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25th International Conference on Neural information processing, ICONIP 2018

作者： Luo, Xiaohui Zhang, Li Li, Fanzhang Hu, Chengxiang School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (纸本)9783030041816

Manifold learning has attracted more and more attention in machine learning for past decades. Unsupervised Large Graph Embedding (ULGE), which performs well on the large-scale data, has been proposed for manifold learning. To improve the clustering performance, a novel Unsupervised Ensemble Learning based on Graph Embedding (UEL-GE) is explored, which takes ULGE to get low-dimensional embeddings of the given data and uses the K-means method to obtain the clustering results. Furthermore, the multiple clusterings are corrected by using the bestMap method. Finally, the corrected clusterings are combined to generate the final clustering. Extensive experiments on several data sets are conducted to show the efficiency and effectiveness of the proposed ensemble learning method. © Springer Nature Switzerland AG 2018.

关键词： K-means clustering

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Fuzzy inference mechanism based automatic elastic registration of two-dimensional gel electrophoresis images

Fuzzy inference mechanism based automatic elastic registrati...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Deng, Zhaohong Shitong Wang Qun Gao Yonghui Pan School of Digital Media Jiangnan University Jiangsu Wuxi China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Jiangsu Suzhou China Jiangsu Engineering R and D Center for Information Fusion Software Jiangsu Jiangyin China

ISBN: (纸本)9781612841816

A key technique for protein analysis is the geometric alignment of two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), i.e. 2-D PAGE image registration. In this study, according to the characteristics of 2-D gel image registration, a fuzzy-inference-rule based flexible model (FIM-FM) is proposed to model the complex geometric transformation and intensity variation between 2-D gel image pairs. Then, by introducing the concept of motion estimation and the corresponding sum-of-squared-difference (SSD) objective function, the parameter learning rules of the proposed model are derived for registration. Accordingly, an adaptive FIR-FM-based automatic elastic image registration algorithm is presented for 2-D gel image registration here. It is distinguished by its strong ability in approximating complex nonlinear transformation inherited from fuzzy inference and its efficiency and adaptability in obtaining precise model parameters through effective parameter learning rules. Our experiments show that the proposed algorithm is highly effective for registration of 2-D gel images. © 2011 IEEE.

关键词： Fuzzy inference

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The application of flash into M-learning in 3G initial stage

The application of flash into M-learning in 3G initial stage

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2010 3rd International Symposium on Knowledge Acquisition and Modeling, KAM 2010

作者： Tang, Yue-Ming Liu, Quan Wang, Ling-Ling Department of Educational Technology Henan Normal University Xinxiang Henan Province China Institute of Computer Science and Technology Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou Jiangsu Province China

ISBN: (纸本)9781424480050

With the development of the 3G technology, M-learning enters a period of rapid development. But meanwhile 3G also restrains the development of M-Iearning due to several drawbacks in its early days. According to the application analysis, it comes out Flash has its unique features: small capacity, easy to develop high-quality and etc, which can make up for 3G initial stage's disadvantages. Through case design, this paper proves that Flash can be a new effective approach and way for M-Iearning in the 3G network. In the end, from the aspects of technical and artistic, this paper makes a further research on how to improve the flash animation to serve the M-Iearning better. © 2010 IEEE.

关键词： E-learning

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Research on intrusion detection model of heterogeneous attributes clustering

Journal of Software

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Journal of Software 2012年第12期7卷 2823-2831页

作者： Xie, Linquan Wang, Ying Yu, Fei Xu, Chen Yue, Guangxue School of Science Jiangxi University of Science and Technology 341000 Ganzhou China School of Science Jiangxi University of Science and Technology 341000 Ganzhou China Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University 215000 Soochow China School of Information Science and Engineering Hunan University 416000 Changsha China Department of Computer Science and Technology Huaihua University Huaihua China

A fuzzy clustering algorithm for intrusion detection based on heterogeneous attributes is proposed in this paper. Firstly, the algorithm modifies the comparability measurement for the categorical attributes according to the formula of Hemingway;then, for the shortages of fuzzy C-means clustering algorithm: initialize sensitively and easy to get into the local optimum, the presented new algorithm is optimized by GuoTao approach. We simulate our algorithm with the KDDCUP99 data set, and the results show that the convergence rate of the new algorithm is faster than the original fuzzy C-means clustering algorithm and the performance of our algorithm is more stable. © 2012 ACADEMY PUBLISHER.

关键词： Intrusion detection

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Multi-target localization using distributed MIMO radar based on spatial sparsity

Multi-target localization using distributed MIMO radar based...

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2021 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2021

作者： Zhao, Chenyang Ke, Wei Wang, Tingting Nanjing Normal University Jiangsu Key Laboratory on Optoelectronic Technology in School of Computer and Electronic Information School of Artificial Intelligence Nanjing China Nanjing University of Information Science Technology Jiangsu Key Lab of Meteorological Observation and Information Processing Nanjing China

ISBN: (纸本)9781665418676

This paper presents a sparsity-based multi-target localization method for multiple-input multiple-output (MIMO) radar systems using distributed antennas. Since targets usually lie at some points within the localization domain, we are able to exploit this sparsity to convert the radar localization problem into a distributed recovery solution. Based on this natural sparsity, in this paper we introduce a block-sparse illustration model for distributed MIMO radar and propose a completely unique block-sparse recovery algorithmic rule supported approximate l0 norm diminution. The novelty of this technique is using l0 norm to push inter-block sparsity within the signals and also the optimisation problem is resolved by an ordered procedure in conjunction with a conjugate-gradient technique for quick reconstruction. Moreover, the amount of targets doesn't be well-known in advance. The effectiveness of this technique is incontestable by simulation results that obtain better localization performance and reduce computation complexness for giant sized data. © 2021 IEEE.

