On the Numerical Solutions of Two Dimensional Maxwell s Equations

Size: px
Start display at page:

Download "On the Numerical Solutions of Two Dimensional Maxwell s Equations"

Transcription

1 ues oea ceces : 8-88 I -9 IOI Pubcaos O e umeca ouos of Two mesoa Mawe s uaos.. weam M.M. Kae a.t. Moame epame of Maemacs acu of ceces Cao Uves Ga gp epame of Maemacs acu of ceces Bea Uves Bea gp epame of Basc ceces Moe Acaem fo geeg a Tecoog Cao gp Absac: I s pape wo ffee meos ae sue o sove umeca me epee Mawe s euaos wo mesos. Te meos ae e scouous Gae Meo GM a e Vaaoa Ieao Meo VIM. scouous Gae meo s ese fo ffee umbe of aguao eemes ffee me evas a ffee oes of bass fucos. Compasos bewee e souos of Mawe s euaos usg GM a VIM ae pesee. I s fou a VIM s va fo age me bu GM ovecome s pobem. Ke wos: scouous Gae meo me-oma mawe s euaos wo mesos vaaoa eao meo ITROUCTIO Mawe s euaos ae oe of e mpoa moes ffee fes; escbes eecomagec peomea suc as cues mco ao a aa waves. I s we ow a Mawe s euaos ae a o sove aaca ca be sove aaca o fo smpe omas suc as a spee a a fe ccua ce []. umeca meos fo e Mawe s euaos ae usua efee o as Compuaoa ecomagec CM. Te moeg of ssems ae vovg eecomagec waves s ow we oe oug e souo of e me oma Mawe euao o space g. uc sems wee sove w ma ffee meos e fs meo fo e umeca smuao of me epee eecomagec waves e e ffeece Tme oma Meo TM was popose []. I s cea a TM ow ue s smpc a effcec bu s ffcu o geeae o usucue o-caesa gs a suffe fom e accuae epeseao of e souo o cuve bouaes []. Moeove TM as accuac mao fo seco oe accuac sevee ms e ab o coec epese wave moo ove og saces uess e g s pobve fe []. Ma ffee pes of meos ave bee popose wc ae base o usucue gs a ca ea w compe geomees e e eme Tme oma Meos TMs [4 5]. Tee ae wo ffcues appea we usg e saa e eme Meo M. s e meo geea use o a goba cofomg mes a mea a mes wou agg coecg oes o msmac of mes pos aog ea bouaes. eco ow we ca epese coe sguaes [4 6]. Te scouous Gae Meos GM meos [7-] ae base o scouous fe eeme spaces. I s eas ae eemes of vaous pes a sapes egua o-cofomg meses a eve oca vag pooma egee. Moeove cou s wea efoce acoss mes efaces b ag suabe bea foms e so-cae umeca fues o e saa vaaoa fomuaos []. e o eaea meses usg Lagage poomas [ ] o o eaea meses usg poucs of Lagage poomas []. O e oe a Vaaoa Ieao Meo VIM [4-] s popose b J.. e [6 7] as a mofcao of a geea Lagage mupe meo. Ts ecue poves a seuece of fucos wc coveges o e eac souo of e pobem. I as bee sow a s poceue s a powefu oo fo sovg vaous s of pobems. Ts ecue soves e pobem wou a ee o sceao of e vaabes eefoe s o affece b comp uao ou off eos a oe s o face w ecess of age compue memo a me. Aso s ecue poves e souo of e pobem a cose fom we e mes po ecues suc as e fe ffeece meo pove e appomao a mes pos o. Ts pape s ogae as foows: I seco e moe pobem a e agom of souo ae Coespog Auo:... weam epame of Maemacs acu of ceces Cao Uves Ga gp 8

2 ues oea c. : 8-88 ouce. I seco VIM s ouce o oba e appomae souo of Mawe s euaos. Te as seco 4 gves a scusso of ou esus. GM OR T MOL PROBLM Le us cose e wo mesoa vacuum Mawe s euaos; wc s ow as Tasvese Magec fom TM [9]; efe Ω [ ] [ ] [ T]. wee ae e compoes of e magec fe a s e eecc fe -eco. uemoe s cae magec pemeab a s cae eecc pemv. o e boua coos assume a e wa of e cav s pefec eecca coucg suc a e agea compoe of e eecc fe vases a e wa.e. a e was aso e a coos of e pobem ae pace sceao: a Assume a e compuaoa oma Ω s compose of K ooveappg -smpces o eemes. Ω Ω K wee s a sag se age a e aguao s assume o be geomeca cofomg; fo eampe Ω s appomae b a pecewse ea pogo w eac e segme beg a face of age. b Assume a e oca souo of e veco of e fe [] T a e oca fu ca be efe: p. wee p s e mumesoa Lagage poomas efe b some g pos a efe as P p spa j ; j p s m s wee m ae aba cosas. o smpc e us we Mawe s euao e fu fom [9]: w a Q.. T [ ] Q e e I e foowg we epa bef e basc ea of GM o sove. e fs pa we pese e space sceao a e seco pa we pese e me sceao. 8 c To mpeme e umeca appomao mup euao. w es fuco e egag b pas wce s ea us o. * [ ] ˆ..4 wc epese e sog fom of GM fo pobem.. Assume a e oca souo s epesse as p p ˆ ψ.5

