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Novel Double-Damped Tuned AC Filters in HVDC Systems

computers, Materials & Continua 2023年第4期75卷 1467-1482页

作者： Rana Shaheer Mehmood Asif Hussain Usman Ali Muhammad Tariq Mahmood Department of Electrical Engineering University of Management and TechnologyLahore54782Pakistan Department of Electrical and Computer Engineering Sungkyunkwan University2066Seobu-roJangan-guSuwon-si16419Gyeonggi-doKorea School of Computer Science and Engineering Korea University of Technology and Education1600ChungjeolnoByeogchunmyunCheonan31253Korea

This paper presents a performance analysis of novel doubledampedtuned alternating current (AC) filters in high voltage direct current(HVDC) systems. The proposed double-damped tuned AC filters offer theadvantages of improved performance of HVDC systems in terms of betterpower quality, high power factor, and lower total harmonic distortion (THD).The system under analysis consists of an 878 km long HVDC transmissionline connecting converter stations at Matiari and Lahore, two major cities inPakistan. The main focus of this research is to design a novel AC filter usingthe equivalent impedance method of two single-tuned and double-dampedtuned AC filters. Additionally, the impact of the damping resistor on the ACchannel is examined. TheTHDof theHVDCsystem with and without currentAC filters was also compared in this research and a double-damped tuned ACfilter was proposed. The results of the simulation represent that the proposeddouble-damped tuned AC filter is far smaller in size, offers better powerquality, and has a much lower THD compared to the AC filters currently inplace in the converter station. The simulation analysis was carried out utilizingpower systems computer-aided design (PSCAD) software.

关键词： Double-damped tuned AC filters harmonics high voltage DC systems long distance transmission power quality total harmonic distortion

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Classification of Skin Diseases in the Internet of Medical Things using Hybrid Deep Learning 2

Classification of Skin Diseases in the Internet of Medical T...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Latha, Dandu Madhavi Anusha, Kondepati Samrin, Rafath Reddy, Pundru Chandra Shaker Sudhakar, Bolleddu Sathish, Goski Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India King Khalid University Department of Computer Science College of Computer Science Asir Abha Saudi Arabia Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Degree Lecturer of Computer Science Mjptbcwreis Telangana India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350372892

When it comes to the IoMT, skin lesion examination is absolutely essential for making an accurate diagnosis. Skin lesion analysis relies heavily on computer-aided design(CAD) technologies to enhance accuracy and efficiency. Using hybrid deep learning approaches, this work focuses on segmenting and classifying skin lesions from dermoscopy images. Mask Region-based Convolutional Neural Network (MRCNN) for semantic separation and ResNet50 for lesion-detection are two state-of-the-art methodologies that are combined in this research's hybrid deep learning model. A skin lesion's exact location can be determined with the use of the MRCNN's border delineation capabilities. For comprehensive model training, we gather a large collection of dermoscopy photos that have been annotated. This dataset is used to train a hybrid deep-learning(DL) model that can capture images' nuanced representations. An accuracy rate of 95.49% in segmenting lesions into different groups demonstrates the model's capability to do so. Another significant improvement over older method is the high reliability and accuracy of skin lesion classification. After being tested on the ISIC2020 Challenge dataset, the model achieved an impressive 96.75% accuracy rate. Classification and segmentation models outperform the state-of-the-art in Internet-of-Medical-Things(IoMT). When it comes to segmenting and classifying skin lesions, this paper's hybrid deep learning approach works wonders. Outperforming the existing gold standards, the results demonstrate that the model can enhance diagnostic accuracy in an IoMT environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of Electronics and Information Engineers.

