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International Conference on Electric Information and Control Engineering

作者： Xia Jiang WenJing Luan Department of Computer Laiwu Vocational and Technical College The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

A new book-lending system is designed and analyzed based on logic Petri nets in this paper. The batch processing function and indeterminacy of readers are included in the system. Its logic Petri net model is establish... 详细信息

ISBN: (纸本)9781424480364

关键词： Book-lending system Batch processing Designing Verification Logic Petri net

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Quantized H2 Filtering for Markovian Jump Linear systems over Networks with Non-accessible Mode Information

Quantized H2 Filtering for Markovian Jump Linear Systems ove...

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The 8th World Congress on Intelligent Control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Tao Liu Qijun Chen Hao Zhang Department of Electronics and Information Engineering Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

This paper is concerned with the problems of quantized *** for discrete-time Markovian jump linear systems over networks with non-accessible mode information. The quantizer considered here is logarithmic quantizer. In this paper, a linear time-invariant mode-independent quantized *** is designed such *** error system is stochastically stable. *** conditions for the existence of *** is given in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed approach.

关键词： Quantization Bismuth Linear matrix inequalities Symmetric matrices Linear systems Upper bound Networked control systems

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Block diagonal precoding based power allocation for coordinated multi-point transmission

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Journal of Communications 2011年第7期6卷 524-528页

作者： Han, Jing Wang, Ping Liu, Fuqiang Zhu, Yin Broadband Wireless Communications and Multimedia Laboratory Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China

This paper studies power allocation in coordinated multi-point (CoMP) transmission of 3GPP LTE-Advanced system with remote radio units(RRUs) power constraints. We apply block diagonal (BD) precoding to downlink transmission, and assume perfect knowledge of downlink channels and transmit messages at each transmit point. We propose a modified water-filling power (MWF) allocation algorithm in order to maximize the downlink sum capacity, at the same time the low complexity is achieved. The interior-point method is also used to solve the optimization problem. Simulations show that interior-point method converges after only a few iterative steps and the system capacity is near-optimal. As for complexity and power efficiency, MWF achieves a good compromise. © 2011 ACADEMY PUBLISHER.

关键词： Iterative methods

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Research on a Remote Network Monitoring Model for Large-scale Materials Manufacturing

Research on a Remote Network Monitoring Model for Large-scal...

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2011 International Conference on Advanced Materials and Computer Science(ICAMCS 2011)

作者： Xiaohui ZENG Manhua LI Wenlang LUO Department of Computer Science and Technology Jinggangshan University The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

A remote network monitoring model for large-scale materials manufacturing is proposed, including five modules: center control module, data collection and fault alarm module, graph drawing module and data storage module. The center control module not only interacts with users, but also controls the other four modules to work together in harmony. According to this monitoring model, a remote network monitoring platform is designed and realized. The user can interact with the control center module through an Internet browser, and the information about the monitored manufacturing machines and devices can be displayed by means of text, chart, graphic and sound, etc. Moreover, the details about the problems or faults from the monitored objects can be obtained in time. The experimental results indicate that the network monitoring platform can accurately get the information of the monitored objects, and users can conveniently get the online running state of those monitored objects.

关键词： remote network monitoring data storage fault alarm materials manufacturing

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Study and implementation of agent-based grid resource monitoring model

Study and implementation of agent-based grid resource monito...

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International Conference on Information Science and Engineering (ICISE)

作者： Liu Ming Junqi Zhang Yu Wang Jie Li Department of Computer Science and Technology The Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China

ISBN: (纸本)9781424476169

Grid is a promising infrastructure which enables scientists and engineers to access geographically distributed resources. Grid computing is a new technology which focuses on aggregating various kinds of resource (e.g., processor cycles, disk storage, and contents) into one computing platform. The realization of grid computing requires a resource agent to manage and monitor available resources. Based on study of past models, this paper presents a new agent-based resource monitoring model whose main feature is multi-layered monitoring architecture, which enhances the ability to monitor related resource efficiently. In the meantime, the new model has been implemented in the development of a resource monitoring module integrated within a academic grid project.

关键词： Monitoring Computational modeling Databases Grid computing Automation Resource management XML

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Lane Detection Method Based on Improved RANSAC Algorithm

Lane Detection Method Based on Improved RANSAC Algorithm

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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Jie Guo Zhihua Wei Duoqian Miao Ministry of Education Key Laboratory of Embedded System and Service Computing Shanghai China Department of Computer Science and Technology Tongji University

ISBN: (纸本)9781479982622

Lane detection based on computer vision is a key technology of Automatic Drive system for intelligent vehicles. In this paper, we propose a real-time and efficient lane detection algorithm that can detect lanes appearing in urban streets and highway roads under complex background. In order to enhance lane boundary information and to be suitable for various light conditions, we adopt canny algorithm for edge detection to get good feature points. We use the generalized curve lane parameter model, which can describe both straight and curved lanes. We propose an improved random sample consensus (RANSAC) algorithm combined with the least squares technique to estimate lane model parameters based on feature extraction. Experiments are conducted on both real road lane videos captured by Tongji University and Caltech Lane Datasets. The experimental results show that our algorithm is can meet the real time requirement and fit lane boundaries well in various challenging road conditions.

