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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Kaligambe, Abraham Nakagawa, Kota Fujita, Goro Graduate School of Engineering and Science Shibaura Institute of Technology Power System Laboratory Tokyo Japan Shibaura Institute of Technology Power System Laboratory Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350327816

As the adoption of renewable energy surges, the global proliferation of solar photovoltaic (PV) systems intensifies. However, PV panels remain vulnerable to defects during transportation, installation, and harsh weather exposures, compromising their longevity and efficiency. Traditional visual and I-V curve tests, frequently employed for PV system maintenance, often fall short due to their restricted defective detection capacity, and extended inspection durations. While infrared (IR) and electroluminescence (EL) imaging methods excel in efficiency, their accompanying equipment is prohibitively expensive. Addressing this gap, the paper introduces a novel, cost-effective EL imaging solution using a modified consumer camera. The findings highlight the efficacy of EL imaging in detecting an array of solar cell defects, underscored by the affordability of the setup. The accessible and cost-friendly nature of the EL methodology offers a promising inspection alternative for small to medium-sized PV systems, marking a significant stride in PV inspection innovations. © 2023 IEEE.

关键词： electroluminescence imaging inspection photovoltaic solar defects

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Incentive design in Noncooperative Dynamical systems for Social Welfare Maximization Using Limited Payoff Information 63

Incentive Design in Noncooperative Dynamical Systems for Soc...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Yan, Yuyue Ye, Maojiao Hashimoto, Kazumune Wu, Yuhu Kong, He Osaka University Graduate School of Engineering Suita565-0871 Japan Nanjing University of Science and Technology School of Automation Nanjing210094 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9798350316339

In this paper, an incentive mechanism is developed to maximize the weighted social welfare for pseudo-gradient-based noncooperative dynamical systems. In the proposed approach, the system manager manipulates agents' decision making by collecting taxes and giving subsidies to the agents under a sustainable budget constraint using the partial knowledge related to the payoff structure from the agents except a secret agent. To avoid direct acquisition of the payoff information of all the agents, our idea is to shift the stationary points of those partial agents and hence indirectly move the Nash equilibrium of the entire system. Sufficient conditions are derived under which the Nash equilibrium is shifted towards the target state achieving Pareto efficiency under the incentive mechanism without knowing any payoff information of the secret agent. Some convergent conditions to ensure agents' state converge to the target state under a sustainable budget are provided for the given initial state. We present a numerical example to illustrate the efficacy of our results. © 2024 IEEE.

关键词： Budget control

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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Computer Modeling in engineering & sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Combined anisotropic and cyclic constitutive model for laser powder bed fusion fabricated aluminum alloy

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Chinese Journal of Aeronautics 2025年第1期38卷 165-184页

作者： Fei-Fan LI Jihong ZHU Weihong ZHANG Shifeng WEN Jingwen SONG Jun MA Gang FANG State IJR Center of Aerospace Design and Additive Manufacturing School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Materials Science and Engineering Northwestern Polytechnical UniversityXi'an 710072China Department of Mechanical and Industrial Engineering Norwegian University of Science and TechnologyTrondheim 7491Norway State Key Laboratory of Tribology in Advanced Equipment Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China

This study presents new methods to effectively model the anisotropic yielding and hardening behavior of laser powder bed fusion fabricated aluminum alloy under both monotonic and cyclic loading *** proposed model combines the yield surface-interpolation method to accurately describe the anisotropic hardening rates in various directions,with the Chaboche kinematic hardening rule to precisely reflect the cyclic *** numerical implementation of the combined anisotropic and cyclic constitutive model,a fully implicit stress integration algorithm based on return mapping method is ***,the multiple parameters associated with the model are categorized and identified in an uncoupled *** isotropic and cyclic hardening parameters are determined by an inverse method,and the stability of the optimization outcomes is validated by applying different starting points for the ***,the back-stress effect on the identification of anisotropic parameters associated with the stress invariant-based Hill48 yield function is considered for the first *** consideration leads to an improved prediction accuracy compared to the identification of anisotropic parameters without considering back-stress *** combined anisotropic and cyclic constitutive model,along with the calibrated parameters,are proven capable of accurately reproducing the intricate deformation behavior of laser powder bed fusion fabricated AlSi10Mg.

关键词： Constitutive models Anisotropy Kinematic hardening Laser powder bed fusion Aluminum alloys

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RAG-Enhanced Large Language Model for Intelligent Assistance from Web-Scraped Data 9

RAG-Enhanced Large Language Model for Intelligent Assistance...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Kanataria, Nikunj Patel, Kunj Pareshbhai Patel, Hetul Niteshbhai Goel, Parth Patel, Krishna Garg, Dweepna Devang Patel Institute of Advance Technology and Research Computer Science & Engineering Department Changa Anand India Computer Science & Design Department Vallabh vidyanagar Anand India

ISBN: (纸本)9798350377972

The explosion of online information necessitates efficient and accurate methods for retrieving and analyzing relevant data. This research proposes a novel framework that leverages Retrieval-Augmented Generation (RAG) techniques to address this challenge. The framework utilizes web scraping to extract information from various sources and employs advanced language models like Llama 3, Mistral, and Gemini to generate concise and informative summaries. Furthermore, the framework incorporates embedding models such as nomic-embed-text and Gemini embedding model text embedding 001 to create semantic representations of the retrieved data and utilize FAISS for efficient indexing and retrieval. The RAGAS framework was used to evaluate the performance of different LLMs, with Llama 3.1 demonstrating the highest accuracy at 86.67%. This framework has significant implications for various applications, including personalized education, where it can be used to provide students with tailored learning materials and personalized tutoring experiences. © 2024 IEEE.

