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Hui-chu Chen - One of the best experts on this subject based on the ideXlab platform.
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heat exchanger network synthesis involving organic rankine cycle for waste heat recovery
Industrial & Engineering Chemistry Research, 2014Co-Authors: Cheng-liang Chen, Tzu-hsiang Chao, Feng-yi Chang, Hui-chu ChenAbstract:This article aims to present a mathematical model for the synthesis of a heat-exchanger network (HEN) which can be integrated with an organic Rankine cycle (ORC) for the recovery of low-grade waste heat from the heat surplus zone of the Background Process. An ORC-incorporated stagewise superstructure considering all possible heat-exchange matches between Process hot/cold streams and the ORC is first presented. On the basis of this superstructure, the model for synthesizing ORC-integrated HENs is formulated as a mixed-integer nonlinear program (MINLP). A two-step solution procedure is proposed to solve the MINLP model. First, a stand-alone HEN is synthesized to minimize the external utility consumption. An ORC is then incorporated into the HEN with the objective of maximizing the work produced from waste heat (in the heat surplus zone below the Process pinch) without increasing the use of a hot utility. A literature example is solved to demonstrate the application of the proposed model for industrial waste...
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heat exchanger network synthesis involving organic rankine cycle for waste heat recovery
Industrial & Engineering Chemistry Research, 2014Co-Authors: Cheng-liang Chen, Tzu-hsiang Chao, Feng-yi Chang, Hui-chu ChenAbstract:This article aims to present a mathematical model for the synthesis of a heat-exchanger network (HEN) which can be integrated with an organic Rankine cycle (ORC) for the recovery of low-grade waste heat from the heat surplus zone of the Background Process. An ORC-incorporated stagewise superstructure considering all possible heat-exchange matches between Process hot/cold streams and the ORC is first presented. On the basis of this superstructure, the model for synthesizing ORC-integrated HENs is formulated as a mixed-integer nonlinear program (MINLP). A two-step solution procedure is proposed to solve the MINLP model. First, a stand-alone HEN is synthesized to minimize the external utility consumption. An ORC is then incorporated into the HEN with the objective of maximizing the work produced from waste heat (in the heat surplus zone below the Process pinch) without increasing the use of a hot utility. A literature example is solved to demonstrate the application of the proposed model for industrial waste...
K. De Turck - One of the best experts on this subject based on the ideXlab platform.
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markov modulated ornstein uhlenbeck Processes
Advances in Applied Probability, 2016Co-Authors: Gang Huang, Michel Mandjes, Hermanus Marinus Jansen, Peter Spreij, K. De TurckAbstract:In this paper we consider an Ornstein-Uhlenbeck (OU) Process (M(t)) t≥0 whose parameters are determined by an external Markov Process (X(t)) t≥0 on a finite state space {1, . . ., d}; this Process is usually referred to as Markov-modulated Ornstein-Uhlenbeck. We use stochastic integration theory to determine explicit expressions for the mean and variance of M(t). Then we establish a system of partial differential equations (PDEs) for the Laplace transform of M(t) and the state X(t) of the Background Process, jointly for time epochs t = t1, . . ., t K . Then we use this PDE to set up a recursion that yields all moments of M(t) and its stationary counterpart; we also find an expression for the covariance between M(t) and M(t + u). We then establish a functional central limit theorem for M(t) for the situation that certain parameters of the underlying OU Processes are scaled, in combination with the modulating Markov Process being accelerated; interestingly, specific scalings lead to drastically different limiting Processes. We conclude the paper by considering the situation of a single Markov Process modulating multiple OU Processes.
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A Functional Central Limit Theorem for a Markov-Modulated Infinite-Server Queue
Methodology and Computing in Applied Probability, 2014Co-Authors: David F. Anderson, Joke Blom, Michel Mandjes, H. Thorsdottir, K. De TurckAbstract:We consider a model in which the production of new molecules in a chemical reaction network occurs in a seemingly stochastic fashion, and can be modeled as a Poisson Process with a varying arrival rate: the rate is λ i when an external Markov Process J(⋅) is in state i. It is assumed that molecules decay after an exponential time with mean μ −1. The goal of this work is to analyze the distributional properties of the number of molecules in the system, under a specific time-scaling. In this scaling, the Background Process is sped up by a factor N α , for some α>0, whereas the arrival rates become N λ i , for N large. The main result of this paper is a functional central limit theorem (F-CLT) for the number of molecules, in that, after centering and scaling, it converges to an Ornstein-Uhlenbeck Process. An interesting dichotomy is observed: (i) if α > 1 the Background Process jumps faster than the arrival Process, and consequently the arrival Process behaves essentially as a (homogeneous) Poisson Process, so that the scaling in the F-CLT is the usual \(\sqrt {N}\), whereas (ii) for α≤1 the Background Process is relatively slow, and the scaling in the F-CLT is N 1−α/2. In the latter regime, the parameters of the limiting Ornstein-Uhlenbeck Process contain the deviation matrix associated with the Background Process J(⋅).
