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Progress in Applied Electrical engineering (PAEE)

作者： Patryk Mendoń Mariusz Pelc Aleksandra Kawala-Sterniuk Mariusz Gola Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Mathematics Physics and Computer Science University of Opole Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

In this paper the authors propose a system architecture which allows product scoring based on sentiment analysis and utilising Machine Learning (ML) and Expert systems technologies. This solution presented in this paper allows decoupling the AI-based part, which is very capable in sentiment analysis but is lacking some flexibility to actually put the sentiment analysis results in a wider context to make a better sense as to what the results actually mean. This task in our solution is performed by the Expert system where the expert may provide some decision making rules in order to determine the final system *** results confirm discrepancy between reviews and star ratings.

关键词： Knowledge engineering Fuzzy logic Sentiment analysis Reviews Systems architecture Stars Machine learning Cognition Expert systems System analysis and design

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A Hybrid Temporal-Magnitude Based Sampling for Event-Based control

A Hybrid Temporal-Magnitude Based Sampling for Event-Based C...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Oscar Miguel-Escrig José Sánchez-Moreno Julio-Ariel Romero-Pérez Sebastián Dormido-Bencomo Department of System Engineering and Design Universitat Jaume I Castelló de la Plana Spain Department of Computer Science and Automatic Control Universidad Nacional de Educación a Distancia (UNED) Madrid Spain

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

In the current industrial environment, which is shifting towards an event-based control prone paradigm, sampling strategies in use must be revisited. Both temporal and magnitude dependent sampling strategies have been demonstrated to be suitable for control systems, reporting benefits in different categories. However, these sampling strategies also present some drawbacks, discussed in this contribution. Aiming to address some of these drawbacks, a new sampling strategy is proposed gathering characteristics of both predecessors.

关键词： Industrial electronics Transient response Quantization (signal) Event detection control systems Hybrid power systems Steady-state Data communication Guidelines

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Tuning the feedback controller gains is a simple way to improve autonomous driving performance

arXiv

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arXiv 2024年

作者： Liang, Wenyu Baldivieso, Pablo R. Drummond, Ross Shin, Donghwan Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Dept. of Computer Science University of Sheffield Sheffield United Kingdom

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle’s closed loop dynamics. The aim of this paper is to highlight the limitations of this approach;it is shown that retuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness. © 2024, CC BY.

关键词： Feedback control

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Low-disturbance Nonlinear control of TBM Cutterhead Speed in Coal Mine Tunneling 50

Low-disturbance Nonlinear Control of TBM Cutterhead Speed in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Liu, Nannan Wang, Tong Wang, Jianmin Fan, Yingda School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Computer Science University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9781665464543

To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability. © 2024 IEEE.

关键词： Hydraulic drives

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Efficient data-driven occupancy detection in office environments and feature impact analysis

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Fouzi, Harrou Ramakrishna, Kini K. Madakyaru, Muddu Ying, Sun Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Computer Thuwal 23955-6900 Saudi Arabia Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Department of Chemical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India

Occupancy detection is crucial in optimizing building energy efficiency and enhancing occupant comfort. This study introduces an innovative data-driven approach for accurate occupancy detection in an office room environment. Specifically, the methodology combines the advantages of Independent Component Analysis (ICA) to extract essential features from multivariate data and Kantorovitch distance (KD)-based schemes for detection sensitivity. The KD scheme’s detection threshold is computed nonparametrically using kernel density estimation to enhance the sensitivity of occupancy detection. The efficacy of this strategy is evaluated utilizing publicly available data recorded during winter in Mons, Belgium, capturing vital environmental parameters such as temperature, humidity, light, and CO2 levels through specialized sensors. Results demonstrate that the ICA-KD approach achieves an averaged accuracy of 98.355%, surpassing conventional approaches like Principal Component Analysis (PCA)-based, ICA-based, and other state-of-the-art methods. Additionally, the study uses Shapley Additive exPlanations (SHAP) with XGBoost to explore the impact of input variables on occupancy detection, highlighting the influence of various factors under different testing conditions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Building occupancy Data-driven Environmental data Occupancy detection Semi-supervised detection

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers

Biological Influence of Low-Frequency Electromagnetic Field ...

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ELEKTRO

作者： Robert Cichorowski Olga Kocikowska Marek Bajtoš Roman Radil Ladislav Janoušek Daria Gendosz De Carrillo Sebastian Student Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology liwice Poland Department of Engineering and Systems Biology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Department of Physiology Faculty of Medical Sciences Silesian University of Medicine Katowice Poland Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Informational Technology Žilina Slovakia Department of Histology Faculty of Medical Sciences in Zabrze Silesian University of Medicine Zabrze Poland Biotechnology Center Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week.

关键词： Proteins Biological system modeling Estimation Electromagnetic fields

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SAGPCN: Self-Attention Graph Pooling Convolutional Network for Battery State of Health Estimation 3

SAGPCN: Self-Attention Graph Pooling Convolutional Network f...

