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arXiv 2019年

作者： Wang, Liupeng Huang, Yunqing Jiang, Kai School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan Hunan411105 China Key Laboratory of Ministry of Education for Intelligent Computing and Information Processing Xiangtan University Xiangtan Hunan411105 China

In this paper, we construct and analyze an energy stable scheme by combining the latest developed scalar auxiliary variable (SAV) approach and linear finite element method (FEM) for phase field crystal (PFC) model, and show rigorously that the scheme is first-order in time and second-order in space for the L2and H1 gradient ow equations. To reduce eficiently computational cost and capture accurately the phase interface, we give a simple adaptive strategy, equipped with a posteriori gradient estimator, i.e. L2norm of the recovered gradient. Extensive numerical experiments are presented to verify our theoretical results and to demonstrate the efiectiveness and accuracy of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite element method

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Research on Optimization Strategy of Power Communication Network for Load Balancing

Research on Optimization Strategy of Power Communication Net...

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作者： Chen Shenghai Jiang Fengying Mi Xianwu College of Electrical and Information Engineering Huaihua University Key Laboratory of Ecological Agriculture Intelligent Control Technology in Wuling Mountain Area of Hunan Provincial College of Mathematics Huaihua University

With the rapid development of communication technology,the power communication network based on power grid construction plays an increasingly important role in improving the information transmission *** to the slow start of China's electric power communication network,the relevant research still needs to be *** addition,as the structure of power communication network becomes more complex and the business needs become more abundant,the performance of communication network load needs to be *** this context,based on the development status of power communication network,the author analyzes the complex network theory,topology and static characteristics,robustness and vulnerability performance for the realization of load balancing,in order to provide relevant reference for the realization of load balancing.

关键词： Load Balancing Power Communication Network Optimization Strategic Research

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A hybrid binary harmony search algorithm for solving the winner determination problem

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International Journal of Innovative Computing and Applications 2019年第1期10卷 59-68页

作者： Lin, Geng Li, Zuoyong Collaborative Innovation Center of IoT Industrialization and Intelligent Production Minjiang University Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

The winner determination problem (WDP) in combinatorial auctions is to determine an allocation of items to bidders such that each item is allocated to at most one bidder, and the auctioneer's revenue is maximised. This paper proposes a hybrid binary harmony search algorithm for the WDP. Firstly, to enhance the global search ability of the proposed algorithm, a modified harmony improvisation mechanism is developed with a modified memory consideration rule and an adaptive pitch adjustment scheme. Next, a repair operator is employed to guarantee the feasibility of the new candidate harmonies. Finally, a tabu search procedure is presented to improve the local search ability. These strategies make a good balance between intensification and diversification. In the experiments, the performance of the proposed algorithm is validated on five groups of 500 instances. Experimental results and comparisons show that the proposed algorithm is very efficient, and the tabu search procedure significantly improves the performance of the proposed algorithm. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Combinatorial optimization

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Solder joint image adaptive block compressive sensing with convex optimisation and Gini index

Solder joint image adaptive block compressive sensing with c...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Zhao, Huihuang Wang, Yaonan Zheng, Jinhua Qiao, Zhijun Zhang, Yun Department of Computer Science and Technology College of Computer Science and Technology Hengyang Normal University Hengyang Hunan China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China Department of Electrical and Engineering College of Electrical and Information Engineering Hunan University Changsha China School of Mathematical and Statistical Sciences University of Texas Rio Grande ValleyTX United States Institute of Radio and TV Technology Communication University of Zhejiang Hangzhou310018 China

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Convex optimization

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Novel Multi-Agent Deep Reinforcement Learning Approach

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Dong Yin Zhe Zhao Yinglong Dai Han Long College of Intelligence Science and Technology National University of Defense Technology Changsha 410073 China College of Liberal Arts and Sciences National University of Defense Technology Changsha 410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha 410081 China

Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks

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HSCS:a hybrid shared cache scheduling scheme for multiprogrammed workloads

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Frontiers of Computer Science 2018年第6期12卷 1090-1104页

