Investigating Singularities of the Observability Jacobian for Nonlinear Power Systems

Size: px
Start display at page:

Download "Investigating Singularities of the Observability Jacobian for Nonlinear Power Systems"

Transcription

1 Investgatng Sngulartes of the Observablty Jacoban for Nonlnear Power Systems C. J. Dafs, Member, IEEE, and C. O. Nwankpa, Member, IEEE Abstract Ths paper focuses on computng and nterpretng the sgnfcance of the observablty Jacoban sngulartes. Prevous efforts have focused on developng an observablty ndex and determnng the effects of model/measurement changes to the observablty formulaton. Usng ths ndex, the sngulartes of the Jacoban are extracted, and prelmnary results ndcate that there s a dualty between the loss of stablty (whether t be phase or voltage stablty), and the loss of observablty. Thus, provdng a sngle dual-use metrc capable of forecastng both stablty and observablty characterstcs of a system. The results are llustrated usng a sample 3-bus system. Index Terms observablty, power systems, system ndex, dfferental algebrac equatons. T I. INTRODUCTION he safe, relable and economc operaton of modern power systems rely on ncreased levels of system automaton, power electronc equpment, and system controllers capable of montorng and controllng the system through varous operatng ponts. One partcular famly of power systems where the effects of system automaton, power converters and rapd system reconfguraton are more profound are naval shpboard power systems. The nherent nature of the shpboard systems, smlar to sland topologes n terrestral systems, has magnfed the need for real-tme system control and therefore the development of dynamc state estmators and nonlnear observers. Shpboard power systems are unque n ther composton, and dffer from terrestral systems n many ways [1]: Sngle dynamc loads consumng a large porton of the generaton capacty Tme constants of the generators are closer to the electrcal tme constants than n terrestral systems System reconfguraton routnes are much faster and a large porton of the system load s senstve to power nterruptons. A small nterrupton of approxmately 1 msec. can cause subsystems to shut-down. Redundancy s ncorporated n all aspects of system desgn to ensure survvablty The power demand on a shp has evolved from klowatts the tens of megawatts, and s constantly ncreasng wth new shp desgns and concepts. One partcular concept affectng drectly the shpboard power system s the vson for an All Electrc Shp [2], whch sgnfes the move of the shpboard power system from an electromagnetc/electromechancal bass to a power electronc bass. Some of the desgn aspects nvolvng the All Electrc Shp concept are: Electrc Drve Integrated Power System Zonal Dstrbuton System Reduced Mannng The enablng technology n all of the above concepts s ntellgent automaton and control. Wth the ncrease n the automaton of system functons wthn the shpboard power system, the dependence on both executve and dstrbuted controllers to mantan the performance of the power system at acceptable levels s becomng more apparent. Tradtonal control approaches based on lnearzed models of the system generaton/dstrbuton capacty wll not be suffcent. The need of ncorporatng the nonlnear dynamcs of the shpboard power system n the control methodology s the soluton to ths problem, and one proposed soluton s the use of nonlnear observer based controllers and dynamc state estmators. Before these solutons are desgned however, the concept of nonlnear observablty s frst nvestgated. Provdng an mplementaton of the observablty formulaton for power systems modeled as Dfferental Algebrac Equatons s the startng pont for the development of nonlnear controllers, dynamc state estmators and performance ndex measures. The authors have nvestgated the problem of system observablty for nonlnear power systems modeled as DAEs [3-6], and ths work emphaszes on the possble dualty between the sngulartes of the observablty Jacoban and the known stablty characterstcs of the system. The prelmnary results ndcate that a system becomes unobservable at the maxmum load pont. Ths work was supported n part by the (NAVSEA-ILIR), under grant number and the Department of Energy under grant number CH C. J. Dafs s wth NAVSEA-Phladelpha, Phladelpha, PA USA (dafscj@nswccd.navy.ml) C. O. Nwankpa s drector of CEPE, Drexel Unversty, Phladelpha, PA 1914 USA (chka@nwankpa.ece.drexel.edu) /5/$2. 25 IEEE. 75 II. PROBLEM FORMULATION The majorty of the system models used n ths work and presented n ths secton are adopted from [4] and repeated here for the sake of contnuty. The Dfferental Algebrac Model of a power system consstng of n generators and m

