Model-Based FDI : the control approach

Size: px
Start display at page:

Download "Model-Based FDI : the control approach"

Transcription

1 Model-Baed FDI : he conrol approach M. Saroweck LAIL-CNRS EUDIL, Unver Llle I Olne of he preenaon Par I : model Sem, normal and no normal condon, fal Par II : he decon problem problem eng noe, drbance, ncerane Par III : fal deecon par pace, oberver baed approache Par IV : fal olaon dreconal, rcred redal Conclon 1

2 Par I : model em : a e of nerconneced componen, fal : preven componen o perform her fncon varable and me conno varable/conno me, conno varable/dcree me, dcree varable/dcree me fal model addve, mlplcave, probabl drbon Wha a em? A em a e of nerconneced componen Each componen acheve ome fncon of nere becae eplo ome phcal prncple whch are epreed b ome relaonhp beween he me evolon of ome em varable. Relaonhp are called conran, Tme evolon of a varable called rajecor. 2

3 The ank eample Poble fncon : orage of lqd, decoplng moohng of he op flow, mng of wo dfferen lqd, ec. Whaever fncon, he ank nrodce a mahemacal conran beween ome varable, namel dl d q q o Conrolled em Acaor : allow o chooe, beween all he poble em rajecore, he one whch wll brng ome epeced rel acheve ome gven objecve. Eablh ome conran beween he proce varable and ome conrol varable, whch called ''conrol gnal''. Conrol gnal : generaed b hman operaor or b conrol algorhm. Cloed loop conrol : he acal vale of ome em varable m be known. Senor : componen whch provde h nformaon. 3

4 4 Acaor eample : np valve Analog valve On-off valve p k q α 1 q q Senor eample : level enor Analog enor conran Qanzed enor 1 2 n l [a,b[ [b,c[ [,l ma ] l

5 5 Conrolled em e of nerconneced componen whch nclde proce componen, acaor, enor and conrol algorhm Eample ank np valve op ppe level enor level conrol algorhm q q d dl o α 1 q q l k q o, Σ N l 1 > < l l l l M m

6 6 Normal and no-normal condon, fal Normal operaon : he mlaneo occrence of wo aon 1 componen perform properl her agned fncon he reall behave a he degner epec conran appled o he varable are he nomnal one IF NOT : INTERNAL FAULT 2 neracon beween he em and envronmen are compable wh he em' objecve. IF NOT : EXTERNAL FAULT Eample of nernal fal Proce fal : he ank leakng. Acaor fal : he np valve blocked open. Senor fal : noe ha mproper acal characerc q q q d dl l o α α 1 q q, 1 Σ µ N l

7 Eample of eernal fal Conrol algorhm objecve : l mn l l ma canno be acheved for oo large op flow 2 1 q d o > l α l In he preence of a leak n he ank nernal fal, he em objecve ma ll be acheved provded he above neqal doe no hold for he overall op flow mn Dfferen knd of model em' fncon, em' archecre he componen and her nerconneon, em' behavor he e of he conran whch lnk he em varable. Degn of MB FDI algorhm behavor model 3 bac feare : varable, me and conran. 7

8 Bac modellng feare Varable whoe me evolon of nere for FDI Bond-graph : power and energ varable conrolled em : conrol and nformaon gnal varable o be condered : all qane conraned b he em componen proce, acaor, enor, algorhm Se of vale varale can be agned qanave varable qalave varable ordered or no abrp ranon avoded ng fzz e. 8

9 Tme conno me dcree me ampled daa em drven b a clock even drven em Conran Markov proper Whole pa hor mmarzed b he em' ae Sae evolon onl depend on crren ae and np Conno me, conno varable algebrac and dfferenal eqaon Comper-baed mearemen, conrol and pervon dcree me, algebrac and dfference eqaon Qalave varable logc eqaon, aomaa, Per ne, e of rle. 9

10 1 Normal operaon Conno varable/dcree me Phcal law obeed b he healh proce componen ae pace, mearemen, operang range,,,,,, 1 h P g v f Eample : Lnear em h H H C v E B A 1

11 11 Fal operaon Mlplcave fal :,,,,, 1 g v f,,,,,,, 1 g v f ϕ ϕ From mlplcave o addve fal 1 C v E B A 1 C C v E E B B A A

12 12 Addve fal Acaor fal : F B, F Senor fal : F, F C 1 F C F Ev B A ϕ Change n acal drbon 1 P P

13 Qalave varable / dcree me Eample : eqenal lnear em gae arra, nchroneo clock {,1}, wo operaor and, conan amplng rae Normal operaon Fal model 1 A B C 1 [ A B ] e [ C ] e j Dfferen fal H : Normal operaon em behavor obe normal ae and mearemen eqaon em behavor obe fal ae and mearemen eqaon wh ϕ. H :Faloccr ϕ φ eample : fal on enor : g ϕ ϕ ke 13

