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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Bonar Sirait, Jaime Christ Togatorop, Parmonangan R. Tambunan, Paulus M.L.P Yanti Marpaung, Yemima Febe Situmeang, Samuel I.G Simanjuntak, Humasak T.A Faculty of Informatics and Electrical Engineering Institute of Del Technology Department of Information Systems Indonesia Faculty of Informatics and Electrical Engineering Institute of Del Technology Lecturer in Department of Information Systems Indonesia

ISBN: (纸本)9798350381382

Predicting student performance stands as a critical task to comprehensively assess their academic achievements, whether they classified as the category of good, sufficient, or bad. The outcome of a student's final exam can be influenced by a multitude of factors, among which the quality of the learning process and teaching methodology hold considerable significance. Recognizing the intricate interplay of these factors, it becomes evident that the integration of a satisfaction survey becomes imperative. In the realm of this research, we harness the potential embedded within both student satisfaction survey data and their ultimate academic grades. Overcoming existing challenges by an imbalanced dataset, we employ a holistic approach by utilizing the Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) to rectify the skewed distribution of data. To further enhance prediction capabilities, we introduced an innovative technique known as Deep Feature Synthesis. This advanced approach generating 36 novel features. The culmination of our research underscores the superiority of the random forest algorithm, especially when enriched by the harmonious merging of Deep Feature Synthesis and SVM-SMOTE through the strategic implementation of Random Search. Notably, our model achieves an impressive accuracy rate of 69%, complemented by 47% precision, 52% recall, and 47% F1-score. copy;2023 IEEE. © 2023 IEEE.

关键词： Support vector machines

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Software Implementation for the ESP32-Based EEG Measurement System 5th

Software Implementation for the ESP32-Based EEG Measurement ...

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5th International Conference on electrical engineering and Control Applications, ICEECA 2022

作者： Bendib, Hafed-Eddine Bahri, Mebarek Boumehraz, Mohamed Mallem, Ali Benaziza, Walid Laboratory of Energy Systems Modeling Department of Electrical Engineering University of Biskra Biskra07000 Algeria Department of Industrial Engineering University of Abbes Laghrour Khenchela40004 Algeria Department of Electrical Engineering University of Batna2 Batna05078 Algeria

ISBN: (纸本)9789819700448

This article integrates the library design, the firmware implementation and python-based streaming framework for the ESP32-based EEG measurement system. The overall system is characterized by the ability to capture and send data to the back-end via the USB interface at a speed of up to 16 KSPS, or via the wireless interface (Bluetooth or Wi-Fi) at a limited speed of 250 SPS. The modeling of software solutions is an important task in the design of the embedded and real-time systems. Therefore, it is important to have a vision on each step during the modeling process. Moreover, to manage the complexity of the system, two approaches were considered in this study, one was the separation of the platform into coherent entities that can be compiled separately;the second was the introduction of the object aspect (OA) which makes the scalability, the maintaining, and the re-usability of the underlying classes a relatively simple task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Firmware

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Multiple learning neural network algorithm for parameter estimation of proton exchange membrane fuel cell models

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Green Energy and Intelligent Transportation 2023年第1期2卷 1-15页

作者： Yiying Zhang Chao Huang Hailong Huang Jingda Wu School of Electrical and Information Engineering Jiangsu UniversityChina Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong Kong School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore

Extracting the unknown parameters of proton exchange membrane fuel cell(PEMFC)models accurately is vital to design,control,and simulate the actual *** order to extract the unknown parameters of PEMFC models precisely,this work presents an improved version of neural network algorithm(NNA),namely the multiple learning neural network algorithm(MLNNA).In MLNNA,six learning strategies are designed based on the created local elite archive and global elite archive to balance exploration and exploitation of *** evaluate the performance of MLNNA,MLNNA is first employed to solve the well-known CEC 2015 test *** results demonstrate that MLNNA outperforms NNA on most test ***,MLNNA is used to extract the parameters of two PEMFC models including the BCS 500 W PEMFC model and the NedStack SP6 PEMFC *** results support the superiority of MLNNA in the parameter estimation of PEMFC models by comparing it with 10 powerful optimization algorithms.

关键词： Neural network algorithm Parameter extraction Proton exchange membrane fuel cell Metaheuristics

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Arabic Handwriting Recognition Using Deep Learning and Simulated Annealing 4

Arabic Handwriting Recognition Using Deep Learning and Simul...

