A Fuzzy Mathematical Programming Approach to DEA Models

Size: px
Start display at page:

Download "A Fuzzy Mathematical Programming Approach to DEA Models"

Transcription

1 Aeca Ja f Apped Scece 5 (0): , 2008 ISSN Scece Pbcat A Fzzy Matheatca Pgag Appach t DEA Mde A. Azadeh, S.F.Ghade, Z. Javahe ad 2 M. Sabe Depatet f Idta Egeeg ad Cete f Eceece f Iteget Baed Epeeta Mechac ad Reeach Ittte f Eegy Maageet ad Pag, Cege f Egeeg, Uvety f Teha, P.O. B , Ia 2 Depatet f Idta Egeeg, Uvety f Tafeh, Ia Abtact: Evaatg the peface f actvte gazat by tadta Data Evepet Aay (DEA) a effcecy fte aay de eqe cp pt/tpt data. Hweve, ea-wd pbe pt ad tpt ae fte pece. Th tdy devep DEA de g pece data epeeted by fzzy et. A ptat tce f aeg eatve effcece wth a gp f Dec Makg Ut (DMU) fzzy data evepet aay t detee effcet DMU. We fd effcecy eae wth fzzy pt ad tpt va pped de. A eape g fzzy data peeted f tatve ppe. We appy th ethd the appcat t the pwe geeat ect f Ia. Key wd: Fzzy gc, ptzat, data evepet aay, pwe pat INTRODUCTION Effcecy fte aay ha bee a ptat appach f evaatg f peface pvate ad pbc ect. Thee have bee ay effcecy fte aay ethd epted the teate. Hweve, the apt ade f each f thee ethd ae etctve. Each f thee ethdge ha t tegth a we a aj tat epecay etvty f fte de t data cae t e a fzzy atheatca pgag appach t the aeet f effcecy wth DEA de deveped. The ft tdy fzzy DEA wa wtte 992 [24]. The ath f tdy eped the e f fzzy et they dec akg [24]. I th tdy, thee type f fzzy de (fzzy atheatca pgag, fzzy ege ad fzzy etpy) wee peeted t tate the type f dec ad t that wee achevabe. Meve, the tdy akg ethd ed f deteg the effcecy f DMU pteted CCR de wth fzzy pt ad fzzy tpt [3]. A the eathp betwee DEA ad Rege Aay (RA) tded. The CCR de ad RA wee cdeed a tw peca cae f the fwg ga pgag pbe: DEARA(cbat f DEA ad RA) : G = (aρ + bη ) T T v y = ρ η T 0 =,v 0 ρ, η 0 =,..., () The the devep e fzzy ve f the caca DEA de by g e akg ethd baed the cpa f -ct [26]. Th ake a appach whch abe t dea wth eact be be age, deabe. T dea qattatvey wth pec dec pce, the t f fzze tdced [24]. I the cveta DEA appach, a et f weght whch atfe a et f ctat eected t gve the hghet pbe effcecy eae f each DMU. Whe e bevat ae fzzy, the ga ad ctat the dec pce bece fzzy a we. Sce the DEA de eetay a ea pga, e taghtfwad dea t appy the etg fzzy Lea Pgag (LP) techqe t the fzzy DEA pbe [3,4,3,6,8,9,26]. Uftatey, t f the etg techqe y pvde cp t ad the ae y tabe f pecfc pbe, athgh they ae abe t pdce pbty dtbt f the pta bjectve vae [3,4,3,68,9,26]. Thee ae atce Cepdg Ath: A. Azadeh, Depatet f Idta Egeeg, Cege f Egeeg, Uvety f Teha, P.O. B , Ia 352

2 A. J. Apped Sc., 5 (0): , 2008 dcg effcecy eae whe the bevat ae ad, yet t fzzy, ate. A tchatc DEA de va pecfc ebehp fct t gve a fzzy pgag tepetat tafed [2,23,25]. Tw DEA de ae fated: e de that gve ppe t (bet cae) effcecy ad e de that gve we t (we cae) effcecy [8]. The a teva-vaed effcecy ca be ctcted f thee tw etee effcece. F the ppe t cae, the de the ae a the CCR de. Hweve, y cp effcecy eae ae pvded. O the whe, thee ae the thee pcede that have bee dced vg fzzy DEA pbe. The ft the pcede that ve the fzzy DEA by the teace appach. The et, vg the fzzy DEA by the akg appach ad the at t ve the fzzy DEA by the paaetc pgag. I th tdy we devep a ethd whch abe t pvde fzzy effcecy eae f DMU wth fzzy bevat. Retated, the ebehp fct, athe tha cp eae, f effcece w be deved. The bac dea t appy the -ct t taf the fzzy DEA de t a ee f cveta cp DEA de. The cveta DEA de ae the ved by the LP ethd. DEA AND FUZZY DEA Data Evepet Aay (DEA) a ethdgy baed a Lea Pgag (LP) de f evaatg eatve effcece f Dec Makg Ut (DMU) wth c pt ad tpt. It ed t akg ad aay f Dec- Makg Ut (DMU), ch a dteete, hpta, cte, facte ayt, etc. [3]. The tw bac DEA de ae CCR ad BCC wth ctat et t cae ad vaabe et t cae, epectvey [3,6]. Each DMU k aged the hghet pbe effcecy ce ( h k ) that the ctat aw f the avaabe data, by chg the pta weght f the tpt ad pt. If DMU k eceve the aa vae h k =, the t effcet, bt f h k, t effcet, ce wth t pta weght, athe DMU eceve the aa effcecy Eq. (). Bacay, the de dvde the DMU t tw gp, effcet ( h k = ) ad effcet ( h k ), by detfyg the effcet f the data. The ga DEA de t capabe f akg effcet t Theefe; the de dfed awg f a akg f the effcet t theeve. The ga facta CCR de () evaate the eatve effcece f DMU (j =,, ), each wth pt ad tpt deted by j, 2j,, j ad y j, y 2j,, y j, epectvey, by azg the at f weghted f tpt t the weghted f pt. (CCR at de) Ma y e j = = v j = y = v j =, j =,..., 0, =,...,, =,...,. (2) I de t cptata cveece the facta pgag de (2) e-epeed LP f a fw: (CCR-LP de) Ma e j = y = y v j 0, j =,..., = = v j = = 0, =,...,, =,...,. (4) Sppe that thee ae DMU, each f whch ce the ae type f pt ad pdce the ae type f tpt. Let be the be f pt ad et be the be f tpt. A pt ad tpt ae aed t be egatve, bt at eat e pt ad e tpt ae ptve. The fwg tat w be ed thght th tdy. DMU the th DMU, DMU taget DMU, NOTATIONS 353