3 ψ p wee { } ues oea c. : 8-88 s a geue wo mesoa pooma bass of oe a p s e umbe of ems e oca epaso wc eae o e oe of e pooma w e eao p. e Te we ave..4 a.5 a afe some eaageme we ge: p p j. j j j p * ˆ.6 j j wc s e sem scee fom of Mawe s euaos wee e mass ma M j j a ca be we as M J I T J v v j.7 wee suc a a j j j j j j j j a e eeme of ege egao ma j ˆ j J I j j.8.9 a w be cacuae e same wa e e sffess ma fo moe eaes fo asfomao a cacuao see [7 ]. Aso o compue e suabe fu we efe Z ± ± Z ± ± w bouaes ˆ ˆ Aso e sffess ma ˆ ˆ Z Z Te e peag boua em ow efe as; ˆ [[ ]] ˆ ˆ [[ ]]. ˆ Z Z ˆ [[ ]] αˆ ˆ [[ ]] ˆ α. f ow e oca semscee sceme of Mawe s euaos geg afe sovg usg GM a fom. a. ae gve b:.a JM ˆ [ ] α ˆ [[ ]] [ ] [[ ]] [ ] JM [ ] ˆ α ˆ [ ] [ ] α JM ˆ ˆ.b.c Tme sceao: To compee e semscee fomuao..a-..c use a epc aw soage Rug- Kua meo o egae me w p sages p s e oe of e pooma of e bass fucos. Te me sep use e compuao s ae eusca o be CL p m ma c wee c 84

4 ues oea c. : 8-88 Tabe : Te mamum eo of e umeca souo a ffee vaues of p a ffee mes T p 4 p 8 p p A e e-6.4e e-4.4e e e e e e e-6.98e-7 A e-8.768e-.85e e e-.4e-..586e e-.6e-.8.74e-7.596e e- A e e e-.445e e-4.97e e-.4656e-.56e e-.666e e- 9.65e e- 5.e-.7e Appomae Appomae g. : umeca souo fo usg GM a p a g. : umeca souo fo usg GM a p a 8 s e wave spee e - eeme; s e eg mamum amee of e eeme; a CLp coo pca aes vaues of /p. Te umeca esus fo sovg Mawe s euao usg e GM ae sow Tabe. Ts abe peses e mamum eo of e souo fo ffee mes umbe of eemes K a e oe of pooma p. UMRICAL IMPLMTATIO O VIM I s seco we app VIM o oba e appomae souo of. w e a a coos. Accog o VIM [6] we ca cosuc e foowg eao fomua: Appomae g. : umeca souo fo usg GM a 5 p a

5 ues oea c. : Appomae ac Z g. 4: Te umeca souo usg VIM a.4 afe 6 eao ef. ac souo fo g ~. ~ ~ wee a ae geea Lagage mupes wc ca be efe opma va vaaoa eo. Te seco em o e g-a se. s cae e coeco a e subscp eoes e - oe appomao. Ue a suabe esce vaaoa assumpo.e. ~ ~ a ~ s cosee as a esce vaao we ca assume a e above coecoa fucoa ae saoa.e. ~ δ ~ δ a ~ δ e e Lagage mupes ca be efe []. Te successve appomaos a of e souo w be ea obae upo usg e obae Lagage mupes a suabe seecve fucos a. Cacuag vaao w espec o a we ca oba e foowg ffeea euaos:. Te Lagage mupes eefoe ca be efe as:. ubsug e efe mupe. o. esus e foowg eao fomua.4 We ca sa w e gve a appomao usg a coos a b e fomua.4 some appomae souos ae se beow:

6 ues oea c. : 8-88 Tabe : Te mamum eo of e umeca souo a ffee vaues of mes T Ma eo T Ma eo s ms m m m m s ms s m s s m s 87 s m s s m s s m s m s m s s m s ece e souo s ma be obae usg: m s ms s m s s m s m.5 Te beavo of e appomae souo of e VIM a e eac souo s gve g. 4 a.4. Te umeca esus fo sovg Mawe s euao usg e VIM ae sow Tabe. Ts abe peses e mamum eo of e souo fo ffee mes. COCLUIO A RMARK I s wo e scouous Gae me oma meo a VIM ae use o su e umeca souo of Mawe s euaos womesos. Te GM s appe fo ffee umbe of eemes ffee me a ffee oe of poomas. ee we e moes mus be sma eoug fo eas souo f beg age gve moe oscaos fo sma moes gve ve goo accuac aso mus be ege o fco. Aoug VIM s a ve smpe meo a as ma avaages suc as ees o sceao space a me vaabes o ee o sove ea o oea ssem of euaos bu fom e obae umeca esus Tabe we fou a VIM s va fo age me a GM ovecome suc s pobem. RRC. Aesso U.. Tme oma Meos fo e Mawe uaos. ocoa sseao Roa Isue of Tecoog ocom.. Tafove A Compuaoa ecoamcs: Te e-ffeece Tme- oma Meo Aec ouse Boso.. ee K umeca souo of a boua vaue pobems vovg Mawe s euao soopc mea. I Tas. Aeas Popagae 4: mes A. a P. Jo 997. e eemes a mass umpg fo Mawe s euaos: Te case umeca Aass C.R. Aca c. Pas 4: Moma. a. Abuasa 5. Appcao of e s vaaoa eao meo o emo euao. Caos oos a acas 7: Mo P.. e eme Meo fo Mawe s uao. Ofo Uves ew o.