关键词： Demand response

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Railway Track Fault Detection Using Mel Frequency Cepstral Coefficient from Acoustic Data using Chi Square

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Chinese Journal of Electronics 2023年

作者： Furqan Rustam Abid Ishaq Muhammad Shadab Alam Hashmi Hafeez-Ur-Rehman Siddiqui Imran Ashraf School of Computer Science University College Dublin Faculty of Computer Science and Information Technology Khawaja Fareed University of Engineering and Information Technology Department of Information and Communication Engineering Yeungnam University

Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erroneous, labor-intensive, and *** automatic inspection provides a fast, reliable, and unbiased solution in this regard, however, ensuring high accuracy for fault detection is challenging due to the lack of public datasets, noisy data, high computer processing requirements, and inefficient models. This study presents an approach that uses Mel frequency cepstral coefficient features from the acoustic data. The dataset gathered using a customized railway cart from our previous research is used for experiments. The focus of the study is to increase the fault detection performance using selective features from the acoustic data. This study employs Chisquare(Chi2) for the selection of important features and involves performance analysis of machine learning and deep learning models using selected features. Experimental results suggest that using 60 features, 40 original features, and 20 Chi2 features, produces optimal results both regarding accuracy and computational complexity. A 100%accuracy can be obtained using the proposed approach with machine learning models. Moreover, this performance is significantly better than existing approaches.

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FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

FAQNet: Frequency-Aware Quaternion Network for Endoscopic Hi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Dingzhou Huang, Guoheng Yuan, Xiaochen Chen, Xuhang Zhong, Guo Pun, Chi-Man Deng, Jie Guangdong University of Technology School of Computer Science and Technology China Macao Polytechnic University Faculty of Applied Sciences China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Guangdong University of Foreign Studies School of Information Science and Technology China First Affiliated Hospital of Sun Yat-Sen University China

ISBN: (纸本)9798350386226

Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual Information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model's performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies. © 2024 IEEE.

关键词： Endoscopy

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Design and Implementation of Intelligent Soccer-picking Robot 3

Design and Implementation of Intelligent Soccer-picking Robo...

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3rd International Conference on computer Vision and Pattern Analysis, ICCPA 2023

作者： Xiang, ShiHao Dong, Chen Hu, ZhanJie Li, HaoZhan Jiang, Yu Zhang, TianYu TianJin University of Technology School of Computer Science and Engineering TianJin China TianJin University of Technology School of Materials Science and Engineering TianJin China TianJin University of Technology College of Mechanical Engineering TianJin China

ISBN: (纸本)9781510667563

To address the time-consuming and laborious task of picking up soccers, an intelligent soccer-picking robot is designed and implemented, which consists of three main parts: mechanical, electronic, and software design. The robot employs a four-wheel drive and hydraulic shock absorber structure to ensure fast and stable operation. The main body structure of the robot is built with aluminum alloy and PLA materials to reduce weight effectively. The Kendryte K210 chip is the main control chip to reduce power consumption and cost. A monocular camera is used to construct a map, and the YOLOX object detection model is used to detect various objects on the soccer field. The artificial potential field algorithm is improved to achieve optimal path planning for the robot. The robot is tested 80 times on a real soccer field, with a successful pickup rate of 92.5%, meeting the actual needs of soccer-picking and filling the gap in the soccer-picking robot market. © 2023 SPIE.

关键词： Object detection

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Study on MRI Slices-based Lightweight Neural Network in Alzheimer's Disease Detection 3

Study on MRI Slices-based Lightweight Neural Network in Alzh...

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3rd International Conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Zhang, Qiongmin Long, Ying Cai, Hongshun School of Computer Science and Engineering Chongqing University of Technology Banan District 400054 China

ISBN: (纸本)9798350335972

Currently, early detection and intervention are still the key to prevent the aggravation of Alzheimer's disease (AD). computer assisted diagnosis can provide great help for the early detection of AD. However, compared with natural images, medical images are generally difficult to collect and always have small data sets. To solve this problem, we propose a magnetic resonance imaging (MRI) slices-based lightweight neural network to effectively identify AD. In the proposed method, we use ShuffleNet as the backbone network for feature extraction. Efficient channel attention is added to capture the information of cross-channel interaction information. Triplet loss and cross entropy loss in deep metric learning are combined to ensure the multi-slices under the same subject are classified consistently. The experimental results indicate that, compare with state-of-the-art lightweight methods, our model achieves better classification performance with only 1.52M parameters and 5.56G FLOPs. Experiments verify that our method is helpful to reduce the number of parameters, FLOPs and improve the model performance. Through the construction of the model, it is expected to contribute to the improvement of AD intelligent detection on devices with limited computing power. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Research on the Transient Potential Rise Characteristics of High - speed Railway Rail Lightning Strike and Its Impact on Trackside Signal Equipment 37

