A New Mathematical Approach for Solving the Equations of Harmonic Elimination PWM

Size: px
Start display at page:

Download "A New Mathematical Approach for Solving the Equations of Harmonic Elimination PWM"

Transcription

1 New atheatcal pproach for Solvg the Equatos of Haroc Elato PW Roozbeh Nader Electrcal Egeerg Departet, Ira Uversty of Scece ad Techology Tehra, Tehra, Ira ad bdolreza Rahat Electrcal Egeerg Departet, Ira Uversty of Scece ad Techology Tehra, Tehra, Ira STRCT Selectve haroc elato s the optal cotrol techque for PW verters. The a challege ths techque s to solve the olear trascedetal equato syste obtaed fro the Fourer trasfor. The elato techque cojucto wth the Pade approato theory ad Resultat theory s oe of the effcet techques to solve ths equato syste. I ths paper, we propose a ew techque for elatg the varables aog odfed polyoal equatos of haroc elato techque based o a ew otato whch splfes the relatoshp betwee polyoal factors. Keywords: Selectve Haroc Elato, Pulse-Wdth odulato, Elato Theory, DC-C Iverter ad Polyoal Equatos.. INTRODUCTION Selectve haroc Elato (SHE s the optal cotrol techque for PW verters. SHE offers several advatages copared to covetoal atural PW techque cludg acceptable perforace wth low swtchg frequecy to fudaetal frequecy ratos, drect cotrol over output wavefor harocs, ad the ablty to leave trple harocs ucotrolled to tae advatage of crcut topology three-phase systes. These ey advatages ae SHE a vable alteratve to other ethods of odulato applcatos such as groud power uts, varable speed drves, or dual-frequecy ducto heatg []. I ths techque, a PW wavefor wth varable swtchg agles s cosdered to Fourer trasfor to tae t to the haroc doa. equato syste s the fored by cotrollg selectve harocs of ths PW wavefor. The resultg equato syste s olear ad trascedetal. Ths equato syste ca be solved by geeral calculatve techques such as Newto ad Iterato ethods or by specalzed techques such as Walsh fuctos, Geetc lgorths, ad Elato techques []. The assued syetry of the tal PW wavefor defes the for of the equato syste. The splest for s the quarter-wave syetry of the PW wavefor whch gves the followg equato syste: ( cos( θ = ( = Where s the uber of swtchg agles the quarter perod, θ are the swtchg agles, ad s costat. For sgle-phase verters s odd ad for three-phase verters s odd ad otrple: sgle-phase: =,,5,7,..., ubers three-phase: =, 5, 7,,,7,... For sgle phase syste the aswer s uque ad for three-phase syste several aswers et. The half-wave syetry assupto creases the soluto space for both sgle-phase ad three-phase systes ad therefore gves a good fleblty to the desger to choose the ost effcet aswer for decreasg total haroc dstorto (THD, acoustc ose, ad electroagetc terferece (EI. However, ths paper we focus o solvg the equato syste for quarter-wave syetry. The elato techque for solvg the equato syste of the SHE techque s based o covertg trgooetrc eleets of each equato to polyoal eleets. The objectve of the elato techque s to elate the varables aog these polyoal equatos ad covert t to a sgle-varable polyoal equato whose roots are the sae as the aswer of ths equato syste. Ths ca be acheved by Pade approato theory [] or Resultat theory [],[4]. Pade approato theory s used to obta the sgle polyoal equato for sgle-phase forulato ad the Resultat theory s used for the ore coplcated case of three-phase syste. I ths paper, we preset a ew atheatcal approach for elato based o the relatoshps betwee eve ad odd costats of polyoal equatos whch eables us to obta the sgle polyoal equato for sgle-phase systes. For the geeral case the proposed techque operates faster tha the Pade approato techque. For the future tred the eteso of ths techque ay be used wth the resultat theory

