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International Joint Conference on Neural Networks (IJCNN)

作者： Rewbenio A. Frota Marley M.B.R. Vellasco Guilherme A. Barreto Candida Menezes de Jesus PETROBRAS Rio de Janeiro Brazil Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro Brazil Graduate Program in Teleinformatics Engineering Center of Technology Federal University of Ceará (UFC) Fortaleza Brazil

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In recent years, Representation Learning (RepL) has experienced a surge, especially for cross-modal applications with mixed-type data. Most current cross-modal RepL approaches rely mainly on supervised deep learning models, with unsupervised models playing auxiliary roles. In this paper, we present a fully unsupervised approach to demonstrate the capabilities of such models for cross-modal RepL. Our method jointly learns representations into topologically coherent cross-modal heteroassociative mappings. We apply the proposed framework to a yet to be solved multi-output prediction problem in petroleum geoscience: generating a complete set of regular petrophysical well logs from acoustic borehole images in highly heterogeneous Brazilian pre-salt reservoirs.

关键词： Self-organizing feature maps Representation learning Deep learning Neural networks Geoscience Reservoirs Probabilistic logic

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Twenty years of e-learning in health science: A bibliometric

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Procedia Computer Science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health Sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health Sciences field, which Scopus index.

关键词： e-learning health science bibliometric vosviewer education

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Crack Catcher Ai – Enabling Smart Fracture Mechanics Approaches for Damage Control of Thin Silicon Cells or Wafers

SSRN

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SSRN 2024年

作者： Budiman, Arief Suriadi Putri, Dianing Novita Nurmala Candra, Henry Djuana, Endang Sari, Tyas Kartika Aji, Daisman P.B. Putri, Larasati Rizky Sitepu, Elioenai Speaks, Derrick Pasang, Tim Klamath FallsOR97601 United States Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Silicon has been one among a few key technologically important materials especially in our modern world. Silicon, especially in its current most useful forms (thin layers), is a brittle material by nature. Thin silicon cells crack easily during manufacturing assembly. Every crack is a defect in any PV manufacturing lines, and if let to grow/propagate it will lead to reliability and quality issues with time. Nevertheless, the global silicon solar PV industry is aggressively reducing the cost of solar PV systems through cutting down the thickness of silicon solar cells. Cell cracks immediately lower PV module efficiency and can lead to premature aging of the entire module. The "Crack Catcher AI" was our research joint collaboration’s entry in the Department of Energy (DOE)’s American-Made Solar Innovation competition in 2022 (Round 6) which later was selected as the national semifinalists and won an award announced by DOE in December 2022. It uses smart stress sensing and smart fracture prediction approaches utilizing fundamental fracture mechanics and big data analytics to reduce crack defects and enhance long-term durability/reliability of PV products/devices. Smart Stress Sensing uses a novel laser-based curvature methodology for fast in-line stress metrology in manufacturing lines. Smart Fracture Prediction uses Artificial Intelligence (AI) for defect control metrology and yield enhancement in PV production lines. It is based on monocrystalline silicon used in solar PV industries, but all scientific principles utilized and technological innovations developed in this article would be applicable as well for single crystal silicon semiconductor wafers. © 2024, The Authors. All rights reserved.

关键词： Cracks

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Condition Assessment of Underground Cable System Using Fuzzy Logic and Scoring and Weighting Average Methods

Condition Assessment of Underground Cable System Using Fuzzy...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Dineenath Romphoyen Nattapon Panmala Thanapong Suwanasri Cattareeya Suwanasri EPE Program The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

In this paper, the overall health index of underground cable system is determined using Fuzzy Logic and Scoring and Weighting Average methods. The relevant data of 73 feeders has been collected in the prepared evaluation forms, divided into five major component groups: cable, joint, termination, manhole, and ductbank. In each component, various testing methods and diagnostic techniques are applied, and then the numerical score is determined according to the technical criteria for condition assessment. Subsequently, the obtained overall health index is multiplied with the conditional factor to incorporate the differences in installation type and configuration as well as the operating and environmental conditions. Finally, the overall health index based on these two methods is compared to verify the accuracy of the obtained results to properly plan the preventive and condition-based maintenance and to improve the reliability of the underground cable system.

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Sodium-ion battery cycle life prediction using machine learning

Sodium-ion battery cycle life prediction using machine learn...

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International Conference on Green Energy, Computing and Intelligent technology 2024 (GEn-CITy 2024)

作者： Linfeng Liu Filbert H. Juwono W. K. Wong Erick Purwanto Tingyan Jin Yuyang Zhen Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing People's Republic of China School of Instrument Science and Technology Beijing University of Technology Beijing People's Republic of China

ISBN: (数字)9781837242863

In this study, we explore the classification and prediction capabilities of three models—Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)—on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.

