LESSON 15: BODE PLOTS OF TRANSFER FUNCTIONS
|
|
- Christal Powers
- 6 years ago
- Views:
Transcription
1 10/8/015 1 LESSON 15: BODE PLOTS OF TRANSFER FUNCTIONS ET 438a Automatc Control Systems Technology Learnng Objectves After ths presentaton you wll be able to: Compute the magntude of a transfer functon for a gven radan frequency. Compute the phase shft of a transfer functon for a gven radan frequency. Construct a Bode plot that shows both magntude and phase shft as functons of transfer functon nput frequency Use MatLAB nstructons t produce Bode plots of transfer functons. 1
2 10/8/015 Constructon of Bode Plots 3 Bode plots consst of two ndvdual graphs: a) a semlog plot of gan vs frequency b) a semlog plot of phase shft vs frequency. Frequency s the logarthmc axs on both plots. Bode plots of transfer functons gve the frequency response of a control system To compute the ponts for a Bode Plot: 1) Replace Laplace varable, s, n transfer functon wth jw ) Select frequences of nterest n rad/sec (w=pf) 3) Compute magntude and phase angle of the resultng complex expresson. Constructon of Bode Plots 4 Bode plot calculatons- magntude/phase X(jw) Transfer Functon Y(jw) Gan: Y(jw) G(jw) X(jw) db 0log( G(jw) ) Where for a gven frequency Y y o X x Phase: o To fnd the magntude and phase shft of a complex number n rectangular form gven: z a jb z a b b tan 1 a
3 10/8/015 Computng Transfer Functon Values 5 Example 15-1: A self-regulatng tank has a transfer functon of the form shown below. H(s) G Q(s) 1 s The tank has a tme constant, =1590 seconds and a gan, G=000 s/m. Determne the ampltude and phase shft of the system to a snusodal flow nput of Hz Soluton: Substtute values of G,, and jw nto transfer functon and compute the gan magntude and phase shft. Example 15-1 Soluton (1) 6 G 000 s/m 1590 s f Hz H(jw) 000 Q(jw) jw Hz 0.001rad/s w pf w p Place radan frequency nto transfer functon and compute complex number H( j0.001) j901 Q( j0.001) j j.590 Convert the result to polar form to fnd magntude and phase shft 3
4 10/8/015 Example 15-1 Soluton () 7 Complex gan Magntude Calculaton a G(j0.001) G(j0.001) G(j0.001) j j j.590 b -901 G(jw) db 0log(1065) 60.5 db a b Ans tan Phase shft -1 b a -1 b tan tan 58 a Ans At rad/sec, the system has a gan of 60.5 db and the output changes n heght lag the flow changes by 58 degrees 8 MatLAB has control system toolbox functons for defnng Lnear Tme-nvarant systems (LTI) and constructng the Bode plots. Use tf and bode functons to create LTI and plot. Introducng zpk functon sys = zpk(z,p,k) Turns arrays of zeros, poles and gans nto LTI called sys Where z = array of transfer functon zeros p = array of transfer functon poles k = array of transfer functon gans 4
5 Magntude (db) Phase (deg) 10/8/015 9 Example 15-: Construct the Bode plot for the gven transfer functon shown n factored form usng MatLAB control toolbox functons. V o(s) V (s) 0.005s 0.001s s 1 Soluton: Transfer functon has one zero at s=0 and two poles at s=-1/0.001= Dvdng the transfer functon denomnator and numerator by places t nto standard form V o(s) V (s) s s s s s s s s 1000s MatLAB code to produce Bode plot of gven transfer functon Enter at command prompt of nto m-fle Magntude plot k= [5] p=[ ] z=[0] sys=zpk(z,p,k) bode(sys) w=1000 Bode Dagram Phase plot Frequency (rad/s) 5
6 Magntude (db) Phase (deg) 10/8/ The bode(sys) functon can plot more than one transfer functon on the same fgure axs. The fgure produced by the bode(sys) functon can be coped and pasted nto wordprocessors and other programs. To plot more than one transfer functon use the followng syntax: bode(sys1,sys, ). Example 15-3: Compare the Bode plots of the transfer functon gven n Example 15- to the functon gven below. Use MatLAB to generate the Bode plots on a sngle set of axs. Note: the only dfference s the gan s ncreased by a factor of 100. V o(s) V (s) 500s s 1000s Enter at command prompt of nto m-fle sys k= [5] p=[ ] z=[0] sys1=zpk(z,p,k) k1=[500] sys=zpk(z,p,k1) bode(sys1, ro-,sys, b- ) Bode Dagram sys Ths changes plot styles (blue sold lne) Frequency (rad/s) sys1 & sys 6
