ALLOCATING TOLERANCES FOR VEE-GROOVE FIBER ALIGNMENT

Size: px
Start display at page:

Download "ALLOCATING TOLERANCES FOR VEE-GROOVE FIBER ALIGNMENT"

Transcription

1 ALLOCATING TOLERANCES FOR VEE-GROOVE FIBER ALIGNMENT Maheu Barraa and R. Ryan Vallance Precson Sysems Laboraory Unversy of Kenucky Lengon KY * S. Kan J. Lehman and Burke Hunsaker Teradyne Connecon Sysems Nashua NH Absrac Ths paper presens a mehod for allocang olerances o dmensons n mcro scale vee-grooves used o algn opcal fbers. The obecve s o reduce he manufacurng cos whou eceedng a lm on he msalgnmen beween wo mang fbers. The allocaon procedure s performed on a D geomerc model of a connecor ha algns an array of mulple fbers. An analycal model of he connecon based upon sascs s used for provdng a relaon beween varaon n manufacured dmensons and varaon n he resulng msalgnmen of he fbers conaned n he array. Opmal olerances are deermned usng a non-lnear consraned opmzaon algorhm ha mnmzes he manufacurng cos whle sasfyng consrans on he varaon of he msalgnmen of any par of fbers n he array. The mehod provdes a useful ool when desgnng mass-produced connecors for mul-fber cables for whch manufacurng cos and accuracy are crcal parameers. Keywords: olerance allocaon vee-grooves opcal fbers algnmen manufacurng cos Inroducon Vee-grooves are wdely used n mcroscale devces especally for posonng cylndrcal obecs. A common applcaon s o algn opcal fbers whch s of prmary neres for communcaons []. When connecng wo fbers a laeral msalgnmen due o he manufacurng errors of he vee-groove generaes a consequenal amoun of sgnal loss. Consderng Marcuse s [] model for sgnal loss due o laeral msalgnmen of sngle-mode fbers he cores of wo fbers should be algned whn abou µm o acheve ~ 0. db sgnal loss. Ths s a challengng proposon for only wo ndvdual fbers bu he challenge s even greaer for mass produced nerconnecs whch on cables conanng egh or more fbers as shown n Fg. Our obecve s o selec olerances ha are suffcen for algnng opcal fbers whou ecessve loss bu smulaneously mnmzng manufacurng coss ha arse from ecessvely gh olerances. Tolerance allocaon s generally formulaed as an opmzaon problem wh an obecve funcon and se of consrans. In hs case he obecve s o mnmze he manufacurng cos whch s a funcon of he olerances. Tolerance relaons for eched slcon vee-grooves are no avalable. However for oher maerals lke zrconum a secondary maeral removal process lke grndng may mprove he olerances. In hs case we employ relaons developed by Chase [3] o relae manufacurng coss o olerances. Boh cos and sgnal loss can be defned as funcons of he olerances allocaed by he desgner. The opmzaon consrans are formulaed as mamum olerable sgnal loss whn an enre connecor. Fbers Ferrules Fg : Mulfber Connecor wh Vee-Grooves In formulang he opmzaon problem he greaes challenge s deermnng a relaonshp * Precson Sysems Laboraory Mechancal Engneerng Unversy of Kenucky 0-A CRMS Buldng Lengon KY hp:// Teradyne Connecon Sysems Nashua NH. hp://

