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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Li, Lianghao He, Cheng Lin, Jianqing Pan, Linqiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350393521

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems. © 2023 IEEE.

关键词： Constraint handling

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Data model driven architecture (DMDA)

Data model driven architecture (DMDA)

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4th International Workshop on Advanced Computational Intelligence, IWACI 2011

作者： Chen, Chen Chen, Xueguang Liu, Hong Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781612843735

Based on the current development of Model Driven Architecture (MDA) in Enterprise Information System (EIS), the paper proposes a DMDA, a new development architecture to improve EIS development speed and quality. The basic idea of the DMDA is to take advantage of formal description, decomposition and re-use on EIS development process to let computer understand development demands of developers and then consequently generate some business models having nothing to do with specific technical realization, combine with specific technical basic platform to finally generate the EIS meeting actual demands based on data model. The practice proves the architecture is capable of simplifying EIS development difficulty, expediting application and development speed, improve development quality and achieve the rapid development purpose. © 2011 IEEE.

关键词： Software design

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A Non-Iterative Network Utilization Pricing Mechanism for P2P Energy Trading

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1301-1306页

作者： Yun Liu Yunqiang Gao Yuanzheng Li Jizhong Zhu Hoay Beng Gooi School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhan 430074China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore IEEE

To sustain operation and maintenance of an active distribution network(ADN),a network fee should be charged by the distribution network service provider(DNSP)for facilitating the P2P energy trading *** this end,this paper models the interaction among the DNSP and multiple prosumers as a Stackelberg game,and then develops a non-iterative and decentralized transactive mechanism to simultaneously achieve optimal network utilization pricing and peer-to-peer(P2P)*** results in an ADN with four prosumers connected to a common substation bus validate the effectiveness and efficiency of the proposed scheme.

关键词： Distribution network service provider network fee non-iterative peer-to-peer trading prosumer

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Epidemic spreading with variant infection rates on scale-free network

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6th International Symposium on Neural Networks, ISNN 2009

作者： Hong, Liu Ouyang, Min Mao, Zijun Chen, Xueguang Institute of System Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)3642015123

In this paper, we proposed a susceptible-infected model with variant infection rates because different individuals have different resistance to diseases in different periods of real epidemic events. We consider two cases: Case 1, we know every individual's infection rate to a kind of epidemic, satisfy a type of distribution. Case 2, assume all individuals have same initial infection rates, a susceptible individual's infection rate will be less than the initial rate if he is not infected after limited number of contacts with infected ones. For both two cases, at the time tD, preventive and control measures bring into effects, every individual's infection rate would decrease. We implemented this models on scale-free networks, and found that the epidemic process before the time tD in Case 1 is almost the same as that in Standard SI model if the infection rate in Standard SI model equals the mean infection rate in Case 1. Furthermore, using numerical simulation, we analysis the effects of the parameter tD, and find the bimodal distribution of final infection rates. Finally, we conclude what we get in this paper and give our future direction. © 2009 Springer Berlin Heidelberg.

关键词： Complex networks

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Asynchronous extended spiking neural P systems with astrocytes

Asynchronous extended spiking neural P systems with astrocyt...

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12th International Conference on Membrane Computing, CMC 2011

作者： Pan, Linqiang Wang, Jun Hoogeboom, Hendrik Jan Image Processing and Intelligent Control Key Laboratory of Education Ministry Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Leiden Institute of Advanced Computer Science Universiteit Leiden Niels Bohrweg 1 2333 CA Leiden Netherlands

ISBN: (纸本)9783642280238

A variant of spiking neural P systems was recently investigated by the authors, using astrocytes that have excitatory and inhibitory influence on synapses. In this work, we consider this system in the non-synchronized (i.e., asynchronous) mode: in any step, when a neuron is enabled, it is not obligatorily fired, making a global clock dispensable. It is proved that asynchronous spiking neural P systems with astrocytes are universal (when using extended rules). The construction is uniform in the sense that the form of the modules used is independent of the simulated register machine. © 2012 Springer-Verlag.

关键词： Artificial intelligence

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Graph based co-training algorithm for web page classification

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2009年第10期37卷 2173-2180+2219页

作者： Hou, Cui-Qin Jiao, Li-Cheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi 710071 China

This paper proposes a novel inductive semi-supervised algorithm for web page classification named GCo-training, exploiting texts in web pages and hyperlinks among them. GCo-training iteratively trains two classifiers-a graph-based semi-supervised classifier based on hyperlinks among web pages and a Bayes classifier based on texts in web pages, under the framework of Co-training. On the one hand, the graph-based semi-supervised classifier obtains high accuracy based on a small set of labeled examples through exploiting links among web pages and can augment labeled examples for the Bayes classifier. On the other hand, the Bayes classifier can also provide labeled example for the graph-based classifier after it learning on labeled set augmented by the graph-based classifier. Therefore, the two classifiers help each other and improve their respective performance during the process of training. Finally, the Bayes classifier can classify a large number of unseen examples. We test GCo-training algorithm, Co-training algorithm based on words occurring on web pages and words occurring in hyperlinks and Bayes algorithm based on EM on the Web&KB dataset. Experimental results show GCo-training performs much better than the other algorithms.

