Hard Real-time Embedded Systems are ubiquitously used today in safety critical commercial applications
|
|
- Dora Golden
- 6 years ago
- Views:
Transcription
1 Extenble and Scalable Tme Trggered Schedulng or Automotve Applcaton We Zheng Advor: Proeor Alberto Sangovann-Vncentell Che Revew Ma Berele CA Agenda Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Concluon Future Wor Problem Problem Model Model Mathematcal Mathematcal Model Model Algorthm Implementaton Implementaton Evaluaton Evaluaton Che Revew Ma
2 Proect Motvaton Hard Real-tme Embedded Stem are ubqutoul ued toda n aet crtcal commercal applcaton X-b-wre Power Tranmon Unt - 6-lne per da ppm redental deect - 5 month valdaton tme FABIO ROMEO Magnet- Marell DAC La Vega June 20th 2001 Vercaton o complex tem tme and reource ntenve For at tme-to-maret Extenble and Scalable tem Che Revew Ma Degn Flow Functonalt Archtecture Control algorthm degn Plant Model degn Fault Model Functonal Smulaton Functonal Model Phcal Archtecture Model ECU archtecture Networ archtecture Allocate Functon to Ta Ta and ther WCET Sgnal Mddleware OS Sotware Model Allocatng ta to ECU Allocatng gnal to BUS Mappng Renement Vrtual Prototpe Schedulng Smulaton capturng computatonal contrant TT behavoral mulaton Che Revew Ma
3 Functonalt: Allocate Functon to Ta TASK A TASK A INPUT PROCESSING TASK B DISTRIBUTED CONTROL AGREEMENT TASK D OUTPUT FAULT MANAGEMENT INPUT FAULT MANAGEMENT OUTPUT PROCESSING SYSTEM COORDINATION BY-WIRE CONTROL TASK C NODE STATE OF HEALTH SOURCE: GM Che Revew Ma Stem Archtecture ECU APP. TT TASKS FT. TT APP. OSEK TASKS TASKS OSEK TIME DISPATCHER... OSEK SCHEDULER Comm. controller BUS DRIVER BUS DRIVER... STATIC SEG COMMUNICATION CYCLE NIT SYMBOL WIN DYNAMIC SEG Che Revew Ma
4 Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Concluon Future Wor Che Revew Ma Problem Statement Ident a et o metrc to capture extenblt and calablt Appl the et o metrc n a degn Evaluate the eectvene o the et o metrc Speccall we want to: Stud a hard real tme embedded tem n the automotve doman Focu on the chedulng apect o tem degn Characterze extenblt and calablt n chedulng Appl the et o metrc n a chedulng algorthm Evaluate the eectvene o the approach wth ndutral cae tud Problem Problem Ident a Set o Metrc Formall Decrbe Metrc Appl Metrc To Degn Evaluate Reult w.r.t. Metrc Che Revew Ma
5 Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Concluon Future Wor Che Revew Ma Prevou Wor Statc cclc chedulng ha been extenvel reearched Clacal chedulng theor ue metrc uch a Mnmzng um o completon tme Mnmzng chedule length Mnmzng reource For real tme tem deadlne added a a contrant Empha hted to ndng eable oluton whle Mnmzng end-to-end dela Mnmzng communcaton cot Cloet problem concept come rom Paul Pop et al Cloet problem ormulaton come rom Armn Bender et al Che Revew Ma
6 Prevou Wor Paul Pop et al wrote about ncremental degn Ue lt chedulng approach to obtan a vald chedule Ue a heurtc to dtrbute lac n the tem Mng everal mportant component Preempton not condered Reultng chedule not utable or uture ta wth urgent deadlne Meage lac not dtrbuted Extenblt not condered Armn Bender et al ued mathematcal programmng or mappng and chedulng Wor motvated b otware-hardware co-degn Obectve to obtan chedule eablt whle Maxmzng Perormance Mnmzng reource Che Revew Ma Reearch Drecton Idle Tme Idle[ ECU1 T5_2] T1_1 T3 T5_1 T1_2 T5_2 ECU1 ECU2 T2_1 T4 T2_2 D 12_1 D 34 D 12_2 Bu Tme Data Slac Slac[ D12_2 T2_2] Focu on optmall utlze redundance n chedule or extenblt and calablt Idle tme and lac are tradtonall ncorporated n hard real tme embedded tem chedule to ncreae tem robutne We hould utlze thee redundance to: Tolerate ncremental degn change Accommodate new ta to be added n uture product update Che Revew Ma
7 Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Concluon Future Wor Che Revew Ma Capture the Metrc Model Model Extenblt Tolerate change o Ta WCET Tolerate change o Data WCTT Motvaton Scalablt Accommodate NEW ta b tatcall chedulng them on a legac tem Mantan Bu Schedule Mantan non-nvolved ECU chedule Mantan nvolved ECU chedule wthout reconguraton Meage let & Rght lac Max Sum o all lac Mn Varance o all lac Implementaton Approach Provde bloc o computaton tme or uture computaton ntenve ta Provde porot n chedule to allow or uture ta wth tght deadlne ECU dle tme dtrbuton Bu dle tme dtrbuton Evenl dtrbute all dle tme Che Revew Ma
8 Applng the Metrc Mathematcal Mathematcal Model Model Develop a ormal repreentaton o the problem ung mathematcal programmng and olve t ung extng olver Modelng Language: AMPL Advantage: obtan optmal oluton w.r.t. cot uncton Dadvantage: computatonall ntenve utable onl or moderatel zed problem Aumpton: Hard real tme deadlne Statcall cheduled ta wth data dependenc Dtrbuted and heterogeneou mult-proceor archtecture Tme trggered bu wth TDMA protocol Preempton allowed on ECU wth no level lmt Mult-rate ta upport wth adaptve ta graph expanon Fxed ta allocaton wth no ta mgraton Che Revew Ma Mathematcal Formulaton 1 Mathematcal Mathematcal Model Model Notaton The et o Ta T = { t = 1... m} The et o ECU E = { e = 1... n} The et o ta par wth data dependenc runnng on the ame ECU σ = {( t t t T t T t < t a = a } The et o ta par wth data dependenc runnng on derent ECU ϖ = {( t t t T t T t < t a a 1} The et o ta non-reachable ta par runnng on the ame ECU = {( t t t T t T a = a t <> t } The et o ta par runnng on the ame ECU π = {( t t t T t T a = a E} The et o ta allocaton or ECU λ { κ E} κ = { t a = 1} = Che Revew Ma
