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International Conference on Condition Monitoring and Diagnosis, CMD

作者： Cattareeya Suwanasri Ittiphong Yongyee Thanapong Suwanasri Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Electrical and Software Systems Engineering The Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9788986510225

ISBN: (纸本)9798350353877

The deterioration of transmission lines has a profound effect on the reliability and safety of the power grid. Accurate estimation of their age is critical for effective maintenance and investment planning. This paper proposes a method that combines the percentage statistical health index (%SHI) with failure probability curve-fitting (FPCF) to estimate the ages of overhead transmission lines. %SHI assesses equipment condition through scoring and weighting, while FPCF correlates SHI with the probability of failure. Evaluation of 924 towers situated in various terrains across Thailand reveals discrepancies between estimated and actual ages, influenced by environmental factors. To enhance reliability, proactive maintenance strategies such as regular inspections and advanced monitoring are recommended.

关键词： Power transmission lines Poles and towers Probability Inspection Maintenance Safety Planning Curve fitting Monitoring Investment

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Hand Written Digit Recognition using Multilayer Deep Convolutional Neural Network

Hand Written Digit Recognition using Multilayer Deep Convolu...

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Smart Technologies, Communication and Robotics (STCR)

作者： S Deepa G D Praveenkumar Vivek Duraivelu R Suguna P Sathishkumar N B Priyananthan Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of computer science VET Institute of Arts and Science(Co-Education)College Erode Tamilnadu Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Final M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

In machine learning, handwritten digit recognition is usually seen as a multi-class classification problems In this approach, the ten possible digits (0-9) are treated as individual classes, and the goal is to train a classifier that can accurately identify them. However, it’s not unusual for a single classifier to have varying levels of success when applied to different datasets, even after being trained using a standard learning algorithm. This indicates that while a given learning algorithm may be effective at training strong classifiers on certain datasets, it may result in weaker classifiers for others. Furthermore, it’s possible for a classifier to exhibit varying levels of performance on multiple test datasets, especially considering that different writers may produce highly diverse image samples of the same numbers. To address this issue, the advancement of ensemble learning methodologies will be critical, as they have the potential to improve overall prediction accuracy and offer more consistent performance across different datasets.

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Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

SSRN

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SSRN 2023年

作者： Li, Ping Zhang, Yu Yuan, Li Xiao, Huaxin Lin, Binbin Xu, Xianghua School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electrical and Computer Engineering Peking University Beijing China College of Systems Engineering National University of Defense Technology Changsha China College of Computer Science Zhejiang University Hangzhou China

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high © 2023, The Authors. All rights reserved.

关键词： Pixels

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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ANFIS and SA Based Approach to Prediction, Scheduling, and Performance Evaluation for Semiconductor Wafer Fabrication

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Chinese Journal of Electronics 2023年第1期22卷 25-30页

作者： Zhengcai Cao Huidan Zhao Yongji Wang College of Information Science and Technology Beijing University of Chemical Technology Beijing China State Key Laboratory of Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

In this paper, an optimized mechanism for Semiconductor wafer fabrication (SWF) by integrating the Adaptive neuro-fuzzy inference system (ANFIS) with Simulated annealing (SA) algorithm is proposed. In this approach, aiming to solve the rush order problem which significantly affect the cycle time and impact the Work in process (WIP) of lots due to the high priority, we build an ANFIS based prediction model which will be embedded into releasing to forecast product codes and quantities of the contingent rush orders. Then a scheduler based on SA algorithm is constructed, the coding of which represents a combination of scheduling policies, including lot releasing policies, dispatching rules, batching rules and settingup rules. When the SA finished its optimization process, an optimal scheduling policy is produced. By using the proposed approach, we will find that the system can be optimized to a large extent and give a better performance.

关键词： Semiconductor device modeling Job shop scheduling System performance Optimal scheduling Simulated annealing Predictive models Semiconductor device manufacture

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Evaluation of Traffic Signal Control at Varying Demand Levels: A Comparative Study

Evaluation of Traffic Signal Control at Varying Demand Level...

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International Conference on Intelligent Transportation

作者： Zhivao Zhang Marcos Quinones-Grueiro William Barbour Yuhang Zhang Gautam Biswas Daniel Work Institute for Software Integrated Systems Vanderbilt University Nashville TN USA Department of Civil and Environmental Engineering Vanderbilt University Nashville TN USA Department of Computer Science Vanderbilt University Nashville TN USA

The topic of traffic signal control (TSC) for urban intersection networks has been a key research area for many years, but comprehensive comparisons among state-of-the-art methodologies remain limited. This calls for a more exhaustive approach to benchmarking TSC techniques. This study builds upon previous TSC benchmarking work by evaluating the performance of both reinforcement learning (RL) and model-based TSC algorithms across a spectrum of demand levels. We adapt new metrics for in-depth simulation analysis and ground our investigation in a real-world urban arterial scenario. Our results unveil a noteworthy observation: the performance of tested algorithms is inconsistent when evaluated under diverse demand levels and across different metrics. This highlights the importance of implementing multidimensional evaluations in future TSC studies for a more nuanced understanding of their performance.

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Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Abiodun E. Onile Juri Belikov Eduard Petlenkov Yoash Levron Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening ‘knowledge-action gap’. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user’s energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction.

