Multi-load Optimal Design of Burner-inner-liner Under Performance Index Constraint by Second-Order Polynomial Taylor Series Method

Size: px
Start display at page:

Download "Multi-load Optimal Design of Burner-inner-liner Under Performance Index Constraint by Second-Order Polynomial Taylor Series Method"

Transcription

1 , 0005 (06) DOI: 0.05/ mecconf/ ICMI 06 Mul-lod Opml Desgn of Burner-nner-lner Under Performnce Index Consrn by Second-Order Polynoml ylor Seres Mehod U Goqo, Wong Chun Nm, Zheng Mn nd ng Kongzheng Lnzhou Unversy of echnology, School of Mechncl nd Elecroncl Engneerng, Lnzhou, Gnsu 70050, Chn Absrc. Usng mxmum expnson pressure of n-decne, he eroengne burner-nner-lner combuson pressure lod s compued. Aerodynmc lods re obned from nernl gs pressure lod nd gs momenum. Mul-lod second-order ylor seres equons re esblshed usng mul-vrn polynomls nd her sensves. Opml desgns re crred ou usng vrous performnce ndex consrns. When 0.5 o 0.8 recfcons of dfferen desgn vrns re mplemened, hey converge under 50 d-norm dfference ro. Inroducon Accordng o he urbne combuson echnque [], mnennce coss on urbne nd combuson chmber ccoun for 60% of he whole rcrf mnennce. herefore, hgh performnce on he opml desgn, operon relbly nd srucurl sfey re demnded on modern eroengne herml componens. As burner nner lner (BIL) s he mor componen n combuson chmber, becomes one of he mos sgnfcn componens of he eroengne. BIL s mellc hn-wll cylnder h conrols he combuson, mxng nd coolng processes. I gudes he combuson chmber cylnder nd roors from herml combuson producs []. rom he mnennce survey [], BIL ccouns for he 6% of he combuson chmber fuls. Combuson chmber cylnder s 5% nd fuel nozzle s %. Snce performnce ndces re deermned by BIL mnly, becomes he reserch focus on cnnulr combuson chmber desgn. In he operon process of BIL, complced pressure lods occurs whch cn be clssfed s follows: ) Due o gs combuson, expnson pressure s exered on s cylnder; ) Pressure lod s genered by he pressure dfferen beween he nner nd ouer cylnder surfces. Mos rdonl opmzon mehods wor on unque lod only one dscplnry nlyss s nvolved. o mprove he performnce of BIL under hese lods n dfferen dscplnry, mul-lod opmzon echnque s mplemened n hs desgn. Mul-lod opmzon echnque llows he mulple responses obecve funcons o be opmzed smulneously under he coupled desgn vrns. hs mehod cers for he complced nercons mong dfferen dscplnry, nd he requremens of vrous performnce ndces. Inequly consrns re mposed by hese ndces. Expnson pressure lod creed n fuel combuson he chnges n recn, produc nd composon mole frcon of n-decne nd von fuel premxed combuson flme re bsclly conssen s ndced by Zeng[]. Alhough he von fuel conns complced ngredens, n-decne cn be used for numercl smulon s lernve of von fuel. Dels of hs proposed recon mechnsm cn descrbe deled dynmc chrcerscs of n-decne premxed combuson. Becuse percenges of C nd H whn hexne hydrogen clsses re he sme, one cn nfer s complee combuson produc proporons re he sme. As resul, n-decne, propne nd cyclohexne possess sme mxmum expnson pressure of 0.86MP. gure. Seconl dmensons n ech rnge. rom hs nown combuson pressure, BIL nsde pressure dsrbuon s obned usng nverse-squre lw P P ( r r) r s n g.. In rnge, r0r 0.6x0.05 y for 0 x r r y for In rnge b, x In rnge c, r r ( y 0.095n( 6)) ( x 0.095) n( 6) 0 he Auhors, publshed by EDP Scences. hs s n open ccess rcle dsrbued under he erms of he Creve Commons Arbuon Lcense.0 (hp://crevecommons.org/lcenses/by/.0/).

