Research Article Centralized Fuzzy Data Association Algorithm of Three-sensor Multi-target Tracking System
|
|
- Brian Chambers
- 5 years ago
- Views:
Transcription
1 Reearch Joural of Appled Scece, Egeerg ad echology 7(6): 55-6, 4 DOI:.96/rjae.7.89 ISSN: ; e-issn: Maxwell Scefc Publcao Corp. Submed: Aprl, Acceped: May 8, Publhed: February 5, 4 Reearch Arcle Ceralzed Fuzzy Daa Aocao Algorhm of hree-eor Mul-arge racg Syem Zhaofeg Su, Zhezhe Yua ad L Zhou School of Iformao ad Elecrcal Egeerg, Ludog Uvery, Yaa 645, Cha School of Egeerg, Ocea Uvery of Cha, Qgdao 66, Cha Abrac: For mprovg he effec of mul-arge racg dee arge ad cluer cearo, a ceralzed fuzzy opmal agme algorhm () of hree-eor mul-arge yem propoed. Ad o he bae of h, a geeralzed probablc daa aocao algorhm (GPDA) baed o algorhm preeed. he fuo algorhm ge effecve -uple of meaureme e by ug compoe of everal afacory oluo of he fuzzy opmal agme problem ad he ue geeralzed probablc daa aocao algorhm o calculae he updae ae of arge. Smulao reul how ha, he apec of mularge racg accuracy, algorhm uperor o he opmal agme () algorhm baed o ae emae ad GPDA algorhm beer ha algorhm. Bu coderg he me pe, algorhm ped a mmum of me ad GPDA algorhm exacly o he corary. herefore, compared wh algorhm, he wo algorhm preeed he udy each ha advaage ad hould be choe accordg o he eed of he acual applcao whe ue. Keyword: Ceralzed, fuzzy, geeralzed probablc daa aocao, mul-arge racg, he opmal agme INRODUCION Wh he rapd developme of cece ad echology, moorg yem ha bee a progre from gle-eor yem o mul-eor yem. Mul-eor mul-arge racg yem a moorg yem ug homogeeou or heerogeeou eor o decrbe he evromeal codo. Daa aocao problem a ey echque for mul-arge racg. Now he ypcal mul-eor mul-arge racg daa aocao algorhm clude Jo Probablc Daa Aocao (JPDA) algorhm, geeralzed probablc daa aocao (GPDA) algorhm, mulple hypohe racg (MH) algorhm, he opmal agme algorhm, ec (He e al., ; Ha e al., ). Each commo mul-arge racg algorhm ha uque performace characerc ad applcao evrome. Boh Jo probablc daa aocao algorhm ad geeralzed probably daa aocao algorhm are more applcable o dee arge ad cluer cearo (Pa e al., 5, 9). I geeral deeco cearo, each of hem ealy caue rac excuro or polymerzao due o whole eghborhood propere of probably calculao. Whle he opmal agme algorhm baed o cera opmzao rule more applcable o daa aocao problem uder geeral deeco evrome. I dee arge ad cluer evrome, oe-o-oe feable rule requred by he opmal agme algorhm ealy lead o more accurae aocao, reulg decreag accuracy of mularge racg (Popp e al., ; Zhag e al., 7). o play he advaage of probablc daa aocao ad he opmal agme algorhm ad avod her dadvaage, Zhou ad Zhag () propoe a geeralzed probablc daa aocao algorhm baed o he opmal agme algorhm. he reul of he ude how ha geeralzed probablc daa aocao algorhm baed o he opmal agme algorhm o oly ca avod defcecy of error accumulao ad lo of formao reuled by equeal proceg of meaureme of mul-eor geeralzed probablc daa aocao algorhm, bu alo ca effecvely avod he hgh wrog aocao rae o racg accuracy of he opmal agme algorhm by ug oe-o-oe feable rule uder dee arge ad cluer evrome. Sude have how ha, fuzzy e heory ca beer deal wh daa aocao problem dee arge ad cluer cearo (Xu ad Che, ). o furher effecvely mprove he a-jammg capably of he opmal agme algorhm of daa aocao uder Correpodg Auhor: Zhaofeg Su, School of Iformao ad Elecrcal Egeerg, Ludog Uvery, Yaa 645, Cha h wor lceed uder a Creave Commo Arbuo 4. Ieraoal Lcee (URL: hp://creavecommo.org/lcee/by/4./). 55
