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Multidimensional Reversible Solid Oxide Fuel Cell Modeling for Embedded Applications

IEEE TRANSACTIONS ON ENERGY CONVERSION 2018年第2期33卷 692-701页

作者： Ma, Rui Gao, Fei Breaz, Elena Huangfu, Yigeng Briois, Pascal Univ Bourgogne Franche Comte FEMTO ST UMR CNRS 6174 Energy Dept F-90010 Belfort France FR CNRS 3539 Fuel Cell Lab F-90010 Belfort France Northwestern Polytech Univ Xian 710072 Shaanxi Peoples R China Tech Univ Cluj Napoca Cluj Napoca 400604 Romania Univ Bourgogne Franche Comte Dept MN2S FEMTO ST UMR CNRS 6174 F-25200 Montbeliard France

This paper presents a multiphysical modeling of a two-dimensional (2-D) reversible tubular solid oxide cell. The developed model can represent both a solid oxide electrolysis cell (SOEC) and solid oxide fuel cell (SOFC) operations. By taking into account of the electrochemical, fluidic, and thermal physical phenomena, the presented model can accurately describe the multiphysical effects inside a cell for both fuel cell and electrolysis cell operation under entire working range of cell current and temperature. In addition, an iterative solver is proposed which is used to solve the 2-D distribution of physical quantities along the tubular cell. The proposed model is suitable for embedded applications, such as real-time simulation or online diagnostic control. The reversible solid oxide cell model is then validated experimentally in both SOEC and SOFC configurations under different species partial pressures, operating temperatures, and current densities conditions.

关键词： Solid oxide electrolysis cell (SOEC) solid oxide fuel cell (SOFC) physical modeling multi-dimensional iterative algorithm

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Finite Frequency H_∞ Deconvolution With Application to Approximated Bandlimited Signal Recovery

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2018年第1期63卷 203-210页

作者： Gao, Huijun Li, Xianwei Qiu, Jianbin Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

This paper investigates a finite frequency H-infinity deconvolution problem for single-input-single-output discrete-time systems. Motivated by the fact that many practical signals are located in a finite frequency range, this paper is focused on developing a design method for achieving better deconvolution performance over a finite frequency range. First, a finite frequency H-infinity deconvolution problem is formulated such that the deconvolution specification is characterized by a modified H-infinity index associated with the corresponding finite frequency range. By an adapted generalized Kalman-Yakubovich-Popov lemma and an auxiliary vector approach, a matrix inequality condition is obtained for parameterizing a required deconvolution filter, based on which a simple iterative algorithm is then constructed for optimizing the filter performance. Multiobjective deconvolution is further discussed in the same framework. Compared with the traditional frequency weighting strategy, the proposed method can directly analyze and synthesize the deconvolution performance within finite frequency ranges, the advantage of which is demonstrated in a bandlimited signal recovery example.

关键词： Bandlimited signal recovery finite frequency H-infinity deconvolution iterative algorithm

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Weighted variational model for selective image segmentation with application to medical images

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PATTERN RECOGNITION 2018年 76卷 367-379页

作者： Liu, Chunxiao Ng, Michael Kwok-Po Zeng, Tieyong Hangzhou Normal Univ Dept Math Hangzhou 310036 Zhejiang Peoples R China Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China HKBU Inst Res & Continuing Educ Shenzhen Virtual Hong Kong Hong Kong Peoples R China

Selective image segmentation is an important topic in medical imaging and real applications. In this paper, we propose a weighted variational selective image segmentation model which contains two steps. The first stage is to obtain a smooth approximation related to Mumford-Shah model to the target region in the input image. Using weighted function, the approximation provides a larger value for the target region and smaller values for other regions. In the second stage, we make use of this approximation and perform a thresholding procedure to obtain the object of interest. The approximation can be obtained by the alternating direction method of multipliers and the convergence analysis of the method can be established. Experimental results for medical image selective segmentation are given to demonstrate the usefulness of the proposed method. We also do some comparisons and show that the performance of the proposed method is more competitive than other testing methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Selective segmentation Mumford-Shah model Thresholding Medical images iterative algorithm

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Secure communications with untrusted relays: a multi-pair two-way relaying approach

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IET COMMUNICATIONS 2018年第20期12卷 2552-2560页

作者： Zhao, Tantan Li, Guobing Lv, Gangming Zhang, Yizhen Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China China Acad Engn Phys Inst Comp Applicat Mianyang 621999 Sichuan Peoples R China ZTE Corp R& Ctr Xian Xian 710049 Shaanxi Peoples R China

Physical-layer security is usually ensured by the injection of artificial noise (AN) at the legitimate source or destination when relays are cooperative but untrusted. In this work, in order to avoid the power-consuming AN at the legitimate user pairs, the authors investigate multi-pair two-way relaying as a solution for secure transmission in wireless relay networks with untrusted relays. They formulate a generalised problem for the joint design of cooperative beamforming (CB) and AN at relays to maximise the secrecy sum-rate in wireless networks with multi-pair legal users, multiple untrusted relays as well as multiple non-colluding eavesdroppers. Further, they reformulate, approximate and relax the original non-convex problem into a sequence of convex sub-problems based on semi-definite relaxation (SDR) and Taylor series approximation. Particularly, for the reformulated problem they prove that the SDR is in fact tight, and hence propose an iterative algorithm for CB and AN design at the relays. In the simulations, they demonstrate the fast convergence and high achievable secrecy sum-rate of the proposed algorithm. Also, they simulate and discuss the impact of the number of user pairs on the secrecy sum-rate, and reveal the change of performance gain with the number of multiple user pairs.