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Enhancing e-KYC Security and Privacy: Harnessing Quantum Computing and Blockchain in Web 3.0

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Distributed Ledger Technologies: Research and Practice 2024年第4期3卷 1-23页

作者： G. M. Faruk Ahmed Imran Hasan Md Soumike Hassan Rahul Khan Abu Jor Al Gefari Zain Buksh Joseph Liu A B M Shawkat Ali Cybersecurity Cell Rupali Bank Limited Dhaka Bangladesh Department of Computer Science and Engineering Islamic University Kushtia Bangladesh Department of Statistics Jahangirnagar University Dhaka Bangladesh Department of Pharmacy Bangladesh University Dhaka Bangladesh Rajshahi Science and Technology University Natore Bangladesh Department of Computer Science and Mathematics The University of Fiji Lautoka Fiji Department of Software Systems and Cybersecurity Monash University Melbourne Australia

By implementing a successful and effective Electronic-Know Your Customer (e-KYC) infrastructure, conventional defects and expenses may be greatly reduced. Strong authentication procedures are used by existing e-KYC solutions to guarantee safety and confidentiality. However, these technologies have shortcomings like as key administration expenses and dependency on encryption. We suggest a revolutionary strategy that integrates blockchain technology, Web 3.0, and quantum computing to get around these restrictions. In this research, we show how current e-KYC processes could possibly be improved, making them faster and more reliable, by integrating Web 3.0 with quantum computing. Our solution makes it simple to quickly verify several consumers, increasing productivity. We provide improved safety for online requests, transcending the weaknesses inherent in traditional encryption techniques by utilizing quantum computing technology and applying the concept of quantum encryption key methodology. We contribute to the development of safe and private solutions by addressing the shortcomings of the present e-KYC systems. The results of our research show how Web 3.0 and quantum computer technology have the power to completely alter the e-KYC environment.

关键词： e-KYC Quantum Computing Blockchain Web 3.0 Privacy Security

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Energy consumption of on-device machine learning models for IoT intrusion detection

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Internet of Things (Netherlands) 2023年 21卷

作者： Tekin, Nazli Acar, Abbas Aris, Ahmet Uluagac, A. Selcuk Gungor, Vehbi Cagri Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University Miami 33174 FL United States Department of Software Engineering Erciyes University Kayseri 38280 Turkey Department of Computer Engineering Abdullah Gul University Kayseri 38080 Turkey

Recently, Smart Home systems (SHSs) have gained enormous popularity with the rapid development of the Internet of Things (IoT) technologies. Besides offering many tangible benefits, SHSs are vulnerable to attacks that lead to security and privacy concerns for SHS users. Machine learning (ML)-based Intrusion Detection systems (IDS) are proposed to address such concerns. Conventionally, ML models are trained and tested on computationally powerful platforms such as cloud services. Nevertheless, the data shared with the cloud is vulnerable to privacy attacks and causes latency, which decreases the performance of real-time applications like intrusion detection systems. Therefore, on-device ML models, in which the user data is kept locally, have emerged as promising solutions to ensure the security and privacy of the data for real-time applications. However, performing ML tasks requires high energy consumption. To the best of our knowledge, no study has been conducted to analyze the energy consumption of ML-based IDS. Therefore, in this paper, we perform a comparative analysis of on-device ML algorithms in terms of energy consumption for IoT intrusion detection applications. For a thorough analysis, we study the training and inference phases separately. For training, we compare the cloud computing-based ML, edge computing-based ML, and IoT device-based ML approaches. For the inference, we evaluate the TinyML approach to run the ML algorithms on tiny IoT devices such as Micro Controller Units (MCUs). Comparative performance evaluations show that deploying the Decision Tree (DT) algorithm on-device gives better results in terms of training time, inference time, and power consumption. © 2022 Elsevier B.V.

关键词： Energy consumption Intrusion detection IoT On-device machine learning Smart home

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Crowdsensed Data-oriented Distributed and Secure Spatial Query Scheme

Crowdsensed Data-oriented Distributed and Secure Spatial Que...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuxi Li Fucai Zhou Jingjing Chen Dong Ji School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Focusing on the privacy concerns and leakage abuse of sensory data collection in mobile crowdsensing (MCS) environment, we propose a crowdsensed data-oriented distributed and secure spatial query scheme while ensuring data privacy as long as query patterns. To cater to the demands of real-world MCS workloads, we designed a distributed multi-layer architecture and leveraged distributed hash functions(DHT) and broadcast encryption(BE) to achieve load-balancing and enforce access control. Our scheme incorporates a recently developed cryptographic tool–function secret sharing (FSS) to safeguard the query pattern and sensory data from potential compromises at the server *** analysis demonstrates that our scheme achieves affordable query complexity while satisfying adaptive $\mathcal{L}$-semantic security. Encouraging experimental results substantiate the efficacy of our scheme, the growth rates of query cost diminishes as the number of records and participants increases. These findings emphasize the suitability of our scheme for crowdsensing applications with fine-grained access control requirements and establish it as an efficient cryptographic tool that holds promise for diverse MCS applications.

关键词： Access control Data privacy Costs Crowdsensing Focusing Data collection Complexity theory

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