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A Zero Harmonic Distortion Grid-Connected Grid-Forming Converter for Battery Energy Storage System Applications

A Zero Harmonic Distortion Grid-Connected Grid-Forming Conve...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Gabriel V. Ramos Thiago M. Parreiras Fangzhou Zhao Xiongfei Wang Braz de J. C. Filho Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Electrical Engineering Centro Federal de Educação Tecnológica de Minas Gerais Belo Horizonte Brazil Department of Energy Aalborg University Aalborg Denmark Division of Electric Power and Energy Systems KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

The work proposes the Zero Harmonic Distortion Converter (ZHD) as a grid-connected grid-forming (GFM) converter due to its unique sinusoidal characteristic without capacitive filters, low parts count, and off-the-shelf power circuitry with a simple open-loop control structure that does not contribute to possible control interaction and resonances in grid-connected mode. These characteristic are reached by a harmonic cancellation in a three-winding transformer along with harmonic elimination using the Selective Harmonic Elimination Pulse Width Modulation (SHE PWM). The ZHD GFM converter is a compelling alternative for GFM battery energy storage systems (BESS) in medium-voltage applications.

关键词： Filters Battery energy storage system Medium voltage Pulse width modulation Harmonic analysis Harmonic distortion Voltage control Power harmonic filters Grid forming Voltage-source converters

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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Investigation of Lightning Overvoltage on 220 kV Transmission Lines in Bhutan Using ATP-EMTP

Investigation of Lightning Overvoltage on 220 kV Transmissio...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Samten Thanapong Suwanasri Cattareeya Suwanasri Nattapon Panmala EPE Program The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

This paper provides an examination of the impact of lightning strikes on a 220 kV double circuit (D/C) high voltage transmission line in Bhutan. The study employs the ATP-EMTP software to identify the leading causes of transmission line faults, which are primarily attributed to lightning strikes. Since the transmission lines run through areas in Bhutan that are prone to severe weather conditions and lightning strikes, they are vulnerable to damage, leading to an unreliable power supply. To address this issue, the author proposes several solutions, such as enhancing the tower footing resistance and installing line surge arresters. The author also created models of different transmission towers and analyzed an 18 km stretch of the transmission line that is highly susceptible to lightning strikes.

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Local sampled-data gain-scheduling control of quasi-LPV systems 4th

Local sampled-data gain-scheduling control of quasi-LPV syst...

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4th IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control CESCIT 2021

作者： Coutinho, Pedro H.S. Peixoto, Márcia L.C. Bernal, Miguel Nguyen, Anh-Tu Palhares, Reinaldo M. Federal University of Minas Gerais Graduate Program in Electrical Engineering Brazil Department of Electrical and Electronics Engineering Sonora Institute of Technology Mexico Université Polytechnique Hauts-de-France LAMIH UMR CNRS 8201 France Federal University of Minas Gerais Department of Electronics Engineering Brazil

This paper deals with the problem of sampled-data gain-scheduling control design for affine quasi-LPV systems. As the control implementation is based on sample-data and its update occurs only at specific sampling instants, the state-dependent scheduling functions of the controller are piecewise continuous. This characteristic causes a mismatch between the system model and the controller parameters, namely asynchronous scheduling functions of the controller with respect to the plant during the inter-sampling behavior. To cope with this phenomenon, a polytopic description of the inter-sampling is constructed based on bounding assumptions on the scheduling functions and their time derivatives. Then, regarding a time-delay approach, a constructive and numerically implementable LMI-based synthesis condition is derived, and a convex procedure is proposed for enlarging the estimate of the region of attraction of the origin of the closed-loop system. The time-derivative bounds of the scheduling functions are explicitly accounted into the local analysis to ensure the method's implementation. A numerical example is provided to illustrate the proposed methodology. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Controllers

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Quantum-Chiplet: A Novel Python-Based Efficient and Scalable Design Methodology for Quantum Circuit Verification and Implementation

arXiv

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arXiv 2025年

作者： Kao, Yu-Ting Lu, Hao-Yu Chang, Yeong-Jar Lu, Darsen Industrial Technology Research Institute Taiwan Intelligent Computing Industrial Doctorate Program Miin Wu School of Computing National Cheng Kung University Taiwan Institute of Microelectronics Department of Electrical Engineering National Cheng Kung University Taiwan

Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiplet, a hierarchical quantum behavior modeling methodology that facilitates rapid integration and simulation. Each chiplet is systematically transformed into quantum gates. For circuits involving n qubits and k quantum gates, the design complexity is reduced from "greater than O(2n)" to O(k). This approach provides an open-source solution, enabling a highly customized solution for quantum circuit simulation within the native Python environment, thereby reducing reliance on traditional simulation packages. A quantum amplitude estimation example demonstrates that this method significantly improves the design process, with more than 10x speed-up compared to IBM Qiskit at 14 qubits. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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