Fragility Based Rehabilitation Decision Analysis


 Brianne Owens
 3 years ago
 Views:
Transcription
1 .171. Fraglty Based Rehabltaton Decson Analyss Cagdas Kafal Graduate Student, School of Cvl and Envronmental Engneerng, Cornell Unversty Research Supervsor: rcea Grgoru, Professor Summary A method s presented for assessng the sesmc performance of structural/nonstructural systems and developng ratonal strateges for ncreasng the sesmc reslence of these systems. The sesmc performance s measured by fraglty surfaces, that s, the probablty of system falure as a functon of moment magntude and stetosource dstance, consequences of system damage and falure, and system recovery tme followng sesmc events. The nput to the analyss conssts of () sesmc hazard, () structural/nonstructural systems propertes, () performance crtera, (v) rehabltaton strateges, and (v) a reference tme. Estmates of losses and recovery tmes can be derved usng fraglty nformaton, fnancal models, and avalable resources. A structural/nonstructural system located n New York Cty s used to demonstrate the methodology. Fragltes are obtaned for structural/nonstructural components and systems for several lmt states. Also, statstcs are obtaned for lfe tme losses and recovery tmes correspondng to dfferent rehabltaton alternatves. Introducton Captal allocaton decsons for a health care faclty nclude, for example, openng a new unt, extendng or closng some exstng unts, buyng new equpment, and relocatng the hosptal buldng. These decsons are based on lfe cycle capacty, vewed as the level of performance defned for a servce, and cost estmates. Exstng geotechncal, structural/nonstructural systems can be left as they are or can be retroftted usng one of the avalable rehabltaton alternatves. Leavng a system as t s seems to be reasonable for shortterm decsons but retrofttng the system, despte ts ntal costs, mght be benefcal n the long run. A probablstc methodology s requred to make a rehabltaton decson snce sesmc hazard and system performance are uncertan. ost of the exstng earthquake loss estmaton methodologes usually calculate losses ncludng drect and ndrect economc and socal losses for a gven regon, based on the maxmum credble earthquake. The ATC13 (ATC, 1985) methodology provdes damage and loss estmates, based on expertopnon, for ndustral, commercal, resdental, utlty and transportaton facltes. HAZUS (FEA, 1999) estmates potental losses on a regonal bass and these estmates are essental to decsonmakng at all levels of government, provdng a bass for developng mtgaton polcy, and response and recovery plannng. Both methods were developed to estmate losses for a large number of facltes n a specfed regon usng the maxmum credble earthquake and should not be appled to an ndvdual faclty. Losses estmated by usng the maxmum credble earthquake may not be accurate (Kafal and Grgoru, 24a). The man objectve of ths paper s the development of a methodology for evaluatng the sesmc performance and development of optmal rehabltaton strateges of ndvdual health care facltes Fraglty Based Rehabltaton Decson Analyss 47
2 durng a specfed tme nterval. The sesmc performance s measured by fraglty surfaces, that s, the probablty of system falure as a functon of moment magntude and stetosource dstance, consequences of system damage and falure, and system recovery tme followng sesmc events. Estmates of losses and recovery tmes, referred to as lfe cycle losses and recovery tmes, can be derved usng fraglty nformaton, fnancal models, and avalable resources. A health care faclty located n New York Cty s used to demonstrate the methodology. Fragltes and statstcs for lfetme losses are obtaned for ths structural system and some f ts nonstructural components. Proposed Loss Estmaton ethod The proposed loss estmaton method s based on () sesmc hazard analyss, () fraglty analyss and () capacty/cost estmaton. Fgure 1 shows a chart summarzng the loss estmaton methodology. Structural/nonstructural system defnton water tank ppng Sesmc hazard USGS sesmc actvty matrx mean annual Sesmc events 7 5 T (,R ).... tme Lfe cycle capacty/cost estmates Fraglty surfaces (for specfed lmt states) P f Damage D.... T Capacty 1% T Cost.. G.. C.... T tme tme tme Fgure 1. Loss estmaton Sesmc Hazard Analyss The nput to the sesmc hazard model conssts of (1) sesmc actvty matrx at the ste, (2) the projected lfe of a system, and (3) sol propertes at the ste. The sesmc actvty matrx s calculated usng the deaggregated matrces avalable at USGS webste ( Deaggregaton matrces at a ste gve the percent contrbuton of earthquakes wth dfferent moment 48
