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arXiv 2019年

作者： Liu, Xiwei Li, Zihan Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

In this paper, we studied the finite time anti-synchronization of master-slave coupled complex-valued neural networks (CVNNs) with bounded asynchronous time-varying delays. With the decomposing technique and the generalized {ξ, ∞}-norm, several criteria for ensuring the finite-time anti-synchronization are obtained. The whole anti-synchronization process can be divided into two parts: first, the norm of each error state component will change from initial values to 1 in finite time, then from 1 to 0 in fixed time. Therefore, the whole time is finite. Finally, one typical numerical example is presented to demonstrate the correctness of our obtained results. Key words: Anti-synchronization, asynchronous, complex-valued neural network, finite time, time-varying delay Copyright © 2019, The Authors. All rights reserved.

关键词： Time varying control systems

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A parallel computing model for container terminal logistics

A parallel computing model for container terminal logistics

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Bin Li Weiming Shen School of Transportation Fujian University of Technology Fuzhou China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Existing planning and scheduling solutions for container terminal logistics systems (CTLS) are not sufficient today due to the highly complexity and uncertain environments. This paper reviews the advantages and shortcomings of the existing solutions and proposes a container terminal conceptual parallel computing model for scheduling and execution based on multi-processor systems. It is built on the computational architecture and fundamental principles of distributed, cooperative, parallel, heterogeneous, and reconfigurable computation in essence. The proposed approach is demonstrated and validated by investigating the stress testing, tailor-made processor affinity, load migration and load balancing of a typical container terminal logistics service case with comprehensive computational experiments.

关键词： Containers Computational modeling Processor scheduling Computer architecture Logistics Marine vehicles

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Global µ-stability and finite-time control of octonion-valued neural networks with unbounded delays✡

arXiv

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arXiv 2020年

作者： Wang, Jingzhu Liu, Xiwei Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

Octonion-valued neural networks (OVNNs) are a type of neural networks for which the states and weights are octonions. In this paper, the global µ-stability and finite-time stability problems for octonion-valued neural networks are considered under unbounded and asynchronous time-varying delays. To avoid the non-communicative and non-associative multiplication feature of the octonions, we firstly decompose the OVNNs into eight real-valued neural networks (RVNNs) equivalently. Through the use of generalized norm and the Cauchy convergence principle, we obtain the sufficient criteria which assure the existence, uniqueness of the equilibrium point and global µ-stability of OVNNs. By adding controllers, the criteria to ensure the finite-time stability for OVNNs are presented by dividing the analysis of finite-time stability process into two phases. Furthermore, we also prove the adaptive finite time stability theory of above networks. At last, the simulation results of specified examples is given to substantiate the effectiveness and correctness of the theoretical results. Copyright © 2020, The Authors. All rights reserved.

关键词： Timing circuits

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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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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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An optimized Apriori algorithm based on sparse matrix for intrusion detection

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Open Cybernetics and systemics Journal 2014年第1期8卷 8-11页

作者： Zeng, Xiaohui Lv, Jingxiang Li, Jingzhong Luo, Wenlang Dept. of Computer Science and Technology Jinggangshan University Ji’an China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

The original Apriori algorithm is widely used in the intrusion detection field, but it may consume incredible computing resources in the process of handling network packets. We propose our optimized-Apriori algorithm which can greatly improves the algorithm efficiency by means of reducing the data storage space and the number of frequent item sets. We take full advantage of the characteristics of sparse matrix to save storage space and shorten the running time. The experimental results show that our optimized-Apriori algorithm can make a larger progress in saving the storage space of network data to be processed, even shortening nearly half of the data mining time in the process of intrusion detection. © Zeng et al.

关键词： Data mining

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Extracting all minimal siphons from maximal unmarked siphons in manufacturing-oriented Petri nets

Extracting all minimal siphons from maximal unmarked siphons...

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2011 7th IEEE International Conference on Automation Science and Engineering, CASE 2011

作者： Wang, ShouGuang Zhou, MengChu Wang, ChengYing College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou 310018 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102-1982 United States MoE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 200092 China

ISBN: (纸本)9781457717307

Deadlock control is an important research issue in automated manufacturing systems that have a high degree of resource sharing and concurrency. Since minimal siphons are closely tied with deadlocks in Petri net models, their efficient extraction is fundamentally important. The existing methods can rapidly extract one minimal siphon given a maximal unmarked siphon that is obtained by using a mixed integer programming approach. This paper for the first time presents an extraction algorithm that can efficiently extract all minimal ones. The idea is based on the generation and use of a subnet tree structure given the places in a maximal unmarked siphon. Several Petri net models of automated manufacturing systems are used to illustrate the proposed concepts and methods. © 2011 IEEE.

关键词： Petri nets

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Scheduling of time constrained dual-arm cluster tools with wafer revisiting

Scheduling of time constrained dual-arm cluster tools with w...

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2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Qiao, Yan Wu, Naiqi Zhou, Mengchu Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou 510006 China Macau University of Science and Technology China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102-1982 United States

ISBN: (纸本)9781479915156

This work intends to schedule a dual-arm cluster tool for the atomic layer deposition (ALD) process with wafer residency time constraints. ALD is a typical wafer revisiting process. Based on the analysis of system properties, a novel scheduling strategy called modified 1-wafer cyclic scheduling is derived. With this strategy, necessary and sufficient schedulability conditions are established. If schedulable, scheduling algorithms are presented to obtain a feasible and optimal schedule. By the proposed method, a schedule can be found by just simple calculation. An illustrative example is given to show the application of the proposed approach. © 2013 IEEE.

关键词： Atomic layer deposition

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Hierarchical attention generative adversarial networks for cross-domain sentiment classification

arXiv

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arXiv 2019年

作者： Zhang, Yuebing Miao, Duoqian Wang, Jiaqi Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification. Due to the domain discrepancy, a sentiment classifier trained on source domain data may not works well on target domain data. In recent years, many researchers have used deep neural network models for cross-domain sentiment classification task, many of which use Gradient Reversal Layer (GRL) to design an adversarial network structure to train a domain-shared sentiment classifier. Different from those methods, we proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which alternately trains a generator and a discriminator in order to produce a document representation which is sentimentdistinguishable but domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a sentence into the document representation. In addition, the HAGAN model use hierarchical attention mechanism to optimize the document representation and automatically capture the pivots and non-pivots. The experiments on Amazon review dataset show the effectiveness of HAGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Bridge the Gap Between Network-Based Inference Method and Global Ranking Method in Personal Recommendation

Bridge the Gap Between Network-Based Inference Method and Gl...

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IEEE Conference on Industrial Electronics and Applications

作者： Xiwei Liu Department of Computer Science and Technology Tongji University China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 200092 China

In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the... 详细信息

ISBN: (纸本)9781467386456

关键词： Eigenvalues and eigenfunctions Heating Bridges Inference algorithms Prediction algorithms Conferences Industrial electronics

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