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GCIS, WRI Global Congress on Intelligent systems

作者： Jianzhu Cui Fuqiang Liu Zhipeng Li Jing Li The Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

This paper presents a novel unsupervised learning framework named image retrieval based on manifold learning and incorporate clustering. The dimensionality of image descriptors used in image retrieval applications is quite high. Given a query image, our algorithm first makes use of manifold learning (LPP) for dimensionality reduction and manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly. Then we use the similarities among target images for improving the performance of the image retrieval systems by cluster-based retrieval of images by unsupervised learning. Our algorithm retrieves image clusters as retrieval results by applying K-means clustering algorithm to a collection of images collected by manifold ranking algorithm. Experimental results on a general-purpose image database show that our algorithm attains a significant improvement over existing systems.

关键词： Image retrieval Clustering algorithms Feedback Unsupervised learning Support vector machines Support vector machine classification Image databases Content based retrieval Intelligent systems Laboratories

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A Traffic Flow Prediction Approach: LSTM with Detrending

A Traffic Flow Prediction Approach: LSTM with Detrending

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Zheng Zhao Yaying Zhang The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781538676738;9781538676721

Traffic flow prediction plays a key role in many Intelligent Transportation system research and applications. It aims to forecast the forthcoming traffic conditions with the help of historical data. Urban traffic always has its morning and afternoon peak hours. We also observed that the urban traffic flow can always be divided into main trend data and its residual part. The main trend data presents a similar trend on different days. The residual data is time-variant part which reflects the short-term fluctuation of traffic condition over each day. Enlighted by detrending, Principal Component Analysis (PCA) method is applied to extract the main trend data in this paper. The residual data is obtained by subtracting the main trend data from the overall traffic flow data. Then Long Short-Term Memory (LSTM) model is proposed to predict the residual data. With main trend data and predicted residual data, the urban traffic flow can be predicted by the joint PCA and LSTM approach. Finally, the empirical study demonstrates the propose method outperforms similar traffic prediction models.

关键词： Market research Predictive models Principal component analysis Logic gates Roads Feature extraction Artificial neural networks

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Accelerating Support Vector Machine Learning with GPU-Based MapReduce

Accelerating Support Vector Machine Learning with GPU-Based ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Tianyao Sun Hanli Wang Yun Shen Jun Wu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781479986989

With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational techniques can be employed such as the Graphics Processing Units (GPUs) and MapReduce model. As it is well known, GPUs are microprocessors on a multi-core architecture which reveal high performance in mass data parallel computing, and MapReduce allows computational tasks to be divided into a plurality of parts, distributed to various computing nodes and combined on a single node. In this paper, we propose a GPU-based MapReduce framework to accelerate SVM learning by jointly utilizing the parallel computing power of GPU and MapReduce. Extensive experimental results have verified the effectiveness and efficiency of the proposed approach.

关键词： Graphics processing units Support vector machines Kernel Training Optimization Acceleration Parallel processing

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Real-Time Communication Method for mHealth Base on Extended XMPP Protocol

Real-Time Communication Method for mHealth Base on Extended ...

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The 9th International Conference on Wireless Communications,Networking and Mobile Computing(WICOM 2013)

作者： Chenjie Shi Yu Fang The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

Considering characteristic of mHealth communication and problems of existing methods, this paper presents a real-time communication method for mHealth based on extended XMPP protocol. The method can maintain the role status efficiently and reduce data latency during the communication process. Meanwhile, it can be extended flexibly to meet increasing communication demands of mHealth services. Furthermore, a system framework is presented to support telemonitoring scene. Finally, system implementation and feasibility tests verify the effectiveness of the method and framework.

关键词： mHealth XMPP Protocol Real-Time Communication

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Adopting WirelessHART for In-Vehicle-Networking

Adopting WirelessHART for In-Vehicle-Networking

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IEEE International Symposium on Cyberspace Safety and Security

作者： Zhuo Bi Deji Chen Cheng Wang Changjun Jiang Ming Chen Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

ISBN: (纸本)9781479989386

It is estimated that the breakthrough in the broad deployment of Internet of Things (IoT) could come from smart cars. Indeed, we have seen multi-facet advances around cars: new material, in vehicle infotainment, driverless cars, smart transportation, electrical vehicles, etc. However, in-vehicle-networking has been mainly by wire;the wiring for a car is largely prebuilt during the design phase. With more and more things networked within a car, wiring has taken up 1-2 percent of the total weight. This translates into burning up to 0.1 kilogram fuel over 100 kilometers. On the other hand, the advances in wireless technology, especially the broad acceptance of WirelssHART in the industrial settings, has proved its capability in harsh environments. This paper studies what could happen if we use WirelessHART mesh network for in-vehicle communication. While new wireless network protocols are needed to perform the task of CAN, the dominant in-vehicle fieldbus, WirelessHART could take on the work performed by LIN, the fieldbus for peripheral devices. A detailed study is provided to compare these buses. Road tests were performed, in which a WirelessHART network keeps running for the whole 20 minute period.

