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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13703 条记录，以下是661-670 订阅

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Fault Classification of Wind Turbine: A Comparison of Hyperparameter optimization Methods 3rd

Fault Classification of Wind Turbine: A Comparison of Hyperp...

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3rd international conference on optimization, learning algorithms and applications (OL2A)

作者： Pinna, Danielle Toso, Rodrigo Semaan, Gustavo de Sa, Fernando Pereira, Ana I. Ferreira, Angela Soares, Jorge Brandao, Diego Ctr Fed Educ Tecnol Celso Suckow Fonseca Rio De Janeiro Brazil Microsoft Redmond WA USA Univ Fed Fluminense Rio De Janeiro Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

the last few years have been marked by the insertion of renewable technologies in the global energy matrix, such as wind and solar energy, which are considered clean energies with low environmental impact. Wind turbines, responsible for the energy conversion process, are complex equipment that are expensive and susceptible to numerous failures. Monitoring turbine components can help detect failures before they occur, reducing equipment maintenance costs. this work compares the training time of different techniques for tuning hyperparameters in supervised machine-learning models for fault detection in wind turbines. Results show the importance of data optimization during model training.

关键词： Wind turbine Machine learning Fault classification

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Teaching learning-Based optimization for Solving CEC2014 Test Suite: A Comparative Study 4th

Teaching Learning-Based Optimization for Solving CEC2014 Tes...

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4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Musa, Zulkifli Ibrahim, Zuwairie Shapiai, Mohd Ibrahim Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Pekan26600 Malaysia Faculty of Manufacturing Universiti Malaysia Pahang Pekan26600 Malaysia Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

ISBN: (纸本)9789819988181

this is a comparative study of Teaching learning-Based optimization (TLBO) as a human-based algorithm against other types of metaheuristic algorithm: single-agent finite impulse response optimizer (SAFIRO), simulated Kalman filter (SKF), particle swarm optimization algorithm (PSO), black hole algorithm (BH), and genetic algorithm (GA), in solving CEC2014 test suite. the TLBO algorithm is inspired by the process of teaching and learning in a classroom. the advantages of TLBO are it only has two main tasks: teaching phase and learning phase and has no parameter setting. the TLBO performance provides a balance between exploration and exploitation. Statistical analysis is then carried out to rank the TLBO results to those obtained by other type of metaheuristic algorithm. the experimental result show that the TLBO algorithm is a promising approach and comparative to SAFIRO and SKF and has better than PSO, BH, and GA. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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A Robust Methodology Design to Predict Student Educational Performance by using Hybrid Deep learning Strategy 4

A Robust Methodology Design to Predict Student Educational P...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Gayathri, J. Vijayalakshmi, K. Kalpana, P. Sri Bhavani, N. Chennai600 077 India

ISBN: (纸本)9798331515720

Predicting student educational performance has become an essential task for educational institutions aiming to enhance learning outcomes and provide timely interventions. Traditional machine learning models have been widely used in this domain;however, they often struggle with the high-dimensional and complex nature of educational data. this study presents a robust methodology for predicting student educational performance using a Hybrid Deep learning Model Design (HDLMD) that integrates Cascaded ResNet with AutoEncoder. the model is developed to address the complexities of educational data, leveraging the deep feature extraction capabilities of ResNet and the dimensionality reduction power of AutoEncoders. the proposed model was tested against nine other machine learning models, demonstrating superior performance with an accuracy of 98.11%. the HDLMD outperformed traditional models such as Random Forest, SVM, and Gradient Boosting, showcasing its effectiveness in capturing complex patterns within student data. the results indicate that the HDLMD is a powerful tool for educational institutions seeking to predict and improve student outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Task Offloading and Resource Allocation Strategies Based on Proximal Policy optimization 6

Task Offloading and Resource Allocation Strategies Based on ...

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6th international conference on Natural Language Processing (ICNLP)

作者： Liu, Kai Yang, Wujun Xian Univ Posts & Telecommun Dept Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

In order to meet the service demands of new network applications and scenarios, computational offloading techniques in MEC have become a popular research issue. Aiming at solving the problems of delay deterioration and server resource scarcity caused by multi-users offloading tasks on edge servers at the same time, this paper firstly constructs a multi-terminal and single-server offloading model for tasks based on the queuing *** the user's delay experience under the delay-sensitive condition, introduces the weighted sum of the waiting probability and the task completion time as the optimization objective. On this basis, a proximal policy optimization-based computational offloading and resource allocation strategy is proposed. Finally, the simulation findings indicate that the algorithm proposed in this paper exhibits superior performance in optimizing the delay experience of user when compared to alternative algorithms.

