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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Krish K Raj Jiuta Tamata Litili Waisale Mansour H Assaf Voicu Groza Rahul R Kumar School of Information Technology Engineering Mathematics and Physics The University of The South Pacific Suva Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Induction Machines (IM) are widely favored in contemporary industry due to their affordability, robustness, and economical maintenance requirements. Despite these advantages, IMs are susceptible to various faults, such as bearing, stator winding, and rotor issues, which can cause significant operational disruptions, production delays, and considerable economic losses. Addressing these concerns, the importance of early fault diagnosis cannot be overstated, as it significantly improves machine reliability and efficiency, prevents further damage, and decreases the dependency on extensive repairs post-breakdown. The study focuses on early detection of Stator Inter-turn Faults (SITF) in IMs through an online diagnostic approach, utilizing a dataset from both Healthy and Faulty IMs equipped with a 2.2kW four-pole squirrel cage. An exploratory analysis has been performed on the dataset to simplify the data structure and understand feature dynamics. The most straightforward classification method has been applied to identify SITF occurrences. The effectiveness of each diagnostic technique was evaluated by comparing their accuracy levels. The research successfully developed both non-neural and neural network models capable of detecting SITFs with minimal severity, demonstrating the potential for enhancing industrial operations through early fault diagnosis.

关键词： Fault diagnosis Support vector machines Accuracy Computational modeling Biological system modeling Stator windings Production

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Investigation of XLPE Power Cable Insulation Degradation Under Chemical, Electrical, and Mechanical Stress

Investigation of XLPE Power Cable Insulation Degradation Und...

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Annual Conference of Industrial Electronics Society

作者： Abdelaziz Abuelrub Shady S. Refaat Sayed Mohammad Kameli Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

High voltage (HV) power cables are exposed to various stresses, such as chemical, electrical, mechanical, and thermal. These stresses contribute to the eventual failure of electrical power cables in an unpredictable manner. Cross-linked polyethylene (XLPE) is an essential component of a power cable's insulation layers, and unwanted stresses negatively affect the insulation layers' performance in various ways. In this paper, three 3D Maxwell finite element analysis (FEA) models of a single core, 30 kV DC power cable are built to study the effects of chemical stress, electrical stress, and mechanical stress on the characteristics of polymer insulation material. The main parameter utilized for determining insulation health in this work is the polarization index. Temporal evolution in the polarization index is investigated, as aging-induced stresses degrade the XLPE's structure. The severity of chemical stress, electrical stress, and mechanical stress is estimated and compared based on the polarization index, with a focus on chemical stress, highlighting the severity of the deformation caused within the inner polymeric structure of XLPE. The severity is obtained for the innermost and outermost insulation layers. It was discovered that the insulation layer's proximity to the cable's core is the main factor influencing degradation. Moreover, the obtained results successfully demonstrate the polarization index's ability to detect degradation after the 95-day mark, indicated by the substantial increase in polarization index values, common between all stress types applied.

关键词： Degradation Solid modeling Three-dimensional displays Temperature Power cables Power cable insulation Indexes Stress Chemicals Thermal stresses

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Interactions and Reconnections of Four-Dimensional Quantum Vortices

arXiv

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arXiv 2024年

作者： Middleton-Spencer, H.A.J. McCanna, B. Proment, D. Price, H.M. School of Physics and Astronomy University of Birmingham United Kingdom School of Computer Science University of Birmingham United Kingdom School of Engineering Mathematics and Physics University of East Anglia United Kingdom