3 magntude ranges and rngs R j to the sesmc hazard at the ste. USGS provdes several deaggregated sesmc hazard matrces for any locaton n the Unted States at hazard levels of 1%, 2%, 5% and 1% probablty of exceedance n 5 years, where a hazard level s defned as the probablty that a ground moton parameter (e.g. peak ground acceleraton) exceeds a reference value durng a gven perod of tme. The mean annual rate ν j of earthquakes from bn (,R j ) can be calculated from deaggregaton matrx (Kafal and Grgoru, 24a). A onte Carlo algorthm can be developed for generatng () random samples of the sesmc hazard at the ste durng a gven perod of tme usng the sesmc actvty matrx, and () sesmc ground acceleraton samples for these sesmc hazard samples. Each sesmc hazard sample s defned by the number of earthquakes durng the tme, temporal dstrbuton, and magntude and sourcetoste dstance of each of them. Total number of earthquakes N() s assumed to follow a Posson dstrbuton wth mean annual rate ν=,j ν j, and the probablty that an earthquake havng a magntude n the range and comng from a source n the rng R j can be obtaned from P[, R R j ] =ν j /ν. The ground acceleraton A(t) s modeled by a nonstatonary stochastc process A(t)=w(t)A s (t), where t s the tme, w(t) s a determnstc envelope functon and A s (t) s a statonary Gaussan process whose spectral densty functon s gven by the specfc barrer model. Input parameters of ths model are the moment magntude, sourcetoste dstance of the earthquake and the sol condton at the ste. The descrpton of specfc barrer model and how to generate samples of ground acceleraton tme hstores can be found elsewhere (Papageorgou and Ak, 1983 a and b; Kafal and Grgoru, 23a). Fgure 2 shows () the deaggregaton matrx for 1% probablty of exceedance n 5 years, () the sesmc actvty matrx, and () a sample of sesmc hazard scenaro over a lfe tme of 5 years, for New York Cty (NYC) area. Deaggregaton matrx of NYC Sesmc actvty matrx of NYC A sample of sesmc hazard contrbuton to hazard mean annual rate Fgure 2. Sesmc hazard Fraglty Analyss The probablty that a system response exceeds a lmt state vewed as a functon of and R s called system fraglty surface. onte Carlo smulaton and crossng theory of stochastc processes can be used to calculate fraglty surfaces of lnear/nonlnear systems and ther components for dfferent lmt states (Kafal and Grgoru, 23b, 24b). Fraglty s used to characterze the damage n the structural/nonstructural systems. Let D be a dscrete random varable characterzng the damage state of a nonstructural system after sesmc event characterzed by (,R ), =1,,N(t), where N(t) s the number of sesmc events n [,t]. Assume that the nonstructural system s n damage state d k, wth probablty p k, for k=1,,n, where n s the number of damage states. The probabltes p k, are obtaned from the fraglty nformaton of the nonstructural system and are functons of the lmt Fraglty Based Rehabltaton Decson Analyss 49
4 state defnng the damage state d k and (,R ). Smlarly, we can defne random varables characterzng the damage n structural system and components of the selected nonstructural system. Capacty and Cost Estmaton Capacty, for example patent per day capacty n a servce, and total cost are estmated for the case of no rehabltaton and for dfferent retrofttng technques. Usng these estmates effcent solutons can be determned. We assume that loss of capacty s caused solely by damage of nonstructural systems. The capacty at tme t s 1 % p % O(t) G exp(γ (tt )) S (p) t O( t ) = 1 N ( t ) = 1 G exp( Γ ( t T )), where T s the arrval tme of event, G and Γ are the loss n the capacty and the rate of recovery, after event, respectvely (Fg.3). Note that T Fgure 3. Capacty model S p ( t ) = N( t ) = 1 S ( p ), represents the total tme the system spends at or below p%level capacty n [,t]. The cost relates to () structural falure, () retrofttng, () repar, (v) loss of capacty n servces, and (v) loss of lfe. Rehabltaton s only consdered for the nonstructural system. Costs due to (), (), (v) and (v) are random. The total cost n dollars at tme t n net present value s N( t ) TC( t ) = c + C /( 1+ dr ) = 1 where c s ntal cost related to the rehabltaton, dr s the dscount rate, T s the tme of arrval of event, and C s the cost related to event. Assume that C =CS, f structure fals and C =CR +CC +CL, otherwse. CS s the cost related to structural falure, CR s the repar cost of the nonstructural system, CC s the cost due to the loss n capacty, and CL s the cost of lfe losses. It s expected that wth an ncreasng ntal cost c, the cost C due to event wll decrease and for some rehabltaton alternatve we wll have the optmum soluton. Numercal Example An CEER Demonstraton Hosptal Project located n NYC s used to demonstrate the proposed