2 beween he olerances and he performance crera. For comple assembles such as a fber-opc connecor Mone Carlo smulaons are effecve means for relang fnal olerance of an assembly o he olerances of he componens [4]. However may be dffcul o mplemen Mone Carlo smulaons whn he opmzaon algorhm due o compuaonal me. We nsead use an alernave approach n whch a few Mone Carlo smulaons proved a mahemacal model relang assembly olerances o componen olerances. Knowng he geomery and he dmensons of he vee-groove s possble o defne he sgnal loss of he fber connecon as a funcon of he olerances of he veegroove. Ths paper presens a process o effcenly allocae he olerances for vee-groove fber algnmen. The frs sep s o consruc a mahemacal model of he dmensons and geomery of vee-grooves. The second sep s o defne hrough a sascal sudy he msalgnmen of he fber as a funcon of he olerances n he vee-grooves. The hrd sep s o esmae a relaon beween he olerances and he manufacurng coss. Fnally olerances are allocaed wh an opmzaon algorhm ha mnmzes he manufacurng cos for a gven mamum lm on sgnal loss. An eample llusraes he mehod. Mahemacal Model of he Dmensons and Geomery of a Vee-Groove Fber-o-fber connecons are a maor source of opcal loss. There es hree ypes of connecon losses [] ha are drecly relaed o he manufacurng errors whn he connecors. The frs one s caused by he laeral msalgnmen due o he offse of he cenerlnes of he mang fbers. The second comes from he end-separaon msalgnmen whch s he gap beween he ends of he conneced fbers. And fnally he hrd loss s generaed by he angular msalgnmen whch occurs when here ess an angle beween he wo aes of he fbers. The laeral msalgnmen s of mos concern for connecon loss snce angular msalgnmen s neglgble and end-separaon s usually resolved by mechancal conac beween he fbers or ndemachng compounds. In consderng only laeral msalgnmen a D model of he vee-grooves s a reasonable appromaon for represenng he veegroove geomery. The modelng plane s he z-plane y beng he drecon along he aes of he fbers. Geomery of a ferrule usng vee-grooves s llusraed n he plane perpendcular o he aes of he fbers n Fg. 0 z Fbers Ferrule Vee-Grooves Fg : Connecor n he z-plane In he D confguraon s possble o esablsh a mahemacal relaon beween he varaon of he dmensons n he ferrules and he laeral msalgnmen beween wo mang fbers. As shown n Fg 3 he h vee-groove of an array can be paramercally represened n wo dmensons wh four parameers and her manufacurng errors:. aperure angle α and angle error δ α. nclnaon angle γ whch s he angle beween he groove s bsecor and a vercal lne (deally γ equals zero) and angle error δ 3. deph o he vrual vere h and error γ δ h and 4. radus of curvaure r a he boom of he groove and error δ r. 0 z r + δ α +δα r Vere δ γ h + δ Fg 3: Manufacurng Errors n a Vee-Groove Merology appled o a ferrule canno drecly measure he value of he nclnaon angle so was decded o represen he aperure angle and he nclnaon angle as a combnaon of wo half-angles: L on he lef sde and R one he rgh sde. The aperure angle s defned as he sum of he wo half- h

3 angles whle he nclnaon angle s calculaed as half her dfference. The geomery of a sngle vee-groove s hen defned by four dmensons ( L R h r ) and her varaons. Furhermore varaon n he pch beween wo successve vee-grooves s also a crcal parameer for algnng fbers. The pch p s hen a ffh dmenson used for modelng he connecor. Varaon Analyss by he Law of Error Propagaon Tolerance allocaon requres a relaon beween dmensonal varaon and connecon loss. Marcuse [] presened a relaon gven n Eq () for he connecon loss T of sngle-mode fbers as a funcon of he laeral msalgnmen d and he wdh parameers w and w of he wo fbers. The dmensons d w and w are epressed n he same lengh un and T has no dmenson. Usually connecon loss s epressed n decbels. w w d T = ep () w + w w + w For mul-mode fbers epermenal daa are used o esablsh he relaon beween laeral msalgnmen and connecon loss as shown n Fg 4. Fg 4: Epermenal Deermnaon of a Relaon beween Loss and Msalgnmen by Curve Fng For boh sngle-mode and mul-mode cases s necessary o esablsh a relaon beween dmensonal varaon and laeral msalgnmen. Ths s done by applyng he law of error propagaon [5] on he mahemacal model of he connecors. Ths mehod s compuaonally effcen when used n a olerance allocaon algorhm. The geomerc model of he vee-groove mus be epressed n erms of sascs. Every dmenson ξ s defned as a randomly dsrbued varable. Is mean µ equals he value of he nomnal dmenson whle s sandard devaon σ s a hrd of he olerance. For a complee represenaon of he connecor he same procedure s appled for he dmensons of he fbers and for he dmensons of he sysem ha algns he wo ferrules. The dsrbuon of errors beween he wo ferrules may be measured epermenally or predced usng anoher geomerc/varaon model. The laeral msalgnmen d for he h par of fbers s modeled as a vecor n he z-plane. I s possble o defne s coordnaes ( d z ) d as a funcon of he dmensons of he vee-grooves he fbers and he algnng sysem as shown n Eqs ()- (3). = f ξ ξ ξ... ξ () z d d (... n ) ( ξ ξ ξ... ξ ) = f z... n (3) n beng he oal number of assgned dmensons whn he connecor. Accordng o he law of error propagaon f he dmensons are ndependen (whch s a reasonable assumpon for mos applcaons) hen he sandard devaon σ of he laeral msalgnmen n he - d drecon s gven by Eq (4). A smlar equaon gves he laeral msalgnmen n he z-drecon. d n = f σ ξ σ (4) For perfec dmensons he msalgnmen equals zero. Hence for random dmensons s varaon s drecly relaed o s sandard devaon. The law of propagaon error hen gves a drec analycal epresson of he varance n laeral msalgnmen as funcon of he varances n he dfferen dmensons of he connecor. Four dmensons L R h and p defne he geomery of a vee-groove. Snce he curren sudy analyzes he sensvy of he laeral msalgnmen o he geomery of he vee-grooves he sandard devaons of he componens n he and z drecons for he laeral msalgnmen are epressed as shown n Eqs (5)-(6):