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Large deviations of an infinite-server system with a linearly scaled Background Process
Performance Evaluation, 2014Co-Authors: K. De Turck, Michel MandjesAbstract:Abstract This paper studies an infinite-server queue in a Markov environment, that is, an infinite-server queue with arrival rates and service times depending on the state of a Markovian Background Process. We focus on the probability that the number of jobs in the system attains an unusually high value. Scaling the arrival rates λ i by a factor N and the transition rates ν i j of the Background Process as well, a large-deviations based approach is used to examine such tail probabilities (where N tends to ∞ ). The paper also presents qualitative properties of the system’s behavior conditional on the rare event under consideration happening.
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A functional central limit theorem for a Markov-modulated infinite-server queue
arXiv: Probability, 2013Co-Authors: David F. Anderson, Joke Blom, Michel Mandjes, H. Thorsdottir, K. De TurckAbstract:The production of molecules in a chemical reaction network is modelled as a Poisson Process with a Markov-modulated arrival rate and an exponential decay rate. We analyze the distributional properties of $M$, the number of molecules, under specific time-scaling; the Background Process is sped up by $N^{\alpha}$, the arrival rates are scaled by $N$, for $N$ large. A functional central limit theorem is derived for $M$, which after centering and scaling, converges to an Ornstein-Uhlenbeck Process. A dichotomy depending on $\alpha$ is observed. For $\alpha\leq1$ the parameters of the limiting Process contain the deviation matrix associated with the Background Process.
William Eric Leifur Grimson - One of the best experts on this subject based on the ideXlab platform.
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learning patterns of activity using real time tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000Co-Authors: Chris Stauffer, William Eric Leifur GrimsonAbstract:Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion segmentation is based on an adaptive Background subtraction method that models each pixel as a mixture of Gaussians and uses an online approximation to update the model. The Gaussian distributions are then evaluated to determine which are most likely to result from a Background Process. This yields a stable, real-time outdoor tracker that reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by accumulating joint co-occurrences of the representations within a sequence. These joint co-occurrence statistics are then used to create a hierarchical binary-tree classification of the representations. This method is useful for classifying sequences, as well as individual instances of activities in a site.
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Adaptive Background mixture models for real-time tracking
Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999Co-Authors: Christian Stauffer, William Eric Leifur GrimsonAbstract:A common method for real-time segmentation of moving regions in image sequences involves Background subtraction, or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of Background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a Background Process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the Background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow
Michel Mandjes - One of the best experts on this subject based on the ideXlab platform.
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markov modulated ornstein uhlenbeck Processes
Advances in Applied Probability, 2016Co-Authors: Gang Huang, Michel Mandjes, Hermanus Marinus Jansen, Peter Spreij, K. De TurckAbstract:In this paper we consider an Ornstein-Uhlenbeck (OU) Process (M(t)) t≥0 whose parameters are determined by an external Markov Process (X(t)) t≥0 on a finite state space {1, . . ., d}; this Process is usually referred to as Markov-modulated Ornstein-Uhlenbeck. We use stochastic integration theory to determine explicit expressions for the mean and variance of M(t). Then we establish a system of partial differential equations (PDEs) for the Laplace transform of M(t) and the state X(t) of the Background Process, jointly for time epochs t = t1, . . ., t K . Then we use this PDE to set up a recursion that yields all moments of M(t) and its stationary counterpart; we also find an expression for the covariance between M(t) and M(t + u). We then establish a functional central limit theorem for M(t) for the situation that certain parameters of the underlying OU Processes are scaled, in combination with the modulating Markov Process being accelerated; interestingly, specific scalings lead to drastically different limiting Processes. We conclude the paper by considering the situation of a single Markov Process modulating multiple OU Processes.
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Markov-modulated infinite-server queues driven by a common Background Process
Stochastic Models, 2015Co-Authors: Michel Mandjes, Koen De TurckAbstract:ABSTRACTThis paper studies a system with multiple infinite-server queues that are modulated by a common Background Process. If this Background Process, being modeled as a finite-state continuous-time Markov chain, is in state j, then the arrival rate into the i-th queue is λi, j, whereas the service times of customers present in this queue are exponentially distributed with mean μ− 1i, j; at each of the individual queues all customers present are served in parallel (thus reflecting their infinite-server nature).Three types of results are presented: in the first place (i) we derive differential equations for the probability-generating functions corresponding to the distributions of the transient and stationary numbers of customers (jointly in all queues), then (ii) we set up recursions for the (joint) moments, and finally (iii) we establish a central limit theorem in the asymptotic regime in which the arrival rates as well as the transition rates of the Background Process are simultaneously growing large.