7 10/8/ ET 438a Automatc Control Systems Technology End Lesson 15: Bode Plots of Transfer Functons 7
Classification / Regression Support Vector Machines
Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM
More informationSolutions to Programming Assignment Five Interpolation and Numerical Differentiation
College of Engneerng and Coputer Scence Mechancal Engneerng Departent Mechancal Engneerng 309 Nuercal Analyss of Engneerng Systes Sprng 04 Nuber: 537 Instructor: Larry Caretto Solutons to Prograng Assgnent
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationy and the total sum of
Lnear regresson Testng for non-lnearty In analytcal chemstry, lnear regresson s commonly used n the constructon of calbraton functons requred for analytcal technques such as gas chromatography, atomc absorpton
More informationSum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints
Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan
More informationRecognizing Faces. Outline
Recognzng Faces Drk Colbry Outlne Introducton and Motvaton Defnng a feature vector Prncpal Component Analyss Lnear Dscrmnate Analyss !"" #$""% http://www.nfotech.oulu.f/annual/2004 + &'()*) '+)* 2 ! &
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationAssignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.
Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton
More informationProblem Set 3 Solutions
Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationIntroduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2
Introducton to Geometrcal Optcs - a D ra tracng Ecel model for sphercal mrrors - Part b George ungu - Ths s a tutoral eplanng the creaton of an eact D ra tracng model for both sphercal concave and sphercal
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationAP PHYSICS B 2008 SCORING GUIDELINES
AP PHYSICS B 2008 SCORING GUIDELINES General Notes About 2008 AP Physcs Scorng Gudelnes 1. The solutons contan the most common method of solvng the free-response questons and the allocaton of ponts for
More informationUSING GRAPHING SKILLS
Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp
More informationMachine Learning 9. week
Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below
More informationR s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes
SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges
More informationGSLM Operations Research II Fall 13/14
GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are
More informationElectrical analysis of light-weight, triangular weave reflector antennas
Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna
More informationCompiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz
Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster
More informationLECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming
CEE 60 Davd Rosenberg p. LECTURE NOTES Dualty Theory, Senstvty Analyss, and Parametrc Programmng Learnng Objectves. Revew the prmal LP model formulaton 2. Formulate the Dual Problem of an LP problem (TUES)
More informationOptimizing Document Scoring for Query Retrieval
Optmzng Document Scorng for Query Retreval Brent Ellwen baellwe@cs.stanford.edu Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng
More informationTEST-05 TOPIC: OPTICS COMPLETE
Q. A boy s walkng under an nclned mrror at a constant velocty V m/s along the x-axs as shown n fgure. If the mrror s nclned at an angle wth the horzontal then what s the velocty of the mage? Y V sn + V
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More information2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements
Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.
More informationSUMMARY... I TABLE OF CONTENTS...II INTRODUCTION...
