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An Improved Particle Swarm Optimization Algorithm-Based MPPT Method for Distributed Photovoltaic Hydrogen Production systems 9

An Improved Particle Swarm Optimization Algorithm-Based MPPT...

引用

9th international conference on Power and Renewable Energy

作者： Zhong, Yin Peng, Yunfeng Chen, Hongtian Hao, Guolin Gao, Fenglin Zhou, Zheng Shanghai Jiao Tong Univ Sch Elect & Elect Engn Shanghai Peoples R China Xiangtan Univ Sch Phys & Optoelect & Hunan Inst Adv Sensing & I Xiangtan Peoples R China Shanghai Jiao Tong Univ Sch Smart Energy Shanghai Peoples R China

ISBN: (纸本)9798350377477;9798350377460

In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as an integrated energy system, is an important pathway to the zero-carbon goal. But distributed PV is prone to face the shading situation, while the PV module power output curve will show the phenomenon of multiple peaks. The traditional maximum power tracking methods are prone to fall into the local optimal situation, and the intelligent tracking methods are inherently more complex. The hydrogen production electrolyzer puts forward the requirement of stability on the output power of the PV power supply. Existing research focuses on the maximum power tracking of PV system, or the control of electrolyzer, lacking the coupled modeling control research of distributed PV hydrogen production system. In order to fill the gap of the existing research, this paper proposes a composite algorithm which combines the Improved Particle Swarm Algorithm (IPSO) with the Improved Perturbation Observation (IP&O) method. The initial population and termination conditions of the particle swarm search method (PSO) are improved to enhance the convergence speed of the algorithm and reduce the oscillations during the convergence process of the algorithm, and the IP&O method is utilized to provide stable inputs for the electrolytic tanks. The performance of the algorithm is verified through the comparative experiments with other algorithms.

关键词： Photovoltaic hydrogen production Maximum power point tracking Particle Swarm Optimization Algorithm

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 ieee/CIC international conference on Communications in China, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping 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 Department of Automation Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 ieee.

关键词： Resource allocation

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Software Interference Prevention and SWaP-C Minimization in cyber-Physical systems Using Hypervisor technology

Software Interference Prevention and SWaP-C Minimization in ...

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international conference on technology, Informatics, Management, Engineering, and Environment (TIME-E)

作者： Bambang Sridadi Endra Joelianto Yul Yunazwin Nazaruddin Doctoral Program in Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Currently, hypervisor technology is an emerging topic of research. Virtualization technology, especially hypervisors and virtual machines (VM), has been widely studied but is still little applied in the cyber-physical systems (CPS) field. A hypervisor, or VM monitor (VMM), is a software program used to run multiple VMs on a single physical machine through space and time partitioning in a real-time operating system. This paper discusses the hypervisor technology to prevent software interference and to minimize size, weight, and power-cost (SWaP-C) in cyber physical systems, as well as, the application of hypervisor technology in the aerospace, automotive and control fields. By partitioning, a hypervisor can increase software safety because application programs with different criticality levels do not affect each other in failure when run in partitioned VMs.

关键词： Virtual machine monitors Prevention and mitigation Operating systems Interference cyber-physical systems Virtual machines Real-time systems Software safety Safety Aerospace control

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Engineering a Multi-agent systems Approach for Realizing Collaborative Asset Administration Shells

Engineering a Multi-agent Systems Approach for Realizing Col...

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2022 ieee international conference on Industrial technology, ICIT 2022

作者： Sakurada, Lucas Leitao, Paulo De La Prieta, Fernando Campus de Santa Apolónia Bragança5300-253 Portugal University of Salamanca Bisite Digital Innovation Hub Edificio Multiusos I+D+i Salamanca37007 Spain

ISBN: (纸本)9781728119489

In recent years, there has been a high demand for flexible and reconfigurable processes to meet the fast-changing market conditions. In this context, Industry 4.0 is promoting the digitization of traditional production systems towards intelligent factories with highly automated and rapidly adaptable capabilities based on Industrial cyber-Physical systems (ICPS). This digitization process is currently being leveraged by the so-called Asset Administration Shell (AAS), a standardized digital representation of an asset that provides the uniform access to the asset information. Additionally, the AASs offer the digital basis for future autonomous systems, where intelligent AASs may perform collaborative functions to enhance industrial processes. However, currently such solutions are still at an early stage of maturity. In this sense, this paper explores the adoption of a Multi-agent systems (MAS) approach to provide the required intelligent and collaborative aspects for the traditional AAS. The proposed MAS-based AAS solution was applied in an industrial automation case study to analyze the feasibility of MAS to perform intelligent and collaborative functions in the AAS context. © 2022 ieee.