3 A. J. Apped Sc., 5 (0): , 2008 R the c vecte f pt cedby DMU R the c vecte f pt f ced by taget DMU R the at f pt f a DMU y R the c vecte f pt f ced by DMU y R the c vecte f pt f ced by taget DMU y R the at f tpt f a tpt λ = ( λ ), λ R the c vecte f a ea cbat f DMU θ the bjectve vae (effcecy) f the CCR de R the c vect f pt eweght v R the c vect f tpt weght Mde (5) a ea pgag. Thee ae va ethd t ve t.i t f thee ethd f vg t cvet the pbtc pgag pbe g -ct, the teva bth de f the ctat ae cpaed wth each the. Thee ae ay ethd f cpag the teva; hece ay ethd ay be ggeted f vg tevapgag pbe. THE PROPOSED MODEL The bac dea t taf the fzzy CCR de t a cp ea pgag pbe by appyg a ateatve -ct appach. Theeby, the pbe cveted t a teva pgag. Dffeet ethdge have bee ggeted f the cpa f the teva th tdy baed Tag Cheg Methd wked. At ft, we e -ct t cvet fzzy DEA t teva pgag a fw: The CCR de wth fzzy data ca be wtte a: Ma e j = y = y v j 0, j =,..., (5) = = v j = = 0, =,...,, =,...,. whee, dcate the fzze. Thee ae dffeet type f fzzy be, bt taga fzzy be ae e ef that we cde the pt ad tpt f DMU a taga fzzy be. Theefe, (3) ca be wtte a fw: Let j = (j, j, j )ad y = (y, y, y ) Ma e j = (y, y, y ) = ( y,y, y ) v ( j, j, j ) 0, = = j =,..., v (,, ) = = 0, =,...,, =,...,. (6) 354 Ma e j = ( y + ( - )y, y + ( - )y ) = ( y + ( )y, y + ( )y ) = v ( ( ), ( ) j + j j + j ) 0, = j =,..., v ( ( ), ( ) + + ) = = 0, =,...,, =,..., (7) Wth cdeg ethd de chage t a fw th de gve ppe bd f effcecy ad et de gve we bd f effcecy. a e j = ( y + ( )y ) = v ( j + ( ) j ) ( y + ( )y ) = v ( + ( ) ) = a e j = ( y + ( )y ) = v ( j + ( )j ) ( y + ( )y ) = v ( + ( ) ) = (8) (9)

4 A. J. Apped Sc., 5 (0): , 2008 The abve de eqvaet t a the fzzy ea pgag pbe wth (0, ]. It ted that f each, we have a pta t. Tabe : Lwe bd et Pwepat pdct = 0 = 0.25 = 0.5 = 0.75 = Mtaze Beat Fz Sa 0 Shazad 0 Rajae 0 Beheht Tabz Mfateh Bt Ra 0 Medhaj Bada Zaad Efeha 0 Mtaze T Mahhad Iahah 0 Th, we ca pvde the dec ake a t tabe wth dffeet [0, ). THE CASE STUDY eae fe cpt te f Tea Je (TJ). I the wd, fge have aeady adjted f the qaty f fe ed dffeet pat. Itea pwe the at f eegy ced ( egawatt h) wth the te (f eectcay pweed eqpet etc.). We ppe a ethd t evaate the peface f pwe pat ad fd the effcece. Lwe Tabe 2: Uppe bd et Pwe pat pdct = 0 = 0.25 = 0.5 = 0.75 = Mtaze Beat Fz Sa Shazad Rajae Beheht Tabz Mfateh Bt Ra Medhaj Bada Zaad Efeha Mtaze T Mahhad Iahah Evaat f cveta thea tea-eectc peface ay be decbed cveety wth a egeeg faewk. I th faewk, petet pt ae the fe qatty ced ad taed pwe, whch the a a pwe the pat ae tay deged. O the the had ab pt ctbte t pdct thgh ct ad ateace evce, whch a eqe e capta. The tpt, f ce, eectca eegy pdct. Bt by tce f tde abt effcecy eaeet f thea pwe geeat Ia whch dcate that ab 't a effectve fact [30]. tdy, eectc pwe ( egawatt h) geeated f thea pwe pat each DMU (P) ed a the tpt vaabe, whe capta (C), fe (F) ad tea pwe (Ic) ae thee pt ed f pwe geeat. Capta eaed te f taed thea geeatg capacty egawatt (MW) Va ata eeet have bee ed a fe the pdct f eectc pwe va tea pat Ia (ata ga, ga ad azte). The chce f fe deped ay fact ch a avaabty, ct ad eveta cce ad each fe ha t tat. O fge 355 Tabe 3: Ret f tw ethd Pwe pat Oday ethd Pped ethd Mtaze Beat 0.88 Fz 0.66 Sa Shazad Rajae Beheht Tabz Mfateh Bt 0.99 Ra Medhaj Bada Zaad 0.83 Efeha 0.88 Mtaze T Mahhad Iahah 0.87 bd epeed Tabe. A, ppe bd epeed Tabe 2. The de t vadate th appach we cpae thee et = wth et day DEA de. Wth egad t bjectve fct f pped de azg that we cde ppe bd f vae. We ca ee Tabe 3 f cpag tw ethd.

5 A. J. Apped Sc., 5 (0): , 2008 CONCLUSION We tafed the fzzy CCR de t a cp ea pgag pbe by appyg a ateatve -ct appach. Theeby, the pbe wa cveted t a teva pgag. Dffeet ethdge have bee ggeted f the cpa f the teva th tdy baed Tag Cheg Methd wked. We ed -ct t cvet fzzy DEA t teva pgag. I pped de, tw ea pgag pbe wee ved t bta the effcecy f a gve DMU wth yetca taga fzzy be. Th de a appcat f fzzy et they DEA. REFERENCE. Aade, C.A., 994. Ug data evepet aay t eae teata agcta effcecy ad pdctvty, Uted State Depatet f Agcte. Ecc Reeach Sevce, Tech. B., 83: Bake, R.D., A. Chae ad W.W. Cpe, 984. Se de f etatg techca ad cae effcecy data evepet aay. Maage. Sc., 30: Bake, R.D., H. Chag ad W.W. Cpe, 996. Sat tde f effcecy, et t cae ad pecfcat wth ea fct DEA. A. Ope. Re., 66: Chae, A. ad W.W. Cpe, 959. Chacectaed pgag. Maage. Sc., 6: Chae, A., W.W. Cpe ad E. Rhde, 978. Meag the effcecy f dec-akg t. E. J. Ope. Re., 2: Chae, A., W.W. Cpe, B. Gay ad L. Sefd, 985. Fdat data evepet aay f Paet-Kpa effcet epca pdct fct. J. Ec., 30: Chae, A., W.W. Cpe, A.Y. Lew ad L.M. Sefd, 994. Data Evepet Aay: They, Methdgy ad Appcat. Kwe Acadec Pbhe, Ld. 8. Cpe, W.W., K.S. Pak ad J.T. Pate, 999. RAM: A age adjted eae f effcecy f e wth addtve de ad eat t the de ad eae DEA. J. Pd. Aa., : Cpe, W.W., L.M. Sefd ad K. Te, Data Evepet Aay: A Cpeheve Tet wth Mde, Appcat. Refeece ad DEA-Sve Sftwae, Kwe Acadec Pbhe, Ld. 0. Cpe, W.W. ad K. Te, 997. Meae f effcecy data evepet aay ad tchatc fte etat. E. J. Ope. Re., 99: Db, D. ad H. Pade, 988. Pbty They: A Appach t Cptezed Pceg f Ucetaty. Pe Pe, New Yk. 2. Fag, S.C. ad S. Pthepa, 993. Lea Optzat ad Ete: They ad Agth. Petce-Ha, Egewd C/, NJ. 3. G, P. ad H. Taaka, 200. Fzzy DEA: Apecepta evaat ethd. Fzzy Set Syt., 9: Kahaa, C. ad E. Tga, 998. Data evepet aay g fzzy ccept. 28th Iteata Syp Mtpe-Vaed Lgc., Ka, C. ad S.T. L, Fzzy effcecy eae data evepet aay. Fzzy Set Syt., 3: Letwak, S., 200. Fzzy data evepet aay f ppy cha deg ad aay. Detat Ppa Idta Egeeg, Nth Caa State Uvety. 7. L, B., 999. Uceta Pgag. A Wey- Itecece Pbcat, New Yk. 8. Meada, Y., Eta, T., Taaka, H., 998. Fzzy DEA wth teva effcecy. 6th Epea Cge Iteget Techqe ad Sft Cptg, 2: Sefd, L.M. ad R.M. Tha, 990. Recet devepet DEA: The atheatca pgag appach t fte aay. J. Ec., 46 : Segpta, J.K., 992. A fzzy yte appach data evepet aay. Cpt. Math. App., 24: Segpta, J.K., 995. Dyac f Data Evepet Aay: They f Syte Eacecy. Kwe Acadec Pbhe, Ld. 22. Wag, X., Kee, E.E., 200. Reaabe ppete f the deg f fzzy qatte (I). Fzzy Set Syt., 8: Wag, X. ad E.E. Kee, 200. Reaabe ppete f the deg f fzzy qatte (II). Fzzy Set Syt., 8: Zadeh, L.A., 978. Fzzy et a a ba f a they f pbty, Fzzy Set Syt., : Zea, H.J., 996. Fzzy Set They ad It Appcat. Kwe Acadec Pbhe, Ld. 356