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4th International Conference on Embedded and Distributed systems, EDiS 2024

作者： Boukenadil, Bahidja Ilyas, Bendjillali Ridha Sofiane, Bendelhoum Mohamed Soumia, Djelaila Imane, Haouam Miloud, Kamline Laboratory of Electronic Systems Telecommunications and Renewable Energies Nour Bachir University Center Department of Electrical Engineering El Bayadh Algeria Salhi Ahmed University Center Department of Electrical Engineering Naama Algeria Tahri Mohammed University Tit Laboratory Department of Electrical Engineering Bechar Algeria

ISBN: (纸本)9798331519872

Arabic handwritten characters are important in many applications, making reliable recognition systems essential. In this study, we present a new method for Arabic Handwritten Recognition (AHR) that doesn't rely on segmentation. Using Simulated Annealing, we optimized the architectures of RegNetY-32GF and NfNet-F5 deep convolutional neural networks (CNNs) for feature extraction and classification. The HACDB database was used to demonstrate the effectiveness of our approach. When compared to other methods, our approach proved to be highly effective and robust. The optimized RegNetY-32GF model achieved an impressive success rate of 98.27%, highlighting the strength of our method. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Source Renewable Energy Forecasting Using CNN-LSTM Hybrid Model 25

Multi-Source Renewable Energy Forecasting Using CNN-LSTM Hyb...

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25th International Middle East Power System Conference, MEPCON 2024

作者： El-Saieed, Asmaa Mohamed Mosallam, Hanan A. Mansoura High Institute of Engineering and Technology Dept. of Computers and Systems Mansoura Egypt Mansoura Higher Institute of Engineering and Technology Department of Electrical Engineering Mansoura Egypt

ISBN: (纸本)9798350379648

Accurate forecasting of renewable energy output is vital for enhancing the efficiency and stability of integrated power systems. This study introduces a novel deep learning approach that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to predict energy production from solar, wind and hydropower sources. The hybrid CNN-LSTM model utilizes historical meteorological data, including solar irradiance, cloud cover, temperature, wind speed, and precipitation, to forecast future energy generation with high precision. The CNN component extracts spatial features from the weather data, while the LSTM component captures temporal dependencies and long-term trends. For solar energy forecasting, the model estimates solar radiation based on factors like temperature and humidity, while wind energy predictions rely on wind speed variations, and hydropower output is forecasted using rainfall and watershed information. This comprehensive approach enables accurate and reliable predictions of renewable energy production. The model's performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), etc. Results demonstrate that the hybrid model significantly improves the accuracy and reliability of renewable energy forecasts. This advanced tool provides valuable insights for energy managers, utility companies, and policymakers, aiding in optimizing resource allocation and enhancing the sustainability and resilience of energy grids. © 2024 IEEE.

关键词： Wind forecasting

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A Burst Mode and Variable Frequency Voltage Sharing Control Mechanism for an Adjustable High Voltage Pulsed Converter

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2023年第2期11卷 1888-1900页

作者： Lin, Chang-Hua Ahmad, Javed Li, Ren-Hao Liu, Hwa-Dong National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan National Taiwan Normal University Undergraduate Program of Vehicle and Energy Engineering Taipei106 Taiwan

This article presents a modular adjustable high-voltage pulse (MAHVP) generation system. The proposed MAHVP generation system uses a combination of full-bridge LLC resonant converters whose input terminals are connected in parallel and whose output terminals are in series to boost the output voltage;input parallel output series configuration efficiently increases the power holding capabilities of the overall system. The MAHVP generation system uses a novel burst mode and variable frequency voltage sharing (BMVFVS) control mechanism, the BMVFVS control mechanism senses the type of load if the MAHVP generation system needs to operate at a heavy load, it uses the variable frequency voltage sharing method to divide the power equally between the modules to enhance the overall performance. For light load conditions, the MAHVP generation system operates in burst mode, and the BMVFVS control mechanism combined with the frequency modulation technique is used to control the amplitude and pulsewidth of the output voltage smoothly and efficiently. A hardware prototype of 2.4 kW is developed, and experimental results are taken to verify the feasibility and validity of the proposed system, the maximum output voltage of the developed two-module prototype is tested up to 6 kV at the high efficiency of 97%. © 2013 IEEE.

关键词： Power converters

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Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology 3rd

Design and Manufacturing of an Efficient Low-Cost Fire Surve...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Marhoon, Hamzah M. Basil, Noorulden Elias, Bashar Bahaa Qas Department of Systems Engineering College of Information Engineering Al-Nahrain University Jadriya Baghdad Iraq Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq Department of Information and Communication Engineering College of Information Engineering Al-Nahrain University Jadriya Baghdad Iraq