7 ues oea c. : Cocbu B. G.. Kaaas a C.W. u. s couous Gae Meos. Teo Compuao a Appcaos. Vo. of Lecue oes Compuaoa cece a geeg pge-veag Be. 8. Kög M. K. Busc a J. egema. Te scouous Gae me-oma meo fo Mawe s euaos w asoopc maeas. Poocs a aosucues-uameas a 9. Goe M.J. A.. Be a. coa 6. Ieo pea scouous Gae meo fo Mawe s euaos: L-om eo esmaes. Pep o. 6- ovembe 6. eave J.. a T. Wabuo 8. oa scouous Gae Meos: Agom Aass a Appcaos pge.. Wabuo T. a J. esave. oa g-oe meos o usucue gs. I: Tme oma souo of Mawe's euaos. J. Compu. Ps. 8 : as. a. Lae. A g-oe ocofomg scouous Gae meo fo me-oma eecomagec. Joua of Compuaoa a Appe Maemacs 4: Wabuo T. a J. esave 4. g-oe oa scouous Gae meos fo e Mawe egevaue pobem. Pos. Tas. Ro. oc. Loo e. A 6: Abass T.A. M.A. -Taw a. Zoe 7. ovg o-ea paa ffeea euaos usg e mofe vaaoa eao Paé ecue. Joua of Compuaoa a Appe Maemacs 7: Abou M.A. a A.A. oma 4. Vaaoa eao meo fo sovg Buge s a coupe Buge s euaos. J. of Compuaoa a Appe Maemacs : e J Vaaoa eao meo-a of o-ea aaca ecue: some ea mpes. Ieaoa Joua of o-lea Mecacs 4: e J Vaaoa eao meo fo ea ffeea euaos. Comm. o-lea c. ume. muao : weam.. a M.M. Kae 7. Vaaoa eao meo fo oe mesoa o-ea emo -easc Caos oos a acas : weam.. a M.M. Kae. Covegece of vaaoa eao meo appe o o-ea coupe ssem of paa ffeea euaos. I. J. of Compue Ma. 87 5: -.. weam.. M.M. Kae a R.. A-Ba 8. o-ea focusg Maaov ssems b vaaoa eao meo a Aoma ecomposo meo. Joua of Pscs: Cofeece ees 96: -7.. as.. Lae a. Rape 8. eveopme of a o-cofomg scouous Gae meo o smpe meses fo eecomagec wave popagao. ou Ieaoa Cofeece o Avace Compuaoa Meos geeg ACOM.. sgos L ffeea uaos a e Cacuus of Vaaos. Tasae fom e Russa b G. aovs M Moscow. Appcaos 8:

Increasing the Image Quality of Atomic Force Microscope by Using Improved Double Tapered Micro Cantilever

Increasing the Image Quality of Atomic Force Microscope by Using Improved Double Tapered Micro Cantilever Rece Reseaces Teecocaos foacs Eecocs a Sga Pocessg ceasg e age Qa of oc Foce Mcope Usg pove oe Tapee Mco aeve Saeg epae of Mecaca Egeeg aava Bac sac za Uves aava Tea a a_saeg@aavaa.ac. sac: Te esoa feqec

More information

Chapter 5. Long Waves

Chapter 5. Long Waves ape 5. Lo Waes Wae e s o compaed ae dep: < < L π Fom ea ae eo o s s ; amos ozoa moo z p s ; dosac pesse Dep-aeaed coseao o mass

More information

INDUCTIVE PULSED THRUSTER WITH SUPERCONDUCTING ACCELERATING ELEMENTS

INDUCTIVE PULSED THRUSTER WITH SUPERCONDUCTING ACCELERATING ELEMENTS DTVE PSED THRSTER WTH SPERODTG AEERATG EEMETS Vas Rashova¹, a Poomaova¹, Oma JRamez, Ae Dashov² ¹suo Poécco acoa e Méco, ESME-HAA,Av Saa Aa,, P, Méoco DFFAX:6-6--8, E-ma: vas@camecacesmecupm ²aoa Aeospace

More information

. The second term denotes the transition [n 1, n 2-1] [n 1, n 2 ] and leads to an increased p ( n1,

. The second term denotes the transition [n 1, n 2-1] [n 1, n 2 ] and leads to an increased p ( n1, . THE MSTE EQUTION OCH Te mase euao oesos o e saeme a e obab of beg a gve sae ages eeg o e obabes of aso o a fom a oe sae e ssem. I oves e fu obab sbuo we a be e sove. Ufouae s s o ofe e ase so we mus

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

Lagrangian & Hamiltonian Mechanics:

Lagrangian & Hamiltonian Mechanics: XII AGRANGIAN & HAMITONIAN DYNAMICS Iouco Hamlo aaoal Pcple Geealze Cooaes Geealze Foces agaga s Euao Geealze Momea Foces of Cosa, agage Mulples Hamloa Fucos, Cosevao aws Hamloa Dyamcs: Hamlo s Euaos agaga

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

Technical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005.

Technical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005. Techc Appedx fo Iveoy geme fo Assemy Sysem wh Poduc o Compoe eus ecox d Zp geme Scece 2005 Lemm µ µ s c Poof If J d µ > µ he ˆ 0 µ µ µ µ µ µ µ µ Sm gumes essh he esu f µ ˆ > µ > µ > µ o K ˆ If J he so

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

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

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

-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

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions EMA5 Lecue 3 Seady Sae & Noseady Sae ffuso - Fck s d Law & Soluos EMA 5 Physcal Popees of Maeals Zhe heg (6) 3 Noseady Sae ff Fck s d Law Seady-Sae ffuso Seady Sae Seady Sae = Equlbum? No! Smlay: Sae fuco

More information

ON THE EXTENSION OF WEAK ARMENDARIZ RINGS RELATIVE TO A MONOID

ON THE EXTENSION OF WEAK ARMENDARIZ RINGS RELATIVE TO A MONOID wwweo/voue/vo9iue/ijas_9 9f ON THE EXTENSION OF WEAK AENDAIZ INGS ELATIVE TO A ONOID Eye A & Ayou Eoy Dee of e Nowe No Uvey Lzou 77 C Dee of e Uvey of Kou Ou Su E-: eye76@o; you975@yooo ABSTACT Fo oo we

