Solving the Kinematics of Welding Robot Based on ADAMS

Size: px
Start display at page:

Download "Solving the Kinematics of Welding Robot Based on ADAMS"

Transcription

1 Itertl Jurl f Reserch Egeerg d cece IJRE IN Ole: -9 IN Prt: -9 Vlume Issue ǁ Jue ǁ PP.-7 lvg the Kemtcs f Weldg Rbt Bsed DM Fegle u Yg Xu llege f Mechcl Egeerg hgh Uverst f Egeerg cece h llege f Mechcl Egeerg hgh Uverst f Egeerg cece h bstrct: slve the rblem f gle culg f the weldg rbt kemtcs equts we buld the D mdel f the weldg rbt lus kemtcs equts v usg methd f D-H d tkg PUM rbt s the stud trget d usg DM sftwre s the smultg tl thrugh ths we cheve the dslcemet curve lg d s. Becuse f the smlr f the result f smultg d e f the stve kemtcs equts ths er verfes the crrectess f ts D mdel. Bsed ths ths er uses the ltc methd t deduce the weldg rbt verse kemtcs equt. hs er l use the dervt methd t slve the rblem f the culg betwee gles d deduce the frmuls f ech gle. d ths methd culd be the bss f the weldg rbt trjectr lg. Kewrds: weldg rbt; kemtcs; ltc slut; DM I. Itrduct Reserchg the verse kemtcs f the weldg rbt s ver mrtt tc the kemtcs d the trjectr lg. It s ver dffcult t buld the geerl lgrthm becuse the verse kemtcs f rbt s becmg mre d mre dffcult lg the cresg f the rbt srts jts. I reset ers t slve the verse kemtcs equt we ut frwrd the ltc methd frwrd ltc methd tertve methd gemetrc methd eurl etwrk ther d s. Uder these methds Pul d Wg Qh ut frwrd the ltc methd but the rblem s tht sme reverse swers the ut frwrd re t the crrect swers. Besdes ths Lu sg gu Wg hgb d he Ng ut frwrd the methds f tertve methd d ew ltc methd but these ct crrectl clculte the gle v etrctg eugh elemets. t slve the gle culg rblem d t cveetl slve the se d cse f ech gle ths er lss the verse kemtcs swer med t the PUM weldg rbt usg the sgle r serl mtr left multl the mtr s the rght f the equt wuld t clude ths gle s tht we culd fd the elemets tht rduce the vlue f the se d cse. Uder ths we culd cheve the crrect gle d fsh the rblem f slvg the verse kemtcs equt. II. he Pstve Kemtcs Mdel f Rbt he lss f kemtcs f weldg rbt s the bss f the reserch f the trjectr lg cludg the rblem f stve kemtcs d verse kemtcs. d the mdel f the stve kemtcs s the ke f buldg verse kemtcs. ths er frstl buld the stve mdel f the weldg rbt usg PUM rbt s the reserch bject. ecdl ths er use DM sftwre d the smult. Lstl cmrg the tw results we vldted the stve kemtcs tht bult s crrect d whch s bss f the ltc f verse kemtcs f the weldg rbt.. he lss f Pstve Kemtcs Pge

2 lvg the Kemtcs f Weldg Rbt Bsed DM Pge hs er shw the reltves f the cect f the tw rd crdte sstems v usg the methd f D-H. d the sce reltsh f the tw djcet lks s cect lk trsfrmt mtr. Buldg the hmgeeus trsfrmt mtr s fllw: d mg the mtr =cs =s α - s the dstce betwee - d mesurg lg -; s the sme - s the rtt gle; d s the dstce lg ; θ s the rtt gle rud ; re the crdte sstem f jt. ccrdg t the mtr bve we cheve the fllwg stve kemtcs equt: I ths equt re the crdte sstems shulder crdte reltve t bse reltve the bg rm crdte reltve t shulder crdte the smll rm crdte reltve t the bg crdte d wrst crdte reltve t the smll rm wrst reltve t wrst d wrst crdte reltve t wrst crdte. Besdes s the tttude mtr s the lct mtr; rereset cs ; rereset s.. he lcult f Pstve Kemtcs f Weldg Rbt hs weldg rbt s csst f bd shulder bg rm smll rm wrst hd d s detls culd see fg. It hs s freedm f jt d the s jts ll re rtt jts s t s jt stle rbt. he frst jts fluece the lct f the ed erfrmer; d the lter jts decde the sture f the ed. he crdte jts c be see fg. ccrdg t the methd f D-H we c cheve the rmeters f ech jt whch culd be see tble. Fg the D mdel f rbt Fg the crdte f rbt

3 lvg the Kemtcs f Weldg Rbt Bsed DM Pge ble the rmeters f the rbt jts Jt α/ -/mm Θ/ D/mm Vrble rge ~7. -~ ~ ~8-9 -~ -~ mg them d Just s the sme we c clculte: we c get.7 9. Further we c get the sture f the ed f the rbt s the fllwg t s the sme s the sture tht we gt the DM sftwre: ccrdg the smult DM we culd gt the dslcemet curve f the ed f the weldg rbt. he curves culd be see fg. dslcemet curve lg dslcemet curve lg

