Results 31 to 40 of about 285,439 (304)
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
core +2 more sources
The Marginal Value of Adaptive Gradient Methods in Machine Learning
CoRR, 2017 Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We show that for simple overparameterized problems, adaptive methods often find drastically different solutions than ...
Ashia C. Wilson +4 more
openaire +3 more sources
Ensemble Learning for Free with Evolutionary Algorithms ? [PDF]
, 2007 Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result.
Gagné, Christian +3 more
core +4 more sources
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
doaj +1 more source
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
doaj +1 more source
Quantum adiabatic machine learning by zooming into a region of the energy surface [PDF]
, 2020 Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification.
Job, Joshua +5 more
core
, 2020 Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE).
Imran, Ali +3 more
core +1 more source