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Resilient event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor attacks.In the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal data.Then,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger event constraint

Surveys on the application of neural networks to event extraction

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The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 43-54,66页

作者： Duan Li Chen Lin Luo Bing School of Electronic Engineering Naval University of EngineeringWuhan 430033China

event extraction(EE)is a significant part of natural language information extraction,and it is widely adopted in other natural language processing(NLP)tasks such as question answering and machine reading comprehension.With the development of the NLP field,numerous datasets and approaches for EE are promoted,raising the need for a comprehensive review.In this paper,the resources for EE are reviewed,and then the numerous neural network models currently employed in EE tasks are classified into three types:Word sequence-based methods,graph-based neural network methods,and external knowledge-based approaches.And then the methods are compared and contrasted in detail,and their flaws and difficulties are analyzed with existing research in this survey.Finally,the future research tendency is discussed for EE.

关键词： event extraction natural language processing event extraction methods neural networks

event Detection and Identification of Influential Spreaders in Social Media Data Streams

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Big Data Mining and Analytics 2018年第1期1卷 34-46页

作者： Leilei Shi Yan Wu Lu Liu Xiang Sun Liang Jiang School of Computer Science and Telecommunication Engineering Jiangsu University Department of Computing and Mathematics University of Derby

Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying influential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which significantly reduces the efficiency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either influential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search(HITS) based Topic-Decision method(TD-HITS), and a Latent Dirichlet Allocation(LDA) based Three-Step model(TS-LDA). TDHITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify influential spreaders of hot event topics based on both post and user information.The experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying influential spreaders.

关键词： event detection microblogging Hypertext-Induced Topic Search(HITS) Latent Dirichlet Allocation(LDA) identification of influential spreader

Abnormal event detection via the analysis of multi-frame optical flow information

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Frontiers of Computer Science 2020年第2期14卷 304-313页

作者： Tian WANG Meina QIAO Aichun ZHU Guangcun SHAN Hichem SNOUSSI School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Computer Science and Technology Nanjing University of TechnologyNanjing 210094China School of Instrumentation Science and Opto-electronics Engineering Beihang UniversityBeijing 100191China Institute Charles Delaunay-LM2S-UMR STMR 6281 CNRS University of Technology of TroyesTroyes 10010France

Security surveillance of public scene is closely relevant to routine safety of individual.Under the stimulus of this concern,abnormal event detection is becoming one of the most important tasks in computer vision and video processing.In this paper,we propose a new algorithm to address the visual abnormal detection problem.Our algorithm decouples the problem into a feature descriptor extraction process,followed by an AutoEncoder based network called cascade deep AutoEncoder(CDA).The movement information is represented by a novel descriptor capturing the multi-frame optical flow information.And then,the feature descriptor of the normal samples is fed into the CDA network for training.Finally,the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure.We validate the proposed method on several video surveillance datasets.

关键词： abnormal event detection multi-frame optical flow cascade deep autoencoder

Discrete event System Framework for Fault Diagnosis with Measurement Inconsistency:Case Study of Rogue DHCP Attack

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 789-806页

作者： Mayank Agarwal Santosh Biswas Sukumar Nandi IEEE the Department of Computer Science and Engineering Indian Institute of Technology Guwahati

Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.

关键词： Fault diagnosis instrasion detection system (IDS) measurement inconsistent discrete event system (DES) rogue dynamic host configuration protocol (DHCP) server

Passive-event-Assisted Approach for the Localizability of Large-Scale Randomly Deployed Wireless Sensor Network

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Tsinghua Science and Technology 2019年第2期24卷 134-146页

作者： Zhiguo Chen Guifa Teng Xiaolei Zhou Tao Chen Collegeof Information Science and Technology Agricultural University of Hebei Nanjing Telecommunication Technology Research Institute National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology

Localizability in large-scale, randomly deployed Wireless Sensor Networks(WSNs) is a classic but challenging issue. To become localizable, WSNs normally require extensive adjustments or additional mobile nodes. To address this issue, we utilize occasional passive events to ease the burden of localization-oriented network adjustment. We prove the sufficient condition for node and network localizability and design corresponding algorithms to minimize the number of nodes for adjustment. The upper bound of the number of adjusted nodes is limited to the number of articulation nodes in a connected graph. The results of extensive simulations show that our approach greatly reduces the cost required for network adjustment and can thus provide better support for the localization of large-scale sparse networks than other approaches.

