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Chinese Automation Congress (CAC)

作者： Nan Yu Jiang-Wen Xiao Ministry of Education Key Laboratory of Image Processing and Intelligent Control Wuhan P.R. China

ISBN: (纸本)9781467371902

This paper proposes a novel system for generating 1-D m-scroll, 2-D m×n-grid multi-scroll, 3-D m×n×l-grid multi-scroll chaotic attractors (GMSCAs) via switching piecewise linear (PWL) function. First, a third-order linear system satisfying certain conditions is introduced as the original system. Then, by adding stair function, saturated function series and hysteresis function series to the third-order linear system, respectively, GMSCAs are generated along a certain line, or plane, or in the whole state space. Besides, some basic dynamical behaviors of the controlled system are investigated and numerical simulations are given to verify the effectiveness of this method.

关键词： Jacobian matrices Cable TV Mathematical model Physics

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DSA image registration based on multiscale Gabor filters and mutual information

DSA image registration based on multiscale Gabor filters and...

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ICIA 2005: 2005 International Conference on Information Acquisition

作者： Cao, Zhiguo Liu, Xiaoxiao Peng, Bo Moon, Yiu-Sang Key Laboratory Huazhong University of Science and Technology Ministry of Education for Image Processing and Intelligent Control Wuhan 430074 China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)0780393031

In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in X-ray image sequences. Different with traditional DSA image registration processes, in our proposed image registration method, the control points are selected from the vessel centerlines using multiscale Gabor filters, and mutual information (MI) is then taken as the similarity criterion to find the correspondences. Experimental results demonstrate our algorithm efficiently yields satisfying registration result for DSA images. © 2005 IEEE.

关键词： Medical imaging

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Generating high-dimensional grid multi-scroll attractors via feedback controller and switching function

Generating high-dimensional grid multi-scroll attractors via...

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Chinese Automation Congress (CAC)

作者： Nan Yu Jiang-Wen Xiao Yan-Wu Wang Ministry of Education Key Laboratory of Image Processing and Intelligent Control Wuhan P.R.China

ISBN: (纸本)9781467371902

This paper proposes a systematically method for generating high-dimensional grid multi-scroll attractors (GMSAs), including 3-D, 4-D, 5-D and even higher-dimensional GMSAs. Firstly, based on a third-order linear system, higher-order linear systems are constructed by introducing state feedback controllers. Then, switching piecewise linear (PWL) functions, including stair function and saturated series function, are introduced, respectively, to make the higher-order systems generate high-dimensional GMSAs. Besides, some basic dynamical properties of GMSA systems are investigated and numerical simulations are given to demonstrate the feasibility of the proposed method.

关键词： Gold Encryption

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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Microgrid energy management strategy considering microgrid economy and rSOC stability: A model-based study 43

Microgrid energy management strategy considering microgrid e...

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43rd Chinese control Conference, CCC 2024

作者： Huang, Haizhou Xia, Zhiping Shan, Xinkai Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

ISBN: (纸本)9789887581581

Microgrids possess the ability to use renewable energy efficiently and play an increasingly significant role in environmental protection and sustainable development. Meanwhile, reversible solid oxide fuel cell (rSOC) is currently a new type of energy conversion and storage. Combining with microgrid systems is a great way to benefit from its high capacity, high efficiency, and cleanliness. However, rSOC's complex system of coupled heat, electricity, and gas presents a significant challenge in finding energy management strategies for microgrid applications containing rSOC. In this study, for a microgrid system combining rSOC, battery energy storage system (BESS), solar photovoltaic (PV), and wind turbine generator (WTG), we propose a model-based energy management strategy that simultaneously takes into account the economy of the microgrid system and the stability of the rSOC system. First of all, the microgrid is modeled with great detail, including the wind turbine, solar panels, battery storage system, and rSOC. In addition, the microgrid energy management problem is framed as a mathematical programming problem, with the attention focusing on the costs of BESS aging and operational energy loss of rSOC. In particular, this study also proposes a penalty strategy to reduce the power fluctuation amplitude of the rSOC system and reduce the mode switching frequency of the rSOC system. Using the solver, solving for each device's target power in a microgrid. The actual power operating curve is output by tracking the target power under slope constraints using the Simulink model. We then conduct a case study using data from Wuhan, China. Our research shows that compared with the control strategy based on rule control, the control strategy proposed in this study can reduce the power variation amplitude of the rSOC system by 79%, reduce the mode switching frequency by 85%, reduce the system operating cost by 4.5% and increase the energy self-sufficiency rate by 9%. © 2024 Techn

关键词： Wind turbines

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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization 11th

Manifold Learning Inspired Mating Restriction for Evolutiona...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Analysis of a memristor-based switching network

Analysis of a memristor-based switching network

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2011 International Conference on Information Science and Technology, ICIST 2011

作者： Wu, Ailong Shen, Yi Zeng, Zhigang Zhang, Jine Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China School of Basic Science East China Jiaotong University Nanchang 330013 China

ISBN: (纸本)9781424494408

In this paper, we formulate and investigate a memristor-based switching network which is directly extended from Itoh and Chua's chaotic circuit. Conditions are derived which ensure the existence of an equilibrium point and the uniformly stable for state trajectories of the memristor-based switching network. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. It is believed that the criteria in this paper is also valuable in the design of memristor-based switching network which can be used to solve efficiently classes of optimization problems arising in practical engineering applications. © 2011 IEEE.

关键词： Memristors

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： image reconstruction

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An improved column generation algorithm for crew scheduling problems

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Journal of Information and Computational Science 2013年第1期10卷 175-183页

作者： Chen, Shijun Shen, Yindong Image Processing and Intelligent Control Key Laboratory of Education Ministry Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Column Generation (CG) technique is popularly applied in solving the crew scheduling problem of large size, which is generally modeled as an Integer Linear Programming (ILP) problem. The traditional CG algorithms for bus and rail crew scheduling encounter difficulties on either the generation of all potential shifts or solving subproblems. A subproblem embedded in the CG is generally represented as a Resource-constrained Shortest Path Problem (RCSPP), which is NP-hard and constitutes a major reason for the CG's slow convergence. This paper presents a novel CG strategy to improve the efficiency of the proposed crew scheduling approach. The main idea is that a reasonably large set of "good" potential shifts (called a shift-pool) is pre-compiled using problem-specific knowledge. During the CG process, the RCSPP is not constructed to generate potential shifts as new columns until no columns with Negative Reduced Cost (NRC) exist in the shift-pool. Experiments are carried out on a series of Chinese real-world problem instances, and the computational results show that the solving process is significantly accelerated. © 2013 by Binary Information Press.

关键词： Integer programming

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Predictive control of vehicle based on Echo state network

Predictive control of vehicle based on Echo state network

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International Conference on Intelligence Science and Information Engineering

作者： Ni, Shaobo Liu, Lei Wang, Zhishen Wang, Zirui Beijing Institute of Technology School of Aerospace Engineering Beijing 100081 China Beijing Aerospace Automatic Control Institute Beijing 100854 China Department of Control Science and Engineering Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769544809

An Echo State Network (ESN) based predictive control method for aircraft system is proposed to increase the flight quality. Aerocraft is a nonlinear and time-varying system and its controllers are usually designed by trajectory linearization method. Furthermore, the predictive controller is designed to adjust the current controlled variables by the trend of output to improve the quick responsiveness and traceability. Then, the stability of the system is proved by Lyapunov function. Finally, the numerical simulation results of step-response and attitude tracking illustrate the robustness and effect of the method. © 2011 IEEE.

关键词： controllers

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