THE INFLUENCE OF HINDCAST MODELING UNCERTAINTY ON THE PREDICTION OF HIGH RETURN PERIOD WAVE CONDITIONS

Size: px
Start display at page:

Download "THE INFLUENCE OF HINDCAST MODELING UNCERTAINTY ON THE PREDICTION OF HIGH RETURN PERIOD WAVE CONDITIONS"

Transcription

1 Prodngs of OMAE4 3rd Intrntonl Confrn on Offshor Mhns nd Art Engnrng Jun -5, 4, Vnouvr, Brtsh Colub, Cnd OM AE4-56 THE INFLUENCE OF HINDCAST MODELING UNCERTAINTY ON THE PREDICTION OF HIGH RETURN PERIOD WAVE CONDITIONS Dnl C. Brookr Shool of Ol nd Gs Engnrng Unvrsty of Wstrn Austrl Ndlnds, W.A. 697 E-l: Goffry K. Col Shool of Ol nd Gs Engnrng Unvrsty of Wstrn Austrl Ndlnds, W.A. 697 E-l: Json D. MConoh Woodsd Enrgy Ltd Adld Trr Prth, W.A. 6 E-l: json.onoh@woodsd.o.u Kywords: Extr Vlu Anlyss, Dsgn Wv, Hndstng, Unrtnty, Dt Anlyss, Mont-Crlo, Bootstrp, Probblty ABSTRACT Extr vlu nlyss for th prdton of long rturn prod t-on ondtons s oftn bsd upon hndst studs of wnd nd wv ondtons. Th rndo rrors ssotd wth hndst odlng r not usully norportd whn fttng n xtr vlu dstrbuton to hndst dt. In ths ppr, odfd probblty dstrbuton funton s drvd so tht odlng unrtnts n b xpltly nludd n xtr vlu nlyss. Mxu lklhood stton s thn usd to norport hndst unrtnty nto rturn vlu stts nd onfdn ntrvls. Th thod prsntd hr s oprd gnst sulton thnqus for ountng for hndst rrors. Th nflun of rndo rrors wthn odld dtsts on prdtd yr rturn wv stts s dsussd. INTRODUCTION Th stndrd pproh to stton of hgh rturn prod wv hghts s to ft n xtr vlu dstrbuton (.g. Wbull, Gubl, Gnrlsd Prto) to xstng wv dt nd prdt th wv hght for gvn rturn prod usng th fttd dstrbuton. Whn hndst dt s usd nstd of tul surd dt, th s produr for dstrbuton fttng nd rturn vlu prdton s usully usd. Wv hndstng nvolvs so dgr of rror du to splftons/ssuptons n th nlytl odl usd nd surnt rrors n th nput dt. Whlst oprson gnst surd dt s oftn usd for lbrton of hndst odl, th fft of hndst odlng rrors on rturn vlu stts s not lwys onsdrd. An portnt pont to onsdr s tht hndst odl n b lbrtd to gv unbsd stts of wv hght, but stll rsult n bsd rturn vlus du to rndo vrton. Gvn tht hndstng unrtnts r known nd n b quntfd, thr s quston s to whthr thr nglt durng xtr vlu nlyss s pproprt. In ths ppr, thod by whh odlng unrtnty n b xpltly nludd n xtr vlu nlyss s prsntd nd oprd wth rsults fro Mont-Crlo nd bootstrp studs. Ths nw thod s bsd upon th us of odfd probblty dstrbuton funton whh norports both nvronntl nd hndst odlng unrtnty. Mxu lklhood stton of ths odfd dstrbuton s usd to norport hndst unrtnty nto rturn vlu stts nd onfdn ntrvls. Th thod prsntd hr s usd to hk th robustnss of yr rturn wv stts for so typl dtsts subjt to known hndst rror. Copyrght 4 by ASME

2 BACKGROUND Hndstng of t-on dt usng torologl hrts s oonly usd s n ltrntv for drt surnt of wnd nd wv vlus durng stors. Suh hndstng studs typlly show so dgr of bs nd rndo vrton whn oprd gnst surd vlus. Bs s usully lntd by lbrtng hndst odl rsults usng ny vlbl surnts n th r (.g. v rgrsson thods). Th rndo vrton howvr rns; typlly n vrton btwn surnts nd odl prdtons of round. s obsrvd for sgnfnt wv hght vlus [,]. A sttr n of -5% s rprsnttv of odrn hndsts, whr th sttr n (S.I.) s dfnd s: σ SI.. = µ s whr σ s th stndrd dvton of th dffrn btwn surd nd odld vlus nd µ s s th n of th surd wv hghts usd for oprson. Drt surnt of on dt (.g. v wv buoys) lso nvolvs rndo rrors, prtulrly th surnt of wv hghts. Ths rrors r, howvr, gnrlly uh sllr thn thos ssotd wth hndstng nd wll not b onsdrd drtly n ths ppr. An xpl plot oprng surd to odld sgnfnt wv hghts s shown n Fgur. Ths dt hs bn obtnd on Austrl s North Wst Shlf; h pont orrspondng to th xu wv hght obsrvd/prdtd durng pssg of tropl ylon. Vlus hv bn norlzd by th prdtd yr rturn prod sgnfnt wv hght, H. Th vlus shown n Fgur r thos ftr lbrton of th hndst odl v lnr rgrsson... th ndvdul odld vlus r orrtd usng: odl H = ( H b) / odl, orr s = H + b whr th onstnts nd b hv bn dtrnd v lnr rgrsson of hndst prdtons gnst surnts : H odl Aftr orrton, th n rror s qul to zro, hn ny systt bs n th hndst vlus s lntd... n s odl ε = ( H H )/ n = = It would usully b pproprt to st b= to nsur tht th hndst odl n b snsbly xtrpoltd byond th hghst vlbl surnt. Ths hs bn don n ths s. Ths typ of lbrton sh s ost pproprt whn th vlbl surnt dtst s ndqut for dstrbuton fttng nd hndstng s bng usd to sgnfntly xtnd th surnt dtst. For ths dtst, th dstrbuton of th rsduls s odl, orr ε = ( H H ) n b wll pproxtd by th norl (Gussn) dstrbuton nd s tkn to b ndpndnt of wv hght (.. n bsolut rror). By nspton of Fgur, th ssupton of ndpndn pprs rsonbl for ths st of dt, wth th rndo vrton ssotd wth th lrgr stors slr to tht sn for th sllr ons. Slr oprsons usng th s hndst odl n dffrnt lotons n Austrln wtrs dsply low orrlton btwn ε nd wv hght, s dos th publshd dt n rf. [] nd []. It should b notd howvr tht th ssupton of ndpndn of rndo rror y not b tru for ll hndst odls n urrnt us; t y sots b or pproprt to dsrb th rror s funton of wv hght. Modld Wv Hght Msurd Wv Hght Fgur - Coprson of Msurd Sgnfnt Wv Hghts (Norlzd by H ) Agnst Corrtd Hndst Prdtons - Austrln North Wst Shlf Tkng ndpndnt, norlly dstrbutd rrors, w n xprss th hndst unrtnty v th pdf: fε ( ε ) = whr σ s th stndrd dvton of ε ι. ε σ Clbrton of hndst odl to lnt bs dos not nssrly rsult n unbsd xtr vlu prdtons. Th Copyrght 4 by ASME

