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Bayesian cognitive trust model based self-clustering algorithm for MANETs

Science China(Information Sciences) 2010年第3期53卷 494-505页

作者： WANG Wei1,2,3 & ZENG GuoSun1,2,31Department of Computer Science & Technology, Tongji University, Shanghai 200092, China 2Tongji Branch, National Engineering & Technology Center of High Performance Computer, Shanghai 200092, China 3key Loboratory of embedded system and service computing, ministry of education, Shanghai 200092, China 1. Department of Computer Science & Technology Tongji University Shanghai 200092 China2. Tongji Branch National Engineering & Technology Center of High Performance Computer Shanghai 200092 China3. Key Loboratory of Embedded System and Service Computing Ministry of Education Shanghai 200092 China

With the introduction of mobile Ad hoc networks (MANETs), nodes are able to participate in a dynamic network which lacks an underlying infrastructure. Before two nodes agree to interact, they must trust that each will satisfy the security and privacy requirements of the other. In this paper, using the cognition inspired method from the brain informatics (BI), we present a novel approach to improving the search efficiency and scalability of MANETs by clustering nodes based on cognitive trust mechanism. The trust relationship is formed by evaluating the level of trust using Bayesian statistic analysis, and clusters can be formed and maintained autonomously by nodes with only partial knowledge. Simulation experiments show that each node can form and join proper clusters, which improve the interaction performance of the entire network. The essence of the underlying reason is analyzed through the theory of complex networks, revealing great scalability of this method.

关键词： trust model MANET self-clustering Bayesian method cognitive mobile

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Automatic Web service composition based on Horn clauses and Petri nets

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Expert systems with Applications 2011年第10期38卷 13024-13031页

作者： Tang, Xianfei Jiang, Changjun Zhou, Mengchu 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 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States

This paper introduces an automatic Web service composition method based on logical inference of Horn clauses and Petri nets. The Web service composition problem is transformed into the logical inference problem of Horn clauses by exploring the dependency relations among services. An algorithm is presented, to determine whether the composite service exists in time that is linear in the size of the knowledge base. It can also return the rule set necessary for the composition. Petri nets are chosen to model the rule set and its structural analysis techniques are used to obtain the composite service. © 2010 Elsevier Ltd. All rights reserved.

关键词： Automatic reasoning Knowledge base Petri nets service composition Web service

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A Cloudedge-Combined Data Placement Strategy Based on User Access Regions 6

A Cloudedge-Combined Data Placement Strategy Based on User A...

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6th International Conference on Big Data and Information Analytics, BigDIA 2020

作者： Cao, Eryang Wang, Pengwei Yan, Chungang Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai200092 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China School of Computer Science and Technology Donghua University Shanghai201620 China

To improve the availability of data in the cloud and avoid vendor lock-in risk, multi-cloud storage is attracting more and more attentions. However, accessing data from the cloud usually has some disadvantages such as higher latency and more bandwidth-consuming. Edge computing, which has become popular in recent years, could effectively solve the above problems while it usually reduces availability. Storage only in the cloud or edge environment is limited by many factors. To this end, we build mathematical model which is abstracted from real access mode of users in consideration of multi-cloud and edge. This paper presents a method combined NSGAII with multi-group method which has better ability of global search to help users determine cloud and edge services to store and access data object. A large number of experiments using real service provider information demonstrate that the proposed algorithm outperforms existing methods and effectiveness of it. © 2020 IEEE.

关键词： Multiobjective optimization

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Biped robot walking using central pattern generator and genetic algorithm

Biped robot walking using central pattern generator and gene...

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IASTED International Conference on Robotics, Robo 2010

作者： Liu, Chengju Chen, Qijun Wang, Danwei School of Electronics and Information Engineering Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9780889868595

Central pattern generator (CPG) plays an important role in rhythmic activities of animals and this mechanism is an important inspiration source for the motion control of legged robots. In this paper, by using CPGs and function mapping mechanism, a high-efficiency distributed CPG control network is constructed to realize the locomotion control of biped NAO robot. To realize stable and coordinated locomotion, the parameters of the CPG network are evolved by multi-object genetic algorithm (MOGA). Simulations with Webots validate the feasibility and efficiency of the presented CPG-based control method.

关键词： Genetic algorithms

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Predictive Control for Visual Servo Stabilization of Nonholonomic Mobile Robots

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自动化学报 2013年第8期39卷 1238-1245页

作者： CAO Zheng-Cai YIN Long-Jie FU Yi-Li LIU Tian-Long College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji UniversityShanghai 201804China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150080China

活动机器人获得了的 nonholonomic 的视觉伺服稳定广泛的注意。当前，这个问题的解决方案不管多么不认为可见性限制和致动器是限制，因此设计控制器是困难的在实际申请认识到令人满意的性能。在这份报纸，为一个活动机器人的视觉伺服稳... 详细信息

活动机器人获得了的 nonholonomic 的视觉伺服稳定广泛的注意。当前，这个问题的解决方案不管多么不认为可见性限制和致动器是限制，因此设计控制器是困难的在实际申请认识到令人满意的性能。在这份报纸，为一个活动机器人的视觉伺服稳定的一个预兆的控制器被介绍。第一，利用产生速度的命令的一个运动学的预兆的稳定控制器被介绍。然后，来临以便 asymptotically 做活动机器人的实际速度到需要的，一个动态预兆的控制器被设计。建议预兆的控制器能容易处理限制。最后，几模拟被执行，并且结果说明建议控制计划是有效的解决视觉伺服稳定问题。

关键词：非完整移动机器人稳定化控制器预测控制器视觉伺服生成速度运动预测稳定问题控制方案

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Cluster synchronization of nonlinearly coupled networks via periodically intermittent pinning control

Cluster synchronization of nonlinearly coupled networks via ...

