Solving Economic Emissions Load Dispatch problem by using Hybrid ACO-MSM approach

Size: px
Start display at page:

Download "Solving Economic Emissions Load Dispatch problem by using Hybrid ACO-MSM approach"

Transcription

1 Th Ol Joual o ow ad Egy Egg (OJEE) Vol. () No. () Solvg Eoo Essos oad Dspath pobl by usg Hybd ACO-MSM appoah a Wal.F. Abd El-Wahd a A.A.Mousa b M.A.Elssy Faulty of Coputs ad Ifoato, Shb El-Ko, Mufya Uvsty, Egypt. b Faulty of Egg, Shb El-Ko, Mufya Uvsty, Egypt. Bha Hgh Isttut of Thology, Bha Uvsty, Egypt. Abstat- Ths pap todus a soluto of th oo ssos load dspath (EED) pobl usg a hybd appoah of at oloy optzato (ACO) ad odfd spl thod (MSM). Th poposd appoah obs ad tds th attatv fatus of both ACO ad MSM, wh t s basd o ACO to gt appoat odoatd st of th pobl followd by MSM to pov th soluto. Th poposd appoah has b appld two tst apls ad th soluto s th opad wth that obtad by so oth thqus to pov th supoty ad fftvss of th poposd algoth. Kywods- Multobtv Dso Mag pobl, ato optal, At Coloy Optzato, Modfd Spl thod, oo ssos load dspath pobl. I. INTRODUCTION Th obtv of th Eoo Esso oad Dspath (EED) pobl of lt pow gato s to shdul th ottd gatg uts outputs to t th qud load dad at u opatg ost wth u sso whl satsfyg all uts ad syst qualty ad qualty ostats. EED pobl s o of th athatal optzato ssus pow syst opato attatg ay sahs tsts. EED pobl s a ultobtv athatal pogag pobl whh s od wth th attpt to obta th optal soluto whh sultaously optzs two ofltg obtvs. May appoahs ad thods w poposd to solv ultobtv oo sso load dspath pobls [4, 7, ]. Th us ad dvlopt of tahusts-basd ultobtv optzato thqus hav sgfatly gow. atal swa algoth s o of tahust algoths that hav b appld optzato tass suh as EED pobl [3, 7,, 3]. At oloy optzato (ACO) s o of th ost t tahust thqus fo appoat optzato so w usd t to solv ths pobl. Th fst ACO algoths w todud by Mao Dogo ad ollagus as a ovl atu-spd tahust fo th soluto of had obatoal optzato (CO) pobls th aly 99 s [,5,6]. ACO blogs to th lass of tahusts [], whh a appoat algoths usd to obta good ough solutos to had CO pobls a asoabl aout of oputato t. It s spd by th ats foagg bhavo, at th o of ths bhavo s th dt ouato btw th ats by as of hal phoo tals, whh abls th to fd shot paths btw th st ad food sous. II. MUTIOBJECTIVE DECISION MAKING ROBEM FORMUATION A ultobtv dso ag pobl (MODM) a b dfd as th pobl of fdg a vto of dso vaabls whh satsfs ostats ad optzs (z o az) a vto futo whos lts pst th obtv futos. Th athatal foulato of a MODM pobl s to optz dfft obtv futos -usually oflt wth ah oth- subt to a st of syst ostats [9]. optz f()=((),(),...,()) f f f subt to (,,...,) T G(),,..., J wh s a dsoal vto of dso vaabls ad also s alld th dso spa o sah spa, G,,,..., J a qualty ostats. Fo a pobl havg o tha o obtv futo (say, { f,,,...,, } ), ay two solutos ad a hav o of two possblts, o doats th oth o odoats th oth. A soluto s sad to doat th oth soluto, f both th followg odto a tu:. Th soluto s o wos tha all obtvs, o f ()() f fo all,,..., obtvs.. Th soluto s sttly btt tha at last o obtv, o f ()() f fo at last o {,,..., }. Dfto [9]: (ato optal soluto): s sad to b a ato optal soluto of ultobtv optzato pobl f th sts o oth fasbl (.., ) suh that, f ()() f fo all,,...,, ad f ()() f fo at last o obtv. III. ECONOMIC EMISSION OAD DISATCH Th oo sso load dspath hav two obtv futo ful ost ad sso obtvs whh a ofltg os. Th pobl a b foulatd as dsbd blow: T () Rf Nub: W9-8 3

2 Th Ol Joual o ow ad Egy Egg (OJEE) Vol. () No. () Obtv Futos Ful ost obtv: Th lassal oo dspath pobl of fdg th optal obato of pow gato, whh zs th total ful ost whl satsfyg th total qud dad a b athatally statd as follows: f ()()() $ / C a b h G G G () wh C : Ful ost of gato. a, b, : Ful ost offts of gato. : Out pow (p.u) by gato. : ub of gato. G Esso obtv: Th sso futo a b pstd as th su of all typs of sso osdd, suh as NO, SO thal sso, t., wth sutabl pg o wghtg o ah pollutat ttd. I th pst study, oly o typ of sso NO s ta to aout wthout loss of galty. Th aout of NO sso s gv as a futo of gato output, that s, th su of a quadat ad potal futo: () [ () p( G )] G / G () f to h wh,,,, : offts of th th gato's NO sso haatst. Costats: ow bala ostat: th total pow gatd ust supply th total dad ad th tassso losss wh (3) G D loss D s load dad, loss loss a alulatd by wh s tassso losss. B loss B a th lts of loss offt at B. Gato apaty ostat: Fo stabl opato al pow output of ah gato s sttd by low ad upp lts as follows: a,..., N (4) G G G Suty ostats: A athatal foulato of th suty ostad EED pobl would qu a vy lag ub of ostats to b osdd. Howv, fo su opato th tassso l loadg S s sttd by ts upp lt as: a S S (5)... : Th ub of tassso ls. IV. THE ROOSED AGORITHM I ths sto, w pst th poposd algoth. Fstly ACO was pltd to gt appoat odoatd st ND(). Sodly, odfd spl thod MSM was usd as a ghbohood sah g to pov th soluto qualty. Th poposd algoth s plad as follows. ACO Algoth Th ACO algoth has b usd to fd appoat odoatd soluto st of MODM. Th a haatst fatus of ths appoah sulats paalll dpdt us statgy [], wh w apply a squ of M olos osttutg a ha o followd th oth ad ah of th has ow paats. Also, t dffs fo oth appoahs slto podu whh, ah at oloy ostut ts solutos basd o a wght su of ultobtv ta, wh th wght attahd to ultobtv ta s ot ostat but adoly gatd. ado (.) w,,,.., (6) ado (.) wh ado (.) s a o-gatv ado ub. Fo quato (6) w a s that s a al ub th losd tval, ad w w (.). Evy at oloy ostuts ts solutos basd o dfft wght valus wly gatd by quato (6). Th obd ftss futo s dfd as: f w f () (7) Sav all th odoatd soluto podud by all M t olos tally th ahv A ND (). Updat ahv, Algoth s usd to updat th ahv. Tll ths ot w gt a appoat ato soluto, w s to pov th soluto qualty by gttg solutos o los to th tu ato optal soluto; w plt loal sah thqu as a ghbohood sah g. Th podu of MSM s dsbd th followg substo. Algoth : Ahv Updat. INUT A,. If ' A ' f th 3. A 4. ls f ' A f ' th 5. A { }\{ D} 6. ls f A ' f th 7. A { } 8. d f 9. OUTUT : A Modfd Spl Algoth Ths sto s otat dsbg o of loal sah thqu usd ola pogag [9] ad ts odfd fo to b sutabl to ultobtv optzato pobls. Th bas da th spl thod s to opa th valus of th obtv futo at th ( + ) vts of a gal spl ad ov th spl gadually towad th optu dug th tatv poss. Th a th opatos, ow as flto, otato, ad paso. Ou thod ts to pov a st of pots to gt odoatd soluto Rf Nub: W9-8 3