4 σ d f + h σ zd f + h f σ L z σ f h z σ L h L f + p σ L f + f + p z σ R p f + σ z p σ R + Cons R σ R + Cons z (5) (6) where he consan erms are due o he varaons n he dmensons of he fbers and he algnng sysem. Eqs (5)-(6) reurn he varances of he componens n he and z drecons for he laeral msalgnmen bu Marcuse s model and Eq () requre he magnude of he msalgnmen d. Is value could be epressed wh a on probably dsrbuon for d and z d bu an unknown correlaon coeffcen beween he wo componens compromses he accuracy of he calculaon. Therefore a Mone Carlo smulaon of he connecor s used o deermne an emprcal relaon beween he connecon loss and he sandard devaons of and z by a wo-sep process. d d The frs sep consss n collecng daa from he Mone Carlo smulaon. Is npus are he nomnal values and he olerances of he dfferen dmensons defnng he geomery of a connecor. A large number of connecors are vrually generaed usng he mahemacal model prevously presened. Ther dmensons are normally dsrbued wh a mean equal o her nomnal value and a sandard devaon equal o one hrd of her olerance. The algorhm calculaes he msalgnmen of each randomly generaed sample hen performs a sascal reamen on he colleced resuls. Fnally reurns he sandard devaon of he componens n and z of he laeral msalgnmen as well as a cumulave dsrbuon funcon (cdf) of he connecon loss (n db) for every par of fbers as shown n Fg 5. Fg 5: Oupus of Mone Carlo Smulaon Every cdf s curve fed wh a wo-varable connuous funcon. Snce he olerance analyss focuses on he hghes par of he cdf (beyond 90%) he curve fng s performed eclusvely on hs par of he cdf n order o ge more relable appromaons. I has been found ha for snglemode fbers he cdf of a Webull random varable s a good appromaon whle a Gamma ncomplee funcon fs well he cdf of he mul-mode fbers. The smulaon s run many mes wh dfferen npu olerances. The resulng cdf s are reduced o wo parameers defnng he fed curve. Hence he frs sep of he process reurns a se of values for he wo fng parameers n funcon of he sandard devaons of and z. d d The second sep s a new curve fng procedure. Ths me one of he fng parameers s ploed n funcon of he sandard devaons of d and z d and s curve fed. The resulng relaons are fnally compared o new Mone Carlo smulaons and has appeared ha hey were eremely relable. These funcons are used as emprcal models of he connecon loss. Thereby a varaon analyss based upon he law of propagaon error followed by an emprcal ye accurae model of he connecor performance provdes a relaon beween he connecon loss and he olerances of he vee-grooves. Cos-Tolerance Relaons The cos of a manufacured par depends upon he seleced manufacurng process and dmensonal olerances. The cos of achevng a parcular olerance depends upon boh he dmenson's nomnal value and olerance. The manufacurng cos generally ncreases f he olerance s ghened and

