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Comparative Performance Investigation of Control Techniques for Solar PV Fed Induction Motor Drives

Comparative Performance Investigation of Control Techniques ...

引用

Australasian Universities Power engineering Conference, AUPEC

作者： Nazmul Islam Nahin Md. Azad Hossain Joysree Nath Shuvra Prokash Biswas Md. Rabiul Islam Parvez Mahmud Narottam Das Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Chattogram Bangladesh School of Electrical Computer & Telecommunications Engineering University of Wollongong NSW Australia Faculty of Science University of Technology Sydney Ultimo NSW Australia School of Engineering and Technology Central Queensland University Melbourne VIC Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

This paper provides a comparative analysis of three key control strategies for solar photovoltaic (PV) fed induction motor drives (IMDs): field-oriented control (FOC), direct torque control (DTC), and model predictive control (MPC). The study begins by examining the theoretical operating principles of each method, followed by an analysis of their control structures and implementation considerations. A comprehensive assessment is conducted to evaluate the performance of these strategies in various operating conditions, including starting response, step torque response, and low-speed operation. The comparative analysis aims to highlight the advantages and limitations of each control method when applied to solar PV-fed IMDs. This work provides valuable insights into the trade-offs between dynamic performance, steady-state behavior, and implementation complexity for each control strategy, assisting engineers in selecting the most suitable method for specific renewable energy applications.

关键词： Induction motor drives Solar irradiance Renewable energy sources Torque Torque control Steady-state Complexity theory Torque measurement Surges Predictive control

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Deep Learning Techniques for Alzheimer’s Disease Prediction

Deep Learning Techniques for Alzheimer’s Disease Prediction

引用

Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Tr Mahesh Ankita Agarwal Amrutha V Nair Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

Deep learning, a cutting-edge approach of machine learning, surpasses classical machine learning when it comes to detecting particular structures in complicated, high-dimensional data in the area of computer vision. The fast improvement of neuroimaging technologies and the associated creation of huge volumes of multimodal neuroimaging data have led in a noteworthy surge in interest in the application of deep learning for automated categorization and early diagnosis of Alzheimer’s disease (AD). The best classification results were achieved utilizing multimodal neuroimaging in conjunction with fluid biomarkers.

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Managing Cloud Networks: A Classification of SDN-Enabled Clouds

Managing Cloud Networks: A Classification of SDN-Enabled Clo...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Vaishali Singh S Kehkeshan Jallal Sunanda Das Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

The idea of separating the management plane from the data-forwarding gear was first introduced by Software-Defined Networking (SDN), which has brought about a revolutionary new age in networking. This innovation makes networks programmable, flexible, and dynamically reconfigurable. These characteristics have great potential for the field of computer science in the cloud, where dynamic adjustments and reconfiguration are essential owing to on-demand consumption patterns. The many simulation and empirical assessment techniques that have been created expressly for SDN-enabled cloud settings are also highlighted by us. Finally, study conducted perform a critical review of the state of the research, highlighting gaps and suggesting possibilities for further study.

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A novel framework for differentiating vessel-like objects in coronarography images

A novel framework for differentiating vessel-like objects in...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Witold Serwatka Katarzyna Heryan Joanna Sorysz Marcin Jarzab Kamil Sterna Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Krakow Poland Autosymed SRL Iasi Romania Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland

Coronary Artery Disease is the leading cause of death worldwide. Its prevalence will grow while access to specialized medical care will be further limited due to staff shortages. Therefore, any facilitation of diagnosis or treatment is of paramount importance. The diagnosis based on Coronary Angiography can be automated to perform a quantitative evaluation of lesions. This requires precise segmentation of coronary arteries. At the moment, the state-of-the-art algorithms fail to eliminate vessel-like artifacts that are wrongly included in segmentation results (e.g. catheters, stitches). This is a bottleneck for the automatization of the diagnosis workflow that precedes clinical action. In this paper, we propose a 2-step post-segmentation refinement algorithm. A binary segmentation of the coronary arteries is used to extract image features - inputs for an XGBoost Classifier. Its predictions are improved by a neighborhood filter that leverages contextual information to assign correct labels. The algorithm is primarily concerned with differentiating vessels from other vessel-like objects and does so with a 99% accuracy rate. It takes advantage of an original local description of Tamura features, which proved to be one of the most influential factors in decision-making. As a result, the segmentation of coronary arteries is cleaned from artifacts, enabling AI-supported diagnosis workflows to be automated. After re-training, the proposed method can be used to eliminate post-segmentation artifacts in other medical *** relevance— The algorithm proposed in this paper allows for the development of software that could automatically calculate the Syntax Score in real time. This would shorten diagnostics time and allow for immediate action in critical cases.

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Cloud-Enhanced Wireless Sensor Networks for Air Quality Monitoring

Cloud-Enhanced Wireless Sensor Networks for Air Quality Moni...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Shreenidhi H S Trapty Agrawal Kiran Kale Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

A growing trend in environmental monitoring involves the use of inexpensive wireless sensors to create densely dispersed networks that provide higher spatial resolution than traditional air quality control systems. In this study, a novel air quality monitoring system—a wireless sensors network, or WSN, made up of a dispersed sensor network connected to a cloud system—is presented. The sensor nodes use low-power ZigBee motes to send field measurements in real time to the cloud via a dedicated gateway. An optimally calibrated cloud computing framework has been instituted to effectively retain, oversee, handle, and present the data originating from the sensor system. Optimised chemical and contaminant identification is made possible by using artificial intelligence tools in the cloud for sophisticated data handling and analysis. This system is unique in that it is small, light, and energy-efficient, allowing for the deployment of several nodes for thorough air quality evaluations in various locations. The effectiveness of the method in identifying common volatile organic chemicals including the compounds benzene, toluene, ethane, and xylene to is validated by a practical example in a laboratory environment. Principal component analysis, perceptron networks with backpropagation learning, and help vector computation methods are all used in the data processing. The results show that the volatile organic compound concentrations may be distinguished and quantified with good accuracy. Cloud computing, air quality, chemical sensors, and wireless sensor networks are among the key themes.

