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3rd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Xiong, Lu Hu, Yingkun Zhang, Dongbo College of Automation Zhongkai University of Agriculture and Engineering Guangdong Guangzhou510070 China School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce Guangzhou510510 China

ISBN: (纸本)9798350380989

In order to solve the problem that the limited number of original data sets cannot be fully close to the real data distribution, thus affecting the effect of image deblurring. This paper proposes the method of generating adversarial network image deblurring based on data set expansion. Firstly, the generated adversarial network is used to expand data sets;Secondly, in order to solve the problem, the expanded data set is expanded again by traditional methods to generate kuochong data set;finally, the GoPro data set, kuochong data set and Kohler data set are input into the network model for training and testing. The experimental results show that the peak SNR PSNR value increased by 19.8%, and the structural similarity SSIM value increased by 2.5%. © 2024 IEEE.

关键词： GAN image deblurring image restore

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Enhancing EEG Signal Reconstruction in Cross-Domain Adaptation Using CycleGAN

Enhancing EEG Signal Reconstruction in Cross-Domain Adaptati...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Russo, Samuele Ahmed, Sameer Tibermacine, Imad Eddine Napoli, Christian Sapienza University of Rome Department of Psychology Rome Italy Sapienza University of Rome Department of Computer Automation and Management Engineering Rome Italy

ISBN: (纸本)9798331527396

This paper explores the applicability of CycleGAN, to reconstruct as well as improve brain-computer interface (BCI) signals. The experimental protocol used in the study involves a real-world table tennis dataset that includes multiple-channel high-density electroencephalography (EEG) data and structural magnetic resonance imaging (MRI) scans from individuals. The overall purpose is to develop a system that would allow enhancing the low-quality or partially damaged EEG signals by using information obtained from MRI, which is represented by Continuous Wavelet Transform (CWT). This technique solves the problem of converting MRI data into effective EEG signals using CycleGAN. The approach may present some practical benefits in cases where signal quality becomes a problem, for example, in wireless or portable BCI equipment. Data preprocessing involves channel filtering of the data, segmentation, and conversion of the data to continuous wavelet transform format to be used by the model. This paper compares three models of CycleGAN, focusing on the various losses and using identity loss to improve them. © 2024 IEEE.

关键词： Wavelet transforms

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Enhancing Agricultural Productivity: A Sensor Fusion, Spectroscopic Near Infrared, and AI-Based Framework for Monitoring Fruit Ripeness 21

Enhancing Agricultural Productivity: A Sensor Fusion, Spectr...

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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Ambreen, Hira Imran, Muhammad Diyin, Tang Zhang, Chongsheng Sir Syed CASE Institute of Technology Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China Henan Univerity Department of EE Kaifeng475001 China School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics Department of Detection and Automation Engineering China School of Computer & Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9798331516680

Fruit ripeness evaluation is essential for harvest optimization and post-harvest loss prevention. Traditional visual inspection is inefficient. This study employs sensor fusion and AI algorithms to evaluate fruit maturity in real-time using gas sensors (i.e., MQ-3, MQ-6, MQ-8, MQ-135) and Near-Infrared spectral sensors (i.e., GY-AS7263). Primarily, data pro-cessing-sensor fusion, data preparation, and AI model application-is crucial to the proposed framework. Subsequently, sensor fusion approaches improve data accuracy by combining signals from various sensors. Secondly, data pretreatment techniques including noise reduction, normalization, and feature extraction prepare data for analysis. Thirdly, we use powerful machine learning algorithms such as Random Forest to judge maturity more accurately and reliably than conventional techniques. Lastly, integrating NIR spectroscopy with gas sensor data resolves cost and model transferability issues, enhancing accuracy and acces-sibility for agricultural use. Static image processing techniques provide challenges for real-time applications, including expensive computer costs and lower reliability. The proposed framework based on multi-sensor fusion system enhances agricultural applications' versatility, scalability, and reactivity, surpassing existing methodologies. © 2024 IEEE.

