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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89770 条记录，以下是441-450 订阅

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Different MPPT algorithms for DC-DC Converter

Different MPPT Algorithms for DC-DC Converter

引用

2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

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

ISBN: (纸本)9798331529833

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. © 2025 IEEE.

关键词： Artificial Neural Network (ANN) Bidirectional DC-DC Converter Fuzzy Logic Control (FLC) Maximum Power Point Tracking (MPPT) Perturb and Observe (P&O) Photovoltaic (PV) Pulse Width Modulation (PWM) Renewable Energy systems (RES)

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Optimization of Stochastic Image Georeferencing algorithms Using Mutual Information 10

Optimization of Stochastic Image Georeferencing Algorithms U...

引用

10th international conference on Information Technology and Nanotechnology, ITNT 2024

作者： Tashlinskii, Alexander Safina, Galina Ibragimov, Radik Ulyanovsk State Technical University Department Of Radio Engineering Telecommunications And Information Security Ulyanovsk Russia National Research Moscow State University Of Civil Engineering Department Of Fundamental Education Moscow Russia

ISBN: (纸本)9798350372939

The possibility of optimization stochastic image referencing algorithms using mutual information as a target function of image referencing quality is investigated. Three types of mutual information Renyi, Tsallis and Shannon are investigated. Experimental studies have shown that optimized algorithms based on Renyi mutual information have the best results in terms of convergence speed and accuracy of deformation parameter estimates when referencing multispectral and multi-temporal images. © 2024 IEEE.

关键词： Stochastic systems

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2023 international conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2023

作者： Sildas, Vanila Chandraman, Preman Venkatesh Raj, Srinivasan Srm Valliammai Engineering College Department of Electrical and Electronics Engineering India Sri Sivasubramaniya Nadar College of Engineering Department of Electrical and Electronics Engineering India Sri Sivasubramaniya Nadar College of Engineering Department of Information Technology India

ISBN: (纸本)9798350300451

A lot of computational power is needed for the intra prediction procedure in the H.264 video encoder. A reconfigurable similarity-based intra prediction method for the H.264 video encoder is thus presented in this paper. The analysis of similarity-based intra-prediction algorithms for H.264 yields a total of five distinct similarity approaches, including sum of absolute differences, sum of squared differences, Hamming distance, Euclidean distance, and cosine similarities. The post-analysis shows that using Hamming distance for similarity-based intra prediction allows for less power and hardware usage, with predicted similarity ranging between 78% and 88%.. © 2023 IEEE.

关键词： Hamming distance

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Anomaly Detection in Power Distribution Networks with Unsupervised Learning algorithms

Anomaly Detection in Power Distribution Networks with Unsupe...

引用

2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Sundaravelrani, K. Charles Prabu, V. Palle, Kowstubha Mallala, Balasubbareddy Nithya, G. Rao, Nynalasetti Kondala Kameswara Sathyabama Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai600 119 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Chennai600062 India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500075 India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam638401 India Sagi Rama Krishnam Raju Engineering Department Computer Science & Engineering Andhra Pradesh Bhimavaram534204 India

ISBN: (纸本)9798331543617

The identification of anomalies in power distribution networks is a critical component in ensuring the reliability and stability of energy systems. This investigation aims to find anomalous patterns and vulnerabilities within applications used for power grids by examining how unsupervised literacy algorithms function. Because conventional monitoring techniques typically miss subtle or developing irregularities, unsupervised procedures are especially helpful. This is a result of unsupervised routine monitoring techniques. We create algorithms that can learn from unlabeled data to identify departures from the standard geste. To create these models, some methods are applied, including insulating timbers, autoencoders, and k-means clustering. It is possible to test these methods using data from the global power distribution system and assess how well they relate to abnormalities such as equipment failures, power outages, and unexpected consumption patterns. The purpose of this examination is to ascertain the practicality of certain styles. Results show that algorithms for unsupervised literacy can provide rapid and accurate discovery, lower the danger of network failures, and increase the effectiveness of functional activities. Furthermore, these algorithms can increase the effectiveness of functional processes. The study's findings provide insight into the possibility of using machine literacy to improve the sustainability and adaptability of cutting-edge electricity distribution networks. © 2024 IEEE.