methodology. Three dfferent levels of rehabltaton, namely, () no rehabltaton (rehab.1), () lfe safety (rehab.2) and () lmted downtme (rehab.3) are consdered. It s assumed that the structure s lnear elastc and cascade analyss apples, that s, the nonstructural system does not affect the dynamcs of the supportng structure. The nonstructural system consdered conssts of a water tank (comp.1) and a power generator (comp.2) located at the roof and at the frst floor, respectvely. It s assumed that () the components are not nteractng, () water tank s drft senstve, () power generator s acceleraton senstve, and (v) both components are lnear sngle degree of freedom oscllators. An llustraton of the hosptal model wth two components attached to t, the requred T, 5
5 modal propertes of the structure (dampng rato s 3% for all modes), and natural frequences and dampng ratos of the components for the dfferent rehabltaton alternatves are show n Fgure 4. comp.1 Structural system Nonstructural system 51 ft mode ω Γ (rad/sec) comp.1 ω (rad/sec) ζ comp.2 ω (rad/sec) rehab ζ comp rehab rehab Fgure 4. System propertes Fraglty surfaces are obtaned for the structural/nonstructural systems, comp.1 and comp.2 for dfferent lmt states assumng statonary ground acceleratons. Structural system s assumed to fal when the roof dsplacement exceeds 5. Lmt states are {.12,.25,.5 } and {1.g,1.5g} for comp.1 and 2, respectvely. The nonstructural system has three damage states () no damage, when both components have no damage; () extensve damage, when ether of ts components fals; and () moderate damage, otherwse. Fgure 5 shows fraglty surfaces of structural/nonstructural systems and comp.1 and comp.2 for dfferent rehabltaton alternatves and lmt states. Structural system Comp.1 rehab.3 ls.3 Comp.2 rehab.1 ls.1 Pf Pf Pf Fgure 5. Fraglty surfaces Estmates of the total tme the system spends at or below 8%level capacty and the total cost TC, durng a projected lfe of =1 years, for the three rehabltaton alternatves are obtaned by onte Carlo smulaton. Followng nformaton s used to obtan these estmates. G and Γ are dscrete random varables takng values {,.5,.9} and {,.5,.3}, respectvely, wth probabltes obtaned from the nonstructural system fraglty. The dscount rate s 7% and the rehabltaton cost c takes values {,1,5}, for no rehabltaton, lfe safety and lmted downtme rehabltaton, respectvely. CR =C 1, +C 2,, where C 1, and C 2, are dscrete random varables takng values {,6,14,2} and {,5,1}, respectvely, wth probabltes obtaned from the correspondng component fraglty surfaces, and they represent the repar costs for comp.1 and 2, respectvely. CS =cs.q, where cs=47 s the cost related to the downtme and the constructon of a new faclty and q s the probablty of system falure obtaned from the structural Fraglty Based Rehabltaton Decson Analyss 51
6 system fraglty. CL =cl.x, where cl=22 s the cost of one person's lfe loss and X s a bnomal random varable wth parameters n x =1 and p x =.1, representng the number of people losng ther lves, respectvely. CC =cc.rt, where cc=23 s the cost due to the loss n capacty per day and RT s the tme to reach 1% capacty gven by RT = for G =Γ =, and RT =ln(.1/g )/Γ, otherwse. Fgure 6 shows P(S p (t)/ >s/) and P(TC(t)>c). P(S p (t)/ >s/) P(TC(t)>c) s/ (=1 years) c (n 1) Fgure 6. Estmates of the capacty and total cost A possble measure for comparng the effectveness of dfferent rehabltaton alternatves s the probablty that the total cost exceeds a level c. Accordngly, the optmal soluton s the one wth the lowest P(TC(t)>c), and depends on the selected value of c (see Fg.6). For example, the optmal solutons are rehabltaton alternatves 1 and 2, for c=5 and c=4, respectvely. Concludng Remarks A method was developed to dentfy an optmal retrofttng technque for structural/nonstructural systems. The method () consders a realstc sesmc hazard model rather than usng the maxmum credble earthquake, () ncludes all components of costs, that s, the costs related to the structural falure and downtme, retrofttng, repar, loss of capacty n servces, and loss of lfe, and () s desgned for ndvdual facltes rather than a large populaton of them. The method s based on onte Carlo smulaton, probablstc sesmc hazard, fraglty surfaces and capacty/cost analyses. Acknowledgements Ths research was carred out under the supervson of Dr.. Grgoru, and supported by the ultdscplnary Center for Earthquake Engneerng Research under NSF award EEC References ATC13 (1985): Earthquake damage evaluaton data for Calforna. Appled Technology Councl, Redwood Cty, CA. FEA (1999): HAZUS 99: Estmated annualzed earthquake losses for the Unted States, FEA, Washngton, D.C. 52
