Estimating the benefits of energy-efficient train driving strategies: a model calibration with real data
|
|
- Robert Randall
- 6 years ago
- Views:
Transcription
1 Urban Transport XIX 201 Estmatng the benefts of energy-effcent tran drvng strateges: a model calbraton wth real data V. De Martns 1, M. Gallo 2 & L. D Acerno 1 1 Department of Cvl, Archtectural and Envronmental Engneerng, Federco II Unversty of Naples, Italy 2 Department of Engneerng, Unversty of Sanno (Benevento), Italy Abstract Ths paper descrbes the frst results of a research project where the man focus s to mplement a Decson Support System (DSS) to optmse energy consumpton of ral systems. In order to acheve ths objectve, we mplement an optmsaton module for the desgn of energy-effcent drvng strateges, n terms of speed profles, that requres a ralway smulaton model as a subroutne. Here we focus on the general framework of the optmsaton module and on the calbraton of the ralway smulaton model.all elaboratons are mplemented n a MatLab envronment, amng at defnng possble energy-effcent speed profles, n accordance wth energy-savng strateges, through optmsed speed profle parameters, n terms of acceleraton, target speed, deceleraton, coastng phase, and drvng behavour, represented by the jerk. The model s calbrated on real data recorded on a double track secton of a ralway lne n the cty of Naples (Italy). Intal results show that consumpton s very varable wth the speed profle and wth drver behavour, but the model s able to reproduce the average consumpton of each drvng strategy and should be able, wthn the DSS, to suggest the best drvng strateges for each ral secton. Keywords: energy-effcent drvng, ralway systems, optmsaton models. 1 Introducton Energy effcency n ralway systems s one of the emergng topcs n transportaton system research, snce ral travel s one of the best solutons for do: /ut130161
2 202 Urban Transport XIX satsfyng moblty needs, gven energy prces, urban growth and envronmental ssues. The optmsaton of the tran speed profle on a ral path s an mportant strategy for obtanng a good qualty of servce together wth meetng safety and energy effcency requrements (as shown by Dcembre and Rcc [1]). In ths specfc feld, ntal solutons can be obtaned by applyng Potryangn s prncple (see Hansen and Pachl [2]) to a smplfed and constraned explct formulaton of the problem where the decson varables are the swtchng ponts,.e. the tme nstants when the runnng regme changes. In recent years, n lght of the new technologes avalable, varous solutons have been proposed for dfferent problem scales, and, by analysng the network status, many optmsaton procedures have been descrbed, for example from Beugn and Maras [3], D Arano and Albrecht [4] and Lu and Golovtcher [5]. Moreover, Xuan [6] used perturbaton analyss to develop an alternatve set of necessary condtons for an optmal drvng strategy n some specfc track condtons, lke steep sectons, where tran drvng operaton usually dffers. Movng on to smulaton modellng of ralway networks, major mprovements were proposed by Mazzeo et al. [7] and Quagletta et al. [8] through the mplementaton of a smulaton framework for optmsng tran operatons n ralway systems, whle smulaton models were ntegrated wth travel demand estmaton by D Acerno et al. [9] n the case of ral falure management and by Gallo et al. [10] n the case of servce frequency optmsaton. Analyss of specfc ralway systems was performed by Lukaszewcz [11] on freght tran operatons and by Ke and Chen [12] on mass rapd transt plannng; the former analyses energy consumpton trends and ther relatonshp wth maxmum tracton rato, maxmum brakng rato, upper and lower restrctons of speed, and pre-brakng coastng dstance; the latter provdes a tool for block layout and runnng speed optmsaton n order to acheve the mnmum energy consumpton wth the maxmum tran capacty. Sgnfcant results can be found n Albrecht et al. [13] who analyse energy effcency n tran operatons and n Bocharnkov et al. [14], who study energy consumpton and ts relaton wth runnng tme. Gven the avalablty of contnuous nformaton systems, rather than the conventonal sgnallng systems that operate wth dscontnuous nformaton, tran operaton smulaton has been tackled wth dfferent technques: non-lnear programmng methods for energysavng control wth movng block sgnallng systems (see Gu et al. [15]); specfc optmal drvng models under fxed block and moble block condtons (Dng [16], Zhou et al. [17]); real tme control tools (Ba et al. [18]) that dynamcally nteract wth the nformaton systems n order to optmse tran operatons for dfferent track condtons and speed restrctons. 2 Problem descrpton and model formulaton The smulaton of complex systems, such as ralways, s one of the most wdely studed and appled methods to support the plannng and management of transportaton servces, accordng to a what f approach; the best soluton s found by smulatng dfferent scenaros and choosng the one whch best meets