关键词： Power distribution networks

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Impact of Distributed Generation on Power systems: Ramarothole Solar Plant Case Study-Status Review 4

Impact of Distributed Generation on Power Systems: Ramarotho...

引用

4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Hetsa, Mamosothoane Lehloka, Motlatsi University of South Africa Department of Electrical Engineering Roodepoort South Africa

ISBN: (纸本)9798350395914

In this literature, we investigate the repercussions of integrating decentralized generation on active power systems networks. This research will further be applied to the recently integrated 70 MW Ramarothole Solar Plant that is currently operating without energy storage systems. System modeling and various existing algorithms and techniques for determining these impacts are discussed and these algorithms differ in terms of computational speed as well as accuracy. Moreover, some typical impacts of distributed electrical energy production such as electrical protection systems failure due to fault currents, voltage profile, and frequency fluctuation are discussed in this paper. Different metrics such as reliability, load-carrying capacity, controllability, voltage stability, power loss, undesired power exchange, and transient response during the consolidation of a decentralized generation are crucial in quantifying the implications of distributed generation on the radial network. Some of the most important factors influencing the technical implications are size, location, and type of distributed generation units utilized in the power system. © 2024 IEEE.

关键词： DC distribution systems

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A Spectral Learning Enhancement method in Cooperative multiagent system for production control system

A Spectral Learning Enhancement method in Cooperative multia...

引用

2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Sangeetha, V. Padmakala, S. Sutha Merlin, J. Prabu, S. Parthiban, K.G. Saveetha School of Engineering Dept of Cse Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India S.A.Engineering College Department of Information Technology Chennai India Mahendra Institute of Technology Department of Ece Namakkal India Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350360660

In modern production control systems, efficient management of resources and coordination among multiple agents are crucial for optimizing performance and achieving operational goals. This study introduces the Spectral Learning Enhancement (SLE) method, a novel approach designed to improve decision-making and coordination in cooperative multi-agent systems. The SLE method leverages spectral learning techniques to analyze and enhance the communication and interaction patterns between agents, leading to more informed and adaptive production control strategies. The SLE method integrates spectral graph theory with advanced learning algorithms to model the relationships between agents and their environment. By analyzing the spectral properties of the interaction graph, the method identifies key patterns and dependencies that inform the learning process. This approach allows for the dynamic adjustment of agent behaviors and strategies based on real-time feedback and evolving production conditions. Results from extensive simulations and real-world case studies demonstrate the efficacy of the SLE method. In a controlled production environment, the application of SLE led to a 15% increase in overall system efficiency, as measured by production output and resource utilization. Additionally, the method improved coordination among agents, reducing communication overhead by 20% and minimizing production downtime by 10%. The enhanced adaptability of the agents, driven by the spectral learning framework, resulted in a 25% reduction in response time to unexpected changes in production demands. The SLE method's ability to enhance both individual and collective agent performance positions it as a significant advancement in the field of production control. By incorporating spectral analysis into multi-agent systems, the method provides a robust and scalable solution for optimizing production processes and achieving greater operational efficiency. © 2024 IEEE.

关键词： Adaptive control systems

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Solar Photovoltaic Supported reconfigurable EV Battery Charger with Grid Feeding 7

Solar Photovoltaic Supported Reconfigurable EV Battery Charg...

引用

7th international conference on Computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Sinha, Akhouri Prateek Gupta, Rajesh Motilal Nehru National Institute of Technology Allahabad Electrical Engineering Department Prayagraj India

ISBN: (纸本)9798350305005

This paper proposes a solar photovoltaic (PV) supported reconfigurable electric vehicle (EV) battery charging with the possibility of grid feeding. The reconfigurable topology has two power transfer options, including solar to grid and solar to EV, so that it fulfils the objectives of an EV battery charging as well as the grid injection of the PV power. In this system, the solar PV power is transferred to an EV battery in a controlled manner. In the absence of an EV battery, the solar power can be transferred to the grid. Both the operations are considered complementary. To provide isolation and convert high voltage to low voltage, a high-frequency transformer is used. The proposed configuration uses a boost DC-DC converter for maximum power point tracking (MPPT), followed by a single-phase H-bridge inverter for DC to high frequency AC power conversion in case of EV battery charging and DC to power frequency output in case of grid injection. A rectifier with high frequency transformer enables battery charging and LCL filter interfaces inverter output to the grid. Suitable choice of control mode diverts the power flow in the desired direction. The results for the proposed topology are verified through the MATLAB/SIMULINK simulations. © 2023 IEEE.

