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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Padakandla, Sindhu Prabuchandran, J.K. Ganguly, Sourav Bhatnagar, Shalabh Indian Institute of Science Dept. of Computer Science and Automation India Iit Dharwad Dept. of Computer Science Engineering India

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Applying reinforcement learning (RL) methods for real world applications pose multiple challenges - the foremost being safety of the system controlled by the learning agent and the learning efficiency. An RL agent learns to control a system by exploring the available actions in various operating states. In some states, when the RL agent exercises an exploratory action, the system may enter unsafe operation, which can lead to safety hazards both for the system as well as for humans supervising the system. RL algorithms thus must learn to control the system respecting safety. In this work, we formulate the safe RL problem in the constrained off-policy setting that facilitates safe exploration by the RL agent. We then develop a sample efficient algorithm utilizing the cross-entropy method. The proposed algorithm's safety performance is evaluated numerically on benchmark RL problems. © 2022 IEEE.

关键词： Reinforcement learning

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Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols

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Science China(Information Sciences) 2022年第1期65卷 56-71页

作者： Yajing ZHANG Qimin XU Xinping GUAN Cailian CHEN Mingyan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management College of Computer Science Chongqing University

The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems(ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service(Qo S) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5 th generation(5 G) wireless communication technology provides ultra-reliable and low-latency communication(URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking(TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network(HTSN) co-designed by 5 G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling(SPS) to reduce signaling delay by reserving resources in *** we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing(PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization.

关键词： industrial cyber-physical systems (ICPS) 5G-TSN co-design wireless/wired integrated network risk-sensitive reinforcement learning deterministic communication

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Comparative Analysis of SVM and CNN Classifiers for EEG Signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Tibermacine, Ahmed Akrour, Djouher Khamar, Rofeida Tibermacine, Imad Eddine Rabehi, Abdelaziz University of Biskra Department of Computer Science Biskra Algeria Sapienza University of Rome Department of Computer Automation and Management Engineering Rome Italy Zian Achour University of Djelfa Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Djelfa17000 Algeria

ISBN: (纸本)9798331527396

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants' favorite music, and Interstellar's main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Shen Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization. © 2024 IEEE.

关键词： Mobile edge computing

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Visual cognition of birds and its underlying neural mechanism:A review

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Avian Research 2022年第2期13卷 216-225页

作者： Xiaoke Niu Zhenyang Jiang Yanyan Peng Shuman Huang Zhizhong Wang Li Shi Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical EngineeringZhengzhou UniversityZhengzhou450001China Department of Automation Tsinghua UniversityBeijing100084China

Birds have acute vision and many remarkable visual cognition abilities,due to their unique living *** underlying neural mechanisms have also attracted interests of researchers in ***,we firstly summarize the visual cognition abilities of birds,and make a comparison with ***,the underlying neural mechanisms are presented,including histological structure of avian brain and visual pathways,typical experimental results and conclusions in electrochemistry and *** latter mainly focuses on several higher brain areas related to visual cognition,including mesopallium ventrolaterale,entopallium,visual Wulst,and nidopallium ***,we make a conclusion and provide a suggestion about future studies on revealing the neural mechanisms of avian visual *** review presents a detailed understanding of avian visual cognition and would be helpful in ornithology studies in the field of cognitive neuroscience.

关键词： Avian Electrophysiology Neural mechanism Visual cognition

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An Evolutionary Framework with Improved Variance-Stabilized Multi-Objective Proximal Policy Optimization and NSGA-II

An Evolutionary Framework with Improved Variance-Stabilized ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Yue, Caiheng Yuan, Haitao Zhai, Jiahui Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Multi-objective optimization algorithms are essential for addressing real-world challenges characterized by conflicting objectives. Although conventional algorithms are effective in exploring solution spaces and generating non-dominated solutions, solution quality and dynamic adaptability of true Pareto fronts need to be improved. This work proposes a multi-objective algorithm that integrates Non-dominated sorting genetic algorithm II (NSGA-II) and Multi-Objective Reinforcement Learning (N-MORL). N-MORL consists of two parts including upstream and downstream components. In the upstream component, this work improves the Variance-stabilized Multi-objective Proximal Policy Optimization (VMPPO) for enhanced convergence stability by adjusting its iteration mechanism. Additionally, this work optimizes variance networks and action sampling to balance exploration and exploitation, which improves experience sampling efficiency. This work adopts high-quality solution sets yielded by MORL as the initial solution set for downstream NSGA-II, guiding the exploration space and increasing the solution number. High-quality initial solutions significantly accelerate the iterative convergence speed of N-MORL. N-MORL provides the quality and the number of solutions, better covering or approaching the true Pareto front. Experimental results with five benchmark multi-objective functions demonstrate that N-MORL outperforms the other three multi-objective evolutionary algorithms regarding high-quality solutions with the same iterations. © 2024 IEEE.