3 Urban Transport XIX 203 the proposed requrements. The energy-effcent speed profle optmsaton procedure for tran operatons proposed n ths paper s based on an optmsaton loop that ntegrates two dfferent modules: an optmsaton module and a ralway smulaton model. The optmsaton module conssts of a constraned gradent descent optmsaton algorthm that allows a local mnmum of the objectve functon to be found (n our case total energy consumpton), coupled wth a speed profle defnton model that verfes the congruence of tme and dstance covered. The gradent descent algorthm conssts n evaluatng, ntally at a startng soluton, the value of the optmsaton functon and ts gradent. It then chooses a second soluton n the drecton ndcated by the gradent, that s accepted as the startng pont for the next teraton f the value of ts objectve functon s lower than the prevous one, and so on. Snce the gradent descent algorthm gves only a local optmal soluton, f the objectve functon s not convex, a mult-start method that consders several startng ponts can be useful for explorng the soluton set, generatng more local optma. The constrants are some condtons on mnmum and maxmum acceleraton, speed and deceleraton, that take account of passenger comfort, speed lmts and safety; other constrants concern the total travel tme avalable, n lght of the reserve tme, whch s the tme that preserves tmetable ntegrty, avodng delays. Moreover, on analysng energy-savng strateges, other condtons on the coastng phase, n terms of startng and endng ponts, have to be consdered. The ralway smulaton model estmates delays, runnng tme reserve, energy consumpton from the mechancal tracton requred for moton, and the tractve effort actng on the wheel, ncludng also brakng acton. The energy-savng optmsaton model can be formulated as follows: subject to: * * * * * a,v,d,t,t arg mn Ea,V,d,T,T max S acc C T fc V * fc mn (1) a V, max,d,t C,T fc max allow max < V V (2) J 1 s < a (3) a max J 1 s < d (4) * C * fc d max T T (5) dec * V T fc Tmax T (6) S S S Dst (7) cruse where: a s the target acceleraton (a* s ts optmal value); V max s the target speed (V* max s ts optmal value); d s the target deceleraton (d* s ts optmal value); s the startng tme of coastng (T* C s ts optmal value); T C coast dec C fc
4 204 Urban Transport XIX T fc s the endng tme of coastng (T* fc s ts optmal value); E(.) s the total mechancal energy spent; V mn s the mnmum target speed that respects the scheduled arrval tme, wthout coastng; V allow s the maxmum speed on the secton allowed by speed lmts; J 1s s the acceleraton at 1 second, obtaned multplyng the jerkng value by 1 second; a max s the maxmum acceleraton compatble wth passenger comfort; d max s the maxmum deceleraton compatble wth passenger comfort; T dec s the tme needed to decelerate from a certan speed; T max s the maxmum travel tme compatble wth tmetable respect (t s the sum of the mnmum runnng tme and the reserve tme); S acc s the space covered durng the acceleraton regme; S cruse s the space covered durng the crusng regme; S coast s the space covered durng the coastng regme; S dec s the space covered durng the deceleraton regme; Dst s the total dstance to cover. Constrants (2), (3) and (4) lmt the values of speed, acceleraton and deceleraton respectvely; constrant (5) mposes that the startng tme of coastng must be lower than ts endng tme; constrant (6) ensures that the sum of the coastng endng tme and the tme necessary for the tran to brake s lower than, or at least equal to, the maxmum travel tme avalable; constrant (7) ensures that the space covered by the dfferent regmes s equal to the real dstance to be covered. The jerk value represents the varaton n acceleraton durng the acceleraton phase, and can be optmsed as well as the other movng parameters. However, due to some consderatons on the sgnfcance of ths parameter, n ths paper we dd not consder t for calbraton and thus assumed a fxed value. The man reason s that acceleraton, speed and deceleraton can be consdered target parameters for the drver, whle the jerk s closer to the drver s behavour. That sad, t can be taken nto consderaton as a target value to optmze n the case of drverless systems. The objectve functon can be formulated consderng the mechancal energy requred to move a vehcle along a gven track wth gven moton parameters, usually expressed as the ntegral of the related mechancal power over tme. The mechancal power s ntended to be the power measured at wheel-ral nterface and can be computed as the product of the tractve effort F and speed V: E = P ( t) dt V F ( V, t) dt (8) mech tt where the tractve effort F s defned n T, that s travel tme on the track (or part of t) under consderaton, and can be computed by solvng the dfferental equaton derved from Newton s theory, also known as Moton General tt
