Tokyo Institute of Technology Tokyo Institute of Technology
|
|
- Lester Harris
- 6 years ago
- Views:
Transcription
1 Outle ult-aget Search usg oroo Partto ad oroo D eermet Revew Itroducto Decreasg desty fucto Stablty Cocluso Fujta Lab, Det. of Cotrol ad System Egeerg, FL07--: July 09,007 Davd Ask ork rogress:. Smulato of oroo D wth desty fucto. Lloyd s Algorthm D eermet Future ork Revew Revew Revew meu:. oroo artto D & D. Lloyd s Algorthm. Objectve fucto: - Sesg erformace - Desty fucto oroo artto: The set of all ots q whose dstace from s less tha or equal to the dstaces from all other j { : } = q j q q j 0 Revew Revew Lloyd s Algorthm: A method for evely dstrbutg ots over a ukow area. The stes: Ste 0: Start wth a radom area, {}, ad radom ots, {}. Ste : Costruct oroo artto {}, geerated by {}. Ste : Udate to be the cetrod of. Retur to Ste. Ste 0: Start wth a radom area, {}, ad radom ots, {}. Ste : Costruct oroo artto {}, geerated by {}. Ste : Udate to be the cetrod of. Retur to Ste. 0
2 Revew Revew Objectve fucto: f ( q ): (, ) = ( ) φ H f q q Sesg erformace (f=bg oor sesg) Desty fucto φ ( q): = aget osto q = object = artto By mmzg H, we get otmum coverage. hy? he H = m, agets move to the area wth the hghest occurrece ossblty. e assume: f q = q Smlfy the objectve fucto usg arallel as theorem. H, = f q φ q To mmze ths, =C w (=cetrod of artto) Therefore, use ths as a ut to make agets go to cetrod of the artto. (, ) (, ) = φ : H = H c c q mass Alcato: Itroducto Autoomous N agets equed wth sesors deloy themselves a otmal way over a ukow area. search & rescue, evrometal motorg, mltary ad defece alcato, etc. Objectve: mult-aget search Agets deloy themselves otmally whle udatg (=reducg) ucertaty desty fucto ad gather formato tll the ucertaty s below a certa level. Decreasg Desty Fucto At each terato, after deloyg themselves otmally, the sesors gather formato about, reducg the desty fucto as: β ( q) ( q) m{ ( q )} φ = φ β φ ( q ) [ ] : desty fucto : sesg erformace : osto of the -th sesor β : a 0, s the factor of reducto hy m{ β ( q )}? Oly the aget wth the smallest β ca reduce the ucertaty by the largest amout. Decreasg Desty Fucto For sesg erformace fucto: ( ) β s mmum. As aroaches q β decreases (=good sesg) As go further away from q β creases (=bad sesg) Cocluso: he = q, β q = ke α k α > 0 ( 0,) Decreasg Desty Fucto For objectve fucto: H ( q) = ma{ φ φ } { φ ( q) φ ( q) m{ β( q )}} φ( q) { m{ β( q )}} = φ { β( )} = φ q { } = φ q ke = φ q ke q q
3 Decreasg Desty Fucto Objectve fucto: = φ H φ ( α)( ) = q ke ( α) ( φ q q) ( α) φ( q) φ ( α) φ( q) φ H q ke { q q } ( α ){ C} = C q q φ ( q) = φ φ = q C = qφ ( q) q ke α Decreasg Desty Fucto H = From C, we ca coclude that the ecessary codto for otmalty s, ( ad are resectvely the mass ad the cetrod of wth resect to ) Assume the system as & = u. Use the result above as a ut: u k = ro C k ro > 0 C = C φ C Ths moves the aget towards. DDF Summary Objectve: Agets deloy themselves otmally whle reducg ucertaty desty fucto ad gather formato tll the ucertaty s below a certa level. Decreasg desty fucto: q φ ( q) = φ( q) m{ β( q )} β ( q ) ke α = Objectve fucto: H ( q ) q ( q ) ke = φ = φ H = C O system &, use ths as a ut: u k = u = ro C Stablty Cosder the X H, where X =,, K, N reresets the cofguratos of N agets. dh dt = & X = δ H = & δ = α C & = α C k ro C =kro C Sce α > 0, k ro > 0, & s a egatve defte. Stablty Cocluso By LaSalle s varace rcle, the trajectores of the agets govered by cotrol law: startg from ay tal cofgurato, wll asymtotcally coverge to cetrodal oroo artto wth resect to the desty fucto: Note: C u k = ro C φ ( q) = φ C = qφ q q ke α = φ q Objectve: Agets deloy themselves otmally, gather formato ther resectve oroo artto ad hece reduce ucertaty desty fucto. (Note: the teratos are cotued tll the ucertaty the s below a requred level) The oe-ste otmal deloymet s the cetrodal oroo cofgurato wth resect to the reduced desty fucto. Prove stable by LaSalle s varace rcle.