3 ddton of ny rndo rror to dtst rsults n or hghly skwd prl dstrbuton provdd th rror s sytr (Fgur ). Hn, th fft of usng spl of hndst dt s to nrs th skwnss of th spl nd so ovr-prdt rturn vlus. Ths ppr s prrly onrnd wth thods for sttng th dgr of ovr-stton tht s lkly gvn tht th gntud of th hndst rror n b rsonbly ssssd usng th σ lultd durng lbrton. Tru Wv Populton 3) A rndoly dtrnd rror s ddd to h spl pont, tkn fro th dstrbuton of hndst rror vlus (.. drwn fro norl dstrbuton of zro n nd stndrd dvton σ ). 4) For h spl, nw xtr vlu ft s ondutd, nd rturn prod stts r d. 5) Th n of th rsultnt rturn vlus s thn found An stt of th ount of bs du to hndstng rrors s obtnd by tkng th dffrn btwn ths vlu nd th rturn vlu stt obtnd fro th orgnl dt. Rndo Error Skwd Populton Wv Hght Wv Hght Wv Hght A brodly slr pproh for ssssng unrtnts s dsrbd n [3]. Usd n ths wy, th ont-rlo pproh ould b qully wll dsrbd s prtr bootstrp thod. Anothr bootstrp thod tht ks or drt us of vlbl dt s th rsdul bootstrp thod dsrbd blow. Th Rsdul Bootstrppng Approh Th rsdul bootstrp thod s slr, but ks us of splng fro th tul dt rthr thn fro fttd dstrbutons: ) For h dt pont n th st of lbrtd hndst vlus, dd rndoly drwn rsdul (rror) fro th st of rsduls, ε ι (spld wth rplnt). ) Rpt ths lrg nubr of ts (.g. 5), nd ondut n xtr vlu ft on h dtst to obtn rturn prod stts. 3) Fnd th n of th st of rturn vlus. Fgur - Efft of Hndst Unrtnty on Spl Dstrbutons NUMERICAL SIMULATION METHODS FOR ACCOUNTING FOR MODELING ERROR Two wll stblshd sulton bsd thods for dlng wth probl of ths typ r Mont-Crlo thods nd bootstrppng. Th Mont-Crlo Approh An xpl of th Mont-Crlo pproh to ount for hndst rror s nurl sulton of th followng stps: ) An xtr vlu dstrbuton s fttd to lbrtd hndst dtst of sz N. ) A lrg nubr of spls (.g. ) of sz N r rndoly drwn fro ths fttd dstrbuton. On gn, n stt of th ount of bs du to hndstng rrors s obtnd by tkng th dffrn btwn ths vlu nd th rturn vlu found fro th orgnl dt. Not tht ths thod bootstrps th hndst rrors only, t s lso possbl to bootstrp th orgnl st of wv hghts s wll to ount for unrtnty du to spl sz. Both ths nurl thods r spl to pply, but suffr fro so potntlly srous drwbks whn dlng wth odlng rror: ) In both ss, w r ddng rror to dt tht lrdy ontns rror. W r ssung tht th bs du to ddtonl rror s quvlnt to th nhrnt bs du to usng ontntd hndst dt. Ths y b vld n lot of sttstl ppltons but s dbtbl for us n xtr wv nlyss du to tl snstvty, splly for lrg rrors. 3 Copyrght 4 by ASME

4 ) Extr wv nlyss usully uss th pksovr-thrshold pproh, whh rqurs dt bov gvn thrshold vlu only. Ethr th xdns ovr th thrshold r odld s Prto dstrbutd or truntd for of trdtonl xtrl dstrbuton (.g. Wbull) s usd. Both Mont-Crlo nd bootstrppng sulton thnqus los thr vldty whn onsdrng suh truntd dt - th ddton of ngtv rror to ponts nr th thrshold rsults n loss of dt, rtflly rdung th populton dnsty nr th thrshold pont. A thod by whh ths probls n b ddrssd s by onvolvng th hndst rror nto th xtr vlu dstrbuton usd to ondut th sttstl nlyss. In ths wy th ffts of hndst rror n b xpltly nludd, nd vld stts of bs for both truntd nd untruntd hndst dtsts n b obtnd. Ths pproh s rfrrd to hr s th onvoluton ntgrl pproh. CONVOLUTION INTEGRAL APPROACH If w ssu tht th hndst dt s rndo vrbl Z oprsd of th su of two rndo vrbls X (dstrbuton of th undrlyng wv populton) nd E (hndst odlng unrtnty), th probblty dnsty funton of Z (f Z ) s gvn n trs of th pdfs of X nd E by th onvoluton ntgrl: Z = X + E f () z = f () x f ( zx) Z X E If X nd E r both Gussn, ths ntgrl hs losdfor soluton, othrws ths ntgrl nnot b sply solvd prt fro so spl ss (s for xpl [4]). Th ntgrl s nonthlss nbl to nurl soluton. Mxu lklhood stton of ths onvolutd dstrbuton to st of hndst dt wll norport th hndst unrtnty nto ll rturn vlu stts nd onfdn ntrvls. If thr s vdn tht hndst rror s proportonl to wv hght n ltrntv forulton ould b usd: Z = XE fz() z = f X() x fe( z/ x) In prnpl, ny ssud dstrbuton for th undrlyng wv populton nd ny dsrpton of hndst unrtnty n b usd wth ths thod. In prt, th xu lklhood thod n gv poor stts for so dstrbutons (.g. th Gnrlsd Prto), n ths s th onvoluton thod y not b fsbl. To llustrt th pplton of th onvoluton ntgrl pproh, w wll ssu tht th wv populton dstrbuton s two-prtr Wbull n for nd th hndst odlng rror s ndpndnt nd norlly dstrbutd. by: As dsussd bfor, th hndst rror n b dsrbd f E ( ε ) = ε σ Th undrlyng populton dstrbuton s gvn by th stndrd Wbull forul wth sl prtr nd shp prtr : x x fx ( x) = Aountng for thrshold vlu, t, blow whh dt s not usd, th onvoluton ntgrl bos: f (; z,,) t = Z = ( z x) x x σ ( z x) x x σ t x ( z x) x σ x ( z x) x σ t dz dz Ths n b solvd to rsonbl lvl of ury usng ny nurl ntgrton thod (.g. dptv Spson's rul) ovr so fnt bounds (.g. -). Applton of th xu lklhood stton thod thn nvolvs xzng th loglklhood funton for prtrs nd for th st of dt z..z n = Loglklhood fn. = log fz( z;,, t) n 4 Copyrght 4 by ASME