5 Urban Transport XIX 205 Equaton, by a dscrete approach. Gven a generc temporal step of 1 second, the followng may be wrtten: F( V ) M f p v ( t t 1 ) R V,TRACK (9) where R(V, TRACK) can be computed by analysng the vehcle and lne resstances. More specfcally, t can be assumed that resstances can be computed wth the Sauthoff formula regardng specfc vehcle resstance: R 2 V K M K M V K V (10) and wth the formula of Roeckl (10), as regards the lne resstances due to curves, and wth the weght force component (11), wth regard to resstances due to the slopes: Rr Rr 6.3 = M V = M V 30 R r 300 m r < 300 m (11) M g (12) 1000 Fnally, R(V, TRACK) can be defned as the sum of (10), (11) and (12): R V,TRACK R(V ) Rr R (13) The acceleraton can be computed wth the followng formula: where: V a a ( t t ) 1 1 J ( t t 1 ) (14) a1 J ( t t 1) (a) a (15) a1 J ( t t 1) (b) consderng both the approach to the target value of acceleraton (a) and the approach to the target value of speed (b). The same consderatons can be supposed for the deceleraton values. The model of speed profle defnton allows energy-effcent results, as n the case of mplementaton of energy-savng strateges, through the defnton of the startng and endng ponts of the coastng phase, T C and T fc. For a gven coastng strategy, the speed profle model verfes the consstency of the profle n terms of travel tme avalable on a gven track and the dstance
6 206 Urban Transport XIX covered,.e. constrants (5), (6) and (7), usng the moton parameters generated by the optmsaton algorthm. In practce, the startng and endng ponts of the coastng regme are defned a pror by a coastng strategy; the drver has a planned coastng regme at a gven track pont. In ths paper we use the ASAP strategy (As Soon As Possble), whch means that the drver starts coastng as soon as he/she can; ths strategy assumes the exstence of a drvng assstance system. The model for speed profle defnton may already be suffcent for the computaton of the energy consumed. However, t does not contemplate the randomness of events on the ralway network, such as nteracton between vehcles. Therefore, from ths pont of vew, ts use could be evaluated wth the presence of drver assstance systems, drverless trans or smple networks such as urban and suburban lnes. Energy Optmzaton Module Ralway Smulaton nput: a,v max, T C, T F, d output: Energy, Delay, Runnng Reserve Tme Optmzaton Algorthm nput: Energy output: a,v max,d Speed profle defnton model: nput: a,v max,d output: Speed profle,t C, T F Yes Congruence on tme and space? No Fgure 1: The optmzaton loop. In fg. 1 the proposed optmzaton model s reported. Gven a set of target parameters of moton (a, V max, d), the model for defnng the speed profles calculates, at each one-second tme step, the relatve speed profle. The startng tme of the coastng phase, T C, s sought at each step wth a parallel algorthm that runs eqn (9), where tractve effort F(V ) s not appled and the varaton of speed and the related resstances at each step has to be computed. In other terms: RV,TRACK V (16) M f t t ) p ( 1
7 Urban Transport XIX 207 and the speed profle wth the coastng phase s accepted f the followng two condtons gven from constrants (6) and (7) are respected: 1. T + T dec (V(t)) = T max 2. Space covered at tme T max = space to be covered These condtons mean that the whole runnng tme reserve has to be used. The frst condton requres complance wth the maxmum tme avalable, T max, makng due allowance for the fact that at tme t we must add the tme T brake (V(t)) requred for brakng from speed V at tme t wth a deceleraton d. The second condton requres that the whole dstance n queston be covered. 3 Calbraton procedure Although the model descrbed n the prevous secton s a useful tool for evaluatng energy-effcent strateges, t cannot guarantee correct numercal results for each specfc case wthout calbraton. Calbratng a smulaton model conssts n fndng the values of some parameters such that the model wll reproduce wth accuracy the measurement observed from the real system. The calbraton procedure s generally performed by formulatng an optmsaton problem n whch the objectve functon to mnmse represents the devaton of the smulated measures from the observed ones. In ths paper, we need to calbrate the resstance parameters n order to better evaluate effectve power requrements and energy consumpton. The model representng the calbraton procedure can be formulated as follows: Kˆ obs sm arg mn f ( E, E( K) ) (17) KI where: Kˆ s the vector of the model parameters we wsh to calbrate,.e. resstance parameters; I s the doman of feasblty of the model parameters, that can eventually be constraned; f s the functon that measures the dstance between observed and smulated measures of performance; n ths paper we use the RMSE%; E obs and E(K) sm are, respectvely, the observed and smulated measures of system performance, where the smulated ones depend on the model parameters to calbrate. In ths paper we use energy consumpton as a measure of performance. 4 Numercal results The proposed model was mplemented on a MatLab platform usng the Optmzaton Toolbox, and some results were obtaned consderng prelmnary tests and data from the Italan natonal research project SFERE. Data refer to
