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Planning of Optimal Phase Balancing for an Unbalanced Distribution System: A Case Study of Cambodia 11

Planning of Optimal Phase Balancing for an Unbalanced Distri...

引用

11th International Conference on Power Science and engineering, ICPSE 2022

作者： Kay, Sopheak Vai, Vannak Eng, Samphors Him, Tivorn Institute of Technology of Cambodia Dept. of Electrical and Energy Engineering Phnom Penh Cambodia

ISBN: (数字)9781665451291

ISBN: (纸本)9781665451291

This paper discusses the problem of minimizing the power loss in unbalanced distribution systems through phase-balancing with the phase swapping concept from substation (HV/MV) to all MV/LV substations. This paper proposes a genetic algorithm (GA) for the optimum phase unbalancing in the medium voltage unbalanced distribution system. To validate the proposed method, the real distribution system test of 47 buses in a rural area, Cambodia is selected. Firstly, the relevant data of the real test system which consists of phase connection, line data, and bus data are collected. Then, the performance with several indicators of the test system is provided. Next, the optimal simulation results are performed in the real distribution system with the proposed method to minimize the power loss and improve the voltage profile using load flow computation in MATLAB considering voltage regulation, maximum voltage unbalance, and total power losses. The simulation results confirm the effectiveness of the proposed method for planning and operation. © 2022 IEEE.

关键词： Genetic algorithms

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Analytical Study of the Features and Parameters of Off-Angle Iris Image vs. Deep Learning Model

Analytical Study of the Features and Parameters of Off-Angle...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Suleiman, Aghiad Khedr Shafek, Darin Mahfuri, Mahmoud Khishe, Mohammad Saeed, Ali Ghwanmeh, Sameh Alma'Moon University College Cybersecurity and Cloud Computing Technologies Engineering Dept. Baghdad Iraq Alma'Moon University College Computer Engineering Techniques Dept. Baghdad Iraq International University for Science and Technology Computer and Information Technology Department Syria Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara Research Center Jadara University Jordan Jordan Computer Center Northern Technical University Ninevah Iraq College of Computer Information Technology Computer Science Dept American University in the Emirates United Arab Emirates

ISBN: (纸本)9798350369106

This paper presents an analytical study and a technique for extracting the features of a common case of images of the iris called off-angle iris which was taken for persons identification systems. The main problem when using biological iris measurements to identify the persons is the difficulty of identifying and extracting features of the iris. This problem is increasing when dealing with off-angle iris and it leads to decreased system accuracy and increased system rate error. In return, all the transfer learning techniques face difficulties in the case of heavily degraded data and partially occluded, and off-angle. A new method has a whole new methodology to deal with the image as it is without transformation processes. A new algorithm has been included for extracting features of the iris through the pupil switching points. The most discriminating points of the iris depend on the biological human eye statistics and analytical study. It has been trained and tested on the common images of the off-angle iris database so-called: 'CASIA Iris 1.0'. It has been implemented in the MATLAB environment. The results showed the efficiency of this technique, high precision and more importantly low failed acceptance rate. It emphasized that it is adaptive as well as efficiency improvement of the system. © 2024 IEEE.

关键词： Contrastive Learning

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Synergistic Skin Cancer Classification: Vision Transformer alongside MobileNetV2 4

Synergistic Skin Cancer Classification: Vision Transformer a...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kumar, M Ranjith Priyanga, S. Anusha, Janani Srinivasan Chatiyode, Veda Santiago, Jerome Revathi, P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Amrita School of Computing Dept. of Computer Science and Engg. Amrita Vishwa Vidyapeetham Chennai India C. Abdul Hakeem College of Engg. and Tech. Dept. of Computer Science and Engg. Melvisharam Vellore India

ISBN: (纸本)9798350349900

Skin cancer is one of the most common types of cancer in the world, and it poses major health risks due to its ability to spread quickly and metastasize. Early and accurate identification is crucial for treatment success and improved patient outcomes. This proposed work combines the MobileNetV2 and Vision Transformer (ViT) architectures to create a hybrid automated skin cancer classification technique. This technique aims to increase the accuracy and efficiency of dermatological diagnosis tools by combining MobileNetV2's effective feature extraction capabilities with ViT's self-attention mechanism. After testing on the HAM10000 dataset, this hybrid model outperformed individual models with a remarkable 96.3% classification accuracy. Not only did the integration of ViT and MobileNetV2 improve the classification accuracy but it also demonstrated how various deep-learning architectures work together to tackle complex image analysis tasks. Classifying skin cancers is critical to the medical industry because it allows for the early diagnosis of various dermatological disorders, allowing prompt intervention and treatment. These benefits can eventually improve patient outcomes and save lives in huge numbers. The findings of this research highlight the potential of deep learning to transform dermatological diagnostics and open the door to creating systems that will significantly impact clinical practice by detecting skin cancer with greater accuracy and efficiency. © 2024 IEEE.

