Inverse Kinematics From Position to Angles

Size: px
Start display at page:

Download "Inverse Kinematics From Position to Angles"

Transcription

1 Invere Knemat From Poton to Ange

2 Invere Knemat Gven a dered poton P & orentaton R o the end-eetor Y,, z, O, A, T z q,, n Fnd the jont varabe whh an brng the robot to the dered onguraton.

3 Sovabt 0 Gven the numera vaue o we attempt to nd vaue o,,, n. The PUMA 560: T Gven 0 a 6 numera vaue, 6T ove or6 jont ange,,,, 6. equaton and 6 unnown 6 equaton and 6 unnown nonnear, tranendenta equaton

4 Invere Knemat More dut. The equaton to ove are nonnear thu temat oed-orm outon not awa avaabe. Souton not unque. Redundant robot. Ebow-up/ebow-down onguraton. Robot dependent. outon!

5 The Worpae Worpae: voume o pae whh an be reahed b the end eetor Detrou worpae: voume o pae where the end eetor an be arbtrar orented Reahabe worpae: voume o pae whh the robot an reah n at eat one orentaton

6 Two-n manpuator I = reahabe wor pae a d o radu. The detrou Worpae a pont: orgn I there no detrou worpae. The reahabe wor pae a rng o outer radu + and nner radu -

7 Etene o Souton A outon to the IKP et the target beong to the worpae. Worpae omputaton ma be hard. In prate t made ea b pea degn o the robot.

8 Method o Souton A manpuator ovabe the jont varabe an be determned b an agorthm. The agorthm houd nd a pobe outon. Souton oed orm outon numera outon

9 umera Souton Reut n a numera, teratve outon to tem o equaton, or eampe ewton/raphon tehnque. Unnown number o operaton to ove. On return a nge outon. Aura dtated b uer. Beaue o thee reaon, th muh e derabe than a oed-orm outon. Can be apped to a robot.

10 Coed-orm outon Anata outon to tem o equaton Can be oved n a ed number o operaton thereore, omputatona at/nown peed Reut n a pobe outon to the manpuator nemat Oten dut or mpobe to nd Mot derabe or rea-tme ontro Mot derabe overa

11 Invere Knemat Probem Gven: Poton & Orentaton Fnd: jont oordnate o ED-EFFECTOR 0 T q, q, q 3,, q eed to ove at mot ndependent equaton n unnown.

12 Invere Knemat Probem ISSUES Etene o outon Worpae Detrou Worpae Le than 6 jont Jont mt prata Mutpe outon Crtera Agebra Sovabt oed orm numera Geometr number o outon = 6 d, r 0 or pont

13 Souton To Invere Knemat 0 T = 0 T T T 3 - T = A A A 3 A Gven: 0 T n n nz o o o z a a a z p p A p z θ -θ θ d θ θ θ - θ d θ r Fnd: q = q, q, q 3,, q jont oordnate

14 Souton To Invere Knemat 3 z z z z...a A A A p a o n p a o n p a o n Equaton 6 ndependent 6 redundant unnown LHS,j = RHS,j row =,, 3 oumn j =,, 3, 4

15 Souton To Invere Knemat Genera Approah: Ioate one jont varabe at a tme A - 0 T = A A 3 A = T unton o q unton o q,, q Loo or ontant eement n T Equate LHS,j = RHS,j Sove or q

16 Souton To Invere Knemat A - A -0 T = A 3 A = T unton o q 3,, q unton o q, q on one unnown q ne q ha been oved or Loo or ontant eement o T Equate LHS,j = RHS,j Sove or q Mabe an nd equaton nvovng q on ote: There no agorthm approah that 00% eetve Geometr ntuton requred

17 A Smpe Eampe Revoute and Prmat Jont Combned, Fndng : θ artan More Spea: θ artan artan pee that t n the rt quadrant Y S Fndng S: X S

18 Invere Knemat o a Two Ln Manpuator, Gven:,,, Fnd:, Redundan: A unque outon to th probem doe not et. ote, that ung the gven two outon are pobe. Sometme no outon pobe.,

19 The Geometr Souton,

20 The Geometr Souton, Ung the Law o Cone: aro θ oθ oθ θ o80 θ o80 o C ab b a Ung the Law o Cone: artan α α θ θ nθ θ n80 nθ n n C b B artan nθ arn θ Redundant ne oud be n the rt or ourth quadrant. Redundan aued ne ha two pobe vaue

21 The Agebra Souton,

22 aro θ n n n The Agebra Souton, θ θ θ 3 n θ o θ o θ On Unnown n o o n n n n o o o : b a b a b a b a b a b a ote

23 n o n o n n n o o o : a b b a b a b a b a b a ote n We now what rom the prevou de. We need to ove or. ow we have two equaton and two unnown n and o Subttutng or and mpng man tme ote th the aw o one and an be repaed b + arn θ