5 W n us ultvrt nurl xzton sh suh s th Qus-Nwton ln srh thod to solv for th prtrs. Ths produr y b slow, but hs th dvntg tht th Hssn trx of th lklhood funton s pproxtd s prt of th xzton lgorth. Du to th syptot proprts of xu lklhood sttors th (pproxt) Hssn trx n b usd drtly to provd stts of th vrn of th sl nd shp prtrs. Durng th ln srh produr th Hssn s pproxtd s: LogL LogL A LogL LogL Th ovrn trx of th prtrs s thn gvn by: Vr() ˆ Cov(,) ˆ ˆ = Cov(,) ˆˆ Vr () ˆ [ A] Th 'tru' rturn vlu (y) for gvn rturn prod (RP) nd nnul rt of ourrn of wv hghts xdng th thrshold (λ) s found by solvng: y = FX λrp / ˆ ˆ t = ˆ log + λrp ˆ whr F X s th uultv dnsty funton of x. Vrn n th rturn vlu s thn obtnd usng th dlt thod: Vr( ˆ) Cov( ˆ, ˆ) y y y y y y Vr( y) = Cov( ˆ, ˆ) Vr( ˆ) λ λ Vr( ˆ λ) Ths thod s n pproxt on nd should b usd wth dgr of uton, s lulton of th Hssn s pron to nurl rror. An ltrntv pproh s to us th profl lklhood thod to drv onfdn ntrvls. Ths thod s pplbl whn lrg nubr of oprsons btwn th hndst odl nd surnts hv bn d nd th stt of σ n b rsonbly xptd to b urt. If, howvr, thr s srty of Mtrx of sond prtl drvts T nforton vlbl for lbrton of th hndst odl, w n nstd us th vlbl rsdul rrors, ε, drtly n th fttng produr... w xs th lklhood of obtnng th two spls z nd ε j : n Loglklhood fn. = log[ f ( z ;,, t, σ )] = Z ε j + log[ πσ ] j= σ In ths forulton, th hndst stndrd dvton, σ, bos on of th prtrs to b lultd long wth nd. In ths wy, w not only ount for th unrtnty n th odld wv hghts but lso th unrtnty n th ount of rror nvolvd wth hndstng. COMPARISON STUDY USING KODIAK WAVE DATA A publly vlbl dtst of stor sgnfnt wv hghts, surd ovr prod of 9 yrs t th Kodk st off th Alskn ost, hs bn dstrbutd by th Intrntonl Assoton for Hydrul Rsrh (IAHR) [6]. W shll rtflly ntrodu rndo rror to ths dtst n ordr to tst th ury of th proposd onvoluton ntgrl pproh for ssssng bs, nd to opr t gnst nurl sulton thods. Th Kodk dtst s shown n Fgur 3, long wth truntd -prtr Wbull ft of th dt. A thrshold lvl of 6.85 s usd hr, orrspondng wth totl of 49 wv hght surnts. A stor ourrn frquny of 49/9=.579 stors/yr s usd to obtn rturn prod stts. Bsd on ths dt, th nonl yr rturn prod wv hght s % onfdn ntrvls du to splng vrblty r nludd on th plot (syptot norl bounds). In ordr to ssss th dgr of bs n th yr rturn vlus du to rndo rror n th dt, odfd dtsts wr rtd. Norlly dstrbutd rndo rrors of zro n nd.8 stndrd dvton wr ddd to th Kodk dtst to provd odfd dtsts 3. Rfttng of th truntd Wbull dstrbuton to h odfd dtst to obtn yr rturn vlus produd th rsults shown n Fgur 4. As xptd, th gnrl trnd s postv bs n th prdtd yr wv vlu, wth th ount of bs rngng fro -.6 to Th n bs du to th ddton of rndo rror ws In othr words, for ths st of dt,.8 stndrd dvton n th ury of h surnt 3 Ths rrors r usd for llustrtv purposs only nd do not nssrly rflt th hndst unrtnty of th Kodk dtst. 5 Copyrght 4 by ASME

6 wll, on vrg, rsult n.388 ovrstt of th yr wv vlu (n ovrstton of round 3.3%). Wv Hght () Rturn Prod (yrs) 5 5 Prdtd Quntl Fgur 3 - Truntd Wbull Plot for Kodk Dt Rltv Frquny Bs n th yr Wv du to th Inluson of Modlng Error () Fgur 4 - Hstogr of Clultd Bs n H du to th Inluson of Modlng Error (Rsults fro Dtsts, Consstng of Norlly Dstrbutd Error of Zro Mn nd.8 Stndrd Dvton ddd to th Kodk Dt) A tst spl wth bs nr ws sltd for us n oprson study of th Mont-Crlo, rsdul bootstrp, nd onvoluton ntgrl thods for ssssng bs. Th rsults r prsntd n Tbl. trtons wr usd for both th Mont-Crlo nd th rsdul bootstrp nlyss. Th rror rsduls wr usd drtly n th onvoluton ntgrl nlyss. Tbl - Coprson of Dffrnt Mthods of Esttng Bs n yr Rturn Prod Wvhghts for Known Hndstng Error (σ =.8) Atul Kodk Dt Avrg Efft of Error Mont- Crlo Rsdul Bootstrp Convoluton Intgrl Prdtd H Bs % Error n Bs Estt % -49 % -4.6 % It n b sn tht th onvoluton ntgrl pproh pprs to ssss odlng bs rsonbly wll. Th Mont- Crlo nd Rsdul Bootstrp thods ppr lss urt. Whlst ths rsults donstrt tht hndst unrtnty n hv n fft on th prdton of dsgn ondtons, th vrton du to ths unrtnty rns qut sll n oprson to th splng unrtnty (s for xpl th onfdn ntrvl n Fgur 3). For yr ondtons, th ddd oplxty of usng th onvoluton ntgrl pproh dos not ppr wrrntd gvn tht th dgr of bs s lkly to b lss thn.5 for typl lvls of hndst unrtnty. Howvr, n ss whr thr s lrg dgr of unrtnty n th hndst odl, prhps du to thr bng only fw rlbl surnts of stor ondtons vlbl n th rgon, or thr s nd to xn vry long rturn prod vlus, suh s n rlblty study, th fft of hndst unrtnty s uh or portnt. Ths pont s llustrtd by Tbl, whh shows rsults for th yr ondton for hndst stndrd dvton of.. Not tht f hndst unrtnty s ngltd, th yr wv hght s ovrsttd by ovr tr. Tbl - Coprson of Dffrnt Mthods of Esttng Bs n yr Rturn Prod Wvhghts for Known Hndstng Error (σ =.) Atul Kodk Dt Avrg Efft of Error Mont- Crlo Rsdul Bootstrp Convoluton Intgrl Prdtd H Bs % Error n Bs Estt % -6.6 % +.3 % 6 Copyrght 4 by ASME

7 CONCLUSIONS It hs bn shown hr tht th us of hndst dt to ondut xtr vlu nlyss rsults n sll but sgnfnt postv bs n th lulton of rturn vlus of t-on prtrs suh s sgnfnt wv hght. Gvn tht hndst unrtnty n b wll sttd v lbrton studs, thr s no rson why th fft of hndst unrtnty nnot b norportd nto n xtrl nlyss. Ths ppr prsnts thod to oplsh ths, nd donstrts tht ths nw thod prfors bttr thn xstng nurl sulton thods n prdtng bs, prtulrly whn onsdrng truntd dtsts. Tsts usng stndrd on wv dtst ndt tht th fft of hndst unrtnty on rturn vlus s of prtulr portn for rlblty studs, nd n rgons whr th surnt dtbs vlbl for odl lbrton s sll. ACKNOWLEDGMENTS Th uthors wsh to grtfully knowldg th support nd ssstn provdd by Woodsd Enrgy Ltd. nd by Prof. In Js of Murdoh Unvrsty. Fnnl support fro th Cooprtv Rsrh Cntr for Wldd Struturs s lso knowldgd. REFERENCES [] R A.M. nd Crdon V.J. 98, Tst of Wv Hndst Modl Rsults Agnst Msurnts Durng Four Dffrnt Mtorologl Systs, Offshor Thnology Confrn, Houston, USA, OTC 433 [] Hvr S., 994, Unrtnts Rltd to Hndst Wv Dt, Pro. Int. Conf. Offshor Mhns nd Art Eng., Vol. II, p57-65 [3] Erl M.D. nd Br, L. 98, Effts of Unrtnts on Extr Wv Hghts, J. Wtrw. Port Costl On Dv. Pro. ASCE, 8, WW4, p [4] Vn Gldr P.H.A.J.M. 999, Sttstl Mthods for th Rsk-Bsd Dsgn of Cvl Struturs, Co. on Hydrul nd Gothnl Eng. [6] vn Vlddr G., God Y., Hwks P., Mnsrd E., Jsus Mrtn M., Mthsn M., Pltr E. nd Thopson E. 993, Cs Studs of Extr Wv Anlyss: A Coprtv Anlyss, Pro. WAVES 93 Conf., 6-8 July, Nw Orlns, USA p Copyrght 4 by ASME

