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IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： G. S. Cheng Chao-Fu Wang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Auhui University Hefei China Temasek Laboratories National University of Singapore Singapore Singapore

ISBN: (数字)9781728134482

ISBN: (纸本)9781728134499

This paper addresses how to develop theory of characteristic modes (TCM) for finite periodic arrays or structures with fully using their periodic property. For ease of reference, the developed method is called periodic characteristic mode analysis (PCMA) method. Its basic idea is that we adopt a small set of specially selected characteristic modes (CMs) of a reference cell (element) of the structure to build up entire-domain basis functions (EDBFs) defined on each cell of the array structure and apply them to represent the characteristic currents (CCs) of the whole structure. Making use of periodicity of the array can greatly reduce the matrix size of the resultant generalized eigenvalue equation and speed up its solution process.

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Community-Grouping Based Particle Swarm optimisation Algorithm for Feature Selection

Community-Grouping Based Particle Swarm optimisation Algorit...

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Congress on Evolutionary Computation

作者： Jianfeng Qiu Jiangchuan Wan Lei Zhang Fan Cheng Yongkang Luo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

As a frequently-used dimensionality reduction technique in machine learning, feature selection has attracted interests in the last decade. Since feature selection is essentially a combinatorial optimization problem, how to search the valuable feature subset is a challenging optimization task. Particle swarm optimization (PSO) algorithm and its variations have shown their competitiveness in solving feature selection problem. However, they have been proven to be easily trapped into the local optimal in high-dimensional space due to their intrinsic characteristic of quick convergence. To this end, an effective binary particle swarm optimization algorithm, named CBPSOFS, is proposed for feature selection, where a community-grouping based adaptive updating strategy is designed to avoid trapping into the local optimum and enhance the performance of PSO algorithm in feature selection. To be specific, the correlationship among features is used to construct the feature network, where multiple feature groups are obtained by dividing the achieved feature network. Considering that a community usually contains multiple similar features, the proposed adaptive updating strategy utilizes these feature groups to make the similar features not be included in the same particle so as to maintain the diversity of the population in the evolution. In addition, an information gain based initialization strategy and a history information based resetting strategy are also developed to improve the quality of obtained feature subset. Experimental results on several real world datasets have demonstrated the effectiveness of CBPSOFS in feature selection when compared with the several state-of-the-art baselines.

关键词： Feature extraction Sociology Statistics Particle swarm optimization Optimization History signal processing algorithms

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Continuous Distributed processing of Software Defined Radar

Continuous Distributed Processing of Software Defined Radar

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IEEE International Conference on Radar

作者： Bing Li Qiang Qiu Shiqi Gong Yongjun Liu Yu Lei CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Institute of Computing Technology Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China National Laboratory of Radar Signal Processing Xidian University Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665468893

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing.

关键词： Protocols Scalability Streaming media Radar imaging Data processing Software Hardware

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DHMM-Based Asynchronous Finite-Time Sliding Mode Control for Markovian Jumping Lur’e Systems

Research Square

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Research Square 2021年

作者： Nie, Rong Du, Wenli Li, Zhongmei Zhou, Zhao He, Shuping Key Laboratory of Smart Manufacturing in Energy Chemical Process East China University of Science and Technology Ministry of Education Shanghai200237 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China

This paper is concerned with the asynchronous problem for a class of Markovian jumping Lur’s system (MJLSs) via sliding mode control (SMC) in continuous-time domain. Specifically, the discrete hidden Markovian model (DHMM) is employed to describe the nonsynchronization between the controller modes and the MJLSs modes. In particular, considering the nonlinearity of MJLSs, a novel Lur’e-integral-type sliding surface is constructed. In order to ensure the finite-time stability of sliding mode dynamics and the accessibility of the specified sliding surface, the asynchronous Lur’e-type SMC law of the detector mode is presented. Finally, an example of DC motor is provided to demonstrate the effectiveness of the proposed technique. © 2021, CC BY.