关键词： Feature extraction Roads Computational modeling Image edge detection Algorithm design and analysis Real-time systems Mathematical model

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keystroke dynamics in password authentication for multi-user account

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Journal of Computational Information systems 2015年第1期11卷 321-331页

作者： Zhang, Xiaomeng Zhao, Peihai Wang, Mimi Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

In the field of identity authentication, existing researches on user keystroke authentication only focus on the situation in which a single user uses a single account. When multiple users share one account, there emerges the problem of inaccurate modeling. The key to construct the keyboard behaviour pattern in a single-account-for-multiple-users case is to cluster and build the unique keyboard behaviour pattern for each user under the shared account. In this paper, the original K-means clustering algorithm is improved by adding variance determination conditions. And when the number of users is unknown, the algorithm will determine whether the account is a multi-user account or not and identify the number of users under the account to carry out keyboard behaviour clustering in a single-account-for-multiple-users situation. We build a model based on keyboard behaviour data after clustering. Compared with other methods, experiments arrive at the expected results. This method can be used for keyboard behaviour authentication in real-world scenarios in which multiple users share one account at home or in companies. ©, 2014, Journal of Computational Information systems. All right reserved.

关键词： Authentication

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An Enhanced Scheduling Solution to Support Quality of service for Broadband Wireless Access Networks

An Enhanced Scheduling Solution to Support Quality of Servic...

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第三届IEEE无线通讯、网络技术暨移动计算国际会议

作者： Fuqiang Liu Yuhui Huang Shangzhi Xu The Key Laboratory of Embedded System and Service Computing supported by Ministry of Education Broadband Wireless Communication and Multimedia Laboratory Tongji University Shanghai China

This paper proposes an enhanced scheduling solution to support different quality of service (QoS) for IEEE 802.16e broadband wireless access network. Based on the existing scheduling algorithm M-LWDF, we do deeper research into how to integrate it into IEEE 802.16e. The proposed scheduling solution combines the key service flow parameters defined by IEEE 802.16e standard with M-LWDF scheme. Simulation results show that the delay character is improved for real-time services using the proposed method.

关键词： scheduling QoS BWA

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A New One-Class Classification Method with Multiple Encoder-Decoder Pairs for Images

A New One-Class Classification Method with Multiple Encoder-...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Dongxiang Chen Chungang Yan Mimi Wang Department of Electronics and Information Engineering Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

One of the main destinations of image classification methods is to screen out the images belonging to the target class (positive) and identify the images of other classes (negative). Although most classifiers are trained on both positive samples and negative samples, in reality, negative samples are often unavailable. One-class classifiers trained only on positive samples, are proposed to solve this problem. However, how to train effectively a classifier remains a daunting challenge. Considering the success of deep learning in the field of computer vision in recent years, we propose a one-class classification model for images based on convolutional neural network (CNN). The model consists of two parts of networks with different responsibilities. The network of the first part works as the discriminator, used to identify whether the images are positive. The networks of the second part, each of which consists of an encoder-decoder pair work as the guiders of the discriminator. They guide the discriminator to learn what images it should identify to be negative. Different encoder-decoder pairs can restore images to different degrees. Images restored to different degrees can be used to train the discriminator. The discriminator learns that images under a specific restored degree don't belong to the target class. The experiment results on CIFAR-10 show that our method can achieve good performance, with less difficulty in training than the GAN-based counterparts'.

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Spatial-Temporal Flow Holistic Interaction Graph Convolution Network for Bidirectional Traffic Flow Forecasting

Spatial-Temporal Flow Holistic Interaction Graph Convolution...

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IEEE International Conference on Big Data

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

Traffic flow forecasting is indispensable in modern urban life. Considering the complexity, variability and strong timeliness of traffic flow, traffic flow forecasting is a worth exploring but challenging research field. To achieve better traffic flow forecasting effect, we focus on two critical aspects that assume noteworthy importance: i) the features inside the traffic outflows and inflows. ii) the supplementary information regarding exterior region which is the area outside the grid division regions. To address these challenges, we propose a novel deep learning model Spatial-Temporal Flow Holistic Interaction Graph Convolution Network (STHGCN). In STHGCN, graph convolution based modules are applied through multi-step simulation. An exterior region feature estimation module is designed to estimate the influence of the special exterior region through the characteristics of complete trajectories, which enables a more comprehensive reasoning for traffic flow forecasting in grid division regions. Furthermore, a flow feature fusion integrator and stackable convolution modules are proposed to aggregate the intermediate features extracted from various perspectives, which simulate the constantly-updating and interlinked states of traffic flows through the process of multi-layer feature separation and fusion. We conduct extensive experiments on real-world traffic datasets and our proposed model outperforms all baselines.

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