9 Mathematcal Formulaton 2 Mathematcal Mathematcal Model Model Parameter and Varable Mappng rom Ta to ECU M = T E = { a = 1... m; = 1... n} 1 ta mapped to ECU a = 0 otherwe Ta and Meage Ta: 6-tuple parameter varable Vector e T r T p T c T T T τ : e r p c WCET Releae Tme Perod Idle tme Startng tme Fnhng tme Meage: 5-tuple parameter varable Vector ς : mt l r m m mt ( ϖ WCTT l ( ϖ Let Slac rl ( ϖ Rght Slac m ( ϖ Startng tme m ( ϖ Fnhng tme Che Revew Ma Mathematcal Formulaton 3 Mathematcal Mathematcal Model Model Parameter and Varable (contnue Idle tme and Integer Varable c dle rt c ater lat c 0 = 1 Idle tme or ta not the rt ta n o t runnng ECU Frt dle tme or each ECU Idle tme or ECU beore the uper perod the tartng tme o ta precede the tartng tme o ta otherwe ( p z l 0 = 1 0 = 1 ta not preempted b ta otherwe data tranmtted rom ta to ta precede data tranmtted rom ta to l otherwe ( ( ϖ ( l ϖ Che Revew Ma
10 Che Revew Ma Mathematcal Formulaton 4 Subect to the ollowng contrant: Releae and Deadlne Contrant Executon Tme/Tranmon Contrant Precedence Contrant Non-negatve and Integer Contrant T d T r T T e p e = ( σ ( <= ϖ ( m ϖ ( mt m ϖ = ( m mt m = Λ Λ ( 1( ( (1 ( l or l bnar z bnar p m m T l = = ( ( ( 0( ϖ ϖ ϖ Mathematcal Model Mathematcal Model Che Revew Ma Mathematcal Formulaton 5 Contrant (contnued: Mutual excluon contrant Idle tme contrant n m or n m z m mt m n m or n m z m mt m m n m n m n m n m n Λ Λ ( ( (1 ( ( ϖ ϖ ϖ ϖ Λ Λ Λ Λ ( 1 ( 1( ( (1 ( p p p p p E e P c c E P c p c c p c lat ater dle lat ater dle dle dle = Λ Λ Λ Λ Λ ( (1 0 ( ( ( κ κ κ π π π Mathematcal Model Mathematcal Model
11 Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Concluon Future Wor Che Revew Ma Multple Obectve Cot Functon Algorthm Extenblt max R = ( ϖ (( m ( m Scalablt mns = a E T dle 2 ater _ lat 2 ( c average ( c average E Jontl Extenblt & Scalablt mn RS K ( R K S = 1 2 Che Revew Ma
12 Extenblt and Scalablt o Tme Trggered Schedulng Algorthm Mappng T1 T5 T2 ECU1 ECU2 T1 T3 T5 T2 T1 T2 T5 T4 T3 T4 T6 FlexRa Ta graph expanon (n a SUPERperod unctonalt archtecture Mnmze Scalable Checng Schedule End Schedulablt Extenble Schedule to End Latenc T1_1 T3 T1_1 T5_1 T3 T3 T5_1 T1_2 T5_1 T1_2 T5_2 Jontl Conderng ECU1 T1_1 Extenble T3 and Scalable T5_1 T1_2 T5_2 ECU1 ECU1 T2_1 T4 T1_1 T3 T2_1 T5_1 T1_2 T4 T4 T2_2 T2_2 ECU2 T2_1 T4 ECU1 ECU2 ECU2 D 12_1 D 34 D 12_2 D 12_1 D 34 D 34 T2_1 D 12_2 T4 T5_2 T2_2 T5_2 T2_2 Bu Bu D 12_1 D 34 D 12_2 ECU2 Bu Tme Tme 0 D 12_1 1 D 34 2 D 12_2 3 4 Tme Bu Extenble Scalable Schedule: Tme Schedule--- 0 Add 1 2 WCET Increae 3 4 WCET New Ta Change T1_1 T3 T3 T5_1 T5_1 T1_2 T5_2 T1_2 T6 T5_2 ECU1 T2_1 T2_1 T4 T4 T2_2 T2_2 ECU2 D 12_1 D 12_1 D 34 D 34 D 12_2 D 12_2 Bu Tme Che Revew 4 Ma T2_2 Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Summar Concluon Future Wor Che Revew Ma
13 Advanced Automotve Control Applcaton Obect Dtance and Speed Current Speed T9 T7 T8 Dered Speed T10 Current throttle Dered brang poton orce Dered Throttle poton Adaptve Crue Control T11 T13 T12 T14 Actuate Throttle Actuate brae T15 T16 T17 T18 Let-Rear Let-Front Rght-Rear Rght-Front Wheel peed Wheel Speed Wheel Speed Wheel Speed Hand-wheel Lateral Yaw Rate poton acceleraton Dered Brang Force Actuate Actuate Brae Throttle Tracton Control T19 T23 T20 T22 T21 T24 Hand-wheel Road-wheel poton orce Dered road- Dered hand- Wheel angle Wheel eort Electrc Power Steerng T3 T1 T4 T2 Actuate teerng Rac motor Force eedbac To drver T5 T6 Applcaton and correpondng ta graph repreentaton Che Revew Ma Archtecture and Ta Allocaton P1 T4T11 P2 T3T12 T22 P3 T10T20 Proceor wth ta allocated Throttle poton Steerng Rac Force Obect Dtance Actuator are drectl connected to the bu Throttle Value Hand- Wheel motor Steerng Rac Motor FlexRa LR Wheel peed LF Wheel Speed RR Wheel Speed RF Wheel Speed Car Speed Accelarator Hand- Wheel angle Brae Senor are drectl connected c to the bu Che Revew Ma
14 Implementaton Inratructure Implementaton Implementaton Cae tud Decrbe the Metrc Automatc AMPL data le generaton AMPL model wth cot uncton and contrant Formalze the Metrc CPLEX olver. Automatc Gant graph generaton T1_1 T3 T5_1 T1_2T5_2 ECU1 T2_1 T4 T2_2 ECU2 D 12_1 D 34 D 12_2 Bu Tme Sel-developed proect nratructure. O-the-hel proect nratructure Get Schedulng Reult Evaluate Reult w.r.t. Metrc Che Revew Ma Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Summar Concluon Future Wor Che Revew Ma