Summary A follow-the-leader robot system s mplemented usng Dscrete-Event Supervsory Control methods. The system conssts of three robots, a leader and two followers. The dea s to get the two followers to
More informationOutline. Third Programming Project Two-Dimensional Arrays. Files You Can Download. Exercise 8 Linear Regression. General Regression
Project 3 Two-densonal arras Ma 9, 6 Thrd Prograng Project Two-Densonal Arras Larr Caretto Coputer Scence 6 Coputng n Engneerng and Scence Ma 9, 6 Outlne Quz three on Thursda for full lab perod See saple
More informationKiran Joy, International Journal of Advanced Engineering Technology E-ISSN
Kran oy, nternatonal ournal of Advanced Engneerng Technology E-SS 0976-3945 nt Adv Engg Tech/Vol. V/ssue /Aprl-une,04/9-95 Research Paper DETERMATO O RADATVE VEW ACTOR WTOUT COSDERG TE SADOWG EECT Kran
More informationPRÉSENTATIONS DE PROJETS
PRÉSENTATIONS DE PROJETS Rex Onlne (V. Atanasu) What s Rex? Rex s an onlne browser for collectons of wrtten documents [1]. Asde ths core functon t has however many other applcatons that make t nterestng
More informationMATHEMATICS FORM ONE SCHEME OF WORK 2004
MATHEMATICS FORM ONE SCHEME OF WORK 2004 WEEK TOPICS/SUBTOPICS LEARNING OBJECTIVES LEARNING OUTCOMES VALUES CREATIVE & CRITICAL THINKING 1 WHOLE NUMBER Students wll be able to: GENERICS 1 1.1 Concept of
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More informationAn Introduction to MATLAB and the Control Systems toolbox Aravind Parchuri, Darren Hon and Albert Honein
E205 Introduction to Control Design Techniques An Introduction to MATLAB and the Control Systems toolbox Aravind Parchuri, Darren Hon and Albert Honein MATLAB is essentially a programming interface that
More informationAssembler. Building a Modern Computer From First Principles.
Assembler Buldng a Modern Computer From Frst Prncples www.nand2tetrs.org Elements of Computng Systems, Nsan & Schocken, MIT Press, www.nand2tetrs.org, Chapter 6: Assembler slde Where we are at: Human Thought
More informationMotivation. EE 457 Unit 4. Throughput vs. Latency. Performance Depends on View Point?! Computer System Performance. An individual user wants to:
4.1 4.2 Motvaton EE 457 Unt 4 Computer System Performance An ndvdual user wants to: Mnmze sngle program executon tme A datacenter owner wants to: Maxmze number of Mnmze ( ) http://e-tellgentnternetmarketng.com/webste/frustrated-computer-user-2/
More informationSome Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.
Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,
More informationLecture #15 Lecture Notes
Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal
More informationSolving two-person zero-sum game by Matlab
Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by
More informationCS1100 Introduction to Programming
Factoral (n) Recursve Program fact(n) = n*fact(n-) CS00 Introducton to Programmng Recurson and Sortng Madhu Mutyam Department of Computer Scence and Engneerng Indan Insttute of Technology Madras nt fact
More information2. Introduction to Matlab Control System Toolbox
. Introduction to Matlab Control System Toolbox Consider a single-input, single-output (SISO), continuous-time, linear, time invariant (LTI) system defined by its transfer function: u(t) Y( S) num y(t)
More informationSequential search. Building Java Programs Chapter 13. Sequential search. Sequential search
Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to
More information11. HARMS How To: CSV Import
and Rsk System 11. How To: CSV Import Preparng the spreadsheet for CSV Import Refer to the spreadsheet template to ad algnng spreadsheet columns wth Data Felds. The spreadsheet s shown n the Appendx, an
More informationMulti-view 3D Position Estimation of Sports Players
Mult-vew 3D Poston Estmaton of Sports Players Robbe Vos and Wlle Brnk Appled Mathematcs Department of Mathematcal Scences Unversty of Stellenbosch, South Afrca Emal: vosrobbe@gmal.com Abstract The problem
More informationAnalysis of Non-coherent Fault Trees Using Ternary Decision Diagrams
Analyss of Non-coherent Fault Trees Usng Ternary Decson Dagrams Rasa Remenyte-Prescott Dep. of Aeronautcal and Automotve Engneerng Loughborough Unversty, Loughborough, LE11 3TU, England R.Remenyte-Prescott@lboro.ac.uk
More informationData Mining For Multi-Criteria Energy Predictions
Data Mnng For Mult-Crtera Energy Predctons Kashf Gll and Denns Moon Abstract We present a data mnng technque for mult-crtera predctons of wnd energy. A mult-crtera (MC) evolutonary computng method has
More informationPrivate Information Retrieval (PIR)
2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market
More informationHierarchical clustering for gene expression data analysis
Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally
More informationForm-factors Josef Pelikán CGG MFF UK Praha.
