System Ageing Assessment in Energy Supply Security Model
|
|
- Christopher Britton Shaw
- 5 years ago
- Views:
Transcription
1 Sytm Agg Amt Ergy Suppy Scurty Mod Juoza AUGUTIS Dpartmt of Mathmatc ad Stattc Vytauta Magu Uvrty Kaua Lthuaa Rčarda KRIKŠTOLAITIS Dpartmt of Mathmatc ad Stattc Vytauta Magu Uvrty Kaua Lthuaa ad Iga ŢUTAUTAITĖ-ŠEPUTIENĖ Laboratory of Nucar Itaato Safty Lthuaa Ergy Ittut Kaua Lthuaa ABSTRACT Th tudy prt a agorthm dvopd for th amt ad updatg tmat of th paramtr mathmatca mod of dvc or ytm agg proc (that charactrzd by a ag faur rat) wth rpct to pror formato ad obtad obrvato (faur data) Th propod agorthm bad o modfd appcato of Baya approach (BA) Th papr prt om mthod for forcatg th rmag ftm wth rpct to th aowd rk marg ug th obtad updatd mathmatca mod of ag faur rat Th dvopd mthodoogy ud for th modg of tchca dturbac rgy curty aay mod [] Kyword: agg ag faur rat Baya approach INTRODUCTION Scurty of rgy uppy ha rcty gad mportac o th pocy agda du to th growg dpdc of dutrazd coom o rgy coumpto ad th ad frqucy of uppy drupto Scurty of rgy uppy a vry mportat fd of atoa curty vry coutry It cud mg covro ad traportato of prmary rgy ourc grato dtrbuto ad uppy of rgy fuctog of fratructur cur f of octy from tchca coomca oco-potca ad vromta pot of vw Ergy curty v [] maurd by a ytm of dcator Accordg to th ytm th rut of ach caro muato ar trafrrd to umrca vau of curty v Scurty maur tgra ad covr th mot mportat tchca coomca vromta ad potca tra of rgy uppy [] Th rarch work focud o ag th rabty of tchca ytm fac of agg phoma Rabty or ffccy of th ytm ca chag ( b dag) bcau of t agg Th agg of th ytm whch coud b udrtood a a gra proc whch th charactrtc of compot ytm ad tructur ("dvc") graduay chag wth tm or u vtuay ad to dgradato of matra ubjctd to rvc codto ad coud cau a rducto compot ad ytm afty marg Uuay om dvc or ytm ca work afy for a ogr tm tha t dotd th tchca pcfcato Morovr th rpacmt of dvc (or om parat part) xpv coty ( mag of th tm) or coud b ratd to ta rk I grg maagmt t mportat to dtrm th maxmum rmag ftm wth th aowd rk v Th proc of ytm agg ca b dfd by varou charactrtc that dpd o tm ( u or othr factor) Th papr aayz th ca wh agg proc charactrzd by a ag faur rat that dpd o tm For th outo of th dcud probm th foowg tak ar gog to b aayzd: Etmato of th momt at whch th agg proc tart (or dtrmg that agg proc ha arady tartd) Dvopmt of a agorthm for obtag ad updatg th tmat of paramtr th mathmatca mod of agg proc wth rpct to pror formato ad w obrvato Forcat of afty durato of th ytm oprato ad th rmag ftm For tac dtrmato of pot ad trva tmat of th tca tm momt rpct of th aowd tca vau of ag faur rat I th papr a chm of modfd appcato of Baya approach (BA) that utab for updatg radom paramtr tmat th mathmatca mod of o-tatoary proc a agg prtd SYSTEM AGEING PROCESS CHARACTERIZED BY INCREASING FAILURE RATE Dvc / ytm oprato tm ca b dvdd to thr part (Fgur ): bur- prod (wh faur rat dag); prod of ufu f (charactrzd by cotat faur rat); ad war-out (or o cad agg) prod (wh faur rat ag): at th tm momt t t tart th tca vau of faur rat markd rachd at th tm momt t Fgur Th bathtub curv Hypothtc faur rat vru tm
2 It vdt that th dpdabty of th codrd ytm / dvc / oftwar dag th thrd agg prod bcau of mor ad mor frqut faur So t mportat to dtrm whthr th agg prod ha tartd for th aayzd ytm / dvc; to dvop a mathmatca mod for th ytm / dvc agg prod that ab mag pot ad trva forcat of th rmag ftm of th dvc ( faur rat do ot xcd th aowd tca vau ) Th tmato of th momt at whch th agg proc tart Thr ar om tt for dtrmg whthr dt vt a proc hav a trd: Lapac tt or o cad ctrod tt [6] vro tt [4 8] two-c tt [3] Faur rat trd updatg Aum that th ytm agg proc ha arady tartd ad t charactrzd by a ag faur rat Somtm th approxmat faur rat dpdc o tm (or othr factor) for th partcuar group of dvc kow advac Howvr pror formato ca ad to om utat thu th paramtr of th dpdc ar aumd a radom varab Lftm dtrbuto of th codrg ytm ctd accordg to th form of faur rat trd Uuay th appd ftm dtrbuto ar charactrzd by ag faur rat ( ) or bath-tub ( U ) hap faur rat fucto: Wbu Brbaum-Saudr ( ); Grazd Modfd Wbu Expotatd