15 Cot Functon Evaluaton Evaluaton Evaluaton Mult-obectve cot uncton an abtracton Mathematcal programmng ormulaton ha lmted emantc Extenblt and calablt metrc are too complex Decrbed n ull the cot uncton would be too computatonall expenve Mut determne the cot uncton abtracton eectvel repreent the metrc Ue the reult o CPLEX olver Extract real lac and dle tme dtrbuton baed on prece denton o the metrc Compare reult wth the chedule wthout extenblt and calablt optmzaton Che Revew Ma Tradtonal Schedulng Reult Evaluaton Evaluaton Optmzng or End to End Latenc Che Revew Ma
16 Optmzed Schedulng Reult Evaluaton Evaluaton Optmzng or Extenblt and Scalablt Che Revew Ma Metrc Evaluaton Evaluaton Evaluaton Our et o metrc one abtracton o the extenblt and calablt concept Mut determne our metrc eectvel handle ncremental degn change Incremental Degn Scenaro: Bac ACC Stop-N-Go ACC Addton o a new Adaptve Crue Control eature Predct dered peed baed on: Dgtal map normaton Forward loong von enor Requre addton o ta and meage Some extng ta wll need more computaton tme Che Revew Ma
17 Adaptve Crue Control Evaluaton Evaluaton Incremental Degn Change: Add new Dgtal Map Computaton ta on P1 More complex algorthm n T10 (Dered Speed Control Dered Speed Control requre new nput rom Hand Wheel Senor Dered Throttle Control requre new nput rom Forward Von Senor Dgtal Map Computaton Hand Wheel Poton Obect Dtance Current Speed and Speed T19 T_add T9 T7 T8 Dered Speed Current throttle poton Dered Throttle poton Dered brang orce T11 T10 T12 Actuate Throttle Actuate brae T13 T14 Che Revew Ma Tradtonal Schedule Evaluaton Evaluaton In Tradton Schedule: Incremental change mpoble wthout ull rechedulng Che Revew Ma
18 Optmzed Schedule Evaluaton Evaluaton In Tradton Schedule: Incremental change mpoble wthout ull rechedulng In Optmzed Schedule: A lot more porot to accommodate new ta and meage Che Revew Ma Optmzed Schedule Evaluaton Evaluaton In Tradton Schedule: Incremental change mpoble wthout ull rechedulng In Optmzed Schedule: A lot more porot to accommodate new ta and meage New uncton added: Wthout dturbng legac chedule Che Revew Ma
19 Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Concluon Future Wor Che Revew Ma Concluon Succeull captured extenblt and calablt metrc Recognzed mplcaton n acceleratng tme-to-maret o embedded tem development Reduce re-vercaton burden n ncremental degn low No ncreae n reource requrement Formulated the chedulng problem a a mathematcal programmng problem Contructed mult-obect cot uncton abtracted rom the metrc The cot uncton hown to be eectve or the metrc The metrc hown to be eectve n ndutr cae tud Che Revew Ma
20 Agenda Proect Motvaton Problem Statement Prevou Wor Invetgatve Approach Metrc Denton Mathematcal Formulaton Mult-Obectve Cot Functon Cae Stud Stem Decrpton Cot Functon Evaluaton Metrc Evaluaton Concluon Future Wor Che Revew Ma Future Wor Protocol Comparon FlexRa V. TTP Slot Sze Optmzaton Slot Sze Exploraton COMMUNICATION CYCLE Read/Wrte Meage Frame Pacng Buer Requrement COMMUNICATION CYCLE Fragmentaton Che Revew Ma
21 Future Wor Control algorthm degn Plant Model degn Fault Model Functonal Smulaton Ta and ther WCET Sgnal Mddleware OS Functonalt Functonal Model Sotware Model Allocate Functon to Ta Archtecture Phcal Archtecture Model ECU archtecture Networ archtecture Renement Allocatng ta to ECU Allocatng gnal to BUS Vrtual Prototpe Mappng Schedulng Tme Trggered Schedulng Meage Schedulng Ta Schedulng Smulaton capturng computatonal contrant TT behavoral mulaton Slot Sze Optmzaton Che Revew Ma Reerence Paul Pop: Anal and Snthe o Communcaton-Intenve Heterogeneou Real-Tme Stem. Ph. D. The No. 833 Dept. o Computer and Inormaton Scence Lnöpng Unvert June 2003 H. Kopetz et al. Real-Tme Stem-Degn Prncple or Dtrbuted Embedded Applcaton Kluwer Academc Publher 1997 N. Kandaam J. P. Hae B. T. Murra. Dependable Communcaton Snthe or Dtrbuted Embedded Stem. Lu Laland Schedulng Algorthm or Multprogrammng n a Hard-Real-Tme Envronment J. ACM 20 p Devller Gooen Lu and Laland chedulablt tet revted Inormaton Proceng Letter p Bn Go. Buttazzo Gu. Buttazzo Rate Monotonc Anal : The Hperbolc Bound p Pradumna K. Mhra and Saneev M. Na. Dtrbuted Control Stem Development or FlexRa-Baed Stem A. Bender Degn o an Optmal Looel Coupled Heterogeneou Multproceor Stem n Proceedng o Electronc Degn and Tet Conerence page Che Revew Ma
Synchronization Protocols. Task Allocation Bin-Packing Heuristics: First-Fit Subtasks assigned in arbitrary order To allocate a new subtask T i,j
End-to-End Schedulng Framework 1. Tak allocaton: bnd tak to proceor 2. Synchronzaton protocol: enforce precedence contrant 3. Subdeadlne agnment 4. Schedulablty analy Tak Allocaton Bn-Packng eurtc: Frt-Ft
More informationSolution Methods for Time-indexed MIP Models for Chemical Production Scheduling
Ian Davd Lockhart Bogle and Mchael Farweather (Edtor), Proceedng of the 22nd European Sympoum on Computer Aded Proce Engneerng, 17-2 June 212, London. 212 Elever B.V. All rght reerved. Soluton Method for
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 informationPreemptive scheduling. Disadvantages of preemptions WCET. Preemption indirect costs 19/10/2018. Cache related preemption delay
