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

共 13504 条记录，以下是541-550 订阅

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Evaluation of Heuristic optimization algorithms for Model Predictive Ventilation Control and Reducing Energy Consumption 9th

Evaluation of Heuristic Optimization Algorithms for Model Pr...

引用

9th international Building Physics conference

作者： Bielenda, Ryan Tariku, Fitsum BCIT Bldg Sci Ctr Excellence Burnaby BC V5G 3H2 Canada Now Stantec Morrison Hershfield Burnaby BC V5C 6S7 Canada

ISBN: (纸本)9789819783199;9789819783175;9789819783168

Research shows that heuristic optimization algorithms can find solutions for complex problems in the physical world. Pairing these algorithms with machine learning models for predictive building control applications has been widely explored. this study investigates the efficacy of determining optimal ventilation rates using a particle swarm optimization (PSO) algorithm, a genetic algorithm (GA), and a hybridized genetic particle swarm optimization (GPSO) algorithm developed using MATLAB within an EnergyPlus building energy simulation model. Weather data from Vancouver is used to exemplify a marine climate, where free-cooling opportunities are relatively abundant. the algorithm performance results are collected for both the heating and cooling seasons and are compared against each other for run times, energy savings, and indoor air quality performance. the results are compared against simulation results using a conventional demand control ventilation (DCV) system. Results indicate that when compared to the DCV controller with an economizer mode, heuristic optimization control methods are capable of reducing HVAC energy consumption during a cooling season with free-cooling opportunities by up to 14.1%. Additionally, all three optimization algorithms are capable of minimizing HVAC energy consumption with zero unmet hours for the indoor carbon dioxide concentration setpoint.

关键词： Particle Swarm optimization Genetic Algorithm Predictive Control Ventilation Rates Free-Cooling Artificial Neural Networks Marine Climate Energy Efficiency Indoor Air Quality

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R2 Score Estimation with Various Time Series Models Incorporating 1000 Epochs for optimization of Accuracy of Renewable Energy Future Trends 4

R2 Score Estimation with Various Time Series Models Incorpor...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chowdhury, Suman Saha, Apurba Kumar Das, Dilip Kumar International University of Business Agriculture and Technology Department of Electrical and Electronic Engineering Dhaka1230 Bangladesh Khulna University of Engineering and Technology Department of Electrical and Electronic Engineering Khulna Bangladesh International University of Business Agriculture and Technology Department of Mathematics Dhaka1230 Bangladesh

ISBN: (纸本)9798331529635

the 1°C rise in global temperature since the pre-industrial era is mainly attributed to the use of fossil fuels and human activities. To mitigate this temperature increase, it is crucial to reduce greenhouse gas emissions. In recent years, renewable energy has gained popularity due to its economic and environmental benefits. this study projects 450 days of renewable energy consumption based on a dataset spanning 5.5 years. three machine learning algorithms - Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Hist Gradient Boosting (HGBoost) - were utilized to predict renewable energy production and consumption. Based on the margin of error, it can be inferred that producing 2000MW to 5000MW of electricity is a viable option for Romania. the obtained r2 score for Random Forest Regressor is 0.95, whereas it is 0.83 for XGBoost and 0.84 for HGBoost Regressor as a performance parameter for the time series analysis. © 2024 IEEE.

关键词： Clean energy

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Deep learning methods for smart grid data analysis 4

Deep learning methods for smart grid data analysis

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4th international conference on Smart Grid and Energy Internet, SGEI 2024

作者： Yang, Jiahao Harvard University BostonMA02115 United States

ISBN: (纸本)9798350368314

this study aims to explore the application of deep learning technology in smart grid data analysis to improve the efficiency and reliability of power grid operation. By using LSTM (LSTM) and convolutional neural network (CNN), this paper analyzes the performance of these models in power load forecasting, fault detection and prevention, and energy distribution optimization in detail. Research results show that deep learning technology can significantly improve the accuracy of power demand forecasting from 70% to 85%, reduce fault response time, and improve fault detection accuracy to 100%. In addition, the optimized energy distribution strategy significantly reduces operating costs and energy waste. the research conclusion shows that deep learning not only optimizes the operating efficiency of the power grid, but also enhances the economy and sustainability of the power grid, confirming its practical value and broad application potential in smart grid management. ©2024 IEEE.