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A Functional Central Limit Theorem for a Markov-Modulated Infinite-Server Queue
Methodology and Computing in Applied Probability, 2014Co-Authors: David F. Anderson, Joke Blom, Michel Mandjes, H. Thorsdottir, K. De TurckAbstract:We consider a model in which the production of new molecules in a chemical reaction network occurs in a seemingly stochastic fashion, and can be modeled as a Poisson Process with a varying arrival rate: the rate is λ i when an external Markov Process J(⋅) is in state i. It is assumed that molecules decay after an exponential time with mean μ −1. The goal of this work is to analyze the distributional properties of the number of molecules in the system, under a specific time-scaling. In this scaling, the Background Process is sped up by a factor N α , for some α>0, whereas the arrival rates become N λ i , for N large. The main result of this paper is a functional central limit theorem (F-CLT) for the number of molecules, in that, after centering and scaling, it converges to an Ornstein-Uhlenbeck Process. An interesting dichotomy is observed: (i) if α > 1 the Background Process jumps faster than the arrival Process, and consequently the arrival Process behaves essentially as a (homogeneous) Poisson Process, so that the scaling in the F-CLT is the usual \(\sqrt {N}\), whereas (ii) for α≤1 the Background Process is relatively slow, and the scaling in the F-CLT is N 1−α/2. In the latter regime, the parameters of the limiting Ornstein-Uhlenbeck Process contain the deviation matrix associated with the Background Process J(⋅).
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analysis of markov modulated infinite server queues in the central limit regime
arXiv: Probability, 2014Co-Authors: Joke Blom, Koen De Turck, Michel MandjesAbstract:This paper focuses on an infinite-server queue modulated by an independently evolving finite-state Markovian Background Process, with transition rate matrix $Q\equiv(q_{ij})_{i,j=1}^d$. Both arrival rates and service rates are depending on the state of the Background Process. The main contribution concerns the derivation of central limit theorems for the number of customers in the system at time $t\ge 0$, in the asymptotic regime in which the arrival rates $\lambda_i$ are scaled by a factor $N$, and the transition rates $q_{ij}$ by a factor $N^\alpha$, with $\alpha \in \mathbb R^+$. The specific value of $\alpha$ has a crucial impact on the result: (i) for $\alpha>1$ the system essentially behaves as an M/M/$\infty$ queue, and in the central limit theorem the centered Process has to be normalized by $\sqrt{N}$; (ii) for $\alpha<1$, the centered Process has to be normalized by $N^{{1-}\alpha/2}$, with the deviation matrix appearing in the expression for the variance.
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Large deviations of an infinite-server system with a linearly scaled Background Process
Performance Evaluation, 2014Co-Authors: K. De Turck, Michel MandjesAbstract:Abstract This paper studies an infinite-server queue in a Markov environment, that is, an infinite-server queue with arrival rates and service times depending on the state of a Markovian Background Process. We focus on the probability that the number of jobs in the system attains an unusually high value. Scaling the arrival rates λ i by a factor N and the transition rates ν i j of the Background Process as well, a large-deviations based approach is used to examine such tail probabilities (where N tends to ∞ ). The paper also presents qualitative properties of the system’s behavior conditional on the rare event under consideration happening.
Cheng-liang Chen - One of the best experts on this subject based on the ideXlab platform.
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heat exchanger network synthesis involving organic rankine cycle for waste heat recovery
Industrial & Engineering Chemistry Research, 2014Co-Authors: Cheng-liang Chen, Tzu-hsiang Chao, Feng-yi Chang, Hui-chu ChenAbstract:This article aims to present a mathematical model for the synthesis of a heat-exchanger network (HEN) which can be integrated with an organic Rankine cycle (ORC) for the recovery of low-grade waste heat from the heat surplus zone of the Background Process. An ORC-incorporated stagewise superstructure considering all possible heat-exchange matches between Process hot/cold streams and the ORC is first presented. On the basis of this superstructure, the model for synthesizing ORC-integrated HENs is formulated as a mixed-integer nonlinear program (MINLP). A two-step solution procedure is proposed to solve the MINLP model. First, a stand-alone HEN is synthesized to minimize the external utility consumption. An ORC is then incorporated into the HEN with the objective of maximizing the work produced from waste heat (in the heat surplus zone below the Process pinch) without increasing the use of a hot utility. A literature example is solved to demonstrate the application of the proposed model for industrial waste...
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heat exchanger network synthesis involving organic rankine cycle for waste heat recovery
Industrial & Engineering Chemistry Research, 2014Co-Authors: Cheng-liang Chen, Tzu-hsiang Chao, Feng-yi Chang, Hui-chu ChenAbstract:This article aims to present a mathematical model for the synthesis of a heat-exchanger network (HEN) which can be integrated with an organic Rankine cycle (ORC) for the recovery of low-grade waste heat from the heat surplus zone of the Background Process. An ORC-incorporated stagewise superstructure considering all possible heat-exchange matches between Process hot/cold streams and the ORC is first presented. On the basis of this superstructure, the model for synthesizing ORC-integrated HENs is formulated as a mixed-integer nonlinear program (MINLP). A two-step solution procedure is proposed to solve the MINLP model. First, a stand-alone HEN is synthesized to minimize the external utility consumption. An ORC is then incorporated into the HEN with the objective of maximizing the work produced from waste heat (in the heat surplus zone below the Process pinch) without increasing the use of a hot utility. A literature example is solved to demonstrate the application of the proposed model for industrial waste...
