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A Monolithic All-1.8 V-Thin-Gate-NMOS KY-Boost Converter With Reused Flying-Capacitor Bootstrap Gate Driver Achieving 94.42% Peak Efficiency

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IEEE Transactions on Power electronics 2024年第6期39卷 7238-7251页

作者： Tsai, Chieh-Ju Hsu, Chan-Hsuan Chen, Ching-Jan Hung, Yu-Ting Hsieh, Chun-Yu National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan National Taiwan University Graduate Institute of Electronics Engineering Taipei10617 Taiwan NovaTek Corporation Taiwan Taipei10617 Taiwan

This article presents a monolithic KY-boost converter for wearable display systems. Traditional boost converter for this application suffers from large chip area and bulky output filter. This article proposes a monolithic KY-boost converter using all-1.8V-thin-gate-NMOS switches to reduce power loss and silicon area. Without an extra off-chip bootstrap capacitor, this proposed converter reuses the flying capacitor as the bootstrap capacitor for one of the high-side switches. Meanwhile, an internal charge-pump-based gate driver is introduced to drive another high-side switch. Furthermore, the soft-charging startup circuit and peak-current mode control for this KY converter are realized. The converter integrated circuit (IC) is realized in a 180 nm CMOS process with an input voltage of 1.5-1.8 V, an output of 2-2.7 V, a maximum power efficiency of 94.42%, and a 0.695 W/mm2 power density. Thanks to the all-thin-gate-NMOS architecture, the proposed converter reduces more than 75% of silicon area with the same IC pin count compared to the conventional boost converter and P-P-NMOS thick-gate KY-boost converter. © 1986-2012 IEEE.

关键词： System-on-chip

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Sensitivity Analysis for Neutral Reactor Optimization 8

Sensitivity Analysis for Neutral Reactor Optimization

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8th Workshop on Communication Networks and Power Systems, WCNPS 2023

作者： Balbino, L.S. Dantas, K.M. Neves, W.L.A. Electrical Engineering Graduate Program Campina Grande Brazil Department of Electrical Engineering PB Campina Grande Brazil

ISBN: (纸本)9798350343571

This paper presents an approach for choosing neutral reactors in transmission lines (TL) with shunt compensation. Data from the Brazilian Power Grid were used for digital simulations with ATPDraw, and a sensitivity analysis was carried out with various parameters (line length, neutral reactor, fault location) to find curves that correlate the optimum reactor value with the transmission line shunt compensation level. The influence of line length and compensation level are discussed. Limitations are presented, and the results show that the approach allows reactors to be obtained efficiently for most of the compensation range. © 2023 IEEE.

关键词： Electric lines

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Visualize the Gas Spreading Over Time as Separate Trajectories with Matrix Decomposition Based on the Linearity of LSPR Gas Sensor Response

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IEEJ Transactions on Sensors and Micromachines 2024年第11期144卷 345-349页

作者： Zheng, Xiaofan Matsuoka, Masato Hayashi, Kenshi Tomiura, Yoichi Graduate School of Information Science and Electrical Engineering Department of Informatics Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan Graduate School of Information Science and Electrical Engineering Department of Electronics Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

Detecting and visualizing gas distributed in two dimensions is enabled by the localized surface plasmon resonance (LSPR) gas sensor. This study provides a method for analyzing measurement data that allows component gases to be visualized separately. The degree of decrease in the intensity of the transmitted light (corresponding to the absorbance) due to the effect of the surrounding gas on the sensor was taken as the response of the sensor, and an approximate linear proportionality between the gas concentration and the response of the sensor was assured through measuring the sample of gas sources in different dilutions. Because the responses of gas sensor to mixed gases can be regarded as the sum of the responses to each component gas with respect to its concentration, this proportionality lead the possibility to estimate the concentration distribution of component gases by applying the algorithm of matrix decomposition. We applied matrix decomposition to real measurement data and visualized the component gases spreading over time. Moreover, we discussed the impact of speculating on the number of components in our case by conducting a simulation experiment. © 2024 The Institute of electrical Engineers of Japan.

关键词： Surface plasmon resonance

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A 0.9-V 50-MS/s 67.3-dB-SNDR SAR-ISDM ADC With an Oscillator-Based Integrator

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IEEE Transactions on Circuits and Systems II: Express Briefs 2023年第9期70卷 3263-3267页

作者： Chan, Yen-Chun Chang, Che-Wei Lee, Tai-Cheng Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan

A two-step analog-to-digital converter (ADC), using a successive approximation register (SAR) ADC and a time-domain incremental σΔ modulator (ISDM) for the coarse and fine conversion, is proposed for power-efficient data conversion. A high-gain gated-delay oscillator (GDO) with a time-domain integration technique is employed to achieve the first-order noise shaping in the fine ISDM conversion. The proposed ADC fabricated in a 40-nm CMOS technology achieves a peak SNDR of 67.3 dB, the Schreier figure of merit (FoMS) of 169.27 dB, and the Walden figure of merit (FoMW ) of 16.76 fJ/conversion-step. © 2023 IEEE.