关键词： Power distribution faults

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Integrating Big Data, ML, and DL for Predictive Analytics in Healthcare: A Comprehensive Analysis and Hybrid Framework Proposal 4

Integrating Big Data, ML, and DL for Predictive Analytics in...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kargotra, Poonam Parray, Irfan Ramzan School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331529635

the paper presents a comprehensive in-depth analysis of big data, machine learning (ML), and deep learning (DL) methodologies in predictive healthcare analytics, with a focus on their comparative strengths, research gaps, and applications in real world. the study synthesizes findings from recent research papers related to same objective, highlighting the evolution of data-driven technologies in healthcare sector and the challenges which are associated with data integration, privacy, and real-time processing. the big data analytics provides foundational insights from vast healthcare datasets, while ML algorithms enhance predictive accuracy on structured data, while DL models achieve high precision even in complex tasks, such as medical imaging and real-time IoT embedded systems monitoring. this article introduces a novel hybrid approach that combines the strength of all these approaches, employing common data for heterogenous data integration, federated learning for privacy, and a feedback-driven adaptive system for continuous improvement. Analyzing quantitative comparisons, the effectiveness of each approach is illustrated: DL demonstrate high acuracy, and ML, in turn, flexibility with a moderate rate. Discussing the research gaps of current methodologies, the proposed framework discus the scalability, adaptability, and privacy enhancements of this new conceptual model to create more precise and proactive healthcare analytics. Possible future investigations indicate that it is necessary to enhance these adaptive learning solutions and to expand the arbitrary federative learning approaches to treat different interdisciplinary domains. © 2024 IEEE.

关键词： Federated learning

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Structured Progress Flow Implementation through GA and so Methods in Cc Networks 4

Structured Progress Flow Implementation Through GA and so Me...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Sharma, Komal Balmiki, Vinod Alabdeli, Haider Kumar, Rakesh Aparna, S. V. Suji Krishnaraj, V. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun248007 India The Islamic University Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Cmr Technical Campus Department of Cse Telangana Hyderabad India Sona College of Technology Department of Mathematics Salem India

ISBN: (纸本)9798350360165

With its Pay-As-You-Use model that allows users to share resources remotely, cloud computing ushers in a new era of network-based computing. However, because user services and sophisticated applications generate vast amounts of data, there are problems with data transfer costs and overall network resource execution costs. this study presents a heuristic scheduling technique based on Cat Swarm optimization (CSO) to distribute application the efficiency of the project for available resources Operating costs in equipment are two also and are evaluated by the CSO algorithm the performance of the proposed CSO algorithm is compared with the current workflow using Particle Set optimization (PSO) workflow tests. Several important conclusions may be drawn from the experimental results: (1) CSOs optimize work-resource (TOR) scheduling systems to minimize overall costs;(2) CSOs outperform PSOs in iteration efficiency;and (3) CSOs agree to equitable load distribution among available resources. the implementation of CSO provides a convenient approach to scheduling efficiency in cloud computing systems, thus optimizing resource utilization and reducing overall operating costs. © 2024 IEEE.

关键词： Genetic algorithms

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Proximal Policy optimization Algorithm for Integrated Energy System Operation with Adaptive learning Rate Decay Strategy 4

Proximal Policy Optimization Algorithm for Integrated Energy...

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4th international conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Wang, Jun Wang, Xin Du, Wei Li, Yuan Tang, Chenghong Liu, Gang Nari Group State Grid Electric Power Research Institute Co. Ltd. Nari Technology Co. Ltd. Nanjing China Nanjing Normal University Nari Group State Grid Electric Power Research Institute Co. Ltd. Nanjing China

ISBN: (纸本)9798350359619

With increasing focus on sustainability and efficiency, Integrated Energy Systems (IES) have gained more attention in the provision of electricity and thermal energy. However, the inherent complexity and uncertainty of IES pose challenges to their optimization and management. this paper proposes a Proximal Policy optimization (PPO) algorithm for the operation of IES, based on an adaptive learning rate decay strategy, aimed at enhancing the operational efficiency and stability of IES. Initially, a Markov decision process is established to simulate the operation of IES. Subsequently, a PPO algorithm with an adaptive learning rate decay strategy is proposed. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional PPO algorithms in reducing overall operational costs and improving the balance of electricity and thermal energy supply and demand. this research provides an effective optimization method for the efficient and sustainable operation of IES. © 2023 IEEE.

关键词： Markov processes

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Swarm optimization-Based Hyperparameter Selection for Machine learning algorithms in Indoor Localization 8

Swarm Optimization-Based Hyperparameter Selection for Machin...