2 Re. J. Appl. Sc. Eg. echol., 7(6): 55-6, 4 dee arge ad cluer cearo, h udy frly propoe a fuzzy opmal agme algorhm for mul-eor mul-arge racg. O h ba, fue he algorhm above ad geeralzed probablc daa aocao algorhm ad ge a geeralzed probablc daa aocao algorhm baed o he fuzzy opmal agme algorhm. Smulao reul how ha, compared wh he opmal agme algorhm baed o ae emae, he fuzzy opmal agme algorhm o oly ha beer mul-arge racg accuracy, bu alo ha le me pe. Whle compared wh he fuzzy opmal agme algorhm, he geeralzed probablc daa aocao algorhm baed o he fuzzy opmal agme algorhm furher mprove mul-arge racg ad locao accuracy dee arge ad cluer cearo SYSEM DESCRIPIONS Le u uppoe ha he ae equao of dcreeme yem a follow: where, X ( + ) φ( ) X ( ) + G( ) V ( ) (), φ ( ) R he ae-rao marx a, me ; G( ) R repree a proce oe drbuo marx; X ( ) R a ae vecor of he arge a me ; V ( ) R a equece of zeromea, whe Gaua proce oe wh covarace marx Q () a me, proce oe of dffere me are depede,.e.: E[ V ( )] () E[ V ( ) V ] Q( ) δ l () he meaureme equao of dcree-me yem : Z ( ) H ( ) X ( ) + W ( ) (4) m where, Z ( ) R meaureme vecor a me ; m H () he meaureme marx; W ( ) R a zero-mea, whe Gaua meaureme oe equece wh covarace R () ad meaureme oe of dffere me are depede,.e.: E[ W ( )] (5) ' E[ W ( ) W ] R( ) δ (6) l V ( ) E W ( ) ~ X ( ) Q ( ) δ l ' ' [ V ( l ) W ( l ) X ~ ( ) ] R ( ) δ where, X ~ ( ) X() Xˆ ( ) l P ( ) HE FUZZY OPIMAL ASSIGNMEN ALGORIHM Fuzzy facor e ad memberhp fuco: Le u uppoe ha he fuzzy facor e U { u, u, L, u, L, u}, where u deoe he h fuzzy facor. he fuzzy facor e uually clude Eucldea dace bewee poo, veloce ad accelerao of he wo arge x ad y dreco ad Eucldea dace bewee headg ad headg chage rae of he wo arge. Becaue ha hee facor have dffere effec o aocao judgme, oly hoe facor ha play a mpora role aocao judgme ca be eleced praccal applcao. h wll o oly eure he accurae racg o he arge wh dffere moveme, bu alo ca avod he algorhm oo complex, o a o ge he goal of decreag he compuaoal burde of he formao fuo yem. Iuvely, he Eucldea dace bewee poo of arge hould be he mo mpora, he he Eucldea dace bewee headg ad dreco coe agle of arge. All hee facor coue he ma body of fuzzy aocao judgme, whle he Eucldea dace bewee accelerao ad chagg rae of headg or dreco coe agle ca be ued a he auxlary judgme, he wegh ca be ae a a very mall value or zero. Le u uppoe ha he wegh agme vecor of he fuzzy facor e U A { a, a, L, a }, where a he correpodg wegh of he h facor u. I geeral, a, where he eleco of (7) a eed o be decded by he mporace or mpac of he yh facor. Uually, a a al a ad he wegh of he fal fuzzy facor are very mall or zero. Accordg o he characerc of fuzzy facor po-rac aocao yem, ormal drbuo memberhp fuco adoped h udy. he al ae decrbed a X () whch Deermao of he fuzzy facor: I h udy, oe elec fuzzy facor bewee meaureme po Z follow a Gau drbuo, wh mea ˆX ( ) ad (l) [x (l), y (l), (l), (l)] ad predced meaureme covarace P ( ) ad: (l l-) a follow: 56
3 Re. J. Appl. Sc. Eg. echol., 7(6): 55-6, 4 u ˆ + ( ˆ ˆ ( l) [(ˆ x x ( l l )) y y ( l l )) ] ( ) [ ˆ ( ) + ˆ ( )] [ ˆ ( ) + ˆ u l x& l y& l l y& l y& ( l l )] u( l) θ,, L, m ; j,, L,. (8) where m he umber of meaureme cofrmao rego a pree me: θ a [ y& / x& ] (9) a ˆ ( ) / ˆ [ y& l l x& ( l l )] Suppoe ha he bac probably agme fuco for meaureme from dffere eor o arge (,, L ) are m, m ad m, repecvely, focal eleme are Ap, Bq ad Cr, repecvely, he he coe meaure of he meaureme of he fr eor, he h h meaureme of he ecod eor ad he h meaureme of he hrd eor ca be decrbed a: c m ( A ) m ( B ) m p q ApIBqICr φ ( C ) r (6) x & [ x xˆ ( l )] / () ˆ ( ) ˆ x& l l x& ( l l ) () where amplg erval. he fuzzy opmal agme algorhm: Le u uppoe ha he memberhp fuco decrbg he mlary bewee he po ad rac baed o he fac : h µ τ σ L () ( u ) exp( ( u / )),,,,. L a, by ug he weghed average mehod, oe ca ge he egraed mlary degree bewee meaureme po Z ( l ) ad rac a: For he fuzzy wegh e A { a, a,, } f a µ,,, L, M,,, L,. () he he fuzzy aocao marx bewee meaureme po Z ( l ) ad rac a me l ca be expreed a: f f L f f f f F L L L L L fm f ( ) ( ) m l L f m l (4) By ormalzg each row of marx (4), oe ca ge he bac probably agme marx of meaureme o dffere arge a:, M ( m ),, L, m ;,, L, (5) where m he bac probably agme fuco for meaureme o arge. 57 where, Ar, B, C U. U he defcao frame of arge. he ) he aocao co C ( c marx of meaureme daa of hree eor ad he daa aocao problem of hree-eor mul-arge yem ca be raformed o a -D agme problem a follow: mc ubjec o: ; ; ;,, L,,, L,,, L, (7a) (7b) where, are dcaor varable. If a -uple of meaureme orgae from a real arge, hould be ; oherwe,. GENERALIZED PROBABILISIC DAA ASSOCIAION ALGORIHM BASED ON FUZZY OPIMAL ASSIGNMEN Coderg ha he fuzzy opmal agme algorhm a globally opmal agme algorhm baed o oe-o-oe feable rule, dee arge ad cluer evrome, wll loe a lo of formao o mpleme po-rac aocao by oly ug compoe of he opmal oluo of (7a-7b) a effecve -uple of meaureme. Ad he geeralzed probablc daa aocao algorhm of equeal proceg of meaureme from mul-eor a cacade algorhm. I he equeal proce, wll loe ome orgal formao ad ca ealy lead o error accumulao, reulg mul-arge racg preco declg. o olve he problem, h eco
4 propoe a geeralzed probablc daa aocao algorhm baed o he fuzzy opmal agme. I eay o ee ha each compoe of oluo of (7a-7b) happe o correpod o a effecve -uple of meaureme. All compoe he eleced good oluo co of he effecve -uple of meaureme e. herefore, oe ca oba a equvale meaureme by emag he poo of he correpodg arge for each effecve -uple of meaureme. Ad by calculag he aocao probably bewee he equvale meaureme ad arge rac, oe ca ae advaage of GPDA algorhm o rac mul-arge. I h way, oe ge he geeralzed probablc daa aocao algorhm baed o he fuzzy opmal agme. he followg he dealed decrpo of he fuo algorhm. For each vald -uple of meaureme, oe ca emae he poo x, y ) of he correpodg ( arge (,, L, ) by ug he followg formula: where, xˆ yˆ, x σ y x y x σ y Re. J. Appl. Sc. Eg. echol., 7(6): 55-6, 4 x σ σ (8) y σ σ are he locao varace x, y dreco, repecvely. x x + u, y y + v are he locao compoe of arge meaured by eor wo coordae axe, repecvely. ae he poo emae of correpodg arge of he h -uple of meaureme a he compreheve meaureme z,,, m, he oe ca calculae he aocao probably bewee he h -uple of meaureme ad arge rac. Furher, he updae ae of arge ca be calculaed by ug GPDA algorhm. he ae ad covarace updae formula are a follow: Xˆ ( ) Xˆ ( ) + K ( ) m β ( ) v ( ) (9) where, ˆ ˆ X ( ) F ( ) X ( ) he ae predco of arge a me, K () fler ga of arge a me, ˆ v ( ) z ( ) Z ( ) meaureme of redual: where, P ( ) β ( ) P ( ) ~ + ( β ( )) P ( ) + P ( ) c () 58 c P ( ) [ I K ( ) H ( )] P ( ) () ~ P ( ) W ( ) P ( ) W ( ) () v P ( ) m [ m v β ( ) v ( ) v β ( ) v ( )][ ( ) m β ( ) v ( )] SIMULAION ANALYSES () I order o verfy he valdy of he propoed algorhm, algorhm, algorhm ad GPDA algorhm are compared dffere mulao codo. I he mulao, aumed ha hree eor rac egh arge ad each arge move a a varable peed a plae. he al velocy are repecvely v x 4 /, v y / ; agle meaureme error ad ragg meaureme error of he hree eor are repecvely he ame; deeco probably Pd. 95 ; gae probably Pg ; he al value of he wegh vecor ae a a. 7, a. ad a. ; radar amplg me ; cluer coeffce λ. he mulao ep are 5 ad mulao umber 5. he mulao reul are a follow: Whe arge-o-arge erval τ m, comparo of roo-mea-quare error (RMSE) of algorhm, algorhm ad GPDA algorhm are a follow: Whe arge-o-arge erval τ 5m, comparo of RMSE of algorhm, algorhm ad GPDA algorhm are a follow able how he average me pe of dffere algorhm whe he arge-o-arge erval 5 m, he ragg meaureme error m ad he agle error.rad. Smulao ep are 5 ad mulao umber 5. I ca be ee from Fg. ha whe arge-o-arge erval o very mall, algorhm beer ha algorhm, bu he opmzao of magude o bg. Ad oe ca ee from Fg. o 4, wh he deerorao of meaureme evrome, he magude of algorhm beg uperor o algorhm correpodgly creae. he reul how ha, compared wh algorhm, algorhm more uable for dee arge ad cluer evrome. I alo ca be ee from Fg. o 4, comparo wh algorhm, GPDA ha more accurae arge racg effec ad beer racg