关键词： Lung cancer

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Application of Pradovera’s Algorithm for Adaptive Frequency Sampling in Electromagnetic Simulation

Application of Pradovera’s Algorithm for Adaptive Frequency...

引用

electrical Design of Advanced Packaging and Systems Symposium, EDAPS

作者： Shilpa T N Rakesh Sinha Dept. of Electrical Engineering National Institute of Technology Rourkela

ISBN: (数字)9798331543259

ISBN: (纸本)9798331543266

In this paper, we have implemented Davide Pradovera’s minimal sampling algorithm for adaptive frequency sampling in electromagnetic simulation. This algorithm is basically a modified AAA (Adaptive Antoulas– Anderson) version. Compared to the conventional AAA algorithm, this approach does not require a prior user-defined dataset to find out the next frequency sample point while meeting the specified error criteria. Pradrovera has introduced a pseudo error to find out the next frequency sample in an adaptive way incorporating the AAA algorithm. MATLAB R2023a was used to demonstrate two examples of electromagnetic simulation. This algorithm shows outstanding accuracy and speed performance.

关键词： Adaptive systems Accuracy Packaging Electromagnetics MATLAB

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Insights into Zooplankton Abundance Dynamics in Tropical Temporary Ponds using Machine Learning and Explainable AI

Insights into Zooplankton Abundance Dynamics in Tropical Tem...

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International Conference on Innovations in Science, engineering and technology (ICISET)

作者： Shahriar Siddique Ayon Sharia Arfin Tanim Hadiur Rahman Nabil Maruful Islam Tonmoy Mohajan Kamruddin Nur Dept. of Computer Science American International University-Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

Determining the health and resilience of ecosystems depends on an understanding of the dynamics of zooplankton abundance in tropical temporary ponds. Using ensemble modelling and explainable artificial intelligence (XAI) techniques, this paper explores the complex dynamics of zooplankton abundance in tropical temporary ponds. This study uses a Machine Learning framework to evaluate the predictive performance of models such as k-nearest Neighbors ($k N N$) regression, Random Forest Regression, Lasso Regression, Passive Aggressive Regression, Ridge Regression, and Ensemble Voting Regression. Among the machine learning models tested, the ensemble model, which combines ridge and ridge CV, performs best, with metrics such as MAE of 0.02, MSE of 0.00, and $\mathbf{R 2}$ of 1.00. The Shapley Additive ExPlanations (SHAP) analysis identifies ‘Rotifers’, ‘Culex’, ‘Chironomodidae’, and ‘Calanoids’ as significant predictors. Data preprocessing techniques improve the dataset derived from a study of fish predation effects in tropical temporary ponds. Our research advances our understanding of zooplankton ecology and provides insights into conservation strategies and management practices in these ecosystems. Using ensemble modelling and explainable AI, we contribute to developing accurate predictive models for the long-term management of tropical temporary pond ecosystems.

关键词： Accuracy Explainable AI Biological system modeling Ecosystems Machine learning Predictive models Ecology Zooplankton Environmental management Resilience

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Modified EfficientNetB3 Deep Learning Model to Classify Colour Fundus Images of Eye Diseases 5

Modified EfficientNetB3 Deep Learning Model to Classify Colo...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Sharma, Riya Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Kumar Gunjan, Vinit Manipal University Jaipur Dept of Computer Science & Engineering Jaipur India Learning Manipal University Jaipur Dept. of Artificial Intelligence & Machine Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

The only way to prevent blindness from eye problems is by early detection and prompt treatment. Although colour fundus photography (CFP) is useful for fundus inspection, there is a need for computer-assisted automated diagnosis tools due to the similarities between the early symptoms of many eye disorders. The suggested approach uses cutting-edge deep learning model to categorize images into several disease categories by learning distinguishing features from the input images. The high-resolution fundus photos from individuals with diabetic retinopathy (DR), glaucoma, cataract, and healthy eyes make up most of the dataset used for this research. The experimental findings show that the suggested system achieve 97% accuracy with modified efficientNetB3 model and surpasses current approaches for categorizing eye diseases. This approach may help doctors diagnose and treat eye conditions earlier, leading to better patient outcomes. © 2023 IEEE.