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

DYNAMICAL NEAR OPTIMAL TRAINING FOR INTERVAL TYPE-2 FUZZY NEURAL NETWORK (T2FNN) WITH GENETIC ALGORITHM

DYNAMICAL NEAR OPTIMAL TRAINING FOR INTERVAL TYPE-2 FUZZY NEURAL NETWORK (T2FNN) WITH GENETIC ALGORITHM DYNAICAL NAR OPIAL RAINING FOR INRVAL YP- FUZZY NURAL NWORK FNN WIH GNIC ALGORIH A hess Submed Fume o he Requemes o he Degee o ase o Phosoph B a Chu-Sheg Cheg Schoo o coeecoc geeg Fau o geeg ad Iomao echoog

More information

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

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

More information

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS Joa of Aed Mahema ad Comaoa Meha 4 3( 6-73 APPLCATON OF A Z-TRANSFORMS METHOD FOR NVESTGATON OF MARKOV G-NETWORKS Mha Maay Vo Nameo e of Mahema Ceohowa Uey of Tehoogy Cęohowa Poad Fay of Mahema ad Come

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

Nonlocal Boundary Value Problem for Nonlinear Impulsive q k Symmetric Integrodifference Equation

Nonlocal Boundary Value Problem for Nonlinear Impulsive q k Symmetric Integrodifference Equation OSR ol o Mec OSR-M e-ssn: 78-578 -SSN: 9-765X Vole e Ve M - A 7 PP 95- wwwojolog Nolocl Bo Vle Poble o Nole lve - Sec egoeece Eo Log Ceg Ceg Ho * Yeg He ee o Mec Yb Uve Yj PR C Abc: A oe ole lve egoeece

More information

Generalized Entropy of Kumaraswamy Distribution Based on Order Statistics

Generalized Entropy of Kumaraswamy Distribution Based on Order Statistics Geeaed Eop o Kumaawam Dbuo Baed o Ode Sac Ra Na M.A.K Bag 2 Javd Ga Da 3 Reeach Schoa Depame o Sac Uve o Kahm Saga Ida 2 Aocae Poeo Depame o Sac Uve o Kahm Saga Ida 3 Depame o Mahemac Iamc Uve o Scece

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 E0 2958 BL K THS PAGE DECLASSFED AW E0 2958

More information

IV. STATICS AND DYNAMICS OF DEFORMABLE MEDIA.

IV. STATICS AND DYNAMICS OF DEFORMABLE MEDIA. V. TT ND DYNM OF DEFOMLE MED. 48. Defoabe eu. Naua sae a efoe sae. The heoes of he efoabe e a he efoabe suface ha we scusse ea a ve aua ae o evsog a oe geea efoabe eu ha he oe ha s habua cosee he heo of

More information

MODELING AND IDENTIFICATION OF A TWO-LINK FLEXIBLE MANIPULATOR

MODELING AND IDENTIFICATION OF A TWO-LINK FLEXIBLE MANIPULATOR ABC Sosu Ss caocs - Vo. 5 Cog b ABC Sco VII - Robocs Pag 9 ODELIN AND IDENIFICAION OF A WO-LINK FLEXIBLE ANIPULAOR og Auguso Bof jog.g@ga.co Fábo L.. Saos fsaos@a.b Cao Rogus Bao caob@ga.co Luz Caos Saova

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

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

3D UNIFIED CFD APPROACH TO MODELING OF BUBBLE PHENOMENA

3D UNIFIED CFD APPROACH TO MODELING OF BUBBLE PHENOMENA e Ieaoa oca eeg o Nucea aco ema-hyaucs (NUREH- Pae: 47 Poes Paace Cofeece Cee Ago Face Ocobe -6 5 3D UNIFIED CFD APPROACH O ODELING OF BUBBLE PHENOENA am Cuao A E Akseoa ae A Pecko Nucea Safey Isue (IBRAE

More information

G OUP S 5 TH TE 5 DN 5. / E/ ' l / DECE 'I E THIS PAGE DECLASSIFIED IAW EO ', - , --,. . ` : - =.. r .

G OUP S 5 TH TE 5 DN 5. / E/ ' l / DECE 'I E THIS PAGE DECLASSIFIED IAW EO ', - , --,. . ` : - =.. r . = ; D p a 0 + 5 TH TE 5 DN 5 506 T F/ GH T G OUP S / E/ 9 4 4 / DECE E = / v c H S T 0 R Y 45 8 TH F HTE S DR N S ) 50 c c o s ) DECE 9 3 DLCE E 9 a L ON J E R E 2 d L Cope s H aca SS L 9/ soca e O 0 THS

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

1. INTRODUCTION In this paper, we consider a general ninth order linear boundary value problem (1) subject to boundary conditions

1. INTRODUCTION In this paper, we consider a general ninth order linear boundary value problem (1) subject to boundary conditions NUMERICAL SOLUTION OF NINTH ORDER BOUNDARY VALUE PROBLEMS BY PETROV-GALERKIN METHOD WITH QUINTIC B-SPLINES AS BASIS FUNCTIONS AND SEXTIC B-SPLINES AS WEIGHT FUNCTIONS K. N. S. Kas Vswaaham a S. V. Kamay

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

MATHEMATICAL DERIVATION OF THE FARADAY INDUCTION LAW AND EXPLANATION OF ITS LORENTZ NON-INVARIANCE

MATHEMATICAL DERIVATION OF THE FARADAY INDUCTION LAW AND EXPLANATION OF ITS LORENTZ NON-INVARIANCE MTHEMTICL DERIVTION OF THE FRDY INDUCTION LW ND EXPLNTION OF ITS LORENTZ NON-INVRINCE.L. Kholmesk Depame of Phscs Belausa Sae Ues 4 F. Skoa eue 0080 Msk Belaus E-mal: kholm@bsu.b The pese pape ees he Faaa

More information

An Interactive Intuitionistic Fuzzy Non-Linear Fractional Programming Problem

An Interactive Intuitionistic Fuzzy Non-Linear Fractional Programming Problem o ou of pp gg R SSN - Voum Num pp - R uo p:wwwpuoom v uo uzz No- o ogmmg om zz m pm of Mm u of S w v o gp O : --- T pp vop w v mo fo ovg o fo pogmmg pom o uo fuzz o v mo f o m M pf g of - v m-m pom ov

More information

Density estimation III. Linear regression.