Research on the Transient Potential Rise Characteristics of ...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Wang, Shulai Xiang, Nianwen Wang, Chaoqun Lyu, Zengwei Chen, Zhiwei School of Electrical Engineering and Automation Hefei University of Technology Hefei China Institute of Energy Hefei Comprehensive National Science Center Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783800764174

Lightning strikes on the catenary will cause a sharp rise in rail potential, which directly threatens the safety of the signal equipment connected to the rail along the trackside. It is urgent to carry out research on the propagation law of transient overvoltage on rails caused by lightning strikes on the catenary. A rail lightning electromagnetic transient calculation model considering the influence of integrated grounding system and catenary is established, and the influence law of different lightning strike point positions, different pier heights, and different lightning current waveforms on the distribution of rail potential rise is calculated. The specific conclusions are as follows: when lightning strikes different positions within a catenary span, the rail potential within 700m near the lightning strike point will exceed the lightning strike tolerance level of the trackside signal equipment, causing equipment damage. The potential rise increases by about 9 kV;for every 5 m increase in pier height, the rail potential rise increases by about 16%. Based on the law of the rail potential rise, the common-mode protection methods for the trackside signal equipment are proposed by appropriately reducing the horizontal connection distance of uplink and downlink ground wires to decrease the rail potential and installing common-mode protection devices. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Signaling equipment

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FLBC-DSS: Federated Learning-Enabled Blockchain-Based Data Sharing Scheme for IIoT 11

FLBC-DSS: Federated Learning-Enabled Blockchain-Based Data S...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Yao, Yan Li, Guangshun Zhang, Huihui Feng, Shanshan Yan, Biwei Cao, Jian School of Computer Science Qufu Normal University Rizhao China Rizhao-Qufu Normal University Joint Technology Transfer Center Rizhao China School of Computer Engineering Weifang University Weifang China Qilu University of Technology Shandong Academy of Sciences Jinan China School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Scienceand Technology Shandong University Qingdao China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798331531904

This paper introduces the Federated Learning-Empowered Blockchain-based Data Sharing Scheme (FLBC-DSS), a novel framework designed to enhance data security and efficiency in Industrial Internet of Things (IIoT) environments. By integrating federated learning with blockchain technology, FLBC-DSS addresses the critical challenges of data privacy and unauthorized access, promoting secure, decentralized data sharing across various industrial sectors. Utilizing zk-SNARKs, the scheme provides a mechanism for anonymous data verification without compromising data integrity. Performance evaluations conducted using Hyperledger Fabric and SecureBoost demonstrate significant improvements in prediction accuracy and computational efficiency compared to traditional approaches. The implementation of non-cooperative game theory further illustrates the economic benefits of FLBC-DSS, highlighting its potential to increase profitability through strategic data sharing. The results affirm the effectiveness of FLBC-DSS in leveraging advanced cryptographic and machine learning techniques to foster a secure, transparent, and efficient digital ecosystem for IIoT. © 2024 IEEE.

关键词： Federated learning

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An Analysis of Cotton Crop for Detection of Pests and Diseases Using ML and DL Techniques

An Analysis of Cotton Crop for Detection of Pests and Diseas...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Susheel, K. Sai Rajkumar, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9781665462631

Agriculture is a significant source of energy that includes several well-known crops that supply food for the population and income for agronomists in India. Cotton is one of the attractive, drought-tolerant crops that can be cultivated commercially in every environment, among many other renowned crops. Although, this cotton harvest is plagued by many diseases and pests that may vary depending on climatic conditions, resulting in significant yield losses. The most liable to pests and diseases are the leaf and seedling stages of the plant, which destroy the whole crop. The cotton pests and diseases based on prior knowledge of IP, ML, and DL techniques are analyzed. Along with studies the comparison of the cotton crop with other crops, and also analyses the status of the area, production, and yield of the cotton crop. Eventually, the SVM classifier models applied fewer portraits than different algorithms regarding reliability and efficiency, while the multi-dimensional convolutional neural network provided better accuracy and effectiveness than further models in this deep analysis. © 2023 IEEE.

关键词： Cotton

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