2 Report Documentaton Page Form Approved OMB No Publc reportng burden for the collecton of nformaton s estmated to average 1 hour per response, ncludng the tme for revewng nstructons, searchng exstng data sources, gatherng and mantanng the data needed, and completng and revewng the collecton of nformaton. Send comments regardng ths burden estmate or any other aspect of ths collecton of nformaton, ncludng suggestons for reducng ths burden, to Washngton Headquarters Servces, Drectorate for Informaton Operatons and Reports, 1215 Jefferson Davs Hghway, Sute 124, Arlngton VA Respondents should be aware that notwthstandng any other provson of law, no person shall be subject to a penalty for falng to comply wth a collecton of nformaton f t does not dsplay a currently vald OMB control number. 1. REPORT DATE JUL REPORT TYPE 3. DATES COVERED --25 to TITLE AND SUBTITLE Investgatng Sngulartes of the Observablty Jacoban for Nonlnear Power Systems 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Surface Warfare Center -Carderock Dvson,NAVSEA-Phladelpha,Phladelpha,PA, PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 1. SPONSOR/MONITOR S ACRONYM(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for publc release; dstrbuton unlmted 11. SPONSOR/MONITOR S REPORT NUMBER(S) 13. SUPPLEMENTARY NOTES See also ADM1931. Proceedngs of the 25 IEEE Electrc Shp Technologes Symposum (ESTS 25) Held n Phladelpha, PA on July 25-27, ABSTRACT see report 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT a. REPORT unclassfed b. ABSTRACT unclassfed c. THIS PAGE unclassfed Same as Report (SAR) 18. NUMBER OF PAGES 6 19a. NAME OF RESPONSIBLE PERSON Standard Form 298 (Rev. 8-98) Prescrbed by ANSI Std Z39-18

3 statc load buses s gven as: n m x = f( x, u, N) f : 2( + 1) 2( n 1) = gxun (,, ) g: y h x N h 2( n+ m 1) 2( m) 2( n+ m 1) = (, ) : where f(.) s the set of dfferental equatons related to the dynamcs of the generaton equpment, g(.) s the set of nonlnear algebrac equatons related to the statc load buses of the system, h(.) are the measurement equatons, u are the system nputs, N descrbes the characterstcs of the ndvdual power lnes n the system, x s the system vector contanng both the dynamc states and statc varables of the system, and y are the avalable measurements. More nformaton on DAE power system models and ther related characterstcs can be found n [7]. By reformulatng (1) n a more condensed verson: F( x, x, N) = Bu (2) y = h( x, N) the observablty Jacoban (J O ) of the system s gven by [8]: Gx Gx Gw JO = (3) H x Hx H w where FxxN (,, ) (1) F : Fx( x, x, N) x Fx( x, x, N) x G + = ( s) ( s) (4) F :( FxxN (,, )) hxn (, ) y (1) (1) h : hx ( x, N) x y H = = ( r) ( r) ( r) h : h( x, N) y and s s the system dfferentaton ndex, r s the measurement dfferentaton ndex, and w are the hgher dervatves of x (2.w). Based on ths Jacoban, the system s sad to be smoothly observable f [8]: Gx Gw 1. rank( JO ) = n + rank H x H w (5) 2. rank( J ) s constant rank on S A. Power System Model O { = g x u N } S: (,, ) In the adopted structure preservng DAE model of the power system, the generators are modeled usng the rotatonal dynamcs provded by the classcal swng equaton, and the loads are modeled as constant PQ loads (consdered power dstrbuton centers), formng the system equatons: I f : δ ω = II D 1 P M f : ω + ( ω) + PG ( δ, θ, V, N) = (6) M M M III * f : P P ( δθ,, V, N) = j j IV * : j j( δθ,,, ) = f Q Q V N r (1) N : Ycd = Gcd + jbcd = 2 n generators; j= n+ 1 m loads; cd, = 1 n+ m where δ, M, D and P M are the nternal angle, nerta, dampng and constant mechancal power of the th generator respectvely, and θ j and V j are the j th load bus angle and voltage respectvely. The desred power demand on the j th load bus s denoted S * =P * +jq *. The system has the form of (1), wth f(.) n (1) comprsed of f I and f II and g(.) n (1) comprsed of f III and f IV, and wth respect to the form gven n (2): I II III IV T F( x, x, N) = [ f f f f ] (7) PM x= [ δ, ω, θ j, Vj], u = M The avalable measurements are all assumed as real power branch flows, gven as: hxn (, ) = { Pcd ( xn, )} (8) Ths system can be further modfed to ncorporate generator excter dynamcs or dynamcs n the system loads. The observablty formulaton for these varatons was dscussed n [3-6], and the addton of other types of generator and load models (voltage dependent loads for example) wll mpact the constructon of the observablty Jacoban only. The methodology for obtanng the Jacoban, the observablty condtons and the degree of observablty measure s consstent. Also, t should be noted that n (6) generator bus 1 was arbtrarly selected as the reference bus, yeldng δ :δ -δ 1 and θ j :θ j -δ 1. B. Observablty Metrc To quantfy the observablty of the system durng a varaton, a measure of observablty s ntroduced, defned as: l λmax ( JO ) η =, l = 1 L (9) λmn ( JO ) where λ are the sngular values of the observablty Jacoban obtaned va a sngular value decomposton at each pont of nterest l. The maxmum number of L, s dctated by the convergence of the numercal solvers used for ether statc studes (ponts along PV Curve), or dynamc studes (length of tme wndow n a tme doman smulaton). The degree of observablty γ s defned as: l γ = max { η } l = 1 L (1) Based on the formulaton of ths measure, a smaller numercal value of γ s preferred a hgher number ndcates that the observablty Jacoban s becomng sngular. III. APPROACH To determne all the sngulartes of the observablty Jacoban, a symbolc computaton approach s used. Also, tme doman smulatons are used to track the observablty of the system along the system state trajectores, as these unobservable regons are approached. A bref descrpton of both the symbolc and numercal approaches s provded. 76