关键词： radio networks convex programming relay networks (telecommunication) iterative methods telecommunication security concave programming cooperative communication array signal processing approximation theory multipair legal users multiple untrusted relays nonconvex problem multiple user pairs secure communications multipair two-way relaying physical-layer security legitimate source legitimate user pairs secure transmission wireless relay networks convex subproblems secrecy sum-rate maximisation artificial noise injection cooperative beamforming multiple noncolluding eavesdroppers semidefinite relaxation Taylor series approximation iterative algorithm

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The daily tail assignment problem under operational uncertainty using look-ahead maintenance constraints

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期264卷 534-547页

作者： Maher, Stephen J. Desaulniers, Guy Soumis, Francois Zuse Inst Berlin Dept Optimizat Takustr 7 D-14195 Berlin Germany Univ Lancaster Dept Management Sci Lancaster LA1 4YX England Ecole Polytech Montreal Montreal PQ H3C 3A7 Canada Gerad Dept Math & Ind Engn Montreal PQ H3C 3A7 Canada

The tail assignment problem is a critical part of the airline planning process that assigns specific aircraft to sequences of flights, called lines-of-flight, to satisfy operational constraints. The aim of this paper is to develop an operationally flexible method, based upon the one-day routes business model, to compute tail assignments that satisfy short-range within the next three days aircraft maintenance requirements. While maintenance plans commonly span multiple days, the methods used to compute tail assignments for the given plans can be overly complex and provide little recourse in the event of schedule perturbations. The presented approach addresses operational uncertainty by using solutions from the one-day routes aircraft maintenance routing approach as input. The daily tail assignment problem is solved with an objective to satisfy maintenance requirements explicitly for the current day and implicitly for the subsequent two days. A computational study will be performed to assess the performance of exact and heuristic solution algorithms that modify the input lines-of-flight to reduce maintenance misalignments. The daily tail assignment problem and the developed algorithms are demonstrated to compute solutions that effectively satisfy maintenance requirements when evaluated using input data collected from three different airlines. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Transportation Tail assignment Maintenance planning Branch-and-price iterative algorithm

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Least-squares-based iterative identification algorithm for Hammerstein nonlinear systems with non-uniform sampling

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2013年第7期90卷 1524-1534页

作者： Li, Xiangli Ding, Ruifeng Zhou, Lincheng Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China

This paper focuses on identification problems for Hammerstein systems with non-uniform sampling. By using the over-parameterization technique, we derive a linear regressive identification model with different input updating rates. To solve the identification problem of Hammerstein output error systems with the unmeasurable variables in the information vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. The performances of the proposed algorithm are analysed and compared by using a numerical example.

关键词： parameter estimation iterative algorithm auxiliary model identification least squares non-uniform sampling 93B30 93A30

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Linear System Identification Based on a Kronecker Product Decomposition

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第10期26卷 1793-1808页

作者： Paleologu, Constantin Benesty, Jacob Ciochina, Silviu Univ Politehn Bucuresti Bucharest 060042 Romania Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada

Linear system identification is a key problem in many important applications, among which echo cancelation is a very challenging one. Due to the long length impulse responses (i.e., echo paths) to he identified, there is always room (and needs) to improve the performance of the echo cancelers, especially in terms of complexity, convergence rate, robustness, and accuracy. In this paper, we propose a new way to address the system identification problem (from the echo cancelation perspective), by exploiting an optimal approximation of the impulse response based on the nearest Kroneeker product decomposition. Also, we make a first step toward this direction, by developing an iterative Wiener filter based on this approach. As compared to the conventional Wiener filter, the proposed solution is much more attractive since its gain is twofold. First, the matrices to be inverted (or, preferably, linear systems to be solved) are smaller as compared to the conventional approach. Second, as a consequence, the iterative Wiener filter leads to a good estimate of the impulse response, even when a small amount of data is available for the estimation of the statistics. Simulation results support the theoretical findings and indicate the good results of the proposed approach, for the identification of different network and acoustic impulse responses.