2 to splfy the equatos ad prove the degree of coplety that the resultat theory ca deal wth.. POLYNOIL EQUTION SYSTE ll the equatos for SHE-PW, Eq. (, are based o the ter cos(θ. Ths ter ca be coverted to su of powers of cos(θ usg the followg forulas: cos( θ = cos ( θ cos( θ = 4cos ( θ cos( θ 4 cos(4 θ = 8cos ( θ 8cos ( θ + 5 cos(5 θ = 6 cos ( θ cos ( θ + 5cos( θ 6 4 cos(6 θ= cos ( θ 48cos ( θ+ 8cos ( θ Iductvely, we have guessed the followg epresso for cos(θ: cos( θ = ( cos ( θ = ( O the other had, we ca epad cos (θ ters of the cose of ultples of θ accordg to the Fourer theory as: cos ( θ = ( cos( θ + cos ( θ = ( cos( θ + cos( θ 4 cos ( θ = ( cos(4 θ + 4 cos( θ cos ( θ = ( cos(5 θ + 5cos( θ + cos( θ 6 5 cos ( θ= cos(6 θ+ 6 cos(4 θ+ 5cos( θ+ ( The geeral forulato s: cos ( θ = cos (( θ = ( We have proved Eq. ( ad Eq. ( apped ad. Cosderg X = (- cos(θ ad usg Eq. ( the equato syste for sgle-phase verter, Eq. (, could be rewrtte as: X = c, j =,,..., (4 = The followg for ay ae a better sese of the polyoal syste: X + X + X + + X = c X + X + X + + X = c X + X + X + + X = c 5 X + X + X + + X = c. NEW NOTTION The relatoshps betwee dfferet hgh order polyoals are very large. Therefore, we preset a ew otato whch eables us to epress the relatoshps: p p p Where s the set of varables. Ths otato represets the su of products of ay possble cobato of varables fro the for of the ter specfed. We call ths otato as SPC, the su of powers of the for (p +p + +p as the order of SPC, ad the uber of varables the for (uber of as the degree of SPC. The followg eaples ae t clear: = a,b,c,d ord. = deg. = { } = ab + ac + ad + bc + bd + cd ord. = 5 deg. = = abd + acd + bcd + abc + adc + bdc + acb + adb + cdb + bca + bda + cda ord. = deg. = 5 = s show the last eaple, f the for s ot achevable usg the set of varables (the degree of SPC s ore tha the uber of estg varables, the result s zero. We also assue the followg specfc case for the otato: = The equato systes we are dealg wth are the for of: = c (5 Therefore we eed to epress ay for of SPC ters of c, c,. Cosder the followg SPC eaple: To epress t ters of c, c, we preset the followg relato: c = Obvously, we have separated fro ad have ultpled ts SPC by the SPC of reag ter (. The result cossts of the SPCs of ay possble for whch ght be geerated by the ultplcato process. The estece of the last ter results two sets of slar ters. Ths s represeted by the coeffcet. We ca etract < > as: = c ( It s clear that we have decoposed our SPC proble to several SPCs of oe ut lower degree. Usg Eq. (5, we repeat ths procedure several tes utl we reach to a epresso whch s oly cossts of c, c,. The followg recursve epresso reduces the proble of fdg the SPC of a specfed for to su of SPCs of other saller fors: cq tes tes tes tes tes tes q p p p p p p q = a tes tes tes tes p p p p+ q P + b = < (p p p The coeffcet a s the degree of q the correspodg SPC ter: f q = p a = + j j j + The coeffcet b s the degree of p q the correspodg SPC ter: f p + q = p b = + j j j If the ter we choose to separate fro the SPC for ( q has the greatest power aog all other ters (q p, p,, p, the all