8 208 Urban Transport XIX drect measurements on a ral track n the cty of Naples (Italy) on whch a vehcle was equpped wth a tran operaton montorng system; the data collected regard consumpton on the tracton unts and speed profle parameters. The ral track consdered s a double track of 1,700 m between two statons at the begnnng and end of the track wth no sgnallng systems. The track s at ground level, and there are no slopes and curves. Gven the characterstcs of the track, ths prelmnary test can be ntended as smlar to a generc staton-tostaton urban lne. Energy [KwH] Energy consumpton Tme [sec] RMSE% = K1 = K2 = K3 = Energy OBSERVED Energy SIMULATED Fgure 2: Energy consumpton observed and smulated for acceleraton and crusng regmes. Model calbraton was approached by fxng n our model the speed profle parameters observed, so that the model can reproduce the observed speed profles, and comparng the energy consumpton. For our purposes, only drvng regmes that requre energy consumpton,.e. acceleraton and crusng, were consdered. Fg. 2 reports the energy consumpton trend of the calbrated model compared wth the energy consumpton measured on board. In the fgure the value of RMSE% between the observed and smulated values s also reported, as are the calbrated values of eqn (10) that computes vehcle resstances. In our case, lne resstances were consdered rrelevant. The frst smulaton results are reported n fg. 3. The tme optmal speed profle s reached by assumng the maxmum allowable speed lmts on the track, n accordance wth the maxmum allowable acceleraton and maxmum allowable deceleraton n comfort condtons, assumng a jerk parameter of 0.3 m/s 3. In ths case the track was covered n 99 seconds. All parameters are summarzed n table 1. For the evaluaton of energy-effcent drvng strateges a runnng tme reserve of 17 s was consdered. The energy-savng speed profle was computed consderng a T max of 116 seconds, wth a coastng phase of 47 seconds The coastng phase begns 44
9 Urban Transport XIX 209 Fgure 3: Speed profle n tme optmal and energy-effcent drvng strateges wth the correspondng energy consumpton. Table 1: Optmsaton results. acc v dec T c T fc E (Kwh) T (s) Res. tme (s) Tme Optmal Energy Savng seconds after the tran starts runnng and t ends at second 91. The energy saved wth ths profle s around 4.45 KwH, that s about 36% less than the tme optmal speed profle energy consumpton. In ths case, as expected, optmsed speed profle parameters are qute dstant from the tme optmal ones and t s worth notng that, for a practcal applcaton
10 210 Urban Transport XIX of the optmsaton results, advanced drvng assstance systems or drverless systems are requred; n other cases drver s error should also be computed. 5 Concluson and future work Ths paper focused on an optmsaton model and ts calbraton, for mnmsng energy consumpton by defnng optmal speed profles. Intal results on a smple double track lne showed the model s ablty to defne the optmal energysavng speed profle for a gven runnng tme reserve and that the energy balance by adoptng energy-savng strateges can be consderable. Buldng on these frst results, future tests wll focus on three man aspects: ) more complex ralway networks for tests, ) mprovement n the optmsaton module for energy recovery applcatons, wth supercapactors both on board and at electrc substatons, and ) senstvty analyss on the optmzaton results consderng both energy savng and energy recovery strateges. Acknowledgement Partally supported under research project PON SFERE grant no. PON01_ References [1] Dcembre, A. and Rcc, S., Ralway traffc on hgh densty urban corrdors: capacty, sgnallng and tmetable. Journal of Ral Transport Plannng and Management, 1(2), pp , [2] Hansen, I.A. and Pachl, J., Ralway tmetable and traffc: analyss, modellng, smulaton, Euralpress: Hamburg, Germany, [3] Beugn, J. and Maras, J., Smulaton-based evaluaton of dependablty and safety propertes of satellte technologes for ralway localzaton. Transportaton Research Part C, 22, pp , [4] D Arano, A. and Albrecht, T., Runnng tme re-optmzaton durng realtme tmetable perturbatons. WIT Transactons on the Bult Envronment, 88, pp , [5] Lu, R. and Golovtcher, I.M., Energy-effcent operaton of ral vehcles. Transportaton Research Part A, 37(10), pp , [6] Xuan, V., Analyss of necessary condtons for the optmal control of a tran. Ph.D. thess, Unversty of South Australa, Adelade, Australa, [7] Mazzeo, A., Mazzocca, N., Nardone, R., D Acerno, L., Montella, B., Punzo, V., Quagletta, E., Lambert, I. and Marmo, P., An ntegrated approach for avalablty and QoS evaluaton n ralway systems. Lecture Notes n Computer Scence, 6894, pp , 2011.
