Results 21 to 30 of about 1,725,169 (334)
IET Electric Power Applications, 2021 As one of the most important assets of the industry, it is crucial to fully characterise all failure modes showing potential to degrade the normal operation of induction motors (IMs).
Fernando Bento +4 more
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Supervised Machine Learning Classification for Short Straddles on the S&P500
Risks, 2022 In this paper, we apply machine learning models to execute certain short-option strategies on the S&P500. In particular, we formulate and focus on a supervised classification task which decides if a plain short straddle on the S&P500 should be executed ...
Alexander Brunhuemer +5 more
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TIME MACHINES AND QUANTUM THEORY [PDF]
The Eleventh Marcel Grossmann Meeting, 2008 There is a deep structural link between acausal spacetimes and quantum theory. As a consequence quantum theory may resolve some "paradoxes" of time travel. Conversely, non-time-orientable spacetimes naturally give rise to electric charges and spin half.
openaire +2 more sources
On Halting Process of Quantum Turing Machine
, 2003 We prove that there is no algorithm to tell whether an arbitrarily constructed Quantum Turing Machine has same time steps for different branches of computation. We, hence, can not avoid the notion of halting to be probabilistic in Quantum Turing Machine.
Deutsch D. +3 more
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Fast iron loss prediction method in the pre-design stage of SRMs
The Journal of Engineering, 2019 To effectively find a valid solution in the development process of electrical machines, it is essential to predict machine iron loss in the pre-design stage. In guidance on the choice of the most suitable configuration of the switched reluctance machine (
Lefei Ge +3 more
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An extended winding function model for induction machine modelling considering saturation effect
IET Electric Power Applications, 2021 Winding function (WF) method is one of the electric machines modelling methods, which is used to simulate electric machines under different conditions.
Mehrdad Gholami +2 more
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RG inspired Machine Learning for lattice field theory
, 2017 Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use Renormalization Group (RG) ideas in the context of machine learning.
Foreman, S. +3 more
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Quantum-chemical insights from deep tensor neural networks
Nature Communications, 2017 Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical ...
Kristof T. Schütt +4 more
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A Study of the Mechanical Response of Nonwovens Excited by Plate Vibration
Applied Mechanics, 2022 Nonwovens are a type of textile that possess a wide range of unique properties, such as lightweight and damping characteristics, which make them suitable for many applications as in medicine and engineering.
Jan-Lukas Archut +6 more
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Actuators, 2023 The paper discusses the synthesis problem of a bistable piston pump, employing a driving mechanism that comprises shape memory alloy wires, a two-section involute cam, and an energy-recuperating spring.
Mihail Kostov +4 more
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