关键词： Topology

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Municipal engineering Construction Management using Elite Individual Reserving strategy-based Fire Fly Optimization algorithm

Municipal Engineering Construction Management using Elite In...

引用

2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Chen, Yueyin Guiyang Information Science and Technology University Guiyang China

ISBN: (纸本)9798350360660

In municipal engineering construction management, to enhance on-site layout progress, a Multi-Objective (MO) optimization model is used to estimate the rationality of Site Layout (SL) based on the most important indicators of safety, time and cost. However, the existing models struggle to adapt to these dynamic changes in real-time, potentially leading to suboptimal layouts or the need for frequent recalculations. To overcome these drawbacks, an Elite Individual Reserving Strategy based Fire Fly Optimization (EIR-FFO) algorithm for analyzing rationality of on-site layout to enhance construction project process, minimizing cost and increasing safety. This EIR-FFO optimization algorithm handle multiple objectives simultaneously, that provide a balanced set of solutions to consider all factors such as cost, safety and time. Initially, the data about the site are acquired and a site-layout is designed to evaluate rationality. After that, various dimensionality of transportation time and cost factors are standardized by min-max standardization method and evaluation of factors of on-site layout are evaluated precisely. The experimental results of proposed method algorithm achieved accuracy of 97% for cost which is higher than existing methods such as Non-dominated Sorting Genetic Algorithm (NSGA). © 2024 IEEE.

关键词： Municipal engineering

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Advanced Citrus Disease Analysis: Integrating ML and DL for Improved Identification and Classification

Advanced Citrus Disease Analysis: Integrating ML and DL for ...

引用

2024 international conference on Smart systems for Electrical, Electronics, Communication and Computer engineering, ICSSEEC 2024

作者： Kalaivani, A. Sathya, J. New Horizon College of Engineering Department of Mca Bengaluru India

ISBN: (纸本)9798350378177

Agriculture is vital for the economy and the country's development. Minimizing plant and fruit diseases is crucial, and early detection has become essential in modern agriculture. Today, agriculture is rapidly evolving due to technological advances, changing consumer behaviour, and environmental challenges. Manual fruit disease identification can be time-consuming and error prone. Artificial intelligence is being utilized to identify and classify fruit diseases and recommend preventive measures. Techniques such as deep learning algorithms, including deep neural networks (DNN) and convolutional neural networks (CNN), as well as machine learning algorithms like SVM and KNN, have proven highly effective. Early detection enhances the quality and quantity of fruit production. This paper presents a hybrid model (CNN-K) that combines CNN for classification with k-means clustering for image segmentation for identifying and classifying the citrus fruit disease. Using a fruit dataset, the hybrid model achieved a classification accuracy of 99.2% for all citrus fruit images. © 2024 IEEE.

关键词： Adversarial machine learning

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Sliding Window Online Anomaly Detection for Dynamic systems with Unsupervised algorithms 4

Sliding Window Online Anomaly Detection for Dynamic Systems ...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Xiao, Wang Xu, Zhicheng Hao, Jiarui He, Chenglong Xu, Linfeng Pan, Junhang Xinjiang University Institute of Intelligent Manufacturing Modern Industry Urumqi China West Pipeline Company National Petroleum and Natural Gas Pipe Network Group Co. Ltd. Urumqi China Shanghai Jiao Tong University The Key Laboratory of Power Machinery and Engineering of Education Ministry China

ISBN: (纸本)9798331531225

Online Anomaly detection in operational data from dynamic systems is challenging due to the inherent complexity and variability of these datasets. This research introduces an innovative approach that leverages the Sliding Window Online Anomaly Detection method combined with online learning to enhance unsupervised algorithms for online anomaly detection within time-series operational data. Compared to traditional offline anomaly detection models, this method offers a significant advantage by allowing the training set to be updated incrementally in real-time. Several unsupervised algorithms were evaluated under different conditions: insertion of outliers, insertion of random noise and real fault data. Our method shows that traditional time-series data anomaly detection algorithms can be further optimized in conjunction with online learning to perform well in dynamic systems. © 2024 IEEE.

关键词： dynamic systems online anomaly detection sliding window unsupervised algorithms

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