关键词： Multi agent systems

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Comparative Analysis of Advanced Optimization Algorithms for Highway Photovoltaic System Layout 22

Comparative Analysis of Advanced Optimization Algorithms for...

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22nd ieee international conference on Industrial Informatics, INDIN 2024

作者： Tao, Yanmeng Liu, Zhengke Ma, Wenxin Ma, Xiaolei School of Transportation Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Transportation Technology and System Ministry of Education Beijing100191 China

ISBN: (纸本)9798331527471

In the context of the current transition in energy structures and the development of renewable energy, optimizing the layout of solar energy systems along highways has become a significant research topic. This study employs five different advanced optimization algorithms, namely NSGA-II, Particle Swarm Optimization (PSO), Simulated Annealing (SA), Differential Evolution (DE), and a combination of NSGA-II and DE (NSGA-II-DE), to minimize total supply cost and maximize energy self-sufficiency. The comparative analysis reveals that the combination of NSGA-II and DE exhibits the best optimization performance. Through detailed experiment and data analysis, the performance and advantages of each algorithm in solving the layout problem of highway solar energy system are demonstrated, and specific implementation strategies are proposed for the optimal solution. In addition, sensitivity analysis is used to understand how changes in parameters such as module efficiency, energy demand fluctuations, and electricity price changes affect the optimization results, adding depth to the findings and making them more applicable to a variety of application scenarios. © 2024 ieee.

关键词： Optimization algorithms

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Study on Multi-Viral Infection on Lungs using Data and Predictive Analysis Techniques 7

Study on Multi-Viral Infection on Lungs using Data and Predi...

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7th international conference on intelligent Computing and control systems, ICICCS 2023

作者： Varalakshmi, Sudha Vijayalakshmi, P. Rajendran, V. Vels Institute of Science Technology and Advanced Studies Department of Electronics and Communication Engineering Tamil Nadu Chennai600117 India

ISBN: (纸本)9798350397253

This research study has used deep learning techniques (DL) to classify spectrograms of acoustic signals. In addition, this study intends to differentiate spectrograms that contain a cough and those that do not contain a cough, and once it is known that a spectrogram contains a cough, it is possible to understand the underlying disease. The main goal is to obtain a system with better performance than those proposed so far in the literature in the field of cough detection and make a first approximation to the classification of diseases based on audio clips with cough. © 2023 ieee.

关键词： Convolutional neural networks

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Enhancing Adaptive Traffic control systems with Deep Reinforcement Learning and Graphical Models

Enhancing Adaptive Traffic Control Systems with Deep Reinfor...

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2024 ieee international conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Sattarzadeh, Ali Reza Pathirana, Pubudu N. School of Engineering Deakin University GeelongVIC3216 Australia

ISBN: (纸本)9798350391213

intelligent traffic signal control plays a crucial role in improving urban transportation networks by reducing congestion and increasing traffic flow efficiency. This paper presents a deep reinforcement learning (DRL) approach for managing traffic signals at multiple intersections. This method enhances both the adaptability and efficiency of traffic control systems. Our model integrates a probabilistic graphical framework with DRL, facilitating the extraction of interpretable and actionable insights from complex traffic data. We have developed and validated our model using a simulated environment that reflects real-world traffic conditions, demonstrating substantial improvements in traffic throughput and reduction in average travel times. The proposed model outperforms traditional traffic signal control algorithms. It also offers a robust framework for real-time traffic management, contributing to the development of future smart city infrastructure. The model achieves a 95% success rate, reduces average queue lengths by over 50%, and cuts intersection delays by up to 80%, compared to traditional methods. These improvements highlight its efficiency in optimizing traffic flow and minimizing congestion. © 2024 ieee.