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1)

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1) Conrgnc Thors for Two Itrt Mthods A sttonry trt thod for solng th lnr syst: Ax = b (.) ploys n trton trx B nd constnt ctor c so tht for gn strtng stt x of x for = 2... x Bx c + = +. (.2) For such n trton

More information

A Solution for multi-evaluator AHP

A Solution for multi-evaluator AHP ISAHP Honoll Hw Jly 8- A Solton for lt-vltor AHP Ms Shnohr Kch Osw Yo Hd Nhon Unvrsty Nhon Unvrsty Nhon Unvrsty Iz-cho Nrshno Iz-cho Nrshno Iz-cho Nrshno hb 7-87 Jpn hb 7-87 Jpn M7snoh@ct.nhon-.c.p 7oosw@ct.nhon-.c.p

More information

SAMPLE CSc 340 EXAM QUESTIONS WITH SOLUTIONS: part 2

SAMPLE CSc 340 EXAM QUESTIONS WITH SOLUTIONS: part 2 AMPLE C EXAM UETION WITH OLUTION: prt. It n sown tt l / wr.7888l. I Φ nots orul or pprotng t vlu o tn t n sown tt t trunton rror o ts pproton s o t or or so onstnts ; tt s Not tt / L Φ L.. Φ.. /. /.. Φ..787.

More information

5. 5. Detection of of Signals in in Noise

5. 5. Detection of of Signals in in Noise 5. 5. Dtton of of Sgnl n n o Dtton probl: Gvn th obrvton vtor, w, thn, prfor ppng dodng fro to n ttd ˆ of th trnttd ybol, n wy tht would nz th probblty of rror n th don kng pro. W wll how tht, n th tht

More information

Having a glimpse of some of the possibilities for solutions of linear systems, we move to methods of finding these solutions. The basic idea we shall

Having a glimpse of some of the possibilities for solutions of linear systems, we move to methods of finding these solutions. The basic idea we shall Hvn lps o so o t posslts or solutons o lnr systs, w ov to tos o nn ts solutons. T s w sll us s to try to sply t syst y lntn so o t vrls n so ts qutons. Tus, w rr to t to s lnton. T prry oprton nvolv s

More information

Minimum Spanning Trees

Minimum Spanning Trees Mnmum Spnnng Trs Spnnng Tr A tr (.., connctd, cyclc grph) whch contns ll th vrtcs of th grph Mnmum Spnnng Tr Spnnng tr wth th mnmum sum of wghts 1 1 Spnnng forst If grph s not connctd, thn thr s spnnng

More information

Special Random Variables: Part 1

Special Random Variables: Part 1 Spcl Rndom Vrbls: Prt Dscrt Rndom Vrbls Brnoull Rndom Vrbl (wth prmtr p) Th rndom vrbl x dnots th succss from trl. Th probblty mss functon of th rndom vrbl X s gvn by p X () p X () p p ( E[X ]p Th momnt

More information

Filter Design Techniques

Filter Design Techniques Fltr Dsgn chnqus Fltr Fltr s systm tht psss crtn frquncy componnts n totlly rcts ll othrs Stgs of th sgn fltr Spcfcton of th sr proprts of th systm ppromton of th spcfcton usng cusl scrt-tm systm Rlzton

More information

Outlier-tolerant parameter estimation

Outlier-tolerant parameter estimation Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln

More information

A general N-dimensional vector consists of N values. They can be arranged as a column or a row and can be real or complex.

A general N-dimensional vector consists of N values. They can be arranged as a column or a row and can be real or complex. Lnr lgr Vctors gnrl -dmnsonl ctor conssts of lus h cn rrngd s column or row nd cn rl or compl Rcll -dmnsonl ctor cn rprsnt poston, loct, or cclrton Lt & k,, unt ctors long,, & rspctl nd lt k h th componnts

More information

Modeling Steel Corrosion in Concrete Structures - Part 1: A New Inverse Relation between Current Density and Potential for the Cathodic Reaction

Modeling Steel Corrosion in Concrete Structures - Part 1: A New Inverse Relation between Current Density and Potential for the Cathodic Reaction Int. J. Eltrohm. S., 5 (010 30-313 Intrntonl Journl of ELECTROCHEMICAL SCIENCE www.ltrohms.org Modlng Stl Corroson n Conrt Struturs - Prt 1: A Nw Invrs Rlton btwn Currnt Dnsty nd Potntl for th Cthod Rton

More information

Bayesian belief networks: learning and inference

Bayesian belief networks: learning and inference CS 1675 Introducton to chn Lrnng Lctur 16 ysn lf ntworks: lrnng nd nfrnc los Huskrcht los@ptt.du 5329 Snnott Squr Dt: Dnsty stton D { D1 D2.. Dn} D x vctor of ttrut vlus Ojctv: try to stt th undrlyng tru

More information

Weighted Graphs. Weighted graphs may be either directed or undirected.

Weighted Graphs. Weighted graphs may be either directed or undirected. 1 In mny ppltons, o rp s n ssot numrl vlu, ll wt. Usully, t wts r nonntv ntrs. Wt rps my tr rt or unrt. T wt o n s otn rrr to s t "ost" o t. In ppltons, t wt my msur o t lnt o rout, t pty o ln, t nry rqur

More information

(A) the function is an eigenfunction with eigenvalue Physical Chemistry (I) First Quiz

(A) the function is an eigenfunction with eigenvalue Physical Chemistry (I) First Quiz 96- Physcl Chmstry (I) Frst Quz lctron rst mss m 9.9 - klogrm, Plnck constnt h 6.66-4 oul scon Sp of lght c. 8 m/s, lctron volt V.6-9 oul. Th functon F() C[cos()+sn()] s n gnfuncton of /. Th gnvlu s (A)

More information

Review - Probabilistic Classification

Review - Probabilistic Classification Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw

More information

Integration Continued. Integration by Parts Solving Definite Integrals: Area Under a Curve Improper Integrals

Integration Continued. Integration by Parts Solving Definite Integrals: Area Under a Curve Improper Integrals Intgrtion Continud Intgrtion y Prts Solving Dinit Intgrls: Ar Undr Curv Impropr Intgrls Intgrtion y Prts Prticulrly usul whn you r trying to tk th intgrl o som unction tht is th product o n lgric prssion

More information

GUC (Dr. Hany Hammad) 9/28/2016

GUC (Dr. Hany Hammad) 9/28/2016 U (r. Hny Hd) 9/8/06 ctur # 3 ignl flow grphs (cont.): ignl-flow grph rprsnttion of : ssiv sgl-port dvic. owr g qutions rnsducr powr g. Oprtg powr g. vill powr g. ppliction to Ntwork nlyzr lirtion. Nois

More information

Bayesian belief networks

Bayesian belief networks CS 1571 Introducton to I Lctur 20 ysn lf ntworks los Huskrcht los@cs.ptt.du 5329 Snnott Squr CS 1571 Intro to I. Huskrcht odlng uncrtnty wth prolts Dfnng th full jont dstruton ks t possl to rprsnt nd rson

More information

COMP108 Algorithmic Foundations

COMP108 Algorithmic Foundations Grdy mthods Prudn Wong http://www.s.liv..uk/~pwong/thing/omp108/01617 Coin Chng Prolm Suppos w hv 3 typs of oins 10p 0p 50p Minimum numr of oins to mk 0.8, 1.0, 1.? Grdy mthod Lrning outoms Undrstnd wht

More information

b.) v d =? Example 2 l = 50 m, D = 1.0 mm, E = 6 V, " = 1.72 #10 $8 % & m, and r = 0.5 % a.) R =? c.) V ab =? a.) R eq =?

