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Disruption prediction and mitigation is of key importance in the development of sustainable tokamakreactors. Machine learning has become a key tool in this endeavour. In this paper multiple machinelearning models will be tested and compared. A particular focus has been placed on their portability.This describes how easily the models can be used with data from new devices. The methods used inthis paper are support vector machine, 2-tiered support vector machine, random forest, gradient boostedtrees and long-short term memory. The results show that the support vector machine performanceis marginally better among the standard models, while the gradient boosted trees performed the worst.The portable variant of each model had lower performance. Random forest obtained the highest portableperformance. Results also suggest that disruptions can be detected as early as 600ms before the event.An analysis of the computational cost showed all models run in less than 1ms, allowing sufficient timefor disruption mitigation.
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As biological gender is one of the aspects of presenting individual human, much work has been done on gender classification based on people names. The proposals for English and Chinese languages are tremendous; still, there have been few works done for Vietnamese so far. We propose a new dataset for gender prediction based on Vietnamese names. This dataset comprises over 26,000 full names annotated with genders. This dataset is available on our website for research purposes. In addition, this paper describes six machine learning algorithms (Support Vector Machine, Multinomial Naive Bayes, Bernoulli Naive Bayes, Decision Tree, Random Forrest and Logistic Regression) and a deep learning model (LSTM) with fastText word embedding for gender prediction on Vietnamese names. We create a dataset and investigate the impact of each name component on detecting gender. As a result, the best F1-score that we have achieved is up to 96% on LSTM model and we generate a web API based on our trained model.
The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper set from historical data for forecasting still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges, therefore machine learning strategies study are needed. In this research, we propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia and the most optimal algorithm, LSTM model with an average of 0.304 mean-square error has been selected as the prediction engine of the proposed system.
The surface current on the plasma-vacuum interface during a disruption event involving kink instability can play an important role in driving current into the vacuum vessel. However, there have been disagreements over the nature or even the sign of the surface current in recent theoretical calculations based on idealized step-function background plasma profiles. We revisit such calculations by replacing step-function profiles with more realistic profiles characterized by strong but finite gradient along the radial direction. It is shown that the resulting surface current is no longer a delta-function current density, but a finite and smooth current density profile with internal structure, concentrated within the region with strong plasma pressure gradient. Moreover, this current density profile has peaks of both signs, unlike the delta-function case with a sign opposite to, or the same as the plasma current. We show analytically and numerically that such current density can be separated into two parts, with one of them, called the convective current density, describing the transport of the background plasma density by the displacement, and the other part that remains, called the residual current density. It is argued that consideration of both types of current density is important and can resolve past controversies.
An updated analysis of New Physics violating Lepton Flavour Universality, by using the Standard Model Effective Field Lagrangian with semileptonic dimension six operators at $Lambda = 1,mathrm{TeV}$ is presented. We perform a global fit, by discussing the relevance of the mixing in the first generation. We use for the first time in this context a Montecarlo analysis to extract the confidence intervals and correlations between observables. Our results show that machine learning, made jointly with the SHAP values, constitute a suitable strategy to use in this kind of analysis.
Results from the last FTU campaigns on the deuterium large (wrt FTU volume) pellet REs suppression capability, mainly due to the induced burst MHD activity expelling REs seed are presented for discharges with 0.5 MA and 5.3T. Clear indications of avalanche multiplication of REs following single pellet injection on 0.36 MA flat-top discharges is shown together with quantitative indications of dissipative effects in terms of critical electrical field increase due to fan-like instabilities. Analysis of large fan-like instabilities on post-disruption RE beams, that seem to be correlated with low electrical field and background density drops, reveal their strong RE energy suppression capability suggesting a new strategy for RE energy suppression controlling large fan instabilities. We demonstrate how such density drops can be induced using modulated ECRH power on post-disruption beams.
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