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International Conference on sciences and Techniques of automatic control and computer engineering (STA)

作者： Taleb Hamdi Khaled Elleuch Hafed Abid Ahmed Toumi National School of Engineering of Sfax Laboratory of Sciences and Techniques of Automatic control & computer engineering (Lab-STA) Sfax Tunisia

ISBN: (纸本)9781665482622

In this work we present a sliding mode controller to control a boost DC-DC converter inserted in a photovoltaic system. The controller includes a combination between Fuzzy logic supervisor and sliding mode control to extract maximum power from PV systems. The proposed algorithms is compared with conventional algorithms such as the Perturb and observe (P&O) algorithm, incremental conductance (InC) and also unconventional algorithms such as FLC while varying irradiation. The simulation results confirm the performance and its robustness compared to other controllers.

关键词： Fuzzy logic Photovoltaic systems Radiation effects Adaptation models Simulation Regulation Robustness

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Sliding Mode control In A Wind Turbine System

Sliding Mode Control In A Wind Turbine System

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International Conference on sciences and Techniques of automatic control and computer engineering (STA)

作者： Atef Jemmali Khaled Elleuch Hafedh Abid Ahmed Toumi Laboratory of Sciences and Techniques of Automatic control & computer engineering (Lab-STA) National School of Engineering of Sfax Sfax Tunisia

ISBN: (纸本)9781665482622

This paper is interested in the control of a DFIG by a sliding mode controller, used in a wind system. The conversion chain comprises a three-blade turbine, the DFIG, a rotor-side converter, a capacitor, grid-side converter and a line-side filter. The sliding mode control is used to control the direct and quadratic current of the rotor; this type of regulator was used especially for its robustness against the parametric variations of the generator.

关键词： Regulators Capacitors Doubly fed induction generators Rotors Robustness Generators Wind turbines

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An Efficient Particle YOLO Detector for Urine Sediment Detection 4th

An Efficient Particle YOLO Detector for Urine Sediment Dete...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Chen, Zejian Hu, Rong Chen, Fukun Fan, Haoyi Ching, Fum Yew Li, Zuoyong Su, Shimei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China School of Electrical Engineering Zhengzhou University Henan Zhengzhou450001 China

ISBN: (纸本)9783031201011

Urine sediment detection is an essential aid in assessing kidney health. Traditional machine learning approaches treat urine sediment particle detection as an image classification task, segmenting particles for detection based on information such as edges or thresholds. However, the segmentation of sediment particles is complex due to the low contrast and weak edge characteristics of urine sediment images. In this paper, we consider urine sediment particle detection as a object detection task and propose the YOLOv5s-CBL, a detector dedicated to particle detection. Specifically, to mitigate the impact of background noise on detection accuracy, we inherit CBAM on the YOLOv5s model to help the network filter useless noise information and find regions of interest to extract target features. Then, we expand the original three-scale feature layer to improve the sensitivity of the model to larger-scale target sediment particles. Finally, we use the BiFPN structure instead of the original PANet combined with the FPN structure, which can more effectively fuse multiple different scales of information to improve the detection performance of the model. We compared state-of-the-art methods on two real-world datasets, and the experimental results demonstrated the effectiveness of YOLOv5s-CBL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis 4th

Domain Adversarial Interaction Network for Cross-Domain Fau...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Lu, Weikai Chen, Jian Zheng, Hao Fan, Haoyi Wei, Eng Yee Cao, Xinrong Zhang, Deyang School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

ISBN: (纸本)9783031200984

Intelligent fault diagnosis has been widely used in the industry and plays a crucial role in the health management of machinery. In recent years, unsupervised domain adaptation (UDA) has been applied to fault diagnosis, showing excellent performance under variable working conditions. However, most existing UDA-based methods do not consider the temporal relations in the fault signal, resulting in sub-optimal performance. In this paper, we proposed a domain adversarial interaction network (DAIN) to solve this problem. By downsampling sub-sequences of fault signals and interacting with their features, DAIN can obtain feature representations containing the temporal relations. In addition, domain adversarial learning and maximum mean discrepancy (MMD) are applied to DAIN to align the domain discrepancy and distribution discrepancy of source and target domains. We conducted extensive experiments on the public Paderborn University (PU) dataset, and the results demonstrate that the proposed method can achieve higher cross-domain fault diagnosis accuracy than the existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Failure analysis

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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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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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Optimal PMU placement in the Southern Cameroon Interconnected Grid considering the effect of a group of ZIBs for complete obervability and dynamic stability

Research Square

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Research Square 2024年

作者： Adolphe, MOULUM Philippe Jacques, MANDENG Jean Hubert, KOM Charles Laboratory of Computer and Automatic Engineering Advanced Teachers’ Training College for Technical Education Douala Cameroon Laboratory of Technology and Applied Sciences University Institute of Technology Douala Cameroon Laboratory of Energy Materials Modelling and Methods Higher National Polytechnic School Douala Cameroon

In this paper, a solution for optimal PMU placement in the Southern Cameroon Interconnected grid (SIG) is proposed in response to a problem of dynamic stability in the network. In this power system, the monitoring of the parameters is ensured so far by the SCADA, a technology that gives unsynchronized measurements and whose state estimation of the grid is inconsistent. Furthermore, a scan rate of data makes the SCADA system inefficient for measuring the dynamic and transient behaviour occurring in the power grid. Present study uses the method of integer linear programming (ILP) considering the effect of a group of zero-injection buses (ZIBs). Then, a comparative analysis of our results is made with those of the algorithms of other existing optimization methods in order to evaluate the performance of the applied method from the viewpoint of the number of PMUs, the number of buses observable from one or more nodes, and the system observability redundancy index (SORI). The different results obtained through MATLAB 2020a clearly show that considering the effect of a group of ZIBs, the ILP method presents the most optimal solution with a number of 10 PMUs installed on the buses 5, 8, 10, 11, 13, 16, 25, 28, 33 and 39, with a complete observation of the SIG. This result is validated insofar as 50% of the buses obtained (5, 8, 11, 33 and 39) are found with all the other comparative methods. In addition, buses 5, 8, 10, 11, 13, 16, 39 represent the dominant interconnection substations of the SIG, bus 33 also being a dominant bus in the Area 3 of the SIG. © 2024, CC BY.

关键词： Phasor measurement units

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Adaptive Kinematic control for the Trajectory Tracking of Unmanned Surface Vehicle with Unknown Ocean Currents

Adaptive Kinematic Control for the Trajectory Tracking of Un...

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International Conference on sciences and Techniques of automatic control and computer engineering (STA)

作者： Sami Souissi Mohamed Boukattaya Laboratory of Sciences and Techniques of Automatic control &computer engineering (Lab-STA) National school of engineering of sfax University of sfax Tunisia

ISBN: (纸本)9781665482622

The problem of trajectory-tracking for Unmanned Surface Vehicle (USV) is presented using a robust adaptive approach. First, a virtual velocity command is developed using backstepping control to ensure the traj ectory tracking of the USV's location and orientation. Then, an adaptive update law is designed for the estimation and compensation of ocean currents disturbances acting on the USV system. The stability analysis and the computation of the adaptive laws are proved using Lyapunov stability. Simulation results show the validity of the proposed method for the trajectory tracking problem.

关键词： Backstepping Trajectory tracking Oceans Simulation Estimation Kinematics Stability analysis

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