b.) v d =? Example 2 l = 50 m, D = 1.0 mm, E = 6 V,  = 1.72 #10 $8 % & m, and r = 0.5 % a.) R =? c.) V ab =? a.) R eq =? xmpl : An 8-gug oppr wr hs nomnl mtr o. mm. Ths wr rrs onstnt urrnt o.67 A to W lmp. Th nsty o r ltrons s 8.5 x 8 ltrons pr u mtr. Fn th mgntu o. th urrnt nsty. th rt vloty xmpl D. mm,.67 A, n N 8.5" 8

More information

Single Source Shortest Paths (with Positive Weights)

Single Source Shortest Paths (with Positive Weights) Snl Sour Sortst Pts (wt Postv Wts) Yuf To ITEE Unvrsty of Qunslnd In ts ltur, w wll rvst t snl sour sortst pt (SSSP) problm. Rll tt w v lrdy lrnd tt t BFS lortm solvs t problm ffntly wn ll t ds v t sm

More information

8. Linear Contracts under Risk Neutrality

8. Linear Contracts under Risk Neutrality 8. Lnr Contrcts undr Rsk Nutrlty Lnr contrcts r th smplst form of contrcts nd thy r vry populr n pplctons. Thy offr smpl ncntv mchnsm. Exmpls of lnr contrcts r mny: contrctul jont vnturs, quty jont vnturs,

More information

A Probabilistic Characterization of Simulation Model Uncertainties

A Probabilistic Characterization of Simulation Model Uncertainties A Proalstc Charactrzaton of Sulaton Modl Uncrtants Vctor Ontvros Mohaad Modarrs Cntr for Rsk and Rlalty Unvrsty of Maryland 1 Introducton Thr s uncrtanty n odl prdctons as wll as uncrtanty n xprnts Th

More information

, each of which is a tree, and whose roots r 1. , respectively, are children of r. Data Structures & File Management

, each of which is a tree, and whose roots r 1. , respectively, are children of r. Data Structures & File Management nrl tr T is init st o on or mor nos suh tht thr is on sint no r, ll th root o T, n th rminin nos r prtition into n isjoint susts T, T,, T n, h o whih is tr, n whos roots r, r,, r n, rsptivly, r hilrn o

More information

minimize c'x subject to subject to subject to

minimize c'x subject to subject to subject to z ' sut to ' M ' M N uostrd N z ' sut to ' z ' sut to ' sl vrls vtor of : vrls surplus vtor of : uostrd s s s s s s z sut to whr : ut ost of :out of : out of ( ' gr of h food ( utrt : rqurt for h utrt

More information

HIGHER ORDER DIFFERENTIAL EQUATIONS

HIGHER ORDER DIFFERENTIAL EQUATIONS Prof Enriqu Mtus Nivs PhD in Mthmtis Edution IGER ORDER DIFFERENTIAL EQUATIONS omognous linr qutions with onstnt offiints of ordr two highr Appl rdution mthod to dtrmin solution of th nonhomognous qution

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

DOI: /jam.v14i2.7401

DOI: /jam.v14i2.7401 Nutrosoph Soft oduls Kml Vlyv Sd Byrmov Dprtmnt of Algbr nd Gomtry of Bku Stt Unvrsty ZKhllov str AZ48 Bku Azrbjn Abstrt kml607@mlru bysd@gmlom olodtsov nttd th onpt of soft sts n [7] j t l dfnd som oprtons

More information

Alignment Based Precision Checking

Alignment Based Precision Checking Algnmnt Bsd Prson Chkng A. Adrnsyh 1, J. Munoz-Gm 2, J. Crmon 2, B.F. vn Dongn 1, nd W.M.P. vn dr Alst 1 1 Dprtmnt of Mthmts nd Computr Sn Endhovn Unvrsty of Thnology P.O. Box 513, 5600 MB Endhovn, Th

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

More information

The Mathematics of Harmonic Oscillators

The Mathematics of Harmonic Oscillators Th Mhcs of Hronc Oscllors Spl Hronc Moon In h cs of on-nsonl spl hronc oon (SHM nvolvng sprng wh sprng consn n wh no frcon, you rv h quon of oon usng Nwon's scon lw: con wh gvs: 0 Ths s sos wrn usng h

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

, between the vertical lines x a and x b. Given a demand curve, having price as a function of quantity, p f (x) at height k is the curve f ( x,

, between the vertical lines x a and x b. Given a demand curve, having price as a function of quantity, p f (x) at height k is the curve f ( x, Clculus for Businss nd Socil Scincs - Prof D Yun Finl Em Rviw vrsion 5/9/7 Chck wbsit for ny postd typos nd updts Pls rport ny typos This rviw sht contins summris of nw topics only (This rviw sht dos hv

More information

Chapter 16. 1) is a particular point on the graph of the function. 1. y, where x y 1

Chapter 16. 1) is a particular point on the graph of the function. 1. y, where x y 1 Prctic qustions W now tht th prmtr p is dirctl rltd to th mplitud; thrfor, w cn find tht p. cos d [ sin ] sin sin Not: Evn though ou might not now how to find th prmtr in prt, it is lws dvisl to procd

More information

Planar Upward Drawings

Planar Upward Drawings C.S. 252 Pro. Rorto Tmssi Computtionl Gomtry Sm. II, 1992 1993 Dt: My 3, 1993 Sri: Shmsi Moussvi Plnr Upwr Drwings 1 Thorm: G is yli i n only i it hs upwr rwing. Proo: 1. An upwr rwing is yli. Follow th

More information

INF5820 MT 26 OCT 2012

INF5820 MT 26 OCT 2012 INF582 MT 26 OCT 22 H22 Jn Tor Lønnng l@.uo.no Tody Ssl hn rnslon: Th nosy hnnl odl Word-bsd IBM odl Trnng SMT xpl En o lgd n r d bygg..9 h.6 d.3.9 rgh.9 wh.4 buldng.45 oo.3 rd.25 srgh.7 by.3 onsruon.33

More information

Analyzing Frequencies

Analyzing Frequencies Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5

More information

RMMP Vianu 2013 Problema 1

RMMP Vianu 2013 Problema 1 RMMP Vnu Probl Dl DAVIDSCU Arn DAFINI sk. Knt nrgy o t r. Soluton Fgur Clulul ontulu nrţ nsty o wl trl ( t lngt o wl s ) s ( ) Consrng t lntry prs ng t ss y t lntry wt rspt o wl s s y r J ( ) wt y r (

More information

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder Nolr Rgrsso Mjor: All Egrg Mjors Auhors: Aur Kw, Luk Sydr hp://urclhodsgusfdu Trsforg Nurcl Mhods Educo for STEM Udrgrdus 3/9/5 hp://urclhodsgusfdu Nolr Rgrsso hp://urclhodsgusfdu Nolr Rgrsso So populr

More information

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation Lctur Rlc nutrnos mpratur at nutrno dcoupln and today Effctv dnracy factor Nutrno mass lmts Saha quaton Physcal Cosmoloy Lnt 005 Rlc Nutrnos Nutrnos ar wakly ntractn partcls (lptons),,,,,,, typcal ractons

More information

Preview. Graph. Graph. Graph. Graph Representation. Graph Representation 12/3/2018. Graph Graph Representation Graph Search Algorithms

Preview. Graph. Graph. Graph. Graph Representation. Graph Representation 12/3/2018. Graph Graph Representation Graph Search Algorithms /3/0 Prvw Grph Grph Rprsntton Grph Srch Algorthms Brdth Frst Srch Corrctnss of BFS Dpth Frst Srch Mnmum Spnnng Tr Kruskl s lgorthm Grph Drctd grph (or dgrph) G = (V, E) V: St of vrt (nod) E: St of dgs

More information

In which direction do compass needles always align? Why?