4 lvg the Kemtcs f Weldg Rbt Bsed DM dslcemet curve lg Fg dslcemet curve f the ed f rbt III. he lss f Iverse Kemtcs f Rbt he lgrthm f verse kemtcs s the bss f trjectr lg f the rbt. But there re lt f culg gles whe we fcg the equts f the kemtcs. slve ths rblem t cheve the vlue f cse d se f ech gle ths er use e r serl mtr multl mtr the left s tht ths equt wuld t clude ths gle. V ths methd we c fd the elemets tht rduce the vlues f cse d se. he we c cheve the gle tht we wt fshg the lss f verse kemtcs f the rbt.. the equt f verse kemtcs slvg ccrdg t the stve kemtcs equt we culd cheve the fllwg vlve f ~ detls s fllw: slvg: Usg multl left bth sde f equt we c get: tht ccrdg t the bve equt the vlue f the thrd d the furth le shuld equl we cheve he rcr r 8 Just s the sme methds we slve the vlue f we c ls get the vlue f ~. eeg s the fllwg: rct rct rct. ccrdg t the lss f stve kemtcs d the bve slved equts we culd use MLB t get the swers f ech jt d the results re: Pge

5 lvg the Kemtcs f Weldg Rbt Bsed DM Obvusl the re the sme wth the vlue tble. ths er crrectl vldte the equts f verse kemtcs. IV. clus hs er use the ltc methd deduce the equts f verse kemtcs s tht we c slve the rblem f the culg betwee gles. V the slvg rcess ths er deduced ech vlue f ~. d ccrdg t the equts f them t m hs 8 verse swers. But becuse f the lmt f the rbt structure ech jt culd t cheve the rtt f degrees. we just eed t chse best swer t meet the wrkg requremets f the rbt bsed the lmts. hs lgrthm l t the stut tht the lst three jts hs ublc tersect r t c l be slved thrugh slvg the mtr r clcultg the verse f the mtr. s we ll kw mst dustrl rbts hve the wrst jts. REFERENE Zhq Wg Xhe Xu hwg Y. PUM ew dervt d slut fr verse kemtc equt f multr. Rbt ggu Lu hqg Zhu Jgb L. Reserch rel tme verse kemtcs lgrthm fr rbt. trl ther d lct Yuch Lu Yume Hug Xue Hug. Iverse kemtcs f rbt kemtcs bsed geetc lgrthm. Rbt.998- Pul RP hm BE Mer G. Kemtc trl Equts fr mle Multrs. IEEERN M 989- Zg. Rbtcs. Qghu uverst ress. Zeggg L. DM detled structs d emles. Ntl defese dustr ress Pge

Kinematics Analysis and Simulation on Transfer Robot with Six Degrees of Freedom

Kinematics Analysis and Simulation on Transfer Robot with Six Degrees of Freedom Sesrs & Trsducers, Vl. 7, Issue 8, August,. 8-89 Sesrs & Trsducers b IFSA Publshg, S. L. htt://www.sesrsrtl.cm Kemtcs Alss d Smult Trsfer Rbt wth S Degrees f Freedm Y Lu, D Lu Jgsu Jhu Isttute, Xuhu, Jgsu,,

More information

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table.

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table. CURVE FITTING Obectve curve ttg s t represet set dscrete dt b uct curve. Csder set dscrete dt s gve tble. 3 3 = T use the dt eectvel, curve epress s tted t the gve dt set, s = + = + + = e b ler uct plml

More information

A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator

A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator Itertl Jurl f Rbts d Autmt (IJRA) Vl N Mrh 5~6 ISSN: 89-856 5 A Neurl Netwrk Arh fr Iverse Kemt f SCARA Multr Phd Jh BB Bswl Dertemet f Idustrl Desg Ntl Isttute f Tehlg Rurkel 7698 Id Artle If Artle hstr:

More information

CS 4758 Robot Kinematics. Ashutosh Saxena

CS 4758 Robot Kinematics. Ashutosh Saxena CS 4758 Rt Kemt Ahuth Se Kemt tude the mt f de e re tereted tw emt tp Frwrd Kemt (ge t pt ht u re gve: he egth f eh he ge f eh t ht u fd: he pt f pt (.e. t (,, rdte Ivere Kemt (pt t ge ht u re gve: he

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt

More information

An Introduction to Robot Kinematics. Renata Melamud

An Introduction to Robot Kinematics. Renata Melamud A Itrdut t Rt Kemt Ret Memud Kemt tude the mt f de A Empe -he UMA 56 3 he UMA 56 hsirevute t A revute t h E degree f freedm ( DF tht defed t ge 4 here re tw mre t the ed effetr (the grpper ther t Revute