关键词： Genetic algorithms

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A Lightweight Flow-based DDoS Detection Approach using Dual Convolutional Kernels

A Lightweight Flow-based DDoS Detection Approach using Dual ...

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2022 Chinese automation Congress, CAC 2022

作者： Liang, Qingrong Liu, Chun Zhong, Yuxuan Ren, Xiaoqiang Shanghai University School of Computer Engineering and Science Shanghai China Shanghai University School of Mechatronic Engineering and Automation Shanghai China

ISBN: (纸本)9781665465335

The damage caused by Distributed Denial of Service (DDoS) attacks is becoming more and more serious with the development of the Internet, especially as we enter the era of the Internet of Things (IoT). Being able to efficiently analyze real-time traffic and detect anomalous flows is one of the current challenges. In this paper, we propose a lightweight detection framework based on Convolutional Neural Network (CNN), including a preprocessing mechanism for live traffic to extract data features, which are then fed into a network with dual convolutional kernels for training. Through different sizes of convolution kernels, we can learn the feature relationships between packets at different distances, while introducing Dilated Convolution to further improve the detection performance. Experimental results show that our method can effectively detect both benign and malicious flows, and has the advantage of high performance and low overhead compared with other state-of-the-art methods, which is suitable for effective DDoS detection in resource-constrained environments. © 2022 IEEE.

关键词： Convolution

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Emotion Recognition in Human Thermal Images with Artificial Intelligence Technology 28

Emotion Recognition in Human Thermal Images with Artificial ...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Pavel, Sorin Moldovanu, Simona Aiordachioaie, Dorel Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania

ISBN: (纸本)9798350339918

This paper presents a case study related to emotion recognition based on human thermal image processing. Three states are considered for human faces: normal, sad, and happy. The thermal images are pre-processed for important feature selection and extraction by using the random forest (RF) classifier. All and selected features set is used as input to a feedforward backpropagation neural network (FFBPN), with binary outputs. The input images are organized in pairs, i.e., normal with sad and normal with happy. The preliminary results show particularly good classification accuracy. © 2023 IEEE.

关键词： Emotion Recognition

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A Few-shot Learning Approach for Anomaly Detection in Cooperative Tracking Control System 21

A Few-shot Learning Approach for Anomaly Detection in Cooper...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Rong, Jieqi Liu, Weirong Yu, Xiaoquan Ren, Wanwan Shu, Boyu Peng, Jun Central South University Computer Science and Engineering Changsha China Central South University School of Traffic and Transportation Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

Cooperative tracking control for multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the false data injection attack for the tracking control system is a serious obstacle, which will lead to imprecise train operation control. To address the issue, a few-shot learning approach for false data injection attack (FDIA) detection for tracking control systems of heavy-haul trains is proposed. First, the physical-cyber model of the tracking control system is established, and the affects of the FDIA on the tracking control system is analyzed. Second, the heavy-haul train anomaly detection simulation model was built by Matlab/Simulink to obtain the dataset for training the GAN model. Third, the Generative Adversarial Network (GAN) method is introduced to augment the anomaly dataset, which can solve the problem of lack of actual operation fault data of heavy trains. Further, the Local Outlier Factor (LOF) algorithm is leveraged to achieve the false data injection attack detection. Simulation results show that the proposed method can effectively increase the anomaly detection accuracy than other supervised methods. © 2023 IEEE.

关键词： Anomaly detection

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Learning Hybrid Representations of Semantics and Distortion for Blind Image Quality Assessment 48

Learning Hybrid Representations of Semantics and Distortion ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Xiaoqi Xiong, Jian Li, Bo Suo, Jinli Gao, Hao Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing China Xihua University School of Computer and Software Engineering Chengdu China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9781728163277

Recently, some studies have shown that semantic and distortion representations both benefit the evaluation of image quality. However, the images of existing synthetic distortion databases are annotated with subjective quality scores and distortion types, lacking labels with semantic objects. Therefore, it is virtually infeasible to learn the representations of image semantics and distortion by co-guiding with semantic and distortion labels. To address this issue, we propose a dual-perception network (DPNet) via an end-to-end multi-task learning method, where knowledge distillation is lever-aged as a semantic label-free strategy. Specifically, semantic representation derived from pre-trained ResNet152 is applied to supervise the output of DPNet, while the output is utilized to construct a distortion recognition task. In this way, image semantics and distortion can be hybridly represented in an identical feature map. Finally, image quality is regressed based on the hybrid representations. Experimental results conducted on five benchmark databases validate that the proposed method can achieve state-of-the-art performance. © 2023 IEEE.

关键词： Blind image quality assessment distortion classification knowledge distillation multi-task learning

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