6 A. J. Apped Sc., 5 (0): , Le, T., V. Le ad J.L. Rz, A fzzy atheatca pgag appach t the aeet f effcecy wth DEA de. Fzzy Set Syt., 39 (2): Letwak, S. ad S.C. Fag, Fzzy data evepet aay (DEA): A pbty appach. Fzzy Set Syt., Bckey, J.J., 989. Svg pbtc ea pgag pbe. Fzzy Set Syt., 3: Degad, M., J.L. Vedegay ad M.A. Va, 990. Reatg dffeet appache t ve ea pgag pbe wth pece ct. Fzzy Set Syt., 37: EaMebd, A., 998. Effcecy cdeat the eectcty ppy dty: The Cae f Ia. Ph.D. The, Uvety f Sey. 3. Azadeh, A., S.F. Ghade, M. Ava ad M. Sabe, Meag peface eectc pwe geeat g atfca ea etwk ad fzzy cteg. I Pceedg f the 2006 IEEE Iteata Cfeece Idta Eectc - IECON 06 (Pa). 357

A note on A New Approach for the Selection of Advanced Manufacturing Technologies: Data Envelopment Analysis with Double Frontiers

A note on A New Approach for the Selection of Advanced Manufacturing Technologies: Data Envelopment Analysis with Double Frontiers A te A New Appach f the Select f Advaced Mafactg Techlge: Data Evelpet Aal wth Dble Fte He Azz Depatet f Appled Matheatc Paabad Mgha Bach Ilac Azad Uvet Paabad Mgha Ia hazz@apga.ac. Recetl g the data evelpet

More information

Cross Efficiency of Decision Making Units with the Negative Data in Data Envelopment Analysis

Cross Efficiency of Decision Making Units with the Negative Data in Data Envelopment Analysis Pceedg f the 202 Iteatal Cfeece Idutal Egeeg ad Opeat Maageet Itabul, Tuey, July 3 6, 202 C Effcecy f Dec Mag Ut wth the Negatve Data Data Evelpet Aaly Ghae Thd Depatet f Matheatc Ilac Azad Uvety - Cetal

More information

Inverse DEA Model with Fuzzy Data for Output Estimation

Inverse DEA Model with Fuzzy Data for Output Estimation Aaabe e at www.. Iaa Ja Optat 2200 388-4 Iaa Ja Optat Iee DEA Mde wt F Data Otpt Etat A Mad Rad a Rea Dea a Faad Heade Lt b a Depatet Mateatc Iac Aad Uet Maedea Bac Ia b Depatet Mateatc Iac Aad Uet Scece

More information

Proposing a Mixed Model Based on Stochastic Data Envelopment Analysis and Principal Component Analysis to Predict Efficiency

Proposing a Mixed Model Based on Stochastic Data Envelopment Analysis and Principal Component Analysis to Predict Efficiency J. Bac. pp. Sc. Re. 0-0 0 0 TextRad Pcat ISSN 090-0 Ja f Bac ad pped Scetfc Reeach www.textad.c Ppg a Mxed Mde Baed Stchatc Data epet a ad Pcpa Cpet a t Pedct ffcec Yagh Mehd Bah Depatet f Idta geeg Teha

More information

Online Open Access publishing platform for Management Research. Copyright 2010 All rights reserved Integrated Publishing association

Online Open Access publishing platform for Management Research. Copyright 2010 All rights reserved Integrated Publishing association Ole Ope cce pblhg plaf f Maagee Reeach Cpgh 00 ll gh eeed Iegaed Pblhg aca Reeach cle ISSN 9 3795 c e f wegh dea eae effcec ad Idef pdc chage Fahad Hezadeh Lf l Paa Reza N Depae f Maheac Scece ad Reeach

More information

Shabnam Razavyan 1* ; Ghasem Tohidi 2

Shabnam Razavyan 1* ; Ghasem Tohidi 2 J. Id. Eg. It., 7(5), 8-4, Fall 0 ISSN: 735-570 IAU, Sth Teha Bach Shaba Razava ; Ghae Thd Atat Pfe, Det. f Matheatc, Ilac Azad Uvet, Sth Teha Bach, Teha-Ia Atat Pfe, Det. f Matheatc, Ilac Azad Uvet, Cetal

More information

Exam-style practice: A Level

Exam-style practice: A Level Exa-tye practce: A Leve a Let X dete the dtrbut ae ad X dete the dtrbut eae The dee the rad varabe Y X X j j The expected vaue Y : E( Y) EX X j j EX EX j j EX E X 7 The varace : Var( Y) VarX VarX j j Var(

More information

Super Efficiency with 2- Stage DEA Model

Super Efficiency with 2- Stage DEA Model Sup Effccy wth 2- Stag DEA Md Sha Ea Put Dpatt f Mathatc, Uvty f Suata Utaa Mda, Ida Abtact DEA d tat a t f vauatd DMU ad u t tat th ffccy c by vauatg ach DMU a data t. Th ach dtd th w ch f 2-tag DEA d

More information

Using Cross Efficiency with Symmetric Weights for the Method DEAHP

Using Cross Efficiency with Symmetric Weights for the Method DEAHP 2, Scece-Le Pblct.cece-le.c ISSN: 222-477 Jl f Edctl d Mgeet Stde J. Edc. Mge. Std.,(4): 84-89, 2 JEMS g C Effcecy th Syetc Weght f the Methd DEAHP Sh Hep, Jf Phd 2, Ne M Deptet f Mthetc, Shbet Bch, Ilc

