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arXiv 2018年

作者： He, Xudong Zhao, Junqiao Sun, Lu Huang, Yewei Zhang, Xinglian Li, Jun Ye, Chen Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07 m. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS. Copyright © 2018, The Authors. All rights reserved.

关键词： Mapping

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TiEV: The tongji intelligent electric vehicle in the intelligent vehicle future challenge of China

arXiv

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arXiv 2018年

作者： Zhao, Junqiao Ye, Chen Wu, Yan Guan, Linting Cai, Lewen Sun, Lu Yang, Tao He, Xudong Li, Jun Ding, Yongchao Zhang, Xinglian Wang, Xinchen Huang, Jinlin Zhang, Enwei Huang, Yewei Jiang, Wei Zhang, Shaoming Xiong, Lu Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of electrics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

TiEV is an autonomous driving platform implemented by the Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, large-scale mapping and updating, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016, and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and discuss the future trends. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Classification of bearing faults combining compressive sampling, laplacian score, and support vector machine 43

Classification of bearing faults combining compressive sampl...

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43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017

作者： Ahmed, H.O.A. Dennis Wong, M.L. Nandi, A.K. Department of Electronic and Computer Engineering Brunei University London UxbridgeUB8 3PH United Kingdom Heriot-Watt University Malaysia Precinct 5 Putrajaya62200 Malaysia Key Laboratory of Embedded Systems and Service Computing College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9781538611272

Rolling element bearings have a pivotal role in rotating machine and their failures are the leading cause of more substantial failures in the machine. In response to their importance, there is a growing body of research looking at condition monitoring of rolling element bearings to avoid machine breakdowns. In this study, by taking advantages of Compressive Sampling (CS), Laplacian Score (LS) and Multi-class Support Vector Machine (MSVM), an intelligent method for rolling bearing fault classification is proposed The CS is used to obtain compressed samples of the raw vibration signals, and the LS is used to rank the features of the obtained compressed samples with respect to their importance and correlations with the core fault characteristics. Then, based on LS ranking, we selected a small amount of the most significant compressed samples to produce the features vector. Finally, classification performance using MSVM shows high classification accuracy with a significantly reduced feature set. © 2017 IEEE.

关键词： Compressed sensing

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General-to-Specialized Analysis Based on Deep Belief Network

General-to-Specialized Analysis Based on Deep Belief Network

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Asian Conference on Pattern Recognition (ACPR)

作者： Renjie Hu Lianghua He Yuqin Wang D. Hu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Key Laboratory of EMW Information Fudan University Shanghai CN

Recently, the deep neural network has achieved great performance in many areas. During analysis, all learned features are used at once, some of which could bring negative affect to specific classes. Recently, cognitive studies show that a human visual cognition process is hierarchical and dynamic, i.e., when meeting different targets, human brain intends to pay attention to different parts. Therefore, in this paper, we introduce this kind of mechanism into deep belief network (DBN) and propose a new general-to-specialized algorithm. Firstly, hierarchical knowledge networks are constructed based on the original learned DBN through pruning and retraining. Because these networks are learned for different discriminate ability, we call them as the general network and the specialized network separately. Secondly, a general-to-specialized analysis method is proposed which is proved theoretically. When predicting the class of an input sample, we select the corresponding specialized networks according to the preliminary analysis result and then make in-depth analysis. Experiments on four benchmark datasets are performed to test the proposed algorithm. The results show that our algorithm is feasible, valid and robust.

关键词： Training Visualization Feature extraction Prediction algorithms Biological neural networks Cognition Recurrent neural networks

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Adaptive morphological reconstruction for seeded image segmentation

arXiv

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arXiv 2019年

作者： Lei, Tao Jia, Xiaohong Liu, Tongliang Liu, Shigang Meng, Hongying Nandi, Asoke K. School of Electronical and Information Engineering Shaanxi University of Science and Technology Xi’an710021 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Electronical and Information Engineering Shaanxi University of Science and Technology Xi’an710021 China School of Computer Science University of Sydney Sydney Australia School of Computer Science Shaanxi Normal University Xi’an710119 China Department of Electronic and Computer Engineering Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom Department of Electronic and Computer Engineering Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Col- lege of Electronic and Information Engineering Tongji University Shanghai200092 China

—Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving meaningful ones. Secondly, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that AMR is useful for improving algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time. Source code is available at https://***/SUST-reynole/AMR. Copyright © 2019, The Authors. All rights reserved.