3 b coeffcets are equal to. Thus, we ca wrte the followg recursve epresso for ay SPC for: tes tes tes p p p = tes tes tes p p p c p (6 tes tes tes tes p p p p+ p P = < (p p p < p Ths recursve epresso ca be easly pleeted by a fucto atheatcal softwares such as atlab, aple, or atheatca. 4. ELIINTING THE VRILES Now, suppose that we wat to obta a polyoal equato wth the roots R. We ca wrte t as: R = X,X,,X = { } (X X = Collectg X ths equato, we have: tes ( X = (7 = R Obvously, to specfy ths equato we eed to calculate the values for <>, <>,. For ths case, we ca splfy Eq. (6 as: tes tes tes = c tes tes tes q q+ = c R R R R q R R Usg these recursve equatos we have obtaed the drect epresso as: tes P p ( c = R Q W p! W : for each ter p = Q : ay possble for I the atr for ths ca be wrtte as the followg deterat: c c c c tes c c c = R c c (8! c However, the equato syste for sgle-phase SHE oly cotas odd powers of (c odd. Thus, we ust obta the relatoshp betwee eve ad odd. I ths case we ca use the epasos of the <> whch are: = + = = ! P P P = Q P!P! P! P + P + P = Q : ay possble for If we cosder the equatos fro [(+/] to, the ter whch oly cossts of ut powers of wll be elated. Usg Eq. (6, we ca reduce the degree of the reag ters to oe. y solvg the resultg equatos, the relatoshp betwee eve ad odd wll be obtaed. Thus we ca substtute c eve Eq. (8 ad obta all < > fors eeded to detere Eq. (7. s a eaple the followg s the fal equato for a syste wth fve varables: 5 4 PX PX + PX PX + PX P = 4 5 P = 9(c 75c c + 6c c 5c c + 5c c 89c c c 5c c c 5 P = 9c (c 75c c + 6c c 5c c + 5c c 89c c c 5c c c 5 P = 4c 5c c + 945c c c 7c c c c c c 5c c 7c c 575c c 5c c c c c 75c 97c c 5 5 P = c + 5c c c 5c c c 5c c c c c c c 675c c 575c c 89c c c 4c c c c 575c c c + 6c c 75c c 54c c c c 5 P = c + 665c c 969c c 675c 8c c c c 475c c c 44c c c 945c c c c c c 855c c c 798c c c 5c c 49c c c c 8c c 5c c c + 68c c + 44c c P = c + 75c c 5c c c + 5c c 5c c c c c + 945c c c + 455c c c + 995c c c c c 555c c c 475c c c 5c c c c c c 5c c 57c 65c 555c c c c c 7875c c c 75c c c 95c c

4 5. CONCLUSION I ths paper we proposed a ew atheatcal approach for the elato of varables aog the polyoal equatos of the SHE PW ethod. Ths ew techque s based o the relatoshps betwee polyoal factors that are splfed through the proposed otato. lthough ths paper focuses o SHE equato syste for sglephase verters, the techque could be used to splfy the equato syste of three-phase verters ad cojucto wth the resultat techque ay prove the degree of coplety that the resultat theory ca deal wth. Ths could be a valuable atter for future research. 6. REFERENCES [] J. R. Wells,.. Nee, P. L. Chapa, ad P. T. Kre, Selectve Haroc Cotrol: Geeral Proble Forulato ad Selected Solutos, IEEE Trasactos o Power Electrocs, Vol., No. 6, Nov. 5, pp [] D. Czarows, D. V. Chudovsy, G. V. Chudovsy, ad I. W. Selesc, Solvg the Optal PW Proble for Sgle- Phase Iverters, IEEE Trasactos o Crcuts ad Systes I, Vol. 49, No. 4, pr., pp [] J. N. Chasso, L.. Tolbert, K. J. ckeze, ad Z. Du, Ufed pproach to Solvg the Haroc Elato Equatos ultlevel Coverters, IEEE Trasactos o Power Electrocs, Vol. 9, No., ar. 4, pp [4] J. N. Chasso, L.. Tolbert, K. J. ckeze, ad Z. Du, Elato of Harocs a ultlevel Coverter Usg the Theory of Syetrc Polyoals ad Resultats, IEEE Trasactos o Cotrol Systes Techology, Vol., No., ar. 5, pp pped pped : We have guessed the followg epresso for cos(θ, ductvely: cos( θ = ( cos ( θ = (9 ccordg to atheatcal ducto prcple, to verfy Eq. (9 we should obta cos((+θ for cos(θ. Cosder the followg trgooetrc detty: cos (( + θ = cos( θcos( θ s( θs( θ ( To calculate Eq. ( the ter s(θ s requred. Calculatg the frst-order dervatve of Eq. (9 wth respect to θ gves the followg epresso for s(θ: s( θ = s( θ ( ( cos( θ = ( Substtutg Eq. (9 ad Eq. ( Eq. (, we have: ( cos ( + θ = cos( θ ( cos ( θ = s ( θ ( ( cos( θ = Splfyg ths equato results : cos ( + θ = ( + ( cos ( θ = + + ( cos ( θ = C ( cos ( θ = ( To equalze the coses power betwee all ters, we should chage the paraeter to - ter C of Eq. (. Thus, we have: = + ew old C + ( + + ( cos ( θ = Regardless of beg ad ed pots of the suato ters, sug up ther geeral epressos/ters ad factorzg a eagful ter gves us: = + C ( + ( + ( + + ( ( (4 ( + + cos ( θ + The ters,, ad C Eq. (4 correspod to the ters,, ad C Eq. (. has a zero at = (+/, therefore we ca crease the fal pot of to [(+/] whle ths has o effect o the result of Eq. (. We ca also crease the fal pot of to [(+/] whle has two zeros at = / ad = (+/. C has a zero at =, thus we ca decrease the start pot of C Eq. ( to =. Therefore we ca eted Eq. (4 to all pots fro = to [(+/]: cos (( + θ = ( cos ( θ = + Clearly, the result cofrs Eq. (9. pped : We have obtaed the followg epresso for cos (θ: cos ( θ = cos (( θ = (5 We ca obta cos + (θ fro cos (θ as:

5 + cos ( θ = cos( θ.cos ( θ = = ( cos ( = cos ( = cos ( θ cos( θ = ( + θ ( (6 + θ To equalze the cose ter betwee ad, we should chage the paraeter to - ter of Eq. (6. Thus we ca rewrte t as: = + ew old + cos ( = ( + θ (7 Substtutg (7 (6 ad factorzg a eagful ter gves us: + cos ( θ = + + cos (( + θ = cos (( + θ = + Note that the startg pot of suato s decreased to whle ths has o effects o the result. The two suatos ca be sply added fro to [(-/]. The reag pots are = (+/ for odd ad = /, /+ for eve: + = + cos ( θ = cos (( + θ = odd = ( = cos( eve = = = + θ We ca erge the resultg two states to oe suato as: + + cos ( θ = cos (( + θ = + + = + + = + + cos (( + θ = + cos ( Obvously, the result cofrs (5. ( + θ

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

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

More information

7.0 Equality Contraints: Lagrange Multipliers

7.0 Equality Contraints: Lagrange Multipliers Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions

A Conventional Approach for the Solution of the Fifth Order Boundary Value Problems Using Sixth Degree Spline Functions Appled Matheatcs, 1, 4, 8-88 http://d.do.org/1.4/a.1.448 Publshed Ole Aprl 1 (http://www.scrp.org/joural/a) A Covetoal Approach for the Soluto of the Ffth Order Boudary Value Probles Usg Sth Degree Sple

More information

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne.

KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS. Peter J. Wilcoxen. Impact Research Centre, University of Melbourne. KURODA S METHOD FOR CONSTRUCTING CONSISTENT INPUT-OUTPUT DATA SETS by Peter J. Wlcoxe Ipact Research Cetre, Uversty of Melboure Aprl 1989 Ths paper descrbes a ethod that ca be used to resolve cossteces

More information

Some results and conjectures about recurrence relations for certain sequences of binomial sums.