14 Par II : The decon problem Two problem eng Obervaon : Z col [U, Y ] U col,-1, - Y col,-1, - Hpohee checkng H he daa have been prodced b he healh em H 1 he daa canno have been prodced b he healh em Change pon deecon H all he daa have been prodced b he healh em H 1 he daa have been prodced b he healh em nll ome me nan; he canno have been prodced b he healh em afer ha me 14

15 No noe, no nceran, no drbance 1 f,, ϕ g, ϕ fal deecon fndanneger and a e E ch ha Z E for whch he healh em prodce he obervaon Z fal olaon fndanneger and a e E.. Z E, ϕ φ for whch he fal em prodce he obervaon Z No noe, no nceran 1 f,, v, ϕ g, ϕ fal deecon fndanneger and a e E ch ha Z E, v V for whch he healh em prodce he obervaon Z fal olaon fndanneger and a e E.. Z E, ϕ φ, v V for whch he fal em prodce he obervaon Z 15

16 1 No noe f,, v,, ϕ g, ϕ, fal deecon fndanneger and a e E.. Z E, v V, Θ for whch he healh em prodce he obervaon Z fal olaon fndanneger and a e E.. Z E, ϕ φ, v V, Θ for whch he fal em prodce he obervaon Z Deermnc deecabl / olabl Non deecable fal Z E healh em OR fal Non olable fal Z E j fal OR fal j 16

17 Sochac em 1 Mearemen noe g, ϕ,, Ξ,, f,, v,, ϕ P, Ξ, are bonded e : forge he drbon, do a for nknown np Sacal decon : replace characerc vecor b condonal probabl π Z Proba {Z Z /fal preen} Sochac deecabl / olabl Generalzaon An nknown em np, em parameer can be decrbed b ome probabl drbon Non-deecable fal π Z π Z Non-olable fal π Z π j Z 17

18 Par III : Fal deecon The problem Bld a paron no wo clae E and E 1 ch ha Z E decde H and Z E 1 decde H 1 Dffcl : he nknown nknown nal condon v nknown np nknown or nceran parameer mearemen noe 1 Elmnae he nknown : analc redndanc approach 2 Emae he nknown : oberver baed approach 18

19 19 Unknown ae : Analc redndanc Obervaon Tranformaon,, 1 g f,, k k U g k k k,, U G Y Ξ,, ] [ U G Y Ξ Ψ Ψ Ξ Ψ Ξ Ψ Ψ ],, [ ], [ ] [ 1 U U Y Analc redndanc relaon Decon ], [ ], [ ] [ Ξ Ξ Ψ Ψ Z U Y ω { }, ; Z Z E ω Y U E

20 2 Unknown ae : oberver Smlaon of he em behavor Dcrepance hold be zero Eample em mlaor error, 1 g f r Ce e Ae e C B A C B A Oberver General cae K ch ha when no fal 1 ] [ Ce e e KC A e C Ke B A C B A,, 1 g K f lm

21 Y E U Unknown np 1 Par pace Y Tranformaon 1 f,, v g, v, G, U, V, Ξ Ψ [ U, Ξ ] Ψ[ Y ] 1 Ψ [, U, V, Ξ ] Rob redndanc relaon ω [ Z, Ξ ] 21

22 Unknown np 2 Eence condon : ome bem whch no affeced b he nknown np Eqvalen aemen : a ranformaon z T. z1 1 f1 z1, z2 1 f2 z1, z2,, v 1 g1 z1, 2 g2 z1, z2, v, An oberver can be degned from bem 1. Unknown np 3 Y E U 22

23 Unknown np 4 When no decoplng poble? No rob oberver, no rob redndanc relaon Se heorec approach 1 { Z ; V V ; ω Z, V, } E How o deermne Lnear eample E PY E E QU RV 1 RV Unknown np 5 Y E E E U 23

24 Uncerane Same a nknown np Par IV : Fal olaon 24

25 Problem Se whch are characerc of each fal whch mgh occr Y E E 2 E 1 U Dreconal and rcred redal Dreconal redal characerc e pace of dmenon one Srcred redal characerc e pace of gven dmenon Fal 3 Fal Fal

26 Compaon / evalaon form 1 f,, ϕ g, ϕ ω[ Z, Φ ] Sppoe can be wren ω ω where [ Z, Φ ] c[ Z,] ωe[ Z, Φ ] ω e [ Z,] Then r ωc[ Z,] ωe[ Z, Φ ] r when no fal occr { Z ; r [ Z,] } E ω c Srcred redal Le Φ col ϕ, ϕ 1,... ϕ when fal preen Sppoe here e a be of componen of ω e 1 ω e [ Z, Φ ], Φ φ ω e Then, nder fal, he redal vecor belong o a gven bpace Ω. Srcred redal : he bpace Ω are dfferen. Degn of rcred redal ng elmnaon n redndanc relaon or pecal oberver degn 26

