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2024 International Conference on electrical Machines, ICEM 2024

作者： Lerch, Tomasz Milewski, Szczepan Drewniak, Michal Lukawski, Krzysztof Kolacz, Tomasz AGH University of Krakow Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland University of Applied Sciences in Tarnow Polytechnic Faculty Tarnów Poland

ISBN: (纸本)9798350370607

This article presents a description of research that has led to the development of a new design permanent magnet machine. The machine is based on the operational principle of the transverse-flux machine (TFM). In the proposed solution, a core commonly used in radial flux machines was employed, significantly simplifying the construction of the TFM machine. This simultaneously reduces flux leakage and enhances the value of the developed electromagnetic torque. The article sequentially outlines the stages of research on the new design, ranging from analytical calculations through Finite Element Method (FEM) simulation to the construction of a machine prototype. © 2024 IEEE.

关键词： Permanent magnets

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A hybrid attention deep learning network for refined segmentation of cracks from shield tunnel lining images

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Journal of Rock Mechanics and Geotechnical engineering 2023年第12期15卷 3105-3117页

作者： Shuai Zhao Guokai Zhang Dongming Zhang Daoyuan Tan Hongwei Huang Department of Civil and Environmental Engineering The Hong Kong Polytechnic University999077Hong KongChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji UniversityShanghai200092China

This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.

关键词： Crack segmentation Crack disjoint problem U-net Channel attention Position attention

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A NOA-AP based Typical Process Route Automatic Discovery Approach for Intelligent Manufacturing 7

A NOA-AP based Typical Process Route Automatic Discovery App...

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7th International Conference on Robotics, Control and Automation engineering, RCAE 2024

作者： Cao, Xinyu Xu, Binzi Huang, Dengchao Wang, Chun Anhui Polytechnic University School of Electrical Engineering Wuhu241000 China Huaibei Normal University School of Computer Science and Technology Huaibei235000 China

ISBN: (纸本)9798350355642

With the continuous advancement of intelligent manufacturing, the efficient extraction of reusable typical process routes based on artificial intelligence for computer-aided process planning (CAPP) has emerged as a critical necessity. Because a good process route can not only effectively improve the efficiency of process planning, but also promote the transformation of traditional manufacturing to intelligent manufacturing. To address this situation, this paper proposes a Nutcracker Optimization Algorithm (NOA) enhanced Affinity Propagation (AP) algorithm under the consideration of clustering effectiveness to automatic discover the typical process routes from the massive historical data. First, two soft constraints (i.e., quantity constraint and size constraint) are introduced simultaneously in the clustering process to ensure the quality and validity of the obtained typical process routes according to the actual demand. In addition, previous research has emphasized that the selection of preference value and damping factor in the AP algorithm significantly influences clustering outcomes. Hence, NOA is implemented to identify the optimal individual within the search space to determine suitable preference values and damping factors, thereby enhancing the clustering quality of the AP algorithm. The numerical illustration shows that the proposed NOA-AP could improve the clustering quality and discover more representative and effective typical process routes. © 2024 IEEE.

关键词： Smart manufacturing

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A Deep Learning Based Denoising Approach for Synthetic Aperture Sonar Imaging 2

A Deep Learning Based Denoising Approach for Synthetic Apert...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Lu, Xicheng Sun, Guanxi Liu, Wei Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798331515669

Synthetic Aperture Sonar (SAS) is one of the techniques used for underwater imaging, and in this paper, we use the range Doppler algorithm for SAS imaging and consider the position errors of sonar platforms in practical scenarios. These errors can seriously degrade the imaging result;however, correcting them and performing compensation with high-precision equipment could be very costly. In this paper, a deep learning approach is proposed using artificial neural networks for image denoising, specifically targeting noise induced by motion uncertainties. As demonstrated by computer simulations, it can recover the images with better quality without employing complex algorithms and hardware compensation. © 2024 IEEE.