19/1/18 Preemptve cedulng Mot o wor on cedulng a been ocued on ully preemptve ytem, becaue tey allow ger reponvene: Preemptve Non Preemptve Dadvantage o preempton However, eac preempton a a cot: ontext
More informationReal-Time Systems. Multiprocessor scheduling. Multiprocessor scheduling. Multiprocessor scheduling
Real-Tme Systems Multprocessor schedulng Specfcaton Implementaton Verfcaton Multprocessor schedulng -- -- Global schedulng How are tasks assgned to processors? Statc assgnment The processor(s) used for
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 informationA FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS
Shervn Haamn A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS INTRODUCTION Increasng computatons n applcatons has led to faster processng. o Use more cores n a chp
More informationMODELLING OF STOCHASTIC PARAMETERS FOR CONTROL OF CITY ELECTRIC TRANSPORT SYSTEMS USING EVOLUTIONARY ALGORITHM
MODELLING OF STOCHASTIC PARAMETERS FOR CONTROL OF CITY ELECTRIC TRANSPORT SYSTEMS USING EVOLUTIONARY ALGORITHM Mkhal Gorobetz, Anatoly Levchenkov Inttute of Indutral Electronc and Electrotechnc, Rga Techncal
More informationMethod Of Fundamental Solutions For Modeling Electromagnetic Wave Scattering Problems
Internatonal Workhop on MehFree Method 003 1 Method Of Fundamental Soluton For Modelng lectromagnetc Wave Scatterng Problem Der-Lang Young (1) and Jhh-We Ruan (1) Abtract: In th paper we attempt to contruct
More informationMODELLING OF TRANSIENT HEAT TRANSPORT IN TWO-LAYERED CRYSTALLINE SOLID FILMS USING THE INTERVAL LATTICE BOLTZMANN METHOD
Journal o Appled Mathematc and Computatonal Mechanc 7, 6(4), 57-65 www.amcm.pcz.pl p-issn 99-9965 DOI:.75/jamcm.7.4.6 e-issn 353-588 MODELLING OF TRANSIENT HEAT TRANSPORT IN TWO-LAYERED CRYSTALLINE SOLID
More informationStart Point and Trajectory Analysis for the Minimal Time System Design Algorithm
Start Pont and Trajectory Analy for the Mnmal Tme Sytem Degn Algorthm ALEXANDER ZEMLIAK, PEDRO MIRANDA Department of Phyc and Mathematc Puebla Autonomou Unverty Av San Claudo /n, Puebla, 757 MEXICO Abtract:
More informationChapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder
S-. The Method of Steepet cent Chapter. Supplemental Text Materal The method of teepet acent can be derved a follow. Suppoe that we have ft a frtorder model y = β + β x and we wh to ue th model to determne
More informationPortioned Static-Priority Scheduling on Multiprocessors
Portoned Statc-Prorty Schedulng on Multproceor Shnpe Kato and Nobuyuk Yamaak School of Scence for Open and Envronmental Sytem Keo Unverty, Japan {hnpe,yamaak}@ny.c.keo.ac.jp Abtract Th paper propoe an
More informationConfidence intervals for the difference and the ratio of Lognormal means with bounded parameters
Songklanakarn J. Sc. Technol. 37 () 3-40 Mar.-Apr. 05 http://www.jt.pu.ac.th Orgnal Artcle Confdence nterval for the dfference and the rato of Lognormal mean wth bounded parameter Sa-aat Nwtpong* Department
More informationTeam. Outline. Statistics and Art: Sampling, Response Error, Mixed Models, Missing Data, and Inference
Team Stattc and Art: Samplng, Repone Error, Mxed Model, Mng Data, and nference Ed Stanek Unverty of Maachuett- Amhert, USA 9/5/8 9/5/8 Outlne. Example: Doe-repone Model n Toxcology. ow to Predct Realzed
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 informationHarmonic oscillator approximation
armonc ocllator approxmaton armonc ocllator approxmaton Euaton to be olved We are fndng a mnmum of the functon under the retrcton where W P, P,..., P, Q, Q,..., Q P, P,..., P, Q, Q,..., Q lnwgner functon
More informationUnsupervised Pattern Classification
Unuperved Pattern Clacaton Chang-Tun L Department o Computer Scence Unvert o Warwck UK Multmeda Securt and Forenc 1 Ba o the Preentaton C.-T. L Y. Yuan and R. Wlon "An Unuperved Condtonal Random Feld Approach
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 informationA Quadratic Constraint Total Least-squares Algorithm for Hyperbolic Location
I. J. Communcaton, Network and Sytem Scence, 8,, 5-6 Publhed Onlne May 8 n ScRe (http://www.srpublhng.org/journal/jcn/). A Quadratc Contrant otal Leat-quare Algorthm for Hyperbolc Locaton Ka YANG, Janpng
More informationOutline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique
Outlne and Readng Dynamc Programmng The General Technque ( 5.3.2) -1 Knapsac Problem ( 5.3.3) Matrx Chan-Product ( 5.3.1) Dynamc Programmng verson 1.4 1 Dynamc Programmng verson 1.4 2 Dynamc Programmng
More informationJoint Source Coding and Higher-Dimension Modulation
Jont Codng and Hgher-Dmenon Modulaton Tze C. Wong and Huck M. Kwon Electrcal Engneerng and Computer Scence Wchta State Unvert, Wchta, Kana 676, USA {tcwong; huck.kwon}@wchta.edu Abtract Th paper propoe
More informationTOPICS MULTIPLIERLESS FILTER DESIGN ELEMENTARY SCHOOL ALGORITHM MULTIPLICATION
1 2 MULTIPLIERLESS FILTER DESIGN Realzaton of flters wthout full-fledged multplers Some sldes based on support materal by W. Wolf for hs book Modern VLSI Desgn, 3 rd edton. Partly based on followng papers:
More informationAn Integrated OR/CP Method for Planning and Scheduling
