The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform
Ling Wang - One of the best experts on this subject based on the ideXlab platform.
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A condition-based replacement and spare provisioning policy for deteriorating systems with uncertain deterioration to failure
European Journal of Operational Research, 2009Co-Authors: Ling WangAbstract:A new policy, referred to as the condition-based replacement and spare provisioning policy, is presented for deteriorating systems with a number of identical units. It combines the condition-based replacement policy with periodical inspections and the (S,s) type inventory policy, noted as the (T,S,s,Lp) policy, where T is the inspection interval, S is the maximum stock level, s is the reorder level, and Lp is the preventive replacement threshold for the deterioration levels of units. The deterioration level of each unit in the system can be described by a Scalar Random Variable, which is continuous and increasing monotonically. Furthermore, the deterioration level just when the unit failure occurs, termed deterioration to failure, is uncertain. Therefore, the condition-based reliability is proposed in order to characterize various and uncertain deterioration levels when unit failure occurs. A simulation model is developed for the system operation under the proposed condition-based replacement and spare provisioning policy. Thus, via the simulation method and the genetic algorithm, the decision Variables T, S, s, and Lp can be jointly optimized for minimizing the cost rate. A case study is given, showing the procedure of applying the proposed policy and the condition-based reliability methodology to optimizing the maintenance scheme of haul truck motors at a mine site based on oil inspections, and proving beneficial for plant maintenance managers to reduce maintenance cost.
Hayakawa Ryo - One of the best experts on this subject based on the ideXlab platform.
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Error Analysis of Douglas-Rachford Algorithm for Linear Inverse Problems: Asymptotics of Proximity Operator for Squared Loss
2021Co-Authors: Hayakawa RyoAbstract:Proximal splitting-based convex optimization is a promising approach to linear inverse problems because we can use some prior knowledge of the unknown Variables explicitly. In this paper, we firstly analyze the asymptotic property of the proximity operator for the squared loss function, which appears in the update equations of some proximal splitting methods for linear inverse problems. The analysis shows that the output of the proximity operator can be characterized with a Scalar Random Variable in the large system limit. Moreover, we investigate the asymptotic behavior of the Douglas-Rachford algorithm, which is one of the famous proximal splitting methods. From the asymptotic result, we can predict the evolution of the mean-square-error (MSE) in the algorithm for large-scale linear inverse problems. Simulation results demonstrate that the MSE performance of the Douglas-Rachford algorithm can be well predicted by the analytical result in compressed sensing with the $\ell_{1}$ optimization.Comment: This work will be submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl
Tomasz Kozlowski - One of the best experts on this subject based on the ideXlab platform.
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inverse uncertainty quantification of input model parameters for thermal hydraulics simulations using expectation maximization under bayesian framework
Journal of Applied Statistics, 2015Co-Authors: Rijan Shrestha, Tomasz KozlowskiAbstract:Quantification of uncertainties in code responses necessitates knowledge of input model parameter uncertainties. However, nuclear thermal-hydraulics code such as RELAP5 and TRACE do not provide any information on input model parameter uncertainties. Moreover, the input model parameters for physical models in these legacy codes were derived under steady-state flow conditions and hence might not be accurate to use in the analysis of transients without accounting for uncertainties. We present a Bayesian framework to estimate the posterior mode of input model parameters' mean and variance by implementing the iterative expectation--maximization algorithm. For this, we introduce the idea of model parameter multiplier. A log-normal transformation is used to transform the model parameter multiplier to pseudo-parameter. Our analysis is based on two main assumptions on pseudo-parameter. First, a first-order linear relationship is assumed between code responses and pseudo-parameters. Second, the pseudo-parameters are assumed to be normally distributed. The problem is formulated to express the Scalar Random Variable, the difference between experimental result and base (nominal) code-calculated value as a linear combination of pseudo-parameters.
Rijan Shrestha - One of the best experts on this subject based on the ideXlab platform.
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inverse uncertainty quantification of input model parameters for thermal hydraulics simulations using expectation maximization under bayesian framework
Journal of Applied Statistics, 2015Co-Authors: Rijan Shrestha, Tomasz KozlowskiAbstract:Quantification of uncertainties in code responses necessitates knowledge of input model parameter uncertainties. However, nuclear thermal-hydraulics code such as RELAP5 and TRACE do not provide any information on input model parameter uncertainties. Moreover, the input model parameters for physical models in these legacy codes were derived under steady-state flow conditions and hence might not be accurate to use in the analysis of transients without accounting for uncertainties. We present a Bayesian framework to estimate the posterior mode of input model parameters' mean and variance by implementing the iterative expectation--maximization algorithm. For this, we introduce the idea of model parameter multiplier. A log-normal transformation is used to transform the model parameter multiplier to pseudo-parameter. Our analysis is based on two main assumptions on pseudo-parameter. First, a first-order linear relationship is assumed between code responses and pseudo-parameters. Second, the pseudo-parameters are assumed to be normally distributed. The problem is formulated to express the Scalar Random Variable, the difference between experimental result and base (nominal) code-calculated value as a linear combination of pseudo-parameters.
Peter M Robinson - One of the best experts on this subject based on the ideXlab platform.
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quantile autoregression commentary
Journal of the American Statistical Association, 2006Co-Authors: Roger Koenker, Marc Hallin, Zhijie Xiao, Jianqing Fan, Yingying Fan, Keith Knight, Bas J M Werker, Christian M Hafner, Oliver Linton, Peter M RobinsonAbstract:We consider quantile autoregression (QAR) models in which the autoregressive coefficients can be expressed as monotone functions of a single, Scalar Random Variable. The models can capture systematic influences of conditioning Variables on the location, scale, and shape of the conditional distribution of the response, and thus constitute a significant extension of classical constant coefficient linear time series models in which the effect of conditioning is confined to a location shift. The models may be interpreted as a special case of the general Random-coefficient autoregression model with strongly dependent coefficients. Statistical properties of the proposed model and associated estimators are studied. The limiting distributions of the autoregression quantile process are derived. QAR inference methods are also investigated. Empirical applications of the model to the U.S. unemployment rate, short-term interest rate, and gasoline prices highlight the model's potential.