2 , 0005 (06) DOI: 0.05/ mecconf/ ICMI x 0.5. Usng he dsrbued pressure for on ech rnge compued bove, s hrus lod s compued by equvlen nodl lods ( sn( ) 0 sn( ) 0 sn( ) 0 sn( ) 0 sn( ) 0 ) dn (),, re he xl ngles of rng,,8 respecvely. n s he pressure lod on rng n. he equvlen nodl lod of rng n. 0 n s Aerodynmc lod genered by gs pressure nd gs momenum Accordng o he erodynmc equon, when he gs psses he desgn model (g. ), ol hrus s he gs momenum dfference beween oule nd nle. Se he gs pressure lod nsde s P. ng he hrus drecon s posve, erodynmc force on BIL s PAP AP. Gs momenum s GV ( V ) 0 n ou wh G 50.7 g / s ou. hus we hve he blnce equon PG( V V ) AP 0 AP V n ou n n ou 50 m/ s, V 600 m/ s ou n wh. Usng he con nd recon rule, hrus lod due o erodynmc force s P. Subsung ll compued prs, one hs G( V V ) P ( A ( S S sn )) P A dn () n ou n 0 ou s he gs hole re, Sn s surfce re of rng n. herefore he ol hrus lod s composed of hese mul-lod s.89 0 dn () gure. Desgn model of BIL. Esblshmen of mul-lod opmzon prncple In hs mehod, second-order ylor Seres expnson equon [5] s ulzed o rele he chnges of he mullod responses wh he recfcons of desgn vrns n he desgn model. By nverse compuon, hese recfcons n he desgn vrns re esmed from he BGS lgorhm[6]. he opmzon process s repeed unl specfc ermnon crer re reched. Le m,,, be he -h upded desgn vrn vecor, denoes he fuel nozzle e ngle, gs flow re, fuel combuson pressure nd compressor,,, oule pressure. Menwhle d d d dm s he cul desgn vrn vecor. he seleced ses of n hrus response vlues nd n S hrus response vecors S correspondng o he upded nd cul srucures re denoed by Y A nd Y D respecvely,,, ns Y A, n, S,, S,, S n ns Y D d, d,, d, S d, Sd,, Sd, nd,. In he hrus lod sysem equon, re hrus force due o he fuel combuson, flow re nd r pressure. S re dsrbued sress due o expnson pressure nd dsrbued r pressure on he cylnder surfce. In generl, one cn use second-order ylor seres expnson[7] o obn he chnge of he -h hrus response vecor due o chnge n desgn vrn (,,..., m): m m m S S S ds! () () S s he resdul error vecor n s expnson. or he p-h order expnson of he -h hrus response vlue, one ges m m m d!, (5) n whch s he resdul error vlue n he expnson. ermnon creron s esblshed o conrol he ccurcy of erve process. or he opml desgn of BIL, he obecve funcon s me when he sum of hrus response resdul errors drop o he globl mnmum. As resul, he d-norm d dfference ro s chosen s he

3 , 0005 (06) DOI: 0.05/ mecconf/ ICMI 06 ermnon ndcor,.e. d d d d Bsed on he m ordered ses, one cn nerpole m-vre hus response vecor polynoml funcon s n ns s s d d Sd S, s. (6) d K K m m m S S L L Lm m m L s he Lgrnge fcor funcon of he h desgn vrn he h nerpoled sffness vlue, gven by L (7) (,,, K ) (8) Menwhle, s hus response vlue polynoml funcon s K K m m m (9) L L L m m m rs-order dervve erms cn be obned by drec dervve on Eqs. (7), (9) wh respec o. or mulple vrns recfcon, he frs-order hus S response vecor nd vlue dervves [7] cn be expressed s L K K m m S m L Lm m m (0) K K L L L m m L K K. On he oher hnd, he second-order dervve consss of wo erms nmely he repeed dfferenl nd he unrepeed dfferenl. or he unrepeed erm, s m m m m () gven by he drec frs-order dervve of Eqs. 7,9 ccordng o nvolved. Specl cre s gven o he repeed erms drec second-order dervve wh respec o re encounered. or mulple vrns, he second-order hus response vecor nd vlue dervves [7] re expressd s K K L L m m m m m S m K K m m S m L L m m m S L L L K K L L m m m m m m K K L m m L m L m m m L L () ()

4 , 0005 (06) DOI: 0.05/ mecconf/ ICMI 06 L K K K m m m m m Subsung Eqs. (0), () no Eq. (), second-order ylor seres expnson of hus response vecor s furnshed. Moreover, second-order hus response vlue equon of Eq. (5) s compleed by Eqs. (), (). 5 Normlzon of ylor seres equon usng ccurcy number Here we crry ou he normlzon of he opmzon equons. or he response vecors, we reduce he d precson s smll s possble, whle no ffecng s vron chrcerscs. We mulply he equons n ech dscplne by ccurcy numbers, so h hey re normlzed o he sme precson level. In hs cse, he compuon problems such s he sngulry, ccurcy nd convergency cn be voded. or exmple, ccurces of he dsrbued sress nd pressure re nd 0 respecvely, hen he normlzon ccurcy numbers 9 6 re [0,0 ]. Under hs remen, fser nd more ccure convergences re ned. When wo desgn vrns, gs flow re nd compressor oule pressure re recfed n 0.5 level, her opmzon nd convergence processes re recorded s follows. rom g., he mnmzon of d s fser up o esmon 0. I becomes slower up o esmon 80, nd sble ferwrds. ermnon creron s me esmon 9 he drops o.8 0. uel nozzle e ngle (rd) gure. Esmon pern of under 0.5 recfcon d Esmon Number 6 Opml desgn of BIL under performnce ndex consrn In hs opmzon pproch, nequly consrns re mposed usng vrous performnce ndces. Specfc hrus s consrned n he rnge 60 G80dNs g consumpon ; specfc fuel 0.8 C 600 f S.0 g ( hdn) f 0.08 ; hrus wegh ro s.5 w= Wg.0 nd compressor oule pressure ro s.5 P= P P.5 P s he mospherc pressure. m 5.00 d-norm dfference ro gure. Convergence of nd. r ou m Esmon Number s d under 0.5 recfcon of Usng dfferen combnons of one o four, vrous 0.5 o 0.8 recfcon cses re esblshed. rom g., rpd dusmen of occurs up o esmon 0. hen becomes sble ferwrds. As s recfcon s relvely smll, wve-le pern hvng pe nd vlley s observed. Gs flow re (g/s) Esmon Number gure 5. Esmon pern of under 0.5 recfcon. Esmon process of s llusred n g.5. Smlr o he pern of d, ncreses rpdly up o esmon 0. Aferwrds, chnges grdully unl ermnon creron s me esmon 9 s percenge error s consrned by he performnce ndces.%. rom he esmon pern of, s rpd dusmen occurs up o esmon 0. hen becomes sble ferwrds. No he sme s, s recfcon cycle s shorer ledng o less sgnfcn pe nd vlley feures. Cler cu pern s observed s n g. 6. As s nomnl vlue s relvely lrge mong he oher vrns, s prory s hgh n he pern. hus s opmzon s

