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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Giriprasad. S S. Sangeetha G Manikandan G Bhuvaneswari B Meenakshi S. Sujatha Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The rising need for sustainable energy solutions in off-grid regions has prompted the investigation of creative predictive modeling methodologies. This research examines Support Vector Machine (SVM) techniques to enhance solar energy production and use. SVM, a powerful machine learning instrument, assesses diverse environmental variables, such as solar radiation, temperature, and humidity, to precisely forecast solar energy production. It presents a SVM model to forecast solar energy production in off-grid areas, optimizing resource distribution and improving sustainability with analysis of environmental variations and energy consumption data. The model improves decision-making for deploying solar systems in distant areas using historical data and real-time observations. The prediction skills of SVM enable the creation of customized solar energy systems that address the specific energy requirements of off-grid settlements. This technique enhances sustainability by optimizing solar installation efficiency, decreasing dependence on fossil fuels, and mitigating environmental consequences. The results indicate that SVM-based predictive modeling substantially enhances the dependability and efficiency of solar energy systems, making them more feasible for off-grid applications. It highlights the capacity of advanced machine learning methodologies to promote sustainable energy solutions, hence aiding the worldwide shift towards renewable energy sources.

关键词： Support vector machines Renewable energy sources Energy consumption Solar energy Production Predictive models Market research Real-time systems Data models Sustainable development

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Classification System Based on Data Mining Technique for Flat-Colored Textiles

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SSRN 2025年

作者： Zhou, Min Lin, Yukai Hu, Wei Dai, Chai Tang, Guangfa He, Fei School of Information and Intelligent Engineering Guangzhou xinhua University Dongguan China School of Intelligent Control Hunan Railway Professional Technology College Zhuzhou China College of Electrical and Electrical Engineering Hunan University Changsha China Dept. of Pingdingshan Branch of China Mobile Communication Group Henan co. LTD Henan Pingdingshan China Dept.of Physics Electrical and Intelligent Manufacturing Huaihua University Changsha China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Manual visual inspection for defect detection in manufacturing can lead to high false positive rates. This paper compares data mining techniques for the binary classification of defects in flat products in a relatively open scenario. The background provides context on research into data mining-based detection. The methodology is divided into five sections: data collection, selection, preprocessing and transformation, classification, and model evaluation. Each section details the techniques applied, tailored to the application context, with specifying examples. Experiments apply real-world data sets self-collected from production lines. Statistics like prediction accuracy are generated by established data mining evaluation algorithms. Results indicate the efficacy of data mining for automated defect detection over manual approaches. Finally, conclusions summarize findings and discuss the limitations of the current research and planned future work to address the drawbacks. Overall, the formalized methodology and supportive experimental results demonstrate both the viability and potential areas of improvement for data mining in manufacturing quality control. © 2025, The Authors. All rights reserved.

关键词： Data accuracy

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Predictive Maintenance in Autonomous Systems using Improved Support Vector Machine

Predictive Maintenance in Autonomous Systems using Improved ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Mamatha Bai B G N. Sree Divya Ammar Hamedshnain S. Senthil Kumar T. Vasumathi Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology Mahatma Gandhi Institute of Technology Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Gojan School of Business and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, rapid development in the domain of Predictive Maintenance have resulted in improved equipment lifetime span, decrease maintenance costs and reduced the unplanned downtime. Predictive maintenance is an approach that addresses any equipment or component failures in the systems proactively. However, integration of this approach in automated systems has several challenges such as ensuring the accuracy of fault predictions, management of large-scale data and maintenance of system reliability under various operational conditions. To overcome these challenges Improved Support Vector Machine (ISVM) is proposed. Initially, the data is collected from the Predictive Maintenance Dataset (PMD) and the preprocessing is performed through Inter Quartile Range (IQR) which enhances the quality of data and modeling efforts. Further relevant features are extracted using Recursive Feature Elimination (RFE). Finally, the prediction is performed using ISVM which achieved better outcomes compared to existing a One-Class Support Vector Machine (SVM) in terms of accuracy as well as recall by achieving 90.58%, 80.79% respectively.