3 Th Ol Joual o ow ad Egy Egg (OJEE) Vol. () No. () los to tu ato optal soluto. Assu th obtad appoat odoatd st s pstd by ND(). Rflto: To pla ths thod, assu th a th pots,, 3 ND ad assu ths pots a aagd aodg to o of th obtv futos,,. A w pot obtad by fltg th pot (subspt dat th ddl pot) th oppost fa to hav w valu. Mathatally, th fltd pot s gv by () (8) s th tod of all pots pt 3 (9) s alld th flto offt.copag th gatd pot wth th st of pots,, 3. s plad by f F () doats F () ad psv ts fasblty, ls f F () ad F () a odoatd to ah oth, th a addd to th st,, 3 podug th st,, 3,. I th oth had f F () was doatd by F (), th otato poss wll b usd to gat a w pot. Algoth dsbs th a fatu of th flto poss. Algoth : Rflto poss. INUT ND (),. If () ND th f 3. ND}\{ U} ad go to Epaso poss 4. ls f () ND th f 6. ad ND go to Cotato poss 7. ls () f ND f th 8. ND () { U } 9. d f. OUTUT : A Epaso: If a flto poss gvs a pot fo whh F () doat F ().., th sultat st s th pots assu t s aly as,, 3 so w a gally pt to gt o aptd pots by ovg alog th dto potg fo to. H w pad to usg th lato: () () Algoth 3: Epaso poss. INUT ND (),. If () ND th f 3. ND}\{ U} 4. ls f () ND th f 6. go ND ba to Rflto poss 7. ls () f ND f th 8. ND} U 9. d f. OUTUT : A wh s alld th paso offt. Algoth 3 dsbs th a fatu of th paso poss. Cotato: If a flto poss gvs a pot fo whh a doatd by ay pot th st,, 3. I ths as w pot a gatd as follows: () () wh s alld th otato offt ( ). Algoth 4 dsbs th a fatu of th otato poss. Algoth 4: Cotato poss. INUT (), ND. If () ND th f 3. () ND}\{ U} 4. ls f () ND f 6. () ND 7.Rpla all () by( ND )/ 8. () ls f ND f th 9. () ND} U. d f. OUTUT : A Th psudo od of th poposd algoth s show fgu (). ACO Costut M olos. Italz th paats fo ah ACO, Italz ahv A t b Fo =: M Whl (Stop to has ot b satsfd) do Soluto Costuto ( ) hoo Updat ( ) Nodoatd solutos of oloy() t t A Updat ( A,) ND Ed Ed Ahv A ota all gatd appoatd odoatd solutos, MSM Fo =:sz( A,)- (,,) (),(),()a A f dsdg f f.. ay w t f Whl (th algoth do ot podu w pots) do Gat If (Rflto poss s sudd) Epaso poss If ls (Cotato poss s sudd) If (Rflto poss s sudd) Epaso poss Ed Gat w st of pots (,,) Ed Ed Fgu (): Th psudo od of th poposd algoth V. IMEMENTATION OF THE ROOSED AROACH Th thqus ad all sulatos dvlopd ths study w pltd o 3. GHz C usg MATAB laguag. Rf Nub: W9-8 33

4 Th Ol Joual o ow ad Egy Egg (OJEE) Vol. () No. () Th algoth dvlopd th pvous sto has b pltd to th stadad IEEE 3-bus 6-gato tst syst. Th valus of ful ost ad sso offts a gv Tabl (). To dostat th pottal of th poposd appoah fo dfft pobl oplts ad tad-off sufas, two dfft ass hav b osdd as follows. Cas ( A), fo opaso puposs wth th potd sults, th syst s osdd as losslss ad th suty osta s lasd. Covg of ful ost ad sso obtvs a show fgu (3). Cas ( B), ths as, th pow loss has b ta to aout. Th tassso loss B-offts a spfd [3]. Covg of ful ost ad sso obtvs wh optzd a show fgu (4). Th valus of th bst ost ad th bst sso obtvs wth th poposd appoah a gv Tabls (). ost Esso pow G G G3 G4 G5 G6 a b E-4 5E-4 E-6 E-3 E-6 E a Tabl (): Gato ost, apats ad sso offts of tst syst B= oposd algoth [3] Cas(A) Cas(B) Cas(B) G G G G G G Cost Esso osss Tabl (): Solutos oposg u ful ost ad sso Th sod tst syst ossts of th plats ad s gatos. Th tst syst osdd was dvd fo [7].Th gato data a gv Tabls (3). Th syst load was 9 w. Two dfft ass hav b osdd as tst syst. Covg of ful ost ad sso obtvs a show fgu (5) fo as (A) ad fgu (6) fo as (B). Th valus of th bst ost ad th bst sso obtvs wth th poposd appoah a gv Tabls (4) Fgu (3): Cas (A), Rsults of tst syst Fgu (4): Cas (B), Rsults of tst syst I ths substo, a opaatv study has b ad out to assss th poposd appoah og qualty of th ato st. O th fst had, volutoay thqus suff fo th qualty of th ato st. Thfo th poposd appoah has b usd to as th soluto qualty by obg th two ts of two hust algoths. Howv, th goal s ot oly to as th soluto qualty, but also to gat a pstatv subst, whh atas th haatsts of th gal st ad ta th soluto dvsty to osdato. O th oth had, lassal thqus a to gv sgl pot at ah tato of pobl solvg by ovtg th ultobtv pobl to a sgl obtv pobl by la obato of dfft obtvs as a wghtd su. O th otay, th poposd appoah s a husts-basd ultobtv optzato thqu wh, t uss a populato of solutos th sah, ultpl ato-optal solutos a, ppl, b foud o sgl u. ost Esso pow G G G3 G4 G5 G6 a b a Tabl (3): Gato ost, apats ad sso offts of tst syst. Rf Nub: W9-8 34