5 s more epensve o make a gven olerance on a large nomnal dmenson. Based on hs Chase [3] recommends epressng olerances as recprocal power funcons for maeral removal processes. Eq (7) epresses he olerance for he h dmenson as a funcon of cos C range R and hree consans a b and c. The values of he hree consans depend upon he range and he manufacurng process. Alhough a consan erm would be necessary for accuracy s praccally mpossble o evaluae and doesn affec he olerance allocaon. a b R = c (7) C Smlar funcons are no avalable for echng processes commonly used wh slcon. Knowng he range and he manufacurng process of every dmenson enables generang he cos-olerance funcons requred o esmae he manufacurng cos of a connecor n funcon of he olerances assgned o s dfferen dmensons. The poron of he oal manufacurng cos ha s arbuable o vee-groove olerancng s hen he sum of he coss for he four dmensons L R h and p as shown n Eq (8). c Cos = c + h h L ah h L al L bh c + b L p c + p a p p R R bp ar R b R (8) can be specfed o preven he opmzaon from drvng he assgned olerances o unreasonably hgh or low values. Snce hs opmzaon only deals wh allocang olerances o he vee-grooves s assumed ha he olerances for he fbers and he sysem ha algns he ferrules s already known emprcally or predced by anoher analyss. The varables of he opmzaon problem are hen he olerances for he four dmensons ( L R h p ) defnng he vee-grooves. The radus of curvaure s no ncluded snce doesn affec he posonng of he fber. Ths mehod was used o allocae olerances o an eemplary connecor. The obecve was o mnmze he manufacurng cos of an 8-fber connecor whle he connecon loss of every par of sngle-mode fbers should be less han 0.5 db. The calculaed connecon losses along he connecors are dsplayed n Fg 6 and he resulng olerances allocaed by he opmzaon procedure are lsed n Table. When olerancng he connecor only one olerance s assgned o a nomnal dmenson even f hs feaure s repeaed several mes n he produc. Tha s he reason why he manufacurng cos depends upon he dmensons of one sngle veegroove nsead of he complee array of grooves. Tolerance Allocaon by Opmzaon Opmal olerances for he dmensons are deermned usng nonlnear consraned opmzaon. The problem s formulaed as a mnmzaon subec o consrans. The funcon o mnmze s he manufacurng cos of he connecor wh respec o s olerances as defned n he prevous secon. Consrans are formulaed by specfyng ha he sandard devaon of he laeral msalgnmen σ d for every par of fbers whn he connecor mus be posve ye below a crcal value. Addonal bounds Fg 6: Compued Losses Table : Compued Tolerances Dmenson Assgned Tolerance Lef Angle radans Rgh Angle radans Deph of he Vere 0.59 mcrons Pch beween Vees mcrons Conclusons Manufacurng cos s a crcal parameer for mass-produced feaures. On he oher hand accurae devces need low varaon n her dmensons. Massproduced mul-fber connecors usng vee-grooves

6 face boh problems. Ther desgn can be opmzed by a olerance allocaon mehod. Ths paper presens a mehod for allocang olerances o he dmensons of he vee-grooves. A mahemacal model of he geomery s consruced and used n a sascal analyss. Applyng he law of error propagaon allows dervaon of a relaon beween dmensonal varance and varance n sgnal loss. Opmal olerances of he vee-grooves are compued by mnmzng he relave manufacurng cos whle respecng consrans on mamum loss accepable for every par of mang fbers. The mehod s demonsraed for an eemplary 8-fber veegroove algnmen sysem. References [] Zanger H. & Zanger C. Fber Opcs Communcaon and Oher Applcaons Macmllan Publshng Company New York 99. [] Marcuse D. Loss Analyss of Sngle-Mode Fber Splces. The Bell Sysem Techncal Journal. May- June 977. pp [3] Chase K. W. Tolerance Allocaon Mehods for Desgners ADCATS Repor No Brgham Young Unversy 999. [4] Rachakonda P. Barraa M. Vallance R.R. Kan S. & Lehman J. D Error Models and Mone Carlo Smulaons for Budgeng Varaon n Opcal-Fber Array Connecors Proceedngs of he ASPE 00 Annual Meeng pp [5] Arenberg J. W. On he Orgns of he Law of Error Propagaon and s Uses Proceedngs of he ASPE 00 Summer Topcal Meeng pp