5 , 0005 (06) DOI: 0.05/ mecconf/ ICMI 06 rpd nd ccure. Usng 0.5 recfcon for sme desgn vrns, 9 esmons re needed. hus, he convergence re s slghly decresed for smller recfcon level. Compressor oule pressure (P) gure 6. Esmon pern of under 0.5 recfcon. When hree desgn vrns,, (m=) re recfed n 0.8 level, her opmzon nd convergence processes re recorded s follows. d-norm dfference ro. gure 7. Convergence of of,,. rom g. 7, d under 0.8 recfcon d s rmped up o he pe 5.5 esmon. hen drops rpdly o.6 nd mnmzes grdully ferwrds. ermnon creron s me esmon 78 drops o.8 0. uel nozzle e ngle (rd).60e+05.0e E+0.00E E Esmon Number Esmon Number d Esmon Number gure 8. Esmon pern of under 0.8 recfcon. Esmon process of s llusred n g. 8. Inlly, rmps up rpdly o 7.9rd esmon. Aferwrds, ncreses grdully o he +0.8 recfcon level wh.7 0 % devon. Esmon process of s llusred n g. 9. I ncreses from esmon grdully ner +0.8 recfcon level of 89.g/s s consrned by he performnce ndces.08%. As recfcon s zero, wve-le pern hvng lrge pe nd vlley of 6 mpludes up o 7.50 P s observed n he nl sge. hen drops grdully o he nl level wh % devon. Gs flow re (g/s) gure 9. Esmon pern of under 0.8 recfcon. Compressor oule pressure (P) E+05.E E+0 6.0E+0.0E+0 gure 0. Esmon pern of under 0.8 recfcon. 5 rom g. 0 drops rpdly from.60 P o mnmum level of.6 0. I remns sonry unl esmon 7, nd ncreses grdully o he -0.8 recfcon level wh. 0 % devon. Usng sme desgn vrns wh 0.5 recfcon, 87 esmons re requred. herefore, he convergence re s lower for lrger recfcon level. When m=, 97 esmons re needed n boh 0.5 nd 0.8 recfcon cses. or m=, here s 69 esmons n 0.5 recfcon cse. In he 0.8 recfcon, esmons re requred. When recfcon level s lrger, convergence re s decresed. Menwhle, for he ncrese n m, he convergence re s ncresed. or he llusred cses, m=,, her convergence res follow hese rends. Summry Esmon Number 0.0E Esmon Number BIL combuson pressure lod s clculed o be dn usng 0.86MP n-decne expnson pressure. Aerodynmc lod s obned s dn. Usng developed equons, 0.5 o 0.8 recfcons of one o four desgn vrns consrned by vrous performnce ndces converge 50 d under creron. In generl, lrger recfcon level leds o slower convergence. hs rend s more sgnfcn for lrger m. When m ncreses, s convergence becomes fser. 5

6 , 0005 (06) DOI: 0.05/ mecconf/ ICMI 06 Acnowledgemens hs reserch s suppored by he Nonl Nurl Scence oundon of Chn under grn numbers nd References. X. Hou, H. J,. Lu, e l., Hgh performnce von gs urbne combuson echnque. Peng: Mlrry Defence Indusrl Press, (00), pp Y. Wng, Aeroengne Prncple. Peng: Behng Unversy Press, (009), pp S. Peng, Aeroengne Combuson Chmber Srucure. Peng: Mlrry Defence Indusrl Press, (978), pp W. Zeng, J. Lu, X. Chen, M. Je, Deled recon nec modelng of n-decne premxed combuson, Journl of Aerospce Power, 6(0): (0) 5. C.N. Wong, A.A. Brhors, Polynoml nerpoled ylor seres mehod for prmeer denfcon of nonlner dynmc sysem, ASME Journl of Compuonl nd Nonlner Dynmcs, ():8-56 (006). 6. C.N. Wong, J. Xong, H. Hung,. Hu, Dmge Deecon of Spce russ usng Second Order Polynoml Mehod wh BGS us-newon Opmzon, Proceedngs of he ASME 00 IDEC/CIE, DEC00-809, (00), pp C.N. Wong, J. Xong, H. Hung, Y.J. Zho, A polynoml lgorhm for model updng of engneerng russ, Mechncs Bsed Desgn of Srucures nd Mchnes, 8:-, (00). 6

Advanced Electromechanical Systems (ELE 847)