Form-factors 1996-2016 Josef Pelkán CGG MFF UK Praha pepca@cgg.mff.cun.cz http://cgg.mff.cun.cz/~pepca/ FormFactor 2016 Josef Pelkán, http://cgg.mff.cun.cz/~pepca 1 / 23 Form-factor F It ndcates the proporton
More informationHigh level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization
What s a Computer Program? Descrpton of algorthms and data structures to acheve a specfc ojectve Could e done n any language, even a natural language lke Englsh Programmng language: A Standard notaton
More informationA SYSTOLIC APPROACH TO LOOP PARTITIONING AND MAPPING INTO FIXED SIZE DISTRIBUTED MEMORY ARCHITECTURES
A SYSOLIC APPROACH O LOOP PARIIONING AND MAPPING INO FIXED SIZE DISRIBUED MEMORY ARCHIECURES Ioanns Drosts, Nektaros Kozrs, George Papakonstantnou and Panayots sanakas Natonal echncal Unversty of Athens
More informationREFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water.
Purpose Theory REFRACTION a. To study the refracton of lght from plane surfaces. b. To determne the ndex of refracton for Acrylc and Water. When a ray of lght passes from one medum nto another one of dfferent
More information6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour
6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the
More informationA fault tree analysis strategy using binary decision diagrams
Loughborough Unversty Insttutonal Repostory A fault tree analyss strategy usng bnary decson dagrams Ths tem was submtted to Loughborough Unversty's Insttutonal Repostory by the/an author. Addtonal Informaton:
More informationAlgorithm To Convert A Decimal To A Fraction
Algorthm To Convert A ecmal To A Fracton by John Kennedy Mathematcs epartment Santa Monca College 1900 Pco Blvd. Santa Monca, CA 90405 jrkennedy6@gmal.com Except for ths comment explanng that t s blank
More information9.5 Polar Coordinates. Copyright Cengage Learning. All rights reserved.
9.5 Polar Coordinates Copyright Cengage Learning. All rights reserved. Introduction Representation of graphs of equations as collections of points (x, y), where x and y represent the directed distances
More informationSubspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;
Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationParallel Computation of the Functions Constructed with
PDCS 013 (Ukrane, Kharkv, March 13-14, 013) Parallel Computaton of the Functons Constructed wth R-operatons usng CUDA Roman A. Uvarov Podgorny Insttute for Mechancal Engneerng Problems of NAS of Ukrane,
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationIntro. Iterators. 1. Access
Intro Ths mornng I d lke to talk a lttle bt about s and s. We wll start out wth smlartes and dfferences, then we wll see how to draw them n envronment dagrams, and we wll fnsh wth some examples. Happy
More informationComputer Animation and Visualisation. Lecture 4. Rigging / Skinning
Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume
More informationLEAST SQUARES. RANSAC. HOUGH TRANSFORM.
LEAS SQUARES. RANSAC. HOUGH RANSFORM. he sldes are from several sources through James Has (Brown); Srnvasa Narasmhan (CMU); Slvo Savarese (U. of Mchgan); Bll Freeman and Antono orralba (MI), ncludng ther
More informationETAtouch RESTful Webservices
ETAtouch RESTful Webservces Verson 1.1 November 8, 2012 Contents 1 Introducton 3 2 The resource /user/ap 6 2.1 HTTP GET................................... 6 2.2 HTTP POST..................................
More informationUser Authentication Based On Behavioral Mouse Dynamics Biometrics
User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA
More informationLobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide
Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.
