Jack Cichoń ad Wojcich Macya Istitut of Mathmatics ad Computr Scic Wrocław Uivrsity of Tchology, Polad Abstract Flash mmory bcoms th a vry popular storag dvic Du to its

More information

1.- L a m e j o r o p c ió n e s c l o na r e l d i s co ( s e e x p li c a r á d es p u é s ).

1.- L a m e j o r o p c ió n e s c l o na r e l d i s co ( s e e x p li c a r á d es p u é s ). PROCEDIMIENTO DE RECUPERACION Y COPIAS DE SEGURIDAD DEL CORTAFUEGOS LINUX P ar a p od e r re c u p e ra r nu e s t r o c o rt a f u e go s an t e un d es a s t r e ( r ot u r a d e l di s c o o d e l a

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,

More information

Authenticated Encryption. Jeremy, Paul, Ken, and Mike

Authenticated Encryption. Jeremy, Paul, Ken, and Mike uthntcatd Encrypton Jrmy Paul Kn and M Objctvs Examn thr mthods of authntcatd ncrypton and dtrmn th bst soluton consdrng prformanc and scurty Basc Componnts Mssag uthntcaton Cod + Symmtrc Encrypton Both

More information

Three Dimensional Interpolation of Video Signals

Three Dimensional Interpolation of Video Signals Three Dmesoal Iterpolato of Vdeo Sgals Elham Shahfard March 0 th 006 Outle A Bref reve of prevous tals Dgtal Iterpolato Bascs Upsamplg D Flter Desg Issues Ifte Impulse Respose Fte Impulse Respose Desged

More information

Speeding up k-means Clustering by Bootstrap Averaging

Speeding up k-means Clustering by Bootstrap Averaging Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg

More information

QUANTITATIVE METHODS CLASSES WEEK SEVEN

QUANTITATIVE METHODS CLASSES WEEK SEVEN QUANTITATIVE METHODS CLASSES WEEK SEVEN Th rgrssion modls studid in prvious classs assum that th rspons variabl is quantitativ. Oftn, howvr, w wish to study social procsss that lad to two diffrnt outcoms.

More information

Magic Message Maker Amaze your customers with this Gift of Caring communication piece

Magic Message Maker Amaze your customers with this Gift of Caring communication piece Magic Mssag Makr maz your customrs with this Gift of aring communication pic Girls larn th powr and impact of crativ markting with this attntion grabbing communication pic that will hlp thm o a World of

More information

11 Multiple Linear Regression

11 Multiple Linear Regression 11 Multpl Lar Rgrsso Multpl lar rgrsso (MLR) s a mthod usd to modl th lar rlatoshp btw a dpdt varabl ad o or mor dpdt varabls. Th dpdt varabl s somtms also calld th prdctad, ad th dpdt varabls th prdctors.

More information

Incomplete 2-Port Vector Network Analyzer Calibration Methods

Incomplete 2-Port Vector Network Analyzer Calibration Methods Incomplt -Port Vctor Ntwork nalyzr Calibration Mthods. Hnz, N. Tmpon, G. Monastrios, H. ilva 4 RF Mtrology Laboratory Instituto Nacional d Tcnología Industrial (INTI) Bunos irs, rgntina ahnz@inti.gov.ar

More information

West Virginia. Income/Business Franchise Tax for S Corps & Partnerships (Pass-Through Entities) INSTRUCTIONS

West Virginia. Income/Business Franchise Tax for S Corps & Partnerships (Pass-Through Entities) INSTRUCTIONS 2012 Wst Vrga Icom/Busss Frachs Tax for S Corps & Partrshps (Pass-Through Etts) INSTRUCTIONS Nw for 2012 Tax Rats For tax yars bgg o or aftr Jauary 1, 2012, th Busss Frachs rat s th gratr of $50 or 0.27%.

More information

West Virginia. Instructions. Income/Business Franchise Tax for S Corps & Partnerships (Pass-Through Entities) Guyandotte River, Mingo County

West Virginia. Instructions. Income/Business Franchise Tax for S Corps & Partnerships (Pass-Through Entities) Guyandotte River, Mingo County 2014 Wst Vrga Icom/Busss Frachs Tax for S Corps & Partrshps (Pass-Through Etts) Istructos Guyadott Rvr, Mgo Couty Nw for 2014 Tax Rats For tax yars bgg o or aftr Jauary 1, 2014, th Busss Frachs rat s th