27 Conclon On model Componen relaed model Hgher abracon level ranformaon n he ae pace Noe, drbance, ncerane Poble knowledge abo he fal Toward hbrd model 27

28 On decon Real me decon of nere Geomerc / acal opmzaon fale alarm, med deecon rae deecon dela Rob decon procedre Logcall ond decon procedre ee IMALAIA On he FDI cone Real me eploaon cone FDI for Fal Toleran Conrol fal accomodaon conrol reconfgraon goal reconfgraon conrol, degraded conrol, change of operang mode, manenance FDI for manenance Sem degn for FDI / FTC enor, acaor elecon 28

Real-Time Trajectory Generation and Tracking for Cooperative Control Systems

Real-Time Trajectory Generation and Tracking for Cooperative Control Systems Real-Tme Trajecor Generaon and Trackng for Cooperave Conrol Ssems Rchard Mrra Jason Hcke Calforna Inse of Technolog MURI Kckoff Meeng 14 Ma 2001 Olne I. Revew of prevos work n rajecor generaon and rackng

More information

CONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS

CONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS APPENDX J CONSSTENT EARTHQUAKE ACCEERATON AND DSPACEMENT RECORDS Earhqake Acceleraons can be Measred. However, Srcres are Sbjeced o Earhqake Dsplacemens J. NTRODUCTON { XE "Acceleraon Records" }A he presen

More information

by Lauren DeDieu Advisor: George Chen

by Lauren DeDieu Advisor: George Chen b Laren DeDe Advsor: George Chen Are one of he mos powerfl mehods o nmercall solve me dependen paral dfferenal eqaons PDE wh some knd of snglar shock waves & blow-p problems. Fed nmber of mesh pons Moves

More information

Stochastic Programming handling CVAR in objective and constraint

Stochastic Programming handling CVAR in objective and constraint Sochasc Programmng handlng CVAR n obecve and consran Leondas Sakalaskas VU Inse of Mahemacs and Informacs Lhana ICSP XIII Jly 8-2 23 Bergamo Ialy Olne Inrodcon Lagrangan & KKT condons Mone-Carlo samplng

More information

ANALYSIS AND MODELING OF HYDROLOGIC TIME SERIES. Wasserhaushalt Time Series Analysis and Stochastic Modelling Spring Semester

ANALYSIS AND MODELING OF HYDROLOGIC TIME SERIES. Wasserhaushalt Time Series Analysis and Stochastic Modelling Spring Semester ANALYSIS AND MODELING OF HYDROLOGIC TIME SERIES Waerhauhal Tme Sere Analy and Sochac Modellng Sprng Semeer 8 ANALYSIS AND MODELING OF HYDROLOGIC TIME SERIES Defnon Wha a me ere? Leraure: Sala, J.D. 99,

More information

Control Systems. Mathematical Modeling of Control Systems.

Control Systems. Mathematical Modeling of Control Systems. Conrol Syem Mahemacal Modelng of Conrol Syem chbum@eoulech.ac.kr Oulne Mahemacal model and model ype. Tranfer funcon model Syem pole and zero Chbum Lee -Seoulech Conrol Syem Mahemacal Model Model are key

More information

Observer Design for Nonlinear Systems using Linear Approximations

Observer Design for Nonlinear Systems using Linear Approximations Observer Desgn for Nonlnear Ssems sng Lnear Appromaons C. Navarro Hernandez, S.P. Banks and M. Aldeen Deparmen of Aomac Conrol and Ssems Engneerng, Unvers of Sheffeld, Mappn Sree, Sheffeld S 3JD. e-mal:

More information

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol

More information

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More information

A Demand System for Input Factors when there are Technological Changes in Production

A Demand System for Input Factors when there are Technological Changes in Production A Demand Syem for Inpu Facor when here are Technologcal Change n Producon Movaon Due o (e.g.) echnologcal change here mgh no be a aonary relaonhp for he co hare of each npu facor. When emang demand yem

More information

H = d d q 1 d d q N d d p 1 d d p N exp

H = d d q 1 d d q N d d p 1 d d p N exp 8333: Sacal Mechanc I roblem Se # 7 Soluon Fall 3 Canoncal Enemble Non-harmonc Ga: The Hamlonan for a ga of N non neracng parcle n a d dmenonal box ha he form H A p a The paron funcon gven by ZN T d d

More information

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables Opmal Conrol Why Use I - verss calcls of varaons, opmal conrol More generaly More convenen wh consrans (e.g., can p consrans on he dervaves More nsghs no problem (a leas more apparen han hrogh calcls of

More information

Let s treat the problem of the response of a system to an applied external force. Again,

