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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Comparative Analysis of The Effective Grounding System on External LPS Modeling in PV Fields 7

Comparative Analysis of The Effective Grounding System on Ex...

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7th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2023

作者： Rohana, Rohana Hardi, Surya Nasaruddin, Nasaruddin Away, Yuwaldi Doctoral Program School of Engineering Universitas Syiah Banda Aceh Indonesia Universitas Muhammadiyah Sumatera Utara Indonesia Universitas Sumatera Utara Dept. of Electrical Engineering Medan Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350385526

Solar energy is environmentally friendly energy and is available in large quantities. The energy produced is commonly converted into electricity by using solar panel (PV) systems for utilization. PV systems are generally located in open areas such as hillsides, deserts, and water surfaces, so they become more sensitive against lightning strikes. Commonly, the PV is equipped with a lightning protection system (LPS). This paper discusses the grounding system at the solar power center (PLTS) Weh Island, Sabang City. Evaluation and comparison under certain conditions are carried out through measurements, calculations, and simulations. The grounding system is modeled for one rod, four rods in parallel, and plate rod. The measurement process during field observations and the induce voltage simulation are carried out to get the results of the analysis calculations. The result obtained shows that the grounding system fulfills the PUIL standard, namely being smaller than 5 Ω. When using plate electrodes, the resistance value is greater than that of rod electrodes. So that the lower conductor can be more resistant and easier to maintain. Improving the quality of the protection system, this research is used as a reference to evaluate LPS conditions in PV fields. © 2023 IEEE.

关键词： Solar energy

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers 5

Observing Generator Performance by Finite Element Analysis C...

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5th International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2023

作者： Habibi, Muhammad Afnan Ali Irfani, Mochamad Farhan Gumilar, Langlang Kusumawardana, Arya Aripriharta Baskoro, Angger Universitas Negeri Malang Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia National Yang Ming Chiao Tung University Dept. Electrical Engineering and Computer Sciences Hsinchu Taiwan

ISBN: (纸本)9798350369717

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies. © 2023 IEEE.

关键词： Finite element method

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A Classifiers Experimentation with Quantum Machine Learning

A Classifiers Experimentation with Quantum Machine Learning

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International Electrical engineering Congress (iEECON)

作者： Chaiyaporn Khemapatapan Thammanoon Thepsena Aomduan Jeamaon Computer Engineering Program College of Innovative Technology and Engineering Dhurakij Pundit University Bangkok Thailand

Quantum Computing is a new way to create a different type of computer system from Quantum Mechanics. Quantum machine learning is one of the applications took the benefit from qauntum computers. In this experiment, we use the IBM Qiskit library to study how well a quantum machine learning classifier works. We made two different colored dots to represent the two-class dataset. The quantum circuit can be collected from the Qiskit library. The 1 st and 2 nd order feature map schemes operated by oracle quantum oracle functions are applied before making a classification. Finally, we can call the quantum support vector machine or QSVM function to classify data. Kernel matrix from the studied qauntum model shows that quantum computing can be used to clearly classify classes of the dataset with 100 percent accuracy.

关键词： Support vector machines Measurement Machine learning Production Quantum state Prediction algorithms Libraries

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Early Diagnosis of Osteoporosis: An Artificial Intelligence-Based Framework

Early Diagnosis of Osteoporosis: An Artificial Intelligence-...

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International Conference on Advances in Biomedical engineering (ICABME)

作者： Amira J. Zaylaa Alaa Daher Mohammad Ayache Niveen Omeirat Razane Mahmoud Bader Abou Hawili Biomedical Engineering Laboratory Biomedical Engineering Program Electrical and Computer Engineering Faculty of Engineering Beirut Arab University Lebanon