关键词： Underwater imaging

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Feasibility and Potential Assessment of Wind Resource a Case Study in North Shewa Zone, Amhara, Ethiopia

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Distributed Generation and Alternative Energy Journal 2023年第6期38卷 1863-1884页

作者： Feleke, Solomon Anteneh, Degarege Khan, Baseem Álvarez, Roberto Marcelo Electrical and computer engineering Debre Berhan University Debre Berhan Ethiopia Electrical and computer engineering Hawassa University Hawassa Ethiopia Higher Polytechnic School Universidad Europea del Atlántico C/Isabel Torres 21 Santander39011 Spain Department of Project Management Universidad Internacional Iberoamericana Campeche24560 Mexico

The assessment of wind resource potential and feasibility is critical for generating and forecasting power generation, as wellas resource identification. In Ethiopia, the majority of the country lacks a wind atlas, making it difficult to determine the availability of sources. Seven different areas (Debre Berhan, Alem Ketema, Mehal Meda, Eneware, Gundo Meskel, Majete and Shewa Robit) were investigated. For the work data was collected from various sources and analyzed using MATLAB software. The basic sources of data that were obtained nationally were from the NMA, which is the National Metrological Agency found in Addis Ababa, and were obtained centrally from each local delegate’s registered report from a height of 2 meters and 10 meters in each listed districts above. According to the results analysis, the average wind speed at most sites is reasonable and 4 m/s at a height of 10 meters, and some of the case study sites have an average wind speed of less than 3 m/s. The extrapolation prediction method produces more realistic results at 30 and 50 meters;for example, when 10 meter is extrapolated to 30 and 50 meters, the wind power densities are 75.2 w/m2, 300.9 w/m2, and 680.5 w/m2, respectively. Similarly, the average yearly energy density for 10 meter, 30 meter, and 50 meter is 2110.8, 4122.6, and 8219.9 Kwh/m2/year, respectively. As per the international standard for wind power and wind speed classification, Eneware and Mehal Meda are categorized under class 7, whereas Debre Berhan is categorized under class 3, while the remaining sites such as Shewarobit, Gunde Meskel, Alem Ketema, and Majete are classified under class 1 for the majority of the year. © 2023 River Publishers.

关键词： Wind power

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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Explaining Machine Learning Based Speed Anomaly Detection System Using eXplainable Artificial Intelligence 1

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13th International Conference on Information Systems and Advanced Technologies, ICISAT 2023

作者： Idris, Hussaini Aliyu Ahmad, Aminu Diginsa, Muhammad Umar Aliyu, Buhari Muhammad, Adamu Yahaya, Abdulgaffar Abubakar Suleiman, Sabiu Usman Alexandria Egypt School of Computer Science and Engineering Central South University Hunan Changsha China Department of Computer Engineering Technology Bilyaminu Usman Polytechnic Hadejia Hadejia Nigeria Department of Computer Science Jigawa State Polytechnic Dutse Jigawa Nigeria Department of Electrical Electronics Engineering Jigawa State Polytechnic Dutse Jigawa Nigeria Department of Computer Science Mewar University Gangarar Chittorgarh Rajasthan India

ISBN: (数字)9783031605949

ISBN: (纸本)9783031605932

The goal of combating road accidents through the application of advanced communication and network technologies in transportation system called intelligent transportation system (ITS) has recorded a remarkable success. Nevertheless, the complex nature of the ITS makes cyberattacks a major concern even with cryptographic techniques employed for authentication of users, as they cannot prevent attacks from authenticated users (insider attackers). To solve this problem, several data-centric approaches for anomaly detection using machine learning (ML) are proposed by the research community. However, those approaches are limited in interpretability and explainability due to the complexity of some ML algorithms and black-box nature of neural networks which makes threat intelligence and analysis difficult. In this paper, we proposed an eXplainable artificial intelligence (XAI)-based speed anomaly detection system using trusted vehicles method with more than 99% accuracy, precision, recall and F1-Score where Shapley Additive exPlanation (SHAP) is further employed to explain the predictions made by the models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Improved Cortical Stimulation Efficiency Using 3D Rotating Fields in Transcranial Magnetic Stimulation