11 Urban Transport XIX 211 [8] Quagletta, E., D Acerno, L., Punzo, V., Nardone, R. and Mazzocca, N., A smulaton framework for supportng desgn and real-tme decsonal phases n ralway systems. Proc. of the 14th IEEE Conference on Intellgent Transportaton Systems (ITSC), art. no , pp , [9] D Acerno, L., Gallo, M., Montella, B. and Placdo, A., Analyss of the nteracton between travel demand and ral capacty constrants. WIT Transactons on the Bult Envronment, 128, pp , [10] Gallo, M., Montella, B. and D Acerno, L., The transt network desgn problem wth elastc demand and nternalsaton of external costs: An applcaton to ral frequency optmsaton. Transportaton Research Part C, 19(6), pp , [11] Lukaszewcz, P., Energy savng drvng methods for freght trans. Advances n Transport, 15, pp , [12] Ke, B.R. and Chen, N., Sgnallng block layout and strategy of tran operaton for savng energy n mass rapd transt systems. IEE Proceedngs: Electrc Power Applcatons, 152(2), pp , [13] Albrecht, T., Gassel, C., Bnder, A. and van Lupen, J., Dealng wth operatonal constrants n energy effcent drvng. Proc. of IET Conference on Ralway Tracton Systems (RTS 2010), IET Semnar Dgest, 2010(13342), art. no. 22, [14] Bocharnkov, Y.V., Tobas, A.M., Roberts, C., Hllmansen, S. and Goodman, C.J., Optmal drvng strategy for tracton energy savng on DC suburban ralways. IET Electrcal Power Applcatons, 1(5), pp , [15] Gu, Q., Lu, X. and Tang, T., Energy savng for automatc tran control n movng block sgnalng system. Proc. of 14th Internatonal IEEE Conference on Intellgent Transportaton Systems (ITSC), art. no , pp , [16] Dng, Y., Study on Tran Movement Calculaton and Operaton Optmzaton Smulaton System, PhD thess, Bejng Jaotong Unversty, Bejng, Chna, [17] Zhu, J., L, H., Wang, Q. and Long, S., Optmzaton analyss on the energy savng control for trans. Chna Ralway Scence, 29(2), pp , [18] Ba, Y., Mao, B., Dng, Y., Zhou, F. and Ja, W., An onboard optmal control system for freght trans. Proc. of the Conference on Traffc and Transportaton Studes (ICTTS), pp , 2008.
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 informationModule 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 informationSome modelling aspects for the Matlab implementation of MMA
Some modellng aspects for the Matlab mplementaton of MMA Krster Svanberg krlle@math.kth.se Optmzaton and Systems Theory Department of Mathematcs KTH, SE 10044 Stockholm September 2004 1. Consdered optmzaton
More informationCHAPTER 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 informationResource 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 informationAnnexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances
ec Annexes Ths Annex frst llustrates a cycle-based move n the dynamc-block generaton tabu search. It then dsplays the characterstcs of the nstance sets, followed by detaled results of the parametercalbraton
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationDesign 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 informationSimultaneous 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 informationEn 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 informationEEE 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 informationSuppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl
RECURSIVE SPLINE INTERPOLATION METHOD FOR REAL TIME ENGINE CONTROL APPLICATIONS A. Stotsky Volvo Car Corporaton Engne Desgn and Development Dept. 97542, HA1N, SE- 405 31 Gothenburg Sweden. Emal: astotsky@volvocars.com
More informationEEL 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 informationMMA and GCMMA two methods for nonlinear optimization
MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons
More informationEvaluation of Validation Metrics. O. Polach Final Meeting Frankfurt am Main, September 27, 2013
Evaluaton of Valdaton Metrcs O. Polach Fnal Meetng Frankfurt am Man, September 7, 013 Contents What s Valdaton Metrcs? Valdaton Metrcs evaluated n DynoTRAIN WP5 Drawbacks of Valdaton Metrcs Conclusons
More informationLecture 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 informationChapter - 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 informationELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM
ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look
More informationUncertainty 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 informationChapter 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 information4DVAR, 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 informationNumerical 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 informationA 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 informationAssortment Optimization under MNL
Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.