Some results and conjectures about recurrence relations for certain sequences of binomial sums. Soe results ad coectures about recurrece relatos for certa sequeces of boal sus Joha Cgler Faultät für Matheat Uverstät We A-9 We Nordbergstraße 5 Joha Cgler@uveacat Abstract I a prevous paper [] I have

More information

3D Reconstruction from Image Pairs. Reconstruction from Multiple Views. Computing Scene Point from Two Matching Image Points

3D Reconstruction from Image Pairs. Reconstruction from Multiple Views. Computing Scene Point from Two Matching Image Points D Recostructo fro Iage ars Recostructo fro ultple Ves Dael Deetho Fd terest pots atch terest pots Copute fudaetal atr F Copute caera atrces ad fro F For each atchg age pots ad copute pot scee Coputg Scee

More information

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming Aerca Joural of Operatos Research, 4, 4, 33-339 Publshed Ole Noveber 4 ScRes http://wwwscrporg/oural/aor http://ddoorg/436/aor4463 A Pealty Fucto Algorth wth Obectve Paraeters ad Costrat Pealty Paraeter

More information

Solutions to problem set ); (, ) (

Solutions to problem set ); (, ) ( Solutos to proble set.. L = ( yp p ); L = ( p p ); y y L, L = yp p, p p = yp p, + p [, p ] y y y = yp + p = L y Here we use for eaple that yp, p = yp p p yp = yp, p = yp : factors that coute ca be treated

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

Parallelized methods for solving polynomial equations

Parallelized methods for solving polynomial equations IOSR Joural of Matheatcs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X. Volue 2, Issue 4 Ver. II (Jul. - Aug.206), PP 75-79 www.osrourals.org Paralleled ethods for solvg polyoal equatos Rela Kapçu, Fatr

More information

D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1

D. L. Bricker, 2002 Dept of Mechanical & Industrial Engineering The University of Iowa. CPL/XD 12/10/2003 page 1 D. L. Brcker, 2002 Dept of Mechacal & Idustral Egeerg The Uversty of Iowa CPL/XD 2/0/2003 page Capactated Plat Locato Proble: Mze FY + C X subject to = = j= where Y = j= X D, j =, j X SY, =,... X 0, =,

More information

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION

PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of

More information

Debabrata Dey and Atanu Lahiri

Debabrata Dey and Atanu Lahiri RESEARCH ARTICLE QUALITY COMPETITION AND MARKET SEGMENTATION IN THE SECURITY SOFTWARE MARKET Debabrata Dey ad Atau Lahr Mchael G. Foster School of Busess, Uersty of Washgto, Seattle, Seattle, WA 9895 U.S.A.

More information

Algorithms behind the Correlation Setting Window

Algorithms behind the Correlation Setting Window Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree

More information

An Innovative Algorithmic Approach for Solving Profit Maximization Problems

An Innovative Algorithmic Approach for Solving Profit Maximization Problems Matheatcs Letters 208; 4(: -5 http://www.scecepublshggroup.co/j/l do: 0.648/j.l.208040. ISSN: 2575-503X (Prt; ISSN: 2575-5056 (Ole A Iovatve Algorthc Approach for Solvg Proft Maxzato Probles Abul Kala

More information

Non-degenerate Perturbation Theory

Non-degenerate Perturbation Theory No-degeerate Perturbato Theory Proble : H E ca't solve exactly. But wth H H H' H" L H E Uperturbed egevalue proble. Ca solve exactly. E Therefore, kow ad. H ' H" called perturbatos Copyrght Mchael D. Fayer,

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates

More information

Stationary states of atoms and molecules

Stationary states of atoms and molecules Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal

More information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Global Optimization for Solving Linear Non-Quadratic Optimal Control Problems

Global Optimization for Solving Linear Non-Quadratic Optimal Control Problems Joural of Appled Matheatcs ad Physcs 06 4 859-869 http://wwwscrporg/joural/jap ISSN Ole: 37-4379 ISSN Prt: 37-435 Global Optzato for Solvg Lear No-Quadratc Optal Cotrol Probles Jghao Zhu Departet of Appled

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

The Geometric Least Squares Fitting Of Ellipses

The Geometric Least Squares Fitting Of Ellipses IOSR Joural of Matheatcs (IOSR-JM) e-issn: 78-578, p-issn: 39-765X. Volue 4, Issue 3 Ver.I (May - Jue 8), PP -8 www.osrourals.org Abdellatf Bettayeb Departet of Geeral Studes, Jubal Idustral College, Jubal