5 Th Ol Joual o ow ad Egy Egg (OJEE) Vol. () No. () B oposd algoth [7] Cas(A) Cas(B) Cas(A) Cas(B) G G G G G G Cost Esso osss Tabl 4: Solutos oposg u ful ost ad sso Fgu (5): Cas (A), Rsults fo tst syst Fgu (6): Cas (B), Rsults of tst syst V. CONCUSION I ths pap, w pst a w appoah fo solvg EED. Th thodology obs ad tds th attatv fatus of both ACO ad MS. Th algoth psts a w slto podu basd o a obd ftss futo ad pal sults show that ou appoah s vy fft to fd th tu ato optal solutos fo EED ad ay b vy hlpful studyg optzato pobls pow systs. 4 REFERENCES [] C. Blu, At oloy optzato: Itoduto ad t tds, hyss of f Rvws , 5. [] C. Blu ad A. Rol, Mtahusts obatoal optzato: ovvw ad optual opaso, ACM Coput., Suvys 35 (3) 68 38, 3 [3] Ca,. Ma, Q.,. ad H. g, A ultobtv haot patl swa optzato fo votal/oo dspath, Egy Covso ad Maagt, atl pss 9. [4] K. T. Chatuvd, M. adt,. Svastava, Modfd o-fuzzy uo-basd appoah fo oo ad votal optal pow dspath, Appld Soft Coputg 8, ,8 [5] M. Dogo ad T. Stützl, At Coloy optzato, Cabdg, MA: MIT ss, 4. [6] M. Dogo,V. Mazzo,A. Colo, ostv fdba as a sah statgy. Thal Rpot 9-6, Dpatto d Elttoa, olto d Mlao, Italy, 99. [7] C. alahay, N. S. Babu, Aalytal soluto fo obd oo ad ssos dspath, Elt ow Systs Rsah 78, 9 37, 8. [8] B.K. agah, V. Ravua ad, Saoy Das, Adaptv patl swa optzato appoah fo stat ad dya oo load dspath, Egy Covso ad Maagt 49, 47 45, 8. [9] S.S. RAO, Egg optzato Thoy ad at, Thd dto, Joh Wly & Sos, I., Wly East td, ublshs, ad Nw Ag Itatoal ublshs, td,996. [].K. Hota, R. Chaabat,.K. Chattopadhyay, Eoo sso load dspath though a tatv fuzzy satsfyg thod, Elt ow Systs Rsah 54,5 57,. [] T. Stützl, aalllzato statgs fo at oloy optzato,,odgs of aalll obl Solvg fo Natu, A. El, T. B.a, M. Shoau, ad H. Shwfl (Eds.), tu Nots Coput S, Vol. 498, pp. 7 74, Spg-Vlag, Bl,998. []. Wag, C. Sgh, Rsv-ostad ult aa votal/ oo dspath basd o patl swa optzato wth loal sah, Egg Applatos of Atfal Itllg, 98 37,9. [3]. Wag ad C. Sgh, Stohast oo sso load dspath though a odfd patl swa optzato algoth, Elt ow Systs Rsah 78, ,8. Rf Nub: W9-8 35

GTAP Eleventh Annual Conference, 2008 "Future of Global Economy" Helsinki

GTAP Eleventh Annual Conference, 2008 Future of Global Economy Helsinki GTAP Elvth Aual Cof, 28 "Futu of Global Eooy" lsk SAM laboato as a ultobtv austt pobl Casao Maqu Laa Pñat Dpto Aálss Eoóo Aplao Uvsa Las Palas Ga Caaa (Spa aqu@aa.ulpg.s Dolos R. Satos-Pñat Dpto Métoos

More information

Homework 1: Solutions

Homework 1: Solutions Howo : Solutos No-a Fals supposto tst but passs scal tst lthouh -f th ta as slowss [S /V] vs t th appaac of laty alty th path alo whch slowss s to b tat to obta tavl ts ps o th ol paat S o V as a cosquc

More information

Today s topics. How did we solve the H atom problem? CMF Office Hours

Today s topics. How did we solve the H atom problem? CMF Office Hours CMF Offc ous Wd. Nov. 4 oo-p Mo. Nov. 9 oo-p Mo. Nov. 6-3p Wd. Nov. 8 :30-3:30 p Wd. Dc. 5 oo-p F. Dc. 7 4:30-5:30 Mo. Dc. 0 oo-p Wd. Dc. 4:30-5:30 p ouly xa o Th. Dc. 3 Today s topcs Bf vw of slctd sults

More information

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t

Lecture 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 information

Noise in electronic components.