In which direction do compass needles always align? Why? AQA Trloy Unt 6.7 Mntsm n Eltromntsm - Hr 1 Complt t p ll: Mnt or s typ o or n t s stronst t t o t mnt. Tr r two typs o mnt pol: n. Wrt wt woul ppn twn t pols n o t mnt ntrtons low: Drw t mnt l lns on

More information

Fundamentals of Continuum Mechanics. Seoul National University Graphics & Media Lab

Fundamentals of Continuum Mechanics. Seoul National University Graphics & Media Lab Fndmntls of Contnm Mchncs Sol Ntonl Unvrsty Grphcs & Md Lb Th Rodmp of Contnm Mchncs Strss Trnsformton Strn Trnsformton Strss Tnsor Strn T + T ++ T Strss-Strn Rltonshp Strn Enrgy FEM Formlton Lt s Stdy

More information

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem Avll t http:pvu.u Appl. Appl. Mth. ISSN: 9-9466 Vol. 0 Issu Dr 05 pp. 007-08 Appltos Appl Mthts: A Itrtol Jourl AAM Etso oruls of Lurll s utos Appltos of Do s Suto Thor Ah Al Atsh Dprtt of Mthts A Uvrst

More information

Formal Concept Analysis

Formal Concept Analysis Forml Conpt Anlysis Conpt intnts s losd sts Closur Systms nd Implitions 4 Closur Systms 0.06.005 Nxt-Closur ws dvlopd y B. Gntr (984). Lt M = {,..., n}. A M is ltilly smllr thn B M, if B A if th smllst

More information

Wave Phenomena Physics 15c

Wave Phenomena Physics 15c Wv hnon hyscs 5c cur 4 Coupl Oscllors! H& con 4. Wh W D s T " u forc oscllon " olv h quon of oon wh frcon n foun h sy-s soluon " Oscllon bcos lr nr h rsonnc frquncy " hs chns fro 0 π/ π s h frquncy ncrss

More information

The Z transform techniques

The Z transform techniques h Z trnfor tchniqu h Z trnfor h th rol in dicrt yt tht th Lplc trnfor h in nlyi of continuou yt. h Z trnfor i th principl nlyticl tool for ingl-loop dicrt-ti yt. h Z trnfor h Z trnfor i to dicrt-ti yt

More information

Matched Quick Switching Variable Sampling System with Quick Switching Attribute Sampling System

Matched Quick Switching Variable Sampling System with Quick Switching Attribute Sampling System Natur and Sn 9;7( g v, t al, Samlng Systm Mathd Quk Swthng Varabl Samlng Systm wth Quk Swthng Attrbut Samlng Systm Srramahandran G.V, Palanvl.M Dartmnt of Mathmats, Dr.Mahalngam Collg of Engnrng and Thnology,

More information

4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling.

4.1 Interval Scheduling. Chapter 4. Greedy Algorithms. Interval Scheduling: Greedy Algorithms. Interval Scheduling. Interval scheduling. Cptr 4 4 Intrvl Suln Gry Alortms Sls y Kvn Wyn Copyrt 005 Prson-Ason Wsly All rts rsrv Intrvl Suln Intrvl Suln: Gry Alortms Intrvl suln! Jo strts t s n nss t! Two os omptl ty on't ovrlp! Gol: n mxmum sust

More information

CSE 373: AVL trees. Warmup: Warmup. Interlude: Exploring the balance invariant. AVL Trees: Invariants. AVL tree invariants review

CSE 373: AVL trees. Warmup: Warmup. Interlude: Exploring the balance invariant. AVL Trees: Invariants. AVL tree invariants review rmup CSE 7: AVL trs rmup: ht is n invrint? Mihl L Friy, Jn 9, 0 ht r th AVL tr invrints, xtly? Disuss with your nighor. AVL Trs: Invrints Intrlu: Exploring th ln invrint Cor i: xtr invrint to BSTs tht

More information

Rate of Molecular Exchange Through the Membranes of Ionic Liquid Filled. Polymersomes Dispersed in Water

Rate of Molecular Exchange Through the Membranes of Ionic Liquid Filled. Polymersomes Dispersed in Water Supportng Informton for: Rt of Molculr Exchng hrough th Mmrns of Ionc Lqud Flld olymrsoms Dsprsd n Wtr Soonyong So nd mothy. Lodg *,, Dprtmnt of Chmcl Engnrng & Mtrls Scnc nd Dprtmnt of Chmstry, Unvrsty

More information

PH427/PH527: Periodic systems Spring Overview of the PH427 website (syllabus, assignments etc.) 2. Coupled oscillations.

PH427/PH527: Periodic systems Spring Overview of the PH427 website (syllabus, assignments etc.) 2. Coupled oscillations. Dy : Mondy 5 inuts. Ovrviw of th PH47 wsit (syllus, ssignnts tc.). Coupld oscilltions W gin with sss coupld y Hook's Lw springs nd find th possil longitudinl) otion of such syst. W ll xtnd this to finit

More information

Handout 11. Energy Bands in Graphene: Tight Binding and the Nearly Free Electron Approach

Handout 11. Energy Bands in Graphene: Tight Binding and the Nearly Free Electron Approach Hdout rg ds Grh: Tght dg d th Nrl Fr ltro roh I ths ltur ou wll lr: rg Th tght bdg thod (otd ) Th -bds grh FZ C 407 Srg 009 Frh R Corll Uvrst Grh d Crbo Notubs: ss Grh s two dsol sgl to lr o rbo tos rrgd

More information

SAMPLE LITANY OF THE SAINTS E/G. Dadd9/F. Aadd9. cy. Christ, have. Lord, have mer cy. Christ, have A/E. Dadd9. Aadd9/C Bm E. 1. Ma ry and. mer cy.

SAMPLE LITANY OF THE SAINTS E/G. Dadd9/F. Aadd9. cy. Christ, have. Lord, have mer cy. Christ, have A/E. Dadd9. Aadd9/C Bm E. 1. Ma ry and. mer cy. LTNY OF TH SNTS Cntrs Gnt flwng ( = c. 100) /G Ddd9/F ll Kybrd / hv Ddd9 hv hv Txt 1973, CL. ll rghts rsrvd. Usd wth prmssn. Musc: D. Bckr, b. 1953, 1987, D. Bckr. Publshd by OCP. ll rghts rsrvd. SMPL

More information

CIVL 8/ D Boundary Value Problems - Rectangular Elements 1/7

CIVL 8/ D Boundary Value Problems - Rectangular Elements 1/7 CIVL / -D Boundr Vlu Prolms - Rctngulr Elmnts / RECANGULAR ELEMENS - In som pplictions, it m mor dsirl to us n lmntl rprsnttion of th domin tht hs four sids, ithr rctngulr or qudriltrl in shp. Considr

More information

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura Moul grph.py CS 231 Nomi Nishimur 1 Introution Just lik th Python list n th Python itionry provi wys of storing, ssing, n moifying t, grph n viw s wy of storing, ssing, n moifying t. Bus Python os not

More information

TOPIC 5: INTEGRATION

TOPIC 5: INTEGRATION TOPIC 5: INTEGRATION. Th indfinit intgrl In mny rspcts, th oprtion of intgrtion tht w r studying hr is th invrs oprtion of drivtion. Dfinition.. Th function F is n ntidrivtiv (or primitiv) of th function