关键词： Volatile organic compounds Wireless communication Wireless sensor networks Cloud computing Chemical sensors Air quality Data processing Sensor systems Sensors Testing

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Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Optimizer: A Study 3

Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Opti...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sankaran, D. Abed Mohammed, Mazin Chander, Shrinithi Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Skin is a major sensory organs and the irregularity in the skin causes various issues. Skin cancer is one major medical emergency, and appropriate detection and treatment are essential. Clinical level skin cancer assessment is normally performed using the Dermoscopy Images (DI). This research aims to develop and implement the VGG-UNet scheme to extract and assess the abnormal section in DI with improved accuracy. This plan includes;(i) Image collection and resizing, (ii) Segmentation of skin lesion in DI, (iii) Comparing the segmented section with the Ground-Truth (GT), and (iv) Performance evaluation and validation. The merit of the proposed technique is confirmed by computing the necessary performance metrics. In this work, the performance of the proposed technique is verified with different optimizers (Adam/SGD) and the average pooling. The experimental work is executed on the ISIC2016 challenge dataset, and the merit of this scheme is verified with Jaccard, Dice, and Accuracy. © 2022 IEEE.

关键词： Diseases

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Fast-OMRA: Fast Online Motion Resolution Adaptation for Neural B-Frame Coding 16

Fast-OMRA: Fast Online Motion Resolution Adaptation for Neur...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Nguyenquang, Sang Gao, Zong-Lin Ho, Kuan-Wei Hoangvan, Xiem Peng, Wen-Hsiao National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan Vnu University of Engineering and Technology Faculty of Electronics and Telecommunications Hanoi Viet Nam

ISBN: (纸本)9798331522124

Most learned B-frame codecs with hierarchical temporal prediction suffer from the domain shift issue caused by the discrepancy in the Group-of-Pictures (GOP) size used for training and test. As such, the motion estimation network may fail to predict large motion properly. One effective strategy to mitigate this domain shift issue is to downsample video frames for motion estimation. However, finding the optimal downsampling factor involves a time-consuming rate-distortion optimization process. This work introduces lightweight classifiers to determine the downsampling factor. To strike a good rate-distortion-complexity trade-off, our classifiers observe simple state signals, including only the coding and reference frames, to predict the best downsampling factor. We present two variants that adopt binary and multi-class classifiers, respectively. The binary classifier adopts the Focal Loss for training, classifying between motion estimation at high and low resolutions. Our multi-class classifier is trained with novel soft labels incorporating the knowledge of the rate-distortion costs of different downsampling factors. Both variants operate as add-on modules without the need to re-train the B-frame codec. Experimental results confirm that they achieve comparable coding performance to the brute-force search methods while greatly reducing computational complexity. © 2025 IEEE.

关键词： Image coding

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Enhanced Bluetooth with Low-Density Parity-Check Codes

Enhanced Bluetooth with Low-Density Parity-Check Codes

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Pawarit Manakul Usana Tuntoolavest La-or Kovavisaruch Kazuhiko Fukawa Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand

In order to enhance Bluetooth, this paper proposes to employ a low-density parity-check (LDPC) code instead of S2 and S8 coding schemes in Bluetooth Low Energy (BLE) coded standard. Since decoding LDPC codes requires log-likelihood ratios (LLRs), the Viterbi algorithm is modified so that it can obtain the LLR from the correlation between received and reference signals. Simulation results under the additive white Gaussian noise (AWGN) channel condition show that when signal-to-noise ratio (SNR) is greater than or equal to 3 dB, Bluetooth LDPC performance with LDPC is superior to both of the standard BLE coded schemes. Moreover, the average decoding time is 16.954 times shorter than that of BLE coded S8. Thus, enhanced Bluetooth LDPC is suitable for the AWGN channel with SNR greater than or equal to 3 dB because it needs fewer redundancy bits to provide lower packet error rate (PER) with shorter decoding time.

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Diagnosis of Parkinson's disease from electroencephalography signals using linear and self-similarity features

Diagnosis of Parkinson's disease from electroencephalography...

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作者： Bhurane, Ankit A. Dhok, Shivani Sharma, Manish Yuvaraj, Rajamanickam Murugappan, Murugappan Acharya, U. Rajendra Nagpur India Ahmedabad India Singapore Electronics and Communication Engineering Kuwait College of Science and Technology Doha Kuwait Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology SUSS Singapore School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya Malaysia

An early stage detection of Parkinson's disease (PD) is crucial for its appropriate treatment. The quality of life degrades with the advancement of the disease. In this paper, we propose a natural (time) domain technique for the diagnosis of PD. The proposed technique eliminates the need for transformation of the signal to other domains by extracting the feature of electroencephalography signals in the time domain. We hypothesize that two inter-channel similarity features, correlation coefficients and linear predictive coefficients, are able to detect the PD signals automatically using support vector machines classifier with third degree polynomial kernel. A progressive feature addition analysis is employed using selected features obtained based on the feature ranking and principal component analysis techniques. The proposed approach is able to achieve a maximum accuracy of 99.1±0.1%. The presented computer-aided diagnosis system can act as an assistive tool to confirm the finding of PD by the clinicians. © 2019 John Wiley & Sons, Ltd.

关键词： Electroencephalography

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Charged particles

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