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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 recent survey on controllable text generation: A causal perspective

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Fundamental Research 2024年第3期5卷 1194-1203页

作者： Wang, Junli Zhang, Chenyang Zhang, Dongyu Tong, Haibo Yan, Chungang Jiang, Changjun Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai 201804 China National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai 201804 China

As an important subject of natural language generation, Controllable Text Generation (CTG) focuses on integrating additional constraints and controls while generating texts and has attracted a lot of attention. Existing controllable text generation approaches mainly capture the statistical association implied within training texts, but generated texts lack causality consideration. This paper intends to review recent CTG approaches from a causal perspective. Firstly, according to previous research on basic types of CTG models, it is discovered that their essence is to obtain the association, and then four kinds of challenges caused by absence of causality are introduced. Next, this paper reviews the improvements to address these challenges from four aspects, namely representation disentanglement, causal inference, knowledge enhancement and multi-aspect CTG respectively. Additionally, this paper inspects existing evaluations of CTG, especially evaluations for causality of CTG. Finally, this review discusses some future research directions for the causality improvement of CTG and makes a conclusion. © 2024

关键词： Causal inference Causality Controllable text generation Knowledge enhancement Representation disentanglement

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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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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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An Improved Foreground Object Detection Method Based on Gaussian Mixture Models

An Improved Foreground Object Detection Method Based on Gaus...

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International Conference on Multimedia Communications (Mediacom)

作者： Xiayi Zhang Fuqiang Liu Zhipeng Li Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

Statistical background subtraction has proved to be a robust and effective approach for segmenting and extracting objects without any prior information of the foreground objects. This paper presents two contributions on this topic. The first contribution of this paper proposes a novel approach which introduces the motion mask into the Gaussian Mixture Models to reduce the errors of classical GMMs, which always classifies the moving objects as background incorrectly, and affects the accuracy of the steps followed by, when the objects are still in long periods. The second contribution regards the connected component labeling based on the contour tracking algorithm. Experimental results validate the effectiveness of the proposed approach.

关键词： Pixel Labeling Proposals Noise Adaptation model Video sequences Real time systems

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Path restoring algorithm based on vehicle trajectory mining

Path restoring algorithm based on vehicle trajectory mining

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Yuanhui Sun Yaying Zhang Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781467395922

Path restoring is a path searching problem in the time-dependent road network with the time constraints of origin and destination. This paper proposes a path restoring algorithm to find the possible path that vehicles may have been driving along. We mined the vehicle trajectories based on historical GPS data and then build a “popular” intersection graph based on its entropy and frequency. Then the restoring path is searched on the sub-graph of the popular intersection graph. The experiment result shows that the proposed algorithm increases 10% compared to that of using time-dependent fastest path method.

关键词： Vehicles Trajectory Roads Entropy TV Heuristic algorithms Algorithm design and analysis

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Optimal stopping theory based fast coding tree unit decision for high efficiency video coding

Optimal stopping theory based fast coding tree unit decision...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Xiuzhe Wu Hanli Wang Zhihua Wei Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781509053179

High Efficiency Video Coding (HEVC) is the most recent video coding standard aiming to further reduce the bitrate over 50% as compared to the state-of-the-art H.264/Advanced Video Coding under the same visual quality. In order to achieve this, a number of advanced coding techniques have been adopted in HEVC, including the quadtree structure of Coding Unit (CU), Prediction Unit (PU) and Transform Unit (TU), etc. However, these coding techniques lead to a tremendous increase in HEVC encoding computations. In order to reduce the HEVC encoding computational complexity, the optimal stopping theory is employed herein to design an efficient algorithm to optimize the decision making process when choosing the best coding parameters of CU, PU and TU. Extensive comparative experimental results are performed by the proposed algorithm and another two recent works, which demonstrate that the proposed algorithm is very efficient and better in reducing the HEVC encoding computations while keeping the video quality and compression efficiency almost intact.

关键词： Encoding Decision making Algorithm design and analysis Optimization High efficiency video coding Bit rate

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Modeling and Control of Urban Expressways with Emergency using Hybrid Petri Nets

Modeling and Control of Urban Expressways with Emergency usi...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Yaying Zhang Yuefeng Fu Wei Qiang Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781509018987

The urban expressway system model based on hybrid petri nets is proposed. With the traffic detecting cameras and coils, the strategies of traffic signal lights and warning lights are enforced with the aim to prevent the large-scale congestion in emergencies (e.g. accidents). The simulation results verify the effectiveness of the proposed model and the warning light strategy is demonstrated to be suitable for traffic accidents and congestion in the accident-prone weaving sections in the urban expressways.

关键词： warning light strategy Hybrid petri nets urban expressway model emergency control

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Using estimation of distribution algorithm to coordinate decentralized learning automata for meta-task scheduling

Using estimation of distribution algorithm to coordinate dec...

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Congress on Evolutionary Computation

作者： Jie Li Junqi Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Shanghai CN

Learning automaton (LA) is a reinforcement learning model that aims to determine the optimal action out of a set of actions. It is characterized by updating a selection probability vector through a sequence of repetitive feedback cycles interacting with an environment. Decentralized learning automata (DLAs) consists of many learning automata (LAs) that learn at the same time. Each LA independently selects an action based on its own selection probability vector. In order to provide an appropriate central coordination mechanism in DLAs, this paper proposes a novel decentralized coordination learning automaton (DCLA) using a new selection probability vector which is combined with the probability vectors derived from both LA and estimation of distribution algorithm (EDA). LA contributes to the own learning experience of each LA while EDA estimates the distribution of the whole swarm's promising individuals. Thus, decentralized LAs can be coordinated by EDA using the swarm's comprehensive knowledge. The proposed automaton is applied to solve the real problem of meta-task scheduling in heterogeneous computing system. Extensive experiments demonstrate a superiority of DCLA over other counterpart algorithms. The results show that the proposed DCLA provides an effective and efficient way to coordinate LAs for solving complicated problems.

关键词： Vectors Learning automata Sociology Statistics Processor scheduling Scheduling Estimation

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DSP Assembler Auto-Generation Technique and ToolChain Integration 21

DSP Assembler Auto-Generation Technique and ToolChain Integr...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Wang, Wanli Wu, Jun Ren, Haoqi Zhang, Zhifeng Tongji University Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Fudan University School of Computer Science Shanghai200438 China

ISBN: (纸本)9781665432061

To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the architecture description language, and migrate the toolchain to adapt to the specified processor architecture. The algorithm uses the DSP architecture description as input to generate intermediate expressions, then uses lexical analysis and syntax analysis tools to migrate the assembly toolchain. In addition, an integrated development environment is developed based on Eclipse, which creates a friendly human-computer interface. Experiments show that this toolchain generation algorithm runs fast and accurately, which solves problems of the development and application of the DSP toolchain, and creates favorable conditions for the promotion of DSP. © 2021 IEEE.

关键词： Syntactics

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