Density estimation III. Linear regression. Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg

More information

Parameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data

Parameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data Avlble ole wwwsceceeccom Physcs Poce 0 475 480 0 Ieol Cofeece o Mecl Physcs Bomecl ee Pmee smo Hyohess es of wo Neve Boml Dsbuo Poulo wh Mss D Zhwe Zho Collee of MhemcsJl Noml UvesyS Ch zhozhwe@6com Absc

More information

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems Vo 3 No Mod Appd Scc Exsc of Nooscaoy Souos fo a Cass of N-od Nua Dffa Sysms Zhb Ch & Apg Zhag Dpam of Ifomao Egg Hua Uvsy of Tchoogy Hua 4 Cha E-ma: chzhbb@63com Th sach s facd by Hua Povc aua sccs fud

More information

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

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

More information

Reliability Equivalence of a Parallel System with Non-Identical Components

Reliability 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 information

β A Constant-G m Biasing

β A Constant-G m Biasing p 2002 EE 532 Anal IC Des II Pae 73 Cnsan-G Bas ecall ha us a PTAT cuen efeence (see p f p. 66 he nes) bas a bpla anss pes cnsan anscnucance e epeaue (an als epenen f supply lae an pcess). Hw h we achee

More information

Summary of Grade 1 and 2 Braille

Summary of Grade 1 and 2 Braille Sa of Gade 1 ad 2 Baie Wiia Pa Seebe 1998, Ai 1999 1 Baie Aabe Te fooig i i of TEX aco ad Baie bo coaied i baie Te e coad \baie{} cove eece of ag o Baie bo A ag ca be oe caace ic aea a i, o i caace ic

More information

Hyperbolic Heat Equation as Mathematical Model for Steel Quenching of L-shape and T-shape Samples, Direct and Inverse Problems

Hyperbolic Heat Equation as Mathematical Model for Steel Quenching of L-shape and T-shape Samples, Direct and Inverse Problems SEAS RANSACIONS o HEA MASS RANSER Bos M Be As Bs Hpeo He Eo s Me Moe o See Qe o L-spe -spe Spes De Iese Poes ABIA BOBINSKA o Pss Mes es o L Ze See 8 L R LAIA e@o MARARIA BIKE ANDRIS BIKIS Ise o Mes Cope

More information

Viewing in 3D. Viewing in 3D. Planar Geometric Projections. Taxonomy of Projections. How to specify which part of the 3D world is to be viewed?

Viewing in 3D. Viewing in 3D. Planar Geometric Projections. Taxonomy of Projections. How to specify which part of the 3D world is to be viewed? Viewig i 3D Viewig i 3D How o speci which pa o he 3D wo is o e viewe? 3D viewig voume How o asom 3D wo cooiaes o D ispa cooiae? Pojecios Cocepua viewig pipeie: Xom o ee coos 3D cippig Pojec Xom o viewpo

More information

(1) Cov(, ) E[( E( ))( E( ))]

(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 information

Lecture 3 summary. C4 Lecture 3 - Jim Libby 1

Lecture 3 summary. C4 Lecture 3 - Jim Libby 1 Lecue su Fes of efeece Ivce ude sfoos oo of H wve fuco: d-fucos Eple: e e - µ µ - Agul oeu s oo geeo Eule gles Geec slos cosevo lws d Noehe s heoe C4 Lecue - Lbb Fes of efeece Cosde fe of efeece O whch

More information

_ J.. C C A 551NED. - n R ' ' t i :. t ; . b c c : : I I .., I AS IEC. r '2 5? 9

_ J.. C C A 551NED. - n R ' ' t i :. t ; . b c c : : I I .., I AS IEC. r '2 5? 9 C C A 55NED n R 5 0 9 b c c \ { s AS EC 2 5? 9 Con 0 \ 0265 o + s ^! 4 y!! {! w Y n < R > s s = ~ C c [ + * c n j R c C / e A / = + j ) d /! Y 6 ] s v * ^ / ) v } > { ± n S = S w c s y c C { ~! > R = n

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

Order Statistics. 1 n. Example Four measurements are taken on a random variable, x, which take on values.

Order Statistics. 1 n. Example Four measurements are taken on a random variable, x, which take on values. Oe Sascs e be couous epee.v. wh sbuo a es (. We eoe K be he oee aom vaable whee < < K < a because he ae couous we ca goe equal sg. m ma ( K ( K The pobabl es uco o a ae easl ou: e be a couous.v. ha has

More information

Continuous Time Markov Chains

Continuous 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 information

Algebraic Properties of Modular Addition Modulo a Power of Two

Algebraic Properties of Modular Addition Modulo a Power of Two Agebac Popees of Moda Addo Modo a Powe of Two S M Dehav Aeza Rahmpo Facy of Mahemaca ad Compe Sceces Khaazm Uvesy Teha Ia sd_dehavsm@hac Facy of Sceces Qom Uvesy Qom Ia aahmpo@sqomac Absac; Moda addo modo

More information

Copyright Birkin Cars (Pty) Ltd

Copyright Birkin Cars (Pty) Ltd E GROU TWO STEERING AND EDAS - R.H.D Aemble clue : K360 043AD STEERING OUMN I u: - : K360 04A STEERING RAK :3 K360 045A EDA OX K360043AD STEERING O UMN Tl eque f embl f u: - mm Alle Ke 3mm Se 6mm Alle