Noise in electronic components. No lto opot5098, JDS No lto opot Th PN juto Th ut thouh a PN juto ha fou opot t: two ffuo ut (hol fo th paa to th aa a lto th oppot to) a thal at oty ha a (hol fo th aa to th paa a lto th oppot to, laka

More information

SIMULTANEOUS METHODS FOR FINDING ALL ZEROS OF A POLYNOMIAL

SIMULTANEOUS METHODS FOR FINDING ALL ZEROS OF A POLYNOMIAL Joual of athmatcal Sccs: Advacs ad Applcatos Volum, 05, ags 5-8 SIULTANEUS ETHDS FR FINDING ALL ZERS F A LYNIAL JUN-SE SNG ollg of dc Yos Uvsty Soul Rpublc of Koa -mal: usopsog@yos.ac. Abstact Th pupos

More information

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor

Control 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 information

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find BSE SMLE ER SOLUTONS LSS-X MTHS SET- BSE SETON Gv tht d W d to fd 7 7 Hc, 7 7 7 Lt, W ow tht Thus, osd th vcto quto of th pl z - + z = - + z = Thus th ts quto of th pl s - + z = Lt d th dstc tw th pot,,

More information

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines

School of Aerospace Engineering Origins of Quantum Theory. Measurements of emission of light (EM radiation) from (H) atoms found discrete lines Ogs of Quatu Thoy Masuts of sso of lght (EM adato) fo (H) atos foud dsct ls 5 4 Abl to ft to followg ss psso ν R λ c λwavlgth, νfqucy, cspd lght RRydbg Costat (~09,7677.58c - ),,, +, +,..g.,,.6, 0.6, (Lya

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information

X-ray Diffraction from Materials

X-ray Diffraction from Materials X-ay ffato fo Matal 8 Spg St Lt; Yag Mo Koo Moday ad Wdday :5~6: . X-ay ff Sattg. Thal ff Sattg d to to Vbato. Odg ad ff Sattg d to Stat plat Howo . X-ay ff Sattg ffato of x-ay: pod aay of ato Ba podty

More information

Ch. 8: Electron Levels In Periodic Potential

Ch. 8: Electron Levels In Periodic Potential C. 8: Elto Lvls I Pod Pottal Wat a t oss of a pod pottal o t fo of t wav ftos sptv of t xat fo of t pottal ad sptv of wt t atal s a odto o a slato? s t fftv olto pottal. Blo s To: T gstats of t o-lto altoa

More information

Handout on. Crystal Symmetries and Energy Bands

Handout on. Crystal Symmetries and Energy Bands dou o Csl s d g Bds I hs lu ou wll l: Th loshp bw ss d g bds h bs of sp-ob ouplg Th loshp bw ss d g bds h ps of sp-ob ouplg C 7 pg 9 Fh Coll Uvs d g Bds gll hs oh Th sl pol ss ddo o h l slo s: Fo pl h

More information

Petru P. Blaga-Reducing of variance by a combined scheme based on Bernstein polynomials

Petru P. Blaga-Reducing of variance by a combined scheme based on Bernstein polynomials Ptru P Blaa-Rdu o vara by a obd sh basd o Brst olyoals REUCG OF VARACE BY A COMBE SCHEME BASE O BERSTE POYOMAS by Ptru P Blaa Abstrat A obd sh o th otrol varats ad whtd uor sal thods or rdu o vara s vstatd

More information

Three-Dimensional Defect in a Plate Boundary Element Modeling for Guided Wave Scattering

Three-Dimensional Defect in a Plate Boundary Element Modeling for Guided Wave Scattering Ky Egg Matals Vols. 70-73 (004 pp. 453-460 ol at http://www.stf.t 004 Tas Th Pblatos, Swtzlad Ctato & Copyght (to b std by th pblsh Th-Dsoal Dft a Plat Boday Elt Modlg fo Gdd Wav Sattg Xaolag Zhao, Josph

More information

RAKE Receiver with Adaptive Interference Cancellers for a DS-CDMA System in Multipath Fading Channels

RAKE Receiver with Adaptive Interference Cancellers for a DS-CDMA System in Multipath Fading Channels AKE v wh Apv f Cs fo DS-CDMA Ss Muph Fg Chs JooHu Y Su M EEE JHog M EEE Shoo of E Egg Sou o Uvs Sh-og Gw-gu Sou 5-74 Ko E-: ohu@su As hs pp pv AKE v wh vs og s popos fo DS-CDMA ss uph fg hs h popos pv

More information

New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables

New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables Sogklaaka J. Sc. Tchol. 4 () 4-48 Ma. -. 8 Ogal tcl Nw bouds o Posso aomato to th dstbuto of a sum of gatv bomal adom vaabls * Kat Taabola Datmt of Mathmatcs Faculty of Scc Buaha Uvsty Muag Chobu 3 Thalad

More information

D. Bertsekas and R. Gallager, "Data networks." Q: What are the labels for the x-axis and y-axis of Fig. 4.2?

D. Bertsekas and R. Gallager, Data networks. Q: What are the labels for the x-axis and y-axis of Fig. 4.2? pd by J. Succ ECE 543 Octob 22 2002 Outl Slottd Aloh Dft Stblzd Slottd Aloh Uslottd Aloh Splttg Algoths Rfc D. Btsks d R. llg "Dt twoks." Rvw (Slottd Aloh): : Wht th lbls fo th x-xs d y-xs of Fg. 4.2?

More information

Shor s Algorithm. Motivation. Why build a classical computer? Why build a quantum computer? Quantum Algorithms. Overview. Shor s factoring algorithm

Shor s Algorithm. Motivation. Why build a classical computer? Why build a quantum computer? Quantum Algorithms. Overview. Shor s factoring algorithm Motivation Sho s Algoith It appas that th univs in which w liv is govnd by quantu chanics Quantu infoation thoy givs us a nw avnu to study & tst quantu chanics Why do w want to build a quantu coput? Pt

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

Priority Search Trees - Part I

Priority Search Trees - Part I .S. 252 Pro. Rorto Taassa oputatoal otry S., 1992 1993 Ltur 9 at: ar 8, 1993 Sr: a Q ol aro Prorty Sar Trs - Part 1 trouto t last ltur, w loo at trval trs. or trval pot losur prols, ty us lar spa a optal

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX 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 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

Handout 30. Optical Processes in Solids and the Dielectric Constant

Handout 30. Optical Processes in Solids and the Dielectric Constant Haut Otal Sl a th Dlt Ctat I th ltu yu wll la: La ut Ka-Kg lat Dlt tat l Itba a Itaba tbut t th lt tat l C 47 Sg 9 Faha Raa Cll Uty Chag Dl, Dl Mt, a lazat Dty A hag l t a gat a a t hag aat by ta: Q Q

More information

Bayesian Estimation of the parameters of the Weibull-Weibull Length-Biased mixture distributions using time censored data

Bayesian Estimation of the parameters of the Weibull-Weibull Length-Biased mixture distributions using time censored data Bys Eso of h s of h Wull-Wull gh-bs xu suos usg so S. A. Sh N Bouss I.S.S. Co Uvsy I.N.P.S. Algs Uvsy shsh@yhoo.o ou005@yhoo.o As I hs h s of h Wull-Wull lgh s xu suos s usg h Gs slg hqu u y I sog sh.