More informationAir Age Equation Parameterized by Ventilation Grouped Time WU Wen-zhong
Appled Mechancs and Materals Submtted: 2014-05-07 ISSN: 1662-7482, Vols. 587-589, pp 449-452 Accepted: 2014-05-10 do:10.4028/www.scentfc.net/amm.587-589.449 Onlne: 2014-07-04 2014 Trans Tech Publcatons,
More information829. An adaptive method for inertia force identification in cantilever under moving mass
89. An adaptve method for nerta force dentfcaton n cantlever under movng mass Qang Chen 1, Mnzhuo Wang, Hao Yan 3, Haonan Ye 4, Guola Yang 5 1,, 3, 4 Department of Control and System Engneerng, Nanng Unversty,
More informationTHE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD
Journal of Appled Mathematcs and Computatonal Mechancs 7, 6(3), 7- www.amcm.pcz.pl p-issn 99-9965 DOI:.75/jamcm.7.3. e-issn 353-588 THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS
More informationA PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS
HCMC Unversty of Pedagogy Thong Nguyen Huu et al. A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS Thong Nguyen Huu and Hao Tran Van Department of mathematcs-nformaton,
More information(Online First)A Lattice Boltzmann Scheme for Diffusion Equation in Spherical Coordinate
Internatonal Journal of Mathematcs and Systems Scence (018) Volume 1 do:10.494/jmss.v1.815 (Onlne Frst)A Lattce Boltzmann Scheme for Dffuson Equaton n Sphercal Coordnate Debabrata Datta 1 *, T K Pal 1
More informationCSci 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 informationPhysics 5153 Classical Mechanics. Principle of Virtual Work-1
P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal
More informationStatistical 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 informationRELIABILITY ASSESSMENT
CHAPTER Rsk Analyss n Engneerng and Economcs RELIABILITY ASSESSMENT A. J. Clark School of Engneerng Department of Cvl and Envronmental Engneerng 4a CHAPMAN HALL/CRC Rsk Analyss for Engneerng Department
More informationOperating 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 informationDETERMINATION 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 informationThe 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 informationPhysics 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 informationStudy on Active Micro-vibration Isolation System with Linear Motor Actuator. Gong-yu PAN, Wen-yan GU and Dong LI
2017 2nd Internatonal Conference on Electrcal and Electroncs: echnques and Applcatons (EEA 2017) ISBN: 978-1-60595-416-5 Study on Actve Mcro-vbraton Isolaton System wth Lnear Motor Actuator Gong-yu PAN,
More informationχ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body
Secton.. Moton.. The Materal Body and Moton hyscal materals n the real world are modeled usng an abstract mathematcal entty called a body. Ths body conssts of an nfnte number of materal partcles. Shown
More informationAdjoint Methods of Sensitivity Analysis for Lyapunov Equation. Boping Wang 1, Kun Yan 2. University of Technology, Dalian , P. R.
