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Aggregated optimal charging and vehicle-to-grid control for electric vehicles under large electric vehicle population

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第8期10卷 2012-2018页

作者： Tang, Yuchen Zhong, Jin Bollen, Math Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China Lulea Univ Technol Elect Power Engn Skelleftea Sweden

Effective charging and vehicle-to-grid (V2G) control strategies can utilise the properties of electric vehicles (EVs) to obtain various benefits. EVs are modelled as individuals in existing charging control algorithms. In this study, a new modelling method of EVs and an optimal charging control strategy are proposed so that all EVs in a control area can be regarded as a single object in the optimisation process. The strategy minimises the total charging cost of EVs, and can be further expanded to serve V2G control. With the new modelling method, the computational burden of the optimisation algorithm can be reduced significantly and does not increase with the number of EVs. Thus the strategy is extremely effective when the number of EVs becomes large, and the implementation cost could be more reasonable since less computational capacity is required. Case studies are presented to illustrate the performance of the strategy.

关键词： Vehicle stock optimal charging CONTROLLED AREAS grid control optimization algorithms Electric vehicles Modeling

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Optimizing network robustness by edge rewiring: a general framework

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DATA MINING AND KNOWLEDGE DISCOVERY 2016年第5期30卷 1395-1425页

作者： Chan, Hau Akoglu, Leman SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

Spectral measures have long been used to quantify the robustness of real-world graphs. For example, spectral radius (or the principal eigenvalue) is related to the effective spreading rates of dynamic processes (e.g., rumor, disease, information propagation) on graphs. Algebraic connectivity (or the Fiedler value), which is a lower bound on the node and edge connectivity of a graph, captures the "partitionability" of a graph into disjoint components. In this work we address the problem of modifying a given graph's structure under a given budget so as to maximally improve its robustness, as quantified by spectral measures. We focus on modifications based on degree-preserving edge rewiring, such that the expected load (e.g., airport flight capacity) or physical/hardware requirement (e.g., count of ISP router traffic switches) of nodes remain unchanged. Different from a vast literature of measure-independent heuristic approaches, we propose an algorithm, called EdgeRewire, which optimizes a specific measure of interest directly. Notably, EdgeRewire is general to accommodate six different spectral measures. Experiments on real-world datasets from three different domains (Internet AS-level, P2P, and airport flights graphs) show the effectiveness of our approach, where EdgeRewire produces graphs with both (i) higher robustness, and (ii) higher attack-tolerance over several state-of-the-art methods.

关键词： Graph robustness Edge rewiring Robustnesss measures Graph spectrum optimization algorithms Attack tolerance

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A moving approach for the Vector Hysteron Model

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PHYSICA B-CONDENSED MATTER 2016年 486卷 92-96页

作者： Cardelli, E. Faba, A. Laudani, A. Antonio, S. Quondam Fulginei, F. Riganti Salvini, A. Univ Perugia Dept Engn Via G Duranti 93 I-06125 Perugia Italy Univ Rome Tre Dept Engn Via V Volterra 62 I-00146 Rome Italy

A moving approach for the VHM (Vector Hysteron Model) is here described, to reconstruct both scalar and rotational magnetization of electrical steels with weak anisotropy, such as the non oriented grain Silicon steel. The hysterons distribution is postulated to be function of the magnetization state of the material, in order to overcome the practical limitation of the congruency property of the standard VHM approach. By using this formulation and a suitable accommodation procedure, the results obtained indicate that the model is accurate, in particular in reproducing the experimental behavior approaching to the saturation region, allowing a real improvement respect to the previous approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Vector Hysteresis Models Vector Hysteresis Measurements optimization algorithms

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Optimal control of a distributed solar collector field

Optimal control of a distributed solar collector field

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American Control Conference

作者： Xiaodong Xu Yuan Yuan Stevan Dubljevic Department of Chemical & Materials Engineering University of Alberta Canada T6G 2V4

