APPLICATION OF MACHINE LEARNING TO FILL IN THE MISSING MONITORING DATA OF AIR QUALITY

In this paper, three machine learning models have been applied to predict and fill in the missing monitoring data of air quality for Gia Lam and Nha Trang stations in Hanoi and Khanh Hoa respectively, including Autoregressive Moving Average (ARMA), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Two air pollutants being NO2 and PM10 were selected for this study. The experimental results showed that the performance of all three studied models is better than that of some traditional approaches, including Multiple Linear Regression (LR) and Spline interpolation. Besides that, ARMA, ANN and SVR can capture the fluctuation of concentrations of the selected pollutants. These results indicated that the machine learning is a feasible approach to deal with the missing of data which is one of the biggest problems of air quality monitoring stations in Viet Nam.

A priori error indicator in the transformation method for problems with geometric uncertainties

Version éditeur de cette publication à l'adresse suivante : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514655To solve stochastic problems with geometric uncertainties, one can transform the original problem in a domain with stochastic boundaries and interfaces to a problem defined in a deterministic domain with uncertainties in the material behavior. The latter problem is then discretized. There exist infinitely many random mappings that lead to identical results in the continuous domain but not in the discretized domain. In this paper, an a priori error indicator is proposed for electromagnetic problems with scalar and vector potential formulations. This leads to criteria for selecting random mappings that reduce the numerical error. In an illustrative numerical example, the proposed a priori error indicator is compared with an a posteriori estimator for both potential formulationsThis work is supported by the program MEDEE funded by the Nord Pas de Calais council and the European Community and supported in part by the National Science Foundation under Grant No. 1216927

Residual-based a posteriori error estimation for stochastic magnetostatic problems

In this paper, we propose an a posteriori error estimator for the numerical approximation of a stochastic magnetostatic problem, whose solution depends on the spatial variable but also on a stochastic one. The spatial discretization is performed with finite elements and the stochastic one with a polynomial chaos expansion. As a consequence, the numerical error results from these two levels of discretization. In this paper, we propose an error estimator that takes into account these two sources of error, and which is evaluated from the residuals.This work is supported by the program MEDEE funded by the Nord Pas de Calais council and the European Community and is supported in part by the Labex CEMPI (ANR-11-LABX-0007-01)

Transformation Methods for Static Field Problems With Random Domains

The numerical solution of partial differential equations onto random domains can be done by using a mapping transforming this random domain into a deterministic domain. The issue is then to determine this one to one random mapping. In this paper, we present two methods-one based on the resolution of the Laplace equations, one based on a geometric transformation-to determine the random mapping. A stochastic magnetostatic example is treated to compare these methods.This work is supported by the program MEDEE funded by the Nord Pas de Calais council and the European Communit

Résolution numérique en électromagnétisme statique de problèmes aux incertitudes géométriques par la méthode de transformation : application aux machines électriques

The numerical models that are more and more used as virtual prototypes require information on the input data as the geometrical dimensions, the physical characteristics of materials and the external solicitations. At present, the models available are very close to the physics they represent and the gap met with the reality can come now partially from a lack of information on the input data. The probabilistic approach which consists in modelling the uncertain quantities by variables or random fields is the solution which was chosen in this thesis to take into account the uncertainties on the geometrical dimensions. To resolve the problem, the transformation method, allowing transposing a problem with uncertainties on the geometry to a problem with uncertainties on the behaviour laws, was chosen. As there are an infinite number of possible transformations, various methods to determine the transformation were implemented and compared. In particular, an a-priori error estimator has been proposed which gives a criterion for the transformation choice. It was also shown that the transformation method can take into account naturally the discontinuities at the stochastic level of the electromagnetic fields. Finally, the method was used to study the influence of the geometrical uncertainties of a stator on the performances of an electrical machine. This study is based on a set of measurements made on a batch of stators.Les modèles numériques, de plus en plus utilisés en tant que prototypes virtuels, requièrent la connaissance de paramètres d'entrée comme les dimensions géométriques, les caractéristiques physiques des matériaux et les sollicitations externes. Les modèles numériques disponibles actuellement sont très proches de la physique qu'il représente et les écarts que l'on constate avec la réalité peuvent maintenant incomber en partie à une méconnaissance des paramètres d'entrée. L'approche probabiliste qui consiste à modéliser les quantités incertaines par des variables ou champs aléatoires est la solution qui a été retenue dans cette thèse pour prendre en compte les incertitudes d'origine géométrique. Pour résoudre le problème, la méthode de transformation, permettant de ramener un problème aux incertitudes portées par la géométrie à un problème aux incertitudes portées par les lois de comportement, a été choisie. Comme il existe une infinité de transformations possibles, différentes méthodes de détermination de la transformation ont été mises en œuvre et comparées. En particulier, un estimateur d'erreur a-priori a été proposé de manière à dégager des critères de choix. Il a été aussi montré que la méthode de transformation peut prendre en compte naturellement des discontinuités au niveau stochastique des grandeurs locales. Enfin, la méthode étudiée a été employée pour étudier l'influence des incertitudes géométriques d'un stator sur les performances d'une machine électrique. Cette étude s'appuie sur un ensemble de mesures faites sur un lot de stators

Solution of Static Field Problems With Random Domains

A method to solve stochastic partial differential equations on random domains consists in using a one-to-one random mapping function which transforms the random domain into a deterministic domain. With this method, the randomness is then borne by the constitutive relationship of the material. In this paper, this method is applied in electrokinetics in the case of scalar potential and vector potential formulations. An example is treated and the proposed method is compared to a nonintrusive method (NIM) based on the remeshing of the random domains.This work is supported by the program MEDEE funded by the Nord Pas de Calais council and the European Communit

Direct access to 2-aryl-3-cyanothiophenes by a base-catalyzed one-pot two-step three-component reaction of chalcones with benzoylacetonitriles and elemental sulfur

International audience3-Cyanothiophenes could be obtained via a three-component reaction of chalcones with benzoylacetonitriles and sulfur

DABCO-Catalyzed DMSO-Promoted Sulfurative 1,2-Diamination of Phenylacetylenes with Elemental Sulfur and <i>o</i>‑Phenylenediamines: Access to Quinoxaline-2-thiones

The oxidative amination of alkynes typically requires transition metal catalysts and strong oxidants. Herein, we alternatively utilize DABCO as a sulfur-activating catalyst to achieve the sulfurative 1,2-diamination of phenylacetylenes with elemental sulfur and o-phenylenediamines. DMSO was found to be particularly suitable for use as a terminal oxidant for this three-component process. A mechanistic study has shown that this cascade reaction is triggered by the addition of active sulfur species to the triple bond of phenylacetylenes