Results 41 to 50 of about 218,805 (280)
Frontiers in Energy ResearchWith the continued development and progress of industrialisation, modernisation, and smart cities, global energy demand continues to increase. Photovoltaic systems are used to control CO2 emissions and manage global energy demand.
Manoharan Madhiarasan
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Energies, 2022 Currently, AC motors are a key element of industrial and commercial drive systems. During normal operation, the machines may become damaged, which may pose a threat to the users.
Maciej Skowron +2 more
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Hyperparameter Tuning Approaches
, 2023 AbstractThis chapter provides a broad overview over the different hyperparameter tunings. It details the process of HPT, and discusses popular HPT approaches and difficulties. It focuses on surrogate optimization, because this is the most powerful approach.
Thomas Bartz-Beielstein +1 more
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Learning Individualized Hyperparameter Settings
Algorithms, 2023 The performance of optimization algorithms, and consequently of AI/machine learning solutions, is strongly influenced by the setting of their hyperparameters. Over the last decades, a rich literature has developed proposing methods to automatically determine the parameter setting for a problem of interest, aiming at either robust or instance-specific ...
Vittorio Maniezzo, Tingting Zhou
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
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Optimizing EMG Classification through Metaheuristic Algorithms
Technologies, 2023 This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the ...
Marcos Aviles +2 more
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Efficient Optimization of Echo State Networks for Time Series Datasets
, 2019 Echo State Networks (ESNs) are recurrent neural networks that only train their output layer, thereby precluding the need to backpropagate gradients through time, which leads to significant computational gains.
Gianniotis, Nikos +2 more
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Accelerating Hyperparameter Optimisation with PyCOMPSs [PDF]
Workshop Proceedings of the 48th International Conference on Parallel Processing, 2019 Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming
Njoroge Kahira, Albert +3 more
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
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The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details
Applied SciencesComputer vision solutions have become widely used in various industries and as part of daily solutions. One task of computer vision is object detection. With the development of object detection algorithms and the growing number of various kinds of image ...
Tautvydas Kvietkauskas +3 more
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