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

Connected Directional Slack-Based Measure of. Efficiency in DEA

Connected Directional Slack-Based Measure of. Efficiency in DEA Appled Mathematcal Scece, Vl. 6, 2012,. 5, 237-246 Cected Dectal Slack-Baed Meaue f Effcecy DEA G. R. Jahahahl Faculty f Mathematcal Scece ad Cmpute Egeeg, Tabat Mallem Uvety, Teha, Ia F. Hezadeh Ltf Depatmet

More information

Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate

More information

A Developed Algorithm for Solving Constrained Linear Quadratic Problems with Time Delay

A Developed Algorithm for Solving Constrained Linear Quadratic Problems with Time Delay Devepe gthm vg Cstae Lea aatc Pbems wth me Deay MMED FIM N Eectca Egeeg Depatmet Kwat Uvesty P..:596936-aat KUWI bstact: Dscete tme ea qaatc ptmzat pbem wth tme eay a system cstats s vestgate. te ematg

More information

Software Process Models there are many process model s in th e li t e ra t u re, s om e a r e prescriptions and some are descriptions you need to mode

Software Process Models there are many process model s in th e li t e ra t u re, s om e a r e prescriptions and some are descriptions you need to mode Unit 2 : Software Process O b j ec t i ve This unit introduces software systems engineering through a discussion of software processes and their principal characteristics. In order to achieve the desireable

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

Born-Oppenheimer Approximation. Kaito Takahashi

Born-Oppenheimer Approximation. Kaito Takahashi o-oppehee ppoato Kato Takahah toc Ut Fo quatu yte uch a ecto ad olecule t eae to ue ut that ft the=tomc UNT Ue a of ecto (ot kg) Ue chage of ecto (ot coulob) Ue hba fo agula oetu (ot kg - ) Ue 4pe 0 fo

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

College of Engineering Department of Electronics and Communication Engineering. Test 2 MODEL ANSWERS

College of Engineering Department of Electronics and Communication Engineering. Test 2 MODEL ANSWERS Nae: tudet Nube: ect: ectue: z at Fazea zlee Jehaa y Jaalud able Nube: llee f ee eatet f lectcs ad ucat ee est O N, Y 050 ubject de : B73 use tle : lectcs alyss & es ate : uust 05 e llwed : hu 5 utes stucts

More information

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9 OH BOY! O h Boy!, was or igin a lly cr eat ed in F r en ch an d was a m a jor s u cc ess on t h e Fr en ch st a ge f or young au di enc es. It h a s b een s een by ap pr ox i ma t ely 175,000 sp ect at

More information

Enhanced Russell measure in fuzzy DEA

Enhanced Russell measure in fuzzy DEA 140 It. J. Data Aaly Techque ad Statege, Vol. 2, No. 2, 2010 Ehaced Ruell eaue fuzzy DEA Meqag Wag* School of Maageet, Guzhou Uvety, Guyag 550025, PR Cha Fax: +86 851 6926767 E-al: wagq@al.utc.edu.c *Coepodg

More information

Parts Manual. EPIC II Critical Care Bed REF 2031

Parts Manual. EPIC II Critical Care Bed REF 2031 EPIC II Critical Care Bed REF 2031 Parts Manual For parts or technical assistance call: USA: 1-800-327-0770 2013/05 B.0 2031-109-006 REV B www.stryker.com Table of Contents English Product Labels... 4

More information

Trignometric Inequations and Fuzzy Information Theory

Trignometric Inequations and Fuzzy Information Theory Iteratoal Joural of Scetfc ad Iovatve Mathematcal Reearch (IJSIMR) Volume, Iue, Jauary - 0, PP 00-07 ISSN 7-07X (Prt) & ISSN 7- (Ole) www.arcjoural.org Trgometrc Iequato ad Fuzzy Iformato Theory P.K. Sharma,

More information

On Probability Density Function of the Quotient of Generalized Order Statistics from the Weibull Distribution

On Probability Density Function of the Quotient of Generalized Order Statistics from the Weibull Distribution ISSN 684-843 Joua of Sac Voue 5 8 pp. 7-5 O Pobaby Dey Fuco of he Quoe of Geeaed Ode Sac fo he Webu Dbuo Abac The pobaby dey fuco of Muhaad Aee X k Y k Z whee k X ad Y k ae h ad h geeaed ode ac fo Webu

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

THIS PAGE DECLASSIFIED IAW E

THIS PAGE DECLASSIFIED IAW E THS PAGE DECLASSFED AW E0 2958 BL K THS PAGE DECLASSFED AW E0 2958 THS PAGE DECLASSFED AW E0 2958 B L K THS PAGE DECLASSFED AW E0 2958 THS PAGE DECLASSFED AW EO 2958 THS PAGE DECLASSFED AW EO 2958 THS

More information

T h e C S E T I P r o j e c t

T h e C S E T I P r o j e c t T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T

More information

-HYBRID LAPLACE TRANSFORM AND APPLICATIONS TO MULTIDIMENSIONAL HYBRID SYSTEMS. PART II: DETERMINING THE ORIGINAL

-HYBRID LAPLACE TRANSFORM AND APPLICATIONS TO MULTIDIMENSIONAL HYBRID SYSTEMS. PART II: DETERMINING THE ORIGINAL UPB Sc B See A Vo 72 I 3 2 ISSN 223-727 MUTIPE -HYBRID APACE TRANSORM AND APPICATIONS TO MUTIDIMENSIONA HYBRID SYSTEMS PART II: DETERMININ THE ORIINA Ve PREPEIŢĂ Te VASIACHE 2 Ace co copeeă oă - pce he

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

A B CDE F B FD D A C AF DC A F

A B CDE F B FD D A C AF DC A F International Journal of Arts & Sciences, CD-ROM. ISSN: 1944-6934 :: 4(20):121 131 (2011) Copyright c 2011 by InternationalJournal.org A B CDE F B FD D A C A BC D EF C CE C A D ABC DEF B B C A E E C A

More information

On Eigenvalues of Nonlinear Operator Pencils with Many Parameters

On Eigenvalues of Nonlinear Operator Pencils with Many Parameters Ope Scece Joual of Matheatc ad Applcato 5; 3(4): 96- Publhed ole Jue 5 (http://wwwopececeoleco/oual/oa) O Egevalue of Nolea Opeato Pecl wth May Paaete Rakhhada Dhabaadeh Guay Salaova Depatet of Fuctoal

More information

Chapter 3 Applications of resistive circuits

Chapter 3 Applications of resistive circuits Chapte 3 pplcat f ete ccut 3. (ptal) eal uce mel, maxmum pwe tafe 3. mplfe mel ltage amplfe mel, cuet amplfe mel 3.3 Op-amp lea mel, etg p-amp, etg p-amp, ummg a ffeece p-amp 3.4-3.5 (ptal) teal p-amp

More information

Fun and Fascinating Bible Reference for Kids Ages 8 to 12. starts on page 3! starts on page 163!