4 A. Symbolc Approach The symbolc approach examnes the observablty crtera as a functon of the system state space. In partcular, the examnaton s focused on fndng where n the state-space the observablty Jacoban undergoes rank defcences. In essence, by examnng where observablty s lost, all other ponts n the state-space are assumed to be observable. Ths s assumed, rather than guaranteed, because of the performance of the symbolc tools n evaluatng rank defcences. Namely ther performance n locatng hgher order degeneraces n the Jacoban s of queston. For the partcular system examned, the symbolc tools were able to capture hgher order degeneraces. Ths technque s powerful n the sense that t provdes the global solutons where the system s unobservable, however t s lmted by the computatonal capablty of the symbolc solvers, and hence, s lmted to the sze of the system that can be evaluated. The symbolc approach process s outlned n Fgure 1. Statc Power Flow Statc or Dynamc Case Dynamc Tme Doman Solver Obtan System Parameters (Network Parameters and Generator Parameters) Obtan System Measurements Construct System Equatons Intalze s and r Indces, s=r=1 Symbolc Evaluaton of Observablty Jacoban Numercal Evaluaton of Observablty Crtera Is the System Observable Yes No Proceed to Next Parameter Set Update s and r Obtan System Parameters (Network Parameters and Generator Parameters) Obtan System Measurements Fg 2. Flow Dagram of the Numercal Approaches n Determnng Observablty Construct System Equatons NR NRS Inta lze s and r Indces, s =r=1 Symbolc Evaluaton of Observablty Jacoban Symbolc Evaluaton of State Space Solutons for: det(jo)= Fg 1. Investgatng Observablty usng Symbolc Tools B. Numercal Approach The numercal approach conssts of two separate cases: Statc Cases Evaluatng observablty for operatng ponts along system PV Curves Dynamc Cases Evaluatng the observablty crteron along trajectores of the system states The general approach n evaluatng these crtera and determnng the approprate s and r to render the system observable s outlned n Fgure 2. For the statc cases, the soluton of the system PV curves s accomplshed n an teratve fashon usng the Newton- Raphson (NR) and Newton-Raphson-Seydel (NRS) algorthms [9]. When the NR algorthm fals, the NRS algorthm s used to obtan the data ponts near and around the nose pont of the PV curve, as llustrated n Fgure 3. For the numercal cases, the ode15s DAE solver was used n the Matlab/Smulnk envronment to generate the tme doman smulatons of nterest. Fg 3. Typcal Power System PV Curve IV. SIMULATION RESULTS A. Sample System To llustrate the symbolc approach for obtanng the unobservable regons, a 3-bus power system depcted n Fgure 4, wth correspondng system parameters n Table 1, was used. There are two sets of results consderng two dfferent measurements, P 12 and P 32. In both cases, r=s=1, and the sngularty of the Jacoban s nvestgated by solvng the followng relatonshp symbolcally: det ( J O ) = For the selected system, the observablty Jacoban has the general form provded n (11), and the states of the system are gven as: x = θ, ω, θ, V [ ]