Wbu Addtv Wbu Modfd Wbu Modfd Wbu Exto ( ad U) tc I rabty / dpdabty aay of tchca dvc tchca ytm or v oftwar o of th mot popuar dtrbuto of faur data Poo dtrbuto (wth paramtr ) Th dtrbuto ca b ud ca of ocotat faur rat: xcpt that Poo mod paramtr rpacd wth (t) fucto of tm t or ay othr that dpd o o (or mor) factor() I th ca t dfd a a mathmatcay mp mod of ftm Apart from that th trd curv of ag faur rat coud b ay rpct of th aayzd ytm or dvc Th xt tp to vauat th paramtr ftm dtrbuto Th maxmum khood tmato (MLE) qut popuar tattca mthod ud for provdg tmat for th mod paramtr Th mtato of th mthod that thr o pobty to u pror formato about radom paramtr; t tmat ar obtad ug oy tattca data If th dtrbuto of th mod paramtr ar kow thy ar vovd to th mod through th aw of tota probabty; th ca th obtad mod compx ad t uag qut compcatd Th mthod that aow vovg tattca data ad pror formato about dtrbuto ad tmat of radom paramtr Baya approach; ug th mthod th obtad potror dtrbuto (paramtr tmat a w) coud b updatd by w avaab tattca data Modfd appcato of Baya approach: Commoy Baya approach appd to updat th tmatd paramtr of tatoary proc wh mor tattca formato bcom avaab I ca of o-tatoary proc aay th avaab tattca data ca ot b ud to updat th charactrtc of th prvou prod bcau t rprt th othr tat of th ytm For aayzg otatoary proc th rqurd formato th foowg: dtrbuto of tattca data; form of th trd of ytm dyamc dbg charactrtc ξ (a a fucto of om factor F F r ad paramtr θ θ : f(θ θ F F r ) for xamp t xpota poyoma ar tc Th xpctd vau of radom otatoary charactrtc ξ atf th rqurmt Eξ = f(θ θ F F r ) () Not that paramtr of dtrbuto of tattca data / obrvato ar xprd rpct of q () mut b atfd BA appd to updat radom ukow paramtr θ θ of th fucto dfd by q () I rpct of th utaty of pror formato th maurmt of o ad th paramtr θ θ of th codrd mod coud b aumd a radom dpdt varab wth pror kow dtrbuto (othrw oformatv for tac uform dtrbuto ca b ud a pror; ot that thr pror pdf ar p (x ) = ) Aumg that th dtrbuto of tattca data y = m ao kow th khood fucto L( ) atf q () th potror mutdmoa dty fucto obtad by th appcato of Baya formua for th formato ( x x y y ) () R R p ( x ) L( y y p ( u ) L( y y x x ) u u )du du = m R rag (t of a pob va of paramtr θ = Som compcato ar practca computato of th tgra that appar th domator of Bay formua Som mpfcato for th probm ar pob (of tac ug cojugatd par of pror dty fucto ad khood fucto = avodac of umrca cacuato of tgra gv covt appcato of BA) Dpdg o th khood fucto om pror dtrbuto ca away ad to th potror dtrbuto whch ha th am fuctoa form a th pror dtrbuto for tac Norma khood ad to th cojugat potror Norma dtrbuto Th tattca proprty ratd to th o-cad cojugat par of pror dtrbuto ad khood [5] Ug th cojugat par th ma ad varac a w a othr paramtr ca b ay tmatd for th potror dtrbuto Ug a cojugat par of khood ad pror umrca rror ad covt agorthm for updatg th dtrbuto ca b dvopd [7] Baya pot tmat (xpctd vau of potror dtrbuto) of paramtr θ ˆ x ( x x x y y ) (3) R R R = Th aymptotc of Baya pot tmat: Th ma tro for vauatg th utaty of th Baya tmat th aay of t varac I gra t dffcut to rarch t bcau th varac xprd a a tgra Oy om ca of th covrgc of Baya tmat ar prtd (wh cojugat par [3] ar ud) For tac ca of th pror Gamma pdf ad Poo khood th xpctd vau ad varac ar b y b y b b E E Var a a a a a
3 If E cot Var ; rat of covrgc hr amout of tattca data Not that a ca of cojugat par th varac of Baya pot tmat dag wth covrgc rat ot owr tha / Th commo covrgc of Baya pot tmat that accuracy of updatd tmat of radom paramtr t a op probm (f a ca th varac of updatd tmat dag) Baya trva tmat: I ytm rabty aay th tmat of mod paramtr ar ot away uffct: t cary to obta thr cofdc trva (wth a gv cofdc v) a w It cary to cotruct a modfd aymmtrc cofdc trva f th bgg (or th dg) of th trva ha a hghr mportac for rabty aay For tac aymmtrc trva a ufu trva tmat for Th dfto of th d of trva a foow: Dfto Aymmtrc cofdc trva of th ukow tru vau of θ for a gv gfcac v α db trva x ˆ ˆ ] that atf th quat [ x P ( x P( x ) c P( ( c) c For tac th aymmtrc cofdc trva of Norma radom varab ( N(μ σ)) [μ u cα σ; μ + u ( c)α σ] hr u quat of