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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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A powder-scale multiphysics framework for powder bed fusion of fiber-reinforced polymer composites

Advanced Powder Materials

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advanced Powder Materials 2024年第4期3卷 47-56页

作者： Pengfei Tan Meixin Zhou Chao Tang Kun Zhou HP-NTU Digital Manufacturing Corporate Lab School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingapore 639798Singapore Singapore Centre for 3D Printing School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingapore 639798Singapore Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Additive manufacturing of fiber-reinforced polymer composites has garnered great interest due to its potential in fabricating functional products with lightweight characteristics and unique material ***,the major concern in polymer composites remains the presence of pore defects,as a thorough understanding of pore formation is *** this study,a powder-scale multiphysics framework has been developed to simulate the printing process of fiber-reinforced polymer composites in powder bed fusion additive *** numerical framework involves various multiphysics phenomena such as particle flow dynamics of fiber-reinforced polymer composite powder,infrared laser–particle interaction,heat transfer,and multiphase fluid flow *** melt depths of one-layer glass fiber–reinforced polyamide 12 composite parts fabricated by selective laser sintering are measured to validate modelling *** numerical framework is employed to conduct an in-depth investigation of pore formation mechanisms within printed *** simulation results suggest that an increasing fiber weight fraction would lead to a lower densification rate,larger porosity,and lower pore sphericity in the composites.

关键词： Additive manufacturing Powder bed fusion Polymeric materials Multiphysics modelling Porosity

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An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks 41

An Improved Finite-time Analysis of Temporal Difference Lear...

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41st International Conference on Machine Learning, ICML 2024

作者： Ke, Zhifa Wen, Zaiwen Zhang, Junyu Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved non-asymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ϵ-1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ϵ-1) complexity under the Markovian sampling, as opposed to the best known Õ(ϵ-2) complexity in the existing literature. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Assessment and Analysis of IoT Protocol Effectiveness in Data Exfiltration Scenario 20

Assessment and Analysis of IoT Protocol Effectiveness in Dat...

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20th Annual International Conference on Distributed computing in Smart Systems and the Internet of Things, DCOSS-IoT 2024

作者： Adesanya, Olanrewaju Mueez Moradpoor, Naghmeh Maglaras, Leandros Lim, Ik Soo Amine Ferrag, Mohamed University of Greenwich School of Computing and Mathematical Sciences London United Kingdom Edinburgh Napier University School of Computing Engineering and the Built Environment Edinburgh United Kingdom Technology Innovation Institute Artificial Intelligence and Digital Science Research Center Masdar Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350369441

The rapid growth of Internet of Things technology has introduced an era of numerous interconnected devices, transforming the communication with the physical world. However, the security and privacy of the data generated and stored on these devices have become significant concerns due to the potential risks associated with unauthorized access and data exfiltration. This paper presents a comprehensive study on the characterization and evaluation of IoT protocols for data exfiltration, with a focus on Message Queuing Telemetry Transport versions 3.1.1 and 5.0, Constrained Application Protocol version 1.0, and Websocket, owing to their widespread adoption and diverse characteristics. These protocols are chosen to meet the constraints of IoT devices and networks, where minimal bandwidth utilization and low power consumption are crucial. We employed a multifaceted methodology, incorporating a comprehensive literature review and the Chiton data encapsulation and exfiltration software tool, to assess the security features, vulnerabilities, and performance metrics of these protocols for data exfiltration. Performance evaluations on the three selected protocols were conducted using a live Client-Server network. A laptop served as the server for exfiltrated data collection, while a Raspberry Pi 3 device acted as the IoT Client Publisher. The evaluation included Wireshark as a network protocol analyser and the enhancement of the Chiton Software Tool with custom codes to encapsulate and exfiltrate data within the IoT protocol messages, empirically measuring the selected protocols' performance, including overhead, latency, and throughput for different data exfiltration scenarios, providing a versatile platform for protocol evaluation. Comparative analysis revealed the strengths and weaknesses of MQTT, CoAP, and Websocket in various data exfiltration scenarios, facilitating the selection of MQTT and Websocket over the CoAP protocol for IoT applications. Lastly, ideas and recommend

关键词： Application programs

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Biogas Fermentation Monitoring research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning 13

Biogas Fermentation Monitoring Research Based on Borderline-...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Xiu, Pengfei Fu, Lijun Sun, Yukun Wang, Hongliang Li, Xuerao Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model. ©2024 IEEE.

关键词： Decision making

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Multi-point discharge model: study on corona discharge of double-ended needle in large space

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Plasma Science and Technology 2023年第3期25卷 62-69页

作者：李丁晨李家玮李传张明王鹏宇刘志杨勇于克训 International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical Engineering and ElectronicsHuazhong University of Science and TechnologyWuhan 430074People’s Republic of China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and ElectronicsHuazhong University of Science and TechnologyWuhan 430074People’s Republic of China Digital Grid Research Institute China Southern Power GridGuangzhou 510670People’s Republic of China The Fifth Bureau of China State Construction Engineering Corporation Changsha 410000People’s Republic of China

Corona discharge, as a common means to obtain non-equilibrium plasma, can generally obtain high-concentration plasma by increasing discharge points to meet production needs. However,the existing numerical simulation models used to study multi-point corona discharge are all calculations of small-scale space models, which cannot obtain the distribution characteristics of plasma in large space. Based on our previous research, this paper proposes a hybrid model for studying the distribution of multi-point discharge plasma in large-scale spaces, which divides the computational domain and computes separately with the hydrodynamic model and the ion mobility model. The simulation results are verified by a needle–ball electrode device. Firstly, the electric field distribution and plasma distribution of the needle electrodes with single tip and double tips are compared and discussed. Secondly, the plasma distribution of the needle electrode with the double tip at different voltages is investigated. Both computational and experimental results indicate that the charged particle concentration and current of the needle electrode with double tips are both twice as high as those of the needle electrode with a single tip. This model can extend the computational area of the multi-point corona discharge finite element model to the sub-meter(25 cm) or meter level, which provides an effective means to study the plasma distribution generated by multiple discharge points in large-scale space.