More information

10.5 Future Value and Present Value of a General Annuity Due

10.5 Future Value and Present Value of a General Annuity Due Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the

More information

Maintenance Scheduling of Distribution System with Optimal Economy and Reliability

Maintenance Scheduling of Distribution System with Optimal Economy and Reliability Egeerg, 203, 5, 4-8 http://dx.do.org/0.4236/eg.203.59b003 Publshed Ole September 203 (http://www.scrp.org/joural/eg) Mateace Schedulg of Dstrbuto System wth Optmal Ecoomy ad Relablty Syua Hog, Hafeg L,

More information

81-1-ISD Economic Considerations of Heat Transfer on Sheet Metal Duct

81-1-ISD Economic Considerations of Heat Transfer on Sheet Metal Duct Air Handling Systms Enginring & chnical Bulltin 81-1-ISD Economic Considrations of Hat ransfr on Sht Mtal Duct Othr bulltins hav dmonstratd th nd to add insulation to cooling/hating ducts in ordr to achiv

More information

Multi-way classification

Multi-way classification CS 75 Mache Learg Lecture Mult-a classfcato Mlos Hauskrecht mlos@cs.tt.edu 59 Seott Suare CS 75 Mache Learg Admstratve aoucemets Homeork 6 due o Wedesda Pla for the ucomg moth: Homeork 7 due o Wedesda

More information

A Smart Machine Vision System for PCB Inspection

A Smart Machine Vision System for PCB Inspection A Smart Mache Vso System for PCB Ispecto Te Q Che, JaX Zhag, YouNg Zhou ad Y Lu Murphey Please address all correspodece to Departmet of Electrcal ad Computer Egeerg Uversty of Mchga - Dearbor, Dearbor,

More information

Protecting E-Commerce Systems From Online Fraud

Protecting E-Commerce Systems From Online Fraud Protctng E-Commrc Systms From Onln Fraud Frst Author P.PhanAlkhya Studnt, Dpartmnt of Computr Scnc and Engnrng, QIS Collg of Engnrng & Tchnology, ongol, Andhra Pradsh, Inda. Scond Author Sk.Mahaboob Basha

More information

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh

More information

INFLUENCE OF DEBT FINANCING ON THE EFFECTIVENESS OF THE INVESTMENT PROJECT WITHIN THE MODIGLIANIMILLER THEORY

INFLUENCE OF DEBT FINANCING ON THE EFFECTIVENESS OF THE INVESTMENT PROJECT WITHIN THE MODIGLIANIMILLER THEORY VOUME 2, 2 NFUENCE OF DEBT FNANCNG ON THE EFFECTVENE OF THE NVETMENT PROJECT WTHN THE MODGANMER THEORY Pr Brusov, Taaa Flaova, Naal Orhova, Pavl Brusov, Nasa Brusova Fac Uvrsy ur h Govrm of h Russa Frao,

More information

Lecture 3: Diffusion: Fick s first law

Lecture 3: Diffusion: Fick s first law Lctur 3: Diffusion: Fick s first law Today s topics What is diffusion? What drivs diffusion to occur? Undrstand why diffusion can surprisingly occur against th concntration gradint? Larn how to dduc th

More information

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute

More information

Modern Portfolio Theory (MPT) Statistics

Modern Portfolio Theory (MPT) Statistics Modrn Portfolo Thory (MPT) Statstcs Mornngstar Mthodology Papr Novmr 30, 007 007 Mornngstar, Inc. All rghts rsrvd. Th nformaton n ths documnt s th proprty of Mornngstar, Inc. Rproducton or transcrpton

More information

Outside Cut 1 of fabric Cut 1 of interfacing

Outside Cut 1 of fabric Cut 1 of interfacing a a Outsi Cut o abric Cut o intracing a a b b Outsi Cut o abric Cut o intracing Placmnt lin or Mony Pockts Dix Not: F. Cut Fol b. Pin t /8 in 5. Nx bottom pics sw th 6. For t Prss, 7. Lay togth on th 8.