Interactions and reconnections of vortices are fundamental in many areas of physics, including classical and quantum fluids where they are central to understanding phenomena such as turbulence. In three-dimensional (3D) superfluids, quantum vortices are one-dimensional (1D) filaments that can intersect, reconnect, and recoil with irreversible dynamics described by near-universal scaling laws. We explore quantum-vortex reconnections in a four-dimensional (4D) superfluid, where a vortex is a two-dimensional (2D) surface. Using real-time numerical simulations, we find much richer behaviour than in 3D, including stable intersecting vortex surfaces which do not reconnect, and unusual vortex reconnections which occur without significant energy dissipation, suggesting quasi-reversible dynamics. Our work raises many interesting questions about vortex physics in extra-dimensional systems, and may be extended in the future to more realistic experimental settings where the fourth dimension is simulated with techniques such as synthetic dimensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Vortex flow

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When liquid crystals encounter structured light 20

When liquid crystals encounter structured light

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Emerging Liquid Crystal Technologies XX 2025

作者： Zhou, Le Liu, Yuanfeng Neyts, Kristiaan Shen, Yang Sun, Jingbo Zhou, Ji Physics Department Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States School of Materials Science and Engineering State Key Laboratory of New Ceramics and Fine Processing Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9781510685222

Structured light refers to the light field tailored by various degrees of freedom including intensity, phase, and polarization states in both spatial and temporal domains, which may greatly vitalize the technologies in both optics, such as the next-generation optical communication as well as imaging and the materials science in both fabrications and characterizations. The structured characteristics of the structure light need materials also with structured optical properties that can generate or manipulate structured light in a straightforward way, which can be well satisfied by liquid crystals or glassy liquid crystals such as special spherulites. © 2025 SPIE.

关键词： Degrees of freedom (mechanics)

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Measuring Electromagnetic Performance and Configuration of Networks in High Interference Environments 15

Measuring Electromagnetic Performance and Configuration of N...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Khan, A.Z. Panwar, Neeraj Rayaguru, N.K. Shelke, Priya M. Giri, Rakesh Kumar Jaishankar, P. Yeshwantrao Chavan College of Engineering Applied Physics Department Nagpur India University Gehu Dehradun Department of School of Computing India Vel Tech Rangarajan Dr sagunthala R & D Institute of Science and Technology Department of Eee India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Sunrise University Department of Computer Science & Engineering Alwar India Sastra Deemed to be University Department of School of Civil Engineering India

ISBN: (纸本)9798350370249

Measuring the electromagnetic overall performance and configuration of networks in high-interference environments can be challenging. In those environments, the indicators of hobby can be infected with unwanted heritage indicators, i.e., noise, leading to degraded overall performance and misguided size readings. This paper investigates the most reliable techniques for measuring networks' electromagnetic performance and configuration in these problematic environments, utilizing introducing a unique multistage framework that combines sign-processing strategies with some community parameters. In particular, the framework includes four degrees: sign reception, sensitivity analysis, pass-correlation, and community configuration optimization-an analysis of the results of varying environmental conditions on sign reception and sensitivity change into conduct. A cross-correlation approach has evolved to decide the extent of correlation among one-of-a-kind alerts and to locate the specific configurations of the particular networks. Sooner or later, an optimization set of rules was carried out to locate the network's optimum configuration. The final result of this research is a reliable method for measuring the electromagnetic performance and configuration of networks in high-interference environments. The proposed technique is anticipated to help researchers and engineers better understand the electromagnetic behaviors of complicated networks in actual-world scenarios. © 2024 IEEE.

关键词： Configuration Electromagnetic Environmental Performance Technique

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Designing Scalable and Energy-Efficient Computation: Magnetoelectric Spin-Orbit Circuitry

Designing Scalable and Energy-Efficient Computation: Magneto...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sharma, Amit Kumar Mehra, Ritika Kumar, Brijesh Balaji, N.V. Kumar, K. Sriram Gaikwad, V.S. Tmu Department of Physics India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India School of Commerece and Management Iimt University Meerut India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

Using a magnetoelectric spin-orbit circuit, this study suggests a revolutionary method for developing scale and cost-effective computer. Despite semiconductors miniaturization, the leading technology of today, CMOS, has basically not evolved in terms of operational ideas throughout the years. The research investigates alternate dimensions expanded logic strategies to boost the performance of numerical control schemes and allow for the growth of novel methods like machine learning. The proposed optical logic device links the spinning energy of the electron with its mobility through spin-orbit communication and magnetism property toggle. The gadget has collaboration and senses due to the incorporation of cutting-edge linked chemicals and quasi constituents. In the areas of shifting energy, voltage, and computation efficacy, the study shows that this novel technique beats the technology known as CMOS. Additionally, it allows for minimum idle power usage and the possibility of multigenerational computation. © 2023 IEEE.

