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International Conference on Multimedia Technology

作者： Huichao Gao Feng Cen Weisheng Xu Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China College of Electronics and Information Engineering University of Tongji Shanghai China

ISBN: (纸本)9781612847719

This paper proposes a novel adaptive interpolation filter (AIF) to reduce computational complexity and bit-rate of video coding. In the proposed AIF, the filter coefficients of B frames, which are predicted from those of adjacent P frames with a novel parabolic model, are unnecessary to transmit. Experimental results show that the coding complexity is reduced by 40.22% with no penalty on coding efficiency and 1.59% bit rate reduction is achieved at low bit-rate against conventional AIF.

关键词： Interpolation Wiener filter Adaptive filters Encoding Information filters Complexity theory

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A service selection method based on web service clusters

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Journal of Applied Sciences 2013年第24期13卷 5734-5738页

作者： Liu, Wei Du, Yu-Yue Yan, Chun College of Information Science and Engineering Shandong University of Science and Technology 266590 Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University 200092 Shanghai China

service selection is an essential issue in service discovery based on Web service clusters. The ratio of trust value to price is introduced to select the most suitable service in a cluster. The method of computing the initial trust, the direct trust and the recommendation trust are, respectively presented and the overall trust is derived based on them. The proposed method keeps the balance between the price and service quality. © 2013 Asian Network for Scientific Information.

关键词： Web services

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Sequential end-to-end network for efficient person search

arXiv

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arXiv 2021年

作者： Li, Zhengjia Miao, Duoqian Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

Person search aims at jointly solving Person Detection and Person Re-identification (re-ID). Existing works have designed end-to-end networks based on Faster R-CNN. However, due to the parallel structure of Faster R-CNN, the extracted features come from the low-quality proposals generated by the Region Proposal Network, rather than the detected high-quality bounding boxes. Person search is a fine-grained task and such inferior features will significantly reduce re-ID performance. To address this issue, we propose a Sequential End-to-end Network (SeqNet) to extract superior features. In SeqNet, detection and re-ID are considered as a progressive process and tackled with two sub-networks sequentially. In addition, we design a robust Context Bipartite Graph Matching (CBGM) algorithm to effectively employ context information as an important complementary cue for person matching. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method achieves state-of-the-art results. Also, our model runs at 11.5 fps on a single GPU and can be integrated into the existing end-to-end framework easily. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Recent advances of sensors for pacemakers

Recent advances of sensors for pacemakers

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IEEE International Conference on Networking, Sensing and Control

作者： Wei Vivien Shi MengChu Zhou ECE Department New Jersey Institute of Technology Newark NJ USA Key Laboratory of MoE for Embedded System and Service Computing University of Tongji Shanghai China

In this paper, the recent advances of sensors incorporated in pacemakers are presented. Based on the functions and operations of modern pacemakers, a variety of sensors are described with their rationales, features and applications. Further improvements and future trend of body sensors are pointed out. The comparison of the sensors' features is performed.

关键词： Sensors Pacemakers Heart rate Ventilation Acceleration Accelerometers Biomedical monitoring

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Properties analysis of substitution of logical workow nets

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Journal of Computational Information systems 2014年第6期10卷 2491-2498页

作者： Liu, Wei Du, Yuyue Yan, Chun College of Information Science and Engineering Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

Logical workow nets (LWNs) provide a good tool for modeling business systems with passing value indeterminacy and batch processing function. However, complicated business processes modeling is a hard work. And thus, to facilitate modeling complex systems, the substitution operations of LWNs in this study are proposed. Substitution operations for control places and ordinary transitions of LWNs are presented and properties of substitution of LWNs are analyzed. The structure, boundedness and liveness of LWNs can be preserved after substitution. Copyright © 2014 Binary Information Press.

关键词： Batch data processing

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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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Construct pairwise test suites based on the Bak-Sneppen model of biological evolution

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World Academy of Science, Engineering and Technology 2009年 35卷 224-229页

作者： Yuan, Jianjun Jiang, Changjun Department of Computer Science and Technology The Key Lab. of 'Embedded System and Service Computing' Ministry of Education of China Tongji University Shanghai 201804 China

Pairwise testing, which requires that every combination of valid values of each pair of system factors be covered by at lease one test case, plays an important role in software testing since many faults are caused by unexpected 2-way interactions among system factors. Although meta-heuristic strategies like simulated annealing can generally discover smaller pairwise test suite, they may cost more time to perform search, compared with greedy algorithms. We propose a new method, improved Extremal Optimization (EO) based on the Bak-Sneppen (BS) model of biological evolution, for constructing pairwise test suites and define fitness function according to the requirement of improved EO. Experimental results show that improved EO gives similar size of resulting pairwise test suite and yields an 85% reduction in solution time over SA.

关键词： Simulated annealing

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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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Intelligent Condition Monitoring for Rotating Machinery Using Compressively-Sampled Data and Sub-space Learning Techniques

Intelligent Condition Monitoring for Rotating Machinery Usin...

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作者： Ahmed, H.O.A. Nandi, A.K. Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom The Key Laboratory of Embedded Systems and Service Computing College of Electronic and Information Engineering Tongji University Shanghai China

Rotating machines are widely used in industry. Unforeseen machine failures affect production schedules, product quality, and production costs. Therefore, condition monitoring of rotating machine can play an important role in machine availability. There is a growing number of methods for Machine Condition Monitoring (MCM). Yet, the performance of these methods is limited by the massive amounts of data need to be collected for MCM. This work proposes a computational method which can greatly reduce the high dimensional vibration dataset to a set of compressively-sampled measurements using Compressive Sampling (CS). Then, to learn fewer features from these compressively-sampled measurements we propose an effective multi-step feature learning algorithm that combines the advantages of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Canonical Correlation Analysis (CCA). Finally, with these learned features, we use multi-class Support Vector Machine (SVM) to classify machine health conditions. Experiments on a roller element bearing fault classification task based on vibration signals are used to evaluate the efficiency of the proposed method. The most obvious finding to emerge from this study is that we are able to achieve high classification accuracy even from highly reduced vibration signal measurements. Moreover, the efficiency of our proposed method outperforms some recently published results. The proposed method offers better accuracy and has lower costs in time and storage requirements. © 2019, Springer Nature Switzerland AG.

关键词： Compressed sensing

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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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