关键词： edge computing deep reinforcement learning computational offloading queuing models proximal policy optimization

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Triadic Elastic Structure Representation for Open-Set Incremental 3D Object Retrieval 24

Triadic Elastic Structure Representation for Open-Set Increm...

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4th Annual international conference on Multimedia Retrieval (ICMR)

作者： Xu, Yang Feng, Yifan Bie, Lin Tsinghua Univ Sch Software Beijing Peoples R China

ISBN: (纸本)9798400706028

In open-set environments, the introduction of unseen classes adds a third stage of data for incremental learning, alongside seen classes of old and new. Existing methods by binary optimization between old and new classes struggle to model the complex triadic relationships among old, new, and unseen classes. In this paper, we introduce the Triadic Elastic Structure learning (TESR) framework for open-set incremental 3D object retrieval. Specifically, to overcome the Bi-Directional Catastrophic Forgetting (BDCF) problem in open-set incremental learning, we employ the Triadic Regularized Embedding (TRE) module. this module helps to globally preserve the triadic relationships among the three stages of classes, while simultaneously facilitating semantic-specific learning for new classes from a local perspective. To address the challenge of Bi-Directional Knowledge Transfer (BDKT), our method leverages high-order correlations among objects of different classes through the Elastic Structure Distillation (ESD) module, by constructing an elastic hypergraph based on incremental and global correlations. We construct four multi-modal datasets for this open-set incremental 3D object retrieval: OIES, OINT, OIMN, and OIAB. Extensive experiments and ablation studies on these four benchmarks demonstrate the superiority of our method over current state-of-the-art approaches.

关键词： 3D Object Retrieval Open-Set learning Incremental learning Multi-Modal Fusion Hypergraph

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Hybrid Equilibrium Optimizer and Differential Evolution Algorithm for optimization Combined Heat and Power Economic Dispatch Problem 4

Hybrid Equilibrium Optimizer and Differential Evolution Algo...

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4th international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2024

作者： Song, Panpan Luo, Qifang Zhou, Yongquan College of Artificial Intelligence Guangxi Minzu University Nanning530006 China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006 China

ISBN: (纸本)9781643685519

the Combined Heat and Power Economic Dispatch (CHPED) problem is one of the most complex energy dispatch problems in terms of find the solution that minimizes the fuel cost while satisfying various constraints. therefore, this paper tries to combine the Differential Evolution (DE) algorithm and the Improved Equilibrium Optimizer (IEO) for the characteristics of equilibrium optimizer (EO) which is easily trapped in local optimum this paper adopts two methods for improvement, which are improved equilibrium pool mechanism and quasi-opposite based learning (QOBL) mechanism. Secondly, DE and IEO are mixed using the selection phase of DE to obtain DE-IEO. the superior performance of DE-IEO is verified by comparative analysis with various other algorithms mentioned in the literature on the large-scale CHPED system. the statistical results show that DE-IEO achieves satisfactory results on the CHPED problem, providing another solution to complex optimization problems. © 2024 the Authors.

关键词： optimization algorithms

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A Metaheuristic algorithms for optimization of Integrated Scheduling of Cranes and Storage Platforms at Automated Container Terminals 4th

A Metaheuristic Algorithms for Optimization of Integrated Sc...

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4th international conference on Digital Technologies and applications (ICDTA)

作者： Garmouch, Hamza Abdoun, Otman Abdelmalek Essaadi Univ Fac Sci ISISA Team Tetouan Morocco

ISBN: (纸本)9783031686528;9783031686535

this article focusses on the demanding situations that semi-automatic container terminals face, that specialize in APM Terminals in Tangier. We introduce our 'System Health Check' dashboard, that is a tool designed to display how properly the terminal's devices are working. this is vital for fixing the problem of makespan (the time for handling a container), because it offers decision-makers stable statistics and real-time updates. this proactive method allows make operations smoother and decreases the time ships live withinside the port. We'll additionally display how this dashboard suits with different studies geared toward enhancing global field terminals. this device is not only a oneoff-it's putting the degree for destiny enhancements. We're making plans to apply a few algorithms to make it even better, so it could adapt to adjustments withinside the delivery enterprise and assist field ports run easily worldwide.