ISBN: (纸本)9789819708918

In addition to recent technological advancements, a noticeable trend has emerged toward the development of smart cities and homes. By integrating intelligent home control components with existing household appliances, it aims to significantly reduce the use of human effort. This integration has led to decreased electricity consumption, enhanced home security against accidents and theft, and an overall improvement in residents’ comfort levels. This paper proposes the utilization of Internet of Things (IoT) technology for the design of an intelligent home fire alarm system. The suggested system is comprised of an Arduino microcontroller, a NodeMCU microcontroller, a GSM shield, a flame sensor, a buzzer, and a servo motor. Given the limitations and relatively low accuracy of flame sensors, the LM35 temperature sensor is employed in conjunction with it. Upon detecting a flame and a subsequent temperature rise beyond 50 °C, the Arduino Uno will promptly dispatch an SMS warning to the homeowner. Simultaneously, a logical "1" signal will be transmitted to the NodeMCU microcontroller. This microcontroller will then utilize IoT technology to exhibit the warning message on the pre-programmed graphical user interface (GUI). To conclude the alert sequence, the Arduino Uno microcontroller will activate the servo motor. This activation serves the purpose of initiating the opening of the exit door and triggering the alarm. Rigorous practical testing has been conducted on the implemented system, yielding consistently positive outcomes and successful results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Deep Learning-based Mitigation of Nonlinear Hardware Impairments for THz Communication 35

Deep Learning-based Mitigation of Nonlinear Hardware Impairm...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Sharma, Vaishali Keshari, Prakhar Sharma, Sanjeev Deka, Kuntal Jain, Sandesh Bhatia, Vimal SaSg Department of Electrical Engineering IIT Indore India Iit Bhu Department of Electronics Engineering Varanasi221005 India Iit Guwahati Guwahati781039 India ABV-IIITM Center for Autonomous Systems Department of Electrical & Electronics Engineering Gwalior India

ISBN: (纸本)9798350362244

Terahertz (THz) wireless communication is a promising technique to meet the rising demand for high-bandwidth services. Propagation of the T H z waves through the atmosphere is influenced by factors like attenuation, water vapour, weather, turbulence, rain and beam misalignment. Furthermore, performance of a THz link is severely degraded by non-linear hardware impairments introduced by power amplifier (PA). Conventional estimators such as zero-forcing (ZF) and minimum mean squared error (MMSE) fall short of handling beam misalignment, rain attenuation, and non-linearity. To circumvent this limitation, we propose a deep neural network (DNN) based receiver for THz communication systems in the presence of PA non-linearity. Simulation results for bit error rate (BER) performance considering non-linear distortion due to power amplifier indicate that the proposed deep learning (DL) based receiver performs better in terms of BER and is more robust compared to ZF, MMSE and orthogonal matching pursuit (OMP) based receiver. © 2024 IEEE.

关键词： Mean square error

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Active and Reactive Power Control in Asymmetrical Cascaded Multilevel Inverter 3

Active and Reactive Power Control in Asymmetrical Cascaded M...

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3rd International Conference on Emerging Trends in electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Tauseef, Muhammad Fahad Uzair, Muhammad Abbas, Ghulam Power Electronic Systems R&D Ohmitron Inc. Lahore Pakistan Department of Electrical Engineering Faculty of Engineering Islamic University of Madinah Madinah Saudi Arabia Department of Electrical Engineering The University of Lahore Lahore Pakistan

ISBN: (纸本)9798331504861

This paper presents a 27-level multilevel inverter topology employing three H-Bridge modules in conjunction with high-efficiency power conversion. The proposed topology utilizes a cascaded configuration of single-phase H-Bridge inverters, following a 1:3:9 voltage ratio, to produce a 27-level staircase waveform. This grid-supporting inverter can supply reactive power as well as active power, and it has a smooth dynamic response and flexible management of reactive power. The design method is in line with grid compliance norms and improves overall power quality by ensuring minimal harmonic distortion. The modeling and simulation of a 600-kVA inverter have been developed, incorporating a liquid cooling system to enhance thermal management, achieving a high-power density of up to 25 kW/L and a full-load efficiency of 98%. The PLECS environment is used to simulate the proposed complete inverter system under varying conditions. The topology is adaptable for varied power ratings and allows great scalability, according to detailed analysis, making it versatile for a variety of renewable energy and industrial applications. The inverter might greatly enhance the present energy infrastructure and grid stability, as shown by its effective deployment and performance results. ©2024 IEEE.

关键词： Electric power system control

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Image-Based Fast Finite-Time Target Tracking Control of Onboard Inertially Stabilized Camera Platform

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IEEE Transactions on Industrial Electronics 2024年第12期71卷 16409-16418页

作者： Sun, Xunhong Zhu, Wenwu Du, Haibo Chen, Weile Chen, Chih-Chiang Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University School of Automation Nanjing210096 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

In this article, we investigate the problem of fast finite-time target tracking control of the inertially stabilized platform (ISP) with a camera that mounted on a mobile robot. At first, the kinematics of the onboard ISP target tracking system is established by the geographic coordinate and pinhole camera model, which includes the state-dependent uncertainties and external disturbances. Then, based on the image information captured by camera, a continuous image-based fast finite-time control algorithm is designed for target tracking. Rigorous analysis proves that the target can be tracked at the center of camera's view in finite time in the absence of disturbances. In the presence of disturbances, the tracking error can converge to a region around the origin in finite time. Simulation and experimental comparisons are presented to confirm the efficiency of the proposed method. © 1982-2012 IEEE.

关键词： Mobile robots

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