More information

Newton s Power Flow algorithm

Newton s Power Flow algorithm Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD

LINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer

More information

Capacitated Plant Location Problem:

Capacitated Plant Location Problem: . L. Brcker, 2002 ept of Mechacal & Idustral Egeerg The Uversty of Iowa CPL/ 5/29/2002 page CPL/ 5/29/2002 page 2 Capactated Plat Locato Proble: where Mze F + C subect to = = =, =, S, =,... 0, =, ; =,

More information

THE TRUNCATED RANDIĆ-TYPE INDICES

THE TRUNCATED RANDIĆ-TYPE INDICES Kragujeac J Sc 3 (00 47-5 UDC 547:54 THE TUNCATED ANDIĆ-TYPE INDICES odjtaba horba, a ohaad Al Hossezadeh, b Ia uta c a Departet of atheatcs, Faculty of Scece, Shahd ajae Teacher Trag Uersty, Tehra, 785-3,

More information

Solving the fuzzy shortest path problem on networks by a new algorithm

Solving the fuzzy shortest path problem on networks by a new algorithm Proceedgs of the 0th WSEAS Iteratoal Coferece o FUZZY SYSTEMS Solvg the fuzzy shortest path proble o etworks by a ew algorth SADOAH EBRAHIMNEJAD a, ad REZA TAVAKOI-MOGHADDAM b a Departet of Idustral Egeerg,

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Basic Concepts in Numerical Analysis November 6, 2017

Basic Concepts in Numerical Analysis November 6, 2017 Basc Cocepts Nuercal Aalyss Noveber 6, 7 Basc Cocepts Nuercal Aalyss Larry Caretto Mecacal Egeerg 5AB Sear Egeerg Aalyss Noveber 6, 7 Outle Revew last class Mdter Exa Noveber 5 covers ateral o deretal

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

Long blade vibration model for turbine-generator shafts torsional vibration analysis

Long blade vibration model for turbine-generator shafts torsional vibration analysis Avalable ole www.ocpr.co Joural of Checal ad Pharaceutcal Research, 05, 7(3):39-333 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Log blade vbrato odel for turbe-geerator shafts torsoal vbrato aalyss

More information

Lecture 8 IEEE DCF Performance

Lecture 8 IEEE DCF Performance Lecture 8 IEEE82. DCF Perforace IEEE82. DCF Basc Access Mechas A stato wth a ew packet to trast otors the chael actvty. If the chael s dle for a perod of te equal to a dstrbuted terfrae space (DIFS), the

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi

SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS ASSOCIATED WITH SALAGEAN DERIVATIVE. Sayali S. Joshi Faculty of Sceces ad Matheatcs, Uversty of Nš, Serba Avalable at: http://wwwpfacyu/float Float 3:3 (009), 303 309 DOI:098/FIL0903303J SUBCLASS OF ARMONIC UNIVALENT FUNCTIONS ASSOCIATED WIT SALAGEAN DERIVATIVE

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Transforms that are commonly used are separable

Transforms that are commonly used are separable Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

DATA DOMAIN DATA DOMAIN

DATA DOMAIN DATA DOMAIN 3//6 Coprght otce: Most ages these sldes are Gozalez ad oods Pretce-Hall Note: ages are [spatall] ostatoar sgals. e eed tools to aalze the locall at dfferet resolutos e ca do ths the data doa or sutable

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

High Dynamic Range 3-Moduli Set with Efficient Reverse Converter

High Dynamic Range 3-Moduli Set with Efficient Reverse Converter Hgh Dyac Rage 3-odul et wth Effcet Resdue to Bary Coverter Hgh Dyac Rage 3-odul et wth Effcet Reverse Coverter A. Harr, R. Rastegar, K. av Abstract-Resdue uber yste (R) s a valuable tool for fast ad parallel