5 J O 1 1 j2,1 j2,2 j2,3 j2,4 1 j3,1 j3,3 j3,4 j4,1 j4,3 j4,4 1 1 = j6,1 j6,3 j6,4 j6,5 j6,6 j6,7 j6,8 1 j7,1 j7,3 j7,4 j7,5 j7,7 j7,8 j8,1 j8,3 j8,4 j8,5 j8,7 j8,8 j9,1 j9,3 j9,4 j1,1 j1,3 j1,4 j1,5 j1,7 j1,8 Bus 1 Bus 2 R12 L12 GS GS R13 L23 L13 R23 Bus 3 (11) frst 2 solutons represent ether a fault on bus 3, or a phase nstablty, and the thrd soluton provdes the bus voltage as a functon of the bus phases. TABLE II SYSTEM OBSERVABILITY IS LOST AND THE CORRESPONDING RANK OF THE OBSERVABILITY JACOBIAN P 32 Soluton x 1 x 2 x 3 x 4 Rank 1 x 1 x 2 x x 2 π/2 x x 2 -π/2 x π x 2 π/2 x π x 2 -π/2 x x 1 x 2 x 3 f(x 1, x 3 ) 8 TABLE III SYSTEM OBSERVABILITY IS LOST AND THE CORRESPONDING RANK OF THE OBSERVABILITY JACOBIAN P 12 Soluton x 1 x 2 x 3 x 4 Rank 1 x 1 x 2 x π/2 x 2 x 3 x x 1 x 2 x 3 f(x 1, x 3 ) 8 P3+jQ3 Fg. 4. Sample 3-bus Power System TABLE I SYSTEM PARAMETERS FOR SAMPLE 3-BUS POWER SYSTEM Lne R X D= M= Bus P ntal Q ntal V 1 = V 2 = B. Symbolc Results Loss of Observablty By computng the determnant symbolcally and equatng t to zero, there are 6 solutons for x whch yeld rank defcences (Table II). In analyzng the solutons, and keepng n mnd the sgnfcance of the respectve system state assgnments, the frst fve solutons are not consdered physcally realzable. For Soluton 1, the bus voltage at the load bus would have to go to zero, meanng the generators are ether offlne or the topology of the system has changed (due to a fault on Bus 3, or faults on both lnes connectng Bus 3 to the two generator buses). Smlarly, for Solutons 2-5, the angle dfference between generator 1 and the load bus s 9 degrees, whch would ndcate a phase nstablty n the system. Therefore, for the frst fve solutons, one can assume that the system wll be smoothly observable for all perturbatons that do not cause these specfc system nstabltes, and that one measurement (P 32 ) s suffcent for the system to be smoothly observable. The only soluton of nterest now s Soluton 6, whch represents the load bus voltage as a functon of the two bus phases n the system. Smlarly for the case of a measurement P 12 (Table III), the Examnng Soluton 6 of Table II and Soluton 3 of Table III, and smlar solutons for a lossless system, an unobservable regon can be constructed, gven a range for the bus angles s specfed. To examne the physcal nterpretaton of these solutons, the ntal operatng pont n Table I s selected wth: x = [.869,,.222,1.1] and an arbtrary PV curve s constructed (Fgure 5). Usng the phase quanttes dctated by the PV curve, the relatonshp of the load bus voltage to the phase dfference between bus 2 and 3 s examned and compared to the results for the load bus voltage obtaned usng the PV curve. The phase dfference was used as a parameter to condense the problem nto 2-D. Examnng Fgure 6, the soluton for the load bus voltage magntude ntersects the unobservable solutons obtaned from Tables II and III, and usng the nformaton obtaned by the PV curve, ths ntersecton corresponds to the maxmum load pont of the system (magnfed n Fgure 7). Ths result s consstent wth all the obtaned solutons, and s nvarant to the selected measurement, and to the mplcaton of a lossless or lossy system. Due to the symmetry of the system topology used, the results for P 13 are the same as those for P 32 and are not ncluded. 78 Load Bus Voltage Magntude (pu) A PV Curve for Sample 3-Bus System Real Power Demand - Bus 3 (pu) N N R R S Fg 5. Arbtrary PV Curve of 3-Bus System B C

6 Load Bus Voltage Magntude (pu) Relatonshp Between System PV Curve and Unobservable Regons Losses-P12 Lossless-P12 Losses-P32 Lossless-P32 System PV Curve Theta2-Theta3 (rad) Fg. 6 Relatonshp Between System PV Curve and Unobservable Solutons.682 Relatonshp Between System PV Curve and Unobservable Regons along the trajectores of the system states. It s expected that the observablty ndex for the trajectores assocated wth pont C wll be hgher. By repeatng ths process for the entre set of ponts along the PV curve, a threshold on the observablty ndex was obtaned (γ= 6), and the ndvdual γ s provded n Table V, for the perturbaton level of 4% on the generator phase. TABLE IV ARBITRARY POINTS ALONG PV CURVE CHOSEN FOR TIME DOMAIN SIMULATIONS Pont x 1 x 3 x 4 P 2 P 3 Q 3 A B C Pont A Load Bus Voltage Magntude (pu) Losses-P12 Lossless-P12 Losses-P32 Lossless-P32 System PV Curve Theta2-Theta3 (rad) Pont B 126 Observablty Measure Pont C 3 25 Fg. 7 Relatonshp Between System PV Curve and Unobservable Solutons Intersecton Magnfed 2 15 Tme (sec.) Therefore, for the selected system, a dualty exsts between the loss of observablty and the voltage stablty problem. At the maxmum load pont, the system s unstable and unobservable. Ths s also present wth cases of phase nstablty, represented by the solutons n Table II and III, where large phase dfferences exst between the generator and load bus. C. Dynamc Smulatons Because of the lmtatons wth symbolc tools, analyzng larger systems are not possble wthout the ntroducton of numerc methods. However, unlke the symbolc approach whch yelded the entre famly of unobservable solutons, the numercal approaches are constraned to only approach a pont of nterest. To llustrate that the numercal results are consstent wth the answers from the symbolc computatons, three arbtrary ponts are selected along the PV curve (ponts A, B and C n Fgure 5, gven n Table IV). A 4% perturbaton s appled to the generator phase at each pont, and the resultng tme doman waveforms are extracted (waveforms provded n Appendx). These tme doman waveforms are then used to analyze the observablty ndex 79 Fg. 8. Observablty Measure along State Trajectores for Ponts A, B and C. TABLE V OBSERVABILITY INDEX FOR EACH SET OF TRAJECTORIES INITIATED FROM POINTS A, B AND C Pont γ A B C As expected, the trajectores produced from the ponts closer to the nose pont produced larger ndex values, ndcatng that the system s becomng less observable, and they are consstent wth the results obtaned usng the symbolc approach. However, at no pont does the system become unobservable n a numercal sense. The observablty crtera of Equaton 5 are satsfed for all the ponts along the PV curve, and system trajectores for ths system. A closer examnaton ndcates that a quanttatve change s occurrng as the system s approachng the maxmum load pont. Ths s evdent when examnng the mnmum sngular value of the observablty Jacoban for each pont along the PV curve. As