关键词： makespan portstay ACT QC Shuttle ARMG sACT YT YC TOS

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Research on Electric Power Dialogue System Based on Deep Reinforcement learning 4

Research on Electric Power Dialogue System Based on Deep Rei...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Geng, Chao Wei, Bengang Zhang, Sujie Zheng, Long Du, Congjin Tan, Kaixiang State Grid Shanghai Electric Power Company Equipment Department Shanghai China State Grid Shanghai Electric Power Company Shanghai China Jiangsu Titan Wisdom Technology Co. LTD Wuxi China

ISBN: (纸本)9798331528676

this paper proposes an innovative deep reinforcement learning framework for building a power dialogue system, which aims to optimize the interactive experience and efficiency of power services. therefore, this paper designs a hybrid reinforcement learning architecture combining Deep Q-Network (DQN) and Proximal Policy optimization (PPO) to solve the complex and variable dialogue scenarios in the power field. DQN is responsible for the preliminary value evaluation and action selection due to its efficient exploration ability in discrete action space. However, PPO, with its robust policy optimization mechanism, focuses on the fine adjustment of policies to promote the continuous improvement and optimization of dialogue strategies. the method designed in this paper not only improves the response quality and user satisfaction of the dialogue system, but also enhances the robustness and adaptability of the system to the dynamic environment of the power system. It shows a significant performance improvement in the power dialogue task, and can well handle unstructured user requests and perform precise power service operations. And we can maintain high performance and high reliability in complex and changing environments. ©2024 IEEE.

关键词： Deep reinforcement learning

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A Review on Uncertainty Modelling Approaches for Stochastic optimization in Power System 4

A Review on Uncertainty Modelling Approaches for Stochastic ...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Goyal, Sunil Kumar Gangil, Gaurav Saraswat, Amit Manipal University Jaipur Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350307757

the uncertainty modelling is the challenging task in the power system. Utilization of distributed energy resources (DER) introduces the complexity and uncertainties in electrical power systems. the utility grid pricing, power demand, renewable generation are the key uncertain parameters for solving the stochastic optimization in power system. To handle the uncertainties, this paper presents the various uncertainty modelling approaches for stochastic optimization of power system problems i.e. optimal scheduling of distributed energy resources (DER) and battery storage system in the transmission and distribution network. Generally, the uncertainty can be handled through probabilistic and possibilistic approaches. A comprehensive review of all uncertainty handling approaches in terms of simplicity, accuracy, and efficacy, as well as their strengths and weaknesses in addressing power system issues. In addition, we examine several major applications of optimization under uncertainty in power systems and discuss future research possibilities. this paper suggest that which uncertainty modelling techniques is suitable for dealing with different stochastic optimization problems accurately and efficiently. © 2024 IEEE.

关键词： Electric power transmission networks

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Selective Harmonic Elimination In 5-Level MLI Using Particle Swarm optimization 4

Selective Harmonic Elimination In 5-Level MLI Using Particle...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Verma, Vikas Kumar Meena, Hemant Kumar Sandeep, N. Mnit Jaipur Electrical Engineering Jaipur India

ISBN: (纸本)9798350343670

Multilevel inverters are highly capable to obtaining higher power ratings and higher output voltage waveforms by their modular multilevel structure. Currently, multilevel inverter applications is increasing in the various industries and renewable energy sources. High performance, modular design, and low harmonic distortion in voltage waveform are the most remarkable characteristics of multilevel inverters. the paper proposes a PSO algorithm for selective harmonic elimination in 5-level multilevel inverters. the algorithm consists of particle swarm optimization (PSO) to obtain optimized firing angles, resulting Elimination of 3rd harmonics from the system witch is shown by FFT analysis, it also provides lower total harmonic distortion (thD). © 2024 IEEE.

关键词： Cascade H-Bridge Multilevel inverters Particle Swarm optimization Technique Selective Harmonic Elimination Total Harmonic Distortion

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