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3rd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Zhang, Jiusi Tian, Jilun Jiang, Yuchen Luo, Hao Huang, Cong-Sheng Chow, Mo-Yuen Yin, Shen Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan North Carolina State University Department of Electrical and Computer Engineering RaleighNC27606 United States Norwegian University of Science and Technology Faculty of Engineering Trondheim7034 Norway

ISBN: (纸本)9798350324754

Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches. © 2023 IEEE.

关键词： Lithium-ion batteries

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Enhancing Wastewater Treatment Sensor Fault Detection through Deep Learning

Enhancing Wastewater Treatment Sensor Fault Detection throug...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Liliana-Maria Ghinea Mihaela Miron Marian Barbu Department of Automatic Control and Electrical Engineering University "Dunărea de Jos" Galați Romania Department of Computer Science and Information Technology University "Dunărea de Jos" Galați Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Efficient operation and maintenance of wastewater treatment plants (WWTPs) are essential for safeguarding public health and the environment. The emergence of mechanical faults within complex systems can lead to disruptions, increased operational costs, and environmental risks. As the world moves towards a digitally connected and sustainable future, the development of Deep Learning (DL) tools for fault detection and isolation (FDI) in wastewater treatment processes is expected to become paramount. Therefore, in this study, we developed two neural models, a Feedforward Neural Network (FFNN) and a Long Short-Term Memory (LSTM), to address the detection of mechanical faults such as bias, stuck, spikes, and precision degradation of the Dissolved Oxygen (DO) sensor. The classification results showed remarkable accuracy performances during testing: for Dataset 1, FFNN achieved 96.56%, while LSTM reached 99.36%; and for Dataset 2, FFNN achieved 99.36%, and LSTM reached 99.57%.

关键词： Deep learning Degradation Accuracy Fault detection Plants (biology) Biological system modeling Data models Wastewater treatment Long short term memory Testing

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Propagation Characteristics of Partial Discharges in an Oil-Filled Power Transformer 17

Propagation Characteristics of Partial Discharges in an Oil-...

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17th IEEE International Conference on Compatibility, Power Electronics and Power engineering, CPE-POWERENG 2023

作者： Kameli, Sayed Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Guzinski, Jaroslaw Texas AandM University at Qatar Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Gdansk University of Technology Electrical and Control Engineering Faculty ul. Gabriela Narutowicza 11/12 Gdansk80-233 Poland

ISBN: (纸本)9798350300048

Power transformers are among the most important assets in the power transmission and distribution grid. However, they suffer from degradation and possible faults causing major electrical and financial losses. Partial discharges (PDs) are used to identify the insulation health status and their degradation level. PDs are incipient, low-magnitude faults caused by localized dielectric breakdown. Those activities emit signals in many forms, including electrical, chemical, acoustic, electromagnetic, and optical, facilitating various detection methods. This paper provides a theoretical basis for the condition evaluation of an oil-filled power transformer and clarifies the relationship between the operating voltage, void location, and electric-field intensity within the void. This was achieved by investigating the propagation characteristics of partial discharge signals in an oil-filled power transformer using a 3D finite element method (FEM) based simulation. Moreover, the characterization of simulated PD sources at different positions is investigated in this paper. The simulation results are curried out to show that air voids near the windings are subject to greatest peak electric field intensity. © 2023 IEEE.

关键词： Power transformers

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Security Issues in Software-Defined Networking (SDN) Environments

Security Issues in Software-Defined Networking (SDN) Environ...

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Roedunet International Conference (RoEduNet)

作者： Khirota Gorgees Yalda Diyar Jamal Hamad Nicolae Tapus Ibrahim Taner Okumus Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania IT department Soran Technical college Erbil Polytechnic University Kurdistan Region Erbil Iraq Computer Engineering Faculty of engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Software-Defined Networking (SDN) represents a significant shift in network architecture, providing exceptional programmability, flexibility, and simplified management. However, this paradigm shift introduces a unique set of security challenges that must be addressed to fully realize the potential of SDN. This paper examines the security issues in SDN environments, detailing the threats and vulnerabilities at various layers of the SDN architecture, including the control plane, data plane, and application plane. Through an extensive review of current literature, critical security challenges such as controller attacks, data plane breaches, and vulnerabilities in inter-plane communications are identified. Existing security solutions and mitigation strategies, such as authentication and authorization mechanisms, encryption techniques, and intrusion detection systems, are also explored. Furthermore, the paper discusses recent advances and emerging trends in SDN security, offering insights into ongoing research and future directions. The findings underscore the importance of robust security measures in ensuring the reliability and integrity of SDN deployments, providing a foundation for future innovation and development in this dynamic field.

关键词： Technological innovation Prevention and mitigation Intrusion detection Distributed databases Organizations Robustness Encryption Security Software defined networking computer crime

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