An Integrated OR/CP Method for Plannng and Schedulng John Hooer Carnege Mellon Unversty IT Unversty of Copenhagen June 2005 The Problem Allocate tass to facltes. Schedule tass assgned to each faclty. Subect
More informationScattering of two identical particles in the center-of. of-mass frame. (b)
Lecture # November 5 Scatterng of two dentcal partcle Relatvtc Quantum Mechanc: The Klen-Gordon equaton Interpretaton of the Klen-Gordon equaton The Drac equaton Drac repreentaton for the matrce α and
More informationPlanning and Scheduling to Minimize Makespan & Tardiness. John Hooker Carnegie Mellon University September 2006
Plannng and Schedulng to Mnmze Makespan & ardness John Hooker Carnege Mellon Unversty September 2006 he Problem Gven a set of tasks, each wth a deadlne 2 he Problem Gven a set of tasks, each wth a deadlne
More informationInterconnect Optimization for Deep-Submicron and Giga-Hertz ICs
Interconnect Optmzaton for Deep-Submcron and Gga-Hertz ICs Le He http://cadlab.cs.ucla.edu/~hele UCLA Computer Scence Department Los Angeles, CA 90095 Outlne Background and overvew LR-based STIS optmzaton
More informationA Simple Inventory System
A Smple Inventory System Lawrence M. Leems and Stephen K. Park, Dscrete-Event Smulaton: A Frst Course, Prentce Hall, 2006 Hu Chen Computer Scence Vrgna State Unversty Petersburg, Vrgna February 8, 2017
More informationEmbedded Systems. 4. Aperiodic and Periodic Tasks
Embedded Systems 4. Aperodc and Perodc Tasks Lothar Thele 4-1 Contents of Course 1. Embedded Systems Introducton 2. Software Introducton 7. System Components 10. Models 3. Real-Tme Models 4. Perodc/Aperodc
More informationChapter 6 The Effect of the GPS Systematic Errors on Deformation Parameters
Chapter 6 The Effect of the GPS Sytematc Error on Deformaton Parameter 6.. General Beutler et al., (988) dd the frt comprehenve tudy on the GPS ytematc error. Baed on a geometrc approach and aumng a unform
More informationModeling of Wave Behavior of Substrate Noise Coupling for Mixed-Signal IC Design
Modelng of Wave Behavor of Subtrate Noe Couplng for Mxed-Sgnal IC Degn Georgo Veron, Y-Chang Lu, and Robert W. Dutton Center for Integrated Sytem, Stanford Unverty, Stanford, CA 9435 yorgo@gloworm.tanford.edu
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 informationOptimization Methods for Engineering Design. Logic-Based. John Hooker. Turkish Operational Research Society. Carnegie Mellon University
Logc-Based Optmzaton Methods for Engneerng Desgn John Hooker Carnege Mellon Unerst Turksh Operatonal Research Socet Ankara June 1999 Jont work wth: Srnas Bollapragada General Electrc R&D Omar Ghattas Cl
More informationClock-Driven Scheduling (in-depth) Cyclic Schedules: General Structure
CPSC-663: Real-me Systems n-depth Precompute statc schedule o-lne e.g. at desgn tme: can aord expensve algorthms. Idle tmes can be used or aperodc jobs. Possble mplementaton: able-drven Schedulng table
More informationm = 4 n = 9 W 1 N 1 x 1 R D 4 s x i
GREEDY WIRE-SIZING IS LINEAR TIME Chr C. N. Chu D. F. Wong cnchu@c.utexa.edu wong@c.utexa.edu Department of Computer Scence, Unverty of Texa at Autn, Autn, T 787. ABSTRACT In nterconnect optmzaton by wre-zng,
More informationTwo Methods to Release a New Real-time Task
Two Methods to Release a New Real-tme Task Abstract Guangmng Qan 1, Xanghua Chen 2 College of Mathematcs and Computer Scence Hunan Normal Unversty Changsha, 410081, Chna qqyy@hunnu.edu.cn Gang Yao 3 Sebel
More informationComputational Biology Lecture 8: Substitution matrices Saad Mneimneh
Computatonal Bology Lecture 8: Substtuton matrces Saad Mnemneh As we have ntroduced last tme, smple scorng schemes lke + or a match, - or a msmatch and -2 or a gap are not justable bologcally, especally
More informationSingle Variable Optimization
8/4/07 Course Instructor Dr. Raymond C. Rump Oce: A 337 Phone: (95) 747 6958 E Mal: rcrump@utep.edu Topc 8b Sngle Varable Optmzaton EE 4386/530 Computatonal Methods n EE Outlne Mathematcal Prelmnares Sngle
More informationELG3336: Op Amp-based Active Filters
ELG6: Op Amp-baed Actve Flter Advantage: educed ze and weght, and thereore paratc. Increaed relablty and mproved perormance. Smpler degn than or pave lter and can realze a wder range o uncton a well a
More informationBatch RL Via Least Squares Policy Iteration
Batch RL Va Leat Square Polcy Iteraton Alan Fern * Baed n part on lde by Ronald Parr Overvew Motvaton LSPI Dervaton from LSTD Expermental reult Onlne veru Batch RL Onlne RL: ntegrate data collecton and
More informationRoot Locus Techniques
Root Locu Technque ELEC 32 Cloed-Loop Control The control nput u t ynthezed baed on the a pror knowledge of the ytem plant, the reference nput r t, and the error gnal, e t The control ytem meaure the output,
More informationForesighted Resource Reciprocation Strategies in P2P Networks
Foreghted Reource Recprocaton Stratege n PP Networ Hyunggon Par and Mhaela van der Schaar Electrcal Engneerng Department Unverty of Calforna Lo Angele (UCLA) Emal: {hgpar mhaela@ee.ucla.edu Abtract We
