Allocation of flexible alternating current transmission system (FACTS) devices to an electric power transmission network may be formulated as a nonlinear mathematical program. Solving such a nonlinear program for a la...
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ISBN:
(纸本)9781479964161
Allocation of flexible alternating current transmission system (FACTS) devices to an electric power transmission network may be formulated as a nonlinear mathematical program. Solving such a nonlinear program for a large transmission network is computationally very expensive, and obtaining the optimal solution may be impossible. We present a Taylor series expansion approximation of the nonlinearities of the problem and propose a mixedintegerlinear program (MILP) for finding the optimum location and proper settings of a Thyristor-Controlled Series Capacitor (TCSC) in an electric power network. The objective of this problem is to minimize total generation cost based on the DC load flow model. The proposed method is implemented for the 118-bus IEEE test case and the results are discussed.
Heterogeneous Multiprocessor System-on-Chip (Ht-MPSoC) architectures represent a promising approach as they allow a higher performance/energy consumption trade-off. In such systems, the processor instruction set is en...
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Heterogeneous Multiprocessor System-on-Chip (Ht-MPSoC) architectures represent a promising approach as they allow a higher performance/energy consumption trade-off. In such systems, the processor instruction set is enhanced by application-specific custom instructions implemented on reconfigurable fabrics, namely FPGA. To increase area utilization and guarantee application constraint respect, we propose a new architecture where Ht-MPSoC hardware accelerators are shared among different processors in an intelligent manner. In this paper, a mixed integer linear programming (MILP) model is proposed to systematically explore the complex design space of the different configurations.
Trains running through railway lines often accumulate some delay. When this happens, rescheduling and rerouting decisions must be quickly taken in real time. Despite the fact that even a single wrong decision may dete...
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Trains running through railway lines often accumulate some delay. When this happens, rescheduling and rerouting decisions must be quickly taken in real time. Despite the fact that even a single wrong decision may deteriorate the performance of the whole railway network, this complex optimization task is still basically performed by human operators. In very recent years, the interest of train operators to implement automated decision systems has grown. Not incidentally, the railway application section (RAS) of INFORMS has issued a challenge devoted to this problem concomitantly with the INFORMS Annual Meeting 2012. In this article, we describe two heuristic approaches to solve the RAS problem based on a mixed integer linear programming formulation, and we report computational results on the three RAS instances and on an additional set of instances defined on a more congested network. Computational results on the challenge test bed show that our algorithms positively compare with other approaches to the RAS problem. (c) 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 62(4), 315-326 2013
This paper deals with the global solution of the general multi-parametric mixed integer linear programming problem with uncertainty in the entries of the constraint matrix, the right-hand side vector, and in the coeff...
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This paper deals with the global solution of the general multi-parametric mixed integer linear programming problem with uncertainty in the entries of the constraint matrix, the right-hand side vector, and in the coefficients of the objective function. To derive the piecewise affine globally optimal solution, the steps of a multi-parametric branch-and-bound procedure are outlined, where McCormick-type relaxations of bilinear terms are employed to construct suitable multi-parametric under- and overestimating problems. The alternative of embedding novel piecewise affine relaxations of bilinear terms in the proposed algorithmic procedure is also discussed.
Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomark...
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Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.
Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detect...
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Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill-Whitham- Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data generated by multiple sensors of different types, we show that the constraints resulting from this partial differential equation are mixedintegerlinear inequalities for a specific decision variable. We use this fact to pose the problem of detecting spoofing cyber attacks in probe-based traffic flow information systems as mixedintegerlinear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offline. A numerical implementation is performed on a cyber attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California.
This paper addresses an extension of the resource-constrained project scheduling problem that takes into account storage resources which may be produced or consumed by activities. To solve this problem, we propose the...
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This paper addresses an extension of the resource-constrained project scheduling problem that takes into account storage resources which may be produced or consumed by activities. To solve this problem, we propose the generalization of two existing mixed integer linear programming models for the classical resource-constrained project scheduling problem, as well as one novel formulation based on the concept of event. Computational results are reported to compare these formulations with each other, as well as with a reference method from the literature. Conclusions are drawn on the merits and drawbacks of each model according to the instance characteristics.
Data classification is one of the fundamental issues in data mining and machine learning. A great deal of effort has been done for reducing the time required to learn a classification model. In this research, a new mo...
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Data classification is one of the fundamental issues in data mining and machine learning. A great deal of effort has been done for reducing the time required to learn a classification model. In this research, a new model and algorithm is proposed to improve the work of Xu and Papageorgiou (2009). Computational comparisons on real and simulated patterns with different characteristics (including dimension, high overlap or heterogeneity in the attributes) confirm that, the improved method considerably reduces the training time in comparison to the primary model, whereas it generally maintains the accuracy. Particularly, this speed-increase is significant in the case of high overlap. In addition, the rate of increase in training time of the proposed model is much less than that of the primary model, as the set-size or the number of overlapping samples is increased. (C) 2013 Elsevier Ltd. All rights reserved.
In most countries, mobile network operators compete through auctions in order to acquire spectrum, necessary resource to deliver sustainable services to customers in the forth-coming decades. These auctions can presen...
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ISBN:
(纸本)9781538674628
In most countries, mobile network operators compete through auctions in order to acquire spectrum, necessary resource to deliver sustainable services to customers in the forth-coming decades. These auctions can present complex mechanism designs which make them hard to analyze, and therefore to participate in. In this paper we propose a new approach based on mixed integer linear programming to address the problem of finding an optimal strategy for a bidder, considering a full information auction. This approach enables to simulate realistic instances of auctions, and to quantify the performances of the optimal strategies found against the Straightforward Bidding strategy, a widely studied participation strategy for multi-round auctions.
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