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13th International electrical engineering Congress, iEECON 2025

作者： Suwanasri, Cattareeya Somboon, Makkrapong Rattanasri, Pongwisit Suwanasri, Thanapong Faculty of Ebgineering Department of Electrical and Computer Engineering Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok The Sirindhorn International Thai-German Graduate School of Engineering Bangkok10800 Thailand

ISBN: (纸本)9798331543952

The effective maintenance of Gas Insulated Switchgear (GIS) in high-voltage (HV) substations is essential for ensuring system reliability and minimizing unplanned downtime. The percentage Health and Importance Indices (%HI and %ImI) are key metrics used to assess the criticality of various factors influencing GIS performance, such as equipment age, maintenance history, insulation quality, environmental conditions, and operational performance. This research performs a sensitivity analysis on the weighting factors applied to the %HI and %ImI, examining how changes in these weightings affect the indices. Using a weighting and scoring technique, the analysis investigates the impact of varying each parameter's weight on the overall %HI and %ImI, identifying the most sensitive factors that influence maintenance decisions. The actual data for both the conditional factors and importance criteria are applied in these calculations. Subsequently, the actual scores of six units GIS are computed using ten different weighting sets. The results demonstrate that the weighting values follow a linear approach, where higher weight values indicate a greater influence on the respective criterion. In addition, the sensitivity of the weighting factors significantly impacts the process of adjusting the range for %HI and %ImI, ultimately affecting GIS health and importance identification and informing maintenance planning. © 2025 IEEE.

关键词： Sensitivity analysis

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A Novel Terahertz Metamaterial Absorber Based on Split-Relief Design for the Application of Refractive Index Sensing

SSRN

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SSRN 2023年

作者： Abdulkarim, Yadgar I. Muhammadsharif, Fahmi F. Appasani, Bhargav Bakır, Mehmet Karaaslan, Muharrem Rezaei, Pejman Khatami, Seyed Amin Daraei, Omid Mohsen Zamzam, Pouria Dong, Jian School of Computer Science and Engineering Central South University Changsha410075 China Physics Department College of Science Charmo University Chamchamal Sulaimania46023 Iraq Department of Physics Faculty of Science and Health Koya University KOY45 Koya Iraq School of Electronics Engineering KIIT University Odisha Bhubaneswar India Department of Computer Engineering Bozok University Yozgat66200 Turkey Department of Electrical-Electronics Engineering Iskenderun Technical University Hatay 31200 Turkey Electrical and Computer Engineering Faculty Semnan University Semnan Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

THz metamaterial absorbers are a subclass of metamaterial-based structures that can absorb THz electromagnetic radiations in the THz range. In this paper, four different designs of THz metamaterial absorbers are proposed which are based on split-relief (SR) design. They consist of sun-shaped, SR sun-shaped, star-shaped and SR star-shaped resonators coated on top of a dielectric substrate. The resonance frequencies of the designs were found to be at 0.70, 1.04, 0.41 and 0.60 THz, respectively. Results showed that the resonance frequency was independent on the incident and polarization angles. The proposed absorbers were used to detect the thickness and refractive index of the analyte. The thickness of the analyte can be detected with a sensitivity of 73 GHz/µm for the Sun design, 238 GHz/µm for the SRSun design, 45.2 GHz/µm for the Star design, and 133.4 GHz/µm for the SRStar design. Similarly, the refractive index of the analyte can be measured with a sensitivity of 1.581 THz/RIU for the Sun design, 4.053 THz/RIU for the SRSun design, 1.077 THz/RIU for the Star design, and 2.067 THz/RIU for the SRStar design when the analyte thickness was fixed at 3 μm. The resonance mechanism was explained considering electric and magnetic field distributions. © 2023, The Authors. All rights reserved.

关键词： Refractive index

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Resource Provisioning in Cloud Computing using Fuzzy Logic control System: An adaptive approach 2

Resource Provisioning in Cloud Computing using Fuzzy Logic C...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alnawayseh, Saif. E. A. Muhammad, Muhammad Hassan Ghulam Hassan, Zahid Fatima, Mayraj Aslam, Muhammad Shoukat Ibrahim, Amer Ateeq, Karamath Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan PMAS-Arid Agriculture University Rawalpindi Pakistan American University in Th Emirates College of Computer and Information Technology Dubai United Arab Emirates Skyline University BSIT Faculty Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

The need for cloud computing has been increasing widely in this era since last two decades due to its continual benefits include security, disaster recovery, increased collaboration, cost saving and virtualization etc. This innovation introduces an enormous transformation of conventional applications in business industry. However, cloud users indulge themselves not acquiring a sufficient amount of computing resources caused by resource confliction. A challenging and significant problem in cloud computing is Resource Provisioning in virtualization environment that relates with selecting, deploying and run time management of software and hardware resources. On the contrary, such situations also exist where resources are found in virtual servers either under or over-utilization. In order to deal with these problems, proposed framework in this paper is demonstrated using Fuzzy Logic control System towards characterization the processes' priorities by means of employing input parameters (workload, processing time, and process age) keeping in view of the fact that expert judgment is required for taking into account these parameters. © 2023 IEEE.