7 Kafal, C. and Grgoru,. (23a): NonGaussan model for spatally coherent sesmc ground motons. In proceedngs of the ICASP9, San Francsco, CA., pp Kafal, C. and Grgoru,. (23b): Fraglty analyss for nonstructural systems n crtcal facltes, In proceedngs of ATC292 semnar, Newport Beach, CA, pp Kafal, C. and Grgoru. (24a): Rehabltaton decson analyss. In preparaton. Kafal, C. and Grgoru,. (24b): Sesmc fraglty analyss. In proceedngs PC24, Albuquerque, N.. Papageorgou, A. S. and Ak, A. K. (1983a): A specfc barrer model for the quanttatve descrpton of the nhomogeneous faultng and the predcton of strong ground moton. Part I, Bulletn of the Sesmologcal Socety of Amerca 73, Papageorgou, A. S. and Ak, A. K. (1983b): A specfc barrer model for the quanttatve descrpton of the nhomogeneous faultng and the predcton of strong ground moton. Part II.. Bulletn of the Sesmologcal Socety of Amerca 73, Fraglty Based Rehabltaton Decson Analyss 53
Riskbased Fatigue Estimate of Deep Water Risers  Course Project for EM388F: Fracture Mechanics, Spring 2008
Rskbased Fatgue Estmate of Deep Water Rsers  Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn
More informationCHAPTER EVALUATING EARTHQUAKE RETROFITTING MEASURES FOR SCHOOLS: A COSTBENEFIT ANALYSIS
CHAPTER 17 EVALUATING EARTHQUAKE RETROFITTING MEASURES FOR SCHOOLS: A COSTBENEFIT ANALYSIS A.W. Smyth, G. Deodats, G. Franco, Y. He and T. Gurvch Department of Cvl Engneerng and Engneerng Mechancs, Columba
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationCommunication Networks II Contents
8 / 1  Communcaton Networs II (Görg)  www.comnets.unbremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP
More informationA DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATIONBASED OPTIMIZATION. Michael E. Kuhl Radhamés A. TolentinoPeña
Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATIONBASED OPTIMIZATION
More information9.1 The Cumulative Sum Control Chart
Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s
More informationProject Networks With MixedTime Constraints
Project Networs Wth MxedTme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationChapter 7. RandomVariate Generation 7.1. Prof. Dr. Mesut Güneş Ch. 7 RandomVariate Generation
Chapter 7 RandomVarate Generaton 7. Contents Inversetransform Technque AcceptanceRejecton Technque Specal Propertes 7. Purpose & Overvew Develop understandng of generatng samples from a specfed dstrbuton
More informationEvaluating Earthquake Retrofitting Measures for Schools: A Demonstration CostBenefit Analysis
Evaluatng Earthquake Retrofttng easures for Schools: A emonstraton CostBeneft Analyss A.W. Smyth, G. eodats 2, G. Franco 3, Y. He 4, and T. Gurvch 4 ept. of Cvl Engneerng & Engneerng echancs Columba Unversty,
More informationRisk Model of LongTerm Production Scheduling in Open Pit Gold Mining
Rsk Model of LongTerm Producton Schedulng n Open Pt Gold Mnng R Halatchev 1 and P Lever 2 ABSTRACT Open pt gold mnng s an mportant sector of the Australan mnng ndustry. It uses large amounts of nvestments,
More informationGENETIC ALGORITHM FOR PROJECT SCHEDULING AND RESOURCE ALLOCATION UNDER UNCERTAINTY
Int. J. Mech. Eng. & Rob. Res. 03 Fady Safwat et al., 03 Research Paper ISS 78 049 www.jmerr.com Vol., o. 3, July 03 03 IJMERR. All Rghts Reserved GEETIC ALGORITHM FOR PROJECT SCHEDULIG AD RESOURCE ALLOCATIO
More informationPSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 12
14 The Chsquared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationNumber of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000
Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from
More informationPowerofTwo Policies for Single Warehouse MultiRetailer Inventory Systems with Order Frequency Discounts
Powerofwo Polces for Sngle Warehouse MultRetaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
More informationSensitivity Analysis in a Generic MultiAttribute Decision Support System
Senstvty Analyss n a Generc MultAttrbute Decson Support System Sxto RíosInsua, Antono Jménez and Alfonso Mateos Department of Artfcal Intellgence, Madrd Techncal Unversty Campus de Montegancedo s/n,
More informationMultivariate EWMA Control Chart
Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant
More informationTraffic State Estimation in the Traffic Management Center of Berlin
Traffc State Estmaton n the Traffc Management Center of Berln Authors: Peter Vortsch, PTV AG, Stumpfstrasse, D763 Karlsruhe, Germany phone ++49/72/965/35, emal peter.vortsch@ptv.de Peter Möhl, PTV AG,
More informationThe Development of Web Log Mining Based on ImproveKMeans Clustering Analysis