Let s treat the problem of the response of a system to an applied external force. Again, Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem

More information

Fundamentals of PLLs (I)

Fundamentals of PLLs (I) Phae-Locked Loop Fundamenal of PLL (I) Chng-Yuan Yang Naonal Chung-Hng Unvery Deparmen of Elecrcal Engneerng Why phae-lock? - Jer Supreon - Frequency Synhe T T + 1 - Skew Reducon T + 2 T + 3 PLL fou =

More information

A. Inventory model. Why are we interested in it? What do we really study in such cases.

A. Inventory model. Why are we interested in it? What do we really study in such cases. Some general yem model.. Inenory model. Why are we nereed n? Wha do we really udy n uch cae. General raegy of machng wo dmlar procee, ay, machng a fa proce wh a low one. We need an nenory or a buffer or

More information

Research Article Cubic B-spline for the Numerical Solution of Parabolic Integro-differential Equation with a Weakly Singular Kernel

Research Article Cubic B-spline for the Numerical Solution of Parabolic Integro-differential Equation with a Weakly Singular Kernel Researc Jornal of Appled Scences, Engneerng and Tecnology 7(): 65-7, 4 DOI:.96/afs.7.5 ISS: 4-7459; e-iss: 4-7467 4 Mawell Scenfc Pblcaon Corp. Sbmed: Jne 8, Acceped: Jly 9, Pblsed: Marc 5, 4 Researc Arcle

More information

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal

More information

Cooling of a hot metal forging. , dt dt

Cooling of a hot metal forging. , dt dt Tranen Conducon Uneady Analy - Lumped Thermal Capacy Model Performed when; Hea ranfer whn a yem produced a unform emperaure drbuon n he yem (mall emperaure graden). The emperaure change whn he yem condered

More information

HOMOGENIZATION METHOD TO PREDICT THREE-DIMENSIONAL PERMEABILITIES CONSIDERING MICRO-MACRO AND SOLID-FLUID INTERACTIONS

HOMOGENIZATION METHOD TO PREDICT THREE-DIMENSIONAL PERMEABILITIES CONSIDERING MICRO-MACRO AND SOLID-FLUID INTERACTIONS HOMOGNIZATION MTHOD TO PRDICT THR-DIMNSIONAL PRMABILITIS CONSIDRING MICRO-MACRO AND SOLID-FLUID INTRACTIONS Nao Taano Maar Zao Tomom ohoa 2 and Kenro Terada 3 Deparmen o Manacrng Scence Oaa Unver 2- amada-oa

More information

Is it necessary to seasonally adjust business and consumer surveys. Emmanuelle Guidetti

Is it necessary to seasonally adjust business and consumer surveys. Emmanuelle Guidetti Is necessar o seasonall adjs bsness and consmer srves Emmanelle Gde Olne 1 BTS feares 2 Smlaon eercse 3 Seasonal ARIMA modellng 4 Conclsons Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01

More information

Gradient Flow Independent Component Analysis

Gradient Flow Independent Component Analysis Graden Fow Independen Componen Anay Mun Sanaćevć and Ger Cauwenbergh Adapve Mcroyem ab John Hopkn Unvery Oune Bnd Sgna Separaon and ocazaon Prncpe of Graden Fow : from deay o empora dervave Equvaen ac

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

Chapter 6: AC Circuits

Chapter 6: AC Circuits Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

Chapter 5 Signal-Space Analysis

Chapter 5 Signal-Space Analysis Chaper 5 Sgnal-Space Analy Sgnal pace analy provde a mahemacally elegan and hghly nghful ool for he udy of daa ranmon. 5. Inroducon o Sacal model for a genec dgal communcaon yem n eage ource: A pror probable

More information

Variational Formulation of the Log-Aesthetic Surface and Development of Discrete Surface Filters

Variational Formulation of the Log-Aesthetic Surface and Development of Discrete Surface Filters Varaonal Formlaon of he Log-Aehec Srface and Deelopmen of Dcree Srface Fler Kenjro T. Mra, Ryo Shrahaa, Shn ch Agar 3, Shn U 4 and R.U. Gobhaaan 5 Shzoa Unery, mmr@pc.hzoa.ac.jp Shzoa Unery, f938@pc.hzoa.ac.jp

More information

Hierarchical Sliding Mode Control for Series Double Inverted Pendulums System

Hierarchical Sliding Mode Control for Series Double Inverted Pendulums System Herarchcal Sldng Mode Conrol for Seres Doble Invered Pendlms Sysem Danwe Qan, Janqang Y, Dongbn Zhao, and Ynxng Hao Laboraory of Complex Sysems and Inellgence Scence Inse of Aomaon, Chnese Academy of Scences