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

More information

[ ] 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

Chapter 6: AC Circuits

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

More information

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

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

Volatility Interpolation

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

More information

Comb Filters. Comb Filters

Comb Filters. Comb Filters The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of

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

Structural Optimization Using Metamodels

Structural Optimization Using Metamodels Srucural Opmzaon Usng Meamodels 30 Mar. 007 Dep. o Mechancal Engneerng Dong-A Unvers Korea Kwon-Hee Lee Conens. Numercal Opmzaon. Opmzaon Usng Meamodels Impac beam desgn WB Door desgn 3. Robus Opmzaon

More information

Advanced Machine Learning & Perception

Advanced Machine Learning & Perception Advanced Machne Learnng & Percepon Insrucor: Tony Jebara SVM Feaure & Kernel Selecon SVM Eensons Feaure Selecon (Flerng and Wrappng) SVM Feaure Selecon SVM Kernel Selecon SVM Eensons Classfcaon Feaure/Kernel

More information

An introduction to Support Vector Machine

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

More information

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

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

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon

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

Math 128b Project. Jude Yuen

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

More information

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

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

( ) () 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

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

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

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

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

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

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

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

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

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

A Tour of Modeling Techniques

A Tour of Modeling Techniques A Tour of Modelng Technques John Hooker Carnege Mellon Unversy EWO Semnar February 8 Slde Oulne Med neger lnear (MILP) modelng Dsuncve modelng Knapsack modelng Consran programmng models Inegraed Models

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

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

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

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth Should Exac Index umbers have Sandard Errors? Theory and Applcaon o Asan Growh Rober C. Feensra Marshall B. Rensdorf ovember 003 Proof of Proposon APPEDIX () Frs, we wll derve he convenonal Sao-Vara prce

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

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

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

Panel Data Regression Models

Panel Data Regression Models Panel Daa Regresson Models Wha s Panel Daa? () Mulple dmensoned Dmensons, e.g., cross-secon and me node-o-node (c) Pongsa Pornchawseskul, Faculy of Economcs, Chulalongkorn Unversy (c) Pongsa Pornchawseskul,

More information

Planar truss bridge optimization by dynamic programming and linear programming

Planar truss bridge optimization by dynamic programming and linear programming IABSE-JSCE Jon Conference on Advances n Brdge Engneerng-III, Augus 1-, 015, Dhaka, Bangladesh. ISBN: 978-984-33-9313-5 Amn, Oku, Bhuyan, Ueda (eds.) www.abse-bd.org Planar russ brdge opmzaon by dynamc

More information

Motion in Two Dimensions

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

More information

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

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

Machine Learning Linear Regression

Machine Learning Linear Regression Machne Learnng Lnear Regresson Lesson 3 Lnear Regresson Bascs of Regresson Leas Squares esmaon Polynomal Regresson Bass funcons Regresson model Regularzed Regresson Sascal Regresson Mamum Lkelhood (ML)

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

10. A.C CIRCUITS. Theoretically current grows to maximum value after infinite time. But practically it grows to maximum after 5τ. Decay of current :

10. A.C CIRCUITS. Theoretically current grows to maximum value after infinite time. But practically it grows to maximum after 5τ. Decay of current : . A. IUITS Synopss : GOWTH OF UNT IN IUIT : d. When swch S s closed a =; = d. A me, curren = e 3. The consan / has dmensons of me and s called he nducve me consan ( τ ) of he crcu. 4. = τ; =.63, n one