Fun and Fascinating Bible Reference for Kids Ages 8 to 12. starts on page 3! starts on page 163! F a Faa R K 8 12 a a 3! a a 163! 2013 a P, I. ISN 978-1-62416-216-9. N a a a a a, a,. C a a a a P, a 500 a a aa a. W, : F G: K Fa a Q &, a P, I. U. L aa a a a Fa a Q & a. C a 2 (M) Ta H P M (K) Wa P a

More information

Sensorless A.C. Drive with Vector Controlled Synchronous Motor

Sensorless A.C. Drive with Vector Controlled Synchronous Motor Seole A.C. Dve wth Vecto Cotolle Sychoo Moto Ořej Fše VŠB-echcal Uvety of Otava, Faclty of Electcal Egeeg a Ifomatc, Deatmet of Powe Electoc a Electcal Dve, 17.ltoa 15, 78 33 Otava-Poba, Czech eblc oej.fe@vb.cz

More information

T T V e g em D e j ) a S D } a o "m ek j g ed b m "d mq m [ d, )

T T V e g em D e j ) a S D } a o m ek j g ed b m d mq m [ d, ) . ) 6 3 ; 6 ;, G E E W T S W X D ^ L J R Y [ _ ` E ) '" " " -, 7 4-4 4-4 ; ; 7 4 4 4 4 4 ;= : " B C CA BA " ) 3D H E V U T T V e g em D e j ) a S D } a o "m ek j g ed b m "d mq m [ d, ) W X 6 G.. 6 [ X

More information

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle Thomas Soesso Mcoecoomcs Lecte Cosme theoy A. The efeece odeg B. The feasble set C. The cosmto decso A. The efeece odeg Cosmto bdle x ( 2 x, x,... x ) x Assmtos: Comleteess 2 Tastvty 3 Reflexvty 4 No-satato

More information

A RWA Performance Comparison for Hybrid Optical Networks combining Circuit and Multi-Wavelength Packet Switching

A RWA Performance Comparison for Hybrid Optical Networks combining Circuit and Multi-Wavelength Packet Switching 1 R c Cp Hd Optc tw c Cct d Mt- ct Swtch Kt Mchd 1,3, Hd Iz 1,2, H Mw 1,2, d J M 3 1 Th Ut T 2 t Ittt It d Cct (ICT) 3 K Ut E-: chd@c.wd.d.jp tct Th pp cp t d w t hd ptc tw chtct c ptc cct wtch (OCS) d

More information

NONDIFFERENTIABLE MATHEMATICAL PROGRAMS. OPTIMALITY AND HIGHER-ORDER DUALITY RESULTS

NONDIFFERENTIABLE MATHEMATICAL PROGRAMS. OPTIMALITY AND HIGHER-ORDER DUALITY RESULTS HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMANIAN ACADEMY, See A, OF HE ROMANIAN ACADEMY Volue 9, Nube 3/8,. NONDIFFERENIABLE MAHEMAICAL PROGRAMS. OPIMALIY AND HIGHER-ORDER DUALIY RESULS Vale PREDA Uvety

More information

University of Pavia, Pavia, Italy. North Andover MA 01845, USA

University of Pavia, Pavia, Italy. North Andover MA 01845, USA Iteatoal Joual of Optmzato: heoy, Method ad Applcato 27-5565(Pt) 27-6839(Ole) wwwgph/otma 29 Global Ifomato Publhe (HK) Co, Ltd 29, Vol, No 2, 55-59 η -Peudoleaty ad Effcecy Gogo Gog, Noma G Rueda 2 *

More information

K E L LY T H O M P S O N

K E L LY T H O M P S O N K E L LY T H O M P S O N S E A O LO G Y C R E ATO R, F O U N D E R, A N D PA R T N E R K e l l y T h o m p s o n i s t h e c r e a t o r, f o u n d e r, a n d p a r t n e r o f S e a o l o g y, a n e x

More information

S U E K E AY S S H A R O N T IM B E R W IN D M A R T Z -PA U L L IN. Carlisle Franklin Springboro. Clearcreek TWP. Middletown. Turtlecreek TWP.

S U E K E AY S S H A R O N T IM B E R W IN D M A R T Z -PA U L L IN. Carlisle Franklin Springboro. Clearcreek TWP. Middletown. Turtlecreek TWP. F R A N K L IN M A D IS O N S U E R O B E R T LE IC H T Y A LY C E C H A M B E R L A IN T W IN C R E E K M A R T Z -PA U L L IN C O R A O W E N M E A D O W L A R K W R E N N LA N T IS R E D R O B IN F

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

< < or a. * or c w u. "* \, w * r? ««m * * Z * < -4 * if # * « * W * <r? # *» */>* - 2r 2 * j j. # w O <» x <» V X * M <2 * * * *

< < or a. * or c w u. * \, w * r? ««m * * Z * < -4 * if # * « * W * <r? # *» */>* - 2r 2 * j j. # w O <» x <» V X * M <2 * * * * - W # a a 2T. mj 5 a a s " V l UJ a > M tf U > n &. at M- ~ a f ^ 3 T N - H f Ml fn -> M - M. a w ma a Z a ~ - «2-5 - J «a -J -J Uk. D tm -5. U U # f # -J «vfl \ \ Q f\ \ y; - z «w W ^ z ~ ~ / 5 - - ^

More information

DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCE-CONSTRAINED PROGRAMMING APPROACH. 1. Introduction

DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCE-CONSTRAINED PROGRAMMING APPROACH. 1. Introduction Iaa Joa of Fzz Sstes Vo. No. 5. -9 DAA ENVEOPMEN ANAYSIS WIH FUZZY ANDOM INPUS AND OUPUS: A CHANCE-CONSAINED POGAMMING APPOACH S. AMEZANZADEH M. MEMAIANI AND S. SAAI ABSAC. I ths ae we dea wth fzz ado

More information

Chapter #2 EEE State Space Analysis and Controller Design

Chapter #2 EEE State Space Analysis and Controller Design Chpte EEE8- Chpte # EEE8- Stte Spce Al d Cotolle Deg Itodcto to tte pce Obevblt/Cotollblt Modle ede: D D Go - d.go@cl.c.k /4 Chpte EEE8-. Itodcto Ae tht we hve th ode te: f, ', '',.... Ve dffclt to td

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

M $ 4 65\ K;$ 5, 65\ M $ C! 4 /2 K;$ M $ /+5\ 8$ A5 =+0,7 ;* C! 4.4/ =! K;$,7 $,+7; ;J zy U;K z< mj ]!.,,+7;

M $ 4 65\ K;$ 5, 65\ M $ C! 4 /2 K;$ M $ /+5\ 8$ A5 =+0,7 ;* C! 4.4/ =! K;$,7 $,+7; ;J zy U;K z< mj ]!.,,+7; V 3U. T, SK I 1393/08/21 :,F! 1393/10/29 ::!n> 2 1 /M + - /E+4q; Z R :'!3Qi M $,7 8$ 4,!AK 4 4/ * /;K "FA ƒf\,7 /;G2 @;J\ M $ 4 65\ K;$ 5, 65\ M $ C! 4 /2 K;$ M $ /+5\ 8$ A5 =+0,7 ;* C! 4.4/ =! K;$,7 $,+7;

