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Observable liveness of Petri nets with controllable and observable transitions

Science China(Information Sciences) 2017年第11期60卷 256-264页

作者： Guanjun LIU Changjun JIANG Department of Computer Science Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University E-commerce Transactions and Information Services Collaborative Innovation Center of Shanghai University Knowledge Service Platform Tongji University

Observability and/or controllability are paid much attention to by researchers not only from the control science field[1–3]but also from the computer science field[4–9].Recently,Desel and Kilinc[4]proposed the notion of observable liveness for Petri nets with(un-)observable and(un-)controllable *** purpose of defining observable liveness is to represent that"a user can always enforce the occurrence of any observable transition only by stimulating the net by choosing appropriate enabled controllable transition"[4].An important assumption of

关键词： net cot PN Observable liveness of Petri nets with controllable and observable transitions

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Identification based on PRT Gravity Tunnel of Continuous Electrocardiogram

Identification based on PRT Gravity Tunnel of Continuous Ele...

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International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery

作者： Min Dai Danyang Meng Feng Jia Miaoling Ma Key Laboratory of Computer Vision and System Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781538680988;9781538680971

An electrocardiogram (ECG) identification method is proposed based on PRT triangle gravity tunnel of continuous electrocardiogram. The PRT center of gravity is defined by the P, R, T fiducial points in ECG signal, and the gravity tunnel of continuous PRT points is described as a matrix. The European distance is used to determine the similarity between two matrices. The performance of the proposed method is tested on MIT-BIH Database and the ECG data collected in practical environment from 25 healthy college students. Several group experiments were carried out and the average recognition accuracy is 99.87%. Comparing with the traditional correlation coefficient method, experimental results show that the average recognition accuracy is increased 1.66% when using the proposed method.

关键词： ECG identification Feature selection PRT gravity ECG Tunnel

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Multi-task variational information bottleneck

arXiv

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arXiv 2020年

作者： Qian, Weizhu Chen, Bowei Zhang, Yichao Wen, Guanghui Gechter, Franck Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté France Adam Smith Business School University of Glasgow United Kingdom Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai200092 China Department of Systems Science School of Mathematics Southeast University Nanjing210016 China School of Engineering RMIT University MelbourneVIC3000 Australia Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté and Mosel LORIA UMR CNRS Université de Lorraine 7503 France

Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence. Its applications to computer vision, signal processing, and speech recognition are ubiquitous. Although this subject has attracted considerable attention recently, the performance and robustness of the existing models to different tasks have not been well balanced. This article proposes an MTL model based on the architecture of the variational information bottleneck (VIB), which can provide a more effective latent representation of the input features for the downstream tasks. Extensive observations on three public data sets under adversarial attacks show that the proposed model is competitive to the state-of-the-art algorithms concerning the prediction accuracy. Experimental results suggest that combining the VIB and the task-dependent uncertainties is a very effective way to abstract valid information from the input features for accomplishing multiple tasks. © 2020, CC BY.

关键词： Deep learning

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A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM Networks

A Multi-label Classifier for Human Protein Subcellular Local...

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud

A genetic algorithm enhanced automatic data flow management ...

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作者： Li, Siguang Huang, Zhengwen Han, Liangxiu Jiang, Changjun The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China College of Electrical Engineering Binzhou University Binzhou China Department of Electronic and Computer Engineering Brunel University London London United Kingdom School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester United Kingdom

The past few years have witnessed a rapid deployment of computing infrastructures in the cloud in support of data intensive applications. The effort of the existing works is mainly focused on data reusing mechanisms without considering data processing routes, which can significantly affect the computation costs when exchanging data among the computing node in the cloud. This paper presents a genetic algorithm enhanced Automatic Data Flow Management Solution (ADFMS) that facilitates automatic routing function and a self-adjustable intermediate data management mechanism to achieve an efficient data processing structure of cloud computing. Experimental results show that ADFMS optimizes costs in managing intermediate data in the cloud. © 2018 John Wiley & Sons, Ltd.