More informationMULTIPLE REGRESSION ANALYSIS For the Case of Two Regressors
MULTIPLE REGRESSION ANALYSIS For the Cae of Two Regreor In the followng note, leat-quare etmaton developed for multple regreon problem wth two eplanator varable, here called regreor (uch a n the Fat Food
More informationOn the SO 2 Problem in Thermal Power Plants. 2.Two-steps chemical absorption modeling
Internatonal Journal of Engneerng Reearch ISSN:39-689)(onlne),347-53(prnt) Volume No4, Iue No, pp : 557-56 Oct 5 On the SO Problem n Thermal Power Plant Two-tep chemcal aborpton modelng hr Boyadjev, P
More informationHybrid Unicast and Multicast Flow Control: A Linear Optimization Approach
Hybrd Uncast and Multcast Flow Control: A Lnear Optmzaton Approach Homayoun Youse zadeh Fatemeh Fazel Hamd Jaarhan Department o EECS Unversty o Calorna, Irvne Flow Control Motvaton Content delvery over
More informationRobust Capacitated Facility Location Problem: Optimization Model and Solution Algorithms
222222222214 Journal of Uncertan Sytem Vol.7, No.1, pp.22-35, 2013 Onlne at: www.u.org.uk Robut Capactated Faclty Locaton Problem: Optmzaton Model and Soluton Algorthm Ragheb Rahmanan *, Mohammad Sad-Mehrabad,
More informationMULTISTART OPTIMIZATION WITH A TRAINABLE DECISION MAKER FOR AVOIDING HIGH-VALUED LOCAL MINIMA
3 rd Internatonal Conference on Experment/Proce/Sytem Modelng/Smulaton & Optmzaton 3 rd IC-EpMO Athen, 8- July, 2009 IC-EpMO MULTISTART OPTIMIZATION WITH A TRAINABLE DECISION MAKER FOR AVOIDING HIGH-VALUED
More information2.3 Least-Square regressions
.3 Leat-Square regreon Eample.10 How do chldren grow? The pattern of growth vare from chld to chld, o we can bet undertandng the general pattern b followng the average heght of a number of chldren. Here
More informationAdditional File 1 - Detailed explanation of the expression level CPD
Addtonal Fle - Detaled explanaton of the expreon level CPD A mentoned n the man text, the man CPD for the uterng model cont of two ndvdual factor: P( level gen P( level gen P ( level gen 2 (.).. CPD factor
More informationSmall signal analysis
Small gnal analy. ntroducton Let u conder the crcut hown n Fg., where the nonlnear retor decrbed by the equaton g v havng graphcal repreentaton hown n Fg.. ( G (t G v(t v Fg. Fg. a D current ource wherea
More informationAn easy way to relate optical element motion to system pointing stability
An eay way to relate optcal element moton to ytem pontng tablty J. H. Burge College o Optcal Scence Unverty o Arzona, Tucon, AZ 85721, USA jburge@optc.arzona.edu, (520-621-8182) ABSTRACT The optomechancal
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 informationA Hybrid Evolution Algorithm with Application Based on Chaos Genetic Algorithm and Particle Swarm Optimization
Natonal Conference on Informaton Technology and Computer Scence (CITCS ) A Hybrd Evoluton Algorthm wth Applcaton Baed on Chao Genetc Algorthm and Partcle Swarm Optmzaton Fu Yu School of Computer & Informaton
More informationAPPLICATIONS OF RELIABILITY ANALYSIS TO POWER ELECTRONICS SYSTEMS
APPLICATIONS OF RELIABILITY ANALYSIS TO POWER ELECTRONICS SYSTEMS Chanan Sngh, Fellow IEEE Praad Enjet, Fellow IEEE Department o Electrcal Engneerng Texa A&M Unverty College Staton, Texa USA Joydeep Mtra,
More informationCHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS
CHAPTER 9 LINEAR MOMENTUM, IMPULSE AND COLLISIONS 103 Phy 1 9.1 Lnear Momentum The prncple o energy conervaton can be ued to olve problem that are harder to olve jut ung Newton law. It ued to decrbe moton
More informationMITSUBISHI ELECTRIC RESEARCH LABORATORIES PSD of UWB Signals. Yves-Paul Nakache, Andreas Molisch. TR December 2006
MISUBISHI ELECRIC RESEARCH LABORAORIES http://www.merl.com PSD o UWB Sgnal Yve-Paul akache, Andrea Molch R5-3 December 6 Abtract h document tude the Power Spectral Denty (PSD) o tme-hoppng mpule rado (H-
More informationMTF for color Bayer pattern detector
MTF or color Baer pattern detector Elor Yotam, Pnk Eph and Yaacob Am. aael Ltd., P.O.B. 5, aa, Irael otame@raael.co.l ABTACT We preent a model or calculatng the patal Frequenc epone (F or Baer pattern
More informationApplication of the Fault Tolerance of Reduced Bond Graph Approach of Parallel Computing of A Matching Network
Applcaton of e Fault Tolerance of Reduced Bond Graph Approach of arallel Computng of A Matchng Networ hv Kumar Gupta, Rajv Kumar, Krhna Kumar erma Abtract In paper we preent a new meod for modelng hgh
More informationDiscrete Simultaneous Perturbation Stochastic Approximation on Loss Function with Noisy Measurements
0 Amercan Control Conference on O'Farrell Street San Francco CA USA June 9 - July 0 0 Dcrete Smultaneou Perturbaton Stochatc Approxmaton on Lo Functon wth Noy Meaurement Q Wang and Jame C Spall Abtract
More informationDigital Simulation of Power Systems and Power Electronics using the MATLAB Power System Blockset 筑龙网
Dgtal Smulaton of Power Sytem and Power Electronc ung the MATAB Power Sytem Blocket Power Sytem Blocket Htory Deeloped by IREQ (HydroQuébec) n cooperaton wth Teqm, Unerté aal (Québec), and École de Technologe