5 Re. J. Appl. Sc. Eg. echol., 7(6): 55-6, 4 able : Comparo of me pe of dffere algorhm CMS- Algorhm FOAGPDA me () Fg. : RMSE of hree algorhm uder codo of e m, e.rad r me Sep θ GPDA GPDA 5 5 Fg. 4: RMSE of hree algorhm uder codo of e r m, e θ.rad feable rule dee arge ad cluer cearo, bu alo her he advaage of a-jammg performace of GPDA algorhm. herefore, varou mulao codo, he fuo algorhm how beer performace of mul-arge racg. able how ha algorhm ped le me ha he correpodg reul of CMS -OA algorhm, whle GPDA ped lghly more me ha he correpodg reul of CMS -OA algorhm. CONCLUSION GPDA 5 5 me Sep 5 5 me Sep Fg. : RMSE of hree algorhm uder codo of e r m, e θ.rad 5 4 GPDA 5 5 me Sep Fg. : RMSE of hree algorhm uder codo of e r m, e θ.rad ably. I maly becaue ha GPDA o oly effecvely avod formao lo ad error accumulao caued by equeal proceg meaureme from dffere eor CMS-GPDA algorhm ad avod he deerorag daa aocao qualy by ug algorhm uder oe-o-oe 59 I order o beer olve mul-eor mul-arge racg problem, a fuzzy opmal agme algorhm of daa aocao of hree eor yem propoed. he algorhm fr ue fuzzy compreheve mlary o coruc po-rac correlao marx ad he ue combao rule of D-S evdece heory o coruc meaureme daa aocao marx of hree eor baed o fuzzy compreheve mlary. Smulao reul how ha he fuzzy opmal agme algorhm uperor o he opmal agme algorhm baed o ae emae. o furher ehace he a-jammg performace of he fuzzy opmal agme algorhm, h udy pu forward a fuo algorhm whch ue compoe of everal good oluo of he fuzzy opmal agme algorhm a effecve -uple of meaureme ad he ae advaage of geeralzed probablc daa aocao algorhm o rac mul-arge. Compared wh he fuzzy opmal agme algorhm, he geeralzed probablc daa aocao algorhm baed o he fuzzy opmal agme algorhm furher mprove he accuracy of mul-arge racg dee arge ad cluer evrome, bu he me pe alo creae accordgly. How o furher opmze he performace of he fuo algorhm a problem whch eed o furher udy he fuure.
6 Re. J. Appl. Sc. Eg. echol., 7(6): 55-6, 4 REFERENCES Ha, C.Z., H.Y. Zhu ad Z.S. Dua,. Mul-ource Iformao Fuo. ghua Uvery Pre, Bejg. He, Y., G.H. Wag, D.J. Lu ad Y.N. Peg,. Muleor Iformao Fuo wh Applcao. Publhg Houe of Elecroc Idury, Bejg. Pa, Q., X.N. Ye ad H.C. Zhag, 5. Geeralzed probably daa aocao algorhm. Aca Elecro. Sca, (): Pa, Q., Y. Lag, F. Yag ad Y.M. Cheg, 9. Moder arge racg ad Iformao Fuo. Naoal Defee Idury Pre, Bejg. Popp, R., K. Papa ad Y. Bar-Shalom,. M-be S-D agme algorhm wh applcao o mularge acg. IEEE. Aero. Elec. Sy., 7(): -9. Xu, Y.Y. ad X.H. Che,. Fuzzy e heory he mul-eor formao fuo. Compu. Appl. Sofw., 8(): -4. Zhag, J.W., Y. He ad W. Xog, 7. Muleor mulpled hypohe algorhm baed o daa compreg echc. J. Bejg Uv., Aeroau. Aroau., (): Zhou, L. ad W.H. Zhag,. Geeral probablc daa aocao algorhm baed o he opmal agme. J. Compu. Iform. Sy., 8(7):
Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination
Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of
More informationEfficient Estimators for Population Variance using Auxiliary Information
Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav
More informationCompetitive Facility Location Problem with Demands Depending on the Facilities
Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg
More informationSome Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling
Vplav Kumar gh Rajeh gh Deparme of ac Baara Hdu Uver Varaa-00 Ida Flore maradache Uver of ew Meco Gallup UA ome Improved Emaor for Populao Varace Ug Two Aular Varable Double amplg Publhed : Rajeh gh Flore
More informationReliability Equivalence of a Parallel System with Non-Identical Components
Ieraoa Mahemaca Forum 3 8 o. 34 693-7 Reaby Equvaece of a Parae Syem wh No-Ideca ompoe M. Moaer ad mmar M. Sarha Deparme of Sac & O.R. oege of Scece Kg Saud Uvery P.O.ox 455 Ryadh 45 Saud raba aarha@yahoo.com
More informationNew approach for numerical solution of Fredholm integral equations system of the second kind by using an expansion method
Ieraoal Reearch Joural o Appled ad Bac Scece Avalable ole a wwwrabcom ISSN 5-88X / Vol : 8- Scece xplorer Publcao New approach or umercal oluo o Fredholm eral equao yem o he ecod d by u a expao mehod Nare
More informationThe Signal, Variable System, and Transformation: A Personal Perspective
The Sgal Varable Syem ad Traformao: A Peroal Perpecve Sherv Erfa 35 Eex Hall Faculy of Egeerg Oule Of he Talk Iroduco Mahemacal Repreeao of yem Operaor Calculu Traformao Obervao O Laplace Traform SSB A