24 Invere Knemat Anata outon on wor or a ar mpe truture umera/teratve outon needed or a ompe truture

25 umera Approahe Invere nemat an be ormuated a an optmzaton probem

26 Funton Optmzaton Fndng the mnmum or nonnear unton

27 Formuaton So how to onvert the IK proe n an optmzaton unton? arg mn F θ θ θ C=C,C Bae 0,0

28 Iteratve Approahe Fnd the jont ange θ that mnmze the dtane between the hpothezed harater poton and uer peed poton arg mn C hpothezed poton peed poton θ θ θ C=C,C Bae 0,0

29 Iteratve Approahe Fnd the jont ange θ that mnmze the dtane between the hpothezed harater poton and uer peed poton arg mn, o o n n θ θ θ C=, Bae 0,0

30 Iteratve Approahe Mathemata, we an ormuate th a an optmzaton probem: arg mn The above probem an be oved b man nonnear optmzaton agorthm: - Steepet deent - Gau-newton - Levenberg-marquardt, et

31 Gradent-baed Optmzaton

32 Gau-ewton Approah Step : ntaze the jont ange wth 0 Step : update the jont ange:

33 Gau-ewton Approah Step : ntaze the jont ange wth 0 Step : update the jont ange: How an we dede the amount o update?

34 Gau-ewton Approah Step : ntaze the jont ange wth 0 Step : update the jont ange: arg mn

35 Gau-ewton Approah Step : ntaze the jont ange wth 0 Step : update the jont ange: arg mn Known!

36 Gau-ewton Approah Step : ntaze the jont ange wth 0 Step : update the jont ange: arg mn Taor ere epanon

37 Gau-ewton Approah Step : ntaze the jont ange wth Step : update the jont ange: mn arg mn arg 0 Taor ere epanon rearrange

38 Gau-ewton Approah Step : ntaze the jont ange wth Step : update the jont ange: mn arg mn arg 0 Taor ere epanon rearrange Can ou ove th optmzaton probem?

39 Gau-ewton Approah Step : ntaze the jont ange wth Step : update the jont ange: mn arg mn arg mn arg 0 Taor ere epanon rearrange Th a quadrat unton o

40 Gau-ewton Approah Optmzng an quadrat unton ea It ha an optma vaue when the gradent zero,...,,...,,..., M M M mn arg

41 Gau-ewton Approah Optmzng an quadrat unton ea It ha an optma vaue when the gradent zero,...,,...,,..., M M M mn arg b J Δθ Lnear equaton!

42 Gau-ewton Approah Optmzng an quadrat unton ea It ha an optma vaue when the gradent zero,...,,...,,..., M M M mn arg b J Δθ

43 Gau-ewton Approah Optmzng an quadrat unton ea It ha an optma vaue when the gradent zero,...,,...,,..., M M M mn arg b J Δθ b J J J T T

Jacobians: Velocities and Static Force.

Jacobians: Velocities and Static Force. Jaoban: Veote and Stat Fore mrkabr Unerty o ehnoogy Computer Engneerng Inormaton ehnoogy Department http://e.aut.a.r/~hry/eture/robot-4/robot4.htm Derentaton o poton etor Derate o a etor: V Q d dt Q m

More information

Numerical integration in more dimensions part 2. Remo Minero

Numerical integration in more dimensions part 2. Remo Minero Numerca ntegraton n more dmensons part Remo Mnero Outne The roe of a mappng functon n mutdmensona ntegraton Gauss approach n more dmensons and quadrature rues Crtca anass of acceptabt of a gven quadrature

More information

MARKOV CHAIN AND HIDDEN MARKOV MODEL

MARKOV CHAIN AND HIDDEN MARKOV MODEL MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not

More information

IV. Performance Optimization

IV. Performance Optimization IV. Performance Optmzaton A. Steepest descent algorthm defnton how to set up bounds on learnng rate mnmzaton n a lne (varyng learnng rate) momentum learnng examples B. Newton s method defnton Gauss-Newton

More information

Boundary Value Problems. Lecture Objectives. Ch. 27

Boundary Value Problems. Lecture Objectives. Ch. 27 Boundar Vaue Probes Ch. 7 Lecture Obectves o understand the dfference between an nta vaue and boundar vaue ODE o be abe to understand when and how to app the shootng ethod and FD ethod. o understand what

More information

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students.

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students. Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.