关键词： Deep reinforcement learning

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Comprehensive Security Analysis and Threat Mitigation Strategies for Software-Defined Networking (SDN) Environments 3

Comprehensive Security Analysis and Threat Mitigation Strate...

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3rd international conference on intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Karki, Ajit Deb, Raktim The ICFAI University Sikkim School of Information Technology Gangtok India The ICFAI University Tripura School of Science and Technology Agartala India

ISBN: (纸本)9798331523893

Software Defined Networking (SDN) is a new networking standard that significantly reduces the complexity of network administration tasks. SDN is considered a networking breakthrough. Additionally, it provides a flexible interface that allows for network innovation by controlling the network's general behavior. However, for many years, traditional IP networks were difficult to maintain, prone to errors, and difficult to grow with new features. The idea is derived from the separation and abstraction of the data plane and the control of conventional network devices. Better segmentation, scalability, programmability, faster delivery, and increased service and experience quality are all advantages of this separation for network operations. To make system administrators' tasks more at ease, SDN has been suggested as a prospective standard for a symbolized and rationally integrated network administration plane. By separating the control and data planes, SDN enables network configuration through programmability. This paper provides a comprehensive explanation of SDN, covering its numerous levels and their purposes, to keep up with the new technologies accelerated development. © 2025 ieee.

关键词： Internet protocols

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A Novel Collaborative Heterogeneous Supervision Network for Small Object Detection Method Based on Panchromatic and Hyperspectral Images

A Novel Collaborative Heterogeneous Supervision Network for ...

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2024 ieee international conference on control Science and systems Engineering, ICCSSE 2024

作者： Li, Yuan Kong, Ziyang Xu, Qizhi School of Mechanical and Electrical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331517199

With the increasing availability of simultaneous panchromatic and hyperspectral images, object detection methods based on them have demonstrated significant application advantages. However, they still face several challenges that limit detection performance: 1) the sizes of small objects remain very small even in the fused images, insufficient texture information and spectral information that is easily confused, leading to lower accuracy in object detection;2) etection-by-Preprocess (DBP) methods often suffer from spectral and spatial detail distortions, compromising target features;3) Preprocess-free detection (PFD) methods extract panchromatic and hyperspectral features directly through networks, but the black-box nature of deep network makes it difficult to ensure precise alignment of these two types of features, thereby hindering further improvements in detection accuracy. Therefore, this paper proposed a novel Collaborative Heterogeneous Supervision Network (CHS-Net) for small object detection on panchromatic and hyperspectral images. First, integrating fusion and detection components into a heterogeneous supervision network enhances learning capabilities by incorporating more empirical knowledge. Second, a unified joint regulation strategy was introduced to enhance integrated learning capabilities using optimized feedback loss functions. This approach enhanced the attention of different components to target features, effectively improving weak small target detection performance. Finally, comparative experiments based on EO-1 dataset demonstrate that the proposed method outperforms many start-of-the-art approaches. © 2024 ieee.

关键词： Self-supervised learning

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cyber-Physical intelligent Transport System Based on Digital-Twin technology 7

Cyber-Physical Intelligent Transport System Based on Digital...

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7th international conference on Computational Intelligence and Applications, ICCIA 2022

作者： Lou, Ren Zhang, Yuxin Zhang, Lei Zhou, Yixuan Shi, Lijuan Hong, Qiang Zhou, Yueqi Li, Xinghua Zhejiang Scientific Research Institute of Transport Hangzhou China Tongji University Department of Traffic Information and Control Engineering Shanghai China College of Transportation Engineering Tongji University Shanghai China

ISBN: (纸本)9781665495844

The transport physical objects and transport information world are combined, mapped and refined, and then they transform 'pedestrians-vehicles-roads-clouds' protocols into transport spatio-temporal data. This paper proposes to combine the theory and method of artificial intelligence and cyber physical systems(CPS) into the intelligent transport system and its application, explore the interdisciplinary innovation of 'cyber Physical Transportation System (CPS-T)'. CPS-T mostly plays a role in the joint cooperation of the computing process and the controlling process, and forms intelligent information judging, real-time feedback control and transport response services for transport scenarios. It was found that CPS-T can effectively express, map, feature and regulate traffic signals and data volume. © 2022 ieee.

关键词： cyber Physical System

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