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department 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 information

BEM with Linear Boundary Elements for Solving the Problem of the 3D Compressible Fluid Flow around Obstacles

BEM with Linear Boundary Elements for Solving the Problem of the 3D Compressible Fluid Flow around Obstacles EM wth L ou Elts o olvg th Pol o th D opssl Flu Flow ou Ostls Lut Gu o Vlsu stt hs pp psts soluto o th sgul ou tgl quto o th D opssl lu low ou ostl whh uss sopt l ou lts o Lgg tp. h sgul ou tgl quto oult

More information

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r.

Statics. Consider the free body diagram of link i, which is connected to link i-1 and link i+1 by joint i and joint i-1, respectively. = r r r. Statcs Th cotact btw a mapulato ad ts vomt sults tactv ocs ad momts at th mapulato/vomt tac. Statcs ams at aalyzg th latoshp btw th actuato dv tous ad th sultat oc ad momt appld at th mapulato dpot wh

More information

Midterm Exam. CS/ECE 181B Intro to Computer Vision. February 13, :30-4:45pm

Midterm Exam. CS/ECE 181B Intro to Computer Vision. February 13, :30-4:45pm Nam: Midtm am CS/C 8B Into to Comput Vision Fbua, 7 :-4:45pm las spa ouslvs to th dg possibl so that studnts a vnl distibutd thoughout th oom. his is a losd-boo tst. h a also a fw pags of quations, t.

More information

In the name of Allah Proton Electromagnetic Form Factors

In the name of Allah Proton Electromagnetic Form Factors I th a of Allah Poto Elctoagtc o actos By : Maj Hazav Pof A.A.Rajab Shahoo Uvsty of Tchology Atoc o acto: W cos th tactos of lcto bas wth atos assu to b th gou stats. Th ct lcto ay gt scatt lastcally wth

More information

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

Convergence tests for the cluster DFT calculations

Convergence tests for the cluster DFT calculations Covgc ss o h clus DF clculos. Covgc wh spc o bss s. s clculos o bss s covgc hv b po usg h PBE ucol o 7 os gg h-b. A s o h Guss bss ss wh csg s usss hs b us clug h -G -G** - ++G(p). A l sc o. Å h c bw h

More information

ENGG 1203 Tutorial. Difference Equations. Find the Pole(s) Finding Equations and Poles

ENGG 1203 Tutorial. Difference Equations. Find the Pole(s) Finding Equations and Poles ENGG 03 Tutoial Systms ad Cotol 9 Apil Laig Obctivs Z tasfom Complx pols Fdbac cotol systms Ac: MIT OCW 60, 6003 Diffc Equatios Cosid th systm pstd by th followig diffc quatio y[ ] x[ ] (5y[ ] 3y[ ]) wh

More information

Kummer Beta -Weibull Geometric Distribution. A New Generalization of Beta -Weibull Geometric Distribution

Kummer Beta -Weibull Geometric Distribution. A New Generalization of Beta -Weibull Geometric Distribution ttol Jol of Ss: Bs Al Rsh JSBAR SSN 37-453 Pt & Ol htt://gss.og/.h?joljolofbsaal ---------------------------------------------------------------------------------------------------------------------------

More information

Part I- Wave Reflection and Transmission at Normal Incident. Part II- Wave Reflection and Transmission at Oblique Incident

Part I- Wave Reflection and Transmission at Normal Incident. Part II- Wave Reflection and Transmission at Oblique Incident Apl 6, 3 Uboudd Mda Gudd Mda Chap 7 Chap 8 3 mls 3 o 3 M F bad Lgh wavs md by h su Pa I- Wav Rlo ad Tasmsso a Nomal Id Pa II- Wav Rlo ad Tasmsso a Oblqu Id Pa III- Gal Rlao Bw ad Wavguds ad Cavy Rsoao

More information

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov

International Journal of Advanced Scientific Research and Management, Volume 3 Issue 11, Nov 199 Algothm ad Matlab Pogam fo Softwa Rlablty Gowth Modl Basd o Wbull Od Statstcs Dstbuto Akladswa Svasa Vswaatha 1 ad Saavth Rama 2 1 Mathmatcs, Saaatha Collg of Egg, Tchy, Taml Nadu, Ida Abstact I ths

More information

Unbalanced Panel Data Models

Unbalanced 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 information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

Today s topic 2 = Setting up the Hydrogen Atom problem. Schematic of Hydrogen Atom

Today s topic 2 = Setting up the Hydrogen Atom problem. Schematic of Hydrogen Atom Today s topic Sttig up th Hydog Ato pobl Hydog ato pobl & Agula Motu Objctiv: to solv Schödig quatio. st Stp: to dfi th pottial fuctio Schatic of Hydog Ato Coulob s aw - Z 4ε 4ε fo H ato Nuclus Z What

More information

3 CP Quantum Mechanics

3 CP Quantum Mechanics Quatu Mas. Bas Assutos by ut at May 6 toug ovb 7 Modlg of s basd o t followg bas assutos: () ota sbls of ato ul dsly ld u a log ad vy aow al. () T dstas btw t ul a so sall tat all ltos boud to ts ul a

More information

3.4 Properties of the Stress Tensor

3.4 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 information

A Review of Dynamic Models Used in Simulation of Gear Transmissions

A Review of Dynamic Models Used in Simulation of Gear Transmissions ANALELE UNIVERSITĂłII ETIMIE MURGU REŞIłA ANUL XXI NR. ISSN 5-797 Zol-Ios Ko Io-ol Mulu A Rvw o ls Us Sulo o G Tsssos Th vsgo o lv s lu gg g olg l us o sov sg u o pps g svl s oug o h ps. Th pupos o h ols

More information

CE 561 Lecture Notes. Optimal Timing of Investment. Set 3. Case A- C is const. cost in 1 st yr, benefits start at the end of 1 st yr

CE 561 Lecture Notes. Optimal Timing of Investment. Set 3. Case A- C is const. cost in 1 st yr, benefits start at the end of 1 st yr CE 56 Letue otes Set 3 Optmal Tmg of Ivestmet Case A- C s ost. ost st y, beefts stat at the ed of st y C b b b3 0 3 Case B- Cost. s postpoed by oe yea C b b3 0 3 (B-A C s saved st yea C C, b b 0 3 Savg

More information

A KAM theorem for generalized Hamiltonian systems without action-angle variables

A KAM theorem for generalized Hamiltonian systems without action-angle variables ho fo gald aloa sss whou ao-agl vaabls Yo u Jo u wa Jog : aual L UG Uvs Pogag oa Popl s publ of oa : Faul of ahas L UG Uvs Pogag oa Popl s publ of oa bsa povd a ho o s of vaa o gald aloa sss whou ao-agl

More information

Posterior analysis of the compound truncated Weibull under different loss functions for censored data.

