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Frontiers of computer Science 2011年第2期5卷 205-215页

作者： Ruixuan LI Kunmei WEN Xiwu GU Yuhua LI Xiaolin SUN Bing LI Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China

Description logics （DLs） are widely employed in recent semantic web application systems. However, classical description logics are limited when dealing with imprecise concepts and roles, thus providing the motivation for this work. In this paper, we present a type-2 fuzzy attributive concept language with complements （ALC） and provide its knowledge representation and reasoning algorithms. We also propose type-2 fuzzy web ontology language （OWL） to build a fuzzy ontology based on type- 2 fuzzy ALC and analyze the soundness, completeness, and complexity of the reasoning algorithms. Compared to type-1 fuzzy ALC, type-2 fuzzy ALC can describe imprecise knowledge more meticulously by using the membership degree interval. We implement a semantic search engine based on type-2 fuzzy ALC and carry out experiments on real data to test its performance. The results show that the type-2 fuzzy ALC can improve the precision and increase the number of relevant hits for imprecise information searches.

关键词： description logic （DL） type-2 fuzzy attributive concept language with complements （ALC） fuzzy ontology reasoning semantic search engine

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Multi-objective Evolutionary Algorithm Based on Target Space Partitioning Method

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Journal of Donghua University(English Edition) 2011年第2期28卷 177-181页

作者：尚兆霞刘弘李焱 School of Information Science and Engineering Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University

Considering the defects of conventional optimization methods, a novel optimization algorithm is introduced in this paper. Target space partitioning method is used in this algorithm to solve multi-objective optimization problem, thus achieve the coherent solution which can meet the requirements of all target functions, and improve the population's overall evolution level. The algorithm which guarantees diversity preservation and fast convergence to the Pareto set is applied to structural optimization problems. The empirical analysis supports the algorithm and gives an example with program.

关键词： optimization algorithm multi-objective target space partitioning method

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Implicit Multi-Scale Swin Transformer Network for Image Denoising

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Qi Ding, Yuwei Zhang, Weiqi Zhu, Yian Zhang, Bob Lin, Jerry Chun-Wei Harbin Institute of Technology at Weihai School of Economics and Management Weihai China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Northwestern Polytechnical University School of Computer Science Xi’an China Northwestern Polytechnical University Yangtze River Delta Research Institute Taicang China University of Macau PAMI Research Group Department of Computer and Information Science Taipa China Department of Distributed Systems and IT Devices Electrical Engineering and Mathematical Sciences Silesian university of technology Poland

Image denoising has been used in various edge computing scenarios such as consumer electronics to improve the image quality and user experience. Existing image denoising methods based on Convolutional Neural Networks (CNNs) and vision Transformers achieve good performance by empirically utilizing residual neural network (ResNet) as basic component. However, ResNet lacks interpretability in network design and also long-term memory of features, potentially restricting the performance of denoising networks. In this paper, we propose an Implicit Multi-scale Swin Transformer Network (IMSNet) for image denoising, which introduces the implicit Euler scheme from the feature memory perspective. Specifically, densely connected implicit feature extraction blocks (IFEBs) are designed to learn the residual mapping between noisy and clean images. The IFEB reformulates the initial skip connection of ResNet based on the implicit Euler discretization, providing both network interpretability and long-term memory. In IFEB, the multi-scale swin Transformer block (MSB) is designed as the implicit layer to capture spatial details and non-local contextual information at different scales. Additionally, a cross-layer feature fusion block (CLFF) is proposed to further improve feature reuse capabilities. Compared to existing denoising networks, the extensive experiments demonstrate the superior performance of our IMSNet in various image denoising tasks, and the flexibility in the practical applications with proposed light model on resource-restricted platforms such as consumer electronic devices. © 1975-2011 IEEE.