More informationSingle-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition
Sngle-Faclty Schedulng over Long Tme Horzons by Logc-based Benders Decomposton Elvn Coban and J. N. Hooker Tepper School of Busness, Carnege Mellon Unversty ecoban@andrew.cmu.edu, john@hooker.tepper.cmu.edu
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 informationAP Statistics Ch 3 Examining Relationships
Introducton To tud relatonhp between varable, we mut meaure the varable on the ame group of ndvdual. If we thnk a varable ma eplan or even caue change n another varable, then the eplanator varable and
More informationSpecification -- Assumptions of the Simple Classical Linear Regression Model (CLRM) 1. Introduction
ECONOMICS 35* -- NOTE ECON 35* -- NOTE Specfcaton -- Aumpton of the Smple Clacal Lnear Regreon Model (CLRM). Introducton CLRM tand for the Clacal Lnear Regreon Model. The CLRM alo known a the tandard lnear
More informationAdaptive multi-point sequential sampling methodology for highly nonlinear automotive crashworthiness design problems
11 th World Congress on Structural and Multdscplnary Optmsaton 07 th -12 th, June 2015, Sydney Australa Adaptve mult-pont sequental samplng methodology or hghly nonlnear automotve crashworthness desgn
More informationRobust cross-directional control of large scale sheet and lm processes
Journal of Proce Control (00) 49±77 www.elever.com/locate/jprocont Robut cro-drectonal control of large cale heet and lm procee Jeremy G. VanAntwerp, Andrew P. Feathertone, Rchard D. Braatz * Large Scale
More informationModule 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 informationPerformance Bounds for P2P Streaming System with Transcoding
Appl. Math. Inf. Sc. 7, No. 6, 2477-2484 2013 2477 Appled Mathematc & Informaton Scence An Internatonal Journal http://dx.do.org/10.12785/am/070641 Performance Bound for P2P Streamng Sytem wth Trancodng
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 informationPhysics 111. CQ1: springs. con t. Aristocrat at a fixed angle. Wednesday, 8-9 pm in NSC 118/119 Sunday, 6:30-8 pm in CCLIR 468.
c Announcement day, ober 8, 004 Ch 8: Ch 10: Work done by orce at an angle Power Rotatonal Knematc angular dplacement angular velocty angular acceleraton Wedneday, 8-9 pm n NSC 118/119 Sunday, 6:30-8 pm
More informationAP Physics 1 & 2 Summer Assignment
AP Physcs 1 & 2 Summer Assgnment AP Physcs 1 requres an exceptonal profcency n algebra, trgonometry, and geometry. It was desgned by a select group of college professors and hgh school scence teachers
More informationModule 5. Cables and Arches. Version 2 CE IIT, Kharagpur
odule 5 Cable and Arche Veron CE IIT, Kharagpur Leon 33 Two-nged Arch Veron CE IIT, Kharagpur Intructonal Objectve: After readng th chapter the tudent wll be able to 1. Compute horzontal reacton n two-hnged
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 informationSeismic Reliability Analysis and Topology Optimization of Lifeline Networks
The 4 th World Conference on Earthquake Engneerng October 2-7, 2008, Beng, Chna Semc Relablty Analy and Topology Optmzaton of Lfelne Network ABSTRACT: Je L and We Lu 2 Profeor, Dept. of Buldng Engneerng,
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 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 informationLecture 2 Solution of Nonlinear Equations ( Root Finding Problems )
Lecture Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods Numercal Methods Bracketng Methods Open Methods Convergence Notatons Root Fndng
More informationThe Two-scale Finite Element Errors Analysis for One Class of Thermoelastic Problem in Periodic Composites
7 Asa-Pacfc Engneerng Technology Conference (APETC 7) ISBN: 978--6595-443- The Two-scale Fnte Element Errors Analyss for One Class of Thermoelastc Problem n Perodc Compostes Xaoun Deng Mngxang Deng ABSTRACT
More informationA NUMERICAL MODELING OF MAGNETIC FIELD PERTURBATED BY THE PRESENCE OF SCHIP S HULL
A NUMERCAL MODELNG OF MAGNETC FELD PERTURBATED BY THE PRESENCE OF SCHP S HULL M. Dennah* Z. Abd** * Laboratory Electromagnetc Sytem EMP BP b Ben-Aknoun 606 Alger Algera ** Electronc nttute USTHB Alger
More informationA 2D Bounded Linear Program (H,c) 2D Linear Programming
A 2D Bounded Lnear Program (H,c) h 3 v h 8 h 5 c h 4 h h 6 h 7 h 2 2D Lnear Programmng C s a polygonal regon, the ntersecton of n halfplanes. (H, c) s nfeasble, as C s empty. Feasble regon C s unbounded
More informationIntroduction to Interfacial Segregation. Xiaozhe Zhang 10/02/2015
Introducton to Interfacal Segregaton Xaozhe Zhang 10/02/2015 Interfacal egregaton Segregaton n materal refer to the enrchment of a materal conttuent at a free urface or an nternal nterface of a materal.
More informationSCALARS AND VECTORS All physical quantities in engineering mechanics are measured using either scalars or vectors.
SCALARS AND ECTORS All phscal uanttes n engneerng mechancs are measured usng ether scalars or vectors. Scalar. A scalar s an postve or negatve phscal uantt that can be completel specfed b ts magntude.