关键词： Websites

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Multi-stage hybrid multiobjective evolutionary algorithm for multi-type vehicle routing problem with simultaneous pickup and delivery and time windows

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Computers & Industrial Engineering 2025年

作者： Wenqiang Zhang Shun Li Miaolei Deng Yashuang Mu Peng Li Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology China School of Artificial Intelligence and Big Data Henan University of Technology China College of Information Science and Engineering Henan University of Technology China Institute for Complexity Science Henan University of Technology China

The vehicle routing problem (VRP) is a fundamental and extensively studied problem in logistics and transportation. Despite its significance, real-world applications often require more complex variants to address specific operational constraints and objectives. To address these limitations, this paper introduces the multi-type vehicle routing problem with simultaneous pickup and delivery and time windows (MTVRPSPDTW). Although hybrid multiobjective algorithms have been successful in combination optimization problems in recent years, it is still challenging to improve the performance of the algorithms by combining them with different timing. We propose a multi-stage hybrid evolutionary multi-objective optimization with a multi-region sampling strategy (MS-HEMO-MRSS) to optimize both vehicle number and wait time of MTVRPSPDTW. The algorithm integrates a three-stage hybrid approach, combining a global search using the multi-region sampling strategy (MRSS) with a local search based on routing sequence differential evolution (RSDE). The initial stage employs MRSS to quickly position the population near the Pareto front from various directions. The second stage utilizes RSDE to accelerate convergence towards central and edge areas of the Pareto front. In the final stage, individuals on Pareto front are selected and RSDE is used again to guide them towards the edge regions to enhance distribution performance. Specialized encoding, decoding techniques, and genetic operators tailored for two vectors are introduced to optimize MTVRPSPDTW. Comparative experiments with traditional multiobjective evolutionary algorithms demonstrate significant convergence and notable distribution performances, highlighting the benefits of employing different optimization strategies at different evolutionary stages.

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Knowledge-Based Scene Text Recognition for Industrial Applications

Knowledge-Based Scene Text Recognition for Industrial Applic...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Deng, Guowei Tu, Jingzheng Chen, Cailian He, Jianping Le, Xinyi Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China 200240 Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China 200240

ISBN: (纸本)9781728119489

Scene text recognition (STR) methods combined with semantic information have made great progress to recognize texts in natural scenes, most of which are daily words. However, research on mining semantic information in industrial texts attracts less attention. Since industrial texts follow a different semantic pattern defined by industry standards, it challenges many existing methods to conduct accurate semantic reasoning. In this paper, we abstract the industry standards into two aspects of prior knowledge: the grouping property and the prior lexicon. Correspondingly, a knowledge-based language model is proposed with several group-wise correlation modules and a lexicon-based reasoning module to learn semantic rules from both data and the prior knowledge. Besides, we transfer the prior knowledge into data by generating synthetic pure text datasets according to the industry standards' rules, which introduces more prior knowledge to the language model. Furthermore, a novel STR framework is presented by combining the knowledge-based language model and an attention-based vision model. For evaluation, two industrial text datasets called CIN and SB are collected from real-world industrial field surveillance. Experiments indicate that our method's word-level accuracy outperforms state-of-the-art methods with 15% and 10.61% on CIN and SB datasets respectively. © 2022 IEEE.

关键词： Knowledge based systems

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Heterogeneous belief formation method based on BP neural network 2

Heterogeneous belief formation method based on BP neural net...

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2nd International Conference on Electronic Information Technology and Computer Engineering, EITCE 2018

作者： Zhuang, Yifan Wang, Hongwei Chen, Xi School of Automation Huazhong University of Science and Technology Wuhan430074 China School of Management Huazhong University of Science and Technology Wuhan430074 China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan430074 China

The BDI model has always been the focus of subject modeling research, which includes three kinds of thinking states of the rational subject: Belief, Desire and Intention. Belief is the cognition of agent to the world;it is a collection of environmental information, other agent information, and its own information that the agent has;and it is also the basis of the agent's thinking activity. Due to differences in the individual's living environment and experience, the formation of heterogeneous beliefs is an important issue in the BDI model study. This article divides individual belief set into two parts: knowledge belief and achievable belief. This article proposes an overall framework for the formation of individual heterogeneity beliefs: First, the individual's knowledge experience is modeled, and the empirical knowledge is structured and quantified into binary propositions;then the BP neural network learn and memory propositions of different combinations to form heterogeneous beliefs. Experiments show that this method can simulate the heterogeneity of individual beliefs caused by the individual's own experience, and can realize the belief generation mechanism of gradual information flow, limited attention and heterogeneous priors. © The Authors, published by EDP Sciences, 2018.

关键词： Neural networks

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DNA Strand Displacement Based on Nicking Enzyme for DNA Logic Circuits 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Wang, Gaiying Wang, Zhiyu Yan, Xiaoshan Liu, Xiangrong School of Information Science and Technology Xiamen University XiamenFujian361005 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

DNA strand displacement is widely used in the construction of DNA molecule computational models. In this work, nicking enzyme is used as the input of the logic calculation model for it can cut one strand of a double-stranded DNA at a specific recognition nucleotide sequences known as a restriction site. Based on this, a variety of logic gates are designed and implemented, and a multi-person voting circuit is constructed. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Enzymes

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