More information

Analytical Evaluation of Multicenter Nuclear Attraction Integrals for Slater-Type Orbitals Using Guseinov Rotation-Angular Function

Analytical Evaluation of Multicenter Nuclear Attraction Integrals for Slater-Type Orbitals Using Guseinov Rotation-Angular Function I. J. Cop. Mh. S Vo. 5 o. 7 39-3 Ay Evuo of Mu u Ao Ig fo S-yp O Ug Guov Roo-Agu uo Rz Y M Ag Dp of Mh uy of uo fo g A-Khj Uvy Kgo of Su A Dp of Mh uy of S o B Auh Uvy Kgo of Su A A. Ug h Guov oo-gu fuo

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

4.1 Schrödinger Equation in Spherical Coordinates

4.1 Schrödinger Equation in Spherical Coordinates Phs 34 Quu Mehs D 9 9 Mo./ Wed./ Thus /3 F./4 Mo., /7 Tues. / Wed., /9 F., /3 4.. -. Shodge Sphe: Sepo & gu (Q9.) 4..-.3 Shodge Sphe: gu & d(q9.) Copuo: Sphe Shodge s 4. Hdoge o (Q9.) 4.3 gu Moeu 4.4.-.

More information

Density estimation III.

Density estimation III. Lecure 6 esy esmao III. Mlos Hausrec mlos@cs..eu 539 Seo Square Oule Oule: esy esmao: Bomal srbuo Mulomal srbuo ormal srbuo Eoeal famly aa: esy esmao {.. } a vecor of arbue values Objecve: ry o esmae e

More information

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002

More information

Instruction Sheet COOL SERIES DUCT COOL LISTED H NK O. PR D C FE - Re ove r fro e c sed rea. I Page 1 Rev A

Instruction Sheet COOL SERIES DUCT COOL LISTED H NK O. PR D C FE - Re ove r fro e c sed rea. I Page 1 Rev A Instruction Sheet COOL SERIES DUCT COOL C UL R US LISTED H NK O you or urc s g t e D C t oroug y e ore s g / as e OL P ea e rea g product PR D C FE RES - Re ove r fro e c sed rea t m a o se e x o duct

More information

11/8/2002 CS 258 HW 2

11/8/2002 CS 258 HW 2 /8/ CS 58 HW. G o a a qc of aa h < fo a I o goa o co a C cc c F ch ha F fo a I A If cc - c a co h aoa coo o ho o choo h o qc? I o g o -coa o o-coa? W ca choo h o qc o h a a h aa a. Tha f o o a h o h a:.

More information

By Joonghoe Dho. The irradiance at P is given by

By Joonghoe Dho. The irradiance at P is given by CH. 9 c CH. 9 c By Joogo Do 9 Gal Coao 9. Gal Coao L co wo po ouc, S & S, mg moocomac wav o am qucy. L paao a b muc ga a. Loca am qucy. L paao a b muc ga a. Loca po obvao P a oug away om ouc o a a P wavo

More information

VARIED SIZED FLOOR PLATE S O N - S I T E B U I L D I N G A M E N I T I E S

VARIED SIZED FLOOR PLATE S O N - S I T E B U I L D I N G A M E N I T I E S VAIED SIZED FLOO PLAE S O - S I E B U I L D I G A E I I E S AVAILABILIIES HIGH-ISE EIE 29H FLOO 16,584 SF LEASE OU ID-ISE PAIAL 18H FLOO 12,459 SF 08/2019 ID-ISE PAIAL 14H FLOO 7,232 SF 08/2019 LOW-ISE

More information

The calculation of the characteristic and non-characteristic harmonic current of the rectifying system

The calculation of the characteristic and non-characteristic harmonic current of the rectifying system The calculato of the chaactestc a o-chaactestc hamoc cuet of the ectfyg system Zhag Ruhua, u Shagag, a Luguag, u Zhegguo The sttute of Electcal Egeeg, Chese Acaemy of Sceces, ejg, 00080, Cha. Zhag Ruhua,

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2 Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads

More information

X-Ray Notes, Part III

X-Ray Notes, Part III oll 6 X-y oe 3: Pe X-Ry oe, P III oe Deeo Coe oupu o x-y ye h look lke h: We efe ue of que lhly ffee efo h ue y ovk: Co: C ΔS S Sl o oe Ro: SR S Co o oe Ro: CR ΔS C SR Pevouly, we ee he SR fo ye hv pxel

More information

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

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

More information

Mathematical Formulation

Mathematical Formulation Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg

More information

1.050 Engineering Mechanics I. Summary of variables/concepts. Lecture 27-37

1.050 Engineering Mechanics I. Summary of variables/concepts. Lecture 27-37 .5 Engneeng Mechancs I Summa of vaabes/concepts Lectue 7-37 Vaabe Defnton Notes & ments f secant f tangent f a b a f b f a Convet of a functon a b W v W F v R Etena wok N N δ δ N Fee eneg an pementa fee

More information

( ) ( ) Last Time. 3-D particle in box: summary. Modified Bohr model. 3-dimensional Hydrogen atom. Orbital magnetic dipole moment

( ) ( ) Last Time. 3-D particle in box: summary. Modified Bohr model. 3-dimensional Hydrogen atom. Orbital magnetic dipole moment Last Time 3-dimensional quantum states and wave functions Couse evaluations Tuesday, Dec. 9 in class Deceasing paticle size Quantum dots paticle in box) Optional exta class: eview of mateial since Exam

More information

A Method for Group Decision-making with Uncertain Preference Ordinals Based on Probability Matrix