关键词： Sliding mode control

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Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization

Iterated Problem Reformulation for Evolutionary Large-Scale ...

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Congress on Evolutionary Computation

作者： Cheng He Ran Cheng Ye Tian Xingyi Zhang Department of Computer Science and Engineering University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Southern University of Science and Technology Shenzhen China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale multiobjective optimization. The first issue is how to improve the efficiency of EAs for reducing computation cost. The second one is how to improve the diversity maintenance of EAs to avoid local optima. Nevertheless, these two issues are somehow conflicting with each other, and thus it is crucial to strike a balance between them in practice. Thereby, we propose an iterated problem reformulation based EA for large-scale multiobjective optimization, where the problem reformulation based method and the decomposition based method are used iteratively to address the aforementioned issues. The proposed method is compared with several state-of-the-art EAs on a variety of large-scale multiobjective optimization problems. Experimental results demonstrate the effectiveness of our proposed iterated method in large-scale multiobjective optimization.

关键词： Sociology Convergence Pareto optimization Indexes Computer science

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Improved Matrix-based Attribute Reduction Algorithm Based on Minimal Elements for Mobile Edge computing

Improved Matrix-based Attribute Reduction Algorithm Based on...

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IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： Yi Xu Zhiqiang Zheng Yan Cheng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

With the development of Internet of Thlngs(IoT) technology, the Mobile Edge computing (MEC) environment plays an important role in different scenarios. This paper focus on the "last-mile" delivery scenario in logistics. In this scenario, due to the limited computation ability of the unmanned aerial vehicle (UAV), high real-time and computational resource-intensive tasks such as obstacle avoidance, route planning, and face recognition are require the assistance by MEC computing resources. However, due to the limited computing power of edge servers, the computing tasks of neural networks has very high delay. It cause the deadline of neural networks computation task may not be satisfied. Besides with the users and environment changed continuously, the neural nework take a long time to update which may affect the work of UAV. Therefore, It is an critical problem to accelerate the calculation and update of neural network on edge server. This paper proposes an attribute reduction algorithm based on rough set, which can reduce redundant attributes and compress the dimensionality of data. The experimental results show that can accelerate the calculation and update of neural network significantly and save the energy consumption.

关键词： Servers Edge computing Task analysis Neural networks Energy consumption Information systems Rough sets

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Solving large-scale multi-objective optimization via probabilistic prediction model

arXiv

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arXiv 2021年

作者： Hong, Haokai Ye, Kai Jiang, Min Cao, Donglin Tan, Kay Chen Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

The main feature of large-scale multi-objective optimization problems (LSMOP) is to optimize multiple conflicting objectives while considering thousands of decision variables at the same time. An efficient LSMOP algorithm should have the ability to escape the local optimal solution from the huge search space and find the global optimal. Most of the current researches focus on how to deal with decision variables. However, due to the large number of decision variables, it is easy to lead to high computational cost. Maintaining the diversity of the population is one of the effective ways to improve search efficiency. In this paper, we propose a probabilistic prediction model based on trend prediction model and generating-filtering strategy, called LT-PPM, to tackle the LSMOP. The proposed method enhances the diversity of the population through importance sampling. At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space. We compared the proposed algorithm with several state-of-the-art algorithms for different benchmark functions. The experimental results and complexity analysis have demonstrated that the proposed algorithm has significant improvement in terms of its performance and computational efficiency in large-scale multi-objective optimization. © 2021, CC BY.

关键词： Decision making

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Local Smoothing Optimization Penalized Least Squares: Application to Spectrum Baseline Correction

Local Smoothing Optimization Penalized Least Squares: Applic...