关键词： Color

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Performance Analysis of the Proposed Isolated LLC Resonant Converter with PFM Control

Performance Analysis of the Proposed Isolated LLC Resonant C...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Mohammad Reza Dashti Mehdi Asadi Faezeh Ebrahimi Omid Shoghy dept. Electrical Engineering Arak University of Technology Arak Iran

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

This article introduces a DC-DC converter designed for various applications, including battery chargers and electric vehicles. The input stage features an EMC/EMI filter to mitigate noise. A Power Factor Correction (PFC) circuit efficiently converts 220 V AC power into 400 V DC for the DC-DC converter's input. The converter utilizes a PID controller and Pulse Frequency (PF) modulation for switching control. This modulation technique adjusts the switching frequency in response to changes in load or input voltage, ensuring output voltage stability while maintaining high system efficiency across different load conditions. Simulation results validate the proposed converter design's effectiveness, demonstrating its ability to maintain a stable output voltage under varying conditions.

关键词： Frequency modulation Switching frequency Simulation Resonant converters DC-DC power converters Switches Power factor correction Stability analysis Voltage control Testing

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Different MPPT Algorithms for DC-DC Converter

Different MPPT Algorithms for DC-DC Converter

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IEEE Students' Conference on electrical, Electronics and computer Science (SCEECS)

作者： Boddepalli Hemanth Kumar Arnab Ghosh Dept. of Electrical Engineering National Institute of Technology Rourkela India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The efficient and appropriate power flow technologies are necessary for better performance of renewable energy system applications. The comparative study of different MPPT algorithms, which are best for RES applications due to their influence on efficiency, low response time and the stability under different intervals of irradiance and temperature. When comes to this context, the key focus is evaluation of algorithms to the converter which is bidirectional by considering the desired operational conditions. The observations of this study are a forward step to optimization of systems with bidirectional converters in improving the reliability and sustainability.

关键词： Maximum power point trackers Photovoltaic systems Renewable energy sources Artificial neural networks DC-DC power converters Pulse width modulation Reliability Time factors Sustainable development Thermal stability

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A Compensation Mechanism for EV Flexibility Services using Discrete Utility Functions

A Compensation Mechanism for EV Flexibility Services using D...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Giraldo, Juan S. Arias, Nataly Banol Duque, Edgar Mauricio Salazar Hoogsteen, Gerwin Hurink, Johann L. University of Twente Mathematics and Computer Science Dept. of Electrical Engineering Netherlands Energy Transition Studies Netherlands Eindhoven University of Technology Electrical Energy Systems Group Netherlands

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Compensation mechanisms are used to counterbalance the discomfort suffered by users due to quality service issues. Such mechanisms are currently used for different purposes in the electrical power and energy sector, e.g., power quality and reliability. This paper proposes a compensation mechanism using EV flexibility management of a set of charging sessions managed by a charging point operator (CPO). Users' preferences and bilateral agreements with the CPO are modelled via discrete utility functions for the energy not served. A mathematical proof of the proposed compensation mechanism is given and applied to a test scenario using historical data from an office building with a parking lot in the Netherlands. Synthetic data for 400 charging sessions was generated using multivariate elliptical copulas to capture the complex dependency structures in EV charging data. Numerical results validate the usefulness of the proposed compensation mechanism as an attractive measure both for the CPO and the users in case of energy not served. © 2022 IEEE.

关键词： Office buildings

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Enhancing State-of-Charge Prediction for PEVs Using Hybrid CNN and LSTM Machine Learning Techniques

Enhancing State-of-Charge Prediction for PEVs Using Hybrid C...

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National Power Systems Conference (NPSC)

作者： Jajna Prasad Sahoo Kau Aditya S. Sivasubramani Dept. of Electrical Engineering Indian Institute of Technology Patna

ISBN: (数字)9798331519568

ISBN: (纸本)9798331519575

The State of Charge (SoC) estimation of lithiumion batteries is a critical aspect in the domain of plug-in electric vehicles (PEVs), influencing their performance, range, and overall efficiency. This paper addresses the improvements and limitations of SoC estimate methods, with an emphasis on lithium-ion batteries used in PEVs. The paper comprehensively analyzes a range of strategies used to determine SoC, including traditional methods and modern approaches, including model-based predictions and machine-learning (ML) algorithms. This work employs ML techniques such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). In order to analyze SoC predictions, this paper proposes a hybrid ML strategy that combines CNN and LSTM. An effective SoC prediction is achieved using CNN, LSTM, and an integration of both. A comparison of the findings shows that the hybrid ML model outperforms CNN and LSTM in predicting the SoC of PEVs. The hybrid model exhibits impressive performance with a mean absolute error (MAE) of 1.92%, indicating a significant enhancement in accuracy compared to CNN and LSTM, which have MAE values of 2.12% and 4.2%, respectively.

关键词： Adaptation models Maximum likelihood estimation Accuracy Predictive models Transformers Power system reliability Convolutional neural networks State of charge Reliability Long short term memory

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