More informationPractice Final Exam (corrected formulas, 12/10 11AM)
Ecoomc Meze. Ch Fall Socal Scece 78 Uvery of Wco-Mado Pracce Fal Eam (correced formula, / AM) Awer all queo he (hree) bluebook provded. Make cera you wre your ame, your ude I umber, ad your TA ame o all
More informationThe MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p
Reearch Joural of Aled Scece Eeer ad Techoloy (6): 28-282 22 ISSN: 2-6 Maxwell Scefc Orazao 22 Submed: March 26 22 Acceed: Arl 22 Publhed: Auu 5 22 The MacWllam Idey of he Lear ode over he R F +uf +vf
More informationDeterioration-based Maintenance Management Algorithm
Aca Polyechca Hugarca Vol. 4 No. 2007 Deerorao-baed Maeace Maageme Algorhm Koréla Ambru-Somogy Iue of Meda Techology Budape Tech Doberdó ú 6 H-034 Budape Hugary a_omogy.korela@rkk.bmf.hu Abrac: The Road
More informationThe Linear Regression Of Weighted Segments
The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed
More informationKey words: Fractional difference equation, oscillatory solutions,
OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg
More informationAnalysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays
Ieraoal Coferece o Appled Maheac Sulao ad Modellg (AMSM 6) Aaly of a Sochac Loa-Volerra Copeve Sye wh Drbued Delay Xagu Da ad Xaou L School of Maheacal Scece of Togre Uvery Togre 5543 PR Cha Correpodg
More informationThe Optimal Combination Forecasting Based on ARIMA,VAR and SSM
Advaces Compuer, Sgals ad Sysems (206) : 3-7 Clausus Scefc Press, Caada The Opmal Combao Forecasg Based o ARIMA,VAR ad SSM Bebe Che,a, Mgya Jag,b* School of Iformao Scece ad Egeerg, Shadog Uversy, Ja,
More informationESTIMATION AND TESTING
CHAPTER ESTIMATION AND TESTING. Iroduco Modfcao o he maxmum lkelhood (ML mehod of emao cera drbuo o overcome erave oluo of ML equao for he parameer were uggeed by may auhor (for example Tku (967; Mehrora
More informationIMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS
Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &
More informationThe algebraic immunity of a class of correlation immune H Boolean functions
Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales
More informationCS344: Introduction to Artificial Intelligence
C344: Iroduco o Arfcal Iellgece Puhpa Bhaacharyya CE Dep. IIT Bombay Lecure 3 3 32 33: Forward ad bacward; Baum elch 9 h ad 2 March ad 2 d Aprl 203 Lecure 27 28 29 were o EM; dae 2 h March o 8 h March
More informationTopic 2: Distributions, hypothesis testing, and sample size determination
Topc : Drbuo, hypohe eg, ad ample ze deermao. The Sude - drbuo [ST&D pp. 56, 77] Coder a repeaed drawg of ample of ze from a ormal drbuo. For each ample, compue,,, ad aoher ac,, where: ( ) The ac he devao
More informationThe ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.
C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)
More informationResearch on portfolio model based on information entropy theory
Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceucal esearch, 204, 6(6):286-290 esearch Arcle ISSN : 0975-7384 CODEN(USA) : JCPC5 esearch o porfolo model based o formao eropy heory Zhag Jusha,
More informationA Survey of Rigid 3D Pointcloud Registration Algorithms
AMBIEN 2014 : he Fourh Ieraoal Coferece o Ambe Compug, Applcao, Servce ad echologe A Survey of Rgd 3D Pocloud Regrao Algorhm Be Belleke, Vce Spruy, Rafael Berkve, ad Maare Wey CoSy-Lab, Faculy of Appled
More informationSolution set Stat 471/Spring 06. Homework 2
oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o
More informationPARAMETER OPTIMIZATION FOR ACTIVE SHAPE MODELS. Contact:
PARAMEER OPIMIZAION FOR ACIVE SHAPE MODELS Chu Che * Mg Zhao Sa Z.L Jaju Bu School of Compuer Scece ad echology, Zhejag Uvery, Hagzhou, Cha Mcroof Reearch Cha, Bejg Sgma Ceer, Bejg, Cha Coac: chec@zju.edu.c
More informationQR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA
QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.