作者： Jingyu ZHANG Chentao WU Dingyu YANG Yuanyi CHEN Xiaodong MENG Liting XU Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410004China School of Electronics and Information Shanghai Dianji UniversityShanghai 200240China

The traditional dynamic random-access memory (DRAM) storage medium can be integrated on chips via modern emerging 3D-stacking technology to architect a DRAM shared cache in multicore systems. Compared with static random-access memory (SRAM), DRAM is larger but slower. In the existing research, a lot of work has been devoted to improving the workload performance using SRAM and stacked DRAM together in shared cache systems, ranging from SRAM structure improvement to optimizing cache tags and data access. However, little attention has been paid to designing a shared cache scheduling scheme for multiprogrammed workloads with different memory footprints in multicore systems. Motivated by this, we propose a hybrid shared cache scheduling scheme that allows a multicore system to utilize SRAM and 3D-stacked DRAM efficiently, thus achieving better workload performance. This scheduling scheme employs (1) a cache monitor, which is used to collect cache statistics; (2) a cache evaluator, which is used to evaluate the cache information during the process of programs being executed; and (3) a cache switcher, which is used to self-adaptively choose SRAM or DRAM shared cache modules. A cache data migration policy is naturally developed to guarantee that the scheduling scheme works correctly. Extensive experiments are conducted to evaluate the workload performance of our proposed scheme. The experimental results showed that our method can improve the multiprogrammed workload performance by up to 25% compared with state-of-the-art methods (including conventional and DRAM cache systems).

关键词： multicore system shared cache workload performance

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Dynamic maintenance of decision rules for decision attribute values’ changing 25th

Dynamic maintenance of decision rules for decision attribute...

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25th International Conference on Neural information processing, ICONIP 2018

作者： Wang, Yingyao Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Rule induction method based on rough set theory (RST) which can generate a minimal set of decision rules by using attribute reduction and approximations has received much attention recently. In real-life, the variation of objects, attributes and attributes’ values affects reducts and approximations, e.g., the coarsening and refining of attributes’ values. The goal of this paper is dynamic maintenance of decision rules for decision attribute values’ coarsening and refining. Two incremental rough-set based methods are proposed to deal with this issue by updating assignment discernibility matrix dynamically without recomputing the reducts from the beginning, which increases the efficiency. © Springer Nature Switzerland AG 2018.

关键词： Rough set theory

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Label distribution learning based on ensemble neural networks 25th

Label distribution learning based on ensemble neural network...

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25th International Conference on Neural information processing, ICONIP 2018

作者： Zhai, Yansheng Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Label distribution learning (LDL), as an extension of multi-label learning, is a new arising machine learning technique to deal with label ambiguity problems. The maximum entropy model is commonly used in label distribution learning. However, it does not consider the correlation between the labels and is not suitable for nonlinear relationships, and the prediction performance is also limited. In this paper, we propose a label distribution learning algorithm based on ensemble neural networks. The algorithm trains neural networks with preferences using training sets with different label sets to construct base learners, and combines the base learners with the weights, which is learned by the combined learner to obtain the final learning results. Experimental results show that the proposed algorithm is effective for label distribution data. © Springer Nature Switzerland AG 2018.

关键词： Learning algorithms

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Mixed Word Embedding Method Based on Knowledge Graph Augment for Text Classification

Mixed Word Embedding Method Based on Knowledge Graph Augment...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Hongzhong Wang Kun Guo Zhanghui Liu Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

This paper presents a self-training word embedding text classification model based on knowledge graph expansion for text classification. Current mixed word embedding methods are overly dependent on the Fasttext pre-training model and here is still a problem of missing words with rich semantic information are not mapped. First, we propose a method for extracting missing nouns based on shape near word filtering. Second, we design a self-training word embedding method based on knowledge graph that mixes with pre-training word embedding to obtain a high-quality mixed word vector with rich semantics and rich semantics. Third, we designed a GRU model based on improved mixed word embedding to improve the quality of text classification. Experiments conducted on multiple text classification datasets demonstrate that our methods can effectively improve the text classification accuracy.

关键词： Text categorization Context modeling Feature extraction Training Task analysis Predictive models

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