More information

Chapter Gauss-Seidel Method

Chapter Gauss-Seidel Method Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos

More information

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl Alyss for Egeers Germ Jord Uversty ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl soluto of lrge systems of ler lgerc equtos usg drect methods such s Mtr Iverse, Guss

More information

Density estimation II

Density estimation II CS 750 Mche Lerg Lecture 6 esty estmto II Mlos Husrecht mlos@tt.edu 539 Seott Squre t: esty estmto {.. } vector of ttrute vlues Ojectve: estmte the model of the uderlyg rolty dstruto over vrles X X usg

More information

ECE570 Lecture 14: Qualitative Physics

ECE570 Lecture 14: Qualitative Physics ECE570 Lecture 14: Quttve Physcs Jeffrey Mrk Sskd Sch f Eectrc d Cmuter Egeerg F 2017 Sskd (Purdue ECE) ECE570 Lecture 14: Quttve Physcs F 2017 1 / 20 A Physc System Sskd (Purdue ECE) ECE570 Lecture 14:

More information

Ionization Energies in Si, Ge, GaAs

Ionization Energies in Si, Ge, GaAs Izt erges S, Ge, GAs xtrsc Semcuctrs A extrsc semcuctrs s efe s semcuctr whch ctrlle muts f secfc t r murty tms hve bee e s tht the thermlequlbrum electr hle ccetrt re fferet frm the trsc crrer ccetrt.

More information

Available online through

Available online through Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo

More information

MTH 146 Class 7 Notes

MTH 146 Class 7 Notes 7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg

More information

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg

More information

MATRIX AND VECTOR NORMS

MATRIX AND VECTOR NORMS Numercl lyss for Egeers Germ Jord Uversty MTRIX ND VECTOR NORMS vector orm s mesure of the mgtude of vector. Smlrly, mtr orm s mesure of the mgtude of mtr. For sgle comoet etty such s ordry umers, the

More information

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS PubH 745: REGRESSION ANALSIS REGRESSION IN MATRIX TERMS A mtr s dspl of umbers or umercl quttes ld out rectgulr rr of rows d colums. The rr, or two-w tble of umbers, could be rectgulr or squre could be

More information

Approximations of Definite Integrals

Approximations of Definite Integrals Approximtios of Defiite Itegrls So fr we hve relied o tiderivtives to evlute res uder curves, work doe by vrible force, volumes of revolutio, etc. More precisely, wheever we hve hd to evlute defiite itegrl

More information

Preliminary Examinations: Upper V Mathematics Paper 1

Preliminary Examinations: Upper V Mathematics Paper 1 relmr Emtos: Upper V Mthemtcs per Jul 03 Emer: G Evs Tme: 3 hrs Modertor: D Grgortos Mrks: 50 INSTRUCTIONS ND INFORMTION Ths questo pper sts of 0 pges, cludg swer Sheet pge 8 d Iformto Sheet pges 9 d 0

More information

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of

More information

The z-transform. LTI System description. Prof. Siripong Potisuk

The z-transform. LTI System description. Prof. Siripong Potisuk The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put

More information

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I

More information

Department of Economics University of Toronto. ECO2408F M.A. Econometrics. Lecture Notes on Simple Regression Model

Department of Economics University of Toronto. ECO2408F M.A. Econometrics. Lecture Notes on Simple Regression Model Deprtmet f Ecmc Uvert f Trt ECO48F M.A. Ecmetrc Lecture Nte Smple Regre Mdel Smple Regre Mdel I the frt lecture we lked t fttg le t ctter f pt. I th chpter we eme regre methd f eplrg the prbbltc tructure

More information

Sequences and summations

Sequences and summations Lecture 0 Sequeces d summtos Istructor: Kgl Km CSE) E-ml: kkm0@kokuk.c.kr Tel. : 0-0-9 Room : New Mleum Bldg. 0 Lb : New Egeerg Bldg. 0 All sldes re bsed o CS Dscrete Mthemtcs for Computer Scece course

More information

Solutions to Problems. Then, using the formula for the speed in a parabolic orbit (equation ), we have

Solutions to Problems. Then, using the formula for the speed in a parabolic orbit (equation ), we have Slutins t Prblems. Nttin: V speed f cmet immeditely befre cllisin. V speed f cmbined bject immeditely fter cllisin, mmentum is cnserved. V, becuse liner + k q perihelin distnce f riginl prblic rbit, s

More information

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ] Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles

More information

OVERVIEW Using Similarity and Proving Triangle Theorems G.SRT.4

OVERVIEW Using Similarity and Proving Triangle Theorems G.SRT.4 OVRVIW Using Similrity nd Prving Tringle Therems G.SRT.4 G.SRT.4 Prve therems ut tringles. Therems include: line prllel t ne side f tringle divides the ther tw prprtinlly, nd cnversely; the Pythgren Therem

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter.4 Newton-Rphson Method of Solvng Nonlner Equton After redng ths chpter, you should be ble to:. derve the Newton-Rphson method formul,. develop the lgorthm of the Newton-Rphson method,. use the Newton-Rphson

More information

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN Euroe Jour of Mthemtcs d omuter Scece Vo. No. 6 ISSN 59-995 ISSN 59-995 ON AN INVESTIGATION O THE MATRIX O THE SEOND PARTIA DERIVATIVE IN ONE EONOMI DYNAMIS MODE S. I. Hmdov Bu Stte Uverst ABSTRAT The

More information

PRINCE SULTAN UNIVERSITY Department of Mathematical Sciences Final Examination First Semester ( ) STAT 271.