关键词： Time domain analysis

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An Ensemble-Based Classifier to Determine the Harmonic Contribution Responsibility Between Utility’s Grid and Microgrids

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IEEE Access 2025年 13卷 85873-85881页

作者： Mella, Matheus C. Fernandes, Ricardo A.S Barbosa, Daniel Federal University of São Carlos Graduate Program in Electrical Engineering São Carlos Brazil University of São Paulo São Carlos School of Engineering Department of Electrical and Computer Engineering São Carlos Brazil Federal University of Bahia Department of Electrical Engineering Salvador Brazil

The integration of nonlinear loads and distributed energy resources in microgrids (MG) has increased the levels of harmonic distortions in distribution feeders. Identifying the responsibility for the harmonic distortions measured at the MG’s point of common coupling (PCC) is essential for managing the power quality. For this purpose, the present paper aims to determine whether the responsibility predominates from the utility’s grid or the MG itself. Thus, an ensemble-based approach was proposed, comparing the behaviour of Random Forest and XGBoost classifiers to identify the side with the predominant harmonic contribution. In order to validate the proposed approach, scenarios were simulated in the IEEE 34-node test feeder using the ATP/ATPDraw software, considering the harmonic sources located at different nodes of the feeder. Three-phase voltage and current signals were acquired at MG’s PCC to train and validate the ensembles. However, a feature extraction stage was performed to obtain time and frequency features used as inputs to the predictive models. The results demonstrated that the proposed approach effectively identified the contribution side, achieving an average F1-score above 99% for both ensembles, representing a feasible solution for determining the harmonic distortion responsibilities at the MG’s PCC. © 2013 IEEE.

关键词： Software testing

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Real Time Chest X-Ray Interpretation Applying Vision Transformer for Tuberculosis Detection

Real Time Chest X-Ray Interpretation Applying Vision Transfo...

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2024 International Conference on Computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Irhamsyah, Muhammad Roslidar Yunidar Al Lail Qadrillah, M. Nasaruddin Arnia, Fitri Munadi, Khairul Universitas Syiah Kuala Post Graduate Program in Engineering Banda Aceh2311 Indonesia Faculty of Engineering Department of Electrical and Computer Engineering 2311 Indonesia

ISBN: (纸本)9798350368802

Tuberculosis (TB) is an infectious disease that can attack the lungs and other organs. In the diagnosis of TB, the interpretation of X-ray radiological images requires special skills from medical specialists. However, the lack of experience of medical personnel and focus on X-ray examinations can be a challenge for radiologists because it has the potential to cause diagnostic errors in image interpretation. Therefore, we proposed a web-based application of real-time chest X-ray interpreter by integrating the deep learning model of Vision Transformer (ViT) to interpret chest X-ray images. The inference model was trained and tested on 652 normal and 652 tuberculosis X-ray images. The data preprocessing of Contrast Adaptive Histogram Equalization (CLAHE) was implemented to increase chest X-ray image contrast as well as inference model performance. The proposed real time system can inteprete the tested chest x-ray images with an accuracy of 95% and with simulation duration time of 4.7 seconds. © 2024 IEEE.

关键词： Diagnosis

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Independent Field Excitation Control of an In-Wheel Wound-Field Flux-Switching Machine for Micro-Hybrid Applications 8

Independent Field Excitation Control of an In-Wheel Wound-Fi...

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8th Southern Power electronics Conference and the 17th Brazilian Power electronics Conference, SPEC / COBEP 2023

作者： Vilela Galo, Diogo Pereira Carvalho Maia, Thales Alexandre De Jesus Cardoso Filho, Braz Ufmg Graduate Program in Electrical Engineering Belo Horizonte Brazil Ufmg Department of Electrical Engineering Belo Horizonte Brazil

ISBN: (纸本)9798350321128

The tightening global emission regulations are com-pelling original equipment manufacturers and researchers to prioritize electrified solutions. In developing countries, fossfl fuel-powered vehicles are expected to continue being the primary choice for light-duty passenger vehicles in the medium term. It is crucial to recognize that micro and mild hybrid electric vehicles can make a significant contribution to reducing emissions and mitigating environmental impact. Electric machine designs with fewer or no rare-earth permanent magnets will play an important role in the widespread adoption of hybrid solutions. In this context, doubly salient reluctance machines exhibit a simple structure, robust mechanical strength, excellent fault tolerance, and a wide speed regulation range, making them well-suited for in-wheel applications. Particular emphasis should be placed on flux-switching ma-chines, which offer great operating flexibility, efficient heat dissipation, and power density of up to 4.8 kW/kg. This paper introduces a wound-field flux-switching machine designed for in-wheel applications, featuring individual field current control. Firstly, the machine is reconfigured to enable independent control of field currents, with a dedicated test bench equipped for precise current control. The primary objective of this research is to enhance the machine's operational versatility by facilitating electronic adjustment of the number of poles, promoting balanced operation' and auamentina field-weakenina capabilities. © 2023 IEEE.

