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Science and Information Conference (SAI)

作者： Peikun Chen Wanhua Li Yuzhong Chen Kun Guo Yuzhen Niu College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou Fuzhou China

ISBN: (纸本)9781509054442

Application of cloud computing technologies in power system has made a great contribution to the establishment of smart grid. Among applications of smart grid, electrical load prediction plays an important role in efficient use of power resource. However, the exponential growth of data has posed a great challenge to the existing algorithms. In this paper, we firstly propose a novel parallel hybrid algorithm, combining the Improved Particle Swarm Optimization (PSO) with ELM, named PIPSO-ELM. Here a modified particle swarm optimization is presented to find the optimal number of hidden neurons as well as the corresponding input weights and hidden biases. Furthermore, in the iterative search process of PSO, an update strategy employs the mutation operator of evolutionary algorithms is introduced for further improving the global search capability and convergence speed of PSO. After that, to handle the large-scale dataset efficiently, the parallel implementation of PIPSO-ELM is achieved using Spark. Finally, extensive experiments on real-life electrical load data and comprehensive evaluation are conducted to verify the performance of PIPSO-ELM in electrical load prediction. Extensive experimental results demonstrate that PIPSO-ELM outperforms the compared algorithms in terms of stability, efficiency and scalability simultaneously.

关键词： Neurons Particle swarm optimization Algorithm design and analysis Prediction algorithms Smart grids Encoding Convergence

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Corrigendum to “Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification”

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Complexity 2018年第1期2018卷

作者： Yong Peng Wanzeng Kong Feiwei Qin Feiping Nie School of Computer Science Hangzhou Dianzi University Hangzhou 310018 *** Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 *** Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an 710072 ***

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Qian, Jin Chang, Yu-Hui Lv, Ping Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou213015 China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781509003891

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets. © 2016 IEEE.

关键词： Decision tables

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Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor data Fusion

arXiv

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arxiv 2017年

作者： Pu, Wenqiang Liu, Ya-Feng Yan, Junkun Liu, Hongwei Luo, Zhi-Quan National Laboratory of Radar Signal Processing Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi'an710071 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518100 China

An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares (LS) formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorithm, with a judicious initialization, to solve the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least squares problems and semidefinite programs (SDPs). In the absence of measurement noise, the proposed algorithm is guaranteed to find the global solution of the problem and the true biases. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error (RMSE). Copyright © 2017, The Authors. All rights reserved.

关键词： Mean square error

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Tri-level combination for image representation 17th

Tri-level combination for image representation

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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Top-k Dominating Queries on Incomplete data

Top-k Dominating Queries on Incomplete Data

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作者： Miao, xiaoye Gao, Yunjun Zheng, Baihua Chen, Gang Cui, Huiyong College of Computer Science Zhejiang University 38 Zheda Road Hangzhou310027 China Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China School of Information Systems Singapore Management University Singapore178902 Singapore

The top-k dominating (TKD) query returns the k objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-k queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first time, we carry out a systematic study of TKD queries on incomplete data, which involves the data having some missing dimensional value(s). We formalize this problem, and propose a suite of efficient algorithms for answering TKD queries over incomplete data. Our methods employ some novel techniques, such as upper bound score pruning, bitmap pruning, and partial score pruning, to boost query efficiency. Extensive experimental evaluation using both real and synthetic datasets demonstrates the effectiveness of our developed pruning heuristics and the performance of our presented algorithms. © 2015 IEEE.

关键词： Decision support systems

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Comparative analysis of identification methods for wheat leaf diseases

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International Agricultural Engineering Journal 2016年第3期25卷 197-203页

作者： Zhang, D.Y. xie, X.H. Liang, D. Huang, L.S. Zhang, X.Q. Teng, L. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University School of Electronic and Information Engineering Hefei Anhui230601 China

How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image processing technique was applied to segment the infected regions of disease leaves. Twenty disease features were extracted, and eighteen larger weight features were selected by Relief-F algorithm to generate the models of Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Back Propagation Neural Network (BPNN). Subsequently, these models were used to identify two kinds of wheat diseases, namely, wheat stripe rust and powdery mildew. Total 136 samples, including 68 training samples and 68 test samples with different infection severities were used to study the recognition capabilities of the three models. Results showed that high predictive accuracies in identification of two wheat diseases with varying severity for all three models. Overall accuracy of RVM was 89.71%, which was superior to 83.82% of SVM and inferior to 92.64% of BPNN. Meanwhile, the recognition accuracies of SVM, RVM and BPNN models for mild-to-moderate disease were 83.33%, 88.33% and 91.67%, respectively. The prediction time of RVM was less than those of SVM and BPNN, with differences as large as 7.96 and 31.68 times, respectively. Therefore, RVM appeared to be the most suitable for real-time identifying wheat leaf diseases among the three models, which can provide important technical support for wheat diseases management.

关键词： Support vector machines

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Yu-Hui Chang Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781509003914

关键词： Cybernetics Acceleration Time complexity Rough sets Heuristic algorithms data mining Algorithm design and analysis

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Flickr group recommendation via heterogeneous information networks 15

Flickr group recommendation via heterogeneous information ne...

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7th International Conference on Internet Multimedia computing and Service, ICIMCS 2015

作者： Wang, Yueyang xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting College of Computer Science and Technology Zhejiang University Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS China

ISBN: (纸本)9781450335287

As an important characteristic of social media (i.e. Flickr or Facebook), user communities or groups are beginning to attract increasing attention. Most of the previous studies on group recommendation only consider a homogeneous relationship, such as the rating of users to groups in social network. In real world, each group has its textual descriptions (e.g. the "Sky & Cloud" group in Flickr), and the describing words about a given group may mention particular entities as well as their belonging categories (e.g. "Nature"). In fact, the group recommendation can be conducted in a heterogeneous information network, where informative cues are in general multi-typed. Motivated by the assumption that different types of relationships in heterogeneous information network can be potentially used to boost the performance of the group recommendation. We propose to combine heterogeneous relationship information for users with implicit user feedback. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user-user similarity via heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation techniques. © 2015 ACM.

关键词： Non-negative matrix factorization

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Research on bearing micro deformation based on the frequency domain technique of white light speckle image

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2016年 37卷 46-52页

作者： Feng, Bo Xu, Chao Shao, Deyang Luo, Shixian Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

In this paper, a new method of non contact and on-line measurement is presented for the change of the axis of the rotation axis. Under the condition of a constant light source, the image of the original position and the offset image in the original position are collected by the micro camera, These two pictures are calculated by the improved White light digital image point by point analysis method in this paper, and get the black and white stripes. The number and the angle of the fringe in the white light digital frequency domain interference image can be known as the offset and direction of the rotating shaft in the rotating process. Because this method only use a camera and a light source, and this method is very simple, and it can always detect, and it does not affect the normal work, so there is a wide range of industrial application prospects. This method can provide alarm before the plastic deformation occurs in the shaft, Reduce the risk of shaft damage. © 2016, Science Press. All right reserved.

关键词： Frequency domain analysis

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