The Development of Web Log Mnng Based on ImproveKMeans Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More information1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 /  Communcaton Networks II (Görg) SS20  www.comnets.unbremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
More informationEfficient Project Portfolio as a tool for Enterprise Risk Management
Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse
More informationAn MILP model for planning of batch plants operating in a campaignmode
An MILP model for plannng of batch plants operatng n a campagnmode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN yfumero@santafeconcet.gov.ar Gabrela Corsano Insttuto de Desarrollo y Dseño
More informationSETTLEMENT PREDICTION OF PILESUPPORTED STRUCTURES IN LIQUEFIABLE SOILS DURING EARTHQUAKE
SETTLEMENT PREDICTION OF PILESUPPORTED STRUCTURES IN LIQUEFIABLE SOILS DURING EARTHQUAKE Chandra Dev Raman 1, Subhamoy Bhattacharya 2 and A Blakeborough 3 1 Research Scholar, Department of Engneerng Scence,Unversty
More informationOptimizing Spare Parts Inventory for TimeVarying Task
A publcaton of CHEMCAL ENGNEERNG TRANSACTONS VOL. 33, 203 Guest Edtors: Enrco Zo, Pero Barald Copyrght 203, ADC Servz S.r.l., SBN 9788895608242; SSN 974979 The talan Assocaton of Chemcal Engneerng
More informationForecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network
700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School
More informationA hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):18841889 Research Artcle ISSN : 09757384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
More informationPortfolio Loss Distribution
Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets holdtomaturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment
More informationEarthquake Vulnerability Reduction Program in Colombia A Probabilistic Costbenefit Analysis
Publc Dsclosure Authorzed Earthquake Vulnerablty Reducton Program n Colomba A Probablstc Costbeneft Analyss WPS3939 Publc Dsclosure Authorzed Abstract Francs Ghesquere, World Bank Lus Jamn, Unversty of
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More informationAN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE
AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE YuL Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent
More informationOnLine Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features
OnLne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: ruoyu.l@skf.com
More informationTime Series Analysis in Studies of AGN Variability. Bradley M. Peterson The Ohio State University
Tme Seres Analyss n Studes of AGN Varablty Bradley M. Peterson The Oho State Unversty 1 Lnear Correlaton Degree to whch two parameters are lnearly correlated can be expressed n terms of the lnear correlaton
More informationI. SCOPE, APPLICABILITY AND PARAMETERS Scope
D Executve Board Annex 9 Page A/R ethodologcal Tool alculaton of the number of sample plots for measurements wthn A/R D project actvtes (Verson 0) I. SOPE, PIABIITY AD PARAETERS Scope. Ths tool s applcable
More informationRobust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School
Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management
More informationPROBABILISTIC DECISION ANALYSIS FOR SEISMIC REHABILITATION OF A REGIONAL BUILDING SYSTEM
3 th Worl Conference on Earthquake Engneerng Vancouver, B.C., Canaa August 6, 4 Paper No. 54 PROBABILISTIC DECISION ANALYSIS FOR SEISMIC REHABILITATION OF A REGIONAL BILDING SYSTEM Joonam PARK, Barry
More informationOptimal Bidding Strategies for Generation Companies in a DayAhead Electricity Market with Risk Management Taken into Account
Amercan J. of Engneerng and Appled Scences (): 86, 009 ISSN 94700 009 Scence Publcatons Optmal Bddng Strateges for Generaton Companes n a DayAhead Electrcty Market wth Rsk Management Taken nto Account
More informationFeasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid
Feasblty of Usng Dscrmnate Prcng Schemes for Energy Tradng n Smart Grd Wayes Tushar, Chau Yuen, Bo Cha, Davd B. Smth, and H. Vncent Poor Sngapore Unversty of Technology and Desgn, Sngapore 138682. Emal:
More informationA Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy Scurve Regression
Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy Scurve Regresson ChengWu Chen, Morrs H. L. Wang and TngYa Hseh Department of Cvl Engneerng, Natonal Central Unversty,
More informationActivity Scheduling for CostTime Investment Optimization in Project Management
PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta San Sebastán, September 8 th 10 th 010 Actvty Schedulng
More information1 Approximation Algorithms
CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons
More informationJ. Parallel Distrib. Comput.