More informationCalibration Approach Based Estimators of Finite Population Mean in Two - Stage Stratified Random Sampling
I.J.Curr.crobol.App.Sc (08) 7(): 808-85 Ieraoal Joural of Curre crobolog ad Appled Scece ISS: 39-7706 olue 7 uber 0 (08) Joural hoepage: hp://www.jca.co Orgal Reearch Arcle hp://do.org/0.0546/jca.08.70.9
More informationVARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,
Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs
More informationMidterm Exam. Tuesday, September hour, 15 minutes
Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.
More informationThe Poisson Process Properties of the Poisson Process
Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad
More informationθ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:
Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log
More informationAML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending
CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral
More informationParameters Estimation in a General Failure Rate Semi-Markov Reliability Model
Joura of Saca Theory ad Appcao Vo. No. (Sepember ) - Parameer Emao a Geera Faure Rae Sem-Marov Reaby Mode M. Fahzadeh ad K. Khorhda Deparme of Sac Facuy of Mahemaca Scece Va-e-Ar Uvery of Rafaja Rafaja
More informationThe Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting
Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad
More informationFault Diagnosis in Stationary Rotor Systems through Correlation Analysis and Artificial Neural Network
Faul Dago Saoary oor Syem hrough Correlao aly ad rfcal Neural Newor leadre Carlo duardo a ad obo Pederva b a Federal Uvery of Ma Gera (UFMG). Deparme of Mechacal geerg (DMC) aceduard@homal.com b Sae Uvery
More informationStandby Redundancy Allocation for a Coherent System under Its Signature Point Process Representation
merca Joural of Operao Reearch, 26, 6, 489-5 hp://www.crp.org/joural/ajor ISSN Ole: 26-8849 ISSN Pr: 26-883 Sadby Redudacy llocao for a Cohere Syem uder I Sgaure Po Proce Repreeao Vaderle da Coa ueo Deparme
More informationAn Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys
"cece as True Here" Joural of Mahemacs ad ascal cece, Volume 06, 78-88 cece gpos Publshg A Effce Dual o Rao ad Produc Esmaor of Populao Varace ample urves ubhash Kumar Yadav Deparme of Mahemacs ad ascs
More informationRedundancy System Fault Sampling Under Imperfect Maintenance
A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce
More informationA moment closure method for stochastic reaction networks
THE JOURNAL OF CHEMICAL PHYSICS 3, 347 29 A mome cloure mehod for ochac reaco ewor Chag Hyeog Lee,,a Kyeog-Hu Km, 2,b ad Plwo Km 3,c Deparme of Mahemacal Scece, Worceer Polyechc Iue, Iue Road, Worceer,
More informationProbability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract
Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.
More informationNature and Science, 5(1), 2007, Han and Xu, Multi-variable Grey Model based on Genetic Algorithm and its Application in Urban Water Consumption
Naure ad Scece, 5, 7, Ha ad u, ul-varable Grey odel based o Geec Algorhm ad s Applcao Urba Waer Cosumpo ul-varable Grey odel based o Geec Algorhm ad s Applcao Urba Waer Cosumpo Ha Ya*, u Shguo School of
More informationModeling by Meshless Method LRPIM (local radial point interpolation method)
ème ogrè Fraça de Mécaque Lyo, 4 au 8 Aoû 5 Modelg by Mehle Mehod LRPM (local radal po erpolao mehod) Abrac: A. Mouaou a,. Bouzae b a. Deparme of phyc, Faculy of cece, Moulay mal Uvery B.P. Meke, Morocco,
More informationDetermination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction
refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad
More informationOptimal Eye Movement Strategies in Visual Search (Supplement)
Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case
More informationReal-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF
EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae
More informationFALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.
Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50
More information(1) Cov(, ) E[( E( ))( E( ))]
Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )
More informationLeast Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters
Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo
More informationFor the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.
The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe
More informationThe Histogram. Non-parametric Density Estimation. Non-parametric Approaches
The Hogram Chaper 4 No-paramerc Techque Kerel Pare Wdow Dey Emao Neare Neghbor Rule Approach Neare Neghbor Emao Mmum/Mamum Dace Clafcao No-paramerc Approache A poeal problem wh he paramerc approache The
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
More informationPosition Coordination of a Linear Teleoperation System with Constant Time Delay
Poo Coordao of a Lear eleoperao Syem wh Coa me Delay Kamra Raz, S. MJ. Yazdapaah, Saeed Shry Ghdary Abrac A lear me vara coroller deg for a blaeral eleoperao of a par of N-DO lear roboc yem uder coa me
More informationGeneral Complex Fuzzy Transformation Semigroups in Automata
Joural of Advaces Compuer Research Quarerly pissn: 345-606x eissn: 345-6078 Sar Brach Islamc Azad Uversy Sar IRIra Vol 7 No May 06 Pages: 7-37 wwwacrausaracr Geeral Complex uzzy Trasformao Semgroups Auomaa
More informationEE 6885 Statistical Pattern Recognition
EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,
More informationSecond Quantization for Fermions
9 Chaper Secod Quazao for Fermo Maro Pr Iuo Superor de Ceca y Tecología Nucleare, Ave Salvador Allede y Luace, Qua de lo Molo, La Habaa 6, Cuba. The objec of quaum chemry co of eracg may parcle yem of
More informationSurvival Prediction Based on Compound Covariate under Cox Proportional Hazard Models
Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae
More informationFundamentals of Speech Recognition Suggested Project The Hidden Markov Model
. Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces
More informationInternational Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.