More information

Simple Linear Regression Analysis

Simple Linear Regression Analysis LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such

More information

FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL SEQUENCES

FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL SEQUENCES Joual of Appled Matheatcs ad Coputatoal Mechacs 7, 6(), 59-7 www.ac.pcz.pl p-issn 99-9965 DOI:.75/jac.7..3 e-issn 353-588 FIBONACCI-LIKE SEQUENCE ASSOCIATED WITH K-PELL, K-PELL-LUCAS AND MODIFIED K-PELL

More information

m = Mass flow rate The Lonely Electron Example 0a:

m = Mass flow rate The Lonely Electron Example 0a: The Lel Elect Exaple 0a: Mass flw ate l Liea velcit Hw fa ut f ptial eeg iteacti? Hge ucleus Bh --- 93: Uest the etu ccept. Liea etu istace eeg ( l ) l F ( tie ) ( tie ) + Like t use the peples ieas (if

More information

ˆ SSE SSE q SST R SST R q R R q R R q

ˆ SSE SSE q SST R SST R q R R q R R q Bll Evas Spg 06 Sggested Aswes, Poblem Set 5 ECON 3033. a) The R meases the facto of the vaato Y eplaed by the model. I ths case, R =SSM/SST. Yo ae gve that SSM = 3.059 bt ot SST. Howeve, ote that SST=SSM+SSE

More information

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder Collapg to Saple ad Reader Mea Ed Staek Collapg to Saple ad Reader Average order to collape the expaded rado varable to weghted aple ad reader average, we pre-ultpled by ( M C C ( ( M C ( M M M ( M M M,

More information

Exponential Generating Functions - J. T. Butler

Exponential Generating Functions - J. T. Butler Epoetal Geeatg Fuctos - J. T. Butle Epoetal Geeatg Fuctos Geeatg fuctos fo pemutatos. Defto: a +a +a 2 2 + a + s the oday geeatg fucto fo the sequece of teges (a, a, a 2, a, ). Ep. Ge. Fuc.- J. T. Butle

More information

Section 4.2 Radians, Arc Length, and Area of a Sector

Section 4.2 Radians, Arc Length, and Area of a Sector Sectin 4.2 Radian, Ac Length, and Aea f a Sect An angle i fmed by tw ay that have a cmmn endpint (vetex). One ay i the initial ide and the the i the teminal ide. We typically will daw angle in the cdinate

More information

The Geometric Proof of the Hecke Conjecture

The Geometric Proof of the Hecke Conjecture The Geometc Poof of the Hecke Cojectue Kada Sh Depatmet of Mathematc Zhejag Ocea Uvety Zhouha Cty 6 Zhejag Povce Cha Atact Begg fom the eoluto of Dchlet fucto ug the e poduct fomula of two fte-dmeoal vecto

More information

CHAPTER 17. Solutions for Exercises. Using the expressions given in the Exercise statement for the currents, we have

CHAPTER 17. Solutions for Exercises. Using the expressions given in the Exercise statement for the currents, we have CHATER 7 Slutin f Execie E7. F Equatin 7.5, we have B gap Ki ( t ) c( θ) + Ki ( t ) c( θ 0 ) + Ki ( t ) c( θ 40 a b c ) Uing the expein given in the Execie tateent f the cuent, we have B gap K c( ωt )c(

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

Chapter 3. Many-Electron Atoms

Chapter 3. Many-Electron Atoms Capte. May-ecto Atom Readg: Bade & Joaca Capte 8 Ceta ed Appoxmato Ampto: eac of te atomc eecto move a effectve pecay ymmetc poteta ceated by te ce ad a te ote eecto. amtoa of te -eecto atom m 4πε e >

More information

THIS PAGE DECLASSIFIED IAW EO IRIS u blic Record. Key I fo mation. Ma n: AIR MATERIEL COMM ND. Adm ni trative Mar ings.

THIS PAGE DECLASSIFIED IAW EO IRIS u blic Record. Key I fo mation. Ma n: AIR MATERIEL COMM ND. Adm ni trative Mar ings. T H S PA G E D E CLA SSFED AW E O 2958 RS u blc Recod Key fo maon Ma n AR MATEREL COMM ND D cumen Type Call N u b e 03 V 7 Rcvd Rel 98 / 0 ndexe D 38 Eneed Dae RS l umbe 0 0 4 2 3 5 6 C D QC d Dac A cesson

More information

CSE 5526: Introduction to Neural Networks Linear Regression

CSE 5526: Introduction to Neural Networks Linear Regression CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto

More information

Agenda Rationale for ETG S eek ing I d eas ETG fram ew ork and res u lts 2

Agenda Rationale for ETG S eek ing I d eas ETG fram ew ork and res u lts 2 Internal Innovation @ C is c o 2 0 0 6 C i s c o S y s t e m s, I n c. A l l r i g h t s r e s e r v e d. C i s c o C o n f i d e n t i a l 1 Agenda Rationale for ETG S eek ing I d eas ETG fram ew ork

More information

Centroid A Widely Misunderstood Concept In Facility Location Problems

Centroid A Widely Misunderstood Concept In Facility Location Problems teatal Jual f dustal Egeeg, 6, 99-7, 9. Cetd A Wdel Msudestd Ccept Faclt Lcat Pbles Wlla V. Gehle ad Mugd Pasc Alfed Lee Cllege f Busess ad Eccs Uvest f Delawae Alfed Lee Hall Newak, Delawae 976 dustal

More information

c- : r - C ' ',. A a \ V

c- : r - C ' ',. A a \ V HS PAGE DECLASSFED AW EO 2958 c C \ V A A a HS PAGE DECLASSFED AW EO 2958 HS PAGE DECLASSFED AW EO 2958 = N! [! D!! * J!! [ c 9 c 6 j C v C! ( «! Y y Y ^ L! J ( ) J! J ~ n + ~ L a Y C + J " J 7 = [ " S!

More information

Future Self-Guides. E,.?, :0-..-.,0 Q., 5...q ',D5', 4,] 1-}., d-'.4.., _. ZoltAn Dbrnyei Introduction. u u rt 5,4) ,-,4, a. a aci,, u 4.

Future Self-Guides. E,.?, :0-..-.,0 Q., 5...q ',D5', 4,] 1-}., d-'.4.., _. ZoltAn Dbrnyei Introduction. u u rt 5,4) ,-,4, a. a aci,, u 4. te SelfGi ZltAn Dbnyei Intdtin ; ) Q) 4 t? ) t _ 4 73 y S _ E _ p p 4 t t 4) 1_ ::_ J 1 `i () L VI O I4 " " 1 D 4 L e Q) 1 k) QJ 7 j ZS _Le t 1 ej!2 i1 L 77 7 G (4) 4 6 t (1 ;7 bb F) t f; n (i M Q) 7S

More information

On-Line Parameter Estimation Scheme for Uncertain Takagi-Sugeno Fuzzy Models

On-Line Parameter Estimation Scheme for Uncertain Takagi-Sugeno Fuzzy Models 68 Iteatoa Joa of Coto Atomato ad Sytem Vo. No. Mach 4 O-Le Paamete Etmato Scheme fo Uceta akag-sgeo Fzzy Mode Yog-Wa Cho ad Chag-Woo Pak* Abtact: I th pape a etmato th a appopate adaptve a fo pdatg paamete

More information

K owi g yourself is the begi i g of all wisdo.