th World Congress on Structural and Multdscplnary Optmsaton 7 th - th, June 5, Sydney Australa Adjont Methods of Senstvty Analyss for Lyapunov Equaton Bopng Wang, Kun Yan Department of Mechancal and Aerospace
More informationTemperature. 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 informationModal Strain Energy Decomposition Method for Damage Detection of an Offshore Structure Using Modal Testing Information
Thrd Chnese-German Jont Symposum on Coastal and Ocean Engneerng Natonal Cheng Kung Unversty, Tanan November 8-16, 2006 Modal Stran Energy Decomposton Method for Damage Detecton of an Offshore Structure
More informationEVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES
EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES Manuel J. C. Mnhoto Polytechnc Insttute of Bragança, Bragança, Portugal E-mal: mnhoto@pb.pt Paulo A. A. Perera and Jorge
More informationFUZZY GOAL PROGRAMMING VS ORDINARY FUZZY PROGRAMMING APPROACH FOR MULTI OBJECTIVE PROGRAMMING PROBLEM
Internatonal Conference on Ceramcs, Bkaner, Inda Internatonal Journal of Modern Physcs: Conference Seres Vol. 22 (2013) 757 761 World Scentfc Publshng Company DOI: 10.1142/S2010194513010982 FUZZY GOAL
More informationrisk and uncertainty assessment
Optmal forecastng of atmospherc qualty n ndustral regons: rsk and uncertanty assessment Vladmr Penenko Insttute of Computatonal Mathematcs and Mathematcal Geophyscs SD RAS Goal Development of theoretcal
More informationWeek3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle
More informationECE559VV Project Report
ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate
More informationSupporting Information
Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to
More informationSOLVING CAPACITATED VEHICLE ROUTING PROBLEMS WITH TIME WINDOWS BY GOAL PROGRAMMING APPROACH
Proceedngs of IICMA 2013 Research Topc, pp. xx-xx. SOLVIG CAPACITATED VEHICLE ROUTIG PROBLEMS WITH TIME WIDOWS BY GOAL PROGRAMMIG APPROACH ATMII DHORURI 1, EMIUGROHO RATA SARI 2, AD DWI LESTARI 3 1Department
More informationSupplementary 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 informationSpeeding up Computation of Scalar Multiplication in Elliptic Curve Cryptosystem
H.K. Pathak et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Speedng up Computaton of Scalar Multplcaton n Ellptc Curve Cryptosystem H. K. Pathak Manju Sangh S.o.S n Computer scence
More informationWavelet chaotic neural networks and their application to continuous function optimization
Vol., No.3, 04-09 (009) do:0.436/ns.009.307 Natural Scence Wavelet chaotc neural networks and ther applcaton to contnuous functon optmzaton Ja-Ha Zhang, Yao-Qun Xu College of Electrcal and Automatc Engneerng,
More informationFeature 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 informationInductance Calculation for Conductors of Arbitrary Shape
CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors
More informationDesign and Analysis of Landing Gear Mechanic Structure for the Mine Rescue Carrier Robot
Sensors & Transducers 214 by IFSA Publshng, S. L. http://www.sensorsportal.com Desgn and Analyss of Landng Gear Mechanc Structure for the Mne Rescue Carrer Robot We Juan, Wu Ja-Long X an Unversty of Scence
More informationDESIGN OPTIMIZATION OF CFRP RECTANGULAR BOX SUBJECTED TO ARBITRARY LOADINGS
Munch, Germany, 26-30 th June 2016 1 DESIGN OPTIMIZATION OF CFRP RECTANGULAR BOX SUBJECTED TO ARBITRARY LOADINGS Q.T. Guo 1*, Z.Y. L 1, T. Ohor 1 and J. Takahash 1 1 Department of Systems Innovaton, School
More informationNote 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 informationKernel 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 informationComparison 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 informationPsychology 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 informationAn Interactive Optimisation Tool for Allocation Problems
An Interactve Optmsaton ool for Allocaton Problems Fredr Bonäs, Joam Westerlund and apo Westerlund Process Desgn Laboratory, Faculty of echnology, Åbo Aadem Unversty, uru 20500, Fnland hs paper presents
More informationProblem 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 informationThis column is a continuation of our previous column
Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard
More informationUncertainty as the Overlap of Alternate Conditional Distributions
Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant
More informationLOW 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 informationAppendix B: Resampling Algorithms
407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles
More informationIntegrated approach in solving parallel machine scheduling and location (ScheLoc) problem
Internatonal Journal of Industral Engneerng Computatons 7 (2016) 573 584 Contents lsts avalable at GrowngScence Internatonal Journal of Industral Engneerng Computatons homepage: www.growngscence.com/ec
More informationSingular Value Decomposition: Theory and Applications
Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real
More informationGlobal 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 informationDUE: 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 informationParameter 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 informationWinter 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 informationEN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st
EN40: Dynamcs and bratons Homework 4: Work, Energy and Lnear Momentum Due Frday March 1 st School of Engneerng Brown Unversty 1. The fgure (from ths publcaton) shows the energy per unt area requred to