4 ork Progress (-0-a) ork Progress (-0-b) Smulato of oroo D wth costat desty fucto φ = : ( = 9 agets) Last osto: ork Progress (-0-c) ork Progress (--a) Trajectory grah: Smulato of oroo D wth desty fucto φ = e y : ( = 9 agets) ork Progress (--b) ork Progress (--c) Last osto: Trajectory grah: 4
5 ork Progress (--a) Smulato of oroo D wth desty fucto φ = e y : ( = 9 agets) Last osto: ork Progress (--b) ork Progress (--c) ork Progress () Trajectory grah: Lloyd s Algorthm D smulato wth desty fucto φ = : Objectve of the eermet: To test the covergece characterstc of Lloyd s Algorthm D usg RC cars. Equmet: ork Progress () Software: ork Progress (). Halco: Orders the camera to cature the osto of the cars.. Smulk: Processes the data catured by Halco. (Lloyd s Algorthm block s wrtte here) 5
6 Software: ork Progress (). Cotrol Desk:. Receves order (outut) from Smulk ad asses t to RC motors.. otors cars osto, seed, voltage gve, etc. Software dagram: ork Progress () 4. crosoft sual C: Lks data betwee Halco ad Smulk. ork Progress () ork Progress () Eermet order:. ake crcles out of cardboard (as cars osto) for camera to read, ad reare the feld.. ake Halco rogram.. ake C rogram to lk data from Halco to Smulk. 4. ake Lloyd s Algorthm block dagram Smulk. 5. Lk Smulk wth Cotrol Desk ad make motors Cotrol Desk. Lloyd s Algorthm D block dagram: Halco oroo C - otor Halco oroo C - otor u u Adjust voltage, cars seed, drecto, camera, etc. Debug ad comle. Halco oroo C - otor u ork Progress () Future ork Trouble: Dfferet tme/day, dfferet car ad camera characterstc. Adjustmet for car seed, drecto. Lmted ower o cars battery reflects o erformace. Frcto betwee the feld ad the tre. Camera s vso s wared (afflcts o vdeo caturg erformace). Backward movemet. Etc. Lloyd s Algorthm D eermet revso. Lloyd s Algorthm D eermet wth desty fucto. ult-aget Search D smulato. Read more coverage cotrol aers, etc. Backward movemet: Forward : Sto :.8 Backward : If ossble, Lloyd s Algorthm D eermet wth costat desty fucto. 6
7 Refereces Gururasad K.R., Debassh Ghose, ult-aget Search usg oroo Parttos, ACODS, 007 Jorge Cortes, Soa artez, Tmur Karatas, Fracesco Bullo, Coverage Cotrol for oble Sesg Networks, IEEE, 007 Bruce Fracs, Dstrbuted Cotrol of Autoomous oble Robots, 006 7
2. Independence and Bernoulli Trials
. Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
More informationChannel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory
Chael Models wth Memory Chael Models wth Memory Hayder radha Electrcal ad Comuter Egeerg Mchga State Uversty I may ractcal etworkg scearos (cludg the Iteret ad wreless etworks), the uderlyg chaels are
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More information1 Lyapunov Stability Theory
Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may
More informationECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014
ECE 4/599 Electrc Eergy Systems 7 Optmal Dspatch of Geerato Istructor: Ka Su Fall 04 Backgroud I a practcal power system, the costs of geeratg ad delverg electrcty from power plats are dfferet (due to
More informationRock-Paper-Scissors: An Example of a Multi-agent Dynamical System
Rock-Paper-Scssors: A Example of a Mult-aget Dyamcal System Krst Lu Departmet of Mathematcs Krst.lu@gmal.com Abstract: I order to study ad uderstad mult-aget systems, I create smulatos of agets playg the
More informationBlock-Based Compact Thermal Modeling of Semiconductor Integrated Circuits
Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud
More informationENGI 3423 Simple Linear Regression Page 12-01
ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable
More informationLaboratory I.10 It All Adds Up
Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.