Improved Cortical Stimulation Efficiency Using 3D Rotating F...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Chinnakkagari, Shashank Manteghi, Majid Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9781665442282

This investigation proposes using 3D rotating fields for coupling the coil with neurons as an alternative to conventional Transcranial Magnetic Stimulation (TMS) techniques. This method requires calculating the field components necessary to rotate the vector field in a two-dimensional plane at a given point of interest Our results indicate that this approach activates neurons in that plane efficiently. In addition, the study demonstrates that selective activation of neurons is possible by restricting the rotation of the vector field plane to a three-dimensional sphere using an external circuit that adjusts the amplitude and phase difference between the field components. computer simulations of human head models with orthogonal coils were used to verify the suggested approach. © 2023 IEEE.

关键词： Neurons

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A Logical Circuit Optimization in Balancing Delay and Energy Consumption 6

A Logical Circuit Optimization in Balancing Delay and Energy...

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6th International Conference on Information Technologies and electrical engineering, ICITEE 2023

作者： Shan, Qihang The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg24061 United States

ISBN: (纸本)9798400708299

The fast-developing chip manufacturing technique and scaling of transistors allow us to fit more transistors on a small chip. The scaling down process, however, is facing a challenge. The smaller transistors are, the more influential quantum channeling and silicon atom size limit become. To improve efficiency, the solution of scaling down is no longer an option. Therefore, to further improve the efficiency of a chip without scaling down transistors, this paper presents a combinational circuit and focuses on an optimization approach where energy consumption is reduced in exchange for increasing delay. By adjusting the size of transistors, energy is saved while maintaining delay to an acceptable range. This approach manages to reduce energy consumption by about 56% while increasing delay by 50%. This paper represents one of many possible approaches that researchers had and has been working on and this tradeoff can benefit some circuit designs depending on the circuit's purpose and hope to bring some insights on further optimization. © 2023 Owner/Author.

关键词： Energy utilization

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The Interplay Between Industry 4.0 Technologies and Business Performance: Evidence From a Multiple Case Study in the Automotive Sector

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IEEE engineering Management Review 2024年第1期52卷 108-120页

作者： Piepoli, Antonio Arcidiacono, Francesco Basile, Luigi Jesus Pellegrino, Roberta Schupp, Florian Zuehlke, Tobias Polytechnic University of Bari Department of Mechanics Mathematics and Management Bari70126 Italy Schaeffler Automotive Buehl GmbH & Co. KG Buehl77815 Germany University of Catania Department of Electrical Electronic and Computer Engineering Catania95124 Italy

Several automotive industry players are investing in the implementation of Industry 4.0 (I4.0). However, the adoption of I4.0 technologies in automotive supply chains (SCs) is still not uniform, which limits the value creation of advanced technologies. In particular, suppliers, which play a crucial role in the automotive SC, often struggle in their I4.0 projects. Since their adoption of I4.0 technologies can significantly impact the overall performance and value creation within the SC, it is crucial to gain insights into actions and strategies that can support suppliers' I4.0 implementations. Building on a multiple case study approach, this article aims at providing guidance to prospective I4.0 adopters by investigating the organizational-level actions that support I4.0 projects, the financial capital and KPIs typically used by suppliers, and the resulting impact of I4.0 technology implementation on business performance. Evidence shows that the planning stage of I4.0 initiatives, from the organizational-level actions to the investment strategy, is a complex and strategic stage for ensuring success. Equally important are the external collaborations, which ensure that firms are in line with technological innovation and market needs. Finally, results show that the implementation of more advanced I4.0 technologies is correlated with a more robust improvement in business performance. © 1973-2011 IEEE.

关键词： Investments

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