Posterior analysis of the compound truncated Weibull under different loss functions for censored data. INRNAIONA JOURNA OF MAHMAIC AND COMUR IN IMUAION Vou 6 oso yss of h oou u Wu u ff oss fuos fo so. Khw BOUDJRDA Ass CHADI Ho FAG. As I hs h Bys yss of gh u Wu suo s os u y II so. Bys sos osog ss hv v usg

More information

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission /0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power

More information

QUASI-STATIC TRANSIENT THERMAL STRESSES IN A DIRICHLET S THIN HOLLOW CYLINDER WITH INTERNAL MOVING HEAT SOURCE

QUASI-STATIC TRANSIENT THERMAL STRESSES IN A DIRICHLET S THIN HOLLOW CYLINDER WITH INTERNAL MOVING HEAT SOURCE Iteatoal Joual of Pyss ad Mateatal Sees ISSN: 77-111 (Ole) A Oe Aess, Ole Iteatoal Joual Aalable at tt://www.bte.og/js.t 014 Vol. 4 (1) Jauay-Ma,. 188-19/Solae ad Duge Resea Atle QUASI-STATIC TRANSIENT

More information

Learning Automata based Algorithms for Solving Stochastic Minimum Spanning Tree Problem

Learning Automata based Algorithms for Solving Stochastic Minimum Spanning Tree Problem Lg Autot bs Algoths fo Solvg Stohst Mu Sg T Pobl Jv Ab Tost Dtt of Cout Egg Isl Az Uvsty A Bh A I b@u.. Moh Rz Mybo Dtt of Cout Egg IT Ab Uvsty of Thology Th I Isttut fo Stus Thotl Physs Mthts IPM Shool

More information

9.6 Spherical Wave Solutions of the Scalar. Chapter 9: Radiating Systems, Multipole Fields and Radiation

9.6 Spherical Wave Solutions of the Scalar. Chapter 9: Radiating Systems, Multipole Fields and Radiation Cha 9: Raag Syss, Muo Fs a Raao A Ovvw of Chas o EM Wavs :(ov hs ous sou wav quao bouay Ch. 7 o a wav sa o wo s- sas saa by h - y a Ch. 8 o oug was - Ch. 9 J, ~ ougog g wav o sb, as a aa - Ch. J, ~ ougog

More information

Structure and Features

Structure and Features Thust l Roll ans Thust Roll ans Stutu an atus Thust ans onsst of a psly ma a an olls. Thy hav hh ty an hh loa apats an an b us n small spas. Thust l Roll ans nopoat nl olls, whl Thust Roll ans nopoat ylnal

More information

ELECTROMAGNETISM, NUCLEAR STRUCTURES & GRAVITATION

ELECTROMAGNETISM, NUCLEAR STRUCTURES & GRAVITATION . l & a s s Vo Flds o as l axwll a l sla () l Fld () l olasao () a Flx s () a Fld () a do () ad è s ( ). F wo Sala Flds s b dd l a s ( ) ad oool a s ( ) a oal o 4 qaos 3 aabls - w o Lal osas - oz abo Lal-Sd

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp

More information

Nuclear Chemistry -- ANSWERS

Nuclear Chemistry -- ANSWERS Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay

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

Chapter 6. pn-junction diode: I-V characteristics

Chapter 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 information

χ be any function of X and Y then

χ be any function of X and Y then We have show that whe we ae gve Y g(), the [ ] [ g() ] g() f () Y o all g ()() f d fo dscete case Ths ca be eteded to clude fuctos of ay ube of ado vaables. Fo eaple, suppose ad Y ae.v. wth jot desty fucto,

More information

DESIGN OF DATA ACQUISITION SYSTEM FOR MEASUREMENT OF POWER AND ENERGY USED IN CHARGING CAPACITOR BANKS.

DESIGN OF DATA ACQUISITION SYSTEM FOR MEASUREMENT OF POWER AND ENERGY USED IN CHARGING CAPACITOR BANKS. NCCI 00 -No Cof o Copuo Iuo CSIO Chdgh, INDIA, 9-0 Mh 00 DESIGN OF DATA ACQUISITION SYSTEM FOR MEASUREMENT OF POWER AND ENERGY USED IN CHARGING CAPACITOR BANKS M Pbh Mko U Shoo of Eo, Dv Ahy Uvy, Ido MP

More information

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures. Lectue 4 8. MRAC Desg fo Affe--Cotol MIMO Systes I ths secto, we cosde MRAC desg fo a class of ult-ut-ult-outut (MIMO) olea systes, whose lat dyacs ae lealy aaetezed, the ucetates satsfy the so-called

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights 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 information

ES 330 Electronics II Homework # 5 (Fall 2016 Due Wednesday, October 4, 2017)

ES 330 Electronics II Homework # 5 (Fall 2016 Due Wednesday, October 4, 2017) Pag1 Na olutions E 33 Elctonics II Howok # 5 (Fall 216 Du Wdnsday, Octob 4, 217) Pobl 1 (25 pots) A coon-itt aplifi uss a BJT with cunt ga = 1 whn biasd at I =.5 A. It has a collcto sistanc of = 1 k. (a)

More information

Integrated Optical Waveguides

Integrated Optical Waveguides Su Opls Faha Raa Cll Uvs Chap 8 Ia Opal Wavus 7 Dl Slab Wavus 7 Iu: A va f ff a pal wavus a us f a u lh a hp Th s bas pal wavu s a slab wavus shw blw Th suu s uf h - Lh s u s h b al al fl a h -la fas Cla

More information

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition ylcal aplace Soltos ebay 6 9 aplace Eqato Soltos ylcal Geoety ay aetto Mechacal Egeeg 5B Sea Egeeg Aalyss ebay 6 9 Ovevew evew last class Speposto soltos tocto to aal cooates Atoal soltos of aplace s eqato

More information

Chapter 11 Solutions ( ) 1. The wavelength of the peak is. 2. The temperature is found with. 3. The power is. 4. a) The power is