More information

Deriving the Dual. Prof. Bennett Math of Data Science 1/13/06

Deriving the Dual. Prof. Bennett Math of Data Science 1/13/06 Dervng the Dua Prof. Bennett Math of Data Scence /3/06 Outne Ntty Grtty for SVM Revew Rdge Regresson LS-SVM=KRR Dua Dervaton Bas Issue Summary Ntty Grtty Need Dua of w, b, z w 2 2 mn st. ( x w ) = C z

More information

Cyclic Codes BCH Codes

Cyclic Codes BCH Codes Cycc Codes BCH Codes Gaos Feds GF m A Gaos fed of m eements can be obtaned usng the symbos 0,, á, and the eements beng 0,, á, á, á 3 m,... so that fed F* s cosed under mutpcaton wth m eements. The operator

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

Associative Memories

Associative Memories Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete

More information

Multicommodity Distribution System Design

Multicommodity Distribution System Design Mummodt strbun stem esgn Consder the probem where ommodtes are produed and shpped from pants through potenta dstrbun enters C usmers. Pants C Cosmers Mummodt strbun stem esgn Consder the probem where ommodtes

More information

Mean Field / Variational Approximations

Mean Field / Variational Approximations Mean Feld / Varatonal Appromatons resented by Jose Nuñez 0/24/05 Outlne Introducton Mean Feld Appromaton Structured Mean Feld Weghted Mean Feld Varatonal Methods Introducton roblem: We have dstrbuton but

More information

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M CIS56: achne Learnng Lecture 3 (Sept 6, 003) Preparaton help: Xaoyng Huang Lnear Regresson Lnear regresson can be represented by a functonal form: f(; θ) = θ 0 0 +θ + + θ = θ = 0 ote: 0 s a dummy attrbute

More information

Homework Math 180: Introduction to GR Temple-Winter (3) Summarize the article:

Homework Math 180: Introduction to GR Temple-Winter (3) Summarize the article: Homework Math 80: Introduton to GR Temple-Wnter 208 (3) Summarze the artle: https://www.udas.edu/news/dongwthout-dark-energy/ (4) Assume only the transformaton laws for etors. Let X P = a = a α y = Y α

More information

Nonlinear optimization and applications

Nonlinear optimization and applications Nonlnear optzaton and applaton CSI 747 / MAH 689 Intrutor: I. Grva Wedneda 7: : p Contraned optzaton proble : R n R n X I E X Inequalt ontrant I { n R :... } Equalt ontrant E { n R :... p} Equalt ontrant....t.

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

A General Column Generation Algorithm Applied to System Reliability Optimization Problems

A General Column Generation Algorithm Applied to System Reliability Optimization Problems A Genera Coumn Generaton Agorthm Apped to System Reabty Optmzaton Probems Lea Za, Davd W. Cot, Department of Industra and Systems Engneerng, Rutgers Unversty, Pscataway, J 08854, USA Abstract A genera

More information

HO 40 Solutions ( ) ˆ. j, and B v. F m x 10-3 kg = i + ( 4.19 x 10 4 m/s)ˆ. (( )ˆ i + ( 4.19 x 10 4 m/s )ˆ j ) ( 1.40 T )ˆ k.

HO 40 Solutions ( ) ˆ. j, and B v. F m x 10-3 kg = i + ( 4.19 x 10 4 m/s)ˆ. (( )ˆ i + ( 4.19 x 10 4 m/s )ˆ j ) ( 1.40 T )ˆ k. .) m.8 x -3 g, q. x -8 C, ( 3. x 5 m/)ˆ, and (.85 T)ˆ The magnetc force : F q (. x -8 C) ( 3. x 5 m/)ˆ (.85 T)ˆ F.98 x -3 N F ma ( ˆ ˆ ) (.98 x -3 N) ˆ o a HO 4 Soluton F m (.98 x -3 N)ˆ.8 x -3 g.65 m.98

More information

CS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras

CS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras CS4495/6495 Introducton to Computer Vson 3C-L3 Calbratng cameras Fnally (last tme): Camera parameters Projecton equaton the cumulatve effect of all parameters: M (3x4) f s x ' 1 0 0 0 c R 0 I T 3 3 3 x1

More information

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or

More information

[WAVES] 1. Waves and wave forces. Definition of waves

[WAVES] 1. Waves and wave forces. Definition of waves 1. Waves and forces Defnton of s In the smuatons on ong-crested s are consdered. The drecton of these s (μ) s defned as sketched beow n the goba co-ordnate sstem: North West East South The eevaton can

More information

EXCE, steepest descent, conjugate gradient & BFGS

EXCE, steepest descent, conjugate gradient & BFGS Club Cast3M, 21th of November 2008 An amazng optmsaton problem P. Pegon & Ph. Capéran European Laboratory for Structural Assessment Jont Research Centre Ispra, Italy An amazng optmsaton problem OUTLINE:

More information

Harmonic oscillator approximation

Harmonic oscillator approximation armonc ocllator approxmaton armonc ocllator approxmaton Euaton to be olved We are fndng a mnmum of the functon under the retrcton where W P, P,..., P, Q, Q,..., Q P, P,..., P, Q, Q,..., Q lnwgner functon

More information

ScienceDirect. Development of the System of Telecontrol by the Multilink Manipulator Installed on the Mobile Robot

ScienceDirect. Development of the System of Telecontrol by the Multilink Manipulator Installed on the Mobile Robot vaabe onne at www.enedret.om SeneDret roeda Engneerng 7 77 th D Internatona Sympoum on Integent anufaturng and utomaton, D Deveopment of the Sytem of Teeontro by the utnk anpuator Intaed on the obe obot

More information

p 1 c 2 + p 2 c 2 + p 3 c p m c 2

p 1 c 2 + p 2 c 2 + p 3 c p m c 2 Where to put a faclty? Gven locatons p 1,..., p m n R n of m houses, want to choose a locaton c n R n for the fre staton. Want c to be as close as possble to all the house. We know how to measure dstance

More information

Supervised Learning. Neural Networks and Back-Propagation Learning. Credit Assignment Problem. Feedforward Network. Adaptive System.