关键词： Support vector machines Temperature sensors Vibrations Accuracy Autonomous systems Predictive models Feature extraction Maintenance Sensors Predictive maintenance

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Multiple Chern Bands in Twisted MoTe2 and Possible Non-Abelian States

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Physical Review Letters 2025年第6期134卷 066601-066601页

作者： Cheng Xu Ning Mao Tiansheng Zeng Yang Zhang Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Department of Physics Tsinghua University Beijing 100084 China Max Planck Institute for Chemical Physics of Solids 01187 Dresden Germany Department of Physics Xiamen University Xiamen 361005 China Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

We investigate the moiré band structures and a possible even-denominator fractional quantum Hall state in small angle twisted bilayer MoTe2, using combined large-scale local basis density functional theory calculation and continuum model exact diagonalization. Via large-scale first-principles calculations at θ=1.89°, we find a sequence of C=1 (Chern number in the K valley) moiré Chern bands in analogy to Landau levels. By constructing the continuum model with multiple Chern bands, we undertake a band-projected exact diagonalization using an unscreened Coulomb repulsion to identify possible non-Abelian states near twist angle θ=1.89° at the half filling of second moiré band.

关键词： Continuum mechanics

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On the Performance of the Energy Detector Subject to Impulsive Noise in κ-μ, α-μ, and η-μ Fading Channels

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Tsinghua science and Technology 2017年第4期22卷 360-367页

作者： Jos V.M.Cardoso Wamberto J.L.Queiroz Hang Liu Marcelo S.Alencar Department of Electrical Engineering Federal University of Campina Grande Department of Electrical Engineering and Computer Science The Catholic University of America

This paper presents a unified theoretical analysis of the energy detection of Gaussian and M-PSK signals in κ-μ,α-μ,and η-μ fading channels at the output of an energy detector subject to impulsive noise（Bernoulli-Gaussian model）. As a result, novel, simple, and accurately approximated expressions for the probability of detection are derived. More precisely, the generalized Gauss-Laguerre quadrature is applied to approximate the probability of detection as a simple finite sum. Monte Carlo simulations corroborate the accuracy and precision of the derived approximations. The results are further extended to cooperative energy detection with hard decision combining information.

关键词： spectrum sensing energy detector fading channel impulsive noise

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Lifecycle-based Optimization of Home Energy Management: PV–Battery–EV Scheduling using Giant Trevally Optimization

Lifecycle-based Optimization of Home Energy Management: PV–...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Karpagam N B. Dhanasakkaravarthi E. Reddy Gowthami Manivannan R Vignesh C J S. Rukmani Devi Department of Electrical and Electronics Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of Mechanical Engineering Agni College of Technology Chennai Tamil Nadu India Department of Mechanical Engineering K.S.R.M. College of Engineering Kadapa Andhra Pradesh India Department of Electrical and Electronics Engineering OMR St. Joseph's Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

In particular, the efficient home energy management systems have become essential as renewable energy technologies such as PV systems, batteries, and EVs utilize in the residential ambiance. Previous techniques in Home Energy Management Systems (HEMS) lacked efficiency in handling dynamic energy scenarios, optimizing battery control, and achieving a cost-effective yet sustainable approach. This study proposes cost-efficient solutions that enhance the durability of Energy Storage Systems (ESS) while promoting environmentally sustainable energy consumption. To make the PV, batteries, EVs and the grid interactive, this study decomposes the full HEMS as elucidated below. As such, the suggested methodology means the detailed system characterization and mathematical modeling of PVs, batteries, and EVs, further, along with the effective approaches to the optimal flow and scheduling of appliances. GTO is used in the study to ensure that critical factors such as battery capacity and DoD are fine tuned to justify the life, viability, and affordability of the system. The proposed system incurs a cost of 3.899 EUR, experiences an energy loss of 0.423 kWh, and extends battery longevity to 7.940 years.