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

1. Online Event Registration 2. Event Marketing 3. Automated Event Progress Reports 4. Web based Point of Sale Terminal 5. Email Marketing System

1. Online Event Registration 2. Event Marketing 3. Automated Event Progress Reports 4. Web based Point of Sale Terminal 5. Email Marketing System 2 t v E S d Ivit 3 M o it o r ro la 1 r g 1 Oli Evt Rgitratio 2 Evt Marktig 3 Automatd Evt rogr Rport 4 Wb bad oit of Sal Trmial 5 Email Marktig Sytm ag 1 of 6 Copyright 2004-2011 myvillag oli Evt Maagmt

More information

Research on Cloud Computing and Its Application in Big Data Processing of Railway Passenger Flow

Research on Cloud Computing and Its Application in Big Data Processing of Railway Passenger Flow 325 A publcato of CHEMICAL ENGINEERING TRANSACTIONS VOL. 46, 2015 Guest Edtors: Peyu Re, Yacag L, Hupg Sog Copyrght 2015, AIDIC Servz S.r.l., ISBN 978-88-95608-37-2; ISSN 2283-9216 The Itala Assocato of

More information

Learning & Development

Learning & Development Larg & Dvlopmt Offrg ad Proc Updat Octobr 29th, 2012 Roara Torra, L&D Global Soluto Archtct Copyrght 2012 E. I. du Pot d Nmour ad Compay. All rght rrvd. Th DuPot Oval Logo, DuPot, Th mracl of cc ad all

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

BLADE 12th Generation. Rafał Olszewski. Łukasz Matras

BLADE 12th Generation. Rafał Olszewski. Łukasz Matras BLADE 12th Generation Rafał Olszewski Łukasz Matras Jugowice, 15-11-2012 Gl o b a l M a r k e t i n g Dell PowerEdge M-Series Blade Server Portfolio M-Series Blades couple powerful computing capabilities

More information

A Formal Model for Data Flow Diagram Rules

A Formal Model for Data Flow Diagram Rules Volum No. MAY 0 ARPN Journal o Sytm and Sotwar 00- AJSS Journal. All rght rrvd htt://www.cntc-ournal.org A Formal Modl or Data Flow Dagram Rul Rozat Ibrahm Sow Yn Yn Dartmnt o Sotwar Engnrng Unvrty Tun

More information

CPU. Rasterization. Per Vertex Operations & Primitive Assembly. Polynomial Evaluator. Frame Buffer. Per Fragment. Display List.

CPU. Rasterization. Per Vertex Operations & Primitive Assembly. Polynomial Evaluator. Frame Buffer. Per Fragment. Display List. Elmntary Rndring Elmntary rastr algorithms for fast rndring Gomtric Primitivs Lin procssing Polygon procssing Managing OpnGL Stat OpnGL uffrs OpnGL Gomtric Primitivs ll gomtric primitivs ar spcifid by

More information

Section 3: Logistic Regression

Section 3: Logistic Regression Scton 3: Logstc Rgrsson As our motvaton for logstc rgrsson, w wll consdr th Challngr dsastr, th sx of turtls, collg math placmnt, crdt card scorng, and markt sgmntaton. Th Challngr Dsastr On January 28,

More information

Basis risk. When speaking about forward or futures contracts, basis risk is the market

Basis risk. When speaking about forward or futures contracts, basis risk is the market Basis risk Whn spaking about forward or futurs contracts, basis risk is th markt risk mismatch btwn a position in th spot asst and th corrsponding futurs contract. Mor broadly spaking, basis risk (also

More information

Key players and activities across the ERP life cycle: A temporal perspective

Key players and activities across the ERP life cycle: A temporal perspective 126 Revsta Iformatca Ecoomcă, r. 4 (44)/2007 Key layers ad actvtes across the ERP lfe cycle: A temoral ersectve Iulaa SCORŢA, Bucharest, Romaa Eterrse Resource Plag (ERP) systems are eterrse wde systems

More information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,

More information

Maximization of Data Gathering in Clustered Wireless Sensor Networks

Maximization of Data Gathering in Clustered Wireless Sensor Networks Maxmzato of Data Gatherg Clustere Wreless Sesor Networks Taq Wag Stuet Member I We Hezelma Seor Member I a Alreza Seye Member I Abstract I ths paper we vestgate the maxmzato of the amout of gathere ata

More information

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral

More information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

More information

Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines

Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines (ICS) Iteratoal oural of dvaced Comuter Scece ad lcatos Vol 6 No 05 romato lgorthms for Schedulg wth eecto o wo Urelated Parallel aches Feg Xahao Zhag Zega Ca College of Scece y Uversty y Shadog Cha 76005

More information

Projections - 3D Viewing. Overview Lecture 4. Projection - 3D viewing. Projections. Projections Parallel Perspective

Projections - 3D Viewing. Overview Lecture 4. Projection - 3D viewing. Projections. Projections Parallel Perspective Ovrviw Lctur 4 Projctions - 3D Viwing Projctions Paralll Prspctiv 3D Viw Volum 3D Viwing Transformation Camra Modl - Assignmnt 2 OFF fils 3D mor compl than 2D On mor dimnsion Displa dvic still 2D Analog

