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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Feng Chen Zhiwei Ye Jun Su Haofeng Lang Xiaoxiao Shi Shuqing Wang School of computer Science Hubei university of Technology Wuhan China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm (SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. The SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), moth-fire suppression optimization (MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. The mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. The Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. The simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. The experiments above indicates that the prediction method is effective and feasible.

关键词： Neural networks Prediction algorithms Predictive models Optimization Load modeling Load forecasting

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Improved Quality Aware network Using Quality and Max Pooling Based Late Feature Fusion for Video-Based Person Re-Identification

Improved Quality Aware Network Using Quality and Max Pooling...

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International Conference on Computer Science and network Technology (ICCSNT)

作者： Yude Shi Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781728133003

Video-based person re-identification is an important research area in computer vision. Quality Aware network (QAN) is a model that is robust to noise in the videos. It calculates the weighted average of all the frames' image features, with each frame's quality score as the weight, and generates a video feature. However, the QAN has poor ability to distinguish pedestrians with highly similar clothing. To address this problem, we analyze the advantages and shortcomings of Quality Pooling and Max Pooling in theory, and propose the Frame Quality and Max Pooling based Late Feature Fusion Method (QMFF). It aims at enhancing the QAN's ability to distinguish pedestrians with similar clothing. First, use Quality Pooling to get the initial ranking list. Then, for each Query, judge whether the top-N candidates in the ranking list are highly similar in clothing to one another. If the Query meets the similarity criterion, Max Pooling with Frame Quality Filter is used. Local salient features selected by Max Pooling help distinguish pedestrians more accurately. Experimental results on the iLIDS-VID dataset show that the proposed method is superior to the QAN and many recently proposed methods.

关键词： Videos Clothing Feature extraction Face recognition Computer science Databases Training

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Image Classification Based on BP Neural network and Sine Cosine Algorithm

Image Classification Based on BP Neural Network and Sine Cos...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Haoqiu Song Zhiwei Ye Chunzhi Wang Lingyu Yan School of Computer Science Hubei University of Technology China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University China

Image classification is one of the most important tasks in image analysis and computer vision. BP neural network is a successful classifier for the task. However, with regard to the low study efficiency and the slow convergence speed in BP algorithm, some optimization algorithms have been proposed for achieving better results. Among all these methods, BP neural network improved by particle swarm optimization (PSO) and genetic algorithm (GA) may be the most successful and classical ones. Nevertheless, both GA and PSO are easy to fall into the local optimal solution, which has a great impact on the precision of classification. As a result, a novel optimization algorithm called sine cosine algorithm (SCA) is presented to improve the classification performance. The experimental results manifest that the proposed method has good performances, and the classification accuracy is better than BP neural network optimized by GA, PSO or other algorithms.

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Automated classification of Wuyi rock tealeaves based on support vector machine

Automated classification of Wuyi rock tealeaves based on sup...

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作者： Lin, Li-Hui Li, Cheng-Hsuan Yang, Sheng Li, Shao-Zi Wei, Yi College of Mathematics and Computer Science Wuyi University Wuyishan354300 China Graduate Institute of Educational Information and Measurement National Taichung University of Education Taichung40306 Taiwan School of Information Science and Engineering Xiamen University Xiamen361005 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing Fujian Education Institutions Wuyishan354300 China

This paper describes a new automated classification method for Wuyi rock tealeaves based on the best penalty parameter selection for the support vector machine with RBF (Radial Basis Function) kernel. A total of 3590 fresh tealeaf images of the representative Rou Gui and Shui Hsien varieties of Wuyi rock tea are collected in their natural habitat. Fourteen image features are extracted in terms of the leaf shape and texture. The automatic selection method is used to find the optimum RBF kernel parameter sigma, which is then applied to design an automatic parameter selection method to screen the best penalty parameter C for the classification of Wuyi rock tealeaves. In this study, the SVM classifier is used for the automated classification and recognition of the 14 image features. The contribution of the various features to the recognition rate of fresh tealeaves is evaluated to identify the key features for the classification and recognition of fresh Wuyi rock tealeaf images. The experimental results show that the proposed method improves the recognition rate of fresh tealeaves to 91.00%. © 2018 John Wiley & Sons, Ltd.

关键词： Feature extraction

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An enhanced cuckoo search using dimension selection

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International Journal of Innovative computing and Applications 2019年第1期10卷 3-11页

作者： Wang, Lijin Zhuang, Yihong Chen, Zhennan Zhong, Yiwen College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou China Key Laboratory of Smart Agriculture and Forestry Fujian Province University Fuzhou China Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan Shi China

This paper proposes an enhanced cuckoo search algorithm using dimension selection. In the proposed strategy, the dimensional distance measure is used to select a part of dimensions of each solution to search for the new solution in two search components. The dimensions of each solution are selected when those dimensional distances are larger than the average distance of all dimensional distance. A suit of 20 benchmark functions are employed to verify the performance of the proposed algorithm, and the results show the improvement in effectiveness and efficiency of dimension selection. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Optimization

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Learning from context： a mutual reinforcement model for Chinese microblog opinion retrieval

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Frontiers of Computer Science 2018年第4期12卷 714-724页

作者： Jingjing WEI Xiangwen LIAO Houdong ZHENG Guolong CHEN Xueqi CHENG College of Mathematics and Computer Science Fuzhou University Fuzhou 350108 China College of Electronics and Information Science Fujian Jiangxia University Fuzhou 350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligence Processing Fuzhou University Fuzhou 350108 China Institute of Computing Technology Chinese Academic of Sciences Beijing 100190 China

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentiment words relying on the document features. However, this approach could not be effectively applied to mi- croblogs that have typical user-generated content with valu- able contextual information：＂user-user＂ interpersonal interactions and ＂user-post/comment＂ intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.