关键词： Fruits

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A Two-layer Framework for Mitigating the Con-gestion of Urban Power Grids Based on Flexible Topology with Dynamic Thermal Rating

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Protection and Control of Modern Power Systems 2024年第4期9卷 83-95页

作者： Yi Su Jiashen Teh Qian Luo Kangmiao Tan Jiaying Yong the School of Automation and Electronic In-formation Xiangtan Universityxiangtan 411100China IEEE the School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Penang 11800Malaysia the School of Computer Sciences Universiti Sains Malaysia(USM)Penang 11800Malaysia the Institute of Power Engineering College of EngineeringUniversiti Tenaga Nasional(UNITEN)Kajang 43000Malaysia

The urban power grid(UPG)combines transmission and distribution *** studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load *** situation is expected to worsen with the proliferation of renewable energy and electric *** this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible *** the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation *** is known as the upper-layer *** lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the *** optimum topology is tested by standard power flow to ascertain its *** optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal *** proposed framework is tested on a 56-node test *** show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.

关键词： Congestion mitigation urban power grid two-layer framework transitioning sequence dynamic thermal rating

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Analysis of lightning strike fault characteristics in data-driven dense channels of new power systems 37

Analysis of lightning strike fault characteristics in data-d...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Wang, Chaoqun Xiang, Nianwen Wang, Shulai Lyu, Zengwei Chen, Zhiwei Hefei China School of Electrical Engineering and Automation Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783800764174

With the rapid development of new power systems, combined with global warming and other background, the new power system faces an obvious increase in extreme disaster weather events such as lightning. The intensive channel of new power system is the only channel of new energy power transmission. The frequent occurrence of lightning strikes has seriously threatened the safe and stable operation of new power system. By conducting research and analysis on the fault characteristics of lightning strikes in dense channels of new power systems, this paper summarizes the lightning strike disaster factors and the weak links of new power systems, and uses K-means clustering and other data-driven analysis methods to analyze the lightning strike failure factors and the lightning strike disaster mechanism. Seven main factors, including amplitude of lightning current, protection Angle, landform and altitude, were obtained. The sigmoid function was used to fit the lightning damage forms of the dense passage, and the judgment of different flashover forms of the dense passage was finally realized. Meanwhile, a simplified lightning damage model was established, and the data model verified the surface, and the accuracy of the model was as high as 93.53%. It can help on-site operation and maintenance personnel accurately determine the type of lightning fault, summarize the lightning fault and analyze the cause. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Lightning protection

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Research on surface defect detection algorithm of steel pipe weld seam based on deep learning

Research on surface defect detection algorithm of steel pipe...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Qian Lu, Zhiyong Li, Lin Liu, Jiangbo Han, Yu College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9781510681514

An improved YOLOv5s model was proposed to address the issue of poor efficiency in steel pipe weld defect detection. First, the network's feature learning and localization capabilities are enhanced by introducing a coordinate attention mechanism. Secondly, compress the model through the lightweight Ghost module to effectively get fewer network parameters. Thirdly, standard convolution and depth-separable convolution are combined in the Neck module for convolution operations, aiming to reduce model complexity while maintaining accuracy. Finally, introduced a new WIoU loss function to speed up model convergence. Based on a self-created dataset, the detection of three types of surface defects, including overlap, spatter, and undercut, is conducted. The experimental findings demonstrate that the improved model achieves a 2.4% increase in mAP. Furthermore, the model also achieved a 22.4% reduction in parameter count. These findings indicate that the improved model can effectively meet the practical detection requirements. The experimental comparison confirms the feasibility and effectiveness of the proposed improved algorithm for detecting surface defects in steel pipe welds. © 2024 SPIE.

关键词： Convolution

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Lightweight Tunnel Leakage Water Detection Algorithm Based on YOLOv8n 25

Lightweight Tunnel Leakage Water Detection Algorithm Based o...