PRINCE SULTAN UNIVERSITY Department of Mathematical Sciences Final Examination First Semester ( ) STAT 271. PRINCE SULTAN UNIVERSITY Deprtment f Mthemticl Sciences Finl Exmintin First Semester (007 008) STAT 71 Student Nme: Mrk Student Number: Sectin Number: Techer Nme: Time llwed is ½ hurs. Attendnce Number:

More information

CURVE FITTING LEAST SQUARES METHOD

CURVE FITTING LEAST SQUARES METHOD Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued

More information

The Simple Linear Regression Model: Theory

The Simple Linear Regression Model: Theory Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79

More information

Review of Linear Algebra

Review of Linear Algebra PGE 30: Forulto d Soluto Geosstes Egeerg Dr. Blhoff Sprg 0 Revew of Ler Alger Chpter 7 of Nuercl Methods wth MATLAB, Gerld Recktewld Vector s ordered set of rel (or cople) uers rrged s row or colu sclr

More information

Chapter 7. Bounds for weighted sums of Random Variables

Chapter 7. Bounds for weighted sums of Random Variables Chpter 7. Bouds for weghted sums of Rdom Vrbles 7. Itroducto Let d 2 be two depedet rdom vrbles hvg commo dstrbuto fucto. Htczeko (998 d Hu d L (2000 vestgted the Rylegh dstrbuto d obted some results bout

More information

CS473-Algorithms I. Lecture 3. Solving Recurrences. Cevdet Aykanat - Bilkent University Computer Engineering Department

CS473-Algorithms I. Lecture 3. Solving Recurrences. Cevdet Aykanat - Bilkent University Computer Engineering Department CS473-Algorthms I Lecture 3 Solvg Recurreces Cevdet Aykt - Blket Uversty Computer Egeerg Deprtmet Solvg Recurreces The lyss of merge sort Lecture requred us to solve recurrece. Recurreces re lke solvg

More information

1.3 Continuous Functions and Riemann Sums

1.3 Continuous Functions and Riemann Sums mth riem sums, prt 0 Cotiuous Fuctios d Riem Sums I Exmple we sw tht lim Lower() = lim Upper() for the fuctio!! f (x) = + x o [0, ] This is o ccidet It is exmple of the followig theorem THEOREM Let f be

More information

Introduction to mathematical Statistics

Introduction to mathematical Statistics Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs

More information

Taylor Polynomials. The Tangent Line. (a, f (a)) and has the same slope as the curve y = f (x) at that point. It is the best

Taylor Polynomials. The Tangent Line. (a, f (a)) and has the same slope as the curve y = f (x) at that point. It is the best Tylor Polyomils Let f () = e d let p() = 1 + + 1 + 1 6 3 Without usig clcultor, evlute f (1) d p(1) Ok, I m still witig With little effort it is possible to evlute p(1) = 1 + 1 + 1 (144) + 6 1 (178) =

More information

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions HK Km Slghtly moded //9 /8/6 Frstly wrtte t Mrch 5 Itertve Methods er Systems: Guss-Sedel Noler Systems Cse Study: Chemcl Rectos Itertve or ppromte methods or systems o equtos cosst o guessg vlue d the

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt

More information

Rendering Equation. Linear equation Spatial homogeneous Both ray tracing and radiosity can be considered special case of this general eq.

Rendering Equation. Linear equation Spatial homogeneous Both ray tracing and radiosity can be considered special case of this general eq. Rederg quto Ler equto Sptl homogeeous oth ry trcg d rdosty c be cosdered specl cse of ths geerl eq. Relty ctul photogrph Rdosty Mus Rdosty Rederg quls the dfferece or error mge http://www.grphcs.corell.edu/ole/box/compre.html

More information

Cambridge Assessment International Education Cambridge Ordinary Level. Published

Cambridge Assessment International Education Cambridge Ordinary Level. Published Cambridge Assessment Internatinal Educatin Cambridge Ordinary Level ADDITIONAL MATHEMATICS 4037/1 Paper 1 Octber/Nvember 017 MARK SCHEME Maximum Mark: 80 Published This mark scheme is published as an aid

