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An Experimental Study of Effect of Dielectric Materials on Wireless Power Transmission at 6.78 MHz 19

An Experimental Study of Effect of Dielectric Materials on W...

19th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Phaebua, Kittisak Lertwiriyaprapa, Titipong Boonpoonga, Akkarat Rattanarungngam, Darupon Torrungrueng, Danai King Mongkut's University of Technology Research Center of Innovation Digital and Electromagnetic Technology Department of Teacher Training in Electrical Engineering North Bangkok Thailand Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9781665485845

This paper presents an experimental study of the effect of dielectric materials on wireless power transmission (WPT) at 6.78 MHz. The dielectric materials such as high-density wood, freshwater, and low-density (lightweight) concrete block are employed. The WPT system consists of a 5 watt E-class power oscillator with a 6.78 MHz operating frequency, a receiver with a radio frequency (RF) to DC converter, a transmitting (Tx), and receiving coil antennas are employed. The wireless power transmission passing through the thickness of the dielectric materials of 14 cm and 21 cm are investigated. It is found that high-density wood achieves the maximum power transmission of 2.85 watts and 0.82 watts with a thickness of 14 cm and 21 cm, respectively. However, freshwater absorbs electromagnetic energy. The transmission power in the case of freshwater of 2.36 watt and 0.32 watt with the thickness of 14 cm and 21 cm are obtained, respectively. © 2022 IEEE.

关键词： Water

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Synthetic generation of hourly photovoltaic power forecasts using Bayesian Regularised Neural Networks 2

Synthetic generation of hourly photovoltaic power forecasts ...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Theocharides, Spyros Lopez-Lorente, Javier Makrides, George Georghiou, George E. University of Cyprus Pv Technology Laboratory Foss Research Center for Sustainable Energy Department of Electrical and Computer Engineering Nicosia1678 Cyprus

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Accurate day-ahead photovoltaic (PV) power output forecasting techniques are important for grid and plant operators. In this study, a methodology for PV power forecasting is proposed based on a synthetic generation of multiple Bayesian regularised neural network (BRNN) models. Specifically, different BRNN models were developed for every hour of each day within the power production period. The final PV power production forecasting was provided by combining the outputs of all the developed models. The accuracy of the developed model was experimentally validated by deploying it to a test system installed in Cyprus. The day ahead power production forecasting performance accuracy of the proposed methodology in terms of normalised root mean square error and mean absolute percentage error was 5.80% and 4.49%, respectively. © 2022 IEEE.

关键词： Neural network models

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

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Light Fidelity and Internet of Things for Medical Applications 4

Light Fidelity and Internet of Things for Medical Applicatio...

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4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Adi, Puput Dani Prasetyo Sariningrum, Ros Rahmatullah, Rizky Novita, Hessy Situngkir, Tiar Lina Adji, Priyo Hartono Lusiastuti, Angela Mariana Wibowo, Antonia Rahayu Rosaria Prastiyono, Yudi Adiprabowo, Tjahjo Yassir, Muhammad Maulana, Fairuz Iqbal Department of Telecommunication Bandung Indonesia Veterinary Research Center Bandung Indonesia Singaperbangsa University Faculty of Economics Department of Management Karawang Indonesia Bandung Indonesia Langlangbuana University Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Institut Teknologi Dan Bisnis Nobel Department of System and Information System Makassar Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

This study focuses on transmitting data utilizing medical sensors that are vital for patients in hospitals and other settings. In this situation, Light Fidelity refers to transmitting medical sensor data or other data comprising the Transmitter (Tx) and the Receiver (Rx). If the Light Fidelity Principle is used, the Transmitter transmits LEDs or light to the Receiver. At the same time, the Receiver contains a receiver or light detection component known as the Light Dependent Resistor (LDR);using programming and this basic working principle, data can be sent from the LED Transmitter to the LDR Receiver using C++ Programming and its Library to send Medical sensor data. Sensors can include pulse sensors, like in prior research, and additional sensors such as SPO2 or blood pressure sensors. For medical data, a modest bit rate is sufficient, but LiFi applications can be created in such a way that they can be hybridized with WiFi and other RF devices such as LoRa and the Internet of Things. © 2024 IEEE.

关键词： C++ (programming language)

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Unlocking MicroLED Potential: Damage-free Anisotropic Etching for Enhanced Pixel Density

Unlocking MicroLED Potential: Damage-free Anisotropic Etchin...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Clarence Y. Chan Henry C. Roberts Yixin Xiao Zetian Mi Xiuling Li Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois USA Department of Electrical and Computer Engineering Microelectronics Research Center The University of Texas Austin TX USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

One of the biggest hurdles in microLED-technology is the efficiency degradation with shrinking pixel-size, due to etching damage. We present the scaling of µLED from 45 to 5 µm by MacEtch, with near size-ind... 详细信息

ISBN: (纸本)9798350369311

关键词： Degradation Electric potential Lasers and electrooptics Anisotropic Etching Electro-optical waveguides

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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2023 China automation Congress, CAC 2023

作者： Bai, Yanxing Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.