关键词： corona discharge numerical simulation multi-point discharge large space double needle tip electrode

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Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares

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Green Energy and Intelligent Transportation 2024年第4期3卷 12-22页

作者： Dong Zhen Jiahao Liu Shuqin Ma Jingyu Zhu Jinzhen Kong Yizhao Gao Guojin Feng Fengshou Gu School of Mechanical Engineering Hebei University of TechnologyTianjin300401 China Advanced Equipment Research Institute Co. Ltd.of HEBUTTianjin300401 China College of Information Science and Engineering Hohai UniversityNanjing210098 China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai200240 China School of Computing and Engineering University of HuddersfieldHuddersfield HD13DHUK

Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery,thereby influencing safety of entire electric *** estimation of battery model parameters using key measured signals is ***,measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors,potentially diminishing model estimation *** the challenge of accuracy reduction caused by noise,this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares(BCFFRLS)***,a variational error model is crafted to estimate the average weighted variance of random ***,an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors,compensating for bias in the parameter *** assess the proposed method's effectiveness in improving parameter identification accuracy,lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule(UDDS),Dynamic Stress Test(DST),and Hybrid Pulse Power Characterization(HPPC).The proposed method,alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares(FFRLS)—was employed for battery model parameter *** analysis reveals substantial improvements,with the mean absolute error reduced by 25%,28%,and 15%,and the root mean square error reduced by 25.1%,42.7%,and 15.9%in UDDS,HPPC,and DST operating conditions,respectively,when compared to the FFRLS method.

关键词： Lithium-ion battery Battery model Recursive least squares Parameter identification

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Guidance on Dataspaces: Development of a Learning Environment for Industrial SMEs 6

Guidance on Dataspaces: Development of a Learning Environmen...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Jonas, Lick Jessica, Lamarz Friederike, Dohmann Jayant, Kulkarni Pranav Julian, Zerbin Christian, Koldewey Fraunhofer Institute for Mechatronic Systems Design Department for Digital Transformation Paderborn Germany Fraunhofer Institute for Mechatronic Systems Design Department for Corporate Innovation Paderborn Germany University of Paderborn Heinz Nixdorf Institute Research Group for Advanced Systems Engineering Paderborn Germany

ISBN: (纸本)9798331518158

The rapid advancements in digital transformation have led to the emergence of dataspaces as a pivotal element for industry-wide and cross-industry data integration and interoperability across various businesses. Despite their potential, the adoption and effective utilization of dataspaces by business stakeholders remain challenging. This paper aims to address this gap by developing a comprehensive learning environment tailored for business stakeholders. Through an interview study and an analysis of the current state of research, we identify problem fields and derive key requirements for the development of the learning environment. The proposed environment includes a demonstrator and a training concept designed to enhance stakeholders' understanding and capabilities in managing and leveraging dataspaces. Our findings contribute to the body of knowledge by providing practical guidance through learning environments for the deployment of dataspaces in business contexts and highlighting areas for future research. © 2024 IEEE.

关键词： Adversarial machine learning

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Design Optimization of Non-Axisymmetric Vane for an Axial Compressor under Inlet Distortion

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Journal of Thermal Science 2023年第4期32卷 1321-1334页

作者： ZHANG Min DU Juan ZHAO Hongliang QIU Jiahui BA Dun CHEN Yang NIE Chaoqun Digital Twin Research Center Institute of Engineering ThermophysicsChinese Academy of SciencesBeijing100190China Key Laboratory of Advanced Energy and Power Chinese Academy of SciencesBeijing100190China Innovation Academy for Light-Duty Gas Turbine Chinese Academy of SciencesBeijing100190China School of Engineering Science University of Chinese Academy of SciencesBeijing100049China

The flow field at the inlet of compressors is generally encountered combined total pressure and swirl distortion for either aircraft engine with S-duct or gas turbine with lateral air *** inevitably deteriorates compressor aerodynamic performance,including not only the efficiency or pressure ratio but also the operation *** order to conquer this issue,appropriate measures such as integrating flow control techniques and modifying inlet or compressor design are of *** to this motivation,this article develops a full-annular two-dimensional(2D)and a partial-annular three-dimension(3D)optimization strategy for non-axisymmetric vane ***,two numerical simulation methods for evaluating performance of full-annular 2D vane and compressor with partial-annular 3D vane are *** swirl patterns at the inlet of a 1.5-stage axial compressor are analyzed and parametrized,and the parameterization is transferred to characterize the circumferential distribution of geometrical parameters of the vane *** approaches dramatically reduce computational simulation costs without violating the non-axisymmetric flow distortion *** various full-annular 2D sections at different radial locations are constructed as design *** designed vane is reconstructed and 3D numerical simulations are performed to examine performance of the non-axisymmetric vane and the compressor with ***,partial annular 3D optimization is conducted for balancing compressor efficiency and stall *** indicate that the designed non-axisymmetric vane based on full-annular optimization approach can decrease the vane total pressure loss under the considered inlet flow distortion,while those using partial-annular optimization achieve positive effects on compressor stall margin.

关键词： combined total pressure and swirl distortion axial compressor non-axisymmetric vane optimization aerodynamic performance

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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