A Method for Group Decision-making with Uncertain Preference Ordinals Based on Probability Matrix 65 A pubcao of CHEMICA ENGINEERINGTRANSACTIONS VO. 51, 016 Gue Edo:Tcu Wag, Hogyag Zag, e Ta Copyg 016, AIDIC Sevz S..., ISBN978-88-95608-43-3; ISSN 83-916 Te Iaa Aocao of Ceca Egeeg Oe a www.adc./ce DOI:

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

A NEW FORMULAE OF VARIABLE STEP 3-POINT BLOCK BDF METHOD FOR SOLVING STIFF ODES

A NEW FORMULAE OF VARIABLE STEP 3-POINT BLOCK BDF METHOD FOR SOLVING STIFF ODES Joual of Pue ad Appled Maemac: Advace ad Applcao Volume Numbe Page 9-7 A NEW ORMULAE O VARIABLE STEP -POINT BLOCK BD METHOD OR SOLVING STI ODES NAGHMEH ABASI MOHAMED BIN SULEIMAN UDZIAH ISMAIL ZARINA BIBI

More information

Optimization Method for Interval Portfolio Selection Based on. Satisfaction Index of Interval inequality Relation

Optimization Method for Interval Portfolio Selection Based on. Satisfaction Index of Interval inequality Relation Opmzao Meho fo Ieval Pofolo Seleco Base o Sasfaco Ie of Ieval equal Relao Yuchol Jog a a Cee of Naual Scece Uves of Sceces Pogag DPR Koea E-mal: ucholog@ahoo.com Absac: I hs pape e cose a eval pofolo seleco

More information

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983).

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983). Ovrvw B r rh r: R-k r -3-4 r 00 Ig L Gør Amor Dm rogrmmg Nwork fow Srg mhg Srg g Comuo gomr Irouo o NP-om Rom gorhm B r rh r -3-4 r Aow,, or 3 k r o Prf Evr h from roo o f h m gh mr h E w E R E R rgr h

More information

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square Lecure 5 esy esmao Mlos Hauskrec mlos@cs..edu 539 Seo Square esy esmaos ocs: esy esmao: Mamum lkelood ML Bayesa arameer esmaes M Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Noaramerc

More information

I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o

I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o u l d a l w a y s b e t a k e n, i n c l u d f o l

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least 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 information

On the hydrogen wave function in Momentum-space, Clifford algebra and the Generating function of Gegenbauer polynomial

On the hydrogen wave function in Momentum-space, Clifford algebra and the Generating function of Gegenbauer polynomial O he hoge we fco Moe-sce ffo geb he eeg fco of egebe oo Meh Hge Hss To ce hs eso: Meh Hge Hss O he hoge we fco Moe-sce ffo geb he eeg fco of egebe oo 8 HL I: h- hs://hches-oeesf/h- Sbe o J 8 HL s

More information

A PATRA CONFERINŢĂ A HIDROENERGETICIENILOR DIN ROMÂNIA,

A PATRA CONFERINŢĂ A HIDROENERGETICIENILOR DIN ROMÂNIA, A PATRA ONFERINŢĂ A HIDROENERGETIIENILOR DIN ROMÂNIA, Do Pael MODELLING OF SEDIMENTATION PROESS IN LONGITUDINAL HORIZONTAL TANK MODELAREA PROESELOR DE SEPARARE A FAZELOR ÎN DEANTOARE LONGITUDINALE Da ROBESU,

More information

The far field calculation: Approximate and exact solutions. Persa Kyritsi November 10th, 2005 B2-109

The far field calculation: Approximate and exact solutions. Persa Kyritsi November 10th, 2005 B2-109 Th fa fl calculao: Appoa a ac oluo Pa K Novb 0h 005 B-09 Oul Novb 0h 005 Pa K Iouco Appoa oluo flco fo h gou ac oluo Cocluo Pla wav fo Ic fl: pla wav k ( ) jk H ( ) λ λ ( ) Polaao fo η 0 0 Hooal polaao

More information

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c L i f e t i m e M a n a g e m e n t o f F l a-b s ah s e d S S D s U s i n g R e c o v e r-a y w a r e D y n a m i c T h r o t t l i n g S u n g j i n L e, e T a e j i n K i m, K y u n g h o, Kainmd J

More information

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT V M Chacko E CONVE AND INCREASIN CONVE OAL IME ON ES RANSORM ORDER R&A # 4 9 Vol. Decembe ON OAL IME ON ES RANSORM ORDER V. M. Chacko Depame of Sascs S. homas Collee hss eala-68 Emal: chackovm@mal.com

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

AC 2-3 AC 1-1 AC 1-2 CO2 AC 1-3 T CO2 CO2 F ES S I O N RY WO M No.

AC 2-3 AC 1-1 AC 1-2 CO2 AC 1-3 T CO2 CO2 F ES S I O N RY WO M No. SHEE OES. OVE PCE HOSS SSOCE PPUCES. VE EW CORO WR. S SE EEVO S EXS. 2. EW SSORS CCOS. S SE EEVO S HOSS. C 2-3 C - C -2 C 2- C -3 C 4- C 2-2 P SUB pproved Filename: :\\2669 RP Performing rts Center HVC\6-C\s\2669-3.dwg

More information

In order to ensure that an overall development in service by those. of total. rel:rtins lo the wapris are

In order to ensure that an overall development in service by those. of total. rel:rtins lo the wapris are AhAY ggkhu e evue he eve us wch my be eese s esu eucs ese mbes buges hve bee ke cvu vs. Css e vse e' he w m ceges cec ec css. Dec Dgqs_1q W qge5ee.pe_s_ v V cuss ke ecy 1 hc huse ees. bse cu wc esb shmes.