Supervised Learning. Neural Networks and Back-Propagation Learning. Credit Assignment Problem. Feedforward Network. Adaptive System. Part 7: Neura Networ & earnng /2/05 Superved earnng Neura Networ and Bac-Propagaton earnng Produce dered output for tranng nput Generaze reaonaby & appropratey to other nput Good exampe: pattern recognton

More information

COMPLEX NUMBERS AND QUADRATIC EQUATIONS

COMPLEX NUMBERS AND QUADRATIC EQUATIONS COMPLEX NUMBERS AND QUADRATIC EQUATIONS INTRODUCTION We know that x 0 for all x R e the square of a real number (whether postve, negatve or ero) s non-negatve Hence the equatons x, x, x + 7 0 etc are not

More information

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem V. DEMENKO MECHANICS OF MATERIALS 05 LECTURE Mohr s Method for Cacuaton of Genera Dspacements The Recproca Theorem The recproca theorem s one of the genera theorems of strength of materas. It foows drect

More information

Small signal analysis

Small signal analysis Small gnal analy. ntroducton Let u conder the crcut hown n Fg., where the nonlnear retor decrbed by the equaton g v havng graphcal repreentaton hown n Fg.. ( G (t G v(t v Fg. Fg. a D current ource wherea

More information

Dynamic Analysis Of An Off-Road Vehicle Frame

Dynamic Analysis Of An Off-Road Vehicle Frame Proceedngs of the 8th WSEAS Int. Conf. on NON-LINEAR ANALYSIS, NON-LINEAR SYSTEMS AND CHAOS Dnamc Anass Of An Off-Road Vehce Frame ŞTEFAN TABACU, NICOLAE DORU STĂNESCU, ION TABACU Automotve Department,

More information

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He

More information

Neural networks. Nuno Vasconcelos ECE Department, UCSD

Neural networks. Nuno Vasconcelos ECE Department, UCSD Neural networs Nuno Vasconcelos ECE Department, UCSD Classfcaton a classfcaton problem has two types of varables e.g. X - vector of observatons (features) n the world Y - state (class) of the world x X

More information

Research Article H Estimates for Discrete-Time Markovian Jump Linear Systems

Research Article H Estimates for Discrete-Time Markovian Jump Linear Systems Mathematca Probems n Engneerng Voume 213 Artce ID 945342 7 pages http://dxdoorg/11155/213/945342 Research Artce H Estmates for Dscrete-Tme Markovan Jump Lnear Systems Marco H Terra 1 Gdson Jesus 2 and

More information

Homework 9 for BST 631: Statistical Theory I Problems, 11/02/2006

Homework 9 for BST 631: Statistical Theory I Problems, 11/02/2006 Due Tme: 5:00PM Thursda, on /09/006 Problem (8 ponts) Book problem 45 Let U = X + and V = X, then the jont pmf of ( UV, ) s θ λ θ e λ e f( u, ) = ( = 0, ; u =, +, )! ( u )! Then f( u, ) u θ λ f ( x x+

More information

Multi-joint kinematics and dynamics

Multi-joint kinematics and dynamics ut-ont nematcs and dnamcs Emo odorov pped athematcs omputer Scence and Engneerng Unverst o Washngton Knematcs n generazed vs. artesan coordnates generazed artesan dm() euas the number o degrees o reedom

More information

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence)

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence) /24/27 Prevew Fbonacc Sequence Longest Common Subsequence Dynamc programmng s a method for solvng complex problems by breakng them down nto smpler sub-problems. It s applcable to problems exhbtng the propertes

More information

Pattern Classification

Pattern Classification Pattern Classfcaton All materals n these sldes ere taken from Pattern Classfcaton (nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wley & Sons, 000 th the permsson of the authors and the publsher

More information

Additional File 1 - Detailed explanation of the expression level CPD

Additional File 1 - Detailed explanation of the expression level CPD Addtonal Fle - Detaled explanaton of the expreon level CPD A mentoned n the man text, the man CPD for the uterng model cont of two ndvdual factor: P( level gen P( level gen P ( level gen 2 (.).. CPD factor

More information

Outline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique

Outline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique Outlne and Readng Dynamc Programmng The General Technque ( 5.3.2) -1 Knapsac Problem ( 5.3.3) Matrx Chan-Product ( 5.3.1) Dynamc Programmng verson 1.4 1 Dynamc Programmng verson 1.4 2 Dynamc Programmng

More information

Problem Points Score Total 100

Problem Points Score Total 100 Physcs 450 Solutons of Sample Exam I Problem Ponts Score 1 8 15 3 17 4 0 5 0 Total 100 All wor must be shown n order to receve full credt. Wor must be legble and comprehensble wth answers clearly ndcated.

