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Extended and improved slicing technologies for Petri nets

High Technology Letters 2013年第1期19卷 98-104页

作者： Yu, Wangyang Yan, Chungang Ding, Zhijun Fang, Xianwen Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Lab. of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

Petri net is an important tool to model and analyze concurrent systems, but Petri net models are frequently large and complex, and difficult to understand and modify. Slicing is a technique to remove unnecessary parts with respect to a criterion for analyzing programs, and has been widely used in specification level for model reduction, but researches on slicing of Petri nets are still limited. According to the idea of program slicing, this paper extends slicing technologies of Petri nets to four kinds of slices, including backward static slice, backward dynamic slice, forward static slice and forward dynamic slice. Based on the structure properties, the algorithms of obtaining two kinds of static slice are constructed. Then, a new method of slicing backward dynamic slice is proposed based on local reachability graph which can locally reflect the dynamic properties of Petri nets. At last, forward dynamic slice can be obtained through the reachability marking graph under a special marking. The algorithms can be used to reduce the size of Petri net, which can provide the basic technical support for simplifying the complexity of formal verification and analysis. © by HIGH TECHNOLOGY LETTERS PRESS.

关键词： Petri nets

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Large-scale video compression： recent advances and challenges

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Frontiers of computer Science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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Modeling and monitoring of online shopping business processes based on system behavior patterns

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Journal of Computational Information systems 2013年第12期9卷 4691-4698页

作者： Yu, Wangyang Yan, Chungang Ding, Zhijun Zhong, Junzhu Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Lab. of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

Online shopping integrating third-party payment platforms (TPPs) has rapidly developed recently. The integration leads to new security problems derived from complex interactions among Application Programming Interfaces (APIs), and the detection of such defects in application-level is costly and difficult. To deal with these security issues at the online stages, this paper presents a framework of modeling and monitoring online shopping business processes from the point of legal behavior patterns. We present Online Shopping Net (OSN) to model the normal business process, and make it as the legal behavior pattern used to monitor system behaviors. Then, a monitor based on OSN is constructed. The system behaviors beyond the legal behavior pattern would be alarmed and blocked. It is a novel attempt to guarantee the security of online shopping from the point of behaviors and online monitoring. Using the active defense tactics, we can effectively prevent the illegal behaviors of the users at application-level instead of constantly fixing bugs of applications. As a case study, we simulate an e-commerce site and a TPP, and the monitor can effectively prevent the malicious behaviors appearing recently. © 2013 by Binary Information Press.

关键词： Application programming interfaces (API)

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An Initialization Method based on The Core Clusters for Locality-weight Fuzzy C-Means Clustering 4th

An Initialization Method based on The Core Clusters for Loca...

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2013 IEEE 4th International Conference on Software Engineering and service Science

作者： Lei Gu The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Computer Science and Technology Nanjing University of Posts and Telecommunication

ISBN: (纸本)9781467349970

The Locality-weight fuzzy c-means clustering method has been presented *** this approach can improve the clustering accuracies,it often gains the unstable clustering results because some random samples are employed for the initial *** this paper,an initialization method based on the core clusters is used for the locality-weight fuzzy c-means *** core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality-weight fuzzy c-means *** investigate the effectiveness of our approach,several experiments are done on three *** results show that our proposed method can improve the clustering performance compared to the previous locality-weight fuzzy c-means clustering.

关键词： the initialization clustering methods core clusters locality-weight fuzzy c-means neighborhood graph

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Adaptive Granulation for Efficient Classification on Noise Labeled Images 9

Adaptive Granulation for Efficient Classification on Noise L...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Cai, Kecan Zhang, Hongyun Miao, Duoqian Tongji University Department of Computer Science and Technology Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9798350352214

Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positively to model training. This paper introduces a novel semi-supervised image data granulation method that employs adaptively generated granular noisy sample subsets in place of the original noisy samples to enhance classification efficiency. The granular data generated retain the essential features of the original data, thereby improving efficiency without compromising classification accuracy. The quality of the granular data is assessed using coverage and specificity criteria, standard metrics for evaluating information granules. The proposed method consists of three main components: (i)selecting clean and noisy samples through network co-training, (ii)calculating granular noisy sample subsets by adaptive granulation, and (iii)optimizing the network model using a semi-supervised strategy. Experimental results on benchmark datasets with varying noise rates demonstrate that our method significantly improves the efficiency of noisy label image classification while maintaining accuracy. © 2024 IEEE.