关键词： Virtualization

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A Study on the Potency of Hybrid Models: Detecting Diseases in Cucumber Leaves with Pre-trained CNNs and SVM

A Study on the Potency of Hybrid Models: Detecting Diseases ...

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International electrical engineering Congress (iEECON)

作者： Teerapon Yodrot Chalermkiat Sutacha Teerapong Orachon Nattapon Jangjongdee Sompol Boonyasuwanno Department of Electrical Engineering Technology Faculty of Industry and Technology Rajamangala University of Technology Rattanakosin Prachuap Khiri Khan Thailand Department of Computer Network Engineering Faculty of Industrial Technology Ubon Ratchathani Rajabhat University Ubon Ratchathani Thailand Department of Computer Engineering KOSEN of King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This study investigates a hybrid machine learning approach for the classification of cucumber leaf diseases, combining the strengths of pre-trained Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM). Focusing on a publicly available dataset, we evaluated four CNN architectures—VGG16, ResNet50, AlexNet, and EfficientNetV2—for feature extraction, followed by SVM for the final classification task. The performance of each model was assessed using F1 scores and confusion matrices, with ResNet50 yielding the highest F1 score, indicative of a strong balance between precision and recall. VGG16 and AlexNet showed potential for improvement, which could be realized through model tuning or integration into an ensemble framework. EfficientNetV2, despite a slightly lower F1 score, highlighted the importance of further training to enhance class distinction. The research underlines the importance of a hybrid approach, which harnesses deep learning's feature extraction capabilities with SVM's classification strength, to improve the accuracy of disease detection in cucumber plants.

关键词： Support vector machines Training Adaptation models Neural networks Feature extraction Convolutional neural networks Task analysis

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Multi-Scale Regularization and Bounding Box Refinement in Semi-Supervised Object Detection Framework

Multi-Scale Regularization and Bounding Box Refinement in Se...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Ramakrishnan Raman Karthick Rajan Falguni Tlajiya Manisha Paliwal Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Electrical Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of International Business (BIIB) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Semi-supervised object detection (SSOD) has emerged as a critical approach to bridge the gap between the extensive labeled data requirements of supervised methods and the abundance of unlabeled data available. However, existing SSOD techniques often suffer from two significant limitations: inconsistent performance across scales and inaccuracies in object localization. To address these challenges, this paper introduces a novel framework that integrates Multi-Scale Regularization (MSR) and Bounding Box Refinement (BBR). The proposed MSR mechanism enforces consistency in predictions across different feature map scales, ensuring robust pseudo-labeling and mitigating scale-related variations. Simultaneously, BBR employs a two-stage optimization process to enhance the precision of bounding box predictions, correcting spatial errors in initial detections. The combined framework leverages both labeled and unlabeled datasets effectively, resulting in improved detection performance. Extensive experiments conducted on benchmark datasets such as COCO and Pascal VOC demonstrate the efficacy of the proposed approach, showing significant improvements in mean Average Precision (mAP) compared to existing state-of-the-art SSOD methods. Ablation studies further highlight the individual contributions of MSR and BBR to the overall performance gains. This study establishes a new benchmark in SSOD, paving the way for more accurate and efficient object detection systems that rely on minimal labeled data while leveraging abundant unlabeled data.

关键词： Location awareness Accuracy Object detection Benchmark testing Semisupervised learning Performance gain Robustness Data models Integrated circuit modeling Optimization

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging

arXiv

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arXiv 2025年

作者： Stevens, Tristan S.W. Overdevest, Jeroen Nolan, Oisín van Nierop, Wessel L. van Sloun, Ruud J.G. Eldar, Yonina C. Department of Electrical Engineering University of Technology Eindhoven Netherlands Signal Processing Algorithms NXP Semiconductors Netherlands Faculty of Mathematics and Computer Science Weizmann institute of Science Israel

Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution. In recent years, generative modeling for Bayesian inference on sensory data has also gained traction. Nevertheless, the direct application of generative modeling techniques initially designed for natural images on raw sensory data is not straightforward, requiring solutions that deal with high dynamic range signals acquired from multiple sensors or arrays of sensors that interfere with each other, and that typically acquire data at a very high rate. Moreover, the exact physical data-generating process is often complex or unknown. As a consequence, approximate models are used, resulting in discrepancies between model predictions and the observations that are non-Gaussian, in turn complicating the Bayesian inverse problem. Finally, sensor data is often used in real-time processing or decision-making systems, imposing stringent requirements on, e.g., latency and throughput. In this paper, we will discuss some of these challenges and offer approaches to address them, all in the context of high-rate real-time sensing applications in automotive radar and medical imaging. © The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://***/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. © 2025, CC BY-SA.