More information

Strain Energy in Linear Elastic Solids

Strain Energy in Linear Elastic Solids Duke Unverst Department of Cv and Envronmenta Engneerng CEE 41L. Matr Structura Anass Fa, Henr P. Gavn Stran Energ n Lnear Eastc Sods Consder a force, F, apped gradua to a structure. Let D be the resutng

More information

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

EEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming

EEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming EEL 6266 Power System Operaton and Control Chapter 3 Economc Dspatch Usng Dynamc Programmng Pecewse Lnear Cost Functons Common practce many utltes prefer to represent ther generator cost functons as sngle-

More information

Calculation of time complexity (3%)

Calculation of time complexity (3%) Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add

More information

Vector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence.

Vector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence. Vector Norms Chapter 7 Iteratve Technques n Matrx Algebra Per-Olof Persson persson@berkeley.edu Department of Mathematcs Unversty of Calforna, Berkeley Math 128B Numercal Analyss Defnton A vector norm

More information

Exercises of Fundamentals of Chemical Processes

Exercises of Fundamentals of Chemical Processes Department of Energ Poltecnco d Mlano a Lambruschn 4 2056 MILANO Exercses of undamentals of Chemcal Processes Prof. Ganpero Gropp Exercse 7 ) Estmaton of the composton of the streams at the ext of an sothermal

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Chapter 6 Hidden Markov Models. Chaochun Wei Spring 2018

Chapter 6 Hidden Markov Models. Chaochun Wei Spring 2018 896 920 987 2006 Chapter 6 Hdden Markov Modes Chaochun We Sprng 208 Contents Readng materas Introducton to Hdden Markov Mode Markov chans Hdden Markov Modes Parameter estmaton for HMMs 2 Readng Rabner,

More information

Optimal Design of Multi-loop PI Controllers for Enhanced Disturbance Rejection in Multivariable Processes

Optimal Design of Multi-loop PI Controllers for Enhanced Disturbance Rejection in Multivariable Processes Proeedng of the 3rd WSEAS/IASME Internatonal Conferene on Dynamal Sytem and Control, Arahon, Frane, Otober 3-5, 2007 72 Optmal Degn of Mult-loop PI Controller for Enhaned Dturbane Rejeton n Multvarable

More information

Example: (13320, 22140) =? Solution #1: The divisors of are 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 18, 20, 27, 30, 36, 41,

Example: (13320, 22140) =? Solution #1: The divisors of are 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 18, 20, 27, 30, 36, 41, The greatest common dvsor of two ntegers a and b (not both zero) s the largest nteger whch s a common factor of both a and b. We denote ths number by gcd(a, b), or smply (a, b) when there s no confuson

More information

Logistic Regression. CAP 5610: Machine Learning Instructor: Guo-Jun QI

Logistic Regression. CAP 5610: Machine Learning Instructor: Guo-Jun QI Logstc Regresson CAP 561: achne Learnng Instructor: Guo-Jun QI Bayes Classfer: A Generatve model odel the posteror dstrbuton P(Y X) Estmate class-condtonal dstrbuton P(X Y) for each Y Estmate pror dstrbuton

More information

Curve Fitting with the Least Square Method

Curve Fitting with the Least Square Method WIKI Document Number 5 Interpolaton wth Least Squares Curve Fttng wth the Least Square Method Mattheu Bultelle Department of Bo-Engneerng Imperal College, London Context We wsh to model the postve feedback

More information

T E C O L O T E R E S E A R C H, I N C.

T E C O L O T E R E S E A R C H, I N C. T E C O L O T E R E S E A R C H, I N C. B rdg n g En g neern g a nd Econo mcs S nce 1973 THE MINIMUM-UNBIASED-PERCENTAGE ERROR (MUPE) METHOD IN CER DEVELOPMENT Thrd Jont Annual ISPA/SCEA Internatonal Conference

More information

Reliability Sensitivity Algorithm Based on Stratified Importance Sampling Method for Multiple Failure Modes Systems

Reliability Sensitivity Algorithm Based on Stratified Importance Sampling Method for Multiple Failure Modes Systems Chnese Journa o Aeronautcs 3(010) 660-669 Chnese Journa o Aeronautcs www.esever.com/ocate/ca Reabty Senstvty Agorthm Based on Strated Importance Sampng Method or Mutpe aure Modes Systems Zhang eng a, u

More information

Body Models I-2. Gerard Pons-Moll and Bernt Schiele Max Planck Institute for Informatics

Body Models I-2. Gerard Pons-Moll and Bernt Schiele Max Planck Institute for Informatics Body Models I-2 Gerard Pons-Moll and Bernt Schele Max Planck Insttute for Informatcs December 18, 2017 What s mssng Gven correspondences, we can fnd the optmal rgd algnment wth Procrustes. PROBLEMS: How