J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationCourse outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
More informationRELIABILITY, RISK AND AVAILABILITY ANLYSIS OF A CONTAINER GANTRY CRANE ABSTRACT
Kolowrock Krzysztof Joanna oszynska MODELLING ENVIRONMENT AND INFRATRUCTURE INFLUENCE ON RELIABILITY AND OPERATION RT&A # () (Vol.) March RELIABILITY RIK AND AVAILABILITY ANLYI OF A CONTAINER GANTRY CRANE
More informationIDENTIFICATION AND CONTROL OF A FLEXIBLE TRANSMISSION SYSTEM
Abstract IDENTIFICATION AND CONTROL OF A FLEXIBLE TRANSMISSION SYSTEM Alca Esparza Pedro Dept. Sstemas y Automátca, Unversdad Poltécnca de Valenca, Span alespe@sa.upv.es The dentfcaton and control of a
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationMODELING DYNAMICS OF POSTDISASTER RECOVERY. Technology, Texas Tech University, Box 43107, Lubbock, Texas 794093107, Email: ali.nejat@ttu.
2200 MODELING DYNAMICS OF POSTDISASTER RECOVERY Al NEJAT 1 and Ivan DAMNJANOVIC 2 1 Assstant Professor, Department of Constructon Engneerng and Engneerng Technology, Texas Tech Unversty, Box 43107, Lubbock,
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationPerformance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application
Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdodong,
More informationDynamic Resource Allocation and Power Management in Virtualized Data Centers
Dynamc Resource Allocaton and Power Management n Vrtualzed Data Centers Rahul Urgaonkar, Ulas C. Kozat, Ken Igarash, Mchael J. Neely urgaonka@usc.edu, {kozat, garash}@docomolabsusa.com, mjneely@usc.edu
More informationFault tolerance in cloud technologies presented as a service
Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce INTRODUCTION Improvements n technques for vrtualzaton and performance
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract  Stock market s one of the most complcated systems
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a twostage stratfed cluster desgn. 1 The frst stage conssted of a sample
More informationBrigid Mullany, Ph.D University of North Carolina, Charlotte
Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte
More informationAn empirical study for credit card approvals in the Greek banking sector
An emprcal study for credt card approvals n the Greek bankng sector Mara Mavr George Ioannou Bergamo, Italy 1721 May 2004 Management Scences Laboratory Department of Management Scence & Technology Athens
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMISP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationStatistical Methods to Develop Rating Models
Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and
More informationLesson 2 Chapter Two Three Phase Uncontrolled Rectifier
Lesson 2 Chapter Two Three Phase Uncontrolled Rectfer. Operatng prncple of three phase half wave uncontrolled rectfer The half wave uncontrolled converter s the smplest of all three phase rectfer topologes.