More information

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1 ps 57 Machne Leann School of EES Washnon Sae Unves ps 57 - Machne Leann Assume nsances of classes ae lneal sepaable Esmae paamees of lnea dscmnan If ( - -) > hen + Else - ps 57 - Machne Leann lassfcaon

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

-Z ONGRE::IONAL ACTION ON FY 1987 SUPPLEMENTAL 1/1

-Z ONGRE::IONAL ACTION ON FY 1987 SUPPLEMENTAL 1/1 -Z-433 6 --OGRE::OA ATO O FY 987 SUPPEMETA / APPR)PRATO RfQUEST PAY AD PROGRAM(U) DE ARTMET OF DEES AS O' D 9J8,:A:SF ED DEFS! WA-H ODM U 7 / A 25 MRGOPf RESOUTO TEST HART / / AD-A 83 96 (~Go w - %A uj

More information

Convexity Preserving C 2 Rational Quadratic Trigonometric Spline

Convexity Preserving C 2 Rational Quadratic Trigonometric Spline Ieraoal Joural of Scefc a Researc Publcaos, Volume 3, Issue 3, Marc 3 ISSN 5-353 Covexy Preservg C Raoal Quarac Trgoomerc Sple Mrula Dube, Pree Twar Deparme of Maemacs a Compuer Scece, R. D. Uversy, Jabalpur,

More information

FI 2201 Electromagnetism

FI 2201 Electromagnetism F Eectomagnetism exane. skana, Ph.D. Physics of Magnetism an Photonics Reseach Goup Magnetostatics MGNET VETOR POTENTL, MULTPOLE EXPNSON Vecto Potentia Just as E pemitte us to intouce a scaa potentia V

More information

A stopping criterion for Richardson s extrapolation scheme. under finite digit arithmetic.

A stopping criterion for Richardson s extrapolation scheme. under finite digit arithmetic. A stoppg crtero for cardso s extrapoato sceme uder fte dgt artmetc MAKOO MUOFUSHI ad HIEKO NAGASAKA epartmet of Lbera Arts ad Sceces Poytecc Uversty 4-1-1 Hasmotoda,Sagamara,Kaagawa 229-1196 JAPAN Abstract:

More information

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I CEE49b Chaper - Free Vbrao of M-Degree-of-Freedo Syses - I Free Udaped Vbrao The basc ype of respose of -degree-of-freedo syses s free daped vbrao Aaogos o sge degree of freedo syses he aayss of free vbrao

More information

Parameters Estimation in a General Failure Rate Semi-Markov Reliability Model

Parameters 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 information

Upper Bound For Matrix Operators On Some Sequence Spaces

Upper Bound For Matrix Operators On Some Sequence Spaces Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah

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

Radioactive Decay and Half Life Simulation 2/17 Integrated Science 2 Redwood High School Name: Period:

Radioactive Decay and Half Life Simulation 2/17 Integrated Science 2 Redwood High School Name: Period: Radioactive Decay and Half Life Simulation 2/17 Integrated Science 2 Redwood High School Name: Period: Background I t was not until the end of the 1800 s that scientists found a method for determining

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

1. Experimental Methodology

1. Experimental Methodology Supporting Information Combining in situ NEXAFS spectroscopy and CO 2 methanation kinetics to study Pt and Co nanoparticle catalysts reveals key insights into the role of platinum in promoted cobalt catalysis.

More information

The Poisson Process Properties of the Poisson Process

The 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 information

Non-Equidistant Multi-Variable Optimum Model with Fractional Order Accumulation Based on Vector Continued Fractions Theory and its Application

Non-Equidistant Multi-Variable Optimum Model with Fractional Order Accumulation Based on Vector Continued Fractions Theory and its Application QIYUN IU NON-EQUIDISN MUI-VRIE OPIMUM MODE WIH FRCION ORDER... No-Equds Mu-V Ou Mod w Fco Od ccuuo sd o Vco Coud Fcos o d s co Qu IU * D YU. S oo o dcd Dsg d Mucu o Vc od Hu Us Cgs Hu 8 C. Cog o Mcc Egg

More information

Dangote Flour Mills Plc

Dangote Flour Mills Plc SUMMARY OF OFFER Opening Date 6 th September 27 Closing Date 27 th September 27 Shares on Offer 1.25bn Ord. Shares of 5k each Offer Price Offer Size Market Cap (Post Offer) Minimum Offer N15. per share

More information

A DUAL-RECIPROCITY BOUNDARY ELEMENT METHOD FOR ANISOTROPIC HEAT CONDUCTION IN INHOMOGENEOUS SOLIDS

A DUAL-RECIPROCITY BOUNDARY ELEMENT METHOD FOR ANISOTROPIC HEAT CONDUCTION IN INHOMOGENEOUS SOLIDS s Ieo ofeece Fo Scefc ou o ouo Eee s I-SE Ahes 8- Seebe 4 I-SE A DUA-REIPROIY BOUDARY EEE EHOD FOR AISOROPI HEA ODUIO I IHOOGEEOUS SOIDS W.. A K. K. hoo Dvso of Eee echcs Schoo of echc Pouco Eee y echooc

More information

SOLUTION TO THE PROBLEM CONTROL OF A DISTRIBUTED PARAMETER PROCESS

SOLUTION TO THE PROBLEM CONTROL OF A DISTRIBUTED PARAMETER PROCESS DAAA INTERNATIONAL SIENTII BOO pp. 69-86 HAPTER 5 SOLUTION TO THE PROBLE ONTROL O A DISTRIBUTED PARAETER PROESS JADLOVSA A.; ATALINI B.; HRUBINA.; AUROVA A. & WESSELY E. Absrac: The chaper eas wh he ssues

More information