More information

MOMENTS OF NONIDENTICAL ORDER STATISTICS FROM BURR XII DISTRIBUTION WITH GAMMA AND NORMAL OUTLIERS

MOMENTS OF NONIDENTICAL ORDER STATISTICS FROM BURR XII DISTRIBUTION WITH GAMMA AND NORMAL OUTLIERS Journa of Matheat and Statt, 9 (): 5-6, 3 ISSN 549-3644 3 Sene Puaton do:.3844/p.3.5.6 Puhed Onne 9 () 3 (http://www.thepu.o/.to) MOMENTS OF NONIDENTIAL ORDER STATISTIS FROM BURR XII DISTRIBUTION WITH

More information

Neural network-based athletics performance prediction optimization model applied research

Neural network-based athletics performance prediction optimization model applied research Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

Implicit Integration Henyey Method

Implicit Integration Henyey Method Implct Integraton Henyey Method In realstc stellar evoluton codes nstead of a drect ntegraton usng for example the Runge-Kutta method one employs an teratve mplct technque. Ths s because the structure

More information

Feature Selection: Part 1

Feature Selection: Part 1 CSE 546: Machne Learnng Lecture 5 Feature Selecton: Part 1 Instructor: Sham Kakade 1 Regresson n the hgh dmensonal settng How do we learn when the number of features d s greater than the sample sze n?

More information

Stat 543 Exam 2 Spring 2016

Stat 543 Exam 2 Spring 2016 Stat 543 Exam 2 Sprng 206 I have nether gven nor receved unauthorzed assstance on ths exam. Name Sgned Date Name Prnted Ths Exam conssts of questons. Do at least 0 of the parts of the man exam. I wll score

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

Chapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder

Chapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder S-. The Method of Steepet cent Chapter. Supplemental Text Materal The method of teepet acent can be derved a follow. Suppoe that we have ft a frtorder model y = β + β x and we wh to ue th model to determne

More information

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

More information

A A Non-Constructible Equilibrium 1

A A Non-Constructible Equilibrium 1 A A Non-Contructbe Equbrum 1 The eampe depct a eparabe contet wth three payer and one prze of common vaue 1 (o v ( ) =1 c ( )). I contruct an equbrum (C, G, G) of the contet, n whch payer 1 bet-repone

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Chapter 6 The Effect of the GPS Systematic Errors on Deformation Parameters

Chapter 6 The Effect of the GPS Systematic Errors on Deformation Parameters Chapter 6 The Effect of the GPS Sytematc Error on Deformaton Parameter 6.. General Beutler et al., (988) dd the frt comprehenve tudy on the GPS ytematc error. Baed on a geometrc approach and aumng a unform

More information

σ τ τ τ σ τ τ τ σ Review Chapter Four States of Stress Part Three Review Review

σ τ τ τ σ τ τ τ σ Review Chapter Four States of Stress Part Three Review Review Chapter Four States of Stress Part Three When makng your choce n lfe, do not neglect to lve. Samuel Johnson Revew When we use matrx notaton to show the stresses on an element The rows represent the axs

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

Mathematics TASKS. GCSE to ASIA-level BRIDGING. tangent lines at a drffarent paint on the surface

Mathematics TASKS. GCSE to ASIA-level BRIDGING. tangent lines at a drffarent paint on the surface Mathematcs GCSE to ASA-eve BRDGNG TASKS tangent nes at a drffarent pant on the surface Pease compete one ne from the task st eow. A students must compete the mdde task: worked examp :!car task *Topc st

More information

Topic 5: Non-Linear Regression

Topic 5: Non-Linear Regression Topc 5: Non-Lnear Regresson The models we ve worked wth so far have been lnear n the parameters. They ve been of the form: y = Xβ + ε Many models based on economc theory are actually non-lnear n the parameters.

More information

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS)

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS) Some Comments on Acceleratng Convergence of Iteratve Sequences Usng Drect Inverson of the Iteratve Subspace (DIIS) C. Davd Sherrll School of Chemstry and Bochemstry Georga Insttute of Technology May 1998

More information

The Fundamental Theorem of Algebra. Objective To use the Fundamental Theorem of Algebra to solve polynomial equations with complex solutions

The Fundamental Theorem of Algebra. Objective To use the Fundamental Theorem of Algebra to solve polynomial equations with complex solutions 5-6 The Fundamental Theorem of Algebra Content Standards N.CN.7 Solve quadratc equatons wth real coeffcents that have comple solutons. N.CN.8 Etend polnomal denttes to the comple numbers. Also N.CN.9,

More information

Review: Fit a line to N data points

Review: Fit a line to N data points Revew: Ft a lne to data ponts Correlated parameters: L y = a x + b Orthogonal parameters: J y = a (x ˆ x + b For ntercept b, set a=0 and fnd b by optmal average: ˆ b = y, Var[ b ˆ ] = For slope a, set