More information

11.2. Infinite Series

11.2. Infinite Series .2 Infinite Series 76.2 Infinite Series An infinite series is the sum f n infinite seuence f numbers + 2 + 3 + Á + n + Á The gl f this sectin is t understnd the mening f such n infinite sum nd t develp

More information

COMPLEX NUMBERS AND DE MOIVRE S THEOREM

COMPLEX NUMBERS AND DE MOIVRE S THEOREM COMPLEX NUMBERS AND DE MOIVRE S THEOREM OBJECTIVE PROBLEMS. s equl to b d. 9 9 b 9 9 d. The mgr prt of s 5 5 b 5. If m, the the lest tegrl vlue of m s b 8 5. The vlue of 5... s f s eve, f s odd b f s eve,

More information

SOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE

SOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE D I D A C T I C S O F A T H E A T I C S No (4) 3 SOE REARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOIAL ASYPTOTE Tdeusz Jszk Abstct I the techg o clculus, we cosde hozotl d slt symptote I ths ppe the

More information

WYSE Academic Challenge Regional Physics 2008 SOLUTION SET

WYSE Academic Challenge Regional Physics 2008 SOLUTION SET WYSE cdemic Chllenge eginl 008 SOLUTION SET. Crrect nswer: E. Since the blck is mving lng circulr rc when it is t pint Y, it hs centripetl ccelertin which is in the directin lbeled c. Hwever, the blck

More information

Basics of heteroskedasticity

Basics of heteroskedasticity Sect 8 Heterskedastcty ascs f heterskedastcty We have assumed up t w ( ur SR ad MR assumpts) that the varace f the errr term was cstat acrss bservats Ths s urealstc may r mst ecmetrc applcats, especally

More information

BC Calculus Review Sheet. converges. Use the integral: L 1

BC Calculus Review Sheet. converges. Use the integral: L 1 BC Clculus Review Sheet Whe yu see the wrds.. Fid the re f the uuded regi represeted y the itegrl (smetimes f ( ) clled hriztl imprper itegrl).. Fid the re f differet uuded regi uder f() frm (,], where

More information

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt

More information

Introduction to Three-phase Circuits. Balanced 3-phase systems Unbalanced 3-phase systems

Introduction to Three-phase Circuits. Balanced 3-phase systems Unbalanced 3-phase systems Intrductin t Three-hse Circuits Blnced 3-hse systems Unblnced 3-hse systems 1 Intrductin t 3-hse systems Single-hse tw-wire system: Single surce cnnected t ld using tw-wire system Single-hse three-wire

More information

Chapter Linear Regression

Chapter Linear Regression Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use

More information

Numbers (Part I) -- Solutions

Numbers (Part I) -- Solutions Ley College -- For AMATYC SML Mth Competitio Cochig Sessios v.., [/7/00] sme s /6/009 versio, with presettio improvemets Numbers Prt I) -- Solutios. The equtio b c 008 hs solutio i which, b, c re distict

More information

HIGHER SCHOOL CERTIFICATE EXAMINATION MATHEMATICS 3 UNIT (ADDITIONAL) AND 3/4 UNIT (COMMON) Time allowed Two hours (Plus 5 minutes reading time)

HIGHER SCHOOL CERTIFICATE EXAMINATION MATHEMATICS 3 UNIT (ADDITIONAL) AND 3/4 UNIT (COMMON) Time allowed Two hours (Plus 5 minutes reading time) HIGHER SCHOOL CERTIFICATE EXAMINATION 999 MATHEMATICS 3 UNIT (ADDITIONAL) AND 3/ UNIT (COMMON) Time llowed Two hours (Plus 5 miutes redig time) DIRECTIONS TO CANDIDATES Attempt ALL questios. ALL questios

More information

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ] Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow

More information

Lecture 2. Basic Semiconductor Physics

Lecture 2. Basic Semiconductor Physics Lecture Basc Semcductr Physcs I ths lecture yu wll lear: What are semcductrs? Basc crystal structure f semcductrs Electrs ad hles semcductrs Itrsc semcductrs Extrsc semcductrs -ded ad -ded semcductrs Semcductrs

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter 0.04 Newton-Rphson Method o Solvng Nonlner Equton Ater redng ths chpter, you should be ble to:. derve the Newton-Rphson method ormul,. develop the lgorthm o the Newton-Rphson method,. use the Newton-Rphson

More information

Announcements. 30 o. The pumpkin is on the left and the watermelon is on the right. The picture on page 138 is better.

Announcements. 30 o. The pumpkin is on the left and the watermelon is on the right. The picture on page 138 is better. Annuncements Em 1 is ne eek. Ems frm revius semesters hve been sted n the ebsite. HITT quiz slutins re sted n ebsite. Td e ill finish Chter 4 nd begin Chter 5. ill st Em 1 brekdn nd revie mteril. Lk fr

More information

PROGRESSIONS AND SERIES

PROGRESSIONS AND SERIES PROGRESSIONS AND SERIES A sequece is lso clled progressio. We ow study three importt types of sequeces: () The Arithmetic Progressio, () The Geometric Progressio, () The Hrmoic Progressio. Arithmetic Progressio.