www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra
More informationFORCED VIBRATION of MDOF SYSTEMS
FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me
More informationPolicy optimization. Stochastic approach
Polcy opmzo Sochc pproch Dcree-me Mrkov Proce Sory Mrkov ch Sochc proce over fe e e S S {.. 2 S} Oe ep ro probbly: Prob j - p j Se ro me: geomerc drbuo Prob j T p j p - 2 Dcree-me Mrkov Proce Sory corollble
More informationReliability Analysis. Basic Reliability Measures
elably /6/ elably Aaly Perae faul Œ elably decay Teporary faul Œ Ofe Seady ae characerzao Deg faul Œ elably growh durg eg & debuggg A pace hule Challeger Lauch, 986 Ocober 6, Bac elably Meaure elably:
More information(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function
MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,
More informationCyclone. Anti-cyclone
Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme
More informationLeast squares and motion. Nuno Vasconcelos ECE Department, UCSD
Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem
More informationNew Entropy Weight-Based TOPSIS for Evaluation of Multi-objective Job-Shop Scheduling Solutions
Eropy Wegh-Baed TOPSIS for Evaluao of Mul-obecve Job-Shop Schedulg Soluo J Wag, J Che, T u 2, George uag 2, Y F Zhag, S D Su Key Laboraory of Coeporary Deg ad Iegraed Maufacurg Techology, Mry of Educao,
More informationFinal Exam Applied Econometrics
Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc
More informationThe Bernstein Operational Matrix of Integration
Appled Mahemacal Sceces, Vol. 3, 29, o. 49, 2427-2436 he Berse Operaoal Marx of Iegrao Am K. Sgh, Vee K. Sgh, Om P. Sgh Deparme of Appled Mahemacs Isue of echology, Baaras Hdu Uversy Varaas -225, Ida Asrac
More informationBEST PATTERN OF MULTIPLE LINEAR REGRESSION
ERI COADA GERMAY GEERAL M.R. SEFAIK AIR FORCE ACADEMY ARMED FORCES ACADEMY ROMAIA SLOVAK REPUBLIC IERAIOAL COFERECE of SCIEIFIC PAPER AFASES Brov 6-8 M BES PAER OF MULIPLE LIEAR REGRESSIO Corel GABER PEROLEUM-GAS
More informationThe Theory of Membership Degree of Γ-Conclusion in Several n-valued Logic Systems *
erca Joural of Operao eearch 0 47-5 hp://ddoorg/046/ajor007 Publhed Ole Jue 0 (hp://wwwscporg/joural/ajor) The Theory of Meberhp Degree of Γ-Cocluo Several -Valued Logc Sye Jacheg Zhag Depare of Maheac
More informationContinuous Time Markov Chains
Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,
More informationRanking Prior Likelihood Distributions for Bayesian Shape Localization Framework
Rag Pror Lelhood Drbuo for Bayea Shape Localzao Framewor Ý Shucheg Ya Ý, Mgg L Ý, Hogag Zhag Ý, Qaheg Cheg Ý Ý School of Mahemacal Scece, Peg Uvery, 87, Cha Mcroof Reearch Aa, Beg Sgma Ceer, Beg, 8, Cha
More informationFully Fuzzy Linear Systems Solving Using MOLP
World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,
More information4. THE DENSITY MATRIX
4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o
More informationModified Integrated Multi-Point Approximation And GA Used In Truss Topology Optimization
Joural of Muldscplary Egeerg Scece ad echology (JMES) Vol. 4 Issue 6, Jue - 2017 Modfed Iegraed Mul-Po Appromao Ad GA sed I russ opology Opmzao Adurahma M. Hasse 1, Mohammed A. Ha 2 Mechacal ad Idusral
More informationCyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles
Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of
More informationComparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution
Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm
More informationReal-time Classification of Large Data Sets using Binary Knapsack
Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule
More informationSolution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations
Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare
More informationPLUTONIUM FECAL AND URINARY EXCRETION FUNCTIONS: DERIVATION FROM A SYSTEMIC WHOLE-BODY RETENTION FUNCTION
T--5, P-a-85 PLUTONIU FECL ND URINRY EXCRETION FUNCTIONS: DERIVTION FRO SYSTEIC WHOLE-BODY RETENTION FUNCTION Dewhey Lee Radao Safey Ceer, Korea Iue of Nuclear Safey, P.O. Box, Yuog, Taejo 05-600, Korea
More informationSpeech, NLP and the Web
peech NL ad he Web uhpak Bhaacharyya CE Dep. IIT Bombay Lecure 38: Uuperved learg HMM CFG; Baum Welch lecure 37 wa o cogve NL by Abh Mhra Baum Welch uhpak Bhaacharyya roblem HMM arg emac ar of peech Taggg