Advanced Electromechanical Systems (ELE 847) (ELE 847) Dr. Smr ouro-rener Topc 1.4: DC moor speed conrol Torono, 2009 Moor Speed Conrol (open loop conrol) Consder he followng crcu dgrm n V n V bn T1 T 5 T3 V dc r L AA e r f L FF f o V f V cn T 4

More information

Supporting information How to concatenate the local attractors of subnetworks in the HPFP

Supporting information How to concatenate the local attractors of subnetworks in the HPFP n Effcen lgorh for Idenfyng Prry Phenoype rcors of Lrge-Scle Boolen Newor Sng-Mo Choo nd Kwng-Hyun Cho Depren of Mhecs Unversy of Ulsn Ulsn 446 Republc of Kore Depren of Bo nd Brn Engneerng Kore dvnced

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

Electromagnetic Transient Simulation of Large Power Transformer Internal Fault

Electromagnetic Transient Simulation of Large Power Transformer Internal Fault Inernonl Conference on Advnces n Energy nd Envronmenl Scence (ICAEES 5) Elecromgnec Trnsen Smulon of rge Power Trnsformer Inernl Ful Jun u,, Shwu Xo,, Qngsen Sun,c, Huxng Wng,d nd e Yng,e School of Elecrcl

More information

Research Article Oscillatory Criteria for Higher Order Functional Differential Equations with Damping

Research Article Oscillatory Criteria for Higher Order Functional Differential Equations with Damping Journl of Funcon Spces nd Applcons Volume 2013, Arcle ID 968356, 5 pges hp://dx.do.org/10.1155/2013/968356 Reserch Arcle Oscllory Crer for Hgher Order Funconl Dfferenl Equons wh Dmpng Pegung Wng 1 nd H

More information

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions II The Z Trnsfor Tocs o e covered. Inroducon. The Z rnsfor 3. Z rnsfors of eleenry funcons 4. Proeres nd Theory of rnsfor 5. The nverse rnsfor 6. Z rnsfor for solvng dfference equons II. Inroducon The

More information

MODEL SOLUTIONS TO IIT JEE ADVANCED 2014

MODEL SOLUTIONS TO IIT JEE ADVANCED 2014 MODEL SOLUTIONS TO IIT JEE ADVANCED Pper II Code PART I 6 7 8 9 B A A C D B D C C B 6 C B D D C A 7 8 9 C A B D. Rhc(Z ). Cu M. ZM Secon I K Z 8 Cu hc W mu hc 8 W + KE hc W + KE W + KE W + KE W + KE (KE

More information

Origin Destination Transportation Models: Methods

Origin Destination Transportation Models: Methods In Jr. of Mhemcl Scences & Applcons Vol. 2, No. 2, My 2012 Copyrgh Mnd Reder Publcons ISSN No: 2230-9888 www.journlshub.com Orgn Desnon rnsporon Models: Mehods Jyo Gup nd 1 N H. Shh Deprmen of Mhemcs,

More information

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files) . Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.

More information

Privacy-Preserving Bayesian Network Parameter Learning

Privacy-Preserving Bayesian Network Parameter Learning 4h WSEAS In. Conf. on COMUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS nd CYBERNETICS Mm, Flord, USA, November 7-9, 005 pp46-5) rvcy-reservng Byesn Nework rmeer Lernng JIANJIE MA. SIVAUMAR School of EECS,

More information

Numerical Simulations of Femtosecond Pulse. Propagation in Photonic Crystal Fibers. Comparative Study of the S-SSFM and RK4IP

Numerical Simulations of Femtosecond Pulse. Propagation in Photonic Crystal Fibers. Comparative Study of the S-SSFM and RK4IP Appled Mhemcl Scences Vol. 6 1 no. 117 5841 585 Numercl Smulons of Femosecond Pulse Propgon n Phoonc Crysl Fbers Comprve Sudy of he S-SSFM nd RK4IP Mourd Mhboub Scences Fculy Unversy of Tlemcen BP.119

More information

Hidden Markov Model. a ij. Observation : O1,O2,... States in time : q1, q2,... All states : s1, s2,..., sn

Hidden Markov Model. a ij. Observation : O1,O2,... States in time : q1, q2,... All states : s1, s2,..., sn Hdden Mrkov Model S S servon : 2... Ses n me : 2... All ses : s s2... s 2 3 2 3 2 Hdden Mrkov Model Con d Dscree Mrkov Model 2 z k s s s s s s Degree Mrkov Model Hdden Mrkov Model Con d : rnson roly from

More information

e t dt e t dt = lim e t dt T (1 e T ) = 1

e t dt e t dt = lim e t dt T (1 e T ) = 1 Improper Inegrls There re wo ypes of improper inegrls - hose wih infinie limis of inegrion, nd hose wih inegrnds h pproch some poin wihin he limis of inegrion. Firs we will consider inegrls wih infinie

More information

Jordan Journal of Physics

Jordan Journal of Physics Volume, Number, 00. pp. 47-54 RTICLE Jordn Journl of Physcs Frconl Cnoncl Qunzon of he Free Elecromgnec Lgrngn ensy E. K. Jrd, R. S. w b nd J. M. Khlfeh eprmen of Physcs, Unversy of Jordn, 94 mmn, Jordn.