关键词： Image denoising

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Sequence generation model of traditional Chinese medicine 12

Sequence generation model of traditional Chinese medicine

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Duan, Chengzhi Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

ISBN: (纸本)9798350310153

The application of artificial intelligence in the field of traditional Chinese medicine is more and more extensive, and the related research is also more and more. Increasingly, the electronic processing of TCM textual information is becoming more and more important. In this paper, we propose a TCM text-based prescription generation task, intending to be able to implement automatic prescription generation functions based on the sequence-to-sequence (seq2seq) model. Traditional TCM prescriptions are given by the attending physicians, and the prescription recommendation system proposed in this paper achieves a certain degree of accuracy compared to real doctors. After a series of system data training, we improved the accuracy rate to 76%. For FI the value reached the best result of 74% at the existing stage. © 2022 IEEE.

关键词： Diagnosis

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Improved artificial bee colony algorithm and application in path planning of crowd animation

International Journal of Control and Automation

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International Journal of Control and Automation 2015年第3期8卷 53-66页

作者： Jing, Bai Hong, Liu School of Information Science and Engineering College Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

Crowd animation is a new and continuous challenge in computer animation. In tradition, crowd animation can be realized by key frame technology, and animators should set every character's expression, action, motion, and behavior. Therefore animators' workload by hand will increase tremendously with characters growing, which lead to difficult to realize crowd animation for low efficiency and poor global controllability, especially in path planning by appointing a target position for each individuals. In order to overcome these, an improved artificial bee colony (IM-ABC) algorithm is proposed to apply on the path planning of crowd animation. The IM-ABC is fit to simulate the crowd motion in animation based on the following two merits over the others in crowd animation. One is the rule of role transformation, which can make the rapid convergence of the result and avoid getting trapped in the local optima. The other is the realization of multi-object optimization in the process of iteration, which reaches the uniformly distributed result of swarm motion and especially fits to realize the path plan. In this paper, we simply reviews classical ABC algorithm proposed by Karaboga at the beginning. Then, in order to speed the convergence and make individuals generate paths more realistic and natural, some measures are taken to modify the classical ABC (called IM-ABC) algorithm, which include initializing colony based on chaos sequence, self-adaptively selecting the follower bees, and adaptively controlling parameters, etc. After the experiments of benchmark functions, the results confirm that the IM-ABC have better performance than the classical ABC algorithm and others. Finally, the IM-ABC algorithm is used for path planning to generate the route from the initial to the destination without collision. Through simulation experiments based on four motion models it is showed that this method can succeed generating the optimum paths with efficiency, intelligence, and natural featu

关键词： Motion planning

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Community Discovery Algorithm Based on Potential Energy in Complex Network 34

Community Discovery Algorithm Based on Potential Energy in C...

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第三十四届中国控制会议

作者： LIU Shuangshuang WANG Hong School of Information Science and Engineering Shandong Normal University Distributed in Shandong province key laboratory of computer software

ISBN: (纸本)9781467374439

In recent years,our understanding of complex networks has *** structure as a common characteristic of complex networks has become an important direction in the study of complex ***,people put forward many community detection *** original Largest Fitness Measure algorithm,the selection of seed node is random,community division needs to be improved,and it is difficult to achieve its end *** on above problems,we propose a kind of Weight Largest Fitness Measure *** to the thought of potential energy,the new algorithm optimizes and handles initial node,simplify node fitness function and expand community according to potential ***,through two groups of experimental validate the performance of the *** experimental results show that,compared with Largest Fitness Measure algorithm,the new algorithm has higher accuracy and shorter run time.

关键词： Complex Network,Community Detection,Potential Energy

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Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence

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电子学报(英文版) 2012年第4期21卷 636-641页

作者： LIU Hong SUN Yuling LI Yuanyuan School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China

Modeling and simulating group behaviours have been an active research topic in the field of com- puter animation and game. This paper presents some novel approaches for supporting entity modeling and path generation in crowd simulation. It analyses related work about crowd simulation first. Then, an entity modeling approach based on CGA (Cellular genetic algorithm) and NURBS (Non uniform relational B splines) technologies is presented. Next, following the analysis to PSO (Particle swarm optimization) and ABC (Artificial bee colony) algo- rithms, a crowd path generative approach based on ABC- PSO is put forward. After that, a simulating example of crowd cohesion and performance comparison are exhibited for showing the efficiency of the algorithms. Finally, the current work is summarized and an outlook for the future work is given.