More informationOptimization of network mesh topologies and link capacities for congestion relief
Optmzaton of networ mesh topologes and ln capactes for congeston relef D. de Vllers * J.M. Hattngh School of Computer, Statstcal and Mathematcal Scences Potchefstroom Unversty for CHE * Emal: rwddv@pu.ac.za
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More information106 M.R. Akbar Jokar and M. Sefbarghy polcy, ndependent Posson demands n the retalers, a backordered demand durng stockouts n all nstallatons and cons
Scenta Iranca, Vol. 13, No. 1, pp 105{11 c Sharf Unversty of Technology, January 006 Research Note Cost Evaluaton of a TwoEchelon Inventory System wth Lost Sales and Approxmately Normal Demand M.R. Akbar
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationPerformance Analysis of Order Fulfillment for Low Demand Items in Etailing
Performance Analyss of Order Fulfllment for Low Demand Items n Etalng Pallav Chhaochhra, Stephen C Graves Massachusetts Insttute of Technology Abstract We study nventory allocaton and order fulfllment
More informationPreventive Maintenance and Replacement Scheduling: Models and Algorithms
Preventve Mantenance and Replacement Schedulng: Models and Algorthms By Kamran S. Moghaddam B.S. Unversty of Tehran 200 M.S. Tehran Polytechnc 2003 A Dssertaton Proposal Submtted to the Faculty of the
More informationGenetic algorithm for searching for critical slip surface in gravity dams based on stress fields CHEN Jianyun 1, WANG Shu 2, XU Qiang 3, LI Jing 4
Advanced Materals Research Onlne: 2030904 ISSN: 6628985, Vol. 790, pp 4649 do:0.4028/www.scentfc.net/amr.790.46 203 Trans Tech Publcatons, Swtzerland Genetc algorthm for searchng for crtcal slp surface
More informationDetermination of Integrated Risk Degrees in Product Development Project
Proceedngs of the World Congress on Engneerng and Computer Scence 009 Vol II WCECS 009, October 0, 009, San Francsco, USA Determnaton of Integrated sk Degrees n Product Development Project D. W. Cho.,
More informationInequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationA Performance Analysis of View Maintenance Techniques for Data Warehouses
A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 6105194390,
More informationLIFETIME INCOME OPTIONS
LIFETIME INCOME OPTIONS May 2011 by: Marca S. Wagner, Esq. The Wagner Law Group A Professonal Corporaton 99 Summer Street, 13 th Floor Boston, MA 02110 Tel: (617) 3575200 Fax: (617) 3575250 www.ersalawyers.com
More informationDamage detection in composite laminates using cointap method
Damage detecton n composte lamnates usng contap method S.J. Km Korea Aerospace Research Insttute, 45 EoeunDong, YouseongGu, 35333 Daejeon, Republc of Korea yaeln@kar.re.kr 45 The contap test has the
More informationFuzzy Set Approach To Asymmetrical Load Balancing In Distribution Networks
Fuzzy Set Approach To Asymmetrcal Load Balancng n Dstrbuton Networks Goran Majstrovc Energy nsttute Hrvoje Por Zagreb, Croata goran.majstrovc@ehp.hr Slavko Krajcar Faculty of electrcal engneerng and computng
More informationSensor placement for leak detection and location in water distribution networks
Sensor placement for leak detecton and locaton n water dstrbuton networks ABSTRACT R. Sarrate*, J. Blesa, F. Near, J. Quevedo Automatc Control Department, Unverstat Poltècnca de Catalunya, Rambla de Sant
More informationFatigue Analysis for Fleet Management Using Bayesian Networks
ESPOO 2005 VTT WORKING PAPERS 35 Fatgue analyss framewor to support fleet management fatgue damage data from montored structures Devaton crtera for Fatgue Lfe Fatgue Lfe comparson Safe Lfe crtera Next
More informationStudy on CET4 Marks in China s Graded English Teaching
Study on CET4 Marks n Chna s Graded Englsh Teachng CHE We College of Foregn Studes, Shandong Insttute of Busness and Technology, P.R.Chna, 264005 Abstract: Ths paper deploys Logt model, and decomposes
More informationDaily OD Matrix Estimation using Cellular Probe Data
Zhang, Qn, Dong and Ran Daly OD Matrx Estmaton usng Cellular Probe Data 0 0 Y Zhang* Department of Cvl and Envronmental Engneerng, Unversty of WsconsnMadson, Madson, WI 0 Phone: 0 Emal: zhang@wsc.edu
More informationLecture 2: Absorbing states in Markov chains. Mean time to absorption. WrightFisher Model. Moran Model.