More information

An improved statistical disclosure attack

An improved statistical disclosure attack In J Grnulr Compung, Rough Ses nd Inellgen Sysems, Vol X, No Y, xxxx An mproved sscl dsclosure c Bn Tng* Deprmen of Compuer Scence, Clforn Se Unversy Domnguez Hlls, Crson, CA, USA Eml: bng@csudhedu *Correspondng

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

ANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES DATA

ANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES DATA Tn Corn DOSESCU Ph D Dre Cner Chrsn Unversy Buchres Consnn RAISCHI PhD Depren of Mhecs The Buchres Acdey of Econoc Sudes ANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES

More information

Simplified Variance Estimation for Three-Stage Random Sampling

Simplified Variance Estimation for Three-Stage Random Sampling Deprmen of ppled Sscs Johnnes Kepler Unversy Lnz IFS Reserch Pper Seres 04-67 Smplfed rnce Esmon for Three-Sge Rndom Smplng ndres Quember Ocober 04 Smplfed rnce Esmon for Three-Sge Rndom Smplng ndres Quember

More information

To Possibilities of Solution of Differential Equation of Logistic Function

To Possibilities of Solution of Differential Equation of Logistic Function Arnold Dávd, Frnše Peller, Rená Vooroosová To Possbles of Soluon of Dfferenl Equon of Logsc Funcon Arcle Info: Receved 6 My Acceped June UDC 7 Recommended con: Dávd, A., Peller, F., Vooroosová, R. ().

More information

MODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB

MODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB MODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB P. Florn, P. Vrání, R. Čermá Fculy of Mechncl Engneerng, Unversy of Wes Bohem Asrc The frs pr of hs pper s devoed o mhemcl modellng

More information

Torsion, Thermal Effects and Indeterminacy

Torsion, Thermal Effects and Indeterminacy ENDS Note Set 7 F007bn orson, herml Effects nd Indetermncy Deformton n orsonlly Loded Members Ax-symmetrc cross sectons subjected to xl moment or torque wll remn plne nd undstorted. At secton, nternl torque

More information

THE EXISTENCE OF SOLUTIONS FOR A CLASS OF IMPULSIVE FRACTIONAL Q-DIFFERENCE EQUATIONS

THE EXISTENCE OF SOLUTIONS FOR A CLASS OF IMPULSIVE FRACTIONAL Q-DIFFERENCE EQUATIONS Europen Journl of Mhemcs nd Compuer Scence Vol 4 No, 7 SSN 59-995 THE EXSTENCE OF SOLUTONS FOR A CLASS OF MPULSVE FRACTONAL Q-DFFERENCE EQUATONS Shuyun Wn, Yu Tng, Q GE Deprmen of Mhemcs, Ynbn Unversy,

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter 0.04 Newton-Rphson Method o Solvng Nonlner Equton Ater redng ths chpter, you should be ble to:. derve the Newton-Rphson method ormul,. develop the lgorthm o the Newton-Rphson method,. use the Newton-Rphson

More information

Optimization of Pollution Emission in Power Dispatch including Renewable Energy and Energy Storage

Optimization of Pollution Emission in Power Dispatch including Renewable Energy and Energy Storage eserch Journl of Appled cences, Engneerng nd Technology (3): 59-556, I: -767 Mxwell cenfc Orgnzon, ubmed: Aprl, Acceped: My, Publshed: ecember, Opmzon of Polluon Emsson n Power spch ncludng enewble Energy

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

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

Introduction. Voice Coil Motors. Introduction - Voice Coil Velocimeter Electromechanical Systems. F = Bli

Introduction. Voice Coil Motors. Introduction - Voice Coil Velocimeter Electromechanical Systems. F = Bli UNIVERSITY O TECHNOLOGY, SYDNEY ACULTY O ENGINEERING 4853 Elecroechncl Syses Voce Col Moors Topcs o cover:.. Mnec Crcus 3. EM n Voce Col 4. orce n Torque 5. Mhecl Moel 6. Perornce Voce cols re wely use

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

Lecture 36. Finite Element Methods

Lecture 36. Finite Element Methods CE 60: Numercl Methods Lecture 36 Fnte Element Methods Course Coordntor: Dr. Suresh A. Krth, Assocte Professor, Deprtment of Cvl Engneerng, IIT Guwht. In the lst clss, we dscussed on the ppromte methods

More information

Solution in semi infinite diffusion couples (error function analysis)

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

More information

Economic and Environmental Dispatch at Highly Potential Renewable Area with Renewable Storage

Economic and Environmental Dispatch at Highly Potential Renewable Area with Renewable Storage Inernonl Journl of Envronmenl Scence nd evelopmen, Vol. 3, No. 2, Aprl 202 Economc nd Envronmenl spch Hghly oenl Reneble Are h Reneble Sorge F. R. zher, M. F. Ohmn, N. H. Mlk, nd Sfoor O. K. Absrc Economc/Envronmenl

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

4.8 Improper Integrals

4.8 Improper Integrals 4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls

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

September 20 Homework Solutions

September 20 Homework Solutions College of Engineering nd Compuer Science Mechnicl Engineering Deprmen Mechnicl Engineering A Seminr in Engineering Anlysis Fll 7 Number 66 Insrucor: Lrry Creo Sepember Homework Soluions Find he specrum