关键词： opinion retrieval sentiment words lexiconweighting mutual reinforcement model

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Copyright Protection Application Based on Blockchain Technology

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

作者： Yi Ouyang Xianghan Zheng Xiaoliang Lu Lin Xiaowei Shengyin Zhang Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China MingByte Technology (Qingdao) Co. Ltd MingByte Technology Co. Ltd Qingdao China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In the traditional copyright protection platform, there are problems such as copyright information security and high registration cost. With the rapid development of blockchain technology, blockchain technology, as a new technology architecture, has the characteristics of decentralization, nontamperability, traceability, openness and transparency, and has a natural combination with copyright protection, which is expected to solve the existence of traditional copyright protection. The pain point of this paper is to apply the blockchain technology to the copyright management platform. It is hoped that in this way, the problems of poor security, difficult rights, complicated transaction procedures, etc. in the traditional copyright management platform are solved. An attempt to combine chain technology with copyright protection.

关键词： Blockchain Peer-to-peer computing Distributed databases Smart contracts Security Fabrics Distributed ledger

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Quantum identity-authentication scheme based on randomly selected third-party 5th

Quantum identity-authentication scheme based on randomly sel...

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5th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2018

作者： Zheng, Xiaobo Zhang, Fuquan Zhao, Zhiwen College of Information and Technology Beijing Normal University Beijing100875 China Information and Network Management Center Beijing Information Science and Technology University Beijing100101 China School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Beijing Normal University Zhuhai China

ISBN: (纸本)9783030037659

In our article we give a scheme to complete identity-authentication by quantum fingerprinting. Our scheme can overcome the vulnerability of one certification authority and tolerate a controlled error. The cost of our scheme is k log2 (n) for n bit classical identity information while the complexity of quantum circuit in our scheme is also k log2 (n). © Springer Nature Switzerland AG 2019.

关键词： Authentication

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Cost Efficient Offloading Strategy for DNN-Based Applications in Edge-Cloud Environment

Cost Efficient Offloading Strategy for DNN-Based Application...

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

作者： Yinhao Huang Bing Lin Yongjie Zheng Junqin Hu Yuchang Mo Xing Chen College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China College of Physics and Energy Fujian Normal University Fuzhou China Fujian Province University Key Laboratory of Computational Science Huaqiao University Quanzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Deep Neural networks (DNNs) are pervasively used in a large number of applications, such as Microsoft Cortana, Apple Siri, and Google Now. The traditional method is to offload them in the cloud, which causes serious data transmission delay and high network resource cost, as well as user privacy leakage. The emergence of Mobile Edge computing(MEC) provides a new solution for the execution of DNN-based applications. With the increase of the computing power of mobile devices and the appearance of edge nodes, offloading partial layers of the DNN-based application to the edge will speed up system response, reduce the burden of cloud center and protect privacy of users. Most of existing researches focus on the optimization of response delay for DNN-based applications in edge computing system, but there is no mature solution to the cost optimization of edge computing system. To effectively reduce the cost of edge computing for offloading DNN-based applications, we propose a offloading strategy based on Discrete Particle Swarm Optimization with Genetic Operators(DPSO-GO). By introducing crossover operator and mutation operator of genetic algorithm, it overcomes the defect that the traditional PSO algorithm is easy to fall into local optimum. The results show that our algorithm can reduce the cost of edge computing system effectively while satisfying the delay requirements of all the DNN-based applications.

关键词： Mobile handsets Task analysis Cloud computing Edge computing Servers Optimization Bandwidth

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Automatic Text Summarization Based on Transformer and Switchable Normalization

Automatic Text Summarization Based on Transformer and Switch...

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

作者： Tao Luo Kun Guo Hong Guo College of Mathematics and Computer Sciences Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the development of text summarization research, the methods based on RNN with the Encoder-Decoder model gradually become the mainstream. However, RNN tends to forget previous context information and leads to the lack of original information. That will reduce the accuracy of the generated summarizations. The Transformer model uses the self-attention mechanism to encode and decode historical information so that it can achieve better performance than RNN in learning context information. In this paper, a text summarization model based on transformer and switchable normalization is proposed. The accuracy of the model is improved by optimizing the normalization layer. Compared with other models, the new model has a great advantage in understanding words' semantics and associations. Experimental results in English Gigaword dataset show that the proposed model can achieve high ROUGE values and make the summarization more readable.

关键词： Decoding Switches Context modeling Vocabulary Task analysis Data mining Semantics

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