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第31届中国过程控制会议（CPCC 2020）

作者： Yuqiang Li Tianhong Pan Haoran Li Shan Chen Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University School of Electrical and Information Engineering Jiangsu University

Baseline shift, as an undesirable component of processing signal, seriously contaminates the essential information of the analysis object and deteriorates the performance of the developed model. The baseline correction methods based on penalized least squares have been widely applied in various industries and obtained better results in the single interval signal. However, the real analyzed signal is composed of multiple intervals with different smoothness, and the current baseline correction methods using constant smoothing parameters throughout the process cannot eliminate the baseline of the multi-interval signal. To address this issue, a local smoothing optimization penalized least squares(lsoPLS) method is presented in this work. The local smoothing optimization strategy using the derivative difference between the previously fitted baseline and the original signal is used to identify the interval location, while smoothing parameter is optimized by the interval characteristics. Two simulated cases and one real experiment were used to evaluate the effectiveness of the proposed lsoPLS method. Experimental results confirm that the proposed method has better performance in the multi-interval baseline correction and can be served as an effective pre-processing method for the complex spectral signal.

关键词： Baseline correction local smoothing optimization penalized least square signal pre-processing spectral analysis

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Semantic Based Heterogeneous Information Network Embedding for Patent Citation Recommendation

Semantic Based Heterogeneous Information Network Embedding f...

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Artificial intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yanping Zhang Shuang Li Xi Chen Fulan Qian Shu Zhao Shuwei Zhu Yulu Wang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

With the increasing number of patents, the task of patent citation recommendation becomes more and more important and meaningful. The core of a patent is textual content, which describes technical ideas and scope of the protection. Therefore, most of the existing patent citation recommendation methods focus on textual content. Recently, some scholars introduce bibliographic (structural) data to construct heterogeneous information network (HIN) on the basis of textual content to further improve the effect of recommendation. Most of these methods build HIN directly based on candidate patents selected in advance according to text similarity, which simplifies the complexity of the problem. However, the latent semantic relationships between patents formed by the textual content are often ignored, which can be used to mine more semantic and structural relationships in HIN. In this paper, in order to capture deep semantic and structural information, we introduce Semantic based Heterogeneous Information Network Embedding (SHINE) which obtains the latent semantic relationships between patents (referred to as semantic links in this paper) by textual content similarity and topic similarity, and links the semantic relationships with network structure to build a novel HIN. First, to obtain semantic links, we not only consider the textual content similarity between patents but also the topic similarity. We use the linear fusion method to combine these two similarities. Secondly, we build a HIN with semantic links and bibliographic information to integrate semantic and structural information, and we use network embedding to map the integration of two kinds of information into a common vector space. Finally, we recommend patent citations by linear combination of multi-modal similarities. The experimental results on two U.S. patent datasets show the effectiveness of SHINE. Compared to state-of-the art of patent citation recommendation, the proposed method SHINE achieves significantl

关键词： Patents Art Computational modeling Semantics Complexity theory Task analysis Artificial intelligence

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Architecture and key Technologies of Parallel Dispatching System for Railway Technical Operation Stations

Architecture and Key Technologies of Parallel Dispatching Sy...

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Complex Systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Gang Xiong Donghu Yang Wei Xu Runmei Li Shichao Chen Bing Song Xisong Dong Fenghua Zhu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Signal and Communication Research Institute China Academy of Railway Science Corporation Limited Beijing China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Beijing Huairou Academy of Parallel Sensing Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

A railroad technical operation station is an extensive and complex operation system and a variety of random disturbing factors further increase the complexity of its command and dispatching. To enhance the railroad technical operation stations' intelligence, and integration level, and enhance its dispatching management ability of complex operation plans, based on the ACP approach, this paper designs the parallel dispatching system (PDS) architecture, and describes its key technologies including the artificial dispatching system (ADS), computing experiments, parallel execution, etc. On the basis of the artificial dispatching system, computational experiments can optimize various types of technical operations and the overall operational process, to achieve parallel execution between the actual dispatching system and ADS. PDS can provide more efficient and intelligent command and dispatching solutions for railroad technical work stations, and promote the development of the railroad transportation industry in the direction of higher quality and efficiency.

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