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

Lower Bounding Procedures for the Single Allocation Hub Location Problem

Lower Bounding Procedures for the Single Allocation Hub Location Problem Lower Boundng Procedures for the Snge Aocaton Hub Locaton Probem Borzou Rostam 1,2 Chrstoph Buchhem 1,4 Fautät für Mathemat, TU Dortmund, Germany J. Faban Meer 1,3 Uwe Causen 1 Insttute of Transport Logstcs,

More information

Multilayer Perceptrons and Backpropagation. Perceptrons. Recap: Perceptrons. Informatics 1 CG: Lecture 6. Mirella Lapata

Multilayer Perceptrons and Backpropagation. Perceptrons. Recap: Perceptrons. Informatics 1 CG: Lecture 6. Mirella Lapata Multlayer Perceptrons and Informatcs CG: Lecture 6 Mrella Lapata School of Informatcs Unversty of Ednburgh mlap@nf.ed.ac.uk Readng: Kevn Gurney s Introducton to Neural Networks, Chapters 5 6.5 January,

More information

ON THE BEHAVIOR OF THE CONJUGATE-GRADIENT METHOD ON ILL-CONDITIONED PROBLEMS

ON THE BEHAVIOR OF THE CONJUGATE-GRADIENT METHOD ON ILL-CONDITIONED PROBLEMS ON THE BEHAVIOR OF THE CONJUGATE-GRADIENT METHOD ON I-CONDITIONED PROBEM Anders FORGREN Technca Report TRITA-MAT-006-O Department of Mathematcs Roya Insttute of Technoogy January 006 Abstract We study

More information

Team. Outline. Statistics and Art: Sampling, Response Error, Mixed Models, Missing Data, and Inference

Team. Outline. Statistics and Art: Sampling, Response Error, Mixed Models, Missing Data, and Inference Team Stattc and Art: Samplng, Repone Error, Mxed Model, Mng Data, and nference Ed Stanek Unverty of Maachuett- Amhert, USA 9/5/8 9/5/8 Outlne. Example: Doe-repone Model n Toxcology. ow to Predct Realzed

More information

CSCI B609: Foundations of Data Science

CSCI B609: Foundations of Data Science CSCI B609: Foundatons of Data Scence Lecture 13/14: Gradent Descent, Boostng and Learnng from Experts Sldes at http://grgory.us/data-scence-class.html Grgory Yaroslavtsev http://grgory.us Constraned Convex

More information

Basic Dynamic Model. Basic Dynamic Model. Unified Motion/Force Control. Unified Motion/Force Control ( ) Operational Space Dynamics.

Basic Dynamic Model. Basic Dynamic Model. Unified Motion/Force Control. Unified Motion/Force Control ( ) Operational Space Dynamics. Moble Robot Manpulaton Oussaa Khatb Robotcs Laboratory Departent of Coputer Scence Stanford Unversty Wth the Stanford-Schenan Ar Wth the PUMA 6 Stanford Free-Flyng Robot Stanford Robotc Platfors (993)

More information

Lecture 10: Multi-body simulation 1 (MBS 1) Steinbachstraße 53 B Tel.: (80)

Lecture 10: Multi-body simulation 1 (MBS 1) Steinbachstraße 53 B Tel.: (80) Leture : Mut-bod uaton (MBS ) eron-n-harge: Dp.-Ing. Mare Fe Stenbahtraße 5 B e.: (8) 744 E-a: M.Fe@wz.rwth-aahen.de Vrtua ahne too Modeng and Suaton Leture: Mut-bod uaton V- Vrtua ahne too Modeng and

More information

Statistical Analysis of Environmental Data - Academic Year Prof. Fernando Sansò CLUSTER ANALYSIS

Statistical Analysis of Environmental Data - Academic Year Prof. Fernando Sansò CLUSTER ANALYSIS Statstal Analyss o Envronmental Data - Aadem Year 008-009 Pro. Fernando Sansò EXERCISES - PAR CLUSER ANALYSIS Supervsed Unsupervsed Determnst Stohast Determnst Stohast Dsrmnant Analyss Bayesan Herarhal

More information

Expectation Maximization Mixture Models HMMs

Expectation Maximization Mixture Models HMMs -755 Machne Learnng for Sgnal Processng Mture Models HMMs Class 9. 2 Sep 200 Learnng Dstrbutons for Data Problem: Gven a collecton of eamples from some data, estmate ts dstrbuton Basc deas of Mamum Lelhood

More information

Optimization Models for Heterogeneous Protocols

Optimization Models for Heterogeneous Protocols Optmzaton Modes for Heterogeneous Protocos Steven Low CS, EE netab.caltech.edu wth J. Doye, S. Hegde, L. L, A. Tang, J. Wang, Catech M. Chang, Prnceton Outne Internet protocos Horzonta decomposton TCP-AQM

More information

Presenters. Muscle Modeling. John Rasmussen (Presenter) Arne Kiis (Host) The web cast will begin in a few minutes.