More information

FINITE ELEMENT ANALYSIS OF

FINITE ELEMENT ANALYSIS OF FINIT LMNT NLYSIS OF D MODL PROBLM WITH SINGL VRIBL Fnt lmnt modl dvlopmnt of lnr D modl dffrntl qton nvolvng sngl dpndnt nknown govrnng qtons F modl dvlopmnt wk form. JN Rddy Modlqn D - GOVRNING TION

More information

Appendix. In the absence of default risk, the benefit of the tax shield due to debt financing by the firm is 1 C E C

Appendix. In the absence of default risk, the benefit of the tax shield due to debt financing by the firm is 1 C E C nx. Dvon o h n wh In h sn o ul sk h n o h x shl u o nnng y h m s s h ol ouon s h num o ssus s h oo nom x s h sonl nom x n s h v x on quy whh s wgh vg o vn n l gns x s. In hs s h o sonl nom xs on h x shl

More information

Present state Next state Q + M N

Present state Next state Q + M N Qustion 1. An M-N lip-lop works s ollows: I MN=00, th nxt stt o th lip lop is 0. I MN=01, th nxt stt o th lip-lop is th sm s th prsnt stt I MN=10, th nxt stt o th lip-lop is th omplmnt o th prsnt stt I

More information

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION CHAPTER 7d. DIFFERENTIATION AND INTEGRATION A. J. Clark School o Engnrng Dpartmnt o Cvl and Envronmntal Engnrng by Dr. Ibrahm A. Assakka Sprng ENCE - Computaton Mthods n Cvl Engnrng II Dpartmnt o Cvl and

More information

INF5820/INF9820 LANGUAGE TECHNOLOGICAL APPLICATIONS. Jan Tore Lønning, Lecture 4, 14 Sep

INF5820/INF9820 LANGUAGE TECHNOLOGICAL APPLICATIONS. Jan Tore Lønning, Lecture 4, 14 Sep INF5820/INF9820 LANGUAGE TECHNOLOGICAL ALICATIONS Jn Tor Lønning Lctur 4 4 Sp. 206 tl@ii.uio.no Tody 2 Sttisticl chin trnsltion: Th noisy chnnl odl Word-bsd Trining IBM odl 3 SMT xpl 4 En kokk lgd n rtt

More information

Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example

Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example Outlin Computr Sin 33 Computtion o Minimum-Cost Spnnin Trs Prim's Alorithm Introution Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #33 3 Alorithm Gnrl Constrution Mik Joson (Univrsity o Clry)

More information

Cooperative vs. Non-Cooperative R&D Incentives under Incomplete Information*

Cooperative vs. Non-Cooperative R&D Incentives under Incomplete Information* Dsusson Pr EU/04 05 Jun 04 oortv vs. Non-oortv &D Inntvs undr Inomlt Informton* Trun Kr Indn Sttstl Insttut Kolkt nd Sroont httodhyy Vdysgr ollg for Womn Kolkt Jun 04 *orrsondn to: Trun Kr Eonom srh Unt

More information

Advances in the study of intrinsic rotation with flux tube gyrokinetics

Advances in the study of intrinsic rotation with flux tube gyrokinetics Adans n th study o ntrns rotaton wth lux tub gyroknts F.I. Parra and M. arns Unrsty o Oxord Wolgang Paul Insttut, Vnna, Aprl 0 Introduton In th absn o obous momntum nput (apart rom th dg), tokamak plasmas

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Bayesian belief networks: Inference

Bayesian belief networks: Inference C 740 Knowd rprntton ctur 0 n f ntwork: nfrnc o ukrcht o@c.ptt.du 539 nnott qur C 750 chn rnn n f ntwork. 1. Drctd ccc rph Nod rndo vr nk n nk ncod ndpndnc. urr rthquk r ohnc rc C 750 chn rnn n f ntwork.

More information

IX. EMPIRICAL ORTHOGONAL FUNCTIONS

IX. EMPIRICAL ORTHOGONAL FUNCTIONS IX. EMPIRICAL ORTHOGONAL FUNCTION A. Eprcl Orthogonl Functons (EOF): T Don Consdr sptl rry of dscrt t srs obsrvtons (for xpl stwrd currnt) t M loctons - u jt, whr j ndcts stton nubr (j =,... M) nd t ndcts

More information

CHAPTER 4. FREQUENCY ESTIMATION AND TRACKING

CHAPTER 4. FREQUENCY ESTIMATION AND TRACKING CHPTER 4. FREQUENCY ESTITION ND TRCKING 4.. Itroducto Estmtg mult-frquc susodl sgls burd os hs b th focus of rsrch for qut som tm [68] [58] [46] [64]. ost of th publshd rsrch usd costrd ft mpuls rspos

More information

A Study on Energetic Efficiency of a Batch Type Fluidized Bed Dryer

A Study on Energetic Efficiency of a Batch Type Fluidized Bed Dryer Rnt Advns n Mhnl Engnrng A Study on Enrgt Effny of Bth Ty Fludzd Bd Dryr EMRAH ÖZAHİ 1, HACIMURAT DEMİR 2 Mhnl Engnrng Drtnt Unvrsty of Gznt 1, Aksry Unvrsty 2 27310 Gznt 1, 68100 Aksry 2 TURKEY ozh@gnt.du.tr

More information

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture:

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture: Lctur 11 Wvs in Priodic Potntils Tody: 1. Invrs lttic dfinition in 1D.. rphicl rprsnttion of priodic nd -priodic functions using th -xis nd invrs lttic vctors. 3. Sris solutions to th priodic potntil Hmiltonin

More information

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula

u x v x dx u x v x v x u x dx d u x v x u x v x dx u x v x dx Integration by Parts Formula 7. Intgration by Parts Each drivativ formula givs ris to a corrsponding intgral formula, as w v sn many tims. Th drivativ product rul yilds a vry usful intgration tchniqu calld intgration by parts. Starting

More information

Improving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e)

Improving Union. Implementation. Union-by-size Code. Union-by-Size Find Analysis. Path Compression! Improving Find find(e) POW CSE 36: Dt Struturs Top #10 T Dynm (Equvln) Duo: Unon-y-Sz & Pt Comprsson Wk!! Luk MDowll Summr Qurtr 003 M! ZING Wt s Goo Mz? Mz Construton lortm Gvn: ollton o rooms V Conntons twn t rooms (ntlly

More information

The University of Sydney MATH 2009

The University of Sydney MATH 2009 T Unvrsty o Syny MATH 2009 APH THEOY Tutorl 7 Solutons 2004 1. Lt t sonnt plnr rp sown. Drw ts ul, n t ul o t ul ( ). Sow tt s sonnt plnr rp, tn s onnt. Du tt ( ) s not somorp to. ( ) A onnt rp s on n

More information

Single Correct Type. cos z + k, then the value of k equals. dx = 2 dz. (a) 1 (b) 0 (c)1 (d) 2 (code-v2t3paq10) l (c) ( l ) x.

