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6th IEEE Conference on Energy Conversion, CENCON 2023

作者： Qasem Mohammed, Sadeq Ali Ali Gamal Al-Kaf, Hasan Lee, Kyo-Beum Ajou University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350325096

This research work introduces an enhanced sensorless-based iterative learning control (ILC) method to elevate the steady-state and dynamic performance of permanent magnet synchronous motors (PMSMs). Unlike traditional sensorless control methods, the proposed method combines feedback control terms and ILC terms to minimize state errors and improve dynamic performance by utilizing recorded data from previous iterations. Precise knowledge of the PMSM parameters is not required, making the control approach less sensitive to system perturbation. Comparative simulation analyses using the PSIM simulation tool are carried out to validate the practicability of the proposed control approach both in transient and steady-state conditions. © 2023 IEEE.

关键词： Sensorless control

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Design and Performance Analysis of a Grid-Connected Solar Power System for Energy Efficient AR Building 1

Design and Performance Analysis of a Grid-Connected Solar Po...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Salem, Nema Asiri, Jameelh Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In 2020, nearly all Saudi Arabia's electricity generation was fueled by natural gas (61%) and crude oil (39%). As part of Saudi Arabia's vision 2030, the Saudi government plans to diversify fuels to increase crude oil exports and reduce carbon dioxide emissions. In Saudi Arabia, the solar irradiance averages 5.2 kWh/m2/day, photovoltaic (PV) technology is being embraced to achieve green growth and increase power generation. As a result of the technology's proximity to the point of consumption, it ensures a continuous supply of energy while reducing the country's transmission and distribution losses. A key parameter affecting the performance of PV panels in a photovoltaic system is the solar radiation incident on the panel. The tilt and azimuth angles of PV modules are two important factors in designing the PV system for the best performance. This study starts by utilizing Excel software to calculate the azimuth angle for the best adjustment of solar modules. Then, PVSyst software is used to design and simulate a grid-connected PV system for the Admission and Registration Building (AR) at Effat University in Jeddah. The study compares the solar system's performance for mono-crystalline, poly-crystalline, and thin-film photovoltaic modules. The simulation results showed the effectiveness of the design in terms of the produced energy, meeting the estimated needs of the AR building, the save CO2, and the annual savings using Saudi Arabia's current electricity tariffs. © 2023 IEEE.

关键词： Carbon dioxide

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HETEROGENEOUS NEURONAL AND SYNAPTIC DYNAMICS FOR SPIKE-EFFICIENT UNSUPERVISED LEARNING: THEORY AND DESIGN PRINCIPLES 11

HETEROGENEOUS NEURONAL AND SYNAPTIC DYNAMICS FOR SPIKE-EFFIC...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chakraborty, Biswadeep Mukhopadhyay, Saibal Department of Electrical and Computer Engineering Georgia Institute of Technology United States

This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning. We analytically show that the diversity in neurons' integration/relaxation dynamics improves an RSNN's ability to learn more distinct input patterns (higher memory capacity), leading to improved classification and prediction performance. We further prove that heterogeneous Spike-Timing-Dependent-Plasticity (STDP) dynamics of synapses reduce spiking activity but preserve memory capacity. The analytical results motivate Heterogeneous RSNN design using Bayesian optimization to determine heterogeneity in neurons and synapses to improve E, defined as the ratio of spiking activity and memory capacity. The empirical results on time series classification and prediction tasks show that optimized HRSNN increases performance and reduces spiking activity compared to a homogeneous RSNN. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Neurons

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Optimizing Hybrid Energy Systems: Employing Smart Monitoring Networks with IoT Integration 8

Optimizing Hybrid Energy Systems: Employing Smart Monitoring...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Taher, Anmar Yahya Kurnaz, Sefer Ahmed, Saadaldeen Rashid Altinbas University Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept Istanbul Turkey Bayan University Computer Science Department Kurdistan Erbil Iraq College of Engineering Al-Ayen University Artificial Intelligence Engineering Department Thi-Qar Iraq

ISBN: (纸本)9798350354423

For effective and sustainable energy solutions, this paper investigates at a hybrid energy system that uses Internet of Things technologies. Three components make up the system: PV system, an AC turbine voltage source, DC batteries, and ThingSpeak, an IoT analytics program hosted on the cloud that allows for real-time monitoring. In order to optimize performance, the study emphasizes the significance of hybrid energy systems as well as the requirement for improved analytics and monitoring. A detailed description of the experimental setup is given, including the measured parameters. The results demonstrate the hybrid system's adaptability to various loads and input voltages. fluctuations in DC battery and turbine voltages cause fluctuations in AC power and AC current, while the AC voltage stays constant at 224.4 V. For efficient analytics and monitoring, the hybrid energy system must include ThingSpeak. The study emphasizes how the system can adjust to changing conditions and how Internet of Things technologies may assist hybrid energy systems grow more powerful. This research adds to the current discourse on the integration of IoT data for enhanced system optimization and sustainable energy solutions. © 2024 IEEE.