More information

A Feedback Problem For Heating Process

A Feedback Problem For Heating Process nernaona Jorna of Reearc n Engneerng and Scence JRES SS Onne: 3-9364 SS Prn: 3-9356 Vome 6 e 8 Ver ǁ 8 ǁ PP 6- A Feedack Proem For Heang Proce Vagf M Adayev Azerajan Sae O and ndry Unvery ak Azerajan ne

More information

Convection and conduction and lumped models

Convection and conduction and lumped models MIT Hea ranfer Dynamc mdel 4.3./SG nvecn and cndcn and lmped mdel. Hea cnvecn If we have a rface wh he emperare and a rrndng fld wh he emperare a where a hgher han we have a hea flw a Φ h [W] () where

More information

Solution of a diffusion problem in a non-homogeneous flow and diffusion field by the integral representation method (IRM)

Solution of a diffusion problem in a non-homogeneous flow and diffusion field by the integral representation method (IRM) Appled and ompaonal Mahemacs 4; 3: 5-6 Pblshed onlne Febrary 4 hp://www.scencepblshnggrop.com//acm do:.648/.acm.43.3 olon of a dffson problem n a non-homogeneos flow and dffson feld by he negral represenaon

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Methods of Study of Power Converters

Methods of Study of Power Converters N N Mehod of udy of Power onerer for a yemac analy by arlo. Marn N - / wh he uppor of - ouloue, rance -8 May 4 N cceleraor chool - Warrngon, UK N opc of he preenaon Par Par he phae plane repreenaon ere-parallel

More information

Calculation of the Resistance of a Ship Mathematical Formulation. Calculation of the Resistance of a Ship Mathematical Formulation

Calculation of the Resistance of a Ship Mathematical Formulation. Calculation of the Resistance of a Ship Mathematical Formulation Ressance s obaned from he sm of he frcon and pressre ressance arables o deermne: - eloc ecor, (3) = (,, ) = (,, ) - Pressre, p () ( - Dens, ρ, s defned b he eqaon of sae Ressance and Proplson Lecre 0 4

More information

Reconstruction of Variational Iterative Method for Solving Fifth Order Caudrey-Dodd-Gibbon (CDG) Equation

Reconstruction of Variational Iterative Method for Solving Fifth Order Caudrey-Dodd-Gibbon (CDG) Equation Shraz Unvery of Technology From he SelecedWor of Habbolla Lafzadeh Reconrcon of Varaonal Ierave Mehod for Solvng Ffh Order Cadrey-Dodd-Gbbon (CDG Eqaon Habbolla Lafzadeh, Shraz Unvery of Technology Avalable

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

Multiple Failures. Diverse Routing for Maximizing Survivability. Maximum Survivability Models. Minimum-Color (SRLG) Diverse Routing

Multiple Failures. Diverse Routing for Maximizing Survivability. Maximum Survivability Models. Minimum-Color (SRLG) Diverse Routing Mulple Falure Dvere Roung for Maxmzng Survvably One-falure aumpon n prevou work Mulple falure Hard o provde 100% proecon Maxmum urvvably Maxmum Survvably Model Mnmum-Color (SRLG) Dvere Roung Each lnk ha

More information

Unknown Input High Gain Observer for Fault Detection and Isolation of Uncertain Systems

Unknown Input High Gain Observer for Fault Detection and Isolation of Uncertain Systems Engneerng Leer, 7:, EL_7 08 Unknown Inp Hgh Gan Observer or Fal Deecon and Isolaon o Unceran Sysems Sharddn Mondal*, G Chakrabory and K Bhaacharyya Absrac An nknown np hgh gan observer (UIHGO) based componen

More information

Lecture VI Regression

Lecture VI Regression Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M

More information

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs

More information

Jurnal Teknologi. A Test for Normality in the Presence of Outliers. Full paper. t t u. Pooi Ah Hin a*, Soo Huei Ching a

Jurnal Teknologi. A Test for Normality in the Presence of Outliers. Full paper. t t u. Pooi Ah Hin a*, Soo Huei Ching a Jrnal Tenolog Fll paper A Te for oraly n he Preence of Oler Poo Ah Hn a Soo He Chng a a Snway Unvery Bne School o 5 Jalan Unver Bandar Snway 650 Pealng Jaya Selangor Malaya Correpondng ahor: ahhnp@nway.ed.y

More information

Laplace Transformation of Linear Time-Varying Systems

Laplace Transformation of Linear Time-Varying Systems Laplace Tranformaon of Lnear Tme-Varyng Syem Shervn Erfan Reearch Cenre for Inegraed Mcroelecronc Elecrcal and Compuer Engneerng Deparmen Unvery of Wndor Wndor, Onaro N9B 3P4, Canada Aug. 4, 9 Oulne of

More information

Sound Transmission Throough Lined, Composite Panel Structures: Transversely Isotropic Poro- Elastic Model