ISBN: (纸本)9781509045839

The dynamics of a distributed solar collector field can be modelled by a nonlinear hyperbolic partial differential equation (PDE) based on the energy balance. The model-based optimal control of the outlet temperature is studied. One of few ways to influence the outlet temperature is by adjusting the oil pump volumetric flow rate. In this work, an optimal algorithm is developed to minimize the mismatch between the outlet temperature and a desired temperature. The method is based on the adjoint approach for constrained optimization problems with a nonlinear hyperbolic PDE applied as an optimization constraint. In particular, the algorithm simplifies a problem by decomposing it into a two-level optimization problem. Unlike the traditional tracking control such as motion planing, the reference tracking equations in this work do not need state trajectory generation. Finally, the proposed approach is verified to perform well via a computer simulation.

关键词： Optimal control Solar collectors VOLUME FLUX optimization algorithms Constrained optimization Hyperbolic Distribute

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Parameter Study on Improved Quantum-behaved Particle Swarm optimizations

Parameter Study on Improved Quantum-behaved Particle Swarm O...

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2017 2nd International Conference on Applied Mathematics, Simulation and Modelling(AMSM2017)

作者： Yi FU Zhi-guo CHEN Ji ZHAO Juan MEI School of Internet of Things Engineering Wuxi City College of Vocational Technology WuXi Research Center for Environmental Science & Engineering School of Internet of Things Engineering Jiangnan University

Inspired by concepts in quantum mechanics and particle swarm optimization(PSO) algorithm,quantum-behaved particle swarm optimization(QPSO) algorithm was proposed as a variant of PSO algorithm with better global search *** the same time,some improved QPSO algorithms are also *** order to determine whether the performance of the algorithm is affected by the location of the parameter,this paper compares four variants of QPSO *** operator is exerted on the mean best position and the particle's previous position to improve the search ability of the QPSO algorithm,***,some empirical studies on popular benchmark functions are performed in order to make a full performance evaluation and comparison among four variants of QPSO *** experimental results show that the new parameter based on individual particles evolutionary process which located in the mean best position algorithm(IEQPSO-1) is more effective approach than others in most cases.

关键词： Quantum-behaved particle swarm optimization Swarm intelligence optimization algorithms

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Generalized Hessian approximations via Stein's lemma for constrained minimization

Generalized Hessian approximations via Stein's lemma for con...

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Information Theory and Applications Workshop, ITA

作者： Murat A. Erdogdu Department of Statistics Stanford University Stanford CA USA

ISBN: (纸本)9781509052943

We consider the problem of convex constrained minimization of an average of n functions, where the parameter and the features are related through inner products. We focus on second order batch updates, where the curvature matrix is obtained by assuming random design and by applying the celebrated Stein's lemma together with subsampling techniques. The proposed algorithm enjoys fast convergence rates similar to the Newton method, yet the per-iteration cost has the same order of magnitude as the gradient descent. We demonstrate its performance on well-known optimization problems where Stein's lemma is not directly applicable, such as M-estimation for robust statistics, and inequality form linear/quadratic programming etc. Under certain assumptions, we show that the constrained optimization algorithm attains a composite convergence rate that is initially quadratic and asymptotically linear. We validate its performance through widely encountered optimization tasks on several real and synthetic datasets by comparing it to classical optimization algorithms.

关键词： convergence convex programming gradient methods Hessian matrices minimisation Hessian matrix convex programming gradient methods optimization algorithms Converge Lemma Hessian

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Analysis of Look-ahead Control on Traffic Flow

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IFAC-PapersOnLine 2016年第3期49卷 261-266页

作者： Németh, Balázs Gáspár, Péter Mihály, András Bokor, József Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Institute for Computer Science and Control Hungarian Academy of Sciences and MTA-BME Control Engineering Research Group Budapest Hungary