tadard Norma dtrbuto Prformg th utaty aay t mportat to cotruct th arrowt cofdc trva of th codrd radom paramtr For tac f th radom paramtr th momt at whch th ytm agg tart t forma dfto may b t a foow: Dfto Crdb trva of th ukow tru vau of θ for a gv gfcac v α th arrowt cofdc trva xˆ ; xˆ ] : [ x x argm x wh Δx dfd ˆ x ( x P x x ) Not Crdb trva potror (obtad by BA) pdf ud for th cacuato Qut oft compx cacuato of th db trva mt thr appcabty practc (for tac ra tm dco bad agorthm that mut b covt ot rqurg o much tm for prformg th cacuato Som apct of BA appcato: Bhavor of th mod (for tac ar or xpota tc) do ot chag wh appyg BA BA updat jut tmat th paramtr th cho mod BA aow updatg th tmat of a paramtr th mod wth a g w obtad obrvato Th mthod do ot dmad to coct obrvato Iformato about th obrvato corporatd to th dtrbuto of mod radom paramtr through th khood fucto Modfd appcato of BA for NHPP (ohomogou Poo proc) data: Aum that dvc faur umbr pr tm ut (markd a k) foow Poo dtrbuto wth a tm dpdt paramtr λ = λ( θ) trprtd a tm ad paramtr θ radom varab wth kow pdf Th th potror pdf obtad ug th obrvato of th ytm faur data ad Baya approach x k k ) ( ( ( ( du (4) Ca Aum that th faur rat trd ar λ( θ) = θ th cojugat pror pdf of radom paramtr θ Gamma Ga(a b) Potror pdf Gamma Ga(a b ) a w wth paramtr a a b b pot tmat of radom paramtr θ k Baya b ˆ (5) a wth varac Var b ˆ k ( a ) I ca of + th obrvato k + th paramtr of a w potror pdf coud b ay rcacuatd a + = a + ( + ) ad b + = b + k + tmat of θ a w Ca (umrca xamp) Aum that faur rat trd ( xpota ) (6) ad pror pdf of radom paramtr θ xpota wth paramtr μ Baya pot tmat of radom paramtr θ (ot: paramtr θ aumd to b kow) ˆ x x x x x x x wth varac x x ( x ˆ ) ˆ Var A umrca xprmt wa prformd to utrat th covrgc ( Fgur ) of Baya pot tmat to tru vau Faur umbr k = th th trva of tm muatd by Poo dtrbuto wth paramtr * ) (θ = 5 θ = ) Th umrca xprmt ( paramtr θ aumd a radom varab (ca : wth a pror o-formatv dtrbuto t dty fucto cotat; ca : wth a pror formatv dtrbuto) For th ca a xpota dtrbuto wth paramtr μ = 3 wa cho a a pror kow formatv dtrbuto A atratv mthod for th tmato of th paramtr th mod wth kow trd fucto at quar mthod Accordg to th obtad rut th um of rror quar of BA (wth o-formatv pror dtrbuto) approxmaty % bggr tha BA (wth pror xpota dtrbuto); th um of rror quar of LSM approxmaty twc bggr tha BA (wth pror xpota dtrbuto) Obvouy f a t of obrvato qut bg a mthod gv qut prc tmat of th paramtr BA powr th combato of
4 pror formato ad obrvato (khood a w) th ca of jut fw obrvato ( th bgg) wth xpota dtrbuto a pror pdf (dah ) wth o-formatv pror pdf (od ) tru vau of paramtr θ = Fgur Baya pot tmat of θ ( = ) Radom paramtr th trd of faur rat rpacd wth th updatd Baya pot tmat (xpctd va I th futur aay t coud b ud to dvop a mathmatca mthod for th amt of th momt at whch th ag faur rat woud xcd th aowd faur rat Forcat of th rmag ftm rpct of ag faur rat I th commo ca a aumpto mad that trd fucto of ag faur rat dpd o paramtr t ) (7) ( tca vau of ag faur rat dfd ( tchca pcfcato or dtrmd by xprt) for aayzd dvc or ytm; t corrpodg tm momt t rmag ftm Th cto aay th probm of vauatg th rmag ftm of a dvc ad propo two agorthm I Th xpctd vau of th rmag ftm t ca b tmatd ovg th quato ( ˆ ˆ tkr ) (8) hr ˆ Baya pot tmat of paramtr θ = obtad by formua (3) Fgur 3 Iag faur rat th prod of agg II a) Faur rat (t) (dfd by (7) quato) a fucto of radom varab() θ θ t probabty dty fucto (pdf) obtad ug th potror pdf of θ = ad traformato formua (om ca ar prtd Tab ) Itrva tmat: aymmtrc or db Tab Probabty dty fucto f(y k k ) of faur rat (t) Expro of (t) ( t tm ) Radom paramtr Pdf of faur rat (t) ( t tm ) λ(t) = θ t θ y f ( y) p t t t ( t) y (ot: θ θ f ( y) p t y kow) t Not: ) = x k k ) potror pdf of radom paramtr dfd by formua (4) For th gv fxd tm momt t * th probabty that th ag faur rat woud ot xcd th tca vau coud b ay cacuatd P( y t t ) f ( y t t )dy (9) * II b) O othr had t rvat to vauat th rmag ftm of th aayzd dvc or ytm wth rpct to th aowd rk v xprd by th tca vau of faur rat Th ma vau of th rmag ftm ot prc ough I fact th ftm t a fucto of radom varab() θ θ dfd