More informationA Constitutive Model for Multi-Line Simulation of Granular Material Behavior Using Multi-Plane Pattern
Joural of Compuer Scece 5 (): 8-80, 009 ISSN 549-009 Scece Publcaos A Cosuve Model for Mul-Le Smulao of Graular Maeral Behavor Usg Mul-Plae Paer S.A. Sadread, A. Saed Darya ad M. Zae KN Toos Uversy of
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 informationRECURSIVE IDENTIFICATION BASED ON NONLINEAR STATE SPACE MODELS APPLIED TO DRUM-BOILER DYNAMICS WITH NONLINEAR OUTPUT EQUATIONS
005 Amerca Corol Coferece Je 8-0, 005 Porlad, OR, UA FrC54 RECURVE DENFCAON BAED ON NONLNEAR AE PACE MODEL APPLED O DRUM-BOLER DYNAMC WH NONLNEAR OUPU EQUAON orbjör Wgre, eor Member, EEE Abrac he paper
More informationSolution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.
ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh
More informationNotes on MRI, Part III
oll 6 MRI oe 3: page oe o MRI Par III The 3 rd Deo - Z The 3D gal equao ca be wre a follow: ep w v u w v u M ddd where Muvw he 3D FT of. I he p-warp ehod for D acquo oe le a a e acqured he D Fourer doa
More informationSolving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision
Frs Jo Cogress o Fuzzy ad Iellge Sysems Ferdows Uversy of Mashhad Ira 9-3 Aug 7 Iellge Sysems Scefc Socey of Ira Solvg fuzzy lear programmg problems wh pecewse lear membershp fucos by he deermao of a crsp
More informationMoments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables
Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.
More informationPartial Molar Properties of solutions
Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a
More informationNew Guaranteed H Performance State Estimation for Delayed Neural Networks
Ieraoal Joural of Iformao ad Elecrocs Egeerg Vol. o. 6 ovember ew Guaraeed H Performace ae Esmao for Delayed eural eworks Wo Il Lee ad PooGyeo Park Absrac I hs paper a ew guaraeed performace sae esmao
More informationFault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview
Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos
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 informationReliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach
Relably Aalyss of Sparsely Coece Cosecuve- Sysems: GERT Approach Pooa Moha RMSI Pv. L Noa-2131 poalovely@yahoo.com Mau Agarwal Deparme of Operaoal Research Uversy of Delh Delh-117, Ia Agarwal_maulaa@yahoo.com
More informationMixed Integral Equation of Contact Problem in Position and Time
Ieraoal Joural of Basc & Appled Sceces IJBAS-IJENS Vol: No: 3 ed Iegral Equao of Coac Problem Poso ad me. A. Abdou S. J. oaquel Deparme of ahemacs Faculy of Educao Aleadra Uversy Egyp Deparme of ahemacs
More informationStudy on Operator Reliability of Digital Control System in Nuclear Power Plants Based on Boolean Network
Sudy o Operaor Relably of Dgal Corol Sysem Nuclear Power Plas Based o Boolea Nework Yahua Zou a,b,c, L Zhag a,b,c, Lcao Da c, Pegcheg L c a Isue of Huma Facors Egeerg ad Safey Maageme, Hua Isue of Techology,
More informationChapter 8. Simple Linear Regression
Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple
More informationA Demand System for Input Factors when there are Technological Changes in Production
A Demand Syem for Inpu Facor when here are Technologcal Change n Producon Movaon Due o (e.g.) echnologcal change here mgh no be a aonary relaonhp for he co hare of each npu facor. When emang demand yem
More informationRATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA
The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao
More informationAsymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse
P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc
More informationEMD Based on Independent Component Analysis and Its Application in Machinery Fault Diagnosis
30 JOURNAL OF COMPUTERS, VOL. 6, NO. 7, JULY 0 EMD Based o Idepede Compoe Aalyss ad Is Applcao Machery Faul Dagoss Fegl Wag * College of Mare Egeerg, Dala Marme Uversy, Dala, Cha Emal: wagflsky997@sa.com
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 informationRegression Approach to Parameter Estimation of an Exponential Software Reliability Model
Amerca Joural of Theorecal ad Appled Sascs 06; 5(3): 80-86 hp://www.scecepublshggroup.com/j/ajas do: 0.648/j.ajas.060503. ISSN: 36-8999 (Pr); ISSN: 36-9006 (Ole) Regresso Approach o Parameer Esmao of a
More information