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IEEE Electron Device Letters 2025年第5期46卷 705-708页

作者： Chen, Yi-Ho Chu, Fu-Chuan Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung-Shan Institute of Science and Technology Solid State Devices Section Electronic System Research Division Taoyuan325 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Recess gate etching is a critical technique for achieving enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) because the interface is susceptible to the etching damage. This study fabricates recess gates using the neutral beam etching (NBE) technique. By adjusting the aperture thickness in the NBE apparatus, we simulate both NB-mode and plasma-mode etching. The electrical characteristics of E-mode HEMTs fabricated using these two modes are analyzed and compared through DC, noise, and pulsed IV measurements. The results demonstrate that NB-recessed HEMTs exhibit superior performance. © 1980-2012 IEEE.

关键词： Aluminum gallium nitride

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Nafion edge passivation and nitrogen annealing treatment to improve solar cell reliability under light and elevated temperature induced degradation (LeTID) investigation

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Current Applied Physics 2025年 75卷 1-7页

作者： Jony, Jaljalalul Abedin Yousuf, Hasnain Khokhar, Muhammad Quddamah Zahid, Muhammad Aleem Madara, Polgampola Chamani Rahman, Rafi Ur Alamgeer Chu, Mengmeng Sanyal, Simpy Wang, Fucheng Kim, Youngkuk Kim, Kyesoo Dhungel, Suresh Kumar Park, Sangheon Yi, Junsin Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of University Tamilnadu 600124 India Research Institute for Clean Energy College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-do Suwon16419 Korea Republic of Faculty of Technology Nepal Academy of Science and Technology 3323 Khumaltar Lalitpur Nepal

This research examines the effectiveness of nitrogen annealing and Nafion treatment in mitigating Light and Elevated Temperature Induced Degradation (LeTID) in Passivated Emitter and Rear Contact (PERC) solar cells, employing a 30-kW photovoltaic (PV) system for evaluation. As a photosensitive device, solar cells are susceptible to damage from laser irradiation, leading to reduced photoelectric conversion efficiency, structural damage, and functional loss. To minimize this loss, nitrogen annealing and Nafion treatments were applied, followed by the analysis of LeTID effects on untreated, Nafion-treated, and combined nitrogen-annealed and Nafion-treated PERC cells. Untreated cells showed an 8 % fall in open-circuit voltage (Voc) and a 7 % fill factor (FF) loss. The combined treatment (Combination of Nitrogen & Nafion Treatment) improved stability, reducing losses to 6 % in Voc and 5 % in FF. The Nafion treatment yielded the best results, limiting deterioration to 4 % in Voc and 3 % in FF. PVsyst simulations, incorporating real-world data, confirmed enhanced stability and efficiency, aiding in optimizing PV system design and maintenance. © 2025 Korean Physical Society

关键词： Annealing

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Multi-Normalization Residual Graph Convolutional Network for 3D Human Pose Estimation

Multi-Normalization Residual Graph Convolutional Network for...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Pramono, Andy Chang, I-Cheng Puspasari, Betty Dewi Animation Study Program Universitas Negeri Malang Jawa Timur Malang Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Informatics Engineering Institut Teknologi Nasional Malang Jawa Timur Malang Indonesia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nodes and reliance on skeleton-based graphs that may miss complex motion *** address these issues, we introduce a Multi-Normalization Residual Graph Convolutional Network that fine-tunes the graph structure through affinity multi-normalization and activation, allowing the representation of additional connections beyond the *** extensive ablation study shows that this approach enhances performance with minimal overhead while maintaining the same model size, consistently outperforming state-of-the-art techniques on two benchmark datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Lumbanraja, Favorisen Rosyking Kurniasari, Dian Borman, Rohmat Indra Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Universitas Lampung Department of Computer Science Bandar Lampung Indonesia Universitas Lampung Department of Mathematics Bandar Lampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia

ISBN: (纸本)9798331513320

Stemming, an essential procedure in natural language processing (NLP), diminishes words to their base forms, facilitating tasks such as information retrieval and sentiment analysis. Although stemming techniques for highresource languages are well-developed, numerous low-resource languages, including dialect of Tulang Bawang, suffer from inadequate solutions owing to a scarcity of linguistic data and resources. Existing systems, including rule-based stemmers, have demonstrated efficacy in processing low-resource languages such as Indonesian and Javanese by utilizing established morphological rules. Nonetheless, these methods encounter considerable obstacles, such as restricted adaptability, inability to accommodate unusual root structures, and excessive dependence on fixed rules that might result in over- or understemming. Rule-based methodologies frequently misidentify roots when faced with intricate affixes or unconventional word forms. We introduce an improved rule-based Tulang Bawang Stemmer aimed at overcoming these constraints by enhancing current linguistic rules and integrating new patterns specific to the language's morphology. Assessed on 500 test samples and 200 independent test samples, our improved stemmer attained gold standard evaluation metrics of 96.2% and 94%, respectively, surpassing prior implementations in both precision and generalization. The findings demonstrate the potential of enhanced rule-based techniques to improving NLP for lowresource languages. Improved stemming performance enables better downstream applications, promotes more efficient text analysis, and advances research in underrepresented languages. © 2025 IEEE.

关键词： Linguistics

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Advanced technologies in InGaN micro-LED fabrication to mitigate the sidewall effect

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光：科学与应用（英文版） 2025年第2期14卷 307-332页

作者： Zhiyuan Liu Haicheng Cao Xiao Tang Tingang Liu Yi Lu Zixian Jiang Na Xiao Xiaohang Li Advanced Semiconductor Laboratory Electrical and Computer Engineering ProgramCEMSE DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The size of InGaN micro-LEDs is continuously decreasing to meet the demands of various emerging applications,especially in tiny micro-displays such as AR/***,the conventional pixel definition based on plasma etching significantly damages the mesa sidewalls,leading to a severe reduction in efficiency as the micro-LED size *** seriously impedes the development and application of *** this work,we comprehensively explain the origin of micro-LED sidewall effects and corresponding physical ***,we systematically review recent progress in micro-LED fabrication aiming at suppressing sidewall ***,we discuss advancements in micro-LED fabrication with"damage-free"techniques,which hold the potential to fundamentally address the issue of plasma damage in the micro-LED *** believe this review will deepen the understanding of micro-LED sidewall effects and provide a better insight into the latest associated fabrication technologies for high-efficient InGaN micro-LEDs.

关键词： technologies micro-led advanced ingan effect fabrication mitigate sidewall

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Feasibility Study: Magnetic Field Monitoring in Cycled Lead Acid Cells for Stationary Applications

A Feasibility Study: Magnetic Field Monitoring in Cycled Lea...

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IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)

作者： Scarleth Vasconcelos Pritpal Singh Ross Lee Sustainable Engineering Program Villanova University Villanova USA Dept of Electrical and Computer Engineering Villanova University Villanova USA

ISBN: (数字)9798331524852

ISBN: (纸本)9798331536411

This research introduces a novel, real-time, non-invasive, non-destructive, and accurate method for battery mon-itoring using magnetic field mapping. This technique detects stratification in flooded Lead Acid Batteries at different states of charge, providing early notification by monitoring variations in hydrogen ion concentration during cycling. The study tracked pH and magnetic field variations across $H_{2}SO_{4}$ electrolyte specific gravities ranging from 1.07 to 1.33, showing a 17%-20% pH reduction over 12 hours, indicating acid stratification. A proof-of-concept using air-core solenoid coils demonstrated a direct correlation between secondary coil output voltage and $H_{2}SO_{4}$ concentrations, influenced by the induced magnetic field at the primary coil and electrolyte concentration changes. The optimal AC input frequency for maximizing the induced magnetic field mapping response was identified as 30–33 kHz, depending on the physical properties of the magnetic field inductors. An inverse correlation was also observed between the induced magnetic field and the distance between the coils. This approach offers a promising alternative for lead acid battery monitoring, potentially improving battery management and lifespan. The experimental tests also included monitored variations in secondary coil output voltage in a single flooded lead acid cell during cycling. The preliminary results demonstrated a decrease in the magnetic field during charge and an increase during discharge.

