Results 21 to 30 of about 140,191 (290)
Investigation of Machine Learning Techniques for Disruption Prediction Using JET Data
Plasma, 2023 Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors. Machine learning has become a key tool in this endeavour. In this paper, multiple machine learning models are tested and compared.
Joost Croonen +2 more
doaj +1 more source
Delivery Guarantees in Predictable Disruption Tolerant Networks [PDF]
, 2007 This article studies disruption tolerant networks (DTNs) where each node knows the probabilistic distribution of contacts with other nodes. It proposes a framework that allows one to formalize the behaviour of such a network. It generalizes extreme cases that have been studied before where either (a) nodes only know their contact frequency with each ...
François, Jean-Marc, Leduc, Guy
openaire +2 more sources
Network disruption prediction based on neural networks [PDF]
, 2017 Network disruptions cause significant financial losses and discomfort of customers. However, communication systems provide various data about equipment condition. This information can be used to predict network disruptions.
null null, Dmitry S. Taimanov
core +1 more source
Disruption prediction for future tokamaks using parameter-based transfer learning
Communications Physics, 2023 Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device.
Wei Zheng +14 more
doaj +1 more source
Analysis and Prediction of Disruptions in Metro Networks
2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2019 Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict ...
Menno Yap, Oded Cats
openaire +3 more sources
Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation [PDF]
, 2023 The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research.
Ding, Yonghua +13 more
core +1 more source
iScience, 2022 Summary: The number of man-made chemicals has increased exponentially recently, and exposure to some of them can induce fetal malformations. Because complex and precisely programmed signaling pathways play important roles in developmental processes ...
Seiya Kanno +5 more
doaj +1 more source
Multivariate statistical models for disruption prediction at ASDEX Upgrade [PDF]
, 2012 In this paper, a disruption prediction system for ASDEX Upgrade has been proposed that does not require disruption terminated experiments to be implemented.
Pautasso, G. +9 more
core +1 more source
Disruption prediction on EAST tokamak using a deep learning algorithm [PDF]
, 2021 Submitted for publication in Plasma Physics and Controlled FusionIn this study, a long short-term memory (LSTM) model is trained on a large disruption warning database to predict the disruption on EAST tokomak.
Rea, Cristina +10 more
core +1 more source
The evolutionary consequences of disrupted male mating signals: an agent-based modelling exploration of endocrine disrupting chemicals in the guppy. [PDF]
PLoS ONE, 2014 Females may select a mate based on signalling traits that are believed to accurately correlate with heritable aspects of male quality. Anthropogenic actions, in particular chemicals released into the environment, are now disrupting the accuracy of mating
Alistair McNair Senior +2 more
doaj +1 more source