More information

1.B Appendix to Chapter 1

1.B Appendix to Chapter 1 Secon.B.B Append o Chper.B. The Ordnr Clcl Here re led ome mporn concep rom he ordnr clcl. The Dervve Conder ncon o one ndependen vrble. The dervve o dened b d d lm lm.b. where he ncremen n de o n ncremen

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

Background and Motivation: Importance of Pressure Measurements

Background and Motivation: Importance of Pressure Measurements Imornce of Pressre Mesremens: Pressre s rmry concern for mny engneerng lcons e.g. lf nd form drg. Cvon : Pressre s of fndmenl mornce n ndersndng nd modelng cvon. Trblence: Velocy-Pressre-Grden ensor whch

More information

Minimum Squared Error

Minimum Squared Error Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > for ll smples y i solve sysem of liner inequliies MSE procedure y i = i for ll smples

More information

Minimum Squared Error

Minimum Squared Error Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > 0 for ll smples y i solve sysem of liner inequliies MSE procedure y i i for ll smples

More information

ISSN 075-7 : (7) 0 007 C ( ), E-l: ssolos@glco FPGA LUT FPGA EM : FPGA, LUT, EM,,, () FPGA (feldprogrble ge rrs) [, ] () [], () [] () [5] [6] FPGA LUT (Look-Up-Tbles) EM (Ebedded Meor locks) [7, 8] LUT

More information

Query Data With Fuzzy Information In Object- Oriented Databases An Approach The Semantic Neighborhood Of Hedge Algebras

Query Data With Fuzzy Information In Object- Oriented Databases An Approach The Semantic Neighborhood Of Hedge Algebras (IJCSIS) Inernonl Journl of Compuer Scence nd Informon Secury, Vol 9, No 5, My 20 Query D Wh Fuzzy Informon In Obec- Orened Dbses An Approch The Semnc Neghborhood Of edge Algebrs Don Vn Thng Kore-VeNm

More information

INVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES

INVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES Brodogrdnj/Shpuldng Volume 65 Numer 3, 214 Ferd Ckc Muhsn Aydn ISSN 7-215X eissn 1845-5859 INVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES UDC 629.5(5) Professonl pper Summry

More information

Introduction. Section 9: HIGHER ORDER TWO DIMENSIONAL SHAPE FUNCTIONS

Introduction. Section 9: HIGHER ORDER TWO DIMENSIONAL SHAPE FUNCTIONS Secon 9: HIGHER ORDER TWO DIMESIO SHPE FUCTIOS Inroducon We ne conder hpe funcon for hgher order eleen. To do h n n orderl fhon we nroduce he concep of re coordne. Conder ere of rngulr eleen depced n he

More information

FINANCIAL ECONOMETRICS

FINANCIAL ECONOMETRICS FINANCIAL ECONOMETRICS SPRING 07 WEEK IV NONLINEAR MODELS Prof. Dr. Burç ÜLENGİN Nonlner NONLINEARITY EXISTS IN FINANCIAL TIME SERIES ESPECIALLY IN VOLATILITY AND HIGH FREQUENCY DATA LINEAR MODEL IS DEFINED

More information

Reinforcement Learning for a New Piano Mover s Problem

Reinforcement Learning for a New Piano Mover s Problem Renforcemen Lernng for New Pno Mover s Problem Yuko ISHIWAKA Hkode Nonl College of Technology, Hkode, Hokkdo, Jpn Tomohro YOSHIDA Murorn Insue of Technology, Murorn, Hokkdo, Jpn nd Yuknor KAKAZU Reserch

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

Lecture 4: Trunking Theory and Grade of Service (GOS)

Lecture 4: Trunking Theory and Grade of Service (GOS) Lecure 4: Trunkng Theory nd Grde of Servce GOS 4.. Mn Problems nd Defnons n Trunkng nd GOS Mn Problems n Subscrber Servce: lmed rdo specrum # of chnnels; mny users. Prncple of Servce: Defnon: Serve user

More information

Power Series Solutions for Nonlinear Systems. of Partial Differential Equations

Power Series Solutions for Nonlinear Systems. of Partial Differential Equations Appled Mhemcl Scences, Vol. 6, 1, no. 14, 5147-5159 Power Seres Soluons for Nonlner Sysems of Prl Dfferenl Equons Amen S. Nuser Jordn Unversy of Scence nd Technology P. O. Bo 33, Irbd, 11, Jordn nuser@us.edu.o

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

Acoustic and flexural wave energy conservation for a thin plate in a fluid

Acoustic and flexural wave energy conservation for a thin plate in a fluid cousc nd fleurl wve energy conservon for hn ple n flud rryl MCMHON 1 Mrme vson efence Scence nd Technology Orgnson HMS Srlng W usrl STRCT lhough he equons of fleurl wve moon for hn ple n vcuum nd flud

More information

Review: Transformations. Transformations - Viewing. Transformations - Modeling. world CAMERA OBJECT WORLD CSE 681 CSE 681 CSE 681 CSE 681

Review: Transformations. Transformations - Viewing. Transformations - Modeling. world CAMERA OBJECT WORLD CSE 681 CSE 681 CSE 681 CSE 681 Revew: Trnsforons Trnsforons Modelng rnsforons buld cople odels b posonng (rnsforng sple coponens relve o ech oher ewng rnsforons plcng vrul cer n he world rnsforon fro world coordnes o cer coordnes Perspecve

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

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

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

[ ] 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

Rotations.