关键词： Coils Lead acid batteries Correlation Voltage measurement Electrolytes Discharges (electric) Lead compounds Magnetic fields Solenoids Monitoring

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Self SOC estimation for second-life lithium-ion batteries

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IEEE Access 2025年 13卷 75037-75046页

作者： Gotz, J. Viana, E.R. Galvão, J.R. Corrêa, F.C. Borsato, M. Badin, A.A. UTFPR Av. Sete de Setembro PR Curitiba3165 Brazil Department of Computer Science PR Curitiba Brazil Department of Physics UTFPR Av. Sete de Setembro PR Curitiba3165 Brazil PR Ponta Grossa Brazil Postgraduate Program in Mechanical and Materials Engineering PR Curitiba Brazil

Lithium-ion batteries (LIB) are the mainstream technology for energy storage in several industrial segments, such as mobility and stationary systems for solar, wind, or other alternative energy source. This technology has a long lifetime, low self-discharge, high capacity and density, and can store energy longer. Despite that, LIB is suitable to supply power for electric mobility if its state of health (SOH) is higher than 80%. Then, an alternative for batteries with SOH below that is recycling or second-life batteries (SLB). The first is expensive and complex;only some companies retain the technology. Still, the SLB can be an excellent solution to maintaining LIBs in operation for slow vehicles or stationary systems. However, SLB requires intelligent battery management systems (BMS) because the packs are composed of cells with different characteristics, which makes the operation more difficult. This work presents a system consisting of two Machine Learning (ML) layers to automatically estimate the state of charge (SOC) of SLB independent of the battery's capacity or age. In the first phase, a Random Forest (RF) model was built and trained to discover the curve capacity of different SLB characteristics and capacities. After the capacity curve selection, in the second phase, a new RF model was built and trained for each capacity curve to make SOC inferences of the batteries. The discharge data curve of one hundred batteries was used for the development, whereas eighteen were used for the training and eighty-two for tests. The results indicated a root square mean error (RSME) below 45 mAh for the capacity estimation (phase 1), and an RSME below 0.87% was found in the second phase for the SOC estimation. Finally, the capacity and SOC models have been inserted in a Raspberry system to measure the main parameters of SLB (voltage and current) and make inferences in real time independent of the cell's age. The results showed an RSME below 100 mAh for the first layer and be

关键词： Lithium-ion batteries

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Identification of Sounds from Construction Sites: A Machine Learning Approach 4th

Identification of Sounds from Construction Sites: A Machin...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Maji, Sayantan Mukherjee, Himadri Dhar, Ankita Marciano, Matteo Roy, Kaushik Department of Computer Science and Engineering Sister Nivedita University Kolkata India TISA Lab Department of Computer Science West Bengal State University Kolkata India Gazelien Records Lab Department of Arts and Humanities Music Program New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819757022

The rate at which the world is evolving is astonishing with cutting-edge technologies being introduced every day. There have been developments in every field ranging from constructing gigantic architectures to enhancing the ease and comfort with which we can commute daily. Nevertheless, this has given rise to different types of pollution, sound pollution being one of them. Although the impact of sound pollution is not instantaneously noticed, it has a distinct effect on humans and nature which is observed over a period of time. There are multiple sources that cause sound pollution. Sounds from ongoing construction sites are one of them. Such sounds disrupt the daily lifestyle of people, their health, and daily productivity. Mostly, these sounds start very early in the day thereby making it difficult for people to manually take precautions like shutting doors and windows or deploying mobile soundproofing solutions. Machine learning has advanced significantly over the years leading to the refinement of artificial intelligence. This has aided in the development of multifarious solutions to ease and simplify our daily lifestyle. This sets the stage for an autonomous system, that can detect sounds from construction sites very early to automatically deploy precautionary measures so that the effect of these sounds on close by residents can be minimized. In this paper, a system is presented that models audio clips using MFCC-based features for the identification of sounds from construction sites in the Urban environment. Experiments were performed on a dataset of over 6K clips composed of the varied construction sounds as well as ambient urban sounds and a maximum accuracy of 98.02% was achieved using random forest-based classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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