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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Wang, Dehai Cheng, Jiujun Tang, Keshuang Li, Xin 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 Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. PudongShanghai China

ISBN: (纸本)9783319681207

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance. © IFIP International Federation for Information Processing 2017.

关键词： Forecasting

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An Effective and Robust Pedestrians Detecting Algorithm & Symposia

An Effective and Robust Pedestrians Detecting Algorithm & Sy...

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International Conference on Intelligent Transportation

作者： Zhipeng Li Yun Sun Fuqiang Liu Wenhuan Shi Key Laboratory of Embedded System and Service Computing supported by Ministry of Education University of Tongji Shanghai China School of Electronics and Information Engineering University of Tongji Shanghai China Department of Control Science and Technology Shanghai Jiaotong University Shanghai China

In this paper, we present a pedestrian detection approach using spatial histograms of oriented gradients feature. As spatial histograms of oriented gradients consist of marginal distributions of an image over local and global patches, they can preserve shape and contour of a pedestrian simultaneously. There are two main contributions in this paper. First of all, we expand the histograms of oriented gradients features from single-size to variable-size which can capture local and global feature of pedestrian automatically. We call theses feature as the "spatial histograms of oriented gradients". Secondly, we employ histogram similarity and Fisher criterion to measure discriminability of features and select some discriminative features to identify the pedestrian. SVM classifier is constructed to train the selected features from target and surrounding background. The proposed algorithm is tested on some public database. Experimental results show that the proposed approach is efficient and rapid in pedestrian detection.

关键词： Robustness Conferences Histograms Support vector machines Shape Intelligent transportation systems Sun Support vector machine classification Computer vision Image edge detection

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Using convolutional neural network with asymmetrical kernels to predict speed of elevated highway 2nd

Using convolutional neural network with asymmetrical kernels...

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Ling, Jiawei Cheng, Jiujun Tang, Keshuang Li, Xin 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 Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. Pudong Shanghai China

ISBN: (纸本)9783319681207

In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn the temporal features of traffic speed data in a hierarchical way, an improved convolutional neural network (CNN) with asymmetric kernels is proposed. Speed data are collected from loop detectors of Yan’an elevated highway of Shanghai. To test the performance of the presented method, we compare it with some conventional approaches of traffic speed estimation. Experimental results demonstrate that our method outperforms all of them. © IFIP International Federation for Information Processing 2017.

关键词： Speed

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On iterative liveness-enforcement for a class of generalized Petri nets

On iterative liveness-enforcement for a class of generalized...

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2012 IEEE International Conference on Automation Science and Engineering: Green Automation Toward a Sustainable Society, CASE 2012

作者： Hou, Yifan Liu, Ding Zhou, Mengchu School of Electro-Mechanical Engineering Xidian University No. 2 South Taibai Road Xi'an 710071 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781467304283

This work presents an iterative liveness-enforcing method for a class of generalized Petri nets, which can model flexible manufacturing systems. The proposed method checks the liveness of net models using mixed integer programming and controls the token allocations of resource places instead of siphons using a liveness and resource usage ratio-enforcing supervisor. The enumeration of a kind of special structures, which is required in the previous work, is avoided and the number of iterations is bounded by the number of shared resource places in the net model. All strict minimal siphons in the controlled systems are minimally controlled. Several explanatory examples are used to illustrate this method. © 2012 IEEE.

关键词： Petri nets

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Observer-based controller design for networked systems with multiple packet losses and mixed delay

Observer-based controller design for networked systems with ...

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第27届中国控制与决策会议

作者： Fengfeng Qian Huaicheng Yan Hongbo Shi Hao Zhang 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 EducationTongji University

The paper focuses on a stabilizing controller design problem for networked systems with quantization,mixed delays,and a series of packet losses due to the signal transmission through the unreliable communication *** of both sensor-to-controller and controller-to-actuator are taken into account for the networked systems,and the distributed time delay is also considered in the network *** conditions for designing the controller as well as the system parameters can be obtained by solving certain linear matrix ***,the effectiveness of the designed method is proved by a numerical example.

关键词： quantization,mixed delays,multiple packet losses

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Rough Sets and Knowledge Technology: 9th International Conference, RSKT 2014, Shanghai, China, October 24-26, 2014, Proceedings 1

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丛书名： Lecture Notes in Computer Science 8818 Lecture Notes in Artificial Intelligence

2014年

作者： Duoqian Miao, Witold Pedrycz, Dominik Ślȩzak, Georg Peters, Qinghua Hu, Ruizhi Wang (eds.)

ISBN: (数字)9783319117409

ISBN: (纸本)9783319117393;9783319117409

This book constitutes the thoroughly refereed conference proceedings of the 9th International Conference on Rough Sets and Knowledge Technology, RSKT 2014, held in Shanghai, China, in October 2014. The 70 papers presented were carefully reviewed and selected from 162 submissions. The papers in this volume cover topics such as foundations and generalizations of rough sets, attribute reduction and feature selection, applications of rough sets, intelligent systems and applications, knowledge technology, domain-oriented data-driven data mining, uncertainty in granular computing, advances in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.

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Novel Clustering-Based Approach for Local Outlier Detection

Novel Clustering-Based Approach for Local Outlier Detection

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IEEE Conference on Computer Communications Workshops

作者： Haizhou Du Shengjie Zhao Daqiang Zhang Jinsong Wu Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Software Engineering Tongji University Department of Electrical Engineering Universidad de Chile

ISBN: (纸本)9781467399562

With the rapid expansion of data scale, big data mining and analysis have attracted increasing attention. Outlier detection as an important task of data mining is widely used in many applications. However, conventional outlier detection methods have difficulty handling large-scale datasets. In addition, most of them typically can only identify global outliers and are over sensitive to parameters variation. In this paper, we propose a novel method for robust local outlier detection with statistical parameters, which incorporates the clustering-based ideas in dealing with big data. Firstly, this method finds some density peaks of dataset by 3σ standard. Secondly, each remaining data object in the dataset is assigned to the same cluster as its nearest neighbor of higher density. Finally, we use Chebyshev's inequality and density peak reachability to identify local outliers of each group. The experimental results demonstrate the efficiency and accuracy of the proposed method in identifying both global and local outliers. Moreover, the method is also proved to be more stability analysis than typical outlier detection methods, such as LOF (Local Outlier Factor) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise).

关键词： Outlier detection Big data Data mining Clustering-based

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OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

OpenDrift: Online Evolving Fraud Detection for Open-Category...

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IEEE International Conference on Web services (ICWS)

作者： Cheng Wang Songyao Chai Hangyu Zhu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

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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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Implementation Analysis of Reachability Graph and Its Application in Net Learning

Implementation Analysis of Reachability Graph and Its Applic...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Mingjian Guang Chungang Yan Zihao Lu Rui Duan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Reachability represents the most fundamental dynamic property of Petri nets. The reachability graph serves as a crucial tool for analyzing reachability, and its generation algorithm has garnered widespread attention. However, most of the existing work has focused primarily on the accuracy of the generation algorithm, often overlooking computational and time costs. Therefore, this paper concentrates on the implementation of reachability graphs and proposes three implementation methods: definition-based (DB), matrix-based (MB), and a fast generation method without self-loops (FG). Through comprehensive experiments, we evaluate and analyze the running speed and applicable scenarios of these three methods. Furthermore, we have successfully applied the proposed methods in net learning.

关键词： Digital control Costs Accuracy Heuristic algorithms Petri nets Benchmark testing Computational efficiency

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