K owi g yourself is the begi i g of all wisdo. I t odu tio K owi g yourself is the begi i g of all wisdo. A istotle Why You Need Insight Whe is the last ti e ou a e e e taki g ti e to thi k a out ou life, ou alues, ou d ea s o ou pu pose i ei g o this

More information

Executive Committee and Officers ( )

Executive Committee and Officers ( ) Gifted and Talented International V o l u m e 2 4, N u m b e r 2, D e c e m b e r, 2 0 0 9. G i f t e d a n d T a l e n t e d I n t e r n a t i o n a2 l 4 ( 2), D e c e m b e r, 2 0 0 9. 1 T h e W o r

More information

Today. The geometry of homogeneous and nonhomogeneous matrix equations. Solving nonhomogeneous equations. Method of undetermined coefficients

Today. The geometry of homogeneous and nonhomogeneous matrix equations. Solving nonhomogeneous equations. Method of undetermined coefficients Today The geometry of homogeneous and nonhomogeneous matrix equations Solving nonhomogeneous equations Method of undetermined coefficients 1 Second order, linear, constant coeff, nonhomogeneous (3.5) Our

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

Ch5 Appendix Q-factor and Smith Chart Matching

Ch5 Appendix Q-factor and Smith Chart Matching Ch5 Appedx -factr ad mth Chart Matchg 5B-1 We-Cha a udwg, F Crcut Deg hery ad Applcat, Chapter 8 -type matchg etwrk w-cmpet Matchg Netwrk hee etwrk ue tw reactve cmpet t trafrm the lad mpedace t the dered

More information

ALGEBRAS WITH THE SPECTRAL EXPANSION PROPERTY

ALGEBRAS WITH THE SPECTRAL EXPANSION PROPERTY ALGEBRAS WITH THE SPECTRAL EXPANSION PROPERTY BY BRUCE ALAN BARNES Introduction Assume that A is the algebra of all completely continuous operators on a Hilbert space. If T is a normal operator in A, then

More information

Homework 1/Solutions. Graded Exercises

Homework 1/Solutions. Graded Exercises MTH 310-3 Abstract Algebra I and Number Theory S18 Homework 1/Solutions Graded Exercises Exercise 1. Below are parts of the addition table and parts of the multiplication table of a ring. Complete both

More information

NACC Uniform Data Set (UDS) FTLD Module

NACC Uniform Data Set (UDS) FTLD Module NACC Uniform Data Set (UDS) FTLD Module Data Template For Initial Visit Packet Version 2.0, January 2012 Copyright 2013 University of Washington Created and published by the FTLD work group of the ADC

More information

f;g,7k ;! / C+!< 8R+^1 ;0$ Z\ \ K S;4 i!;g + 5 ;* \ C! 1+M, /A+1+> 0 /A+>! 8 J 4! 9,7 )F C!.4 ;* )F /0 u+\ 30< #4 8 J C!

f;g,7k ;! / C+!< 8R+^1 ;0$ Z\ \ K S;4 i!;g + 5 ;* \ C! 1+M, /A+1+> 0 /A+>! 8 J 4! 9,7 )F C!.4 ;* )F /0 u+\ 30< #4 8 J C! 393/09/0 393//07 :,F! ::!n> b]( a.q 5 O +D5 S ١ ; ;* :'!3Qi C+0;$ < "P 4 ; M V! M V! ; a 4 / ;0$ f;g,7k ;! / C+!< 8R+^ ;0$ Z\ \ K S;4 "* < 8c0 5 *

More information

Active Load. Reading S&S (5ed): Sec. 7.2 S&S (6ed): Sec. 8.2

Active Load. Reading S&S (5ed): Sec. 7.2 S&S (6ed): Sec. 8.2 cte La ean S&S (5e: Sec. 7. S&S (6e: Sec. 8. In nteate ccuts, t s ffcult t fabcate essts. Instea, aplfe cnfuatns typcally use acte las (.e. las ae w acte eces. Ths can be ne usn a cuent suce cnfuatn,.e.

More information

Scripture quotations marked cev are from the Contemporary English Version, Copyright 1991, 1992, 1995 by American Bible Society. Used by permission.

Scripture quotations marked cev are from the Contemporary English Version, Copyright 1991, 1992, 1995 by American Bible Society. Used by permission. N Ra: E K B Da a a B a a, a-a- a aa, a a. T, a a. 2009 Ba P, I. ISBN 978-1-60260-296-0. N a a a a a, a,. C a a a Ba P, a 500 a a aa a. W, : F K B Da, Ba P, I. U. S a a a a K Ja V B. S a a a a N K Ja V.

More information

Objectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method)

Objectives. Learning Outcome. 7.1 Centre of Gravity (C.G.) 7. Statics. Determine the C.G of a lamina (Experimental method) Ojectves 7 Statcs 7. Cete of Gavty 7. Equlum of patcles 7.3 Equlum of g oes y Lew Sau oh Leag Outcome (a) efe cete of gavty () state the coto whch the cete of mass s the cete of gavty (c) state the coto

More information

NACC Uniform Data Set (UDS) FTLD Module

NACC Uniform Data Set (UDS) FTLD Module NACC Uniform Data Set (UDS) FTLD Module Data Template For FOLLOW-UP Visit Packet Version 2.0, January 2012 Copyright 2013 University of Washington Created and published by the FTLD work group of the ADC

More information

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1

such that for 1 From the definition of the k-fibonacci numbers, the firsts of them are presented in Table 1. Table 1: First k-fibonacci numbers F 1 Scholas Joual of Egeeg ad Techology (SJET) Sch. J. Eg. Tech. 0; (C):669-67 Scholas Academc ad Scetfc Publshe (A Iteatoal Publshe fo Academc ad Scetfc Resouces) www.saspublshe.com ISSN -X (Ole) ISSN 7-9

More information

A Hallelujah for My Father

A Hallelujah for My Father Univeity o New Mexico UNM Digital Repoitoy New Mexico Compoe' chive Reeach Collection and Data 1312011 Hallelujah o My Fathe lan Stinge Robet Fanci Follow thi and additional wok at: http://digitalepoitoyunmedu/nm_compoe_achive

More information

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx Iteatoal Joual of Mathematcs ad Statstcs Iveto (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 www.jms.og Volume Issue 5 May. 4 PP-44-5 O EP matces.ramesh, N.baas ssocate Pofesso of Mathematcs, ovt. ts College(utoomous),Kumbakoam.