Single Correct Type. cos z + k, then the value of k equals. dx = 2 dz. (a) 1 (b) 0 (c)1 (d) 2 (code-v2t3paq10) l (c) ( l ) x. IIT JEE/AIEEE MATHS y SUHAAG SIR Bhopl, Ph. (755)3 www.kolsss.om Qusion. & Soluion. In. Cl. Pg: of 6 TOPIC = INTEGRAL CALCULUS Singl Corr Typ 3 3 3 Qu.. L f () = sin + sin + + sin + hn h primiiv of f()

More information

Errata for Second Edition, First Printing

Errata for Second Edition, First Printing Errt for Scond Edition, First Printing pg 68, lin 1: z=.67 should b z=.44 pg 1: Eqution (.63) should rd B( R) = x= R = θ ( x R) p( x) R 1 x= [1 G( x)] = θp( R) + ( θ R)[1 G( R)] pg 15, problm 6: dmnd of

More information

Modeling Analysis and Parameters Calculation of Permanent Magnet Linear Synchronous Motor

Modeling Analysis and Parameters Calculation of Permanent Magnet Linear Synchronous Motor JOURNA OF COMPUTERS, VO. 8, NO. 2, FEBRUARY 21 46 Modlng Anlyss nd Prmtrs Clulton of Prmnnt Mgnt nr Synhronous Motor Hng Wng Shool of Eltrl Engnrng nd Automton, Hnn Polythn Unvrsty, Jozuo, Chn Eml: wnghng@hpu.du.n

More information

More Statistics tutorial at 1. Introduction to mathematical Statistics

More Statistics tutorial at   1. Introduction to mathematical Statistics Mor Sttstcs tutorl t wwwdumblttldoctorcom Itroducto to mthmtcl Sttstcs Fl Soluto A Gllup survy portrys US trprurs s " th mvrcks, drmrs, d lors whos rough dgs d ucompromsg d to do t thr ow wy st thm shrp

More information

Minimum Spanning Trees

Minimum Spanning Trees Minimum Spnning Trs Minimum Spnning Trs Problm A town hs st of houss nd st of rods A rod conncts nd only houss A rod conncting houss u nd v hs rpir cost w(u, v) Gol: Rpir nough (nd no mor) rods such tht:

More information

Speed Control of Brushless DC Motors Using Emotional Intelligent Controller

Speed Control of Brushless DC Motors Using Emotional Intelligent Controller Intllgn Systms n Eltrl Engnrng Journl, 4 yr, No. 4, Wntr 214 6 Spd Control of Brushlss DC Motors Usng Emotonl Intllgnt Controllr Ehsn Drybg 1 nd Gholmrz Arb Mrkdh 2 1 - Young Rsrhrs Club, Nfbd brnh, Islm

More information

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline Introucton to Ornar Dffrntal Equatons Sptmbr 7, 7 Introucton to Ornar Dffrntal Equatons Larr artto Mchancal Engnrng AB Smnar n Engnrng Analss Sptmbr 7, 7 Outln Rvw numrcal solutons Bascs of ffrntal quatons

More information

Fractions. Mathletics Instant Workbooks. Simplify. Copyright

Fractions. Mathletics Instant Workbooks. Simplify. Copyright Frctons Stunt Book - Srs H- Smplfy + Mthltcs Instnt Workbooks Copyrht Frctons Stunt Book - Srs H Contnts Topcs Topc - Equvlnt frctons Topc - Smplfyn frctons Topc - Propr frctons, mpropr frctons n mx numbrs

More information

Principle Component Analysis

Principle Component Analysis Prncple Component Anlyss Jng Go SUNY Bufflo Why Dmensonlty Reducton? We hve too mny dmensons o reson bout or obtn nsghts from o vsulze oo much nose n the dt Need to reduce them to smller set of fctors

More information

Linear Algebra Existence of the determinant. Expansion according to a row.

Linear Algebra Existence of the determinant. Expansion according to a row. Lir Algbr 2270 1 Existc of th dtrmit. Expsio ccordig to row. W dfi th dtrmit for 1 1 mtrics s dt([]) = (1) It is sy chck tht it stisfis D1)-D3). For y othr w dfi th dtrmit s follows. Assumig th dtrmit

More information

A Relationship Between Different Costs of Container Yard Modelling in Port Using Queuing Approach

A Relationship Between Different Costs of Container Yard Modelling in Port Using Queuing Approach Romo Mštrovć Profssor Unvrsty of Montngro Mrtm Fulty Kotor Montngro Brnslv Drgovć Profssor Unvrsty of Montngro Mrtm Fulty Kotor Montngro Nnd Zrnć Profssor Unvrsty of Blgrd Fulty of Mhnl Engnrng Držn Drgojvć

More information

Section 3: Antiderivatives of Formulas

Section 3: Antiderivatives of Formulas Chptr Th Intgrl Appli Clculus 96 Sction : Antirivtivs of Formuls Now w cn put th is of rs n ntirivtivs togthr to gt wy of vluting finit intgrls tht is ct n oftn sy. To vlut finit intgrl f(t) t, w cn fin

More information

4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n, h r th ff r d nd

4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n, h r th ff r d nd n r t d n 20 20 0 : 0 T P bl D n, l d t z d http:.h th tr t. r pd l 4 4 N v b r t, 20 xpr n f th ll f th p p l t n p pr d. H ndr d nd th nd f t v L th n n f th pr v n f V ln, r dn nd l r thr n nt pr n,

More information

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

Case Study VI Answers PHA 5127 Fall 2006

Case Study VI Answers PHA 5127 Fall 2006 Qustion. A ptint is givn 250 mg immit-rls thophyllin tblt (Tblt A). A wk ltr, th sm ptint is givn 250 mg sustin-rls thophyllin tblt (Tblt B). Th tblts follow on-comprtmntl mol n hv first-orr bsorption

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

From Structural Analysis to FEM. Dhiman Basu

From Structural Analysis to FEM. Dhiman Basu From Structural Analyss to FEM Dhman Basu Acknowldgmnt Followng txt books wr consultd whl prparng ths lctur nots: Znkwcz, OC O.C. andtaylor Taylor, R.L. (000). Th FntElmnt Mthod, Vol. : Th Bass, Ffth dton,

More information

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands Hdout 7 Popts of Bloch Stts d Elcto Sttstcs Eg Bds I ths lctu ou wll l: Popts of Bloch fuctos Podc boud codtos fo Bloch fuctos Dst of stts -spc Elcto occupto sttstcs g bds ECE 407 Spg 009 Fh R Coll Uvst

More information

n r t d n :4 T P bl D n, l d t z d th tr t. r pd l

n r t d n :4 T P bl D n, l d t z d   th tr t. r pd l n r t d n 20 20 :4 T P bl D n, l d t z d http:.h th tr t. r pd l 2 0 x pt n f t v t, f f d, b th n nd th P r n h h, th r h v n t b n p d f r nt r. Th t v v d pr n, h v r, p n th pl v t r, d b p t r b R

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

5/1/2018. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees

5/1/2018. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees. Huffman Coding Trees /1/018 W usully no strns y ssnn -lnt os to ll rtrs n t lpt (or mpl, 8-t on n ASCII). Howvr, rnt rtrs our wt rnt rquns, w n sv mmory n ru trnsmttl tm y usn vrl-lnt non. T s to ssn sortr os to rtrs tt our

More information

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY

Study of Dynamic Aperture for PETRA III Ring K. Balewski, W. Brefeld, W. Decking, Y. Li DESY Stud of Dnamc Aprtur for PETRA III Rng K. Balws, W. Brfld, W. Dcng, Y. L DESY FLS6 Hamburg PETRA III Yong-Jun L t al. Ovrvw Introducton Dnamcs of dampng wgglrs hoc of machn tuns, and optmzaton of stupol

More information

Divided. diamonds. Mimic the look of facets in a bracelet that s deceptively deep RIGHT-ANGLE WEAVE. designed by Peggy Brinkman Matteliano

Divided. diamonds. Mimic the look of facets in a bracelet that s deceptively deep RIGHT-ANGLE WEAVE. designed by Peggy Brinkman Matteliano RIGHT-ANGLE WEAVE Dv mons Mm t look o ts n rlt tt s ptvly p sn y Py Brnkmn Mttlno Dv your mons nto trnls o two or our olors. FCT-SCON0216_BNB66 2012 Klm Pulsn Co. Ts mtrl my not rprou n ny orm wtout prmsson

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information