Chapter 11 Solutions ( ) 1. The wavelength of the peak is. 2. The temperature is found with. 3. The power is. 4. a) The power is Chapt Solutios. Th wavlgth of th pak is pic 3.898 K T 3.898 K 373K 885 This cospods to ifad adiatio.. Th tpatu is foud with 3.898 K pic T 3 9.898 K 50 T T 5773K 3. Th pow is 4 4 ( 0 ) P σ A T T ( ) ( )

More information

VISUALIZATION OF TRIVARIATE NURBS VOLUMES

VISUALIZATION OF TRIVARIATE NURBS VOLUMES ISUALIZATIO OF TRIARIATE URS OLUMES SAMUELČÍK Mat SK Abstact. I ths pap fcs patca st f f-f bcts a ts sazat. W xt appach f g cs a sfacs a ppa taat s bas z a -sp xpsss. O a ga s t saz g paatc s. Th sazat

More information

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating 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 information

4 CP Quantum Mechanics

4 CP Quantum Mechanics 4 Quatu Mas 4. Bas Assutos y ut at May 6 toug St 8 Modlg of s asd o t followg as assutos: () ota sls of ato ul dsly ld u a log ad vy aow al. () T dstas tw t ul a so sall tat all ltos oud to ts ul a dloald

More information

Chapter 3 Binary Image Analysis. Comunicação Visual Interactiva

Chapter 3 Binary Image Analysis. Comunicação Visual Interactiva Chapt 3 Bnay Iag Analyss Counação Vsual Intatva Most oon nghbohoods Pxls and Nghbohoods Nghbohood Vznhança N 4 Nghbohood N 8 Us of ass Exapl: ogn nput output CVI - Bnay Iag Analyss Exapl 0 0 0 0 0 output

More information

Analysis of a M/G/1/K Queue with Vacations Systems with Exhaustive Service, Multiple or Single Vacations

Analysis of a M/G/1/K Queue with Vacations Systems with Exhaustive Service, Multiple or Single Vacations Analyss of a M/G// uu wth aatons Systms wth Ehaustv Sv, Multpl o Sngl aatons W onsd h th fnt apaty M/G// uu wth th vaaton that th sv gos fo vaatons whn t s dl. Ths sv modl s fd to as on povdng haustv sv,

More information

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures Chpt Rcpocl Lttc A mpott cocpt o lyzg podc stuctus Rsos o toducg cpocl lttc Thoy o cystl dcto o x-ys, utos, d lctos. Wh th dcto mxmum? Wht s th tsty? Abstct study o uctos wth th podcty o Bvs lttc Fou tsomto.

More information

The Real Hydrogen Atom

The Real Hydrogen Atom T Ra Hydog Ato ov ad i fist od gt iddt of :.6V a us tubatio toy to dti: agti ffts si-obit ad yfi -A ativisti otios Aso av ab sift du to to sfitatio. Nd QD Dia q. ad dds o H wavfutio at sou of ti fid. Vy

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total 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 information

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi Assgmet /MATH 47/Wte Due: Thusday Jauay The poblems to solve ae umbeed [] to [] below Fst some explaatoy otes Fdg a bass of the colum-space of a max ad povg that the colum ak (dmeso of the colum space)

More information

International Journal of Mathematical Archive-6(5), 2015, Available online through ISSN

International Journal of Mathematical Archive-6(5), 2015, Available online through  ISSN Itratoal Joural of Mathmatal Arhv-6), 0, 07- Avalabl ol through wwwjmafo ISSN 9 06 ON THE LINE-CUT TRANSFORMATION RAPHS B BASAVANAOUD*, VEENA R DESAI Dartmt of Mathmats, Karatak Uvrsty, Dharwad - 80 003,

More information

Design maintenanceand reliability of engineering systems: a probability based approach

Design maintenanceand reliability of engineering systems: a probability based approach Desg mateaead relablty of egeerg systems: a probablty based approah CHPTER 2. BSIC SET THEORY 2.1 Bas deftos Sets are the bass o whh moder probablty theory s defed. set s a well-defed olleto of objets.

More information

A NEW GENERALIZATION OF KUMARASWAMY LINDLEY DISTRIBUTION

A NEW GENERALIZATION OF KUMARASWAMY LINDLEY DISTRIBUTION ou of Sttt: dv Thoy d ppto Vou 4 Nu 5 Pg 69-5 v t http://tfdv.o. DOI: http://d.do.og/.864/t_754 NW GNRLIZTION OF KUMRSWMY LINDLY DISTRIBUTION M. MHMOUD M. M. NSSR d M.. F Dptt of Mtht Futy of S Sh Uvty

More information

Consider two masses m 1 at x = x 1 and m 2 at x 2.

Consider two masses m 1 at x = x 1 and m 2 at x 2. Chapte 09 Syste of Patcles Cete of ass: The cete of ass of a body o a syste of bodes s the pot that oes as f all of the ass ae cocetated thee ad all exteal foces ae appled thee. Note that HRW uses co but

More information

Nonlinear System Identification Using Takagi-Sugeno-Kang Type Interval-valued Fuzzy Systems via Stable Learning Mechanism

Nonlinear System Identification Using Takagi-Sugeno-Kang Type Interval-valued Fuzzy Systems via Stable Learning Mechanism IANG Itatoa Joua of Cout S 8: IJCS_8 9 Noa St Idtfato Ug aag-sugo-kag Itva-vaud uzz St va Stab Lag ha Chg-Hug L ad Y-Ha L Abtat I th a w oo a tab ag ha fo ov aag-sugo-kag t tva-vaud ua fuzz t wth at fuzz

More information

Analysis of Laser-Driven Particle Acceleration from Planar Infinite Conductive Boundaries *

Analysis of Laser-Driven Particle Acceleration from Planar Infinite Conductive Boundaries * LAC-P-637 Jauay 6 Aalyss of Las-Dv Patl Alato fom Plaa ft Codutv oudas * T. Pltt.L. Gzto Laboatos tafod vsty tafod CA 9435 Abstat Ths atl xplos th gy ga fo a sgl latvst lto fom a moohomat laly polazd pla

More information

Compactness in Multiset Topology

Compactness in Multiset Topology opatess ultset Topolog Sougata ahata S K Saata Depatet of atheats Vsva-haat Satketa-7335 Ida Abstat The pupose of ths pape s to todue the oept of opatess ultset topologal spae e vestgate soe bas esults

More information

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19) TOTAL INTRNAL RFLTION Kmacs pops Sc h vcos a coplaa, l s cosd h cd pla cocds wh h X pla; hc 0. y y y osd h cas whch h lgh s cd fom h mdum of hgh dx of faco >. Fo cd agls ga ha h ccal agl s 1 ( /, h hooal

More information

A study on Ricci soliton in S -manifolds.