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Department of Agricultural Economics. PhD Qualifier Examination. August 2011 Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

x y exp λ'. x exp λ 2. x exp 1.

x y exp λ'. x exp λ 2. x exp 1. egecosmcd Egevalue-egevector of the secod dervatve operator d /d hs leads to Fourer seres (se, cose, Legedre, Bessel, Chebyshev, etc hs s a eample of a systematc way of geeratg a set of mutually orthogoal

More information

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction

Application of Legendre Bernstein basis transformations to degree elevation and degree reduction Computer Aded Geometrc Desg 9 79 78 www.elsever.com/locate/cagd Applcato of Legedre Berste bass trasformatos to degree elevato ad degree reducto Byug-Gook Lee a Yubeom Park b Jaechl Yoo c a Dvso of Iteret

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

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Fibonacci Identities as Binomial Sums

Fibonacci Identities as Binomial Sums It. J. Cotemp. Math. Sceces, Vol. 7, 1, o. 38, 1871-1876 Fboacc Idettes as Bomal Sums Mohammad K. Azara Departmet of Mathematcs, Uversty of Evasvlle 18 Lcol Aveue, Evasvlle, IN 477, USA E-mal: azara@evasvlle.edu

More information

The theoretical background of

The theoretical background of he theoretcal backgroud of -echologes he theoretcal backgroud of FactSage he followg sldes gve a abrdged overvew of the ajor uderlyg prcples of the calculatoal odules of FactSage. -echologes he bbs Eergy

More information

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3 Adrew Powuk - http://www.powuk.com- Math 49 (Numercal Aalyss) Root fdg. Itroducto f ( ),?,? Solve[^-,] {{-},{}} Plot[^-,{,-,}] Cubc equato https://e.wkpeda.org/wk/cubc_fucto Quartc equato https://e.wkpeda.org/wk/quartc_fucto

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific CIS 54 - Iterpolato Roger Crawfs Basc Scearo We are able to prod some fucto, but do ot kow what t really s. Ths gves us a lst of data pots: [x,f ] f(x) f f + x x + August 2, 25 OSU/CIS 54 3 Taylor s Seres

More information

3.1 Introduction to Multinomial Logit and Probit

3.1 Introduction to Multinomial Logit and Probit ES3008 Ecooetrcs Lecture 3 robt ad Logt - Multoal 3. Itroducto to Multoal Logt ad robt 3. Estato of β 3. Itroducto to Multoal Logt ad robt The ultoal Logt odel s used whe there are several optos (ad therefore

More information

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

CS 2750 Machine Learning. Lecture 7. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x CS 75 Mache Learg Lecture 7 Lear regresso Mlos Hauskrecht los@cs.ptt.edu 59 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear cobato of put copoets f + + + K d d K k - paraeters eghts

More information

Queueing Networks. γ 3

Queueing Networks. γ 3 Queueg Networks Systes odeled by queueg etworks ca roughly be grouped to four categores. Ope etworks Custoers arrve fro outsde the syste are served ad the depart. Exaple: acket swtched data etwork. γ µ

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two Overvew of the weghtg costats ad the pots where we evaluate the fucto for The Gaussa quadrature Project two By Ashraf Marzouk ChE 505 Fall 005 Departmet of Mechacal Egeerg Uversty of Teessee Koxvlle, TN

More information

The Mathematics of Portfolio Theory

The Mathematics of Portfolio Theory The Matheatcs of Portfolo Theory The rates of retur of stocks, ad are as follows Market odtos state / scearo) earsh Neutral ullsh Probablty 0. 0.5 0.3 % 5% 9% -3% 3% % 5% % -% Notato: R The retur of stock

More information

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis) We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

Physics 114 Exam 2 Fall Name:

Physics 114 Exam 2 Fall Name: Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout

More information

Coherent Potential Approximation

Coherent Potential Approximation Coheret Potetal Approxato Noveber 29, 2009 Gree-fucto atrces the TB forals I the tght bdg TB pcture the atrx of a Haltoa H s the for H = { H j}, where H j = δ j ε + γ j. 2 Sgle ad double uderles deote