Lecture 2: Absorbng states n Markov chans. Mean tme to absorpton. WrghtFsher Model. Moran Model. Antonna Mtrofanova, NYU, department of Computer Scence December 8, 2007 Hgher Order Transton Probabltes
More informationRealistic Image Synthesis
Realstc Image Synthess  Combned Samplng and Path Tracng  Phlpp Slusallek Karol Myszkowsk Vncent Pegoraro Overvew: Today Combned Samplng (Multple Importance Samplng) Renderng and Measurng Equaton Random
More informationL10: Linear discriminants analysis
L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss
More informationivoip: an Intelligent Bandwidth Management Scheme for VoIP in WLANs
VoIP: an Intellgent Bandwdth Management Scheme for VoIP n WLANs Zhenhu Yuan and GabrelMro Muntean Abstract Voce over Internet Protocol (VoIP) has been wdely used by many moble consumer devces n IEEE 802.11
More informationOn the Optimal Control of a Cascade of HydroElectric Power Stations
On the Optmal Control of a Cascade of HydroElectrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More informationDynamic Pricing for Smart Grid with Reinforcement Learning
Dynamc Prcng for Smart Grd wth Renforcement Learnng ByungGook Km, Yu Zhang, Mhaela van der Schaar, and JangWon Lee Samsung Electroncs, Suwon, Korea Department of Electrcal Engneerng, UCLA, Los Angeles,
More informationExtending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σalgebra: a set
More informationS. Malasri, D.A.Halijan and M.L.Keough Department of Civil Engineering Christian Brothers University Memphis, TN 38104. Abstract
S. Malasr, D.A.Haljan and M.L.Keough Department of Cvl Engneerng Chrstan Brothers Unversty Memphs, TN 38104 Abstract Ths paper demonstrates an applcaton of the natural selecton process to the desgn of
More informationLecture 3: Force of Interest, Real Interest Rate, Annuity
Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annutymmedate, and ts present value Study annutydue, and
More informationThe Application of Fractional Brownian Motion in Option Pricing
Vol. 0, No. (05), pp. 738 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qngxn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com
More informationLETTER IMAGE RECOGNITION
LETTER IMAGE RECOGNITION 1. Introducton. 1. Introducton. Objectve: desgn classfers for letter mage recognton. consder accuracy and tme n takng the decson. 20,000 samples: Startng set: mages based on 20
More informationMODELING AND SCHEDULING INTELLIGENT METHOD S APPLICATION IN INCREASING HOSPITALS EFFICIENCY
MODELING AND SCHEDULING INTELLIGENT METHOD S APPLICATION IN INCREASING HOSPITALS EFFICIENCY 1 NEDA DARVISH, 2 MAHNAZ VAEZI 1 Darvsh, Neda :,PhD student of modelng networkng, Islamc Azad Unversty Tehran
More informationEffective Network Defense Strategies against Malicious Attacks with Various Defense Mechanisms under Quality of Service Constraints
Effectve Network Defense Strateges aganst Malcous Attacks wth Varous Defense Mechansms under Qualty of Servce Constrants Frank YeongSung Ln Department of Informaton Natonal Tawan Unversty Tape, Tawan,
More informationAnts Can Schedule Software Projects
Ants Can Schedule Software Proects Broderck Crawford 1,2, Rcardo Soto 1,3, Frankln Johnson 4, and Erc Monfroy 5 1 Pontfca Unversdad Católca de Valparaíso, Chle FrstName.Name@ucv.cl 2 Unversdad Fns Terrae,
More informationSPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:
SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and
More informationBusiness Process Improvement using Multiobjective Optimisation K. Vergidis 1, A. Tiwari 1 and B. Majeed 2
Busness Process Improvement usng Multobjectve Optmsaton K. Vergds 1, A. Twar 1 and B. Majeed 2 1 Manufacturng Department, School of Industral and Manufacturng Scence, Cranfeld Unversty, Cranfeld, MK43
More informationOverview of monitoring and evaluation
540 Toolkt to Combat Traffckng n Persons Tool 10.1 Overvew of montorng and evaluaton Overvew Ths tool brefly descrbes both montorng and evaluaton, and the dstncton between the two. What s montorng? Montorng
More informationAnalysis of Premium Liabilities for Australian Lines of Business
Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton
More informationFramework for estimating congestion performance measures: from data collection to reliability analysis. Case study Stockholm
Framework for estmatng congeston performance measures: from data collecton to relablty analyss. Case study Stockholm Carlos A. Morán Toledo Lcentate Thess Dvson of Traffc and Logstcs Department of Transport
More informationAn InterestOriented Network Evolution Mechanism for Online Communities
An InterestOrented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationTitle: A Queuing Network Model with Blocking: Analysis of Congested Patient Flows in Mental Health Systems
Ttle: A Queung Network Model wth Blockng: Analyss of Congested Patent Flows n Mental Health Systems AUTHO INFOMATION Naoru Kozum (Correspondng author) Department of lectrcal and Systems ngneerng, Unversty
More informationSketching Sampled Data Streams
Sketchng Sampled Data Streams Florn Rusu, Aln Dobra CISE Department Unversty of Florda Ganesvlle, FL, USA frusu@cse.ufl.edu adobra@cse.ufl.edu Abstract Samplng s used as a unversal method to reduce the
More information