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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lei, Dehao Yu, Zhiyong Li, Peiguan Han, Lei Huang, Fangwan College of Mathematics and Computer Science 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 and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

Air quality is widely concerned by the governments and people. To save cost, air quality monitoring stations are deployed at only a few locations, and the stations are actuated at partial time. Therefore, it is necessary to study how to actively collect a subset of air quality data to maximize the estimation accuracy of air quality at other locations and time. In order to solve this challenge, we propose the active variational adversarial model (AVAM) that selects the most valuable unlabeled samples through two iterative phases of active learning. In the first phase of our model, a candidate set with unlabeled samples is selected through traditional active learning. In the second phase, variational auto-encoder (VAE) is used to obtain the compressed representation of the candidate set and the training set with labeled samples, then a discriminator based on three-layer neural network is trained from the compressed representation. Finally the discriminator can output the most valuable unlabeled samples from the candidate set. The experimental results show that the AVAM proposed in this paper is superior to active learning models with the first or second phase only. © 2020, Springer Nature Switzerland AG.

关键词： Air quality

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An Overlapping Local Community Detection Algorithm Based on Node Transitivity and Modularity Density 15th

An Overlapping Local Community Detection Algorithm Based on ...

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15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Huang, Xintong Wu, Ling Guo, Kun College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

ISBN: (纸本)9789811625398

The local community detection (LCD) method can discover the local community structure in which the seed node is located. Compared with global community detection, local community detection is characterized by its low cost and high efficiency. However, most existing LCD methods only return a non-overlapping community. Individuals in the real world may participate in multiple communities, which can only be discovered by using overlapping local community detection methods. In this study, an overlapping local community detection algorithm based on modularity and node transitivity. First, the scope and structure information of the overlapping communities are obtained according to the node transitivity. Second, NMF is used to obtain the number of overlapping communities. Finally, the local modularity density based on edge weights is used to refine the detected local communities. The experimental results validate the high performance of our method to the other method in comparison. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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The Design and Implementation of KMeans Based on Unified Batch and Streaming processing 15th

The Design and Implementation of KMeans Based on Unified Bat...

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15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Chen, Hao Guo, Kun Chen, Yuzhong Guo, Hong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

Clustering algorithms aim at gathering similar data points from a dataset in an unsupervised manner. Although the batch clustering algorithms have relatively high accuracy, they cannot make use of the dynamic clustering results efficiently. The requirement of using the whole dataset in calculation results in the problems of resource waste and high time cost. On the contrary, incremental clustering only needs to update the varied part of a model upon the arrival of new data, which makes it unnecessary to recluster the whole data all the time. The feature is very suitable for the streaming data process, but it decreases the accuracy of the algorithms and cannot satisfy the low latency requirement of real-time data processing. In response to this problem, the paper proposes a novel unified batch and streaming clustering model (UBSCM) based on streaming computation, which includes a streaming cluster feature updating mechanism (SCFUM). The Flink framework is used to implement a new streaming KMeans algorithm based on UBSCM (KMeansUBSP). The experiments on the real-world datasets validate that the new streaming KMeans algorithm is effective in clustering the batch and streaming data in a unified manner. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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An Improved Sparse Representation Classifier Based on Data Augmentation for Time Series Classification 15th

An Improved Sparse Representation Classifier Based on Data A...

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lu, Juhong Huang, Fangwan Yu, Zhiyong College of Mathematics and Computer Science 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 China

ISBN: (纸本)9783030642426

Sparse Representation-based Classification (SRC), which has achieved good performance in face recognition and other image classification, has been successfully extended to time series classification in recent years. As a generalization of the nearest subspace classifier, the performance of SRC depends on a rich set of training samples for each class, which can span as many variations of each class as possible under testing conditions. However, due to the difficulty of sample collection, many important applications can only provide a few or even a single sample for each class, which inevitably affects the performance of SRC. To address the problem of insufficient training samples, ESRC (Extended SRC) was proposed to put some samples that reflect the variations within the class into the dictionary, to obtain better generalization ability than SRC for under-sampled face recognition. In this paper, a new approach IESRC (Improved extend sparse representation based classification) is developed by splitting the dictionary into an original dictionary and auxiliary dictionary. The proposed model was first evaluated in 30 baseline data sets in the University of California, Riverside time series classification archive and then applied to classify the effects of aerobic exercise intervention in 24 young hypertensive patients based on the time series of the cardiopulmonary exercise test. Experimental results show that the proposed model can achieve better performance than SRC and ESRC in time series classification. © 2020, Springer Nature Switzerland AG.

关键词： Face recognition

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Electric Load Forecasting Based on Sparse Representation Model 13th

Electric Load Forecasting Based on Sparse Representation Mod...

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13th International Conference on Green, Pervasive, and Cloud computing, GPC 2018

作者： Huang, Fangwan Zheng, Xiangping Yu, Zhiyong Yang, Guanyi Guo, Wenzhong College of Mathematics and Computer Science 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 and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030150921

Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural network, SVM, and Random Forest. © 2019, Springer Nature Switzerland AG.