关键词： Energy loss Home appliances Schedules Renewable energy sources Costs Reinforcement learning Batteries US Department of Defense Energy management systems Optimization

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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

作者： Abhay Chaturvedi Praveena Rao Venkata Maruti Prasad S Malini T S. Prabagar K. Bharathi Department of Electronics and Communication Engineering GLA University Mathurai India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad India Department of BS & H Aditya Institute of Technology & Management Tekkali India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering School of Advanced Computing Alliance University Bangalore India Department of Agricultural Engineering Erode Sengunthar Engineering College Perundurai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture.

关键词： Support vector machines Precision agriculture Irrigation Accuracy Predictive models Feature extraction Internet of Things Water resources Long short term memory Mutual information

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Role of high energy photons in dual spectral source rapid isothermal CVD

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JOURNAL OF ELECTRONIC MATERIALS 1997年第10期26卷 1184-1188页

作者： Singh, R Chen, Y Department of Electrical and Computer Engineering Material Science and Engineering Program Clemson University Clemson Department of Electrical and Computer Engineering Clemson University Clemson

The use of tungsten halogen lamps and the deuterium lamp as the source of thermal and optical energies has been exploited to deposit thin films of Ta2O5 on Si and conducting substrates, The leakage current densities are as low as 10(-10) A/cm(2) for gate voltage under 5V. Photons in visible, ultraviolet, and vacuum ultraviolet (lambda < 800 nm) regions provide higher bulk and surface diffusion coefficients as well as reduced activation energy for the chemical process involved in the chemical vapor deposition process. The low thermal mass of the substrate provides limited reaction processing capability. The photochemical and photophysical processes allow the participating atoms and molecules to adjust their bond geometries and occupy sites which result in overall reduction of stress and strain energy and provide materials with overall low microscopic defects at low processing temperature and with high throughput. New experimental results of Al-Ta2O5-Si3N4-poly Si-Al structure are presented. The leakage current-voltage characteristics are better than those reported by other researchers.

关键词： dielectrics metalorganic chemical vapor deposition (MOCVD) rapid isothermal processing (RIP) Ta2O5

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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International electrical engineering Congress (iEECON)

作者： Pruek Vanna-Iampikul Visutr Boonnateephisit Somphop Limsoontharakul Jednipat Moonrinta Matthew Dailey Mongkol Ekpanyapong Department of Electrical Engineering Burapha University Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. Computational Science Prince of Songkla University Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. of Industrial Systems Engineering School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments.

关键词： Geometry Accuracy Laser radar Plantations Estimation Vegetation Production Real-time systems Time measurement Monitoring

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Design of Miniaturized Metamaterial Reflector with Doubly Interdigital Structure for 5G Use Cases

Design of Miniaturized Metamaterial Reflector with Doubly In...

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International electrical engineering Congress (iEECON)

作者： Tanee Somwong Pongsathorn Chomtong Jirayut Khamsuthat Nonchanutt Chudpooti Prayoot Akkaraekthalin Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents the design of a miniaturized metamaterial reflector composing of an open-loop transmission line unit cell structure. The transmission line ends are connected by using a double interdigital technique, resulting in a large capacitive value compared to the conventional methods. When combined with the open-loop line employing the trisection step-impedance structure with adjusting of the capacitive value, its second and third resonant frequencies will be controllable. As a result, this approach can reduce the unit cell size from a typical value of λ/2 to an approximate value of λ/16, therefore overall size of the reflector is significantly decreased. The designed unit cell is measured to be 1.05 cm × 1.0 cm. We implemented the proposed reflector on an FR-4 PCB with a relative permittivity of about 4.4, a substrate height (h) of 1.6 mm, and a loss tangent of about 0.02. The operating frequency range targets of 5G use cases were set to be at 1.8 GHz, 2.6 GHz and 3.5 GHz, respectively. When used with a dipole antenna, the bandwidths of 390 MHz, 604 MHz, and 1.1 GHz and the antenna gains of about 8.23 dB, 8.14 dB, and 8.19 dB were obtained at the operating frequencies, respectively.

关键词： Wireless communication 5G mobile communication Dipole antennas Simulation Resonant frequency Reflector antennas Transmission line measurements Metamaterials Gain Substrates

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