More information

Countdown: 9 Weeks. 3. Kimi has posted3 x 3,.3 " 3 puppyphotos on a social network. 6.EE.1. [-l. rhe power i.l-le. Course{. Countdown.

Countdown: 9 Weeks. 3. Kimi has posted3 x 3,.3  3 puppyphotos on a social network. 6.EE.1. [-l. rhe power i.l-le. Course{. Countdown. Cuntwn: 9 Weeks AT PRIO Lin stke fruit t sell t mrket. The rti f pples t rnges is 1:4. Lin stke n mre thn 200 f eh type f fruit. 6.RP.3,6.RP.3 Pqrt A: Cmplete the tble t etermine hw mny pples n rnges Lin

More information

ALGEBRA. Set of Equations. have no solution 1 b1. Dependent system has infinitely many solutions

ALGEBRA. Set of Equations. have no solution 1 b1. Dependent system has infinitely many solutions Qudrtic Equtios ALGEBRA Remider theorem: If f() is divided b( ), the remider is f(). Fctor theorem: If ( ) is fctor of f(), the f() = 0. Ivolutio d Evlutio ( + b) = + b + b ( b) = + b b ( + b) 3 = 3 +

More information

13.3. The Area Bounded by a Curve. Introduction. Prerequisites. Learning Outcomes

13.3. The Area Bounded by a Curve. Introduction. Prerequisites. Learning Outcomes The Are Bounded b Curve 3.3 Introduction One of the importnt pplictions of integrtion is to find the re bounded b curve. Often such n re cn hve phsicl significnce like the work done b motor, or the distnce

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1

More information

lower lower median upper upper proportions. 1 cm= 10 mm extreme quartile quartile extreme 28mm =?cm I I I

lower lower median upper upper proportions. 1 cm= 10 mm extreme quartile quartile extreme 28mm =?cm I I I Sxth Grde Buld # 7 :!l. ':S.,. (6)()=_ 66 + () = 6 + ()= 88(6)= e :: : : c f So! G) Use the box d whsk plot to sw questos bout the dt ovt the ut of esure usg jjj [ low low ed upp upp proportos. c= 8 =?c

More information

Problem Set 4 Solutions

Problem Set 4 Solutions 4 Eoom Altos of Gme Theory TA: Youg wg /08/0 - Ato se: A A { B, } S Prolem Set 4 Solutos - Tye Se: T { α }, T { β, β} Se Plyer hs o rte formto, we model ths so tht her tye tke oly oe lue Plyer kows tht

More information

As we have already discussed, all the objects have the same absolute value of

As we have already discussed, all the objects have the same absolute value of Lecture 3 Prjectile Mtin Lst time we were tlkin but tw-dimensinl mtin nd intrduced ll imprtnt chrcteristics f this mtin, such s psitin, displcement, elcit nd ccelertin Nw let us see hw ll these thins re

More information

Designing Information Devices and Systems I Discussion 8B

Designing Information Devices and Systems I Discussion 8B Lst Updted: 2018-10-17 19:40 1 EECS 16A Fll 2018 Designing Informtion Devices nd Systems I Discussion 8B 1. Why Bother With Thévenin Anywy? () Find Thévenin eqiuvlent for the circuit shown elow. 2kΩ 5V

More information

Fuzzy Neutrosophic Equivalence Relations

Fuzzy Neutrosophic Equivalence Relations wwwijirdm Jur 6 Vl 5 ssue SS 78 Olie u eutrsphi Equivlee eltis J Mrti Je eserh Shlr Deprtmet f Mthemtis irml Cllege fr Wme Cimtre mil du di rkiri riipl irml Cllege fr Wme Cimtre mil du di strt: his pper

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

Ch5 Appendix Q-factor and Smith Chart Matching

Ch5 Appendix Q-factor and Smith Chart Matching h5 Appedx -factr ad mth hart Matchg 5B-1 We-ha a udwg, F rcut Desg Thery ad Applcat, hapter 8 Frequecy espse f -type Matchg Netwrks 5B- Fg.8-8 Tw desg realzats f a -type matchg etwrk.65pf, 80 f 1 GHz Fg.8-9

More information

than 1. It means in particular that the function is decreasing and approaching the x-

than 1. It means in particular that the function is decreasing and approaching the x- 6 Preclculus Review Grph the functions ) (/) ) log y = b y = Solution () The function y = is n eponentil function with bse smller thn It mens in prticulr tht the function is decresing nd pproching the

More information

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com

More information

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Mg: cepts ad Techques 3 rd ed. hapter 10 1 Evaluat f lusterg lusterg evaluat assesses the feasblty f clusterg aalyss a data set ad the qualty f the results geerated by a clusterg methd. Three mar