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Chinese Control Conference (CCC)

作者： Xiwei Liu Jiujun Cheng Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology the Key Laboratory of Embedded System Service Computing Shanghai China

In this paper, the cluster synchronization problem for nonlinearly coupled networks under periodically intermittent pinning control is studied. At first, a sufficient condition to guarantee cluster synchronization is presented. Next, an adaptive intermittent control algorithm is designed to the control strength and its validity is rigorously proved. Finally, some numerical examples are presented to demonstrate the correctness of obtained theoretical results.

关键词： Synchronization Couplings Symmetric matrices Adaptive systems educational institutions Equations Neural networks

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Non-Intrusive Load Disaggregation Using Semi-Supervised Learning Method

Non-Intrusive Load Disaggregation Using Semi-Supervised Lear...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Nan Miao Shengjie Zhao Qingjiang Shi Rongqing Zhang The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

With the emerging of smart metering around the world, there is a growing demand to analyse the residential energy usage. In this paper, we propose a Deep Neural Network (DNN)-based approach for non-intrusive load monitoring (NILM), which can achieve effective and efficient estimation of individual appliance usage according to a single main meter reading in a non-intrusive manner. Considering practical situations, two training methods are provided. The first training approach is fully supervised learning, which requires a ground truth of label, indicating the state of the appliance (ON/OFF), to build a prediction model. The second training approach is semi-supervised learning, leading to better performance by F-Measure metric while only requiring some more unlabelled training data. Experimental results on the low-sample rate REDD dataset demonstrate the superior performance of our proposed DNN-based method compared with Hidden Markov Model (HMM)based and Graph Signal Processing (GSP)-based approaches.

关键词： Training Hidden Markov models Home appliances Neural networks Convolution Security Pattern analysis

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Information Security Evaluation by Information Flow Analysis Based on Stochastic Petri Nets

Information Security Evaluation by Information Flow Analysis...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Tu, Hanqian Xiang, Dongming Lin, Wang Liu, Guanjun Zhejiang Sci-Tech University Department of Computer Science and Technology Hangzhou310018 China Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Shanghai201804 China

ISBN: (纸本)9781665410205

The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quantitative evaluation on information leakage. In this paper, we propose a novel method for quantitative evaluation of information security based on stochastic labeled Petri nets (SLPNs) and information flow analysis. Specifically, we introduce four different levels of security metrics, and provide a methodology for evaluating the information security. Furthermore, a case study is presented to show the feasibility of our method. © 2024 IEEE.

关键词： Information leakage

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Spatial-Temporal Dynamic Graph Diffusion Convolutional Network for Traffic Flow Forecasting

Spatial-Temporal Dynamic Graph Diffusion Convolutional Netwo...

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IEEE International Conference on Big Data

作者： Yang Li Yaying Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Artificial Intelligence Laboratory Shanghai China

Traffic forecasting plays a crucial role in intelligent transportation systems and finds application in various domains. Accurate traffic forecasting remains challenging due to the time-varying correlations within the data and the heterogeneous correlations between regions. Although various dynamic spatial-temporal graph models have been proposed to address these challenges in recent years, most of them are burdened by high computation costs and not intuitive to understand. In this paper, we propose a spatial-temporal graph model, Spatial-Temporal Dynamic Graph Diffusion Convolutional Network (SDGDN) that provides an effective and efficient approach to traffic forecasting. From the perspective of traffic flow transition probabilities, SDGDN learns dynamic graph structures to capture the time-varying traffic transition relationships. Besides dynamic graph structures, static node features are employed in diffusion convolution to better capture heterogeneous regional features. Furthermore, we utilize temporal encoding and also generate varying graphs in each stacked layer to enhance the forecasting performance. Experiments results on five real-world datasets demonstrate that SDGDN outperforms most baseline models in terms of both performance and computation efficiency.

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Low-Rate Non-Intrusive Appliance Load Monitoring Based on Graph Signal Processing

Low-Rate Non-Intrusive Appliance Load Monitoring Based on Gr...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bing Zhang Shengjie Zhao Qingjiang Shi Rongqing Zhang The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

Thanks to the large-scale smart meters deployments around the world, non-intrusive appliance load monitoring (NILM) is receiving popularity. It aims to disaggregate the total electricity load of a home into individual appliances without resorting to any specific appliance power monitors. NILM is worthy of broad attention owing to its facilitation in energy savings. This paper regards NILM as a classification task and proposes a two-step method based on graph signal processing (GSP). In the first step, a smoothest solution is obtained by minimizing the regularization term. In the second step, gradient projection method, which uses the obtained minimizer as a start point, is adopted to optimize the while objective function, where NILM is regarded as a constrained nonlinear programming problem. The experiment results based on the open-access data set REDD clearly demonstrate that the proposed GSP-based method achieves improved performance compared with other state-of-the-art low-rate NILM approaches.

关键词： domestic appliances gradient methods graph theory nonlinear programming power system measurement signal processing smart meters Smart meters Power system measurements signal processing Nonlinear Programming gradient methods Graph theory Appliances load monitoring

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