Presenters. Muscle Modeling. John Rasmussen (Presenter) Arne Kiis (Host) The web cast will begin in a few minutes. Muscle Modelng If a part of my screen n mssng from your Vew, please press Sharng -> Vew -> Autoft The web cast wll begn n a few mnutes. Introducton (~5 mn) Overvew (~5 mn) Muscle knematcs (~10 mn) Muscle

More information

Estimation of a proportion under a certain two-stage sampling design

Estimation of a proportion under a certain two-stage sampling design Etmaton of a roorton under a certan two-tage amng degn Danutė Kraavcatė nttute of athematc and nformatc Lthuana Stattc Lthuana Lthuana e-ma: raav@tmt Abtract The am of th aer to demontrate wth exame that

More information

Chapter Eight. Review and Summary. Two methods in solid mechanics ---- vectorial methods and energy methods or variational methods

Chapter Eight. Review and Summary. Two methods in solid mechanics ---- vectorial methods and energy methods or variational methods Chapter Eght Energy Method 8. Introducton 8. Stran energy expressons 8.3 Prncpal of statonary potental energy; several degrees of freedom ------ Castglano s frst theorem ---- Examples 8.4 Prncpal of statonary

More information

Machine Learning: and 15781, 2003 Assignment 4

Machine Learning: and 15781, 2003 Assignment 4 ahne Learnng: 070 and 578, 003 Assgnment 4. VC Dmenson 30 onts Consder the spae of nstane X orrespondng to all ponts n the D x, plane. Gve the VC dmenson of the followng hpothess spaes. No explanaton requred.

More information

1 Matrix representations of canonical matrices

1 Matrix representations of canonical matrices 1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:

More information

EN40: Dynamics and Vibrations. Homework 7: Rigid Body Kinematics

EN40: Dynamics and Vibrations. Homework 7: Rigid Body Kinematics N40: ynamcs and Vbratons Homewor 7: Rgd Body Knematcs School of ngneerng Brown Unversty 1. In the fgure below, bar AB rotates counterclocwse at 4 rad/s. What are the angular veloctes of bars BC and C?.

More information

Inthem-machine flow shop problem, a set of jobs, each

Inthem-machine flow shop problem, a set of jobs, each THE ASYMPTOTIC OPTIMALITY OF THE SPT RULE FOR THE FLOW SHOP MEAN COMPLETION TIME PROBLEM PHILIP KAMINSKY Industra Engneerng and Operatons Research, Unversty of Caforna, Bereey, Caforna 9470, amnsy@eor.bereey.edu

More information

Kinematics and geometry review. Based on figures from J Xiao

Kinematics and geometry review. Based on figures from J Xiao Knematcs and geometr ree Based on fgres from J Xao Otlne ee obot Manlators obot onfgraton obot ecfcaton Nmber of Aes DOF recson eeatablt Knematcs relmnar World frame ont frame end-effector frame otaton

More information

Newton s Method for One - Dimensional Optimization - Theory

Newton s Method for One - Dimensional Optimization - Theory Numercal Methods Newton s Method for One - Dmensonal Optmzaton - Theory For more detals on ths topc Go to Clck on Keyword Clck on Newton s Method for One- Dmensonal Optmzaton You are free to Share to copy,

More information

Classification as a Regression Problem

Classification as a Regression Problem Target varable y C C, C,, ; Classfcaton as a Regresson Problem { }, 3 L C K To treat classfcaton as a regresson problem we should transform the target y nto numercal values; The choce of numercal class

More information

Root Locus Techniques

Root Locus Techniques Root Locu Technque ELEC 32 Cloed-Loop Control The control nput u t ynthezed baed on the a pror knowledge of the ytem plant, the reference nput r t, and the error gnal, e t The control ytem meaure the output,

More information

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2 Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................

More information

CS 331 DESIGN AND ANALYSIS OF ALGORITHMS DYNAMIC PROGRAMMING. Dr. Daisy Tang

CS 331 DESIGN AND ANALYSIS OF ALGORITHMS DYNAMIC PROGRAMMING. Dr. Daisy Tang CS DESIGN ND NLYSIS OF LGORITHMS DYNMIC PROGRMMING Dr. Dasy Tang Dynamc Programmng Idea: Problems can be dvded nto stages Soluton s a sequence o decsons and the decson at the current stage s based on the

More information

PHYS 705: Classical Mechanics. Calculus of Variations II

PHYS 705: Classical Mechanics. Calculus of Variations II 1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary

More information

12 th National Congress on Theoretical and Applied Mechanics September 2013, Saints Constantine and Helena, Varna, Bulgaria

12 th National Congress on Theoretical and Applied Mechanics September 2013, Saints Constantine and Helena, Varna, Bulgaria th Natonal Congress on Theoretal and Appled Mehans 3-6 September 03 Sants Constantne and Helena Varna Bulgara APPLICATION OF GEARING PRIMITIVES TO SKEW AXES GEAR SET SYNTHESIS PART : MATHEMATICAL MODEL

More information