7 llustrated n Fgure 9, the value approaches zero at the maxmum load pont. From a numercal standpont, the system s stll observable, and ths problem s hence transformed nto a numercal senstvty ssue. The approprate selecton of thresholds on γ provdes a better depcton; however, ths process nvolves extensve system smulatons to set these thresholds. Generator Phase (rad) Generator Speed Trajectores After Perturbaton from Pont B -.1 Mnmum Sngular Value N R N R S Mnmum Sngular Value of Jacoban Along PV Curve Maxmum Load Level Ponts Along PV Curve Fg 9. Mnmum Sngular Value of Jacoban Along PV Curve V. CONCLUSIONS Sngulartes of the observablty Jacoban correspond wth system sngulartes that are typcally assocated wth phase and voltage stablty problems n power systems. Ths dualty can be exploted by usng the observablty ndex as a system metrc capable of forecastng both the stablty and observablty classfcatons of a power system. The dual use of a sngle metrc s very encouragng. In the case of system stablty, snce the common ponts are fewer than the entre famly of unstable ponts for a system, ths metrc would serve as a necessary but not suffcent condton. VI. APPENDIX Tme doman smulatons for the sample system consdered n prevous secton are provded n Fgures Generator Phase (rad) Generator Speed Bus 3 Phase Bus 3 Voltage Magntude Trajectores After Perturbaton from Pont A Tme (sec.) Fg 1. Trajectores After Perturbaton from Pont A Bus 3 Phase Bus 3 Voltage Magntude Tme (sec.) Fg 11. Trajectores After Perturbaton from Pont B Generator Phase (rad) Generator Speed Bus 3 Phase Bus 3 Voltage Magntude Trajectores After Perturbaton from Pont C Tme (sec.) Fg 12. Trajectores After Perturbaton from Pont C VII. REFERENCES [1] K. L. Butler-Perry, N. D. R. Sarma, I. V. Hcks, Servce Restoraton n Naval Shpboard Power Systems, IEE Proceedngs on Generaton, Transmsson and Dstrbuton, Vol. 151, Issue 1, pp [2] Natonal Research Councl, Technology for the Unted States Navy and Marne Corps Becomng a 21st Century Force, Volume 2: Technology, Natonal Academc Press, Washngton, [3] C. J. Dafs, "An Observablty Formulaton for Nonlnear Power Systems Modeled as Dfferental Algebrac Systems," Ph.D. Dssertaton, Dept. Elect. & Comp. Eng., Drexel Unversty, Phladelpha, Feb. 25. [4] C. J. Dafs, C. O. Nwankpa, Characterstcs of Degree of Observablty Measure for Nonlnear Power Systems, n Proc. 25 Hawa Internatonal Conference on System Scence. [5] C. J. Dafs, C. O. Nwankpa, A Nonlnear Observablty Formulaton for Power Systems Incorporatng Generator and Load Dynamcs, n Proc. 22 IEEE Conference on Decson and Control, Vol.3, pp , 22. [6] C. J. Dafs, C. O. Nwankpa, Examnng Characterstcs of an Observablty Formulaton for Nonlnear Power Systems, n Proc. 23 IEEE Internatonal Symposum on Crcuts and Systems, Vol. 3, pp [7] P.W. Sauer, M. A. Pa, Power System Dynamcs and Stablty, Prentce Hall, Upper Saddle Rver, New Jersey, [8] W. J. Terrell, Observablty of Nonlnear Dfferental Algebrac Systems, Crcuts, Systems, and Sgnal Processng, Vol. 16, No.2, 1997, pp [9] R. Seydel, Practcal Bfurcaton and Stablty Analyss From Equlbrum to Chaos, Sprnger-Verlag, New York,

Root Locus Properties of Adaptive Beamforming and Capon Estimation for Uniform Linear Arrays

Root Locus Properties of Adaptive Beamforming and Capon Estimation for Uniform Linear Arrays Root Locus Propertes of Adaptve Beamformng and Capon Estmaton for Unform Lnear Arrays Allan Stenhardt Alphatech phone: 703-284-8426 emal: astenhardt@dc.alphatech.com Abstract In ths paper we explore propertes

More information

Chapter - 2. Distribution System Power Flow Analysis

Chapter - 2. Distribution System Power Flow Analysis Chapter - 2 Dstrbuton System Power Flow Analyss CHAPTER - 2 Radal Dstrbuton System Load Flow 2.1 Introducton Load flow s an mportant tool [66] for analyzng electrcal power system network performance. Load

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Note 10. Modeling and Simulation of Dynamic Systems

Note 10. Modeling and Simulation of Dynamic Systems Lecture Notes of ME 475: Introducton to Mechatroncs Note 0 Modelng and Smulaton of Dynamc Systems Department of Mechancal Engneerng, Unversty Of Saskatchewan, 57 Campus Drve, Saskatoon, SK S7N 5A9, Canada

More information

6.3.7 Example with Runga Kutta 4 th order method

6.3.7 Example with Runga Kutta 4 th order method 6.3.7 Example wth Runga Kutta 4 th order method Agan, as an example, 3 machne, 9 bus system shown n Fg. 6.4 s agan consdered. Intally, the dampng of the generators are neglected (.e. d = 0 for = 1, 2,