关键词： Carrier transfer CMOS technology Electricity generation Energy usage Energy-efficient computation Ferroelectricity switching Interfacial and workload component control Logic complexity Logic efficiency Microprocessor innovation Scaling Semiconductor technology Spin-flux conversion Spin-orbit transmission Strain

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Classification of Citrus Plant Diseases Using Deep Transfer Learning

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computers, Materials & Continua 2022年第1期70卷 1401-1417页

作者： Muhammad Zia Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Sajjad Shaukat Jamal Jawad Ahmad Iqtadar Hussain Department of Electrical Engineering HITEC UniversityTaxilaPakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia School of Computing Edinburgh Napier UniversityUK Department of Mathematics Statisticsand PhysicsQatar UniversityDoha2713Qatar

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and *** in turn has helped in improving the quality and production of vegetables and *** fruits arewell known for their taste and nutritional *** are one of the natural and well known sources of vitamin C and planted *** are several diseases which severely affect the quality and yield of citrus *** this paper,a new deep learning based technique is proposed for citrus disease *** different pre-trained deep learning models have been used in this *** increase the size of the citrus dataset used in this paper,image augmentation techniques are ***,to improve the visual quality of images,hybrid contrast stretching has been *** addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature *** fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus *** proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

关键词： Citrus plant disease classification deep learning feature fusion deep transfer learning

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks 48

Classification of Mechanical Faults in Rotating Machines Usi...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Messaoudi, Maher Refaat, Shady S. Massaoudi, Mohamed Ghrayeb, Ali Abu-Rub, Haitham Texas A&m University at Qatar Department of Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom Texas A&m University Department of Electrical and Computer Engineering College Station United States

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine's operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state. © 2022 IEEE.

关键词： Condition monitoring

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Exploring the Experimental Possibilities of AI based Real- Time Virtual Reality Assisted Touchless Home Appliance Control 1

Exploring the Experimental Possibilities of AI based Real- T...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Kumar, C. Ashok Gnanasekar, A. Ramya, R. Narmatha, S.U. Arunadevi, R. Ravi, C.N. Engineering Mlr Institute of Technology Department of Electronics and Communication Hyderabad India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Physics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350384246

The design and execution of home automation systems have been greatly enhanced by recent technological breakthroughs, which have increased the capabilities and usefulness of embedded systems. There is a significant demand for smart home automation systems that include Virtual Reality (VR). One possible use of the proposed technology is to let the owner operate their home appliances through virtual reality. Using this is a breeze. In this work, we offer a modular, practical, and secure automation control structure for smart homes that is tied to virtual reality and has a cheap price tag. In this study, we take a simple look at how virtual reality may be used to regulate voltage for common home equipment including lights, fans, air conditioners, motor controls, and more. An AI-based Virtual Appliance Control System (AIVACS) is the name given to the proposed system because of its foundation in AI logic. In order to determine how effective the proposed model is, it is cross-validated using a standard Home Appliance Control System (HACS). The hardware has been assembled and tested in a scaled-down prototype with three options for controlling it: illumination, motors, and a fan in a domestic setting. The primary rationale for this research is the significant practical benefit that this VR-based hands-free operation would provide to physically impaired and elderly individuals, who wouldn't be required to physically go to the location of the switch. © 2024 IEEE.

关键词： Domestic appliances

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