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

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

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

Lecture 11 SVM cont

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

More information

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

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

More information

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

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

Computing Relevance, Similarity: The Vector Space Model

Computing Relevance, Similarity: The Vector Space Model Compung Relevance, Smlary: The Vecor Space Model Based on Larson and Hears s sldes a UC-Bereley hp://.sms.bereley.edu/courses/s0/f00/ aabase Managemen Sysems, R. Ramarshnan ocumen Vecors v ocumens are

More information

Bayesian Inference of the GARCH model with Rational Errors

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

More information

CHAPTER 5: MULTIVARIATE METHODS

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

More information

Introduction to Boosting

Introduction to Boosting Inroducon o Boosng Cynha Rudn PACM, Prnceon Unversy Advsors Ingrd Daubeches and Rober Schapre Say you have a daabase of news arcles, +, +, -, -, +, +, -, -, +, +, -, -, +, +, -, + where arcles are labeled

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

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

A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION

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

More information

[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

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

How about the more general "linear" scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )?

How about the more general linear scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )? lmcd Lnear ransformaon of a vecor he deas presened here are que general hey go beyond he radonal mar-vecor ype seen n lnear algebra Furhermore, hey do no deal wh bass and are equally vald for any se of

More information

Attribute Reduction Algorithm Based on Discernibility Matrix with Algebraic Method GAO Jing1,a, Ma Hui1, Han Zhidong2,b

Attribute Reduction Algorithm Based on Discernibility Matrix with Algebraic Method GAO Jing1,a, Ma Hui1, Han Zhidong2,b Inernaonal Indusral Informacs and Compuer Engneerng Conference (IIICEC 05) Arbue educon Algorhm Based on Dscernbly Marx wh Algebrac Mehod GAO Jng,a, Ma Hu, Han Zhdong,b Informaon School, Capal Unversy

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

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

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

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL

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

More information

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

MANY real-world applications (e.g. production

MANY real-world applications (e.g. production Barebones Parcle Swarm for Ineger Programmng Problems Mahamed G. H. Omran, Andres Engelbrech and Ayed Salman Absrac The performance of wo recen varans of Parcle Swarm Opmzaon (PSO) when appled o Ineger

More information

Stochastic Maxwell Equations in Photonic Crystal Modeling and Simulations

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

More information

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

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

Polymerization Technology Laboratory Course

Polymerization Technology Laboratory Course Prakkum Polymer Scence/Polymersaonsechnk Versuch Resdence Tme Dsrbuon Polymerzaon Technology Laboraory Course Resdence Tme Dsrbuon of Chemcal Reacors If molecules or elemens of a flud are akng dfferen

More information

Chapter 5. Circuit Theorems

Chapter 5. Circuit Theorems Chaper 5 Crcu Theorems Source Transformaons eplace a olage source and seres ressor by a curren and parallel ressor Fgure 5.-1 (a) A nondeal olage source. (b) A nondeal curren source. (c) Crcu B-conneced

More information

PHYS 1443 Section 001 Lecture #4

PHYS 1443 Section 001 Lecture #4 PHYS 1443 Secon 001 Lecure #4 Monda, June 5, 006 Moon n Two Dmensons Moon under consan acceleraon Projecle Moon Mamum ranges and heghs Reerence Frames and relae moon Newon s Laws o Moon Force Newon s Law

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 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

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs

More information

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

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

More information

CS 268: Packet Scheduling

CS 268: Packet Scheduling Pace Schedulng Decde when and wha pace o send on oupu ln - Usually mplemened a oupu nerface CS 68: Pace Schedulng flow Ion Soca March 9, 004 Classfer flow flow n Buffer managemen Scheduler soca@cs.bereley.edu

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

Normal Random Variable and its discriminant functions

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

More information

. 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

Decentralised Sliding Mode Load Frequency Control for an Interconnected Power System with Uncertainties and Nonlinearities

Decentralised Sliding Mode Load Frequency Control for an Interconnected Power System with Uncertainties and Nonlinearities Inernaonal Research Journal of Engneerng and echnology IRJE e-iss: 2395-0056 Volume: 03 Issue: 12 Dec -2016 www.re.ne p-iss: 2395-0072 Decenralsed Sldng Mode Load Frequency Conrol for an Inerconneced Power

More information