关键词： Reliable Wireless Sensor Network Smart Car CAN LIN WirelessHART

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A Method to Check Liveness of WS3PR 33

A Method to Check Liveness of WS3PR

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第三十三届中国控制会议

作者： WANG ShouGuang LIU Miao ZHOU MengChu WU WenHui IEEE School of Information & Electronic Engineering Zhejiang Gongshang University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University School of Electro-Mechanical Engineering Xidian University The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA

ISBN: (纸本)9781479947249

Petri nets are widely used to model flexible manufacturing systems(FMSs) because they can help analyze the properties and synthesize deadlock-free supervisory controllers of *** system of Simple Sequential Processes with Resources(WS3PR) is an important subclass of Petri nets that can well model many *** work first gives new algorithms to check liveness for a WS3 PR net via its subnet trees and *** the computation complexity for the proposed method is shown in this paper,to be polynomial under certain ***,sufficient conditions for deciding liveness of a WS3 PR are *** example is used to illustrate the results.

关键词： Liveness,flexible manufacturing system,deadlock,Petri net.

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Vehicle classification based on the fusion of deep network features and traditional features

Vehicle classification based on the fusion of deep network f...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Hua Qian Yaying Zhang Chunmei Liu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Consider to the defect of traditional features which have high empirical components. A vehicle classification algorithm based on the fusion of higher-layer features of a deep network and traditional features was proposed. Firstly, the traditional features of PHOG and LBP-EOH were extracted. Secondly, the higher-layer features excavated from the vehicle pictures by deep belief networks were added, making these three kinds of features together by feature fusion. Finally, support vector machine is used to train and classify the vehicle. When the number of training samples is large enough, the algorithm has a significant effect compared to those with traditional features. It can achieve the accuracy of 95% in the six categories of vehicles.

关键词： Vehicles Support vector machines Feature extraction Algorithm design and analysis

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Vehicle to Vehicle and Roadside Sensor Communication for Intelligent Navigation

Vehicle to Vehicle and Roadside Sensor Communication for Int...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Rongyan Xia Chen Ye Dongdong Zhang Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

Hybrid sensor network technology is a key component of future ITS applications. In this paper, we propose a hybrid architecture that combined VANETs and roadside WSNs for intelligent navigation. Different from traditional ITS application, Sensors in WSNs and VANETs are used to perceive and exchange roadside and vehicular information to support the intelligent navigating decision process. We firstly give the system and protocol architecture, and then we discuss the scenarios and use cases of our system in intelligent navigation. After that, we describe the software and hardware implementation of our prototype, conduct a simulation on the discussed scenarios, and present a detailed data communication experimental result to prove the feasibility of our prototype.

关键词： Vehicles Wireless sensor networks Navigation Data communication Roads Prototypes Path planning

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HSim: A novel method on similarity computation by hybrid measure

HSim: A novel method on similarity computation by hybrid mea...

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International Conference on Information and Communication systems (ICICS)

作者： Qin Zhao Cheng Wang Changjun Jiang The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781479973507

Link similarity is widely applied in measuring the similarity between objects, e.g., web pages, scientific papers and social networks. However, there are a lot of drawbacks in existing methods of measuring link similarity. In brief, these methods can not handle some semantic-similar content. Moreover, the computation of them are not accurate in some scenes. In this paper, we present a novel method of measuring link similarity called HSim. It introduces the semantic similarity to calculate the similarity between objects, and overcomes the drawback that existing methods ignore the semantic information of objects. We also develop a novel computation function to make the result of similarity more accurate.

关键词： Semantics Web pages Complexity theory Data mining Internet Ontologies

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Composite behavioral modeling for identity theft detection in online social networks

arXiv

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arXiv 2018年

作者： Wang, Cheng Yang, Bo Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education China

In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values 0:956 in Foursquare and 0:947 in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to 65:3% in Foursquare and 72:2% in Yelp with the corresponding disturbance rate (False Positive Rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns. Copyright © 2018, The Authors. All rights reserved.

关键词： Behavioral research

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