More information

The equation of motion of a dynamical system is given by a set of differential equations. That is (1)

The equation of motion of a dynamical system is given by a set of differential equations. That is (1) Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence

More information

Higher Order Wall Boundary Conditions for Incompressible Flow Simulations

Higher Order Wall Boundary Conditions for Incompressible Flow Simulations THE 5 TH ASIAN COMPUTAITIONAL FLUID DYNAMICS BUSAN KOREA OCTOBER 7-30 003 Hgher Order Wall Boundary Condtons for Incompressble Flow Smulatons Hdetosh Nshda. Department of Mechancal and System Engneerng

More information

Appendix B. The Finite Difference Scheme

Appendix B. The Finite Difference Scheme 140 APPENDIXES Appendx B. The Fnte Dfference Scheme In ths appendx we present numercal technques whch are used to approxmate solutons of system 3.1 3.3. A comprehensve treatment of theoretcal and mplementaton

More information

Global Sensitivity. Tuesday 20 th February, 2018

Global Sensitivity. Tuesday 20 th February, 2018 Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Second Order Analysis

Second Order Analysis Second Order Analyss In the prevous classes we looked at a method that determnes the load correspondng to a state of bfurcaton equlbrum of a perfect frame by egenvalye analyss The system was assumed to

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Finite Element Modelling of truss/cable structures

Finite Element Modelling of truss/cable structures Pet Schreurs Endhoven Unversty of echnology Department of Mechancal Engneerng Materals echnology November 3, 214 Fnte Element Modellng of truss/cable structures 1 Fnte Element Analyss of prestressed structures

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

6.3.4 Modified Euler s method of integration

6.3.4 Modified Euler s method of integration 6.3.4 Modfed Euler s method of ntegraton Before dscussng the applcaton of Euler s method for solvng the swng equatons, let us frst revew the basc Euler s method of numercal ntegraton. Let the general from

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Parameter Estimation for Dynamic System using Unscented Kalman filter

Parameter Estimation for Dynamic System using Unscented Kalman filter Parameter Estmaton for Dynamc System usng Unscented Kalman flter Jhoon Seung 1,a, Amr Atya F. 2,b, Alexander G.Parlos 3,c, and Klto Chong 1,4,d* 1 Dvson of Electroncs Engneerng, Chonbuk Natonal Unversty,

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

ELE B7 Power Systems Engineering. Power Flow- Introduction

ELE B7 Power Systems Engineering. Power Flow- Introduction ELE B7 Power Systems Engneerng Power Flow- Introducton Introducton to Load Flow Analyss The power flow s the backbone of the power system operaton, analyss and desgn. It s necessary for plannng, operaton,

More information

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL The Synchronous 8th-Order Dfferental Attack on 12 Rounds of the Block Cpher HyRAL Yasutaka Igarash, Sej Fukushma, and Tomohro Hachno Kagoshma Unversty, Kagoshma, Japan Emal: {garash, fukushma, hachno}@eee.kagoshma-u.ac.jp

More information

A Modified Approach for Continuation Power Flow

A Modified Approach for Continuation Power Flow 212, TextRoad Publcaton ISSN 29-434 Journal of Basc and Appled Scentfc Research www.textroad.com A Modfed Approach for Contnuaton Power Flow M. Beragh*, A.Rab*, S. Mobaeen*, H. Ghorban* *Department of

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Simultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals

Simultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals Smultaneous Optmzaton of Berth Allocaton, Quay Crane Assgnment and Quay Crane Schedulng Problems n Contaner Termnals Necat Aras, Yavuz Türkoğulları, Z. Caner Taşkın, Kuban Altınel Abstract In ths work,

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

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

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

More information

COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN

COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN Int. J. Chem. Sc.: (4), 04, 645654 ISSN 097768X www.sadgurupublcatons.com COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN R. GOVINDARASU a, R. PARTHIBAN a and P. K. BHABA b* a Department

More information

1 Derivation of Point-to-Plane Minimization

1 Derivation of Point-to-Plane Minimization 1 Dervaton of Pont-to-Plane Mnmzaton Consder the Chen-Medon (pont-to-plane) framework for ICP. Assume we have a collecton of ponts (p, q ) wth normals n. We want to determne the optmal rotaton and translaton

More information

The Study of Teaching-learning-based Optimization Algorithm

The Study of Teaching-learning-based Optimization Algorithm Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

Chapter Newton s Method

Chapter Newton s Method Chapter 9. Newton s Method After readng ths chapter, you should be able to:. Understand how Newton s method s dfferent from the Golden Secton Search method. Understand how Newton s method works 3. Solve

More information

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud Resource Allocaton wth a Budget Constrant for Computng Independent Tasks n the Cloud Wemng Sh and Bo Hong School of Electrcal and Computer Engneerng Georga Insttute of Technology, USA 2nd IEEE Internatonal

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

10.34 Numerical Methods Applied to Chemical Engineering Fall Homework #3: Systems of Nonlinear Equations and Optimization