More informationIndeterminate pin-jointed frames (trusses)
Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all
More informationCalculation of time complexity (3%)
Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add
More informationCS : Algorithms and Uncertainty Lecture 17 Date: October 26, 2016
CS 29-128: Algorthms and Uncertanty Lecture 17 Date: October 26, 2016 Instructor: Nkhl Bansal Scrbe: Mchael Denns 1 Introducton In ths lecture we wll be lookng nto the secretary problem, and an nterestng
More informationVARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES
VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES BÂRZĂ, Slvu Faculty of Mathematcs-Informatcs Spru Haret Unversty barza_slvu@yahoo.com Abstract Ths paper wants to contnue
More informationSTATIC OPTIMIZATION: BASICS
STATIC OPTIMIZATION: BASICS 7A- Lecture Overvew What s optmzaton? What applcatons? How can optmzaton be mplemented? How can optmzaton problems be solved? Why should optmzaton apply n human movement? How
More informationA new Approach for Solving Linear Ordinary Differential Equations
, ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of
More informationOPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming
OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or
More informationLab 2e Thermal System Response and Effective Heat Transfer Coefficient
58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),
More informationNON-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 informationAn Admission Control Algorithm in Cloud Computing Systems
An Admsson Control Algorthm n Cloud Computng Systems Authors: Frank Yeong-Sung Ln Department of Informaton Management Natonal Tawan Unversty Tape, Tawan, R.O.C. ysln@m.ntu.edu.tw Yngje Lan Management Scence
More informationAn Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors
An Algorthm to Solve the Inverse Knematcs Problem of a Robotc Manpulator Based on Rotaton Vectors Mohamad Z. Al-az*, Mazn Z. Othman**, and Baker B. Al-Bahr* *AL-Nahran Unversty, Computer Eng. Dep., Baghdad,
More informationMathematical Modeling to Support Gamma Radiation Angular Distribution Measurements
Mathematcal Modelng to Support Gamma Radaton Angular Dstrbuton Measurements V. Baty, O. Stoyanov Insttute for Safety Problems of Nuclear Power Plants, Natonal Academy of Scences of Ukrane Ukrane D. Fedorchenko,
More informationComparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy
Comparatve Studes of Law of Conservaton of Energy and Law Clusters of Conservaton of Generalzed Energy No.3 of Comparatve Physcs Seres Papers Fu Yuhua (CNOOC Research Insttute, E-mal:fuyh1945@sna.com)
More informationA Bayesian Approach to Arrival Rate Forecasting for Inhomogeneous Poisson Processes for Mobile Calls
A Bayesan Approach to Arrval Rate Forecastng for Inhomogeneous Posson Processes for Moble Calls Mchael N. Nawar Department of Computer Engneerng Caro Unversty Caro, Egypt mchaelnawar@eee.org Amr F. Atya
More informationSimulated 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 informationVQ widely used in coding speech, image, and video
at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng
More informationMarkov 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 informationBasic Statistical Analysis and Yield Calculations
October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general
More informationChapter 2 A Class of Robust Solution for Linear Bilevel Programming
Chapter 2 A Class of Robust Soluton for Lnear Blevel Programmng Bo Lu, Bo L and Yan L Abstract Under the way of the centralzed decson-makng, the lnear b-level programmng (BLP) whose coeffcents are supposed
More informationTransfer 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 informationCHAPTER 9 CONCLUSIONS
78 CHAPTER 9 CONCLUSIONS uctlty and structural ntegrty are essentally requred for structures subjected to suddenly appled dynamc loads such as shock loads. Renforced Concrete (RC), the most wdely used
More informationmodeling of equilibrium and dynamic multi-component adsorption in a two-layered fixed bed for purification of hydrogen from methane reforming products
modelng of equlbrum and dynamc mult-component adsorpton n a two-layered fxed bed for purfcaton of hydrogen from methane reformng products Mohammad A. Ebrahm, Mahmood R. G. Arsalan, Shohreh Fatem * Laboratory
More informationClock-Gating and Its Application to Low Power Design of Sequential Circuits
Clock-Gatng and Its Applcaton to Low Power Desgn of Sequental Crcuts ng WU Department of Electrcal Engneerng-Systems, Unversty of Southern Calforna Los Angeles, CA 989, USA, Phone: (23)74-448 Massoud PEDRAM
More informationOnline Appendix to: Axiomatization and measurement of Quasi-hyperbolic Discounting
Onlne Appendx to: Axomatzaton and measurement of Quas-hyperbolc Dscountng José Lus Montel Olea Tomasz Strzaleck 1 Sample Selecton As dscussed before our ntal sample conssts of two groups of subjects. Group
More informationHigh 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 informationOptimum Design of Steel Frames Considering Uncertainty of Parameters
9 th World Congress on Structural and Multdscplnary Optmzaton June 13-17, 211, Shzuoka, Japan Optmum Desgn of Steel Frames Consderng ncertanty of Parameters Masahko Katsura 1, Makoto Ohsak 2 1 Hroshma
More informationprinceton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg
prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there
More information