More informationIS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model
IS 79/89: Comutatoal Methods IS Research Smle Marova Queueg Model Nrmalya Roy Deartmet of Iformato Systems Uversty of Marylad Baltmore Couty www.umbc.edu Queueg Theory Software QtsPlus software The software
More information7.0 Equality Contraints: Lagrange Multipliers
Systes Optzato 7.0 Equalty Cotrats: Lagrage Multplers Cosder the zato of a o-lear fucto subject to equalty costrats: g f() R ( ) 0 ( ) (7.) where the g ( ) are possbly also olear fuctos, ad < otherwse
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationLecture 9. Some Useful Discrete Distributions. Some Useful Discrete Distributions. The observations generated by different experiments have
NM 7 Lecture 9 Some Useful Dscrete Dstrbutos Some Useful Dscrete Dstrbutos The observatos geerated by dfferet eermets have the same geeral tye of behavor. Cosequetly, radom varables assocated wth these
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationSequential Approach to Covariance Correction for P-Field Simulation
Sequetal Approach to Covarace Correcto for P-Feld Smulato Chad Neufeld ad Clayto V. Deutsch Oe well kow artfact of the probablty feld (p-feld smulato algorthm s a too large covarace ear codtog data. Prevously,
More informationCSE 5526: Introduction to Neural Networks Linear Regression
CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto
More informationComputations with large numbers
Comutatos wth large umbers Wehu Hog, Det. of Math, Clayto State Uversty, 2 Clayto State lvd, Morrow, G 326, Mgshe Wu, Det. of Mathematcs, Statstcs, ad Comuter Scece, Uversty of Wscos-Stout, Meomoe, WI
More informationSpecial Instructions / Useful Data
JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth
More informationLikewise, properties of the optimal policy for equipment replacement & maintenance problems can be used to reduce the computation.
Whe solvg a vetory repleshmet problem usg a MDP model, kowg that the optmal polcy s of the form (s,s) ca reduce the computatoal burde. That s, f t s optmal to replesh the vetory whe the vetory level s,
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationKLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames
KLT Tracker Tracker. Detect Harrs corers the frst frame 2. For each Harrs corer compute moto betwee cosecutve frames (Algmet). 3. Lk moto vectors successve frames to get a track 4. Itroduce ew Harrs pots
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationMulti Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.
It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A
More information9.1 Introduction to the probit and logit models
EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos
More information2SLS Estimates ECON In this case, begin with the assumption that E[ i
SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll
More informationThe Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)
We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More informationEntropy ISSN by MDPI
Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,
More informationFeature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)
CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.
More information4. Standard Regression Model and Spatial Dependence Tests
4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.
More informationConvergence of the Desroziers scheme and its relation to the lag innovation diagnostic
Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato
More informationTwo Fuzzy Probability Measures
Two Fuzzy robablty Measures Zdeěk Karíšek Isttute of Mathematcs Faculty of Mechacal Egeerg Bro Uversty of Techology Techcká 2 66 69 Bro Czech Reublc e-mal: karsek@umfmevutbrcz Karel Slavíček System dmstrato
More informationRandom Variate Generation ENM 307 SIMULATION. Anadolu Üniversitesi, Endüstri Mühendisliği Bölümü. Yrd. Doç. Dr. Gürkan ÖZTÜRK.
