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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hanli Wang Ming Ma Tao Tian Key Laboratory of Embedded System and Service Computing Tongji University Shanghai P. R. China

With the increasing popularity of social network, more and more people tend to store and transmit information in visual format, such as image and video. However, the cost of this convenience brings about a shock to traditional video servers and expose them under the risk of overloading. Among the huge amount of online videos, there are quite a number of Near-Duplicate Videos (NDVs). Although many works have been proposed to detect NDVs, few researches are investigated to compress these NDVs in a more effective way than independent compression. In this work, we utilize the data redundancy of NDVs and propose a video coding method to jointly compress NDVs. In order to employ the proposed video coding method, a number of pre-processing functions are designed to explore the correlation of visual information among NDVs and to suit the video coding requirements. Experimental results verify that the proposed video coding method is able to effectively compress NDVs and thus save video data storage.

关键词： Videos Video coding Image coding Bit rate Joints Indexes Encoding

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Finite time cluster consensus of fractional-order multi-agent systems with directed topology

Finite time cluster consensus of fractional-order multi-agen...

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第33届中国控制与决策会议

作者： Weijin Liu Xiwei Liu Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education

In this paper,the finite time cluster consensus(FnTCC) of fractional-order multi-agent systems(FOMAS)with directed topology is *** fractional-order system is converted into an integer-order system by defining a neighborhood-based error variable,and suitable control rules are designed for the obtained first-order multi-agent *** to the exponential finite-time stability theorem,suitable Lyapunov functions are ***,the settling time function is *** simulation results prove the feasibility and validity of our theory.

关键词： Fractional-order Cluster consensus Exponential finite time Multi-agent systems Directed topology

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A survey on distributed compressed sensing： theory and applications

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Frontiers of Computer Science 2014年第6期8卷 893-904页

作者： Hongpeng YIN Jinxing LI Yi CHAI Simon X. YANG College of Automation Chongqing University Chongqing 400030 China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing 400030 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing 400030 China School of Engineering University of Guelph Guelph N1G 2W1 Canada

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

关键词： compressed sensing distributed compressed sensing sparse representation measurement matrix joint reconstruction joint sparsity model

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Incremental Sensor Node Deployment for Low Cost and Highly Available WSNs

Incremental Sensor Node Deployment for Low Cost and Highly A...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Safwan Al-Omari Weisong Shi Jordan University of Science and Technology Jordan Key Laboratory of Embedded System and Service Computing University of Tongji China

We attack the sensor network deployment problem. We define the deployment problem as the problem of deciding how many sensor nodes should be deployed in the sensor field over how many phases during its lifetime. We target the optimal deployment strategy that meets user-defined availability requirement with minimum total cost taking into consideration node failures and changing field trip to sensor node cost ratio. We model WSN availability and total cost as functions of the deployment plan, then, we formalize the deployment problem as a 2D optimization problem. Our modeling enables us to explore cost-benefit tradeoffs, we believe, this is a solid step toward bringing cost as an explicit dimension in the design space of WSN protocols. We compare the performance of the optimized solution (denoted as pro-active) to more ad-hoc solutions: on-demand and at-front. The former strategy schedules future deployments only on demand. The latter strategy deploys all nodes at front with no later field trips. Using extensive simulations, we show that proactive outperforms at-front and on-demand in terms of total cost per availability unit in all application scenarios. For example, using pro-active costs $7 compared to $40 and $280 per total uptime in case of on-demand and at-front, respectively.

关键词： Availability Wireless sensor networks Equations Optimization Mathematical model Business Exponential distribution

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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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Process-aware service composition based on Petri net

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Journal of Harbin Institute of Technology (New Series) 2008年第SUPPL.期15卷 162-166页

作者： Sun, Ping Li, Xiangmei Department of Computer Science and Engineering Tongji University Shanghai 201804 China Embedded System and Service Computing Key Laboratory Shanghai 201804 China

One of the most promising advantages of Web service technology is the possibility of creating value-added services by combining existing ones. A major challenge is how to discover and select concrete service according to user requirements. This paper addresses the topic of service discovery composite Web services. The main feature is that we take the process model as well as service profile into account. Firstly, the process models of Web services are translated into Petri nets. Based on this, we propose a service matchmaking algorithm, via comparing the functionality compatibility and process consistency, thus leading to more accurate matchmaking.

关键词： Web services

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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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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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General-to-Specialized Analysis Based on Deep Belief Network

General-to-Specialized Analysis Based on Deep Belief Network

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Asian Conference on Pattern Recognition (ACPR)

作者： Renjie Hu Lianghua He Yuqin Wang D. Hu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Key Laboratory of EMW Information Fudan University Shanghai CN

Recently, the deep neural network has achieved great performance in many areas. During analysis, all learned features are used at once, some of which could bring negative affect to specific classes. Recently, cognitive studies show that a human visual cognition process is hierarchical and dynamic, i.e., when meeting different targets, human brain intends to pay attention to different parts. Therefore, in this paper, we introduce this kind of mechanism into deep belief network (DBN) and propose a new general-to-specialized algorithm. Firstly, hierarchical knowledge networks are constructed based on the original learned DBN through pruning and retraining. Because these networks are learned for different discriminate ability, we call them as the general network and the specialized network separately. Secondly, a general-to-specialized analysis method is proposed which is proved theoretically. When predicting the class of an input sample, we select the corresponding specialized networks according to the preliminary analysis result and then make in-depth analysis. Experiments on four benchmark datasets are performed to test the proposed algorithm. The results show that our algorithm is feasible, valid and robust.

关键词： Training Visualization Feature extraction Prediction algorithms Biological neural networks Cognition Recurrent neural networks

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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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