关键词： Semantics

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Physiological Glucose Sensing in in-vitro 3D Spheroids using SERS Based Nanosensors 26

Physiological Glucose Sensing in in-vitro 3D Spheroids using...

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26th International Conference on Miniaturized systems for Chemistry and Life sciences, MicroTAS 2022

作者： Dey, Koyel Goudar, Venkanagouda S. Tseng, Fan-Gang Santra, Tuhin Subhra Engineering and System Science National Tsing Hua University Hsinchu Taiwan Department of Engineering Design Indian Institute of Technology Madras India

ISBN: (纸本)9781733419048

During growth, tumor spheroids develop gradients of glucose. In this work, we unveiled real time glucose gradient in tumor spheroid using Confocal Raman Spectroscopy. For sensor preparation, 50 nm silver nanoparticle is conjugated with Raman based dye, mercapto-phenylboronic acid(MPBA). The sensor approach a detection limit of 1 mM and dynamic range from 1-100 mM of glucose. MPBA -glucose bonding being reversible, helps for continuous measurement inside 3D microenvironment. Sensors are mixed with co-cultured cells(colon cancer and fibroblast) and seeded in 3D culture chip for spheroid formation. Raman measurement showed an increased glucose concentration from center to surface of spheroid. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life sciences. All rights reserved.

关键词： Glucose

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Passivity-Based Distributed Averaging Control for Wide-Area Frequency Synchronisation with Time-Varying Power Demand

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IET Control Theory and Applications 2025年第1期19卷

作者： Kang, Heng Namerikawa, Toru Graduate School of Science and Technology Keio University Kanagawa Japan Department of System Design Engineering Keio University Kanagawa Japan

The frequency synchronisation problem of wide-area control from the higher level of networked power systems is considered. We investigate the asymptotic convergence and robustness of the passivity-based distributed averaging control used in damping the inter-area oscillations of power networks and coping with the time-varying power demand. A strict composite storage function is constructed that allows us to quantify the asymptotic convergence rate of the closed-loop system. Furthermore, the power sharing and (Formula presented.) gain performance are utilized as a tool to quantify the impact of time-varying power demand on control areas. We simulate the controller performance on a four-area network which is equivalent to the IEEE New England 39-Bus system. © 2025 The Author(s). IET Control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Robust control

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Nodes2STRNet for structural dense displacement recognition by deformable mesh model and motion representation

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International Journal of Mechanical system Dynamics 2023年第3期3卷 229-250页

作者： Jin Zhao Hui Li Yang Xu Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbinChina Key Lab of Structures Dynamics Behavior and Control of the Ministry of Education Harbin Institute of TechnologyHarbinChina School of Civil Engineering Harbin Institute of TechnologyHarbinChina Department of System Design and Simulation Goldwind Science and Technology Co.Ltd.BeijingChina

Displacement is a critical indicator for mechanical systems and civil *** vision-based displacement recognition methods mainly focus on the sparse identification of limited measurement points,and the motion representation of an entire structure is very *** study proposes a novel Nodes2STRNet for structural dense displacement recognition using a handful of structural control nodes based on a deformable structural three-dimensional mesh model,which consists of control node estimation subnetwork(NodesEstimate)and pose parameter recognition subnetwork(Nodes2PoseNet).NodesEstimate calculates the dense optical flow field based on FlowNet 2.0 and generates structural control node ***2PoseNet uses structural control node coordinates as input and regresses structural pose parameters by a multilayer perceptron.A self-supervised learning strategy is designed with a mean square error loss and L2 regularization to train *** effectiveness and accuracy of dense displacement recognition and robustness to light condition variations are validated by seismic shaking table tests of a four-story-building *** studies with image-segmentation-based Structure-PoseNet show that the proposed Nodes2STRNet can achieve higher accuracy and better robustness against light condition *** addition,NodesEstimate does not require retraining when faced with new scenarios,and Nodes2PoseNet has high self-supervised training efficiency with only a few control nodes instead of fully supervised pixel-level segmentation.

关键词： structural dense displacement recognition deformable structural mesh model deep-learning-based monocular vision self-supervised learning

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How Advanced technology design Meets Senior Adults’ Emotional Needs: A Case Study of Japanese Senior Adults’ Experience 10th

How Advanced Technology Design Meets Senior Adults’ Emotion...

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10th International Conference on Kansei engineering and Emotion Research, KEER 2024

作者： Wang, Yu-Han Muraki, Satoshi Choi, Jeewon Fan, Yuk-Wa Department of Multimedia Design National Taichung University of Science and Technology Taichung Taiwan Department of Human Life Design and Science Kyushu University Fukuoka Japan Department of Industrial and Management Systems Engineering Dong-A University Busan Korea Republic of

ISBN: (纸本)9789819799091

The pilot study aims to investigate the psychological perception of senior adults’ advanced technology adoption in Japan. The advanced technology in this study refers to the latest technology such as artificial intelligence and Internet of Things. A qualitative approach, interview, is adopted to investigate senior adults’ perception of advanced technology. The result suggests that the design of future advanced technology may consider about autonomy, communication, emotional engagement and social impact. This might help to support older adults’ emotional needs for advanced technology which can enhance their adoption of technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Advanced technology design Autonomy Emotional Engagement Senior Adults Social Impact

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A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

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Soft Computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

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