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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang provincial key laboratory of networking and intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

Text Sentiment Analysis based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure

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intelligent Automation & Soft computing 2023年第7期37卷 639-654页

作者： Zhengfang He Cristina E.Dumdumaya Ivy Kim D.Machica School of intelligent Science and Engineering Yunnan Technology and Business UniversityKunming650000China College of Information and computing University of Southeastern PhilippinesDavao CityDavao del SurPhilippines

Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective *** analy-sis algorithms include emotion lexicon,traditional machine learning,and deep *** the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too ***,this paper proposes a Bi-directional LSTM with a trapezoidal structure *** design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and *** classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep *** are two benefits to using the Bi-directional LSTM with a trapezoidal *** is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the *** is that using the trapezoidal structure can reduce the model’s *** paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for *** can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar ***,the trapezoidal structure model parameters are 35.75%less than the normal structure model.

关键词： Text sentiment Bi-directional LSTM Trapezoidal structure

Parallel-Data-based Social Evolution Modeling

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Tsinghua Science and Technology 2021年第6期26卷 878-885页

作者： Weishan Zhang Zhaoxiang Hou Xiao Wang Zhidong Xu Xin Liu Fei-Yue Wang China University of Petroleum Qingdao 266580China Qingdao Academy of intelligent Industry Qingdao 266111China State key laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100049China Institute of National Security National Defense UniversityBeijing 100081China

Abnormal or drastic changes in the natural environment may lead to unexpected events,such as tsunamis and earthquakes,which are becoming a major threat to national ***,no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster *** this study,we propose a social evolution modeling approach and construct a deduction model for self-playing,self-learning,and self-upgrading on the basis of the idea of parallel data and reinforcement *** proposed approach can evaluate the impact of an event,deduce the situation,and provide optimal strategies for *** the breakage of a submarine cable caused by earthquake as an example,we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.

关键词： paral el data reinforcement learning decision-making

Hierarchical Representations Feature Deep Learning for Face Recognition

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Journal of Data Analysis and Information Processing 2020年第3期8卷 195-227页

作者： Haijun Zhang Yinghui Chen Guangdong provincial key laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Moun-tainous Areas Meizhou China School of computing Jiaying University Meizhou China School of Mathematics Jiaying University Meizhou China

Most modern face recognition and classification systems mainly rely on hand-crafted image feature descriptors. In this paper, we propose a novel deep learning algorithm combining unsupervised and supervised learning named deep belief network embedded with Softmax regress (DBNESR) as a natural source for obtaining additional, complementary hierarchical representations, which helps to relieve us from the complicated hand-crafted feature-design step. DBNESR first learns hierarchical representations of feature by greedy layer-wise unsupervised learning in a feed-forward (bottom-up) and back-forward (top-down) manner and then makes more efficient recognition with Softmax regress by supervised learning. As a comparison with the algorithms only based on supervised learning, we again propose and design many kinds of classifiers: BP, HBPNNs, RBF, HRBFNNs, SVM and multiple classification decision fusion classifier (MCDFC)—hybrid HBPNNs-HRBFNNs-SVM classifier. The conducted experiments validate: Firstly, the proposed DBNESR is optimal for face recognition with the highest and most stable recognition rates;second, the algorithm combining unsupervised and supervised learning has better effect than all supervised learning algorithms;third, hybrid neural networks have better effect than single model neural network;fourth, the average recognition rate and variance of these algorithms in order of the largest to the smallest are respectively shown as DBNESR, MCDFC, SVM, HRBFNNs, RBF, HBPNNs, BP and BP, RBF, HBPNNs, HRBFNNs, SVM, MCDFC, DBNESR;at last, it reflects hierarchical representations of feature by DBNESR in terms of its capability of modeling hard artificial intelligent tasks.

关键词： Face Recognition Unsupervised Hierarchical Representations Hybrid Neural networks RBM Deep Belief network Deep Learning

Towards Privacy-Preserving Cloud Storage:A Blockchain Approach

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Computers, Materials & Continua 2021年第12期69卷 2903-2916页

作者： Jia-Shun Zhang Gang Xu Xiu-Bo Chen Haseeb Ahmad Xin Liu Wen Liu Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Inner Mongolia University of Science&Technology School of Information EngineeringBaotou014010China State key laboratory of Media Convergence and Communication Communication University of China School of Computer Science and Cybersecurity Communication University of China key laboratory of Convergent Media and intelligent Technology Communication University of China Ministry of EducationBeijing102204China