The paper analyzes the relationship between the traffic flow and the look-ahead control. The purpose of the analysis is to map the impact of the unique speed profiles used by the look-ahead controlled vehicles on the traffic flow consisting of conventional vehicles. The analysis is carried out by considering different traffic volumes, penetration rates of the look-ahead vehicles and the energy-scaling parameter of the look-ahead optimization algorithm. The analysis is evaluated both in the Matlab simulation environment and in the microscopic traffic simulation environment VISSIM using COM interface. © 2016

关键词： Street traffic control MATLAB Numerical control systems Vehicles Energy scaling Look ahead control Matlab simulations Microscopic traffic simulation optimization algorithms Penetration rates Speed profile Traffic volumes

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A Computationally Efficient Commutation Algorithm for Parasitic Forces and Torques Compensation in Ironless Linear Motors

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IFAC-PapersOnLine 2016年第21期49卷 267-273页

作者： Nguyen, Tuan T. Lazar, Mircea Butler, Hans Eindhoven University of Technology Eindhoven Netherlands

Ideally, ironless linear motors can reach very high precision with simple classical commutation using three-phase sinusoidal currents. However, in reality, due to deviations from the design parameters, there are various parasitic forces and torques for which the classical commutation cannot compensate. An alternative solution is to formulate the commutation as an optimization problem and solve it numerically. This paper proposes a new optimization algorithm which is computationally efficient and well-suited to the commutation problem in the sense that it is capable of compensating for parasitic forces and torques while minimizing the dissipated power in the motors. Simulation results with a finite element method model are presented to demonstrate the effectiveness of the proposed commutation algorithm. © 2016

关键词： Linear motors Computational efficiency Finite element method optimization Alternative solutions Commutation problem Computationally efficient Finite element method models Forces and torques optimization algorithms optimization problems Sinusoidal currents

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Unsupervised adaptation for deep neural networks using Alternating Direction Method of Multipliers

Unsupervised adaptation for deep neural networks using Alter...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Roger Hsiao Tim Ng Man-Hung Siu Raytheon BBN Technologies 10 Moulton Street Cambridge MA 02138 USA

ISBN: (纸本)9781509041183

In this paper, we continue our work on linear least squares based adaptation (LLS) for deep neural networks. We show that our previously proposed algorithm is a special case of an optimization algorithm called Alternating Direction Method of Multipliers (ADMM). We demonstrate that the adaptation algorithm can improve the performance on various deep neural networks including the bidirectional long short term memory (BLSTM). On the Switchboard subset of the Hub5 2000 evaluation set, we show that LLS adaptation can achieve 6 to 9% relative word error rate (WER) reduction, and improve our two-pass system to 7.5% WER. In this paper, we also analyze the factors that could contribute to the success of an adaptation algorithm. This helps us to understand under what circumstances, adaptation could improve the system performance.

关键词： Speech recognition unsupervised adaptation Switchboard Speech recognition Multiplying circuits Neural network optimization algorithms System performance

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Compressive imaging with iterative forward models

Compressive imaging with iterative forward models

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hsiou-Yuan Liu Ulugbek S. Kamilov Dehong Liu Hassan Mansour Petros T. Boufounos Department of Electrical Engineering & Computer Sciences University of California Berkeley 94720 USA Mitsubishi Electric Research Laboratories (MERL) Cambridge MA 02139 USA

ISBN: (纸本)9781509041183

We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements. Our method relies on a novel, nonlinear measurement model that can account for the multiple scattering phenomenon, which makes the method preferable in applications where linear measurement models are inaccurate. We construct the measurement model by expanding the scattered wave-field with an accelerated-gradient method, which is guaranteed to converge and is suitable for large-scale problems. We provide explicit formulas for computing the gradient of our measurement model with respect to the unknown image, which enables image formation with a sparsity-driven numerical optimization algorithm. We validate the method both analytically and with numerical simulations.

关键词： Computational imaging inverse scattering sparse reconstruction total variation regularization inverse scattering Scattering optimization algorithms Image formation Imaging Linear measurement Inaccurate Iterative

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