Sound Transmission Throough Lined, Composite Panel Structures: Transversely Isotropic Poro- Elastic Model Prde nvery Prde e-pb Pblcaon of he Ray. Herrc aboraore School of Mechancal Engneerng 8-5 Sond Tranmon Throogh ned, Comoe Panel Srcre: Tranverely Ioroc Poro- Elac Model J Sar Bolon Prde nvery, bolon@rde.ed

More information

u(t) Figure 1. Open loop control system

u(t) Figure 1. Open loop control system Open loop conrol v cloed loop feedbac conrol The nex wo figure preen he rucure of open loop and feedbac conrol yem Figure how an open loop conrol yem whoe funcion i o caue he oupu y o follow he reference

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information

Robustness Experiments with Two Variance Components

Robustness Experiments with Two Variance Components Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference

More information

Simulation of Contaminant Concentrations in Drinking-Water Distribution Systems

Simulation of Contaminant Concentrations in Drinking-Water Distribution Systems Maer Degree n hemcal Engneerng Smlaon of onamnan oncenraon n Drnkng-Waer Drbon Syem Maer The Developed n he amb of he bjec Developmen Projec Dogo Morera da oa Deparmen of hemcal Engneerng Spervor: Prof.

More information

Different kind of oscillation

Different kind of oscillation PhO 98 Theorecal Qeson.Elecrcy Problem (8 pons) Deren knd o oscllaon e s consder he elecrc crc n he gre, or whch mh, mh, nf, nf and kω. The swch K beng closed he crc s copled wh a sorce o alernang crren.

More information

A Principled Approach to MILP Modeling

A Principled Approach to MILP Modeling A Prncpled Approach o MILP Modelng John Hooer Carnege Mellon Unvers Augus 008 Slde Proposal MILP modelng s an ar, bu need no be unprncpled. Slde Proposal MILP modelng s an ar, bu need no be unprncpled.

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

OPTIMIZATION OF A NONCONVENTIONAL ENGINE EVAPORATOR

OPTIMIZATION OF A NONCONVENTIONAL ENGINE EVAPORATOR Jornal of KONES Powerran and Transpor, Vol. 17, No. 010 OPTIMIZATION OF A NONCONVENTIONAL ENGINE EVAPORATOR Andre Kovalí, Eml Toporcer Unversy of Žlna, Facly of Mechancal Engneerng Deparmen of Aomove Technology

More information

On the Boyd- Kuramoto Model : Emergence in a Mathematical Model for Adversarial C2 Systems

On the Boyd- Kuramoto Model : Emergence in a Mathematical Model for Adversarial C2 Systems On he oyd- Kuramoo Model : Emergence n a Mahemacal Model for Adversaral C2 Sysems Alexander Kallonas DSTO, Jon Operaons Dvson C2 Processes: many are cycles! oyd s Observe-Oren-Decde-Ac Loop: Snowden s

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

1.B Appendix to Chapter 1

1.B Appendix to Chapter 1 Secon.B.B Append o Chper.B. The Ordnr Clcl Here re led ome mporn concep rom he ordnr clcl. The Dervve Conder ncon o one ndependen vrble. The dervve o dened b d d lm lm.b. where he ncremen n de o n ncremen

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

A Random Walk through Seasonal Adjustment: Noninvertible Moving Averages and Unit Root Tests

A Random Walk through Seasonal Adjustment: Noninvertible Moving Averages and Unit Root Tests CONFERENCE ON SEASONAIY, SEASONA ADJUSMEN AND HEIR IMPICAIONS FOR SHOR-ERM ANAYSIS AND FORECASING 0- MAY 006 A Random al hrogh Seaonal Admen: Nonnverble Movng Average and Un Roo e oma del Barro Caro Dene

More information

( )a = "t = 1 E =" B E = 5016 V. E = BHv # 3. 2 %r. c.) direction of induced current in the loop for : i.) "t < 1

( )a = t = 1 E = B E = 5016 V. E = BHv # 3. 2 %r. c.) direction of induced current in the loop for : i.) t < 1 99 3 c dr b a µ r.? d b µ d d cdr a r & b d & µ c µ c b dr µ c µ c b & ' ln' a +*+* b ln r ln a a r a ' µ c b 'b* µ c ln' * & ln, &a a+ ncreang no he page o nduced curren wll creae a - feldou of he page

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

SSRG International Journal of Thermal Engineering (SSRG-IJTE) Volume 4 Issue 1 January to April 2018

SSRG International Journal of Thermal Engineering (SSRG-IJTE) Volume 4 Issue 1 January to April 2018 SSRG Inernaonal Journal of Thermal Engneerng (SSRG-IJTE) Volume 4 Iue 1 January o Aprl 18 Opmal Conrol for a Drbued Parameer Syem wh Tme-Delay, Non-Lnear Ung he Numercal Mehod. Applcaon o One- Sded Hea