Osteoporosis is a common disease characterized by low bone density and structural deterioration of bone tissue. For a successful course of treatment and fracture avoidance, early diagnosis of this disease is essential. The aim was to provide a novel method for osteoporosis prediction using Artificial Intelligence (AI)-based framework. The purpose was to predict the likelihood of osteoporosis based on the Bone Mineral Density (BMD), along with other characteristics such as age, weight, height, gender, and Body Mass Index (BMI) extracted from medical reports and images collected from a comprehensive medical center in Lebanon. Three machine-learning algorithms were implemented and tested, Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT). Variety of quantitative statistical metrics were used to evaluate the performance of our framework, upon training and testing our algorithms. The metrics that were employed to evaluate our results included accuracy, precision, sensitivity, and F-score, in addition to the Receiver Operating Characteristic (ROC) Curve and the Area Under the Curve (AUC). Experimental Results demonstrated that both the SVM and LR algorithms achieved the highest accuracy of detection of osteoporosis as compared to existing algorithms applied in this field, with an accuracy of 89%. The sensitivity of diagnosis obtained was 98% by LR and 97% by SVM and surpassed the sensitivity obtained by DT. As such LR showed the best performance. The output of the algorithms could help medical doctors assess patients automatically. These findings demonstrated the potential of AI in osteoporosis prediction and thus prevention, highlighting the significance of early diagnosis. Thereby as a future prospect, choosing carefully the framework is crucial and additional algorithms have to be considered and tested.

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Single-Phase Transformerless UPS with Interleaved Boost Converters for Battery Discharging

Single-Phase Transformerless UPS with Interleaved Boost Conv...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Pinto, Alan R. A. Bahia, Filipe A. C. Souza, Reuben P. R. Pinheiro, José R. Costa, Fabiano F. Federal University of Bahia Post-Graduate Program in Electrical Engineering - Ppgee Salvador Brazil Federal University of Bahia Dept. of Electrical and Computer Engineering - Deec Salvador Brazil Federal University of Campina Grande Dept. of Electrical Engineering - Dee Campina Grande Brazil

ISBN: (纸本)9798350316445

This paper proposes a reconfiguration for a single-phase, double conversion uninterruptible power supply (UPS). With the addition of two static switches to the original circuit, the UPS mitigates the ripple in the current, through battery discharge performed by two legs. Other than these switches, the circuit topology is preserved. The preliminary results show a satisfactory level of ripples for the inductor current and suitable power quality for the output voltage. © 2023 IEEE.

关键词： battery mode discharger interleaved boost normal mode

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Residential Power Satisfaction as a Demand Side Management Approach in Puerto Rico

Residential Power Satisfaction as a Demand Side Management A...

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IEEE Central America and Panama Convention (CONCAPAN)

作者： Carlos A. Peña Becerra Willian Pacheco Cano Marcel Castro Sitiriche Samira Ortiz Department of Electrical and Computer Engineering University of Puerto Rico Mayaguez PR Electrical and Computer Engineering Program Universidad Ana G. Méndez – Recinto de Gurabo

ISBN: (数字)9798350366723

ISBN: (纸本)9798350366730

Puerto Rico's electrical infrastructure powers over three million residents, yet energy consumption overcomes generation and continues to rise. This imbalance results in an unreliable power supply and frequent blackouts across the island. One solution to this issue is to increase generation capacity by adding new generating units and enhancing transmission networks, though this is expensive. Another approach is to promote the adoption of rooftop solar generation with storage systems through microgrids. Alternatively, demand-side management (DSM) offers a more cost-effective solution by regulating demand and reducing energy use at the consumer level. This paper explores the unique integration of power satisfaction within DSM strategies to reduce energy consumption while preserving user satisfaction. Utilizing an agent-based model to simulate household appliance behavior, we demonstrate how power satisfaction can reduce energy demand. This innovative approach offers a promising pathway toward a more resilient and sustainable energy future for Puerto Rico.

关键词： Energy consumption Renewable energy sources Demand side management Power system stability Agent-based modeling Stability analysis Solar power generation Security Sustainable development Resilience

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Smart Home Security System Using Face Recognition Based on IoT- CNN 2

Smart Home Security System Using Face Recognition Based on I...

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2nd International Conference on Information Technology Research and Innovation, ICITRI 2023

作者： Febriantono, M. Aldiki Zuhair, Alvin Khaeruddin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Islam Blitar Electrical Engineering Department Blitar Indonesia University of Muhammadiyah Malang Diploma III Electronic Technology Vocational Study Program Malang Indonesia

ISBN: (纸本)9798350324945

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners. © 2023 IEEE.

关键词： Face recognition

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