关键词： Electric load forecasting

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A Distributed Storage Middleware Based on HBase and Redis 15th

A Distributed Storage Middleware Based on HBase and Redis

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15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Xu, Lingling Chen, Yuzhong Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

In the era of big data, traditional relational databases face challenges in massive data storage. HBase is a non-relational database based on column storage which is used widely for big data storage. The writing performance of HBase is high, but the unbalanced load caused by its uneven data storage strategy is the bottleneck of its reading performance. HBase needs to access disks to get query results. Therefore, the efficiency of disk access has a great impact on the query performance of HBase. In view of the above problems, this paper proposes a storage middleware which utilizes HBase for load balancing and Redis for memory caching. Specifically, we improve HBase’s original load balancing algorithm for Regions and RegionServers and customize a Redis cache eviction algorithm according to the data’s query and update frequency. Furthermore, the coprocessor of HBase is used to synchronize data between HBase and Redis. Experiments on synthetic datasets show that the proposed storage middleware achieves better writing and reading performance than HBase. The load balancing algorithm employed in the middleware is better than HBase's original algorithm. The hit rate of the customized cache eviction algorithm is also higher than that of the LRU algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cache memory

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An Improved Leaky-ESN for Electricity Load Forecasting 15th

An Improved Leaky-ESN for Electricity Load Forecasting

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lin, Qiaoying Huang, Fangwan Yu, Zhiyong Li, Li College of Mathematics and Computer Science 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 and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

In the recent decade, recurrent neural networks become a hot research field, with the powerful capability to capture temporal information from time series. To avoid the vanishing/exploding gradient problem caused by the gradient descent algorithm involved in most recurrent networks, echo state network (ESN) was proposed to contain a large but sparse reservoir instead of traditional hidden layers. However, the performance of ESN is very sensitive to the parameters of the reservoir. In this paper, we focus on the improvements of ESN in the background of electricity load forecasting. With the goal of effectively and efficiently computation in the context of pervasive and cloud computing, two versions of adaptive echo state network (AESN) are designed to adopt a modular control strategy to automatically adjust some parameters of the reservoir. Applying AESN on two synthetic datasets and two real world electricity datasets, experimental results demonstrate that AESN is viable. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Stochastic SEIR epidemic models with virus mutation and logistic growth of susceptible populations

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Mathematics and Computers in Simulation 2023年第1期212卷 289-309页

作者： Wang, Qi Xiang, Kainan Zhu, Chunhui Zou, Lang Hunan Key Laboratory for Computation and Simulation in Science and Engineering & Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education & School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City411105 China National Center for Applied Mathematics in Hunan China

This article introduces a kind of stochastic SEIR models containing virus mutation and logistic growth of susceptible populations, whose deterministic versions have a positively invariant set and globally asymptotically stable equilibrium points. For these stochastic SEIR epidemic models, we prove they have a unique global positive solution, and also obtain sufficient conditions respectively for survival and extinction of the infectious disease. Eventually, we validate our theoretical findings using numerical simulations. © 2023 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： Viruses

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Block-Based Projection Matrix Design for Compressed Sensing

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Chinese Journal of Electronics 2016年第3期25卷 551-555页

作者： LI Zhetao XIE Jingxiong ZHU Gengming PENG Xin XIE Yanrong CHOI Youngjune The College of Information Engineering Xiangtan University School of Computer National University of Defense Technology Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University School of Computer Science and Engineering Hunan University of Science and Technology College of Information and Communication Engineering Hunan Institute of Science and Technology Department of Information and Computer Engineering Ajou University

The objective of optimizing a projection matrix is to decrease the mutual coherence between a projection matrix and a basis matrix. In this paper, a novel block-based method is proposed to design a projection matrix in compressed sensing. Here, the projection matrix is divided into two blocks. The relationship between the two blocks was obtained by reasoning and proving. Theoretical analysis demonstrates that the mutual coherence between the whole projection matrix and the whole basis matrix keeps as good as the mutual coherence between the block matrix and blocked basis matrix. Experimental results show that the proposed method obtains better performance compared to existing methods.

关键词： Compressed sensing Projection matrix Block optimization Mutual coherence

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A throughput aware with collision-free MAC for wireless LANs

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Science China(information Sciences) 2016年第2期59卷 240-242页

作者： Tingrui PEI Yafeng DENG Zhetao LI Gengming ZHU Gaofeng PAN Youngjune CHOI Hiroo SEKIYA College of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Computer Science & Engineering Hunan University of Science and Technology School of Electronic and Information Engineering Southwest University Department of Information and Computer Engineering Ajou University Graduate School of Advanced Integration Science Chiba University

Dear editor,The Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA)hybrid with the resource reservation approach from Time Division Multiple Address(TDMA)has been emerged as a promising method to solve collision problems in wireless LANs[1–6].In the hybrid method,a TDMA circle contains multiple slots for nodes to contend for the ***,it allows a

关键词： MAC CSCF free TDMA A throughput aware with collision-free MAC for wireless LANs

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