More information

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all

More information

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max ecure 8 7. Sabiliy Analyi For an n dimenional vecor R n, he and he vecor norm are defined a: = T = i n i (7.) I i eay o how ha hee wo norm aify he following relaion: n (7.) If a vecor i ime-dependen, hen

More information

Higher-order Graph Cuts

Higher-order Graph Cuts Example: Segmenaon Hgher-orer Graph Hroh Ihkawa 石川博 Deparmen of omper Scence & Engneerng Waea Unery 早稲田大学 Boyko&Jolly IV 3 Example: Segmenaon Local moel ex.: Moel of pxel ale for each kn of e Pror moel

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL Sco Wsdom, John Hershey 2, Jonahan Le Roux 2, and Shnj Waanabe 2 Deparmen o Elecrcal Engneerng, Unversy o Washngon, Seale, WA, USA

More information

Scattering at an Interface: Oblique Incidence

Scattering at an Interface: Oblique Incidence Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may

More information

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue

More information

The Distribution of Multiple Shot Noise Process and Its Integral

The Distribution of Multiple Shot Noise Process and Its Integral Appled Mahemac 4 5 478-489 Pblhed Onlne Febrary 4 (hp://www.crp.org/jornal/am hp://dx.do.org/.46/am.4.547 The Drbon of Mlple Sho Noe Proce and I Inegral Jwook Jang Deparmen of Appled Fnance & Acaral Sde

More information

2/20/2013. EE 101 Midterm 2 Review

2/20/2013. EE 101 Midterm 2 Review //3 EE Mderm eew //3 Volage-mplfer Model The npu ressance s he equalen ressance see when lookng no he npu ermnals of he amplfer. o s he oupu ressance. I causes he oupu olage o decrease as he load ressance

More information

Multi-Fuel and Mixed-Mode IC Engine Combustion Simulation with a Detailed Chemistry Based Progress Variable Library Approach

Multi-Fuel and Mixed-Mode IC Engine Combustion Simulation with a Detailed Chemistry Based Progress Variable Library Approach Mul-Fuel and Med-Mode IC Engne Combuson Smulaon wh a Dealed Chemsry Based Progress Varable Lbrary Approach Conens Inroducon Approach Resuls Conclusons 2 Inroducon New Combuson Model- PVM-MF New Legslaons

More information

PHYS 1443 Section 001 Lecture #4

PHYS 1443 Section 001 Lecture #4 PHYS 1443 Secon 001 Lecure #4 Monda, June 5, 006 Moon n Two Dmensons Moon under consan acceleraon Projecle Moon Mamum ranges and heghs Reerence Frames and relae moon Newon s Laws o Moon Force Newon s Law

More information

Lecture 11 SVM cont

Lecture 11 SVM cont Lecure SVM con. 0 008 Wha we have done so far We have esalshed ha we wan o fnd a lnear decson oundary whose margn s he larges We know how o measure he margn of a lnear decson oundary Tha s: he mnmum geomerc

More information

Response of MDOF systems

Response of MDOF systems Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss

More information

CS626 Speech, Web and natural Language Processing End Sem

CS626 Speech, Web and natural Language Processing End Sem CS626 Speech, Web and naural Language Proceng End Sem Dae: 14/11/14 Tme: 9.30AM-12.30PM (no book, lecure noe allowed, bu ONLY wo page of any nformaon you deem f; clary and precon are very mporan; read

More information

L N O Q. l q l q. I. A General Case. l q RANDOM LAGRANGE MULTIPLIERS AND TRANSVERSALITY. Econ. 511b Spring 1998 C. Sims

L N O Q. l q l q. I. A General Case. l q RANDOM LAGRANGE MULTIPLIERS AND TRANSVERSALITY. Econ. 511b Spring 1998 C. Sims Econ. 511b Sprng 1998 C. Sm RAD AGRAGE UPERS AD RASVERSAY agrange mulpler mehod are andard fare n elemenary calculu coure, and hey play a cenral role n economc applcaon of calculu becaue hey ofen urn ou

More information

7.6 Disjoint Paths. 7. Network Flow Applications. Edge Disjoint Paths. Edge Disjoint Paths

7.6 Disjoint Paths. 7. Network Flow Applications. Edge Disjoint Paths. Edge Disjoint Paths . Nework Flow Applcaon. Djon Pah Algorhm Degn by Éva Tardo and Jon Klenberg Copyrgh Addon Weley Slde by Kevn Wayne Algorhm Degn by Éva Tardo and Jon Klenberg Copyrgh Addon Weley Slde by Kevn Wayne Edge

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

Li An-Ping. Beijing , P.R.China

Li An-Ping. Beijing , P.R.China A New Type of Cpher: DICING_csb L An-Png Bejng 100085, P.R.Chna apl0001@sna.com Absrac: In hs paper, we wll propose a new ype of cpher named DICING_csb, whch s derved from our prevous sream cpher DICING.