关键词： network localizability random deployment Wireless Sensor Networks (WSNs) passive event

Efficient Deviation Detection Between a Process Model and event Logs

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1352-1364页

作者： Lu Wang Yuyue Du Liang Qi the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and TechnologyQingdao 266590China

Business processes described by formal or semi-formal models are realized via information systems.event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.

关键词： Detect deviations event log model repair Petri net process model

A Retrospective Analysis on 1330Adverse event Reports of Qingkailing in China:Further Perception of Its Risks and Rational Use

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Current Medical Science 2018年第6期38卷 1103-1108页

作者： Bi-li WU Wen-xi HE Meng KE Xiao-fang SHANG-GUAN Guo-fang HE Rui HUANG Department of Oncology Tongii HospitalTongji Medical CollegeHuazhong Universityof Science and TechnologyWuhan 430030China Department of Pharmacy Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China School of Pharmacy Tongji Medical CollegeHuazhong University of Science and Technology.Wuhan 430030China

Qingkailing (QKL)is a modern preparation exploited according to the traditional Chinese medicine theory.It becomes the second leading cause of adverse drug events (ADEs)in all traditional Chinese medicine injections.The safety evaluation and rational use of QKL are of special importance.This retrospective study used data from Adverse Drug Reaction Monitoring Center of Hubei Province in China from January 2012 to December 2014.ADE cases induced by QKL were collected and analyzed according to patients'demographics,characteristics of drugs involved,characteristics of ADEs,causality,and outcomes.A total of 1330 qualified ADEs were included.Most ADEs occurred within 30 min after administration and the 0-10 years old age group had the highest number of ADEs.The common ADEs included anaphylactic reaction,dyspnea and nausea.Serious reactions accounted for 5.19%.Combination with cephalosporin (74/146,50.69%) caused more ADEs than other drugs did.Serious attention should be paid when QKL is used for children,and combination with cephalosporin should be avoided.

关键词： adverse drug event anaphylaxis drug safety Qingkailing

AED-Net:An Abnormal event Detection Network

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Engineering 2019年第5期5卷 930-939页

作者： Tian Wang Zichen Miao Yuxin Chen Yi Zhou Guangcun Shan Hichem Snoussi School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electronic Engineering Dalian Maritime UniversityDalian 116026China School of Instrumentation Science and Opto-electronic Engineering&International Research Institute for Multidisciplinary Science Beihang UniversityBeijing 100191China Institute Charles Delaunay-LM2S-UMR STMR 6281 CNRS University of Technology of TroyesTroyes 10010France

It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component analysis network(PCAnet)and kernel principal component analysis(kPCA),is proposed to address this problem.Using surveillance video sequences of different scenes as raw data,the PCAnet is trained to extract high-level semantics of the crowd’s situation.Next,kPCA,a one-class classifier,is trained to identify anomalies within the scene.In contrast to some prevailing deep learning methods,this framework is completely self-supervised because it utilizes only video sequences of a normal situation.Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota(UMN dataset)and Anomaly Detection dataset from University of California,San Diego(UCSD dataset),and competitive results that yield a better equal error rate(EER)and area under curve(AUC)than other state-of-the-art methods are observed.Furthermore,by adding a local response normalization(LRN)layer,we propose an improvement to the original AED-Net.The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity.

关键词： Abnormal events detection Abnormal event detection network Principal component analysis network Kernel principal component analysis