More information

PH2200 Practice Exam I Summer 2003

PH2200 Practice Exam I Summer 2003 PH00 Prctice Exm I Summer 003 INSTRUCTIONS. Write yur nme nd student identifictin number n the nswer sheet.. Plese cver yur nswer sheet t ll times. 3. This is clsed bk exm. Yu my use the PH00 frmul sheet

More information

_3-----"/- ~StudI_G u_id_e_-..,...-~~_~

_3-----/- ~StudI_G u_id_e_-..,...-~~_~ e- / Dte Period Nme CHAPTR 3-----"/- StudIG uide-..,...- [-------------------- Accelerted Motion Vocbulry Review Write the term tht correctly completes the sttement. Use ech term once. ccelertion verge

More information

Lecture 3-4 Solutions of System of Linear Equations

Lecture 3-4 Solutions of System of Linear Equations Lecture - Solutos of System of Ler Equtos Numerc Ler Alger Revew of vectorsd mtrces System of Ler Equtos Guss Elmto (drect solver) LU Decomposto Guss-Sedel method (tertve solver) VECTORS,,, colum vector

More information

INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS)

INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS) Mthemtics Revisio Guides Itegrtig Trig, Log d Ep Fuctios Pge of MK HOME TUITION Mthemtics Revisio Guides Level: AS / A Level AQA : C Edecel: C OCR: C OCR MEI: C INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS)

More information

Chapter 2 Infinite Series Page 1 of 9

Chapter 2 Infinite Series Page 1 of 9 Chpter Ifiite eries Pge of 9 Chpter : Ifiite eries ectio A Itroductio to Ifiite eries By the ed of this sectio you will be ble to uderstd wht is met by covergece d divergece of ifiite series recogise geometric

More information

Do the one-dimensional kinetic energy and momentum operators commute? If not, what operator does their commutator represent?

Do the one-dimensional kinetic energy and momentum operators commute? If not, what operator does their commutator represent? 1 Problem 1 Do the one-dimensionl kinetic energy nd momentum opertors commute? If not, wht opertor does their commuttor represent? KE ˆ h m d ˆP i h d 1.1 Solution This question requires clculting the

More information

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University Advced Algorthmc Prolem Solvg Le Arthmetc Fredrk Hetz Dept of Computer d Iformto Scece Lköpg Uversty Overvew Arthmetc Iteger multplcto Krtsu s lgorthm Multplcto of polyomls Fst Fourer Trsform Systems of

More information

5.1 Properties of Inverse Trigonometric Functions.

5.1 Properties of Inverse Trigonometric Functions. Inverse Trignmetricl Functins The inverse f functin f( ) f ( ) f : A B eists if f is ne-ne nt ie, ijectin nd is given Cnsider the e functin with dmin R nd rnge [, ] Clerl this functin is nt ijectin nd

More information

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases Itertol Jourl of Advced Reserch Physcl Scece (IJARPS) Volume, Issue 5, September 204, PP 6-0 ISSN 2349-7874 (Prt) & ISSN 2349-7882 (Ole) www.rcourls.org Alytcl Approch for the Soluto of Thermodymc Idettes

More information

under the curve in the first quadrant.

under the curve in the first quadrant. NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )

More information

Schrödinger Equation Via Laplace-Beltrami Operator

Schrödinger Equation Via Laplace-Beltrami Operator IOSR Jourl of Mthemtics (IOSR-JM) e-issn: 78-578, p-issn: 39-765X. Volume 3, Issue 6 Ver. III (Nov. - Dec. 7), PP 9-95 www.iosrjourls.org Schrödiger Equtio Vi Lplce-Beltrmi Opertor Esi İ Eskitşçioğlu,

More information

4. Eccentric axial loading, cross-section core

4. Eccentric axial loading, cross-section core . Eccentrc xl lodng, cross-secton core Introducton We re strtng to consder more generl cse when the xl force nd bxl bendng ct smultneousl n the cross-secton of the br. B vrtue of Snt-Vennt s prncple we

More information

Topic 1 Notes Jeremy Orloff

Topic 1 Notes Jeremy Orloff Topic 1 Notes Jerem Orloff 1 Introduction to differentil equtions 1.1 Gols 1. Know the definition of differentil eqution. 2. Know our first nd second most importnt equtions nd their solutions. 3. Be ble

More information

The fuzzy decision of transformer economic operation

The fuzzy decision of transformer economic operation The fuzzy decs f trasfrmer ecmc perat WENJUN ZHNG, HOZHONG CHENG, HUGNG XIONG, DEXING JI Departmet f Electrcal Egeerg hagha Jatg Uversty 954 Huasha Rad, 3 hagha P. R. CHIN bstract: - Ths paper presets

More information

SPH3U1 Lesson 06 Kinematics

SPH3U1 Lesson 06 Kinematics PROJECTILE MOTION LEARNING GOALS Students will: Describe the mtin f an bject thrwn at arbitrary angles thrugh the air. Describe the hrizntal and vertical mtins f a prjectile. Slve prjectile mtin prblems.