More information

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions

Sebastián Martín Ruiz. Applications of Smarandache Function, and Prime and Coprime Functions Sebastá Martí Ruz Alcatos of Saradache Fucto ad Pre ad Core Fuctos 0 C L f L otherwse are core ubers Aerca Research Press Rehoboth 00 Sebastá Martí Ruz Avda. De Regla 43 Choa 550 Cadz Sa Sarada@telele.es

More information

ON WEIGHTED INTEGRAL AND DISCRETE OPIAL TYPE INEQUALITIES

ON WEIGHTED INTEGRAL AND DISCRETE OPIAL TYPE INEQUALITIES M atheatcal I equaltes & A pplcatos Volue 19, Nuber 4 16, 195 137 do:1.7153/a-19-95 ON WEIGHTED INTEGRAL AND DISCRETE OPIAL TYPE INEQUALITIES MAJA ANDRIĆ, JOSIP PEČARIĆ AND IVAN PERIĆ Coucated by C. P.

More information

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

Polyphase Filters. Section 12.4 Porat

Polyphase Filters. Section 12.4 Porat Polyphase Flters Secto.4 Porat .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful buldg

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Generalization of the Dissimilarity Measure of Fuzzy Sets

Generalization of the Dissimilarity Measure of Fuzzy Sets Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Carbonyl Groups. University of Chemical Technology, Beijing , PR China;

Carbonyl Groups. University of Chemical Technology, Beijing , PR China; Electroc Supplemetary Materal (ESI) for Physcal Chemstry Chemcal Physcs Ths joural s The Ower Socetes 0 Supportg Iformato A Theoretcal Study of Structure-Solublty Correlatos of Carbo Doxde Polymers Cotag

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Power Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1

Power Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1 ower Flow uses wth ether or both Geerator Load G G G D D 4 5 D4 D5 ecto G Net Comple ower ecto - D D ecto s egatve sg at load bus = _ G D mlarl Curret ecto = G _ D At each bus coservato of comple power

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar

More information

Relations to Other Statistical Methods Statistical Data Analysis with Positive Definite Kernels

Relations to Other Statistical Methods Statistical Data Analysis with Positive Definite Kernels Relatos to Other Statstcal Methods Statstcal Data Aalyss wth Postve Defte Kerels Kej Fukuzu Isttute of Statstcal Matheatcs, ROIS Departet of Statstcal Scece, Graduate Uversty for Advaced Studes October

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

More information

General Method for Calculating Chemical Equilibrium Composition

General Method for Calculating Chemical Equilibrium Composition AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

Pseudo-random Functions

Pseudo-random Functions Pseudo-radom Fuctos Debdeep Mukhopadhyay IIT Kharagpur We have see the costructo of PRG (pseudo-radom geerators) beg costructed from ay oe-way fuctos. Now we shall cosder a related cocept: Pseudo-radom

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

Consensus Control for a Class of High Order System via Sliding Mode Control

Consensus Control for a Class of High Order System via Sliding Mode Control Cosesus Cotrol for a Class of Hgh Order System va Sldg Mode Cotrol Chagb L, Y He, ad Aguo Wu School of Electrcal ad Automato Egeerg, Taj Uversty, Taj, Cha, 300072 Abstract. I ths paper, cosesus problem

More information

5 Short Proofs of Simplified Stirling s Approximation

5 Short Proofs of Simplified Stirling s Approximation 5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:

More information

Symmetry of the Solution of Semidefinite Program by Using Primal-Dual Interior-Point Method

Symmetry of the Solution of Semidefinite Program by Using Primal-Dual Interior-Point Method Syetry of the Soluto of Sedefte Progra by Usg Pral-Dual Iteror-Pot Method Yoshhro Kao Makoto Ohsak ad Naok Katoh Departet of Archtecture ad Archtectural Systes Kyoto Uversty Kyoto 66-85 Japa kao@s-jarchkyoto-uacjp

More information