More information

( ) [ ] MAP Decision Rule

( ) [ ] MAP Decision Rule Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure

More information

Robust and Accurate Cancer Classification with Gene Expression Profiling

Robust and Accurate Cancer Classification with Gene Expression Profiling Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem

More information

TSS = SST + SSE An orthogonal partition of the total SS

TSS = SST + SSE An orthogonal partition of the total SS ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs)

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs) USOD 005 uecal Sudy of Lage-aea An-Reonan Reflecng Opcal Wavegude (ARROW Vecal-Cavy Seconduco Opcal Aplfe (VCSOA anhu Chen Su Fung Yu School of Eleccal and Eleconc Engneeng Conen Inoducon Vecal Cavy Seconduco

More information

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6) Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen

More information

Clustering (Bishop ch 9)

Clustering (Bishop ch 9) Cluserng (Bshop ch 9) Reference: Daa Mnng by Margare Dunham (a slde source) 1 Cluserng Cluserng s unsupervsed learnng, here are no class labels Wan o fnd groups of smlar nsances Ofen use a dsance measure

More information

Chapter Lagrangian Interpolation

Chapter Lagrangian Interpolation Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The

More information

Slovak University of Technology in Bratislava Institute of Information Engineering, Automation, and Mathematics PROCEEDINGS

Slovak University of Technology in Bratislava Institute of Information Engineering, Automation, and Mathematics PROCEEDINGS Slovak Unvery of echnology n Bralava Inue of Informaon Engneerng, Auomaon, and Mahemac PROCEEDINGS 17 h Inernaonal Conference on Proce Conrol 9 Hoel Baník, Šrbké Pleo, Slovaka, June 9 1, 9 ISBN 978-8-7-381-5

More information

National Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration

National Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration Naonal Exams December 205 04-BS-3 Bology 3 hours duraon NOTES: f doub exss as o he nerpreaon of any queson he canddae s urged o subm wh he answer paper a clear saemen of any assumpons made 2 Ths s a CLOSED

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

Variable Structure Control ~ Basics

Variable Structure Control ~ Basics Varable Structure Control ~ Bac Harry G. Kwatny Department of Mechancal Engneerng & Mechanc Drexel Unverty Outlne A prelmnary example VS ytem, ldng mode, reachng Bac of dcontnuou ytem Example: underea

More information

with Unmodelled Environment J.K. Tar *, I.J. Rudas *, J.F. Bitó ** Fax: , Abstract

with Unmodelled Environment J.K. Tar *, I.J. Rudas *, J.F. Bitó ** Fax: , Abstract Group Theorecal Approach n Ung Canoncal Tranformaon and Symplecc Geomery n he Conrol of Appromaely Modelled Mechancal Syem Ineracng wh Unmodelled Envronmen J.K. Tar *, I.J. Ruda *, J.F. Bó ** *Deparmen

More information

XIII International PhD Workshop OWD 2011, October Three Phase DC/DC Boost Converter With High Energy Efficiency

XIII International PhD Workshop OWD 2011, October Three Phase DC/DC Boost Converter With High Energy Efficiency X nernaonal Ph Workshop OW, Ocober Three Phase C/C Boos Converer Wh Hgh Energy Effcency Ján Perdľak, Techncal nversy of Košce Absrac Ths paper presens a novel opology of mlphase boos converer wh hgh energy

More information

CS344: Introduction to Artificial Intelligence

CS344: Introduction to Artificial Intelligence C344: Iroduco o Arfcal Iellgece Puhpa Bhaacharyya CE Dep. IIT Bombay Lecure 3 3 32 33: Forward ad bacward; Baum elch 9 h ad 2 March ad 2 d Aprl 203 Lecure 27 28 29 were o EM; dae 2 h March o 8 h March

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary

More information

Math 128b Project. Jude Yuen

Math 128b Project. Jude Yuen Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally

More information

Method of Characteristics for Pure Advection By Gilberto E. Urroz, September 2004

Method of Characteristics for Pure Advection By Gilberto E. Urroz, September 2004 Mehod of Charaerss for Pre Adveon By Glbero E Urroz Sepember 004 Noe: The followng noes are based on lass noes for he lass COMPUTATIONAL HYDAULICS as agh by Dr Forres Holly n he Sprng Semeser 985 a he

More information

Let us start with a two dimensional case. We consider a vector ( x,

Let us start with a two dimensional case. We consider a vector ( x, Roaion marices We consider now roaion marices in wo and hree dimensions. We sar wih wo dimensions since wo dimensions are easier han hree o undersand, and one dimension is a lile oo simple. However, our

More information