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2023 China automation Congress, CAC 2023

作者： Bai, Yanxing Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.

关键词： Emotion Recognition

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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2023 China automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

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Hydrodynamic Studies of Underground Gas Storages 13

Hydrodynamic Studies of Underground Gas Storages

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Pyanylo, Yaroslav Prytula, Zoia Prytula, Myroslav Hymko, Olha Prytula, Nazar Pyanylo, Halyna Pidstryhach Institute for Applied Problems of Mechanics and Mathematics National Academy of Sciences of Ukraine Department of Mathematical Modeling of Transfer Processes in Distributed Systems Lviv Ukraine Pidstryhach Inst. for Appl. Prob. of Mechanics and Math. National Academy of Sciences of Ukraine Laboratory of Mathematical Problems of Mechanics of Inhomogeneous Bodies Lviv Ukraine Research and Design Institute of Gas Transport of PJSC 'Ukrtransgaz' Department of Development of Systems for Optimal Planning and Forecasting of UGS Operating Modes Lviv Ukraine Lviv Polytechnic National University Department of Automation and Computer-Integrated Technologies Lviv Ukraine

Problems of research of underground gas storage (UGS) wells are given. The analysis of the results of well research is carried out and the constant variability of filtration resistance coefficients of bottomhole zones of wells is substantiated. Studies of the influence of bottomhole pressures of wells on the calculated pressure at gas gathering stations (GGS) of underground storage facilities have been carried out. It was found that the calculated filtration resistance coefficients do not allow to calculate the pressures at the gas gathering stations of the underground storage plant with sufficient accuracy. The magnitude of the discrepancy between the measured and calculated pressures on the GGS varies depending on the flow rate and operating time of wells. It is established that the calculation of depression (repression) of pressure in the bottomhole zones of wells in non-stationary conditions of their operation gives underestimated results. © 2023 IEEE.

关键词： Hydrodynamics

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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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Engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Solar-driven photothermal catalytic CO_(2) conversion:a review

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Rare Metals 2024年第7期43卷 2913-2939页

作者： Bachirou Guene Lougou Bo-Xi Geng Ru-Ming Pan Wei Wang Tian-Tian Yan Fang-Hua Li Hao Zhang Oraléou SanguéDjandja Yong Shuai Meisam Tabatabaei Daniel Sabi Takou School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin 150090China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Guangdong Provincial Key Laboratory of Distributed Energy Systems Engineering Research Center of None-Food Biomass Efficient Pyrolysis and Utilization Technology of Guangdong Higher Education InstitutesDongguan University of TechnologyDongguan 523808China Higher Institution Centre of Excellence(HICoE) Institute of Tropical Aquaculture and Fisheries(AKUATROP)Universiti Malaysia Terengganu21030 Kuala NerusMalaysia Department of Biomaterials Saveetha Dental CollegeSaveetha Institute of Medical and Technical SciencesChennai600077India école Polytechnique d'Abomey-Calavi University of Abomey-Calavi2009CotonouBenin

It is highly desirable to seek green and sustainable technologies,such as employing photo thermal effects to drive energy catalysis processes to address the high energy demand and associated environmental impacts induced by the current *** photothermocatalysis process is an emerging research area with great potential in efficiently converting solar energy through various catalytic ***,achieving simultaneously high conversion efficiency,cyclability,and durability is still a daunting ***,tremendous work is still needed to enhance solar photo thermal catalytic conversion and promote its large-scale *** review developed the principles of coupling solar photon and thermal fields underlying the photothermal effect,exploration of efficient nanocatalysts,development of optofluidic reactor model,and photo thermal synergistic-driven CO_(2) reduction *** ultimate goal was to provide an effective approach that can effectively convert solar energy into photocarriers/hot-electrons and heat,and importantly,can couple them to regulate catalysis reaction pathways toward the production of value-added fuel and chemical energy.

关键词： Photochemistry Photothermal CO_(2)catalytic conversion Nanomaterial Solar fuels and chemicals

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DNN-Based Velocity Estimator Using Inertial Sensor for Robot Localization

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IFAC-PapersOnLine 2023年第2期56卷 5027-5032页

作者： Chul-hong Kim Dong-il “Dan” Cho Department of Electrical and Computer Engineering and Automation and Systems Research Institute (ASRI) Seoul National University Seoul Korea Inter-university Semiconductor Research Center (ISRC) Seoul National University Seoul Korea

This paper presents an inertial sensor-based localization method using a deep neural network (DNN)-based velocity estimator. Among various sensors commonly used in localization, inertial sensors are less affected by the surrounding environment. The inertial sensor consists of an accelerometer and a gyroscope, which can estimate the poses of a robot. However, inertial sensors need to be used with other sensors for localization because inherent large drift errors are difficult to prevent. To overcome this problem, a DNN-based velocity estimator that reduces the position error by learning the data pattern of the inertial sensor and by limiting the range of the estimated velocity is proposed. The DNN-based velocity estimator comprises a convolutional neural network, a fully connected layer, and a smoothing filter. To limit the range of the estimated velocity, the range of velocity is divided by the total number of classes, and the divided range is assigned to each class. The relationship between consecutive classes is learned using a smoothing filter. Dataset-based experiments are performed to train the DNN-based velocity estimator and to evaluate the performance of the proposed localization. The experimental result shows that the proposed localization reduces the position errors by 99% compared to integration-based localization and by 91% compared to extended Kalman filter-based localization.

关键词： Localization Mobile robot Inertial sensor Deep learning Kalman filter

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