关键词： Hybrid power

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Quality Assurance of Autonomous Vehicle's Sensor Data based on Classifying Level of Difficulty

Quality Assurance of Autonomous Vehicle's Sensor Data based ...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Kim, Kana Lee, Sangjun Kim, Hakil Inha University Department of Electrical and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350320213

Self-driving technology using deep learning has achieved a lot of research and excellent performance, but the process of training a model requires a large amount of high-quality data and it is still difficult for humans to directly inspect the processed data and prove its quality. In addition, classifying the training difficulty of the data for contextual and functional separation of deep learning algorithm is also an important challenge. This paper proposes a framework for validating the quality of constructed new datasets, which also classifies the training difficulty of datasets, thereby ensuring the versatility of valid datasets and introducing strategies to classify contextual datasets. Experiments on the AI hub dataset proved its quality and were able to be reorganized into datasets classified by difficulty level. © 2023 IEEE.

关键词： Autonomous Vehicle Dataset Quality Level of Difficulty Quality Assurance

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Advanced Malware Detection Methods for Polymorphic Virus Identification 5

Advanced Malware Detection Methods for Polymorphic Virus Ide...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Anil, D. Shreeshayana, R. Kiran, B. Preethi Uvce Bangalore University Department of Computer Science and Engineering Bengaluru India Atme College of Engineering Department of Electrical and Electronics Engineering Mysuru India Pes University Department of Computer Science and Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

Polymorphic viruses pose a significant challenge to traditional malware detection methods due to their ability to modify their code structure with each infection, effectively evading signature-based detection. This paper explores advanced malware detection methods tailored for identifying polymorphic viruses, focusing on the integration of machine learning, deep learning and hybrid approaches. Through a comprehensive analysis of static and dynamic features extracted from executables and URLs, this study employs classifiers such as Random Forests and Support Vector Machines (SVMs) to enhance detection accuracy. The SVM classifier demonstrated superior performance, achieving a detection accuracy of 96.23% compared to the 93.67% accuracy of the Random Forest classifier. This research highlights the potential of combining static and dynamic analysis with machine learning techniques to address the limitations of traditional methods, offering a robust framework for detecting and mitigating polymorphic and zero-day threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Reconfigurable Hybrid Asymmetrical Load Modulated Balanced Amplifier with High Linearity, Wide Bandwidth, and Load Insensitivity

Reconfigurable Hybrid Asymmetrical Load Modulated Balanced A...

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Guo, Jiachen Chen, Kenle University of Central Florida Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350347647

This paper presents the first-ever load-insensitive and highly linear three-way load-modulation power amplifier (PA) based on reconfigurable hybrid asymmetrical load modulated balanced amplifier (H-ALMBA). Through proper amplitude and phase controls, the carrier, control amplifier (CA), and two peaking balanced amplifiers (BA1 and BA2) can form a linear high-order load modulation over wide bandwidth. Moreover, it is theoretically unveiled that once CA reaches saturation at the pre-determined power back-off (PBO), its current- and voltage-source duality makes the primary (BA1) and secondary (BA2) peaking amplifiers have complementary load dependence. As such, the PA's linearity and efficiency profiles can be maintained against arbitrary load mismatch through ZL-dependent reconfiguration of CA supply voltage (VDD,CA) and turning-on sequence of BA1 and BA2. Based on the proposed theory, an RF-input H-ALMBA is developed with GaN transistors and wideband quadrature hybrids. Over the design bandwidth from 1.7-2.9 GHz, an efficiency of 57%-72% at peak power and 43%-53% at 10-dB PBO are measured together with linear AMAM and AMPM responses. In modulated evaluation with 4GLTE signal, an EVM of 3.1%, ACPR of -39 dB, and average efficiency of up to 52% are measured, which are well sustained against 2 : 1 VSWR of load through reconfiguration. © 2023 IEEE.

关键词： Peak to average power ratio

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An Efficient Beam-Sharing Algorithm for RIS-aided Simultaneous Wireless Information and Power Transfer Applications 48

An Efficient Beam-Sharing Algorithm for RIS-aided Simultaneo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tran, Nguyen Minh Amri, Muhammad Miftahul Park, Je Hyeon Kim, Dong In Choi, Kae Won Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781728163277

Simultaneous wireless information and power transfer (SWIPT) is a key technology for enabling future high-tech lifestyles by guaranteeing the perpetual operation of trillions of low-power IoT devices. Currently, reconfigurable intelligent surface (RIS) is a promising technology for achieving cost- effective and energy-efficient wireless technologies. In this paper, we propose an efficient beam-sharing algorithm for RIS-aided SWIPT systems. The proposed algorithm maximizes the power transferred from the power transmitter (Ptx) to the power user (PU) while minimizing that to the data user (DU) but maximizing the signal from the data transmitter (Dtx) to DU. Simulation results demonstrate that the proposed beam-sharing algorithm effectively delivers power from Ptx to PU while limiting the power sent by Ptx to DU but maximizing the received signal from Dtx. © 2023 IEEE.

关键词： beam splitting reconfigurable intelligent surface SWIPT

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Improved Retrieval of Effective Parameters in Hyperbolic Metamaterials for Oblique Incidence 16

Improved Retrieval of Effective Parameters in Hyperbolic Met...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Jusung Park, Seungkyun Kim, Kyuho Yu, Sunkyu Park, Namkyoo Seoul National University Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Seoul National University Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

An improved retrieval method for effective permittivity and permeability of hyperbolic metamaterials at oblique incidence is presented. We revisit a branching problem at oblique incidence. © 2024 The Authors OCIS...

ISBN: (纸本)9798350372076

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