A study on Ricci soliton in S -manifolds. IO Joual of Mathmatc IO-JM -IN: 78-578 p-in: 9-765 olum Iu I Ja - Fb 07 PP - wwwojoualo K dyavath ad Bawad Dpatmt of Mathmatc Kuvmpu vtyhaaahatta - 577 5 hmoa Kaataa Ida Abtact: I th pap w tudy m ymmtc

More information

( ) ( ) ( ) 0. Conservation of Energy & Poynting Theorem. From Maxwell s equations we have. M t. From above it can be shown (HW)

( ) ( ) ( ) 0. Conservation of Energy & Poynting Theorem. From Maxwell s equations we have. M t. From above it can be shown (HW) 8 Conson o n & Ponn To Fo wll s quons w D B σ σ Fo bo n b sown (W) o s W w bo on o s l us n su su ul ow ns [W/ ] [W] su P su B W W 4 444 s W A A s V A A : W W R o n o so n n: [/s W] W W 4 44 9 W : W F

More information

Analyzing Control Structures

Analyzing Control Structures Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred

More information

CONTENTS. Hugo Reitzel, the pickles enthusiast CERTIFICATIONS JARS POUCHES & CANS SALAD DRESSING MINI-TUBES

CONTENTS. Hugo Reitzel, the pickles enthusiast CERTIFICATIONS JARS POUCHES & CANS SALAD DRESSING MINI-TUBES v4. APRIL 2018. Hugo Rz, h pk hu Th of h uhd gu g ou Hugo Rz. I 1909, Ag, Sw vg, h uhd h ow op wh vo: o p up h wod of od! Ad fo ov o hudd ow, w hoo h hg b ug ou xp o o ov ou bu od o bo THE od p. W ufu

More information

Power Spectrum Estimation of Stochastic Stationary Signals

Power 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 information

VIII Dynamics of Systems of Particles

VIII Dynamics of Systems of Particles VIII Dyacs of Systes of Patcles Cete of ass: Cete of ass Lea oetu of a Syste Agula oetu of a syste Ketc & Potetal Eegy of a Syste oto of Two Iteactg Bodes: The Reduced ass Collsos: o Elastc Collsos R whee:

More information

ONLY AVAILABLE IN ELECTRONIC FORM

ONLY AVAILABLE IN ELECTRONIC FORM OPERTIONS RESERH o.287/opr.8.559c pp. c c8 -copao ONLY VILLE IN ELETRONI FORM fors 28 INFORMS Elctroc opao Optzato Mols of scrt-evt Syst yacs by Wa K (Vctor ha a L Schrub, Opratos Rsarch, o.287/opr.8.559.

More information

COHERENCE SCANNING INTERFEROMETRY

COHERENCE SCANNING INTERFEROMETRY COHERENCE SCANNING INTERFEROMETRY Pt 1. Bscs, Cto Austt Sus K. Rsy PD Mc 2013 OUTLINE No cotct suc sut systs Coc sc tot Sts ISO, ASME Pt sts Vto tst Cto, ustt pocus Octv ocus optzto Scc stts w sut stts

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 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 information

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which? 5 9 Bt Ft L # 8 7 6 5 GRAPH IN CIENCE O of th thg ot oft a rto of a xrt a grah of o k. A grah a vual rrtato of ural ata ollt fro a xrt. o of th ty of grah you ll f ar bar a grah. Th o u ot oft a l grah,

More information

Load Equations. So let s look at a single machine connected to an infinite bus, as illustrated in Fig. 1 below.

Load Equations. So let s look at a single machine connected to an infinite bus, as illustrated in Fig. 1 below. oa Euatons Thoughout all of chapt 4, ou focus s on th machn tslf, thfo w wll only pfom a y smpl tatmnt of th ntwok n o to s a complt mol. W o that h, but alz that w wll tun to ths ssu n Chapt 9. So lt

More information

Reliability of time dependent stress-strength system for various distributions

Reliability 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 information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary 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 information

The tight-binding method

The tight-binding method Th tight-idig thod Wa ottial aoach: tat lcto a a ga of aly f coductio lcto. ow aout iulato? ow aout d-lcto? d Tight-idig thod: gad a olid a a collctio of wa itactig utal ato. Ovla of atoic wav fuctio i

More information

Introduction to logistic regression

Introduction 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 information

Formula overview. Halit Eroglu, 04/2014 With the base formula the following fundamental constants and significant physical parameters were derived.

Formula overview. Halit Eroglu, 04/2014 With the base formula the following fundamental constants and significant physical parameters were derived. Foula ovviw Halit Eolu, 0/0 With th bas foula th followin fundantal onstants and sinifiant physial paats w divd. aiabl usd: Spd of liht G Gavitational onstant h lank onstant α Fin stutu onstant h dud lank

More information

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction

Face Detection and Recognition. Linear Algebra and Face Recognition. Face Recognition. Face Recognition. Dimension reduction F Dtto Roto Lr Alr F Roto C Y I Ursty O solto: tto o l trs s s ys os ot. Dlt to t to ltpl ws. F Roto Aotr ppro: ort y rry s tor o so E.. 56 56 > pot 6556- stol sp A st o s t ps to ollto o pots ts sp. F

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

and integrated over all, the result is f ( 0) ] //Fourier transform ] //inverse Fourier transform

and integrated over all, the result is f ( 0) ] //Fourier transform ] //inverse Fourier transform NANO 70-Nots Chapt -Diactd bams Dlta uctio W d som mathmatical tools to dvlop a physical thoy o lcto diactio. Idal cystals a iiit this, so th will b som iiitis lii about. Usually, th iiit quatity oly ists

More information

Planar convex hulls (I)

Planar convex hulls (I) Covx Hu Covxty Gv st P o ots 2D, tr ovx u s t sst ovx oyo tt ots ots o P A oyo P s ovx or y, P, t st s try P. Pr ovx us (I) Coutto Gotry [s 3250] Lur To Bowo Co ovx o-ovx 1 2 3 Covx Hu Covx Hu Covx Hu

More information