More informationCell Count Method on a Network with SANET
CSIS Dscusson Paper No.59 Cell Count Method on a Network wth SANET Atsuyuk Okabe* and Shno Shode** Center for Spatal Informaton Scence, Unversty of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
More informationReview of approximation techniques
CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated
More informationGeneral Vector Machine. Hong Zhao Department of Physics, Xiamen University
General Vector Machne Hong Zhao (zhaoh@xmu.edu.cn) Department of Physcs, Xamen Unversty The support vector machne (SVM) s an mportant class of learnng machnes for functon approach, pattern recognton, and
More informationNews. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example
Unversty of Brtsh Columba CPSC, Intro to Computaton Jan-Apr Tamara Munzner News Assgnment correctons to ASCIIArtste.java posted defntely read WebCT bboards Arrays Lecture, Tue Feb based on sldes by Kurt
More informationA DATA ANALYSIS CODE FOR MCNP MESH AND STANDARD TALLIES
Supercomputng n uclear Applcatons (M&C + SA 007) Monterey, Calforna, Aprl 15-19, 007, on CD-ROM, Amercan uclear Socety, LaGrange Par, IL (007) A DATA AALYSIS CODE FOR MCP MESH AD STADARD TALLIES Kenneth
More informationComplex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.
Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationFinite Element Analysis of Rubber Sealing Ring Resilience Behavior Qu Jia 1,a, Chen Geng 1,b and Yang Yuwei 2,c
Advanced Materals Research Onlne: 03-06-3 ISSN: 66-8985, Vol. 705, pp 40-44 do:0.408/www.scentfc.net/amr.705.40 03 Trans Tech Publcatons, Swtzerland Fnte Element Analyss of Rubber Sealng Rng Reslence Behavor
More informationMathematics 256 a course in differential equations for engineering students
Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the
More informationControl System Toolbox
The Almighty University of Mohaghegh Ardabili Control System Toolbox The SISO Design Tool 1 Transfer Function TF = 2s + 4 s 2 + 6s + 5 TF = TF = 2(s + 2) (s + 1)(s + 5) 2(s + 2) (s + 1)(s + 5) 0 1 A= 5
More informationWelcome to the Three Ring %CIRCOS: An Example of Creating a Circular Graph without a Polar Axis
PharmaSUG 2018 - Paper DV14 Welcome to the Three Rng %CIRCOS: An Example of Creatng a Crcular Graph wthout a Polar Axs Jeffrey Meyers, Mayo Clnc ABSTRACT An nternal graphcs challenge between SAS and R
More informationDesign of an interactive Web-based e-learning course with simulation lab: a case study of a fuzzy expert system course
World Transactons on Engneerng and Technology Educaton Vol.8, No.3, 2010 2010 WIETE Desgn of an nteractve Web-based e-learnng course wth smulaton lab: a case study of a fuzzy expert system course Che-Chern
More informationExercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005
Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed
More informationTN348: Openlab Module - Colocalization
TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages
More informationMachine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law)
Machne Learnng Support Vector Machnes (contans materal adapted from talks by Constantn F. Alfers & Ioanns Tsamardnos, and Martn Law) Bryan Pardo, Machne Learnng: EECS 349 Fall 2014 Support Vector Machnes
More informationGreedy Technique - Definition
Greedy Technque Greedy Technque - Defnton The greedy method s a general algorthm desgn paradgm, bult on the follong elements: confguratons: dfferent choces, collectons, or values to fnd objectve functon:
More informationCMPS 10 Introduction to Computer Science Lecture Notes
CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not
More information7/12/2016. GROUP ANALYSIS Martin M. Monti UCLA Psychology AGGREGATING MULTIPLE SUBJECTS VARIANCE AT THE GROUP LEVEL
GROUP ANALYSIS Martn M. Mont UCLA Psychology NITP AGGREGATING MULTIPLE SUBJECTS When we conduct mult-subject analyss we are tryng to understand whether an effect s sgnfcant across a group of people. Whether