关键词： Hybrid Electric Vehicles In-wheel drives Independent Field-Current Control Wound-Field Flux-Switching Machine

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Deep Convolutional Neural Networks for Accurate Solar Module Classification in Thermal Images 12

Deep Convolutional Neural Networks for Accurate Solar Module...

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12th International electrical engineering Congress, iEECON 2024

作者： Phungket, Chutinun Nangthin, Prasit Pathumwan Institute Of Technology The Graduate Program Faculty Of Engineering Faculty Of Engineering Bangkok Thailand Pathumwan Institute Of Technology Faculty Of Engineering Department Of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

The rapid and continuous technological advancements in computer and internet technologies, combined with data management techniques, find applications in various domains. For example, they aid in predictive maintenance systems, evaluate the performance of solar modules, and differentiate efficiently performing solar modules from less efficient ones using thermal imagery. The accuracy of solar module classification significantly impacts the subsequent assessment. In this study, we employ advanced techniques, particularly deep learning through Convolutional Neural Networks (CNN), to classify thermal images of solar modules. The practical aspects of applying CNN to the dataset of thermal images are explored, including transfer learning from pretrained CNN architectures and custom CNN architecture training. Experiments are conducted with three prominent types of solar module conditions: normal, cracked, and dusty. The results reveal that the most effective approach involves learning and fine-tuning pre-trained architectures on detailed studies, achieving a classification accuracy of up to 94.5% © 2024 IEEE.

关键词： Convolutional neural networks

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Object detection of Train Line Equipment using SSD and YOLOv7 9

Object detection of Train Line Equipment using SSD and YOLOv...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Majima, Daisuke Surantha, Nico Graduate School of Integrative Science and Engineering Tokyo City University Department of Electrical Engineering and Chemistry Tokyo Japan Faculty of Science and Engineering Tokyo City University Department of Electrical Electronics and Communication Engineering Tokyo Japan

ISBN: (纸本)9798331510732

In modern society, the early detection of damage to train line equipment and regular inspections were essential for ensuring the safe operation of trains. Traditionally, inspections of train line equipment were conducted manually at night. However, this method posed issues such as the need for significant labor and limited mobility. To address these problems, efforts were made to improve mobility by installing cameras on the roofs of test vehicles. However, the analysis of the images captured by these cameras was still conducted manually. In recent years, the use of drones, high-resolution cameras, and AI for inspections had been explored. This paper reported on a preliminary study concerning damage detection of train line equipment using deep learning and its application to autonomous drones. In this study, two models were used for object detection of train line equipment. The target equipment in this study included three components: hanger, connector, and insulator. The dataset contained a total of 2,400 images, with 800 images each for the hanger, connector, and insulator. To evaluate the training progress of the models, the mean Average Precision (mAP) was used for both the training and test datasets. The training was conducted on a Windows PC with CUDA support. The object detection models used were the Single Shot multibox Detector (SSD) and You Only Look Once (YOLOv7). After simulations, the following results were obtained. For the training data, the SSD model achieved an mAP of 73.51%, while the YOLOv7 model achieved an mAP of 97.5%. For the test data, the SSD model achieved an mAP of 53.74%, while the YOLOv7 model achieved an mAP of 76.30%. © 2024 IEEE.

关键词： Inspection equipment

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Comparison of Bioinspired MLP Controllers for Mobile Robots Obstacle Following/Avoidance

Comparison of Bioinspired MLP Controllers for Mobile Robots ...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Luiz, Matheus De S. Pastrana, M.A. Da Silva, Lukas. A. Campagnucci, Pamela. R. A. N. Mendoza-Penaloza, Jose Benevides, Rafael R.L. Munoz, Daniel M. University of Brasilia Mechatronics Graduate Program Department of Mechanical Engineering Brasilia Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Proportional, integral, and derivative (PID) controllers have been widely adopted for industrial applications. However, these controllers are not very efficient for non-linear systems. Artificial neural networks (ANN) based on the Multilayer Perceptron (MLP) have great potential to replace PID controllers due to their polynomial structure, allowing complex non-linear systems to be controlled. This article introduces the integration of four MLPs as alternatives to a traditional PID controller. These MLPs were trained through four bioinspired algorithms tailored for following tasks in mobile robots. The bioinspired algorithms employed for MLP network training include Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Month-Flame Optimization (MFO), and Artificial Hummingbird Algorithm (AHA). A comparative analysis was conducted between these MLPs and a classic PID controller, focusing on parameters such as overshoot (OS), settling time (ST), and steady-state error for different simulated scenarios. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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