关键词： Emotion Recognition

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Perfomance comparison of Machine Learning (ML) based fault detection scheme for a compensated transmission line

Perfomance comparison of Machine Learning (ML) based fault d...

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IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： N. Mazibuko K. T. Akindeji K Moloi Department of Electrical Power Engineering Department of electronics and computer engineering Smartgrid research center Durban University of Technology Durban South Africa

ISBN: (数字)9798350389388

ISBN: (纸本)9798350389395

Compensated transmission lines are employed in power system networks to increase energy transmission capacity, improve system stability, decrease transmission losses, and provide more flexibility in power management. However, the effectiveness of protection relays in detecting, classifying, and locating faults in such networks is significantly impacted. This is due to changes in impedance within the fault loop caused by devices used in compensated networks, which alters the apparent impedance seen by the relay. This study introduces three different fault detection and classification schemes for compensated transmission lines. Initially, an IEEE 9-bus system with series compensation at line 4–6 is utilized for data collection. Feature extraction using Principal Component Analysis (PCA) is then conducted, followed by the implementation of fine k-nearest neighbors (KNN), Medium Nuero network (MNN), and SQVM classifiers. A comprehensive performance analysis is carried out, revealing that SQVM achieves the highest overall accuracy of 99.3%, while the fine tree outperforms SQVM in single-line fault detection.

关键词： Power transmission lines Fault detection Power system stability Nearest neighbor methods Propagation losses Stability analysis Impedance Relays Protection Principal component analysis

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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

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computer Systems Science & engineering 2023年第8期46卷 1533-1547页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Civil Engineering Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop *** agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental *** control has become one of the significant problems in the agricultural *** traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same *** more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed *** may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable *** study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision *** HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision *** image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise *** addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature *** detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection *** experimental results of the HHOGCN-WD technique are investigated under the benchmark *** results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

关键词： Weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning

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Optimizing Antenna Design for 6G Applications Using Machine Learning Techniques

Optimizing Antenna Design for 6G Applications Using Machine ...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Praveen Kumar Yadav, Vivek Prakash Mukesh, Aashiwal Nirmal Jawale, Sourabh Kumar, Ankit Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul01811 Korea Republic of FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Pilani India FEAL: Flexible Electronics and Antenna Lab Department of Electronics and Communication Engineering Mumbai India Medicaps University Department of Computer Science and Engineering Indore India Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9798350379716

The emergence of 6G networks requires innovative antenna designs that operate at extremely high frequencies. The proposed paper explores the application of advanced machine learning techniques to optimize the design of antennas for 6G applications at 15 GHz. Traditional design methods are often time-consuming and depend on expert knowledge, whereas machine learning can automate and expedite this process. We developed a dataset incorporating critical antenna design parameters such as geometric dimensions and applied algorithms to predict key performance metrics like return loss, gain, and bandwidth. Our results demonstrate that neural networks, in particular, offer superior predictive accuracy, significantly reducing design iteration times. This integration of machine learning into antenna design not only improves performance but also facilitates the development of adaptive antenna systems, meeting the stringent demands of 6G networks. This research highlights the potential of machine learning to revolutionize antenna design, enabling rapid and efficient development of next-generation communication systems. © 2024 IEEE.

关键词： Smart antennas

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Distributed Control for a Microgrid Cluster: Implementation and Experimental Validation

Distributed Control for a Microgrid Cluster: Implementation ...

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International Conference on Environment and electrical engineering (EEEIC)

作者： David Carrasco-González Raúl Sarrias-Mena Pablo Horrillo-Quintero Ehsan Hosseini Francisco Llorens-Iborra Luis M. Fernández-Ramírez SURET Research Group Department of Electrical Engineering University of Cadiz Algeciras Spain SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks University of Cadiz Algeciras Spain

ISBN: (数字)9798350355185

ISBN: (纸本)9798350355192

In response to growing energy demands and concerns about climate change, microgrid clusters (MGCs) are gaining widespread attention. Their ability to integrate technologies for energy consumption, generation, and storage in AC, DC or a compound of both offer a high degree of flexibility and resiliency. This research introduces a new control strategy for an MGC composed of two MGs interconnected to a local grid via a point of common coupling. The first one is a DC MG comprising an ultracapacitor (UC), wind turbine (WT), DC loads and hydrogen system. The second is an AC MG with a battery bank, AC loads and photovoltaic (PV) generator. The control strategy employs local controllers for each device and distributed control via two control agents, coordinating the power distribution among the energy storage systems (ESSs) of the MGC. This system is tested and validated on a real-time experimental setup, using two Raspberry Pi microcontrollers and an OPAL-RT unit, under different working conditions. The results obtained in this work show that the control strategy implemented in the MGC works correctly.

关键词： Energy consumption Microcontrollers Wind speed Decentralized control Hydrogen Microgrids Supercapacitors Real-time systems Wind turbines Solar radiation

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