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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Molaei, Aref Kanathipan, Kajanan Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350376067

A new single-stage high-gain interleaved boost modular CL resonant converter is proposed in this paper for photovoltaic-powered magnetron application. The proposed converter is able to provide high voltage gain for the magnetron which is a device for microwave applications. Perturb and Observe (P&O) method was used to achieve Maximum Power Point Tracking (MPPT) in the integrated interleaved boost stage by controlling the duty-cycle of the two primary leg switches. The operational principles of the converter are briefly discussed in this paper. Results on a 1.7kW, 230V/3.8kV design are presented to verify the proposed converter performance. © 2024 IEEE.

关键词： Magnetrons

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A Hybrid Non-Conformal Volume Surface Integral Equation Method

A Hybrid Non-Conformal Volume Surface Integral Equation Meth...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Jiang, Yifei Shao, Hanru Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350355581

Traditional volume surface integral equation for analysis of metal-dielectric composites requires the target mesh to be conformal, which leads to over-meshing of the multi-scale model and reduces the solution efficiency. In order to deal with this problem, a non-conformal volume surface integral equation (VSIE) method based on the discontinuous Galerkin volume integral equation (DGVIE) and the grid-robust higher order vector basis (GRHOVB) surface integral equation is proposed in this paper to enhance the computing efficiency. In this method, the DGVIE is used to deal with dielectric target, which avoids the computation of higher-order singular integrals and facilitates the use of volume non-conformal meshing and mixed basis function discretization. The GRHOVB function is used to deal with PEC target, so that the surface mesh also does not need to be conformal. Numerical results verify the good performance of this non-conformal method. © 2024 IEEE.

关键词： Mesh generation

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Optimistic Online Non-stochastic Control via FTRL 63

Optimistic Online Non-stochastic Control via FTRL

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Mhaisen, Naram Iosifidis, George Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

ISBN: (纸本)9798350316339

This paper brings the concept of 'optimism' to the new and promising framework of online Non-stochastic Control (NSC). Namely, we study how NSC can benefit from a prediction oracle of unknown quality responsible for forecasting future costs. The posed problem is first reduced to an optimistic learning with delayed feedback problem, which is handled through the Optimistic Follow the Regularized Leader (OFTRL) algorithmic family. This reduction enables the design of OptFTRL-C, the first Disturbance Action Controller (DAC) with optimistic policy regret bounds. These new bounds are commensurate with the oracle's accuracy, ranging from O (1) for perfect predictions to the order-optimal O(ST) even when all predictions fail. By addressing the challenge of incorporating untrusted predictions into online control, this work contributes to the advancement of the NSC framework and paves the way toward effective and robust learning-based controllers. © 2024 IEEE.

关键词： Stochastic control systems

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Multiple Relays Assisted MEC System for Dynamic Offloading and Resource Scheduling with Energy Harvesting 18th

Multiple Relays Assisted MEC System for Dynamic Offloading ...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Wang, Jiming Qu, Long Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Ningbo China

ISBN: (纸本)9789819998951

Mobile Edge Computing (MEC) has become an indispensable way to reduce the execution delay of devices. However, for some devices located far away from the MEC server, the transmission delay of communication with MEC is still large. In this case, we consider using multiple relay devices to assist Internet of Things (IoT) devices to communicate with MEC servers. To enhance the energy efficiency of the system, we introduce Energy Harvesting (EH) devices to provide energy for the IoT devices. Our objective is to maximize the utilization of EH devices while minimizing the overall delay in task offloading for the IoT devices. We tackle the problem by formulating it as a Markov Decision Problem (MDP). However, due to the significant expansion of the state space, traditional methods such as relative value iteration and linear iterative reconstruction are ineffective in solving this problem. Hence, we propose an approach called Multi-Relay Assisted Dynamic Computation Offloading (MRADCO) algorithm, which leverages the Lyapunov optimization technique. It is important to note that our proposed algorithm makes decisions solely based on the current state, without requiring the distribution information of the wireless channel and EH process. This characteristic enhances the algorithm’s practicality and reduces complexity in real-world implementations. Through rigorous theoretical derivation and comprehensive simulation experiments, we demonstrate that our algorithm is asymptotically optimal. And compared with the benchmark algorithm LODCO, our algorithm reduces the time by 50%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Energy harvesting

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Evaluation of software aging in Hyperledger Fabric 35

Evaluation of software aging in Hyperledger Fabric

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35th IEEE International Symposium on Software Reliability engineering Workshops, ISSREW 2024

作者： Soeda, Kojiro Xiao, Xiao Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350367041

Software aging is the gradual decline in performance that occurs with continuous software operation. As the demands on software systems increase, assessing the risk of software aging becomes crucial, especially in modern systems requiring high availability, such as blockchain platforms. Herein, we quantitatively evaluated the risk of software aging in Hyperledger Fabric, a permissioned blockchain. We subjected Hyperledger Fabric to various loads in an experimental environment and monitored memory and CPU usage fluctuations. Through statistical tests - including the Mann-Kendall test, modified Cox-Stuart test, and Sen's slope estimation - we identified the risk of software aging in terms of memory and CPU usage across all peers. © 2024 IEEE.