关键词：仿真建模生成方法人群计算智能路径遗传算法实体建模计算机动画 Crowd simulation, Entity modeling, Cellular genetic algorithm, PSO （Particle swarm optimization） algorithm, ABC algorithm Path generation.

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Double filtration of topic tracking based on KL divergence

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Journal of Computational Information Systems 2014年第19期10卷 8543-8552页

作者： Lu, Ran Wei, Jingxuan Xun, Jing School of Information Science and Engineering Shandong Normal University Ji'nan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Ji'nan China

In order to overcome the shortcoming of the static threshold and the problem of the false reports, the method of double filtering topic tracking based on Kullback-Leibler divergence is put forward. In this method, it uses the KL divergence to obtain the initial threshold and the candidate reports. Then, depending on the time characteristics of reports, it studies dynamic threshold method. Finally, it extracts named entities of the reports to calculate similarity of named entities, which completes topic tracking. The experiments prove the feasibility of the algorithm and improves the efficiency of the topic tracking to a certain extent. In addition, it reduces the false negative rate and the false alarm rate.

关键词： Information systems

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A path planning method based on deep reinforcement learning for crowd evacuation

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Journal of Ambient Intelligence and Humanized Computing 2024年第6期15卷 2925-2939页

作者： Meng, Xiangdong Liu, Hong Li, Wenhao School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China

Deep reinforcement learning (DRL) is suitable for solving complex path-planning problems due to its excellent ability to make continuous decisions in a complex environment. However, the increase in the population size in the crowd evacuation path-planning problem causes a substantial computational burden for the algorithm, which leads to an unsatisfactory efficiency of the current DRL algorithm. This paper presents a path planning method based on DRL for crowd evacuation to solve the problem. First, we divide crowds into groups based on their relationship and distance from each other and select leaders from them. Next, we expand the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to propose an Optimized Multi-Agent Deep Deterministic Policy Gradient (OMADDPG) algorithm to obtain the global evacuation path. The OMADDPG algorithm uses the Cross-Entropy Method (CEM) to optimize policy and improve the neural network’s training efficiency by applying the Data Pruning (DP) algorithm. In addition, the social force model is improved, incorporating the relationship between individuals and psychological factors into the model. Finally, this paper combines the improved social force model and the OMADDPG algorithm. The OMADDPG algorithm transmits the path information to the leaders. Pedestrians in the environment are driven by the improved social force model to follow the leaders to complete the evacuation simulation. The method can use a leader to guide pedestrians safely arrive the exit and reduce evacuation time in different environments. The simulation results prove the efficiency of the path planning method. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Motion planning

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Heuristic algorithm for multi-vehicle ride matching problem

Heuristic algorithm for multi-vehicle ride matching problem

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2012 International Symposium on Information Technologies in Medicine and Education, ITME 2012

作者： Song, Chaochao Wang, Hongguo Ding, Yanhui Yang, Fuping Institute of Management Science and Engineering Shandong Normal University Shandong Province Key Laboratory for Distributed Computer Software Novel Technology Jinan China Institute of Information Science and Engineering Shandong Normal University Shandong Province Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9781467321068

Multi-vehicle ride matching problem studies how to take passengers as much as possible through optimizing vehicles' route and matching between vehicles and passengers. But at present, problems exist in the researches like that models divorced from reality and low efficiency of algorithms. For this problem, this paper presents a solution based on clustering of vehicles' route order. Transferring the MRMP to RMP through clustering of vehicles' route order, it forms the first matching result between vehicles and passengers. And looking for the best sort order through sorting the first matching result making use of priori clustering. At last, we optimize the solution one times more through optimized rules. Experimental result shows that, the method based on clustering of vehicles' route order can solve the problem with high matching efficiency and low cost. © 2012 IEEE.

关键词： Vehicles

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