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element » elements (Expand Search)
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81
Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment
Published 2023“…First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. …”
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Estimating Construction Project Duration Using a Machine Learning Algorithm
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masterThesis -
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Development of an Optimization Scheme for A Fixed-Wing UAV Long Endurance with PEMFC and Battery
Published 2018Get full text
doctoralThesis -
86
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87
Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem
Published 2024“…We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. …”
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88
Real-Time Selective Harmonic Mitigation Technique for Power Converters Based on the Exchange Market Algorithm
Published 2020“…<p dir="ltr">Hand-in-hand with the smart-grid paradigm development, power converters used in high-power applications are facing important challenges related to efficiency and power quality. …”
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Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control
Published 2021“…The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set.…”
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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study
Published 2022“…This methodology includes a variance-based sensitivity analysis to determine building parameters that significantly influence indoor air temperatures, the Multi-Objective Genetic Algorithm to calibrate different rooms simultaneously based on the significant param eters identified by the sensitivity analysis, and new evaluation criteria to achieve a high-accuracy calibrated model. …”
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93
Detecting latent classes in tourism data through response-based unit segmentation (REBUS) in Pls-Sem
Published 2016“…This research note describes Response-Based Unit Segmentation (REBUS), a “latent class detection” technique used in partial least squares–structural equation modeling (PLS-SEM) to examine data heterogeneity. …”
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94
Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques
Published 2022“…Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. …”
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95
Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data
Published 2023“…Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics.…”
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96
Deep learning-based user experience evaluation in distance learning
Published 2023“…More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. …”
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97
Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems
Published 2022“…Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. …”
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Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
Published 2021“…However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. …”
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Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
Published 2021“…Raw data preprocessing and outliers detection and filtering algorithms were applied at the first stage of the analysis, and consequently, an activity-based travel matrix was developed for each household. …”