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,
More informationRadial Basis Functions
Radal Bass Functons Mesh Reconstructon Input: pont cloud Output: water-tght manfold mesh Explct Connectvty estmaton Implct Sgned dstance functon estmaton Image from: Reconstructon and Representaton of
More informationAnalysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress
Analyss of 3D Cracks n an Arbtrary Geometry wth Weld Resdual Stress Greg Thorwald, Ph.D. Ted L. Anderson, Ph.D. Structural Relablty Technology, Boulder, CO Abstract Materals contanng flaws lke nclusons
More informationJ-DSP-CONTROL: A CONTROL SYSTEMS SIMULATION ENVIRONMENT +
J-DSP-CONTROL: A CONTROL SYSTEMS SIMULATION ENVIRONMENT + T. Thrasyvoulou, K. Tsakals and A. Spanas MIDL Department of Electrcal Engneerng Arzona State Unversty, Tempe, AZ 85287-7206 thrassos@asu.edu,
More informationOutline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1
4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationAn Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane
An Approach n Colorng Sem-Regular Tlngs on the Hyperbolc Plane Ma Louse Antonette N De Las Peñas, mlp@mathscmathadmueduph Glenn R Lago, glago@yahoocom Math Department, Ateneo de Manla Unversty, Loyola
More informationOutline. Midterm Review. Declaring Variables. Main Variable Data Types. Symbolic Constants. Arithmetic Operators. Midterm Review March 24, 2014
Mdterm Revew March 4, 4 Mdterm Revew Larry Caretto Mechancal Engneerng 9 Numercal Analyss of Engneerng Systems March 4, 4 Outlne VBA and MATLAB codng Varable types Control structures (Loopng and Choce)
More informationBarycentric Coordinates. From: Mean Value Coordinates for Closed Triangular Meshes by Ju et al.
Barycentrc Coordnates From: Mean Value Coordnates for Closed Trangular Meshes by Ju et al. Motvaton Data nterpolaton from the vertces of a boundary polygon to ts nteror Boundary value problems Shadng Space
More informationIMAGE MATCHING WITH SIFT FEATURES A PROBABILISTIC APPROACH
IMAGE MATCHING WITH SIFT FEATURES A PROBABILISTIC APPROACH Jyot Joglekar a, *, Shrsh S. Gedam b a CSRE, IIT Bombay, Doctoral Student, Mumba, Inda jyotj@tb.ac.n b Centre of Studes n Resources Engneerng,
More informationAPPLICATION OF A COMPUTATIONALLY EFFICIENT GEOSTATISTICAL APPROACH TO CHARACTERIZING VARIABLY SPACED WATER-TABLE DATA
RFr"W/FZD JAN 2 4 1995 OST control # 1385 John J Q U ~ M Argonne Natonal Laboratory Argonne, L 60439 Tel: 708-252-5357, Fax: 708-252-3 611 APPLCATON OF A COMPUTATONALLY EFFCENT GEOSTATSTCAL APPROACH TO
More informationRange images. Range image registration. Examples of sampling patterns. Range images and range surfaces
Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples
More informationELEC 377 Operating Systems. Week 6 Class 3
ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems
More informationRules for Using Multi-Attribute Utility Theory for Estimating a User s Interests
Rules for Usng Mult-Attrbute Utlty Theory for Estmatng a User s Interests Ralph Schäfer 1 DFKI GmbH, Stuhlsatzenhausweg 3, 66123 Saarbrücken Ralph.Schaefer@dfk.de Abstract. In ths paper, we show that Mult-Attrbute
More informationPerformance Evaluation of Information Retrieval Systems
Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence
More informationStudy on Fuzzy Automatic Transmission Strategy of vehicles
Study on Fuzzy Automatc Transmsson Strategy of vehcles ZhY Zhang BeHua Unversty Engneerng Tranng Center JLn Cty, P.R.Chna zyzhang6@163.com Dngxuan Zhao JLn Unversty College of Mechancal Scence and Engneerng
More informationMultilevel Analysis with Informative Weights
Secton on Survey Research Methods JSM 2008 Multlevel Analyss wth Informatve Weghts Frank Jenkns Westat, 650 Research Blvd., Rockvlle, MD 20850 Abstract Multlevel modelng has become common n large scale
More information