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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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Designing and Analysis of a New Book-Lending system Based on Logic Petri Nets

Designing and Analysis of a New Book-Lending System Based on...

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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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Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment

Cross-Domain Semantic Segmentation of Urban Scenes via Multi...

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International Conference on Pattern Recognition

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

Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.

关键词： Image segmentation Solid modeling Semantics Neural networks Virtual reality Feature extraction Robustness

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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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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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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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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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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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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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Extended and improved slicing technologies for Petri nets

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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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