by quato (mpct form) ( t ) () Pdf of t obtad ug th potror pdf of θ = ad traformato formua (om ca ar prtd Tab ) Tab Probabty dty fucto g(z k k ) of th rmag ftm t Trd Lftm t a fucto fucto of Pdf of rmag ftm t (t) radom ( t tm ) varab λ(t) = θ t radom t g z ( ) p paramtr θ z z t ( t) radom paramtr θ t g( z) p z z (ot: θ kow) Not: ) = x k k ) potror pdf of radom paramtr dfd by formua (4) Pdf g(z) that cota formato about th obrvato ad pror dtrbuto of radom paramtr gv pot ad trva tmat of ftm t I th pot of trva tmat th cotructo of aymmtrc (wth mor attto gv to th bgg Fgur 3) or db trva ha mor advatag tha caca cofdc trva wth quata For tac db trva th hortt of a cofdc trva ad cota mor probab vau of th rmag ftm 3 RESULTS AND CONCLUSIONS Th agorthm for th tmato of th tm momt at whch th agg proc of dvc or ytm tart wa dvopd (t wa bad o ttg of paramtrc hypoth wth rpct to faur data) *
5 Th papr prtd th agorthm for obtag (ad updatg) th tmat of radom paramtr th mathmatca mod of charactrtc that db ytm dvc or ubtac agg: th bhavor (trd fucto dpdt o om factor ad paramtr()) of th aayzd charactrtc prory pror kow Th propod agorthm bad o th modfd appcato of Baya approach I th papr th utrato of th dvopd agorthm appcabty wa prtd by a umrca xprmt: ca wh agg proc charactrzd by a ag faur rat wth pror kow trd; th covrgc of Baya pot tmat of th paramtr of faur rat trd fucto wa dmotratd; th obtad rut wr compard wth th o cacuatd ug at quar mthod (LSM): th um of rror quar of LSM wa approxmaty twc bggr tha th um of rror quar of BA Th agorthm for th amt pot ad trva tmat of th dvc or ytm rmag ftm ( forcat of afty durato of th dvc or ytm oprato) wr prtd ca wh th aowd rk v wa dtrmd ug tca vau of ag faur rat Th dvopd mthodoogy ud for tchca dturbac modg rgy curty aay mod ACKNOWLEDGMENTS Th rarch wa fudd by a grat (No ATE-8/ ad ATE-/) from th Rarch Couc of Lthuaa 4 REFERENCES [] Augut J Matuzė V Krkštoat R Pčuytė S Norvaša E 8 Aay of curty of rgy uppy amt mthod Ergtka Vo 54(4) -9 [] Augut J Krkštoat R Pčuytė S Kotatavčūtė I Sutaab Dvopmt ad Ergy Scurty Lv Aftr Igaa NPP Shutdow Tchoogca ad Ecoomc Dvopmt of Ecoomy Vo 7() 5- [3] Atwood C Crova O Patrk M Rodoov A 7 Mod ad data ud for ag th agg of ytm tructur ad compot (Europa twork o u of probabtc afty amt (PSA) for vauato of agg ffct to th afty of rgy fact) EUR 483 EN Ptt: EC DG JRC Ittut for Ergy [4] Bdat J S Pro A G 986 Radom data: aay ad maurmt procdur Nw York: Wy [5] Brardo J M Smth A F M 3 Baya thory Joh Wy & So [6] Bro A 7 Rabty Egrg Thory ad Practc ISBN thdSprgr Br Hdbrg Nw York [7] Lttwood B Popov P Strg L Amt of th Rabty of Faut-Torat Softwar: a Baya Approach Proc 9th Itratoa Cofrc o Computr Safty Rabty ad Scurty SAFECOMP' Rottrdam th Nthrad Sprgr [8] Rodoova A Atwood C L Krchtgr C Patrk M 8 Dmotrato of tattca approach to dtfy compot agg by opratoa data aay A ca tudy for th agg PSA twork Rabty Egrg ad Sytm Safty vo
APPLICATION OF THE DISTRIBUTED TRANSFER FUNCTION METHOD AND THE RIGID FINITE ELEMENT METHOD FOR MODELLING OF 2-D AND 3-D SYSTEMS
ODELOWIE IŻYIERSKIE ISS 896-77X 9. 97- Gc PPLICIO O HE DISRIBUED RSER UCIO EHOD D HE RIGID IIE ELEE EHOD OR ODELLIG O -D D -D SYSES RŁ HEI CEZRY ORLIKOWSKI chaca Egrg Dpartt Gdak Uvrt o choog -a: rah@pg.gda.p
More informationReliability of time dependent stress-strength system for various distributions
IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,
More informationResearch on the Massive Data Classification Method in Large Scale Computer Information Management huangyun
Itratoa Crc o Automato, Mchaca Cotro ad Computatoa Egrg (AMCCE 05) Rsarch o th Massv Data Cassfcato Mthod Larg Sca Computr Iformato Maagmt huagyu Chogqg ctroc grg Carr Acadmy, Chogqg 4733, Cha Kywords:
More informationChapter Discrete Fourier Transform
haptr.4 Dscrt Fourr Trasform Itroducto Rcad th xpota form of Fourr srs s Equatos 8 ad from haptr., wt f t 8, h.. T w t f t dt T Wh th abov tgra ca b usd to comput, h.., t s mor prfrab to hav a dscrtzd
More informationCourse 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:
Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght
More informationPositive electrical circuits with zero transfer matrices and their discretization
omptr ppcato Ectrca Egrg Vo. 6 DO.8/j.58-8.6. Potv ctrca crct wt zro trafr matrc a tr crtzato Taz Kaczork Baytok Uvrty of Tcoogy 5 5 Baytok. Wjka 5D ma: kaczork@.pw..p Potv coto tm a crt tm ar ctrca crct
More informationON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL
IJRRAS (3) Ju 22 www.arpapr.com/volum/voliu3/ijrras 3_5.pdf ON RANKING OF ALRNAIVS IN UNCRAIN GROUP DCISION MAKING MODL Chao Wag * & Lag L Gul Uvrty of chology Gul 544 Cha * mal: wagchao244@63.com llag6666@26.com
More informationEstimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek
Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd
More informationLecture 5. Estimation of Variance Components
Lctur 5 Etmato of Varac Compot Gulhrm J. M. Roa Uvrt of Wco-Mado Mxd Modl Quattatv Gtc SISG Sattl 8 0 Sptmbr 08 Etmato of Varac Compot ANOVA Etmato Codr th data t blow rlatd to obrvato of half-b faml of
More informationPROBABILITY OF STABILITY AND RELIABILITY OF DISCRETE DYNAMICAL SYSTEMS UDC
FACTA UNIVERSITATIS Srs: Automatc Cotro ad Robotcs Vo. 8, N o, 009, pp. 7-36 ROBABILITY OF STABILITY AND RELIABILITY OF DISCRETE DYNAMICAL SYSTEMS UDC 57.93 59. 6.3.09.3 Bojaa M. Zatovć, Bjaa Samardžć
More informationLine Matching Algorithm for Localization of Mobile Robot Using Distance Data from Structured-light Image 1
Advacd Scc ad Tchoogy Lttrs Vo.86 (Ubqutous Scc ad Egrg 015), pp.37-4 http://dx.do.org/10.1457/ast.015.86.08 L Matchg Agorthm for Locazato of Mob Robot Usg Dstac Data from Structurd-ght Imag 1 Soocho Km
More informationOn Ranking of Alternatives in Uncertain Group Decision Making Model
H COMPUING SCINC AND CHNOLOGY INRNAIONAL JOURNAL VOL NO 3 Aprl 22 ISSN (Prt) 262-66 ISSN (Ol) 262-687 Publhd ol Aprl 22 (http://wwwrarchpuborg/oural/c) 3 O Rag of Altratv Ucrta Group Dco Mag Modl Chao
More informationLECTURE 6 TRANSFORMATION OF RANDOM VARIABLES
LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt
More informationIntroduction to logistic regression
Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data
More informationMODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f
MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu
More informationUnbalanced Panel Data Models
Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr
More information3.4 Properties of the Stress Tensor
cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato
More informationNotation for Mixed Models for Finite Populations
30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p
More informationDepartment of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis
Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..
More informationTotal Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are
Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,
More informationNetwork reliability importance measures : combinatorics and Monte Carlo based computations
7th WSEAS Itratoal Cofrc o APPLIED COMPUTER SCIENCE, Vc, Italy, Novmr 2-2, 2007 88 Ntwork rlalty mportac maur : comatorc ad Mot Carlo ad computato ILYA GERTSAKH Dpartmt of Mathmatc Guro Uvrty PO 65 r Shva
More informationTHE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED UNDER LINEX LOSS FUNCTION
Joural of Stattc: Advac Thory ad Applcato Volum 4 Numbr 5 Pag - Avalabl at http://ctfcadvac.co. DOI: http://dx.do.org/.864/jata_746 THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED
More informationRobust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment
Robut adaptv uro-fuzzy cotrollr for hybrd poto/forc cotrol of robot mapulator cotact wth ukow vromt Arah Faa ad Mohammad Farrokh * Ira Uvrty of Scc ad chology, Dpartmt of Elctrcal Egrg, Narmak, hra 6844,
More informationLecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t
Cla ot fo EE6318/Phy 6383 Spg 001 Th doumt fo tutoal u oly ad may ot b opd o dtbutd outd of EE6318/Phy 6383 tu 7 Dffuo Ou flud quato that w dvlopd bfo a: f ( )+ v v m + v v M m v f P+ q E+ v B 13 1 4 34
More informationRepeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.
Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms
More informationComparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek
Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar
More informationChiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since
56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,
More informationImproved Exponential Estimator for Population Variance Using Two Auxiliary Variables
Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of
More informationChapter 6. pn-junction diode: I-V characteristics
Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc
More informationCOMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES
COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld
More informationPower Spectrum Estimation of Stochastic Stationary Signals
ag of 6 or Spctru stato of Stochastc Statoary Sgas Lt s cosr a obsrvato of a stochastc procss (). Ay obsrvato s a ft rcor of th ra procss. Thrfor, ca say:
More informationComplex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)
th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc
More informationIndependent Domination in Line Graphs
Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG
More informationChemistry 222 DO NOT OPEN THE EXAM UNTIL YOU ARE READY TO TAKE IT! You may allocate a maximum of 80 continuous minutes for this exam.
Chmtry Sprg 09 Eam : Chaptr -5 Nam 80 Pot Complt fv (5) of th followg problm. CLEARLY mark th problm you o ot wat gra. You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat
More informationControl Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor
Cotol Syt ctu 8 Root ocu Clacal Cotol Pof. Eugo Schut hgh Uvty Root ocu Cotoll Plat R E C U Y - H C D So Y C C R C H Wtg th loo ga a w a ttd tackg th clod-loo ol a ga va Clacal Cotol Pof. Eugo Schut hgh
More informationOn Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data
saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord
More informationWashington State University
he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us
More informationWeights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )
Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also
More informationInternational Journal in Foundations of Computer Science & Technology (IJFCST) Vol.8, No.3, May Amin Ghodousian *
AN ALGORIHM OR SOLVING LINEAR OPIMIZAION PROBLEMS SUBJECE O HE INERSECION O WO UZZY RELAIONAL INEQUALIIES EINE BY RANK AMILY O -NORMS Am Ghodoua aculty of Egrg Scc, Collg of Egrg, Uvrty of hra, POBox 365-4563,
More informationANOVA- Analyisis of Variance
ANOVA- Aalii of Variac CS 700 Comparig altrativ Comparig two altrativ u cofidc itrval Comparig mor tha two altrativ ANOVA Aali of Variac Comparig Mor Tha Two Altrativ Naïv approach Compar cofidc itrval
More informationSuzan Mahmoud Mohammed Faculty of science, Helwan University
Europa Joural of Statstcs ad Probablty Vol.3, No., pp.4-37, Ju 015 Publshd by Europa Ctr for Rsarch Trag ad Dvlopmt UK (www.ajourals.org ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN WEIBULL DISTRIBUTION
More informationImproved Exponential Estimator for Population Variance Using Two Auxiliary Variables
Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug
More informationLecture 1: Empirical economic relations
Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.
More informationNumerical Method: Finite difference scheme
Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from
More informationDifferent types of Domination in Intuitionistic Fuzzy Graph
Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationEstimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable
Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl
More informationChapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University
Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod
More informationAPPENDIX: STATISTICAL TOOLS
I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.
More informationBayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution
Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt
More informationChp6. pn Junction Diode: I-V Characteristics II
Ch6. Jucto od: -V Charactrstcs 147 6. 1. 3 rvato Pror 163 Hols o th quas utral -sd For covc s sak, df coordat as, - Th, d h d' ' B.C. 164 1 ) ' ( ' / qv L P qv P P P P L q d d q J '/ / 1) ( ' ' 같은방법으로
More informationOnline MPL Scheduling of Backward Type for Repetitive Systems with MIMO-FIFO Structure
Procdgs of th 7th WSS Itratoa ofrc o Suato Modg ad Optzato Bg ha Sptbr 5-7 27 2 O MP Schdug of Bacward p for Rpttv Ssts wth MIMO-IO Structur HIROYUKI GOO MSRU ONUM ad KZUHIRO YMD Dpartt of Maagt ad Iforato
More informationMath Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)
Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts
More informationCorrelation in tree The (ferromagnetic) Ising model
5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.
More informationECEN 5005 Crystals, Nanocrystals and Device Applications Class 14 Group Theory For Crystals
ECEN 5005 Cryta Naocryta ad Dvic Appicatio Ca 14 Group Thory For Cryta Spi Aguar Motu Quatu Stat of Hydrog-ik Ato Sig Ectro Cryta Fid Thory Fu Rotatio Group 1 Spi Aguar Motu Spi itriic aguar otu of ctro
More informationEntropy Equation for a Control Volume
Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S
More information(1) Then we could wave our hands over this and it would become:
MAT* K285 Spring 28 Anthony Bnoit 4/17/28 Wk 12: Laplac Tranform Rading: Kohlr & Johnon, Chaptr 5 to p. 35 HW: 5.1: 3, 7, 1*, 19 5.2: 1, 5*, 13*, 19, 45* 5.3: 1, 11*, 19 * Pla writ-up th problm natly and
More informationEstimation Theory. Chapter 4
Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,
More informationPriority-Driven Scheduling of Periodic Tasks. Why Focus on Uniprocessor Scheduling?