More informationStability of an Adaptive Switched Controller for Power System Oscillation Damping using Remote Synchrophasor Signals
Stablty of an Adaptve Swtched Controller for Power Sytem Ocllaton Dampng ung Remote Synchrophaor Sgnal Farhad R. Pour Safae 1, Scott G. Ghocel 2, João P. Hepanha 1, and Joe H. Chow 3 Abtract Th paper concerned
More informationMulti-Fidelity Surrogate Based on Single Linear Regression
echncal Note Mult-Fdelty Surrogate Based on Sngle near Regresson Ymng Zhang, Nam-o Km, Chanyoung Park, Raphael. atka Department o Mechancal and Aerospace Engneerng Unversty o Florda Ganesvlle, Florda,
More informationAn Integrated Asset Allocation and Path Planning Method to to Search for a Moving Target in in a Dynamic Environment
An Integrated Asset Allocaton and Path Plannng Method to to Search for a Movng Target n n a Dynamc Envronment Woosun An Mansha Mshra Chulwoo Park Prof. Krshna R. Pattpat Dept. of Electrcal and Computer
More informationEE 330 Lecture 24. Small Signal Analysis Small Signal Analysis of BJT Amplifier
EE 0 Lecture 4 Small Sgnal Analss Small Sgnal Analss o BJT Ampler Eam Frda March 9 Eam Frda Aprl Revew Sesson or Eam : 6:00 p.m. on Thursda March 8 n Room Sweene 6 Revew rom Last Lecture Comparson o Gans
More informationA Novel Approach for Testing Stability of 1-D Recursive Digital Filters Based on Lagrange Multipliers
Amercan Journal of Appled Scence 5 (5: 49-495, 8 ISSN 546-939 8 Scence Publcaton A Novel Approach for Tetng Stablty of -D Recurve Dgtal Flter Baed on Lagrange ultpler KRSanth, NGangatharan and Ponnavakko
More informationResonant FCS Predictive Control of Power Converter in Stationary Reference Frame
Preprnt of the 9th World Congre The Internatonal Federaton of Automatc Control Cape Town, South Afrca. Augut -9, Reonant FCS Predctve Control of Power Converter n Statonary Reference Frame Lupng Wang K
More informationOPTIMAL COMPUTING BUDGET ALLOCATION FOR MULTI-OBJECTIVE SIMULATION MODELS. David Goldsman
Proceedng of the 004 Wnter Smulaton Conference R.G. Ingall, M. D. Roett, J. S. Smth, and B. A. Peter, ed. OPTIMAL COMPUTING BUDGET ALLOCATION FOR MULTI-OBJECTIVE SIMULATION MODELS Loo Hay Lee Ek Peng Chew
More informationGlobal Optimization of Bilinear Generalized Disjunctive Programs
Global Optmzaton o Blnear Generalzed Dsunctve Programs Juan Pablo Ruz Ignaco E. Grossmann Department o Chemcal Engneerng Center or Advanced Process Decson-mang Unversty Pttsburgh, PA 15213 1 Non-Convex
More informationOutline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]
DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm
More information= 1.23 m/s 2 [W] Required: t. Solution:!t = = 17 m/s [W]! m/s [W] (two extra digits carried) = 2.1 m/s [W]
Secton 1.3: Acceleraton Tutoral 1 Practce, page 24 1. Gven: 0 m/s; 15.0 m/s [S]; t 12.5 s Requred: Analyss: a av v t v f v t a v av f v t 15.0 m/s [S] 0 m/s 12.5 s 15.0 m/s [S] 12.5 s 1.20 m/s 2 [S] Statement:
More informationApplication of Nonbinary LDPC Codes for Communication over Fading Channels Using Higher Order Modulations
Applcaton of Nonbnary LDPC Codes for Communcaton over Fadng Channels Usng Hgher Order Modulatons Rong-Hu Peng and Rong-Rong Chen Department of Electrcal and Computer Engneerng Unversty of Utah Ths work
More informationReliable Power Delivery for 3D ICs
Relable Power Delvery for 3D ICs Pngqang Zhou Je Gu Pulkt Jan Chrs H. Km Sachn S. Sapatnekar Unversty of Mnnesota Power Supply Integrty n 3D Puttng the power n s as mportant as gettng the heat out Hgher
More informationAdaptive Memory Programming for the Robust Capacitated International Sourcing Problem
Adaptve Memory Programmng for the Robut Capactated Internatonal Sourcng Problem Joé Lu González Velarde Centro de Stema de Manufactura, ITESM Monterrey, Méxco Lugonzal@campu.mty.tem.mx Rafael Martí Departamento
More informationLocal Approximation of Pareto Surface
Proceedngs o the World Congress on Engneerng 007 Vol II Local Approxmaton o Pareto Surace S.V. Utyuzhnkov, J. Magnot, and M.D. Guenov Abstract In the desgn process o complex systems, the desgner s solvng
More informationLast Time. Priority-based scheduling. Schedulable utilization Rate monotonic rule: Keep utilization below 69% Static priorities Dynamic priorities
Last Tme Prorty-based schedulng Statc prortes Dynamc prortes Schedulable utlzaton Rate monotonc rule: Keep utlzaton below 69% Today Response tme analyss Blockng terms Prorty nverson And solutons Release
More informationVague environment based two-step fuzzy rule interpolation method
Johanyák, Z. C., Kovác, Sz.: Vague Envronment-baed Two-tep Fuzzy Rule Interpolaton Method, 5th Slovakan-Hungaran Jont Sympoum on ppled Machne Intellgence and Informatc (SMI 27), January 25-26, 27 Poprad,
More informationJoint Energy-Efficient Cooperative Spectrum Sensing and Power Allocation in Cognitive Machine-to-Machine Communications
Jont Energy-Effcent Cooperatve Spectrum Senng and Power Allocaton n Cogntve Machne-to-Machne Communcaton Ha Ngoc Pham, Yan Zhang, Tor Skee, Paal E. Engeltad, Frank Elaen Department of Informatc, Unverty
More informationResearch Report. Eiichi Ono, Yoshikazu Hattori, Yuji Muragishi. Abstract. Special Issue Estimation and Control of Vehicle Dynamics for Active Safety
Specal Issue Estmaton and Control of Vehcle Dynamcs for Actve Safety 7 Research Report Estmaton of Tre Frcton Crcle and Vehcle Dynamcs Integrated Control for Four-wheel Dstrbuted Steerng and Four-wheel
More information