Rotations. oons j.lbb@phscs.o.c.uk To s summ Fmes of efeence Invnce une nsfomons oon of wve funcon: -funcons Eule s ngles Emple: e e - - Angul momenum s oon geneo Genec nslons n Noehe s heoem Fmes of efeence Conse

More information

Parameter estimation method using an extended Kalman Filter

Parameter estimation method using an extended Kalman Filter Unvers o Wollongong Reserch Onlne cul o Engneerng nd Inormon cences - Ppers: Pr A cul o Engneerng nd Inormon cences 007 Prmeer esmon mehod usng n eended lmn ler Emmnuel D. Blnchrd Unvers o Wollongong eblnch@uow.edu.u

More information

Developing Communication Strategy for Multi-Agent Systems with Incremental Fuzzy Model

Developing Communication Strategy for Multi-Agent Systems with Incremental Fuzzy Model (IJACSA) Inernonl Journl of Advnced Compuer Scence nd Applcons, Developng Communcon Sregy for Mul-Agen Sysems wh Incremenl Fuzzy Model Sm Hmzeloo, Mnsoor Zolghdr Jhrom Deprmen of Compuer Scence nd Engneerng

More information

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce

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

RL for Large State Spaces: Policy Gradient. Alan Fern

RL for Large State Spaces: Policy Gradient. Alan Fern RL for Lrge Se Spce: Polcy Grden Aln Fern RL v Polcy Grden Serch So fr ll of our RL echnque hve red o lern n ec or pprome uly funcon or Q-funcon Lern opml vlue of beng n e or kng n con from e. Vlue funcon

More information

A Kalman filtering simulation

A Kalman filtering simulation A Klmn filering simulion The performnce of Klmn filering hs been esed on he bsis of wo differen dynmicl models, ssuming eiher moion wih consn elociy or wih consn ccelerion. The former is epeced o beer

More information

6. Gas dynamics. Ideal gases Speed of infinitesimal disturbances in still gas

6. Gas dynamics. Ideal gases Speed of infinitesimal disturbances in still gas 6. Gs dynmics Dr. Gergely Krisóf De. of Fluid echnics, BE Februry, 009. Seed of infiniesiml disurbnces in sill gs dv d, dv d, Coninuiy: ( dv)( ) dv omenum r r heorem: ( ( dv) ) d 3443 4 q m dv d dv llievi

More information

Chapter 2 Linear Mo on

Chapter 2 Linear Mo on Chper Lner M n .1 Aerge Velcy The erge elcy prcle s dened s The erge elcy depends nly n he nl nd he nl psns he prcle. Ths mens h prcle srs rm pn nd reurn bck he sme pn, s dsplcemen, nd s s erge elcy s

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

Motion Feature Extraction Scheme for Content-based Video Retrieval

Motion Feature Extraction Scheme for Content-based Video Retrieval oon Feure Exrcon Scheme for Conen-bsed Vdeo Rerevl Chun Wu *, Yuwen He, L Zho, Yuzhuo Zhong Deprmen of Compuer Scence nd Technology, Tsnghu Unversy, Bejng 100084, Chn ABSTRACT Ths pper proposes he exrcon

More information

Unscented Transformation Unscented Kalman Filter

Unscented Transformation Unscented Kalman Filter Usceed rsformo Usceed Klm Fler Usceed rcle Fler Flerg roblem Geerl roblem Seme where s he se d s he observo Flerg s he problem of sequell esmg he ses (prmeers or hdde vrbles) of ssem s se of observos become

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter.4 Newton-Rphson Method of Solvng Nonlner Equton After redng ths chpter, you should be ble to:. derve the Newton-Rphson method formul,. develop the lgorthm of the Newton-Rphson method,. use the Newton-Rphson

More information

Physics 201 Lecture 2

Physics 201 Lecture 2 Physcs 1 Lecure Lecure Chper.1-. Dene Poson, Dsplcemen & Dsnce Dsngush Tme nd Tme Inerl Dene Velocy (Aerge nd Insnneous), Speed Dene Acceleron Undersnd lgebrclly, hrough ecors, nd grphclly he relonshps

More information

EEM 486: Computer Architecture

EEM 486: Computer Architecture EEM 486: Compuer Archecure Lecure 4 ALU EEM 486 MIPS Arhmec Insrucons R-ype I-ype Insrucon Exmpe Menng Commen dd dd $,$2,$3 $ = $2 + $3 sub sub $,$2,$3 $ = $2 - $3 3 opernds; overfow deeced 3 opernds;

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

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

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

The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems Swiss Federl Insiue of Pge 1 The Finie Elemen Mehod for he Anlysis of Non-Liner nd Dynmic Sysems Prof. Dr. Michel Hvbro Fber Dr. Nebojs Mojsilovic Swiss Federl Insiue of ETH Zurich, Swizerlnd Mehod of

More information

The solution is often represented as a vector: 2xI + 4X2 + 2X3 + 4X4 + 2X5 = 4 2xI + 4X2 + 3X3 + 3X4 + 3X5 = 4. 3xI + 6X2 + 6X3 + 3X4 + 6X5 = 6.