10.34 Numerical Methods Applied to Chemical Engineering Fall Homework #3: Systems of Nonlinear Equations and Optimization 10.34 Numercal Methods Appled to Chemcal Engneerng Fall 2015 Homework #3: Systems of Nonlnear Equatons and Optmzaton Problem 1 (30 ponts). A (homogeneous) azeotrope s a composton of a multcomponent mxture

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

Solving Nonlinear Differential Equations by a Neural Network Method

Solving Nonlinear Differential Equations by a Neural Network Method Solvng Nonlnear Dfferental Equatons by a Neural Network Method Luce P. Aarts and Peter Van der Veer Delft Unversty of Technology, Faculty of Cvlengneerng and Geoscences, Secton of Cvlengneerng Informatcs,

More information

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

High resolution entropy stable scheme for shallow water equations

High resolution entropy stable scheme for shallow water equations Internatonal Symposum on Computers & Informatcs (ISCI 05) Hgh resoluton entropy stable scheme for shallow water equatons Xaohan Cheng,a, Yufeng Ne,b, Department of Appled Mathematcs, Northwestern Polytechncal

More information

Fuzzy Boundaries of Sample Selection Model

Fuzzy Boundaries of Sample Selection Model Proceedngs of the 9th WSES Internatonal Conference on ppled Mathematcs, Istanbul, Turkey, May 7-9, 006 (pp309-34) Fuzzy Boundares of Sample Selecton Model L. MUHMD SFIIH, NTON BDULBSH KMIL, M. T. BU OSMN

More information

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments

More information

A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS. Dr. Derald E. Wentzien, Wesley College, (302) ,

A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS. Dr. Derald E. Wentzien, Wesley College, (302) , A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS Dr. Derald E. Wentzen, Wesley College, (302) 736-2574, wentzde@wesley.edu ABSTRACT A lnear programmng model s developed and used to compare

More information

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Term Project - select journal paper and outline. Completed analysis due end

Term Project - select journal paper and outline. Completed analysis due end EE 5200 - Lecture 30 Fr ov 4, 2016 Topcs for Today: Announcements Term Project - select journal paper and outlne. Completed analyss due end of Week 12. Submt va e-mal as mn-lecture.ppt wth voce narraton.

More information

Statistical Energy Analysis for High Frequency Acoustic Analysis with LS-DYNA

Statistical Energy Analysis for High Frequency Acoustic Analysis with LS-DYNA 14 th Internatonal Users Conference Sesson: ALE-FSI Statstcal Energy Analyss for Hgh Frequency Acoustc Analyss wth Zhe Cu 1, Yun Huang 1, Mhamed Soul 2, Tayeb Zeguar 3 1 Lvermore Software Technology Corporaton

More information

Irregular vibrations in multi-mass discrete-continuous systems torsionally deformed

Irregular vibrations in multi-mass discrete-continuous systems torsionally deformed (2) 4 48 Irregular vbratons n mult-mass dscrete-contnuous systems torsonally deformed Abstract In the paper rregular vbratons of dscrete-contnuous systems consstng of an arbtrary number rgd bodes connected

More information

Uncertainty and auto-correlation in. Measurement

Uncertainty and auto-correlation in. Measurement Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at

More information

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems Chapter. Ordnar Dfferental Equaton Boundar Value (BV) Problems In ths chapter we wll learn how to solve ODE boundar value problem. BV ODE s usuall gven wth x beng the ndependent space varable. p( x) q(

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased

More information

4DVAR, according to the name, is a four-dimensional variational method.

4DVAR, according to the name, is a four-dimensional variational method. 4D-Varatonal Data Assmlaton (4D-Var) 4DVAR, accordng to the name, s a four-dmensonal varatonal method. 4D-Var s actually a drect generalzaton of 3D-Var to handle observatons that are dstrbuted n tme. The

More information

Determining Transmission Losses Penalty Factor Using Adaptive Neuro Fuzzy Inference System (ANFIS) For Economic Dispatch Application

Determining Transmission Losses Penalty Factor Using Adaptive Neuro Fuzzy Inference System (ANFIS) For Economic Dispatch Application 7 Determnng Transmsson Losses Penalty Factor Usng Adaptve Neuro Fuzzy Inference System (ANFIS) For Economc Dspatch Applcaton Rony Seto Wbowo Maurdh Hery Purnomo Dod Prastanto Electrcal Engneerng Department,

More information

Operating conditions of a mine fan under conditions of variable resistance

Operating conditions of a mine fan under conditions of variable resistance Paper No. 11 ISMS 216 Operatng condtons of a mne fan under condtons of varable resstance Zhang Ynghua a, Chen L a, b, Huang Zhan a, *, Gao Yukun a a State Key Laboratory of Hgh-Effcent Mnng and Safety

More information

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems Mathematca Aeterna, Vol. 1, 011, no. 06, 405 415 Applcaton of B-Splne to Numercal Soluton of a System of Sngularly Perturbed Problems Yogesh Gupta Department of Mathematcs Unted College of Engneerng &

