Results 11 to 20 of about 283,145 (183)
Pharmaceuticals, 2023 Predicting compound potency is a major task in computational medicinal chemistry, for which machine learning is often applied. This study systematically predicted compound potency values for 367 target-based compound activity classes from medicinal ...
Tiago Janela, Jürgen Bajorath
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IEEE Access, 2020 This article presents a machine learning approach to predict the long-term voltage stability margin as represented by the Loadability Margin (LM). LM is an intuitive and easily understandable indicator of voltage stability.
Kalana Dulanjith Dharmapala +3 more
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IEEE Access, 2021 This paper proposes a computationally efficient method based on deep neural network and a meta-heuristic optimization algorithm known as bat algorithm to coordinate power oscillation damping controllers incorporated in renewable energy stations to ...
Samundra Gurung +2 more
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Data-centric approach for online P-margin estimation from noisy phasor measurements
Energy Reports, 2023 A new estimation method for load P-margin of transmission systems is proposed by using machine learning techniques. The estimation solution uses a reduced number of features as inputs to the machine learning algorithm and does not rely on power flow ...
Felipe Proença de Albuquerque +4 more
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Fast dynamic voltage security margin estimation: concept and development
IET Smart Grid, 2020 This study develops a machine learning-based method for a fast estimation of the dynamic voltage security margin (DVSM). The DVSM can incorporate the dynamic system response following a disturbance and it generally provides a better measure of security ...
Hannes Hagmar +3 more
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Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches
Energies, 2021 Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching ...
Qian Sun +4 more
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MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis [PDF]
, 2018 Interpretability has emerged as a crucial aspect of machine learning, aimed at providing insights into the working of complex neural networks. However, existing solutions vary vastly based on the nature of the interpretability task, with each use case ...
Anirudh, Rushil +3 more
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Prediction of Rainfall in Australia Using Machine Learning
Information, 2022 Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic
Antonio Sarasa-Cabezuelo
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Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization [PDF]
Journal of Biomedical Optics, 2021 Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes. To date, the vibrational spectroscopy systems developed for medical applications include single-point measurement probes and intraoperative ...
Daoust, François +11 more
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Dental Hypotheses, 2022 Objective: The aim of this study was to compare the marginal microleakage between bulk-fill, preheated bulk-fill, and bulk-fill flowable composite resins above and below cemento-enamel junction (CEJ) using micro-computed tomography.
Nidhal Salim Dilian, Aláa Jawad Kadhim
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