The solution is often represented as a vector: 2xI + 4X2 + 2X3 + 4X4 + 2X5 = 4 2xI + 4X2 + 3X3 + 3X4 + 3X5 = 4. 3xI + 6X2 + 6X3 + 3X4 + 6X5 = 6. [~ o o :- o o ill] i 1. Mrices, Vecors, nd Guss-Jordn Eliminion 1 x y = = - z= The soluion is ofen represened s vecor: n his exmple, he process of eliminion works very smoohly. We cn elimine ll enries

More information

Lecture 4: Piecewise Cubic Interpolation

Lecture 4: Piecewise Cubic Interpolation Lecture notes on Vrtonl nd Approxmte Methods n Appled Mthemtcs - A Perce UBC Lecture 4: Pecewse Cubc Interpolton Compled 6 August 7 In ths lecture we consder pecewse cubc nterpolton n whch cubc polynoml

More information

Active Model Based Predictive Control for Unmanned Helicopter in Full Flight Envelope

Active Model Based Predictive Control for Unmanned Helicopter in Full Flight Envelope he 2 IEEE/RSJ Inernonl Conference on Inellgen Robos nd Sysems Ocober 8-22, 2, pe, wn Acve Model Bsed Predcve Conrol for Unmnned Helcoper n Full Flgh Envelope Dle Song, Junong Q, Jnd Hn, nd Gungjun Lu Absrc-

More information

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC Introducton Rnk One Updte And the Google Mtrx y Al Bernsten Sgnl Scence, LLC www.sgnlscence.net here re two dfferent wys to perform mtrx multplctons. he frst uses dot product formulton nd the second uses

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

4. Eccentric axial loading, cross-section core

4. Eccentric axial loading, cross-section core . Eccentrc xl lodng, cross-secton core Introducton We re strtng to consder more generl cse when the xl force nd bxl bendng ct smultneousl n the cross-secton of the br. B vrtue of Snt-Vennt s prncple we

More information

rank Additionally system of equation only independent atfect Gawp (A) possible ( Alb ) easily process form rang A. Proposition with Definition

rank Additionally system of equation only independent atfect Gawp (A) possible ( Alb ) easily process form rang A. Proposition with Definition Defiion nexivnol numer ler dependen rows mrix sid row Gwp elimion mehod does no fec h numer end process i possile esily red rng fc for mrix form der zz rn rnk wih m dcussion i holds rr o Proposiion ler

More information

Software Reliability Growth Models Incorporating Fault Dependency with Various Debugging Time Lags

Software Reliability Growth Models Incorporating Fault Dependency with Various Debugging Time Lags Sofwre Relbly Growh Models Incorporng Ful Dependency wh Vrous Debuggng Tme Lgs Chn-Yu Hung 1 Chu-T Ln 1 Sy-Yen Kuo Mchel R. Lyu 3 nd Chun-Chng Sue 4 1 Deprmen of Compuer Scence Nonl Tsng Hu Unversy Hsnchu

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

DCDM BUSINESS SCHOOL NUMERICAL METHODS (COS 233-8) Solutions to Assignment 3. x f(x)

DCDM BUSINESS SCHOOL NUMERICAL METHODS (COS 233-8) Solutions to Assignment 3. x f(x) DCDM BUSINESS SCHOOL NUMEICAL METHODS (COS -8) Solutons to Assgnment Queston Consder the followng dt: 5 f() 8 7 5 () Set up dfference tble through fourth dfferences. (b) Wht s the mnmum degree tht n nterpoltng

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

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

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

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

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng

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

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

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

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

The Characterization of Jones Polynomial. for Some Knots

The Characterization of Jones Polynomial. for Some Knots Inernon Mhemc Forum,, 8, no, 9 - The Chrceron of Jones Poynom for Some Knos Mur Cncn Yuuncu Y Ünversy, Fcuy of rs nd Scences Mhemcs Deprmen, 8, n, Turkey m_cencen@yhoocom İsm Yr Non Educon Mnsry, 8, n,

More information

Chapter Runge-Kutta 2nd Order Method for Ordinary Differential Equations

Chapter Runge-Kutta 2nd Order Method for Ordinary Differential Equations Cter. Runge-Kutt nd Order Metod or Ordnr Derentl Eutons Ater redng ts cter ou sould be ble to:. understnd te Runge-Kutt nd order metod or ordnr derentl eutons nd ow to use t to solve roblems. Wt s te Runge-Kutt

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

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

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

Skilled Labor, Economic Transition and Income Inequality

Skilled Labor, Economic Transition and Income Inequality Sklled Lbor, Economc Trnson nd Income Inequly We ZOU; Yong LIU (Insue for Advnced Sudy, Wuhn Unversy, Wuhn, Chn 4372 Absrc: We propose dynmc model of economc rnson n whch he supply consrn of sklled lbor

More information