关键词： Image segmentation

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A QUANTUM MULTI-AGENT BASED NEURAL NETWORK MODEL FOR FAILURE PREDICTION

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Journal of systems Science and systems Engineering 2016年第2期25卷 210-228页

作者： Wei Wu Min Liu Qing Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai 200092 China

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.

关键词： Failure prediction complex equipment quantum-inspired multi-agent algorithm back propagation neural network

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Image captioning with deep LSTM based on sequential residual

Image captioning with deep LSTM based on sequential residual

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Kaisheng Xu Hanli Wang Pengjie Tang Tongji University Shanghai Shanghai CN Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai CN Key Laboratory of Embedded System and Service Computing Tongji University Shanghai P. R. China Tongji University Shanghai P. R. China

ISBN: (纸本)9781509060689

Image captioning is a fundamental task which requires semantic understanding of images and the ability of generating description sentences with proper and correct structure. In consideration of the problem that language models are always shallow in modern image caption frameworks, a deep residual recurrent neural network is proposed in this work with the following two contributions. First, an easy-to-train deep stacked Long Short Term Memory (LSTM) language model is designed to learn the residual function of output distributions by adding identity mappings to multi-layer LSTMs. Second, in order to overcome the over-fitting problem caused by larger-scale parameters in deeper LSTM networks, a novel temporal Dropout method is proposed into LSTM. The experimental results on the benchmark MSCOCO and Flickr30K datasets demonstrate that the proposed model achieves the state-of-the-art performances with 101.1 in CIDEr on MSCOCO and 22.9 in B-4 on Flickr30K, respectively.

关键词： Computational modeling Feature extraction Semantics Training Logic gates Recurrent neural networks

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Cluster Synchronization of Complex Networks With Unbounded Time-varying Delays 35

Cluster Synchronization of Complex Networks With Unbounded T...

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第35届中国控制会议

作者： LIU Xiwei CHEN Wenwen Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9789881563910

In this paper, we investigate the cluster synchronization problem with unbounded time-varying delays for complex networks by adding some external controllers. Previous related works mainly focused on bounded time-varying or constant time delays, which may not be consistent with the real world. Therefore, unbounded time-varying delays is considered in this paper,which can be regarded as the main difference between this paper and previous related works. We discuss the necessary condition for cluster synchronization with external controllers, which can be used to guide the design of external controllers. Then, by using the Lyapunov function method, we prove that cluster synchronization could be achieved under some sufficient ***, the effects of time delays for the convergence of cluster synchronization are also discussed, which are named as theμ-cluster synchronization. Finally, numerical simulations are presented to show the validity of the obtained criteria.

关键词： Cluster Synchronization Complex Networks Unbounded Delay

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Finite-Time Synchronization of Nonlinearly Coupled systems With Delay 35

Finite-Time Synchronization of Nonlinearly Coupled Systems W...

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第35届中国控制会议

作者： LIU Xiwei LI Ping Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9789881563910

In this paper, we consider a general and practical complex network model, in which there exist couplings with and without time delays, and the coupled functions among nodes are also nonlinear. For this network model, we investigate its finite-time synchronization by designing some general static and adaptive controllers respectively. Using the finite-time stability theory, some useful finite-time synchronization criteria are derived. Finally, numerical simulations are given to demonstrate the correctness of our theoretical results.

关键词： Synchronization Finite-time Nonlinear Coupling Time Delay

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Traffic parameters prediction using a three-channel convolutional neural network 2nd

Traffic parameters prediction using a three-channel convolut...

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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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