关键词： MIMO radar

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A privacy policy of P3P based on relational database

A privacy policy of P3P based on relational database

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First International Multi- Symposiums on computer and Computational Sciences, IMSCCS'06

作者： Jiang, Bin Zhang, Da-Fang Yang, Chao Yue, Guangxue Yu, Fei Software School Hunan University Changsha Hunan 410082 College of Computer and Communication Hunan University Changsha Hunan 410082 Provincial Key Laboratory of Computer Information Processing Technology Nanjing Jiangsu 215006

ISBN: (纸本)0769525814

P3P is an industry standard method that enables users to gain powerful control over their processing of personal information on Web sites being visited Traditional methods of database access control places great dependence on the use application of statically defined views. In this paper, we propose the procedure by which current RDBMS can be transformed into their privacy-preserving equivalents, In addition, architectural for implementing P3P based on RDBMS is dissected and a server-centric implementation that reuses database querying technology is suggested. © 2006 IEEE.

关键词： Relational database systems

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Reliable neighbors-based collab.rative filtering for recommendation systems 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Sun, Xiaohan Cheng, Jiangfei Li, Zepeng School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

The sparsity of data and the diversity of ratings have a great effect on the performance of recommendation systems. To deal with these two issues, this paper proposes a reliable neighbors-based collab.rative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on the recommendation effect. Then, RNCF finds a set of reliable users for each item by defining a reliable degree for each item. Next, RNCF adopts the K-means clustering algorithm to find the similar users for a target user. The set of reliable neighbors is the intersection of sets of reliable users and similar users. Finally, RNCF provides a predicted rating of a target user on some item using the pre-estimated ratings of corresponding reliable neighbors. Experimental analysis shows that RNCF can improve the performance at the extreme levels of sparsity and rating diversity in recommendation systems. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Collab.rative filtering

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Semi-supervised feature selection using sparse laplacian support vector machine 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Zheng, Xiaohan Xu, Zhiqiang School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

Semi-supervised feature selection is an active topic in machine learning and data mining. Laplacian support vector machine (LapSVM) has been successfully applied to semi-supervised learning. However, LapSVM cannot be directly applied to feature selection. To remedy it, we propose a sparse Laplacian support vector machine (SLapSVM) and apply it to semi-supervised feature selection. On the basis of LapSVM, SLapSVM introduces the 1-norm regularization, which means the solution of SLapSVM has sparsity. In addition, the training procedure of SLapSVM can be formulated as solving a quadratic programming problem, which indicates that the solution of SLapSVM is unique and global. SLapSVM can perform feature selection and classification at the same time. Experimental results on semi-supervised classification problems show the feasibility and effectiveness of the proposed semi-supervised learning algorithms. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Support vector machines

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Indirect current control of active power filter using novel sliding mode controller

Indirect current control of active power filter using novel ...

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2012 IEEE 13th Workshop on Control and Modeling for Power Electronics, COMPEL 2012

作者： Fei, Juntao Li, Tianhua Zhang, Shenglei Jiangsu Key Lab. of Power Transmission and Distribution Equipment Technology College of Computer and Information Hohai University Changzhou 213022 China

ISBN: (纸本)9781424493739

A novel sliding mode control (SMC) method for indirect current controlled three-phase parallel active power filter is presented in this paper. There are two designed closed-loops in the system, one is the DC voltage controlling loop and the other is the reference current tracking loop. The first loop with a PI regulator is used to control the DC voltage approximating to the given voltage of capacitor and the output of PI regulator through a low-pass filter is applied as the input of the power supply reference currents. The second loop implements the tracking of the reference currents using integral sliding mode controller, which can improve the harmonic treating performance. Compared with the direct current control technique, it is convenient to be implemented with digital signal processing system because of simpler system structure and better harmonic treating property. Simulation results verify that the generated reference currents have the same amplitude with the load currents, demonstrating the superior harmonic compensating effects with the proposed shunt active power filter compared with the hysteresis method. © 2012 IEEE.

关键词： Sliding mode control

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Extracting backbones from weighted complex networks with incomplete information

Abstract and Applied Analysis

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Abstract and Applied Analysis 2015年第Spe2期2015卷 1-11页

作者： Qian, Liqiang Bu, Zhan Lu, Mei Cao, Jie Wu, Zhiang School of Computer Science and Technology Soochow University Suzhou 215006 China Jiangsu Provincial Key Lab. of E-Business Nanjing University of Finance and Economics Nanjing 210046 China

The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency - the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity. © 2015 Liqiang Qian et al.

关键词： information theory information filtering systems FEATURE extraction (Data processing) OUTLIERS (Statistics) MATHEMATICAL models

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