With the rapid development of cloud computing technology,cloud services have now become a new business model for information *** cloud server provides the IT resources required by customers in a selfservice manner through the network,realizing business expansion and rapid ***,due to the insufficient protection of data privacy,the problem of data privacy leakage in cloud storage is threatening cloud *** address the problem,we propose BC-PECK,a data protection scheme based on blockchain and public key searchable ***,all the data is protected by the encryption *** privacy data is encrypted and stored in a cloud server,while the ciphertext index is established by a public key searchable encryption scheme and stored on the ***,based on the characteristics of trusted execution of smart contract technology,a control mechanism for data accessing and sharing is *** transaction is automatically recorded on the blockchain,which is fairer under the premise of ensuring the privacy and security of the data sharing ***,we analyzed the security and fairness of the current *** the comparison with similar schemes,we have shown the advantages of the proposed scheme.

关键词： Blockchain privacy protection smart contract public encryption keyword search

Online Learning Behavior Analysis and Prediction based on Spiking Neural networks

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Journal of Social computing 2024年第2期5卷 180-193页

作者： Yanjing Li Xiaowei Wang Fukun Chen Bingxu Zhao Qiang Fu the Institute of Education Science Research Heilongjiang UniversityHarbin 150080China the School of Cyberspace Security Shandong University of Political Science and LawJinan 250014China the School of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the Faculty of Electrical and Computer Engineering University of VictoriaVictoriaV8P 5C2Canada. the Guangxi key Lab of Brain-Inspired computing and intelligent Chips School of Electronic and Information EngineeringGuangxi Normal UniversityGuilin 541004China

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic *** have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these *** goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures *** results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.

关键词： online learning learning outcomes prediction learning behavior analysis spiking neural network

A Sensor network Coverage Planning based on Adjusted Single Candidate Optimizer

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intelligent Automation & Soft computing 2023年第9期37卷 3213-3234页

作者： Trong-The Nguyen Thi-Kien Dao Trinh-Dong Nguyen Fujian provincial key laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China University of Information Technology Ho Chi Minh CityVietnam Vietnam National University Ho Chi Minh City700000Vietnam

Wireless sensor networks(WSNs)are widely used for various practical applications due to their simplicity and *** quality of service in WSNs is greatly influenced by the coverage,which directly affects the monitoring capacity of the target ***,low WSN coverage and uneven distribution of nodes in random deployments pose significant *** study proposes an optimal node planning strategy for net-work coverage based on an adjusted single candidate optimizer(ASCO)to address these *** single candidate optimizer(SCO)is a metaheuristic algorithm with stable implementation ***,it has limitations in avoiding local optimum traps in complex node coverage optimization *** ASCO overcomes these limitations by incorporating reverse learning and multi-direction strategies,resulting in updated *** performance of the ASCO algorithm is compared with other algorithms in the literature for optimal WSN node *** results demonstrate that the ASCO algorithm offers efficient performance,rapid convergence,and expanded coverage ***,the ASCO achieves an archival coverage rate of 88%,while other approaches achieve coverage rates below or equal to 85%under the same conditions.

关键词： Wireless sensor network coverage and connection adapted single candidate optimizer objective function optimization

A Survey of Edge Caching:key Issues and Challenges

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Tsinghua Science and Technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China shandong provincial University laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture Internet of Things(IoT)

An intelligent method for contact fatigue reliability analysis of spur gear under EHL

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Journal of Central South University 2015年第9期22卷 3389-3396页

作者：胡贇刘少军常继华张建阁 School of Mechanical and Electrical Engineering Central South University State key laboratory for High Performance Complex Manufacturing (Central South University)

To complete the contact fatigue reliability analysis of spur gear under elastohydrodynamic lubrication(EHL) efficiently and accurately, an intelligent method is proposed. Oil film pressure is approximated using quadratic polynomial with intercrossing term and then mapped into the Hertz contact zone. Considering the randomness of the EHL, material properties and fatigue strength correction factors, the probabilistic reliability analysis model is established using artificial neural network(ANN). Genetic algorithm(GA) is employed to search the minimum reliability index and the design point by introducing an adjusting factor in penalty function. Reliability sensitivity analysis is completed based on the advanced first order second moment(AFOSM). Numerical example shows that the established probabilistic reliability analysis model could correctly reflect the effect of EHL on contact fatigue of spur gear, and the proposed intelligent method has an excellent global search capability as well as a highly efficient computing performance compared with the traditional Monte Carlo method(MCM).

关键词： reliability contact fatigue spur gear artificial neural network(ANN) genetic algorithm(GA) elastohydrodynamic lubrication(EHL)