More information

The linear system. The problem: solve

The linear system. The problem: solve The ler syste The prole: solve Suppose A s vertle, the there ests uue soluto How to effetly opute the soluto uerlly??? A A A evew of dret ethods Guss elto wth pvotg Meory ost: O^ Coputtol ost: O^ C oly

More information

Model Fitting and Robust Regression Methods

Model Fitting and Robust Regression Methods Dertment o Comuter Engneerng Unverst o Clorn t Snt Cruz Model Fttng nd Robust Regresson Methods CMPE 64: Imge Anlss nd Comuter Vson H o Fttng lnes nd ellses to mge dt Dertment o Comuter Engneerng Unverst

More information

MATH 118 HW 7 KELLY DOUGAN, ANDREW KOMAR, MARIA SIMBIRSKY, BRANDEN LASKE

MATH 118 HW 7 KELLY DOUGAN, ANDREW KOMAR, MARIA SIMBIRSKY, BRANDEN LASKE MATH 118 HW 7 KELLY DOUGAN, ANDREW KOMAR, MARIA SIMBIRSKY, BRANDEN LASKE Prt 1. Let be odd rime d let Z such tht gcd(, 1. Show tht if is qudrtic residue mod, the is qudrtic residue mod for y ositive iteger.

More information

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol CAPER 5 ENROPY GENERAION Istructr: Pr. Dr. Uğur Atkl Chapter 5 Etrpy Geerat (Exergy Destruct Outle st Avalable rk Cycles eat ege cycles Rergerat cycles eat pump cycles Nlw Prcesses teady-flw Prcesses Exergy

More information

Module B3. VLoad = = V S V LN

Module B3. VLoad = = V S V LN Mdule B Prblem The -hase lads are cnnected n arallel. One s a urely resste lad cnnected n wye. t cnsumes 00kW. The secnd s a urely nducte 00kR lad cnnected n wye. The thrd s a urely caacte 00kR lad cnnected

More information

F Fou n even has domain o. Domain. TE t. Fire Co I. integer. Logarithmic Ty. Exponential Functions. Things. range. Trigonometric Functions.

F Fou n even has domain o. Domain. TE t. Fire Co I. integer. Logarithmic Ty. Exponential Functions. Things. range. Trigonometric Functions. Cve Functins Midterm 1 Review Plnmils Rtinl Functins Pwer Functins rignmetric Functins nverse rignmetric Functins Expnentil Functins Functins Dmin Lgrithmic Review Definitins nd bsic prperties Dmin f f

More information

Differentiation Applications 1: Related Rates

Differentiation Applications 1: Related Rates Differentiatin Applicatins 1: Related Rates 151 Differentiatin Applicatins 1: Related Rates Mdel 1: Sliding Ladder 10 ladder y 10 ladder 10 ladder A 10 ft ladder is leaning against a wall when the bttm

More information

Similar idea to multiplication in N, C. Divide and conquer approach provides unexpected improvements. Naïve matrix multiplication

Similar idea to multiplication in N, C. Divide and conquer approach provides unexpected improvements. Naïve matrix multiplication Next. Covered bsics of simple desig techique (Divided-coquer) Ch. of the text.. Next, Strsse s lgorithm. Lter: more desig d coquer lgorithms: MergeSort. Solvig recurreces d the Mster Theorem. Similr ide

More information

Definite Integral. The Left and Right Sums

Definite Integral. The Left and Right Sums Clculus Li Vs Defiite Itegrl. The Left d Right Sums The defiite itegrl rises from the questio of fidig the re betwee give curve d x-xis o itervl. The re uder curve c be esily clculted if the curve is give

More information

The graphs of Rational Functions

The graphs of Rational Functions Lecture 4 5A: The its of Rtionl Functions s x nd s x + The grphs of Rtionl Functions The grphs of rtionl functions hve severl differences compred to power functions. One of the differences is the behvior

More information

NUMBERS, MATHEMATICS AND EQUATIONS

NUMBERS, MATHEMATICS AND EQUATIONS AUSTRALIAN CURRICULUM PHYSICS GETTING STARTED WITH PHYSICS NUMBERS, MATHEMATICS AND EQUATIONS An integral part t the understanding f ur physical wrld is the use f mathematical mdels which can be used t

More information

: : 8.2. Test About a Population Mean. STT 351 Hypotheses Testing Case I: A Normal Population with Known. - null hypothesis states 0

: : 8.2. Test About a Population Mean. STT 351 Hypotheses Testing Case I: A Normal Population with Known. - null hypothesis states 0 8.2. Test About Popultio Me. Cse I: A Norml Popultio with Kow. H - ull hypothesis sttes. X1, X 2,..., X - rdom smple of size from the orml popultio. The the smple me X N, / X X Whe H is true. X 8.2.1.

More information