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

Supplementary Notes for Chapter 9 Mixture Thermodynamics

Supplementary Notes for Chapter 9 Mixture Thermodynamics Supplementary Notes for Chapter 9 Mxture Thermodynamcs Key ponts Nne major topcs of Chapter 9 are revewed below: 1. Notaton and operatonal equatons for mxtures 2. PVTN EOSs for mxtures 3. General effects

More information

A MODIFIED METHOD FOR SOLVING SYSTEM OF NONLINEAR EQUATIONS

A MODIFIED METHOD FOR SOLVING SYSTEM OF NONLINEAR EQUATIONS Journal of Mathematcs and Statstcs 9 (1): 4-8, 1 ISSN 1549-644 1 Scence Publcatons do:1.844/jmssp.1.4.8 Publshed Onlne 9 (1) 1 (http://www.thescpub.com/jmss.toc) A MODIFIED METHOD FOR SOLVING SYSTEM OF

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k) ISSN 1749-3889 (prnt), 1749-3897 (onlne) Internatonal Journal of Nonlnear Scence Vol.17(2014) No.2,pp.188-192 Modfed Block Jacob-Davdson Method for Solvng Large Sparse Egenproblems Hongy Mao, College of

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

Introduction. - The Second Lyapunov Method. - The First Lyapunov Method

Introduction. - The Second Lyapunov Method. - The First Lyapunov Method Stablty Analyss A. Khak Sedgh Control Systems Group Faculty of Electrcal and Computer Engneerng K. N. Toos Unversty of Technology February 2009 1 Introducton Stablty s the most promnent characterstc of

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

En Route Traffic Optimization to Reduce Environmental Impact

En Route Traffic Optimization to Reduce Environmental Impact En Route Traffc Optmzaton to Reduce Envronmental Impact John-Paul Clarke Assocate Professor of Aerospace Engneerng Drector of the Ar Transportaton Laboratory Georga Insttute of Technology Outlne 1. Introducton

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

Lectures on Multivariable Feedback Control

Lectures on Multivariable Feedback Control Lectures on Multvarable Feedback Control Al Karmpour Department of Electrcal Engneerng, Faculty of Engneerng, Ferdows Unversty of Mashhad June 200) Chapter 9: Quanttatve feedback theory Lecture Notes of

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM Ganj, Z. Z., et al.: Determnaton of Temperature Dstrbuton for S111 DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM by Davood Domr GANJI

More information

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

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

More information

Assignment 5. Simulation for Logistics. Monti, N.E. Yunita, T.

Assignment 5. Simulation for Logistics. Monti, N.E. Yunita, T. Assgnment 5 Smulaton for Logstcs Mont, N.E. Yunta, T. November 26, 2007 1. Smulaton Desgn The frst objectve of ths assgnment s to derve a 90% two-sded Confdence Interval (CI) for the average watng tme

More information

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites IOP Conference Seres: Materals Scence and Engneerng PAPER OPE ACCESS An dentfcaton algorthm of model knetc parameters of the nterfacal layer growth n fber compostes o cte ths artcle: V Zubov et al 216

More information

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method

More information

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

2016 Wiley. Study Session 2: Ethical and Professional Standards Application 6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton

More information

AGC Introduction

AGC Introduction . Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed

More information

Inexact Newton Methods for Inverse Eigenvalue Problems

Inexact Newton Methods for Inverse Eigenvalue Problems Inexact Newton Methods for Inverse Egenvalue Problems Zheng-jan Ba Abstract In ths paper, we survey some of the latest development n usng nexact Newton-lke methods for solvng nverse egenvalue problems.

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Feature Selection: Part 1

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

More information

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

Markov Chain Monte Carlo Lecture 6

Markov Chain Monte Carlo Lecture 6 where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

Experience with Automatic Generation Control (AGC) Dynamic Simulation in PSS E

Experience with Automatic Generation Control (AGC) Dynamic Simulation in PSS E Semens Industry, Inc. Power Technology Issue 113 Experence wth Automatc Generaton Control (AGC) Dynamc Smulaton n PSS E Lu Wang, Ph.D. Staff Software Engneer lu_wang@semens.com Dngguo Chen, Ph.D. Staff

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

More information

(1) L. are all the parameter variables in the polynomials above. Wu Elimination intends to reduce the number of

(1) L. are all the parameter variables in the polynomials above. Wu Elimination intends to reduce the number of Wu Elmnaton Method Appled n Statc Analyss of the Power System QING ZHANG, and CHEN CHEN Department of Electrcal Engneerng Shangha Jao Tong Unversty 800 Dong Chuan oad, Shangha 0040 CHINA chchen@sjtu.edu.cn

More information

Lecture Notes on Linear Regression

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

More information

Power law and dimension of the maximum value for belief distribution with the max Deng entropy

Power law and dimension of the maximum value for belief distribution with the max Deng entropy Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng

More information

Time-Varying Systems and Computations Lecture 6

Time-Varying Systems and Computations Lecture 6 Tme-Varyng Systems and Computatons Lecture 6 Klaus Depold 14. Januar 2014 The Kalman Flter The Kalman estmaton flter attempts to estmate the actual state of an unknown dscrete dynamcal system, gven nosy

More information