关键词： Self-supervised learning

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Particle swarm optimization based on pairwise comparisons 9th

Particle swarm optimization based on pairwise comparisons

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Zhang, JunQi Chen, JianQing Zhu, XiXun Wang, ChunHui Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9783319938141

Particle swarm optimization (PSO) is a widely-adopted optimization algorithm which is based on particles’ fitness evaluations and their swarm intelligence. However, it is difficult to obtain the exact fitness evaluation value and is only able to compare particles in a pairwise manner in many real applications such as dose selection, tournament, crowdsourcing and recommendation. Such ordinal preferences from pairwise comparisons instead of exact fitness evaluations lead the traditional PSO to fail. This paper proposes a particle swarm optimization based on pairwise comparisons. Experiments show that the proposed method enables the traditional PSO to work well by using only ordinal preferences from pairwise comparisons. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Swarm intelligence

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Fuzzy H∞ Filtering for Nonlinear Networked systems with Multiple Stochastic Time-Varying Delays, Packet Dropouts and Sensor Saturation 34

Fuzzy H∞ Filtering for Nonlinear Networked Systems with Mul...

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第三十四届中国控制会议

作者： XU Xiaoli YAN Huaicheng ZHANG Hao SHI Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9781467374439

This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. system conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

关键词： H ∞ filtering,multiple time-varying delays,randomly occurring sensor saturation,packet dropouts,piecewise quadratic Lyapunov function

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A New MPI Parallel Computing Environment Based on Fibre-channel Token-Routing Network for Materials Simulation Research

A New MPI Parallel Computing Environment Based on Fibre-chan...

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International Conference on Advanced Materials in Microwaves and Optics (AMMO 2011)

作者： Zeng, Xiaohui Liu, Dongsheng Luo, Wenlang Fu, Lixin School of Electronics and Information Engineering Jinggangshan University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9783037852736

In order to apply our high efficiency fibre-channel token-routing network (shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, and independently developed a parallel programming environment FC-TR-MPI based on FC-TR network. In FC-TR-MPI, a new method was applied to point-to-point communication that the network communications between processes in the same computing node were changed into memory operations;moreover, according to the underlying software and hardware features of FC-TR network, new algorithms were proposed to optimize the communication performance of some collective communications. Experimental results show that, compared with ScaMPI parallel programming environment, FC-TR-MPI has a higher parallel efficiency and speedup.

关键词： MPI parallel computing environment fibre-channel materials simulation

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Associating semantic sensor web with domain ontology: The way to obtain meaningful sensor data

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International Journal of u- and e- service, Science and Technology 2014年第5期7卷 271-282页

作者： Ji, Chaoqun Liu, Jin Wang, Xiaofeng College of Info. Engineering Shanghai Maritime University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

To realize the sharing and reuse of sensor data and improve interoperability, semantic sensor web(SSW)is proposed to add semantics information to existing sensor networksby utilizing domain, spatial and temporal anthologies and other related semantic *** these is seldom research on how to fully utilize the sensor data through a semantic way such as domain ontology based inference. This paperpresents stateofthe art of SSWin various aspects, and proposesthe method to associate data from semantic sensor web with domain ontology to realize the communication between different domain ontologies and SSW. In addition, this paperalso proposes a new calculation method of semantic similarity amongdifferent entities in different ontology. Experiments show that this method can effectively find the similar entitiesandrealize the knowledge sharing and ontologies reuse. © 2014 SERSC.

关键词： Ontology

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Covariance free incremental principal component analysis with exact mean update

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Journal of Computational Information systems 2013年第16期9卷 6703-6710页

作者： Zeng, Xueqiang Li, Guozheng The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Computer Center Nanchang University Nanchang 330029 China Department of Control Science and Engineering Tongji University Shanghai 201804 China

Incremental feature extraction is an essential data preprocessing technique for large-scale and streaming data mining. Among various covariance matrix-free Incremental Principal Component Analysis (IPCA) methods, Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) is a state-of-the art algorithm. Since the training samples are required to be centred, CCIPCA applies an approximate centric alignment on the input data, where only the current sample is correctly centred and all historical data are not updated properly. In this paper, we propose a novel centred incremental principal component analysis algorithm with exact historical mean update, where not only the current sample is centred, but also all historical data are updated by the current mean correctly. Compared to CCIPCA, the proposed method converges more quickly, and the performance improvement is especially obvious when the data's inherent covariance is not stable. The experiments on real streaming dataset show that the proposed method is much superior to CCIPCA in convergence speed. © 2013 Binary Information Press.

关键词： Principal component analysis

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