CPSC-663: Ra-m Systms Prorty-Drv Schdu Prorty-Drv Schdu of Prodc ass Prorty-drv vs. coc-drv schdu: coc-drv: cycc schdu xcutv rocssor tass a ror! rorty-drv: tass rorty quu rocssor Assumtos: tass ar rodc
More informationJordan Representation of Perfect Reconstruction Filter Banks using Nilpotent Matrices
Procdgs of th 5th WSEAS Itratoa Cofrc o Sga Procssg, Istabu, ury, ay 7-9, 6 (pp-6) Jorda Rprstato of Prfct Rcostructo Ftr Bas usg Npott atrcs ASHA VIJAYAUAR *, G. ABHILASH Dpartmt of Ectrocs ad Commucato
More informationA COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY
Colloquum Bomtrcum 44 04 09 7 COMPISON OF SEVEL ESS FO EQULIY OF COEFFICIENS IN QUDIC EGESSION MODELS UNDE HEEOSCEDSICIY Małgorzata Szczpa Dorota Domagała Dpartmt of ppld Mathmatcs ad Computr Scc Uvrsty
More informationEstimation of Population Variance Using a Generalized Double Sampling Estimator
r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
More informationChannel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto {t-asano,
School of Iformato Scc Chal Capacty 009 - Cours - Iformato Thory - Ttsuo Asao ad Tad matsumoto Emal: {t-asao matumoto}@jast.ac.jp Japa Advacd Isttut of Scc ad Tchology Asahda - Nom Ishkawa 93-9 Japa http://www.jast.ac.jp
More informationReaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4
CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.
More informationTolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data
Itratoal Rfrd Joural of Egrg ad Scc (IRJES) ISSN (Ol) 319-183X, (Prt) 319-181 Volum, Issu 10 (Octobr 013), PP. 6-30 Tolrac Itrval for Expotatd Expotal Dstrbuto Basd o Groupd Data C. S. Kaad 1, D. T. Shr
More informationThe real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.
Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /
More informationProblem Set 3: Model Solutions
Ecoomc 73 Adaced Mcroecoomc Problem et 3: Model oluto. Coder a -bdder aucto wth aluato deedetly ad detcally dtrbuted accordg to F( ) o uort [,]. Let the hghet bdder ay the rce ( - k)b f + kb to the eller,
More informationNote: Torque is prop. to current Stationary voltage is prop. to speed
DC Mach Cotrol Mathmatcal modl. Armatr ad orq f m m a m m r a a a a a dt d ψ ψ ψ ω Not: orq prop. to crrt Statoary voltag prop. to pd Mathmatcal modl. Fld magtato f f f f d f dt a f ψ m m f f m fλ h torq
More informationThe equilibrium distribution of firms in a monopolistically competitive model with the removal of zero-profit conditions
Tzukayama RIEB Dcuo apr Sr No. 3 T qulbrum dtrbuto o rm a moopoltcally compttv modl wt t rmoval o zro-prot codto Wataru Jodo aculty o Ecoomc, Tzukayama Uvrty Octobr 204 Tzukayama Uvrty Rarc Ittut or Ecoomc
More informationIntroduction to logistic regression
Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots
More informationA Simple Representation of the Weighted Non-Central Chi-Square Distribution
SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch
More informationChemistry 350. The take-home least-squares problem will account for 15 possible points on this exam.
Chmtry 30 Sprg 08 Eam : Chaptr - Nam 00 Pot You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Th tak-hom lat-quar problm wll accout for pobl pot
More informationBinary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit
(c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,
More informationsignal amplification; design of digital logic; memory circuits
hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll
More informationERDOS-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 informationA 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 informationAotomorphic Functions And Fermat s Last Theorem(4)
otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral
More informationr y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation
Maatee Klled Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6.11 A Smple Regreo Problem 1 I there relato betwee umber of power boat the area ad umber of maatee klled?