关键词： Petri nets

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CurveCluster: Automated Recognition of Hard Landing Patterns Based on QAR Curve Clustering

CurveCluster: Automated Recognition of Hard Landing Patterns...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Xu Li Jiaxing Shang Linjiang Zheng Dajiang Liu Lin Qi Liu Liu College of Computer Science Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University China College of Computer Science Chongqing University China Data Analysis Department Sichuan Hantai Technology Co. Ltd. Chengdu China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Flight safety is of vital importance to the aviation industry. As one of the most typical security events, hard landing is extremely concerned by airlines and related studies have received extensive attention in recent years. However, existing regression or risk based models either suffers from low prediction accuracy, or cannot provide good interpretability, making themselves impractical in real applications. To solve these problems, in this paper we propose CurveCluster: a curve clustering-based approach which is able to automatically recognize hard landing patterns from quick access recorder (QAR) data. We first provide a two-level hierarchical classification of hard landing events based on different hard landing patterns. Then we extract curve features from several key QAR parameters through interpolation and resampling. Finally, we apply K-means clustering algorithm on the curve features to automatically recognize the hard landing patterns. We test our approach on a dataset of 9,203 A320 flight QAR data samples and the overall recognition accuracy reaches up to 93.1%. Moreover, our results directly reflect the reasons of different types of hard landing events, which show strong interpretability.

关键词： Feature extraction Interpolation Atmospheric modeling Pattern recognition Aircraft Machine learning Safety

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Greedy signal space recovery algorithm with overcomplete dictionaries in compressive sensing

arXiv

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arXiv 2019年

作者： Zhu, Jianchen Zhao, Shengjie Shi, Qingjiang Arce, Gonzalo R. College of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Software Engineering Tongji University Shanghai201804 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Compressed sensing

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A Novel Mutual Information based Ant Colony Classifier 5

A Novel Mutual Information based Ant Colony Classifier

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2017 International Conference on Progress in Informatics and computing(PIC)

作者： Hang Yu Xiaoxiao Qian Yang Yu Jiujun Cheng Ying Yu Shangce Gao College of Computer Science and Technology Taizhou University Faculty of Engineering University of Toyama Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

ISBN: (纸本)9781538619797;9781538619780

By constructing a list of IF-THEN rules, the traditional ant colony optimization(ACO) has been successfully applied on data classification with not only a promising accuracy but also a user comprehensibility. However, as the collected data to be classified usually contain large volumes and redundant features, it is challenging to further improve the classification accuracy and meanwhile reduce the computational time for *** paper proposes a novel hybrid mutual information based ant colony algorithm(mrAM+) for classification. First, a maximum relevance minimum redundancy feature selection method is used to select the most informative and discriminative attributes in a dataset. Then, we use the enhanced ACO classifier(i.e., AM+)to perform the classification. Experimental results show that the proposed mrAM+ outperforms other seven state-of-art related classification algorithms in terms of accuracy and the size of model.

关键词： ant colony optimization mutual information classification data mining feature selection

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Clustering and Analysis of Household Power Load Based on HMM and Multi-factors

Clustering and Analysis of Household Power Load Based on HMM...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hao Fang Yue Zhang Min Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the advance of the information and communication technology, smart grid, and smart metering, residential electricity usage data are available for analyzing household usage pattern. However, most such usage pattern analyses have been based on smart meter data. Geographical location and environment factors have not been well considered. In order to have a better understanding of residential electricity usage pattern, this paper studies the usage pattern based on both environment data and smart meter data. A Hidden Markov Model (HMM) is applied to learn the consumption dynamic behavior under the corresponding environments and a clustering method is applied to discover the typical usage patterns. The environmental adaptation which indicates the household reaction to the environment during the electricity consumption is revealed.

关键词： Hidden Markov models Clustering algorithms Smart meters Meteorology Pattern analysis Analytical models Biological system modeling

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arXiv

引用

arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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