关键词： Inverse problems

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Towards Integrating Physiological Data in UI/UX Design Learning and engineering Education

Towards Integrating Physiological Data in UI/UX Design Learn...

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International Conference on Communications and Electronics, ICCE

作者： Montri Phothisonothai Korawit Orkphol Natthapon Pannurat Apirath Limmanee Warayost Lamaisri Chaowat Euafua Department of Computer Engineering Faculty of Engineering at Sriracha Kasetsart University Sriracha Chonburi Thailand School of Industrial Education and Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand Faculty of Economics at Sriracha Kasetsart University Sriracha Chonburi Thailand Department of Electrical Engineering Faculty of Engineering Burapha University Chonburi Thailand

ISBN: (数字)9798350379792

ISBN: (纸本)9798350379808

This paper introduces an educational framework for incorporating physiological data into engineering classroom activities to enrich the learning process of User Interface/User eXperience (UI/UX) design. Informed by Bloom’s Taxonomy and well-established software engineering design principles, this approach incorporates a variety of key advanced physiological technologies–including eye tracking, palm and fingers movements, brain wave monitoring, muscle movement, and virtual reality–into project-based learning (PBL) scenarios within human-machine interaction (HMI) design. We have analyzed six PBL initiatives in UI/UX design to evaluate the effectiveness of this method. Our findings suggest a transformative shift in engineering education, promoting a more cohesive, data-driven methodology in teaching and learning not only deepens understanding but also stimulates innovation in the field of UI/UX design, particularly within the context of HMIs.

关键词： Knowledge engineering Technological innovation Navigation Taxonomy Muscles Biomedical monitoring engineering education

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Efficient Optimization of Energy Consumption at Home through Machine Learning

Efficient Optimization of Energy Consumption at Home through...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sharada Prasad N Lakshmi Shree M S Manjunatha S Vipul Vekariya Ganga D Benal Harshal Patil Department of Electrical and Electronics Engineering RNS Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

In recent years, the imperative need for efficient energy utilization in residential buildings has become increasingly evident due to the unwarranted wastage of electrical energy. This has spurred significant interest in optimizing energy consumption while maintaining user comfort. Accurate energy prediction is a crucial component of this optimization. This study focus on minimizing energy consumption by accurately predicting it through advanced Machine Learning (ML) models and optimization techniques. The study begins with the collection of energy data from a reliable source, Kaggle, comprising 29 features. To streamline the dataset, unnecessary features are discarded, and data normalization is performed to ensure consistency and reliability. Subsequently, ML models, specifically Long Short-Term Memory (LSTM), are designed and optimized through the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) to fine-tune hyperparameters. The prediction results are evaluated using error values to assess the accuracy and reliability of the models. Notably, our findings indicate that the GWO-LSTM model outperforms the others, exhibiting minimal errors and therefore showcasing its superior predictive capabilities. Accurate energy prediction not only serves as a valuable tool in its own right but also plays a pivotal role in enabling proactive energy management. By accurately forecasting future energy requirements, it becomes possible to optimize energy consumption further. Such precision paves the way for intelligent scheduling of home appliances, which, in turn, leads to significant reductions in energy consumption.

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Empirical Based Irrigation Model Using Predicted Soil Moisture for Durian Plantation 3rd

Empirical Based Irrigation Model Using Predicted Soil Moistu...

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3rd International Conference on control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Ramli, Muhammad Shahrul Azwan Abidin, Mohamad Shukri Zainal Hean, Pui Boon Rahman, Mohd Amiruddin Abd Perumal, Thinagaran Reba, Mohd Nadzri Md Division of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai Malaysia IoT Department MIE Industrial Sdn. Bhd Selangor Puchong Malaysia Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Skudai Malaysia

ISBN: (纸本)9789811939228

It is vital to the agricultural activities to have sufficient water supply for its operation and maintenance mainly for cultivation to keep it in good condition Therefore, it is important to determine the soil moisture levels existing while designing a precise irrigation system. Installing soil moisture sensors in each tree is complicated or excessively expensive. Forecasting the value using climate data is a viable solution in this scenario. Climate data are used to forecast soil moisture and then utilized in this irrigation model. This study uses an Artificial Neural Network (ANN) to forecast soil moisture values. The statistical method is used to determine the predicted values’ correctness. After the process, the irrigation volume and schedule are calculated based on the most accurate prediction findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soil moisture

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