adom Varate Geerato ENM 307 SIMULATION Aadolu Üverstes, Edüstr Mühedslğ Bölümü Yrd. Doç. Dr. Gürka ÖZTÜK 0 adom Varate Geerato adom varate geerato s about procedures for samplg from a varety of wdely-used
More informationRandom Variables. ECE 313 Probability with Engineering Applications Lecture 8 Professor Ravi K. Iyer University of Illinois
Radom Varables ECE 313 Probablty wth Egeerg Alcatos Lecture 8 Professor Rav K. Iyer Uversty of Illos Iyer - Lecture 8 ECE 313 Fall 013 Today s Tocs Revew o Radom Varables Cumulatve Dstrbuto Fucto (CDF
More informationParameter Estimation
arameter Estmato robabltes Notatoal Coveto Mass dscrete fucto: catal letters Desty cotuous fucto: small letters Vector vs. scalar Scalar: la Vector: bold D: small Hgher dmeso: catal Notes a cotuous state
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationTHE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA
THE ROYAL STATISTICAL SOCIETY 3 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER I STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad
More informationEntropy, Relative Entropy and Mutual Information
Etro Relatve Etro ad Mutual Iformato rof. Ja-Lg Wu Deartmet of Comuter Scece ad Iformato Egeerg Natoal Tawa Uverst Defto: The Etro of a dscrete radom varable s defed b : base : 0 0 0 as bts 0 : addg terms
More informationECON 5360 Class Notes GMM
ECON 560 Class Notes GMM Geeralzed Method of Momets (GMM) I beg by outlg the classcal method of momets techque (Fsher, 95) ad the proceed to geeralzed method of momets (Hase, 98).. radtoal Method of Momets
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationTHE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA
THE ROYAL STATISTICAL SOCIETY EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER II STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationCS 2750 Machine Learning Lecture 5. Density estimation. Density estimation
CS 750 Mache Learg Lecture 5 esty estmato Mlos Hausrecht mlos@tt.edu 539 Seott Square esty estmato esty estmato: s a usuervsed learg roblem Goal: Lear a model that rereset the relatos amog attrbutes the
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More informationOutline. Point Pattern Analysis Part I. Revisit IRP/CSR
Pot Patter Aalyss Part I Outle Revst IRP/CSR, frst- ad secod order effects What s pot patter aalyss (PPA)? Desty-based pot patter measures Dstace-based pot patter measures Revst IRP/CSR Equal probablty:
More informationarxiv: v1 [cs.lg] 22 Feb 2015
SDCA wthout Dualty Sha Shalev-Shwartz arxv:50.0677v cs.lg Feb 05 Abstract Stochastc Dual Coordate Ascet s a popular method for solvg regularzed loss mmzato for the case of covex losses. I ths paper we
More informationSTA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1
STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationG S Power Flow Solution
G S Power Flow Soluto P Q I y y * 0 1, Y y Y 0 y Y Y 1, P Q ( k) ( k) * ( k 1) 1, Y Y PQ buses * 1 P Q Y ( k1) *( k) ( k) Q Im[ Y ] 1 P buses & Slack bus ( k 1) *( k) ( k) Y 1 P Re[ ] Slack bus 17 Calculato
More informationUnit 9. The Tangent Bundle
Ut 9. The Taget Budle ========================================================================================== ---------- The taget sace of a submafold of R, detfcato of taget vectors wth dervatos at
More informationPerformance of Energy Efficient Relaying for Cluster-Based Wireless Sensor Networks
Commucatos of the IIMA Volume 7 Issue 3 Artcle 8 007 Performace of Eerg Effcet Relag for Cluster-Based Wreless Sesor Networs Yug-Fa Huag Graduate Isttute of Networg ad Commucato Egeerg Chaoag Uverst of
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationSource-Channel Prediction in Error Resilient Video Coding
Source-Chael Predcto Error Reslet Vdeo Codg Hua Yag ad Keeth Rose Sgal Compresso Laboratory ECE Departmet Uversty of Calfora, Sata Barbara Outle Itroducto Source-chael predcto Smulato results Coclusos
More informationManipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department
Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:
More informationChapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance
Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss
More informationPPCP: The Proofs. 1 Notations and Assumptions. Maxim Likhachev Computer and Information Science University of Pennsylvania Philadelphia, PA 19104
PPCP: The Proofs Maxm Lkhachev Computer ad Iformato Scece Uversty of Pesylvaa Phladelpha, PA 19104 maxml@seas.upe.edu Athoy Stetz The Robotcs Isttute Carege Mello Uversty Pttsburgh, PA 15213 axs@rec.r.cmu.edu
More informationLecture Note to Rice Chapter 8
ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,
More informationConsensus Control for a Class of High Order System via Sliding Mode Control
Cosesus Cotrol for a Class of Hgh Order System va Sldg Mode Cotrol Chagb L, Y He, ad Aguo Wu School of Electrcal ad Automato Egeerg, Taj Uversty, Taj, Cha, 300072 Abstract. I ths paper, cosesus problem
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationSimulation Output Analysis
Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5
More informationECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013
ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport
More informationResearch on Efficient Turbo Frequency Domain Equalization in STBC-MIMO System
Research o Effcet urbo Freuecy Doma Eualzato SBC-MIMO System Wau uag Bejg echology ad Busess Uversty Bejg 00048.R. Cha Abstract. A effcet urbo Freuecy Doma Eualzato FDE based o symbol-wse mmum mea-suare
More informationFor combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.
Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the
More informationUNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS
Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method
More informationAnalysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed
Amerca Joural of Mathematcs ad Statstcs. ; (: -8 DOI:.593/j.ajms.. Aalyss of a Reparable (--out-of-: G System wth Falure ad Repar Tmes Arbtrarly Dstrbuted M. Gherda, M. Boushaba, Departmet of Mathematcs,
More informationTHE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5
THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should
More informationHomework #2 Solutions, EE/MSE 486, Spring 2017 Problem 1:
Homework # Solutos, EE/MSE 486, Sprg 017 Problem 1: P o p N N A ( N N A) Here / for type dopg; 4 p p N A N ( N A N) / for p type dog. 4 At 1000C, 3.1*10 16 3/ From the table the otes, we have T 0.603eV
More informationA class of Liu-type estimators based on ridge regression under multicollinearity with an application to mixture experiments
A class of Lu-te estmators based o rdge regresso uder multcolleart wth a alcato to mture eermets Preseter: A-Chu Che 陳愛群 Advsor: aesh Emura Jue 6, 5 Graduate Isttute of Statstcs, NCU Outle Itroducto Methodolog
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK
Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION
More information2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006
.6 System Idetfcato, Estmato, ad Learg Lectre Notes No. 7 Aprl 4, 6. Iformatve Expermets. Persstece of Exctato Iformatve data sets are closely related to Persstece of Exctato, a mportat cocept sed adaptve
More informationMedian as a Weighted Arithmetic Mean of All Sample Observations
Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of
More informationChapter 9 Jordan Block Matrices
Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.
More informationSingular Value Decomposition. Linear Algebra (3) Singular Value Decomposition. SVD and Eigenvectors. Solving LEs with SVD
Sgular Value Decomosto Lear Algera (3) m Cootes Ay m x matrx wth m ca e decomosed as follows Dagoal matrx A UWV m x x Orthogoal colums U U I w1 0 0 w W M M 0 0 x Orthoormal (Pure rotato) VV V V L 0 L 0
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationRegression and the LMS Algorithm
CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a
More informationρ < 1 be five real numbers. The
Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationMechanics of Materials CIVL 3322 / MECH 3322
Mechacs of Materals CVL / MECH Cetrods ad Momet of erta Calculatos Cetrods = A = = = A = = Cetrod ad Momet of erta Calculatos z= z A = = Parallel As Theorem f ou kow the momet of erta about a cetrodal
More informationε. Therefore, the estimate
Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model
More informationBayesian Classification. CS690L Data Mining: Classification(2) Bayesian Theorem: Basics. Bayesian Theorem. Training dataset. Naïve Bayes Classifier
Baa Classfcato CS6L Data Mg: Classfcato() Referece: J. Ha ad M. Kamber, Data Mg: Cocepts ad Techques robablstc learg: Calculate explct probabltes for hypothess, amog the most practcal approaches to certa
More informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More informationNonparametric Density Estimation Intro
Noarametrc Desty Estmato Itro Parze Wdows No-Parametrc Methods Nether robablty dstrbuto or dscrmat fucto s kow Haes qute ofte All we have s labeled data a lot s kow easer salmo bass salmo salmo Estmate
More informationFactorization of Finite Abelian Groups
Iteratoal Joural of Algebra, Vol 6, 0, o 3, 0-07 Factorzato of Fte Abela Grous Khald Am Uversty of Bahra Deartmet of Mathematcs PO Box 3038 Sakhr, Bahra kamee@uobedubh Abstract If G s a fte abela grou
More informationLecture 2 - What are component and system reliability and how it can be improved?
Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected
More informationChapter 3 Sampling For Proportions and Percentages
Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys
More informationChapter 4: Linear Momentum and Collisions
Chater 4: Lear oetu ad Collsos 4.. The Ceter o ass, Newto s Secod Law or a Syste o artcles 4.. Lear oetu ad Its Coserato 4.3. Collso ad Iulse 4.4. oetu ad Ketc Eergy Collsos 4.. The Ceter o ass. Newto
More informationA Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter
A Robust otal east Mea Square Algorthm For Nolear Adaptve Flter Ruxua We School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049, P.R. Cha rxwe@chare.com Chogzhao Ha, azhe u School of Electroc
More informationTraining Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ
Stat 74 Estmato for Geeral Lear Model Prof. Goel Broad Outle Geeral Lear Model (GLM): Trag Samle Model: Gve observatos, [[( Y, x ), x = ( x,, xr )], =,,, the samle model ca be exressed as Y = µ ( x, x,,
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationMidterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..
More informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More information