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8th international conference on Computer Science and Engineering, UBMK 2023

作者： Kam, Yasin Bayraktar, Mert Ulusar, Umit Deniz Akdeniz University Electrical-Electronics Engineering Department Antalya Turkey Akdeniz University Computer Engineering Department Antalya Turkey

ISBN: (纸本)9798350340815

In this study, we compare multiple machine learning algorithms for indoor positioning applications, offering insights into the application of swarm optimization algorithms for hyperparameter selection in indoor positioning tasks. the study's findings demonstrate that both Particle S warm optimization (PSO) and Whale optimization algorithms (WOA) improve the performance of machine learning models. Specifically, Random Forest (RF)-based classification demonstrates the highest accuracy, precision, and recall. the results also indicate that an increased number of access points results in improved performance and decreased standard deviations. the study contributes to the optimization and refinement of machine learning algorithms for indoor localization, opening avenues for more accurate and reliable positioning systems. © 2023 IEEE.

关键词： Wireless sensor networks

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A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization 27

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empir...

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27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Dagreou, Mathieu Moreau, thomas Vaiter, Samuel Ablin, Pierre Univ Paris Saclay INRIA CEA Palaiseau France Univ Cote Azur CNRS LJAD Nice France Apple Paris France

Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the upper and the lower objectives correspond to empirical risk minimization problems and therefore have a sum structure. In this context, we propose a bilevel extension of the celebrated SARAH algorithm. We demonstrate that the algorithm requires O((n + m)(1/2) epsilon(-1)) oracle calls to achieve e-stationarity with n + m the total number of samples, which improves over all previous bilevel algorithms. Moreover, we provide a lower bound on the number of oracle calls required to get an approximate stationary point of the objective function of the bilevel problem. this lower bound is attained by our algorithm, making it optimal in terms of sample complexity.

关键词： optimization

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Curriculum-enhanced GroupDRO: Challenging the Norm of Avoiding Curriculum learning in Subpopulation Shift Setups 4

Curriculum-enhanced GroupDRO: Challenging the Norm of Avoidi...

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

作者： Barbalau, Antonio Bitdefender Deep Learning Research Unit Bucharest Romania University of Bucharest Faculty of Mathematics and Computer Science Bucharest Romania

ISBN: (纸本)9798331531225

In subpopulation shift scenarios, a Curriculum learning (CL) approach would only serve to imprint the model weights, early on, with the easily learnable spurious correlations featured. To the best of our knowledge, none of the current state-of-the-art subpopulation shift approaches employ any kind of curriculum. To overcome this, we design a CL approach aimed at initializing the model weights in an unbiased vantage point in the hypothesis space which sabotages easy convergence towards biased hypotheses during the final optimization based on the entirety of the available data. We hereby propose a Curriculum-enhanced Group Distributionally Robust optimization (CeGDRO) approach, which prioritizes the hardest bias-confirming samples and the easiest bias-conflicting samples, leveraging GroupDRO to balance the initial discrepancy in terms of difficulty. We benchmark our proposed method against the most popular subpopulation shift datasets, showing an increase over the state-of-the-art results across all scenarios, up to 6.2% on Waterbirds and 2.9% on CivilComments. © 2024 IEEE.

关键词： Contrastive learning

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Research on Intelligent Car Interface Interaction optimization Algorithm Based on User Experience Human Factors Engineering 4

Research on Intelligent Car Interface Interaction Optimizati...

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

作者： Shouxi, Wu Jiaxun, You Fan, Zhang Hongjing, He China Automotive Technology and Research Center Co. Ltd. Tianjin China Co. Ltd Tianjin China

ISBN: (纸本)9798331528676

this study proposes an intelligent car interface interaction optimization algorithm based on user experience human factors engineering. the algorithm combines the basic principles of human factors engineering, designs the interface interaction of intelligent cars, and adopts deep reinforcement learning (DRL) technology to adapt to diverse user needs and complex driving scenarios. By collecting and analyzing the interactive behaviors of users in different situations, the algorithm can adaptively adjust the interface layout, information presentation and interaction mode to improve the overall user experience. through simulation verification, the experimental results show that the optimization algorithm can effectively improve the user's operating experience in a variety of driving scenarios, significantly reducing the user's cognitive load, and improving the interaction efficiency. Simulation data analysis shows that the interface response time after optimization is reduced by an average of 23.5%, and user satisfaction is increased by 15.8%. the results of this study provide new ideas for the design and optimization of intelligent car interface interaction, and lay the foundation for the optimization of human-computer interaction in future intelligent driving systems. ©2024 IEEE.

关键词： Deep reinforcement learning

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