关键词： Software testing

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On Deep Learning for Condition Assessment of Power Transformers 16

On Deep Learning for Condition Assessment of Power Transform...

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16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Jiang, Hanjun Ekanayake, Chandima Ma, Hui School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798350386127

Accurately assessing power transformers' health condition and calculating their probability of failure (PoF) can facilitate decision-making with respect to the operation and maintenance of transformers. Among various data and information used for transformer condition assessment and PoF calculation, the Dissolved Gas Analysis (DGA) plays a key role in diagnosing transformer incipient faults. In this paper, we have developed a deep learning-based model to improves the effectiveness of DGA interpretation for transformer condition assessment. Then, the refined DGA results are used alongside other transformer measurement data to support the PoF calculation and risk cost analysis. The method developed in this paper has been validated using the dataset collected from numerous in-service power transformers. © 2024 IEEE.

关键词： Distribution transformers

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Transmission of Electromagnetic Waves Through a Clay Material 1

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32nd International Conference on Organization and Technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Bilandžija, Domagoj Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper deals with the simulation calculation of transmission (S21) and reflection (S22) parameters in a material parametrically based on clay (brick). The electromagnetic parameters of the clay that are the subject of the study are: relative permittivity Εr, loss tangent tan δ and electrical conductivity σ, while the only structural parameter is the thickness d of the sample. The values of all considered parameters are within the range of data from available literature and values that could occur due to external causes (e. g. an increase in clay moisture leads to an increase in electrical conductivity). The simulation model was carried out with the software tool ANSYS HFSS and validated with a laboratory measurement model. The laboratory model consists of a circular waveguide with an inner diameter of 150 mm and a total length of 700 mm, which is excited by 52 mm long probes placed 50 mm from the last wall of the waveguide. The lower cut-off frequency of this measurement system is 1.173 GHz, so all results (simulation and measurement) are limited by this lower cut-off frequency of the waveguide. All simulation calculations were performed for three clay sample thicknesses: 18 mm, 36 mm and 54 mm. These calculations were made with the aim of determining brick composites that have high attenuation of electromagnetic EM waves (low transmission parameter and high reflection parameter) in order to reduce the strength of EM fields inside the object made of such bricks. The measured results were used to verify the simulation model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Circular waveguides

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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

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Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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Current Trends, Challenges and Techniques in XAI Field;A Tertiary Study of XAI Research 47

Current Trends, Challenges and Techniques in XAI Field;A Ter...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Brdnik, Saša Šumak, Boštjan University Of Maribor Institute Of Informatics Faculty Of Electrical Engineering And Computer Science Slovenia

ISBN: (纸本)9798350382495

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field. © 2024 IEEE.

关键词： Artificial intelligence

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A Deep Neural Network Approach for Fault Detection in Industrial Motors: Enhancing Reliability and Efficiency in Industry 4.0 21

A Deep Neural Network Approach for Fault Detection in Indust...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ali, Hassan Ahyen, Mostafa Yaqub, Raziq Badawi, Ahmed Shifa, Noora Khandakar, Amith University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar Alabama A and M University Department of Electrical Engineering and Computer Science Huntsville United States Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350378078

In the era of Industry 4.0, the integration of sophisticated monitoring and control technologies is essential for the reliability and efficiency of industrial equipment. This paper presents a deep neural network (DNN)-based approach for fault detection and classification in industrial motors. The approach identifies common motor faults, including bent shaft, loose vibration, loosely mounted components, electrical faults, and bearing vibration. The proposed DNN model demonstrated robust performance, achieving an overall training accuracy of 83.49% ± 0.43% and a test accuracy of 81.91 % ± 0.82%. The model also achieved a mean precision of 82.05% ± 0.77%, mean recall of 81.91 % ± 0.82%, and mean F1-Score of81.86%± 0.78%. The 95% confidence intervals for accuracy, precision, recall, and F1-Score were (80.77%, 83.04%), (80.98%, 83.11 %), (80.77%, 83.04%), and (80.78%,82.95%), respectively. These results demonstrate the DNN model's ability to precisely classify various motor faults with minimal variability, as evidenced by its narrow confidence intervals and low standard deviations. The model's reliability and efficacy for real-world applications in proactive maintenance and fault detection are confirmed by the consistent performance that is observed across cross-validation folds. © 2024 IEEE.

关键词： Predictive maintenance

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