More information

Images of Linear Block Codes over Fq ufq vfq uvfq

Images of Linear Block Codes over Fq ufq vfq uvfq Oe Joua of ed Sceces, 03, 3, 7-3 do:036/oas033006 Pubshed Oe 03 (htt://wwwscog/oua/oas) Iages of Lea oc Codes ove u v uv Jae D Paaco, Vgo P Sso Isttute of Matheatca Sceces ad Physcs, Uvesty of the Phes

More information

Math K (24564) - Homework Solutions 02

Math K (24564) - Homework Solutions 02 Math 39100 K (24564) - Homework Solutions 02 Ethan Akin Office: NAC 6/287 Phone: 650-5136 Email: ethanakin@earthlink.net Spring, 2018 Contents Reduction of Order, B & D Chapter 3, p. 174 Constant Coefficient

More information

Harmonic Curvatures in Lorentzian Space

Harmonic Curvatures in Lorentzian Space BULLETIN of the Bull Malaya Math Sc Soc Secod See 7-79 MALAYSIAN MATEMATICAL SCIENCES SOCIETY amoc Cuvatue Loetza Space NEJAT EKMEKÇI ILMI ACISALIOĞLU AND KĀZIM İLARSLAN Aaa Uvety Faculty of Scece Depatmet

More information

International Journal of Industrial Engineering Computations

International Journal of Industrial Engineering Computations Iteatoal Joal of Idtal Egeeg Coptato (200) 65-72 Cotet lt avalable at GowgScece Iteatoal Joal of Idtal Egeeg Coptato hoepage: www.gowgscece.co/ec A epcal tdy of Iaa egoal apot g obt data evelopet aaly

More information

Stillma. Uun. B. Al.'ca ha. already her cargo. - CALENDAR. Island Notes. ua.. Eo'" e"'lej- - :" THE PAOIPXC P. C ADVERTISER CO. i&tilistmtnts.

Stillma. Uun. B. Al.'ca ha. already her cargo. - CALENDAR. Island Notes. ua.. Eo' e'lej- - : THE PAOIPXC P. C ADVERTISER CO. i&tilistmtnts. B E PF B E PEE ED PBED B E PP P DEE D P F B F E F BBEE E F z z Q F E F F F G G F F D D PY B E D B B Pxx BE D B B Q D PY x E D E P D F BE D E E D E E FFE DE D P F BE D D P P G F P F Bx P B B B G FE E PY

More information

Regression and the LMS Algorithm

Regression and the LMS Algorithm CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a

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

Solutions of Schrödinger Equation with Generalized Inverted Hyperbolic Potential

Solutions of Schrödinger Equation with Generalized Inverted Hyperbolic Potential Solto of Schödge Eqato wth Geealzed Ieted Hypebolc Potetal Akpa N.Ikot*,Oladjoye A.Awoga, Lo E.Akpabo ad Beedct I.Ita Theoetcal Phyc gop, Depatmet of Phyc,Uety of Uyo,Ngea. Theoetcal Qatm chemty gop,depatmet

More information

Question 1. Typical Cellular System. Some geometry TELE4353. About cellular system. About cellular system (2)

Question 1. Typical Cellular System. Some geometry TELE4353. About cellular system. About cellular system (2) TELE4353 Moble a atellte Commucato ystems Tutoal 1 (week 3-4 4 Questo 1 ove that fo a hexagoal geomety, the co-chael euse ato s gve by: Q (3 N Whee N + j + j 1/ 1 Typcal Cellula ystem j cells up cells

More information

Standard Signs Manual

Standard Signs Manual Sadad S Maa NEW, CHANGED, OR DELETED 1/2017 "X" SERIES: MISCELLANEOUS G 1 - Ga Ia X1-1... BEGIN MILE ODOMETER CHECK FT AHEAD X1-2... BEGIN (END) ODOMETER CHECK HERE X1-3... MILE (Od Cc) X1-5... AIRCRAFT

More information

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y. .46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure

More information

H STO RY OF TH E SA NT

H STO RY OF TH E SA NT O RY OF E N G L R R VER ritten for the entennial of th e Foundin g of t lair oun t y on ay 8 82 Y EEL N E JEN K RP O N! R ENJ F ] jun E 3 1 92! Ph in t ed b y h e t l a i r R ep u b l i c a n O 4 1922

More information

ECEN474/704: (Analog) VLSI Circuit Design Spring 2018

ECEN474/704: (Analog) VLSI Circuit Design Spring 2018 EEN474/704: (Anal) LSI cut De S 08 Lectue 8: Fequency ene Sa Pale Anal & Mxed-Sal ente Texa A&M Unety Annunceent & Aenda HW Due Ma 6 ead aza hate 3 & 6 Annunceent & Aenda n-suce A Fequency ene Oen-cut

More information

Generalized Super Efficiency Model for Ranking Efficient Decision Making Units in Data Envelopment Analysis

Generalized Super Efficiency Model for Ranking Efficient Decision Making Units in Data Envelopment Analysis Autala Joual of Bac ad Appled Scece, 5(12): 2952-2960, 2011 ISSN 1991-8178 Geealzed Supe Effcecy Model fo Rakg Effcet Deco Makg Ut Data Evelopet Aaly 1 M. Fallah Jeloda, 2 G.R. Jahahahloo, 2 F. Hoezadeh

More information

The Performance of Feedback Control Systems

The Performance of Feedback Control Systems The Performace of Feedbac Cotrol Sytem Objective:. Secify the meaure of erformace time-domai the firt te i the deig roce Percet overhoot / Settlig time T / Time to rie / Steady-tate error e. ut igal uch

More information

A New Measure of Probabilistic Entropy. and its Properties

A New Measure of Probabilistic Entropy. and its Properties Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com

More information

PRISON POLICY INITIATIVE ANNUAL REPORT

PRISON POLICY INITIATIVE ANNUAL REPORT PRISON POLICY INITIATIVE 2015-2016 2016-2017 ANNUAL REPORT N 2016 2017 PO Bx 127 N MA 01061 :// (413) 527-0845 1 T Ex D 1 W 3 P k 4 C R - 7 S j 8 B j 10 P x 12 P j 14 P 16 Wk 18 C x 19 Y P Nk S R 15 B

More information

IMPROVING LINEARITY AND SENSITIVITY IN LOW NOISE AMPLIFIERS

IMPROVING LINEARITY AND SENSITIVITY IN LOW NOISE AMPLIFIERS Pceed f he 6h WSEAS eaal Cfeece Appled fac ad Cuca Eluda eece Auu 8-0 006 pp6-0 MPON LNEATY AND SENSTTY N LOW NOSE AMPLES EDA ALEJANDO ANDADE ONZÁLEZ MAO EYES AYALA JOSÉ ALEDO TADO MÉNDEZ Elecc Depae Mepla

More information

I N A C O M P L E X W O R L D

I N A C O M P L E X W O R L D IS L A M I C E C O N O M I C S I N A C O M P L E X W O R L D E x p l o r a t i o n s i n A g-b eanste d S i m u l a t i o n S a m i A l-s u w a i l e m 1 4 2 9 H 2 0 0 8 I s l a m i c D e v e l o p m e

More information

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v Mg: Pcess Aalyss: Reac ae s defed as whee eac ae elcy lue M les ( ccea) e. dm he ube f les ay lue s M, whee ccea M/L les. he he eac ae beces f a hgeeus eac, ( ) d Usually s csa aqueus eeal pcesses eac,

More information