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Wang, Shiwen Mo, Jiaqing Xu, Liang Zheng, Xinqi Xinjiang University School of Computer Science and Technology Urumqi China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China

ISBN: (纸本)9798350366600

Tunnel leakage is one of the biggest hazards to shield tunnels, this paper proposes a lightweight tunnel water leakage target detection algorithm that improves YOLOv8n for the shortcomings of the existing tunnel water leakage target detection algorithms, such as poor precision and too many model parameters. To make feature fusion richer and reduce the model parameters, this paper proposes to use a bi-directional feature pyramid network on the head network. In addition, this paper uses multi-scale convolutional attention to adapt to the complex features of the leakage area in the tunnel. Finally, use the dilated reparam block to reduce the computational costs and expand the receptive field. The algorithm in this paper not only improves the detection accuracy compared to the original algorithm, but also significantly reduces the model parameters and computational costs. In addition, the superiority of this paper's algorithm is verified through ablation experiments and comparisons with other mainstream target detection algorithms. © 2024 IEEE.

关键词： Target detection Tunnel leakage YOLOv8n

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

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IEEE Transactions on automation Science and engineering 2024年第3期21卷 4829-4850页

作者： Liu, Anbang Luh, Peter B. Sun, Kailai Bragin, Mikhail A. Yan, Bing Department of Automation and BNRist Beijing100084 China University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tsinghua University Chaired Professors Group Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish 'surrogate' part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions, and calculate the solutions of the original part subproblems based on the predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. Note to Practitioners - Scheduling is important for the planning and operation of job shops, and high-quality schedules need to be obtained quickly at the beginning of each shift. To take advantage of ML, in this paper, a DNN is integrated within our recent decomposition and coordination approach to learn to predict 'good-enough' solutions to part subproblems. To be able t

关键词： Deep neural networks

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Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 576-587页

作者： Pavlidis, Nikolaos Perifanis, Vasileios Yilmaz, Selim F. Wilhelmi, Francesc Miozzo, Marco Efraimidis, Pavlos S. Koutsiamanis, Remous-Aris Mulinka, Pavol Dini, Paolo Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi Greece Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Nokia Bell Labs Stuttgart Germany Sustainable Artificial Intelligence Barcelona Spain Imt Atlantique Department of Automation Production and Computer Sciences Inria LS2N Nantes France

The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) in Barcelona (Spain). Our study encompasses relevant aspects within the federated experience, including model aggregation techniques, outlier management, the impact of individual clients, personalized learning, and the integration of exogenous sources of data. The performed evaluation is based on both prediction accuracy and sustainability, thus showcasing the environmental impact of employed FL algorithms in various settings. The findings from our study highlight FL as a promising and robust solution for mobile traffic prediction, emphasizing its twin merits as a privacy-conscious and environmentally sustainable approach, while also demonstrating its capability to overcome data heterogeneity and ensure high-quality predictions, marking a significant stride towards its integration in mobile traffic management systems. © 2016 IEEE.

关键词： Federated learning

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Transformers Unveiled: A Comprehensive Evaluation of Emotion Detection in Text Transcription 7

Transformers Unveiled: A Comprehensive Evaluation of Emotion...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Ali, Sara Naz, Bushra Narejo, Sanam Ali, Sahil Pabani, Jitander Kumar Mehran University of Engineering and Technology National Center of Robotics and Automation Cms Lab Jamshoro Pakistan Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan Mehran University of Engineering and Technology National Center of Rorotics and Automation Cms Lab Jamshoro Pakistan Dawood University of Engineering and Technology Department of Telecommunication Engineering Karachi Pakistan

ISBN: (纸本)9798331534271

The fast growth of digital communication via smartphones and social media has made textual data as one of the most essential forms of people's expressions. Accurate emotion discovery from text is thus important for applications in customer sentiment analysis and interactive technologies, be it a web, desktop, or mobile application, an intelligent robot, or minor connected devices. This paper is intended to evaluate the performance of five state-of-the-art transformer models, including BERT, ALBERT, DistilBERT, Transformer XL, and RoBERTa, for multiclass emotion detection using the IEMOCAP dataset. From the results, it is observed that DistilBERT has the highest accuracy of 79.2%, implying an effective approach in balancing model size and performance. These research show that smaller models like DistilBERT often outperform larger models by reducing their overfitting and computational demands. The findings also highlights the need for task-specific fine-tuning and optimization in emotion detection. © 2024 IEEE.

关键词： DistilBERT IEMOCAP NLP Text Emotion Detection Transformers

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