关键词： System identification impulse response decomposition nearest Kronecker product Wiener filter iterative algorithm echo cancellation

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Primal dual based ontology sparse vector learning for similarity measuring and ontology mapping

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第4期35卷 4525-4531页

作者： Gong, Shu Tian, Liwei Imran, Muhammad Gao, Wei Guangdong Univ Sci & Technol Dept Comp Sci Dongguan 523083 Peoples R China United Arab Emirates Univ Dept Math Sci Al Ain U Arab Emirates Yunnan Normal Univ Sch Informat Sci & Technol Kunming Yunnan Peoples R China

From the mathematical point of view, the goal of ontology learning is to obtain the dimensionality function f : R-p -> R, and the p-dimensional vector corresponding to the ontology vertex is mapped into one-dimensional real number. In the background of big data applications, the ontology concept corresponds to the high complexity of information, and thus sparse tricks are used in ontology learning algorithm. Through the ontology sparse vector learning, the ontology function f is obtained via ontology sparse vector beta, and then applied to ontology similarity computation and ontology mapping. In this paper, the ontology optimization strategy is obtained by coordinate descent and dual optimization, and the optimal solution is obtained by iterative procedure. Furthermore, the greedy method and active sets are applied in the iterative process. Two experiments are presented where we will apply our algorithm to plant science for ontology similarity measuring and to mathematics ontologies for ontology mapping, respectively. The experimental data show that our primal dual based ontology sparse vector learning algorithm has high efficiency.

关键词： Ontology similarity measure ontology mapping machine learning iterative algorithm

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L1-Norm Distance Minimization-Based Fast Robust Twin Support Vector k-Plane Clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第9期29卷 4494-4503页

作者： Ye, Qiaolin Zhao, Henghao Li, Zechao Yang, Xubing Gao, Shangbing Yin, Tongming Ye, Ning Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Jiangsu Peoples R China Huaiyin Inst Technol Lab Internet Things & Internet Technol Jiangsu Pr Nanjing 223003 Jiangsu Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Coll Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China Huaiyin Inst Technol Coll Comp Engn Huaian Peoples R China Nanjing Forestry Univ Coll Forestry Nanjing 210037 Jiangsu Peoples R China

Twin support vector clustering (TWSVC) is a recently proposed powerful k-plane clustering method. It, however, is prone to outliers due to the utilization of squared L2-norm distance. Besides, TWSVC is computationally expensive, attributing to the need of solving a series of constrained quadratic programming problems (CQPPs) in learning each clustering plane. To address these problems, this brief first develops a new k-plane clustering method called L1-norm distance minimization-based robust TWSVC by using robust L1-norm distance. To achieve this objective, we propose a novel iterative algorithm. In each iteration of the algorithm, one CQPP is solved. To speed up the computation of TWSVC and simultaneously inherit the merit of robustness, we further propose Fast RTWSVC and design an effective iterative algorithm to optimize it. Only a system of linear equations needs to be computed in each iteration. These characteristics make our methods more powerful and efficient than TWSVC. We also conduct some insightful analysis on the existence of local minimum and the convergence of the proposed algorithms. Theoretical insights and effectiveness of our methods are further supported by promising experimental results.

关键词： iterative algorithm k-plane clustering L1-norm distance linear equations twin support vector clustering (TWSVC)

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Energy-efficient power allocation scheme for distributed antenna system over spatially correlated Rayleigh channels

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IET COMMUNICATIONS 2018年第5期12卷 533-542页

作者： Xu, Weiye Wen, Benben Lin, Min Yu, Xiangbin Nanjing Inst Technol Sch Commun Engn Nanjing Jiangsu Peoples R China PLA Univ Sci & Technol Coll Commun Engn Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Jiangsu Peoples R China Nanjing Inst Telecommun Technol Nanjing Jiangsu Peoples R China

The energy-efficient power allocation (PA) of distributed antenna system using multiple receive antennas is studied over spatially correlated Rayleigh channels. A new energy efficiency (EE) defined as the ratio of the average transmission rate to the consumed total power is presented. According to this definition, subject to the maximum power constraint, an optimal PA scheme is developed by maximising the EE. It is shown that the solution of PA in the constrained EE optimisation does exist and is unique. A Newton method with iterative algorithm is proposed to attain the PA. To avoid iterative calculation of the optimal scheme, a suboptimal PA scheme is derived by means of the Lambert function. As a result, closed-form PA is obtained. The developed schemes only need the statistical channel information and large scale information, and thus they have lower overhead and better robustness. Moreover, they may include the ones over spatially independent channel as special cases. The theoretical EE is further derived for performance evaluation, and the resultant closed-form expression is obtained. Computer simulation indicates that the theoretical EE agrees well the simulation result, and the suboptimal PA scheme can achieve the EE value near to the optimal one with lower complexity.

关键词： optimisation statistical analysis Newton method receiving antennas energy conservation telecommunication power management Rayleigh channels distributed antenna system spatially correlated Rayleigh channels multiple receive antennas average transmission rate optimal PA scheme system EE maximization constrained EE optimisation Newton method iterative algorithm iterative calculation complexity reduction suboptimal PA scheme Lambert function closed-form PA statistical channel information spatially independent channel performance evaluation closed-form theoretical expression computer simulation

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