More informationMinimum and maximum Power Adaptation Methods using Haar Wavelet for Image Transmission using QPSK Modulation
ISSN: 78-7798 Itratoal Joural of Scc, Egrg ad chology Rarch (IJSER) Volum, Iu, Augut 0 Mmum ad mamum Mthod ug Haar Wavlt for Imag ramo ug QPSK Modulato M. Padmaja, Dr. P. Satyaarayaa, K. Praua 3,G.Nav
More informationChapter (8) Estimation and Confedence Intervals Examples
Chaptr (8) Estimatio ad Cofdc Itrvals Exampls Typs of stimatio: i. Poit stimatio: Exampl (1): Cosidr th sampl obsrvatios, 17,3,5,1,18,6,16,10 8 X i i1 17 3 5 118 6 16 10 116 X 14.5 8 8 8 14.5 is a poit
More informationPower System Dynamic Security Region and Its Approximations
h artcl ha b accpt for publcato a futur u of th joural, but ha ot b fully t. Cott may chag pror to fal publcato. > REPLACE HIS LINE WIH YOUR PAPER IDENIFICAION NUMBER (DOUBLE-CLICK HERE O EDI) < Powr Sytm
More informationFOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of
Do Bgyoko () FOURIER SERIES I. INTRODUCTION Srs psos r ubqutous too o scc d grg. Th kds o pso to utz dpd o () th proprts o th uctos to b studd d (b) th proprts or chrctrstcs o th systm udr vstgto. Powr
More informationOrder Statistics from Exponentiated Gamma. Distribution and Associated Inference
It J otm Mth Scc Vo 4 9 o 7-9 Od Stttc fom Eottd Gmm Dtto d Aoctd Ifc A I Shw * d R A Bo G og of Edcto PO Bo 369 Jddh 438 Sd A G og of Edcto Dtmt of mthmtc PO Bo 469 Jddh 49 Sd A Atct Od tttc fom ottd
More informationSimple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation.
Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6. A Smple Regreo Problem I there relato betwee umber of power boat the area ad umber of maatee klled? Year NPB( )
More informationChapter 4: Model Adequacy Checking
Catr : Modl Adquacy Cckg I t catr, w dcu om troductory act of modl adquacy cckg, cludg: Rdual Aaly, Rdual lot, Dtcto ad tratmt of outlr, T PRE tattc Ttg for lack of ft. T major aumto tat w av mad rgro
More informationAlmost all Cayley Graphs Are Hamiltonian
Acta Mathmatca Sca, Nw Srs 199, Vol1, No, pp 151 155 Almost all Cayly Graphs Ar Hamltoa Mg Jxag & Huag Qogxag Abstract It has b cocturd that thr s a hamltoa cycl vry ft coctd Cayly graph I spt of th dffculty
More informationUsing Nonlinear Filter for Adaptive Blind Channel Equalization
HAMDRZA BAKHSH Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Us oar Ftr or Adaptv Bd Cha quazato MOHAMMAD POOYA Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Abstract: trsybo trrc
More informationBayesian Test for Lifetime Performance Index of Ailamujia Distribution Under Squared Error Loss Function
Pur ad Appld Mathmatcs Joural 6; 5(6): 8-85 http://www.sccpublshggroup.com/j/pamj do:.648/j.pamj.656. ISSN: 36-979 (Prt); ISSN: 36-98 (Ol) Baysa Tst for ftm Prformac Idx of Alamuja Dstrbuto Udr Squard
More informationReliability Equivalence of Independent Non-identical Parallel and Series Systems.
Lf Scc Jua 0;9(3) h://wwwfccc aby Euvac f Idd N-dca Paa ad S Sy Yuy Abdad 3 ; A I Shawy ad M I A-Ohay D f Mah acuy f Scc Uvy f Daa KSA D f Sac acuy f Scc Kg Abduazz Uvy PO Bx 8003 Jddah 589 Saud Aaba 3
More informationThey must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei.
37 1 How may utros ar i a uclus of th uclid l? 20 37 54 2 crtai lmt has svral isotops. Which statmt about ths isotops is corrct? Thy must hav diffrt umbrs of lctros orbitig thir ucli. Thy must hav th sam
More informationESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION
Joural of Stattc: Advac Thor ad Applcato Volu 9 Nubr 03 Pag 9-35 ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION G. SRINIVASA RAO ad CH. RAMESH
More information1985 AP Calculus BC: Section I
985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b
More informationIn 1991 Fermat s Last Theorem Has Been Proved
I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral
More information[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then
SYSTEM PERFORMANCE Lctur 0: Stady-tat Error Stady-tat Error Lctur 0: Stady-tat Error Dr.alyana Vluvolu Stady-tat rror can b found by applying th final valu thorm and i givn by lim ( t) lim E ( ) t 0 providd
More informationEstimation of the Present Values of Life Annuities for the Different Actuarial Models
h Scod Itratoal Symposum o Stochastc Modls Rlablty Egrg, Lf Scc ad Opratos Maagmt Estmato of th Prst Valus of Lf Auts for th Dffrt Actuaral Modls Gady M Koshk, Oaa V Guba omsk Stat Uvrsty Dpartmt of Appld
More informationEngineering Differential Equations Practice Final Exam Solutions Fall 2011
9.6 Enginring Diffrntial Equation Practic Final Exam Solution Fall 0 Problm. (0 pt.) Solv th following initial valu problm: x y = xy, y() = 4. Thi i a linar d.. bcau y and y appar only to th firt powr.
More informationStatistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -
Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts
More informationReduced nonlinear description of Farley-Buneman instability
Rducd olar dcrpto of Farly-Buma tablty A. S. Volot 1, B. Atamau 1 Ittut of rrtral Magtm, th Ioophr, ad Rado Wav Propagato, RAN, IZMIRAN, 149 rot, Mocow Rgo, Rua. Ittut of Fudamtal chologcal Rarch PAS (IFR
More informationSecond Handout: The Measurement of Income Inequality: Basic Concepts
Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart
More information