More information

Whole Systems Approach to CO 2 Capture, Transport and Storage

Whole Systems Approach to CO 2 Capture, Transport and Storage Whol Systms Approach to CO 2 Captur, Transport and Storag N. Mac Dowll, A. Alhajaj, N. Elahi, Y. Zhao, N. Samsatli and N. Shah UKCCS Mting, July 14th 2011, Nottingham, UK Ovrviw 1 Introduction 2 3 4 Powr

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

Adverse Selection and Moral Hazard in a Model With 2 States of the World

Adverse Selection and Moral Hazard in a Model With 2 States of the World Advrs Slction and Moral Hazard in a Modl With 2 Stats of th World A modl of a risky situation with two discrt stats of th world has th advantag that it can b natly rprsntd using indiffrnc curv diagrams,

More information

Factorials! Stirling s formula

Factorials! Stirling s formula Author s not: This articl may us idas you havn t larnd yt, and might sm ovrly complicatd. It is not. Undrstanding Stirling s formula is not for th faint of hart, and rquirs concntrating on a sustaind mathmatical

More information

tis, cis cunc - cunc - tis, cis tis, cis cunc - tis, func - def - def - tis, U func - def - func - tis, pa - tri pa - tri pa - tri tu - per - tu -

tis, cis cunc - cunc - tis, cis tis, cis cunc - tis, func - def - def - tis, U func - def - func - tis, pa - tri pa - tri pa - tri tu - per - tu - 1 B Ihsu dulcs cuncts [Supr 1] [Supr 2] Tnr B B B B - B - B - Ih - Ih - Ih - su su su cs cs cs cunc - cunc - cunc - Amns, Bblthèqu Cntl L Agn, ms 162 D, ff 2v-10 ts, ts, ts, E-tr - E-tr - E-tr - n p n

More information

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree , pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

IP Network Topology Link Prediction Based on Improved Local Information Similarity Algorithm

IP Network Topology Link Prediction Based on Improved Local Information Similarity Algorithm Iteratoal Joural of Grd Dstrbuto Computg, pp.141-150 http://dx.do.org/10.14257/jgdc.2015.8.6.14 IP Network Topology Lk Predcto Based o Improved Local Iformato mlarty Algorthm Che Yu* 1, 2 ad Dua Zhem 1

More information

Integrating Production Scheduling and Maintenance: Practical Implications

Integrating Production Scheduling and Maintenance: Practical Implications Proceedgs of the 2012 Iteratoal Coferece o Idustral Egeerg ad Operatos Maagemet Istabul, Turkey, uly 3 6, 2012 Itegratg Producto Schedulg ad Mateace: Practcal Implcatos Lath A. Hadd ad Umar M. Al-Turk

More information

BASIC DEFINITIONS AND TERMINOLOGY OF SOILS

BASIC DEFINITIONS AND TERMINOLOGY OF SOILS 1 BASIC DEFINITIONS AND TERMINOLOGY OF SOILS Soil i a thr pha atrial hich coit of olid particl hich ak up th oil klto ad void hich ay b full of atr if th oil i aturatd, ay b full of air if th oil i dry,

More information

AP Calculus AB 2008 Scoring Guidelines

AP Calculus AB 2008 Scoring Guidelines AP Calculus AB 8 Scoring Guidlins Th Collg Board: Conncting Studnts to Collg Succss Th Collg Board is a not-for-profit mmbrship association whos mission is to connct studnts to collg succss and opportunity.

More information

AN EVALUATION OF SHORT TERM TREATMENT PROGRAM FOR PERSONS DRIVING UNDER THE INFLUENCE OF ALCOHOL 1978-1981. P. A. V a le s, Ph.D.

AN EVALUATION OF SHORT TERM TREATMENT PROGRAM FOR PERSONS DRIVING UNDER THE INFLUENCE OF ALCOHOL 1978-1981. P. A. V a le s, Ph.D. AN EVALUATION OF SHORT TERM TREATMENT PROGRAM FOR PERSONS DRIVING UNDER THE INFLUENCE OF ALCOHOL 1978-1981 P. A. V a le s, Ph.D. SYNOPSIS Two in d ep en d en t tre a tm e n t g ro u p s, p a r t ic ip

More information

Credibility Premium Calculation in Motor Third-Party Liability Insurance

Credibility Premium Calculation in Motor Third-Party Liability Insurance Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53

More information