References of "Sainlez, Matthieu 50002971"
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See detailComparison of Supervised Learning Techniques for Atmospheric Pollutant Monitoring in a Kraft Pulp Mill
Sainlez, Matthieu UL; Heyen, Georges

in J. Comput. Appl. Math. (2013), 246

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See detailMachine learning techniques for atmospheric pollutant monitoring
Sainlez, Matthieu UL; Heyen, Georges

Poster (2012, January 27)

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See detailComparison of Machine Learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill
Sainlez, Matthieu UL; Heyen, Georges

Scientific Conference (2011, November)

In this paper, machine learning techniques are compared to predict nitrogen oxide (NOx) pollutant emission from the recovery boiler of a Kraft pulp mill. Starting from a large database of raw process data ... [more ▼]

In this paper, machine learning techniques are compared to predict nitrogen oxide (NOx) pollutant emission from the recovery boiler of a Kraft pulp mill. Starting from a large database of raw process data related to a Kraft recovery boiler, we consider a regression problem in which we are trying to predict the value of a continuous variable. Generalization is done on the worst case configuration possible to make sure the model is adequate: the training period concerns stationary operations while test periods mainly focus on NOx emissions during transient operations. This comparison involves neural network techniques (i.e., static multilayer perceptron and dynamic NARX network), tree-based methods and multiple linear regression. We illustrate the potential of a dynamic neural approach compared to the others in this prediction task. [less ▲]

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See detailApproche neuronale dynamique pour la prédiction de polluants atmosphériques: application à l'industrie papetière.
Sainlez, Matthieu UL; Heyen, Georges; Lumen, Philippe

Scientific Conference (2011, May 27)

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See detailDynamic neural network approach for atmospheric pollutant prediction: A pulp mill case study
Sainlez, Matthieu UL

Scientific Conference (2011, May 27)

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See detailSupervised learning for a Kraft recovery boiler: a data mining approach with Random Forests.
Sainlez, Matthieu UL; Heyen, Georges; Lafourcade, Sébastien

in Favrat, Daniel; Maréchal, François (Eds.) ECOS 2010 Volume IV (Power plants and Industrial processes) (2011, January 11)

A data mining methodology, the random forests, is applied to predict high pressure steam production from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data ... [more ▼]

A data mining methodology, the random forests, is applied to predict high pressure steam production from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data, the goal is to identify the input variables that explain the most significant output variations and to predict the high pressure steam flow. [less ▲]

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See detailRecurrent neural network prediction of steam production in a Kraft recovery boiler
Sainlez, Matthieu UL; Heyen, Georges

in E.N. Pistikopoulos, M. C. Georgiadis; Kokossis, A. C. (Eds.) 21st European Symposium on Computer Aided Process Engineering (2011)

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See detailPerformance monitoring of an industrial boiler: classification of relevant variables with Random Forests
Sainlez, Matthieu UL; Heyen, Georges

in Pierucci, S.; Ferraris, G. Buzzi (Eds.) 20th European Symposium on Computer Aided Process Engineering (2010)

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See detailGene expression data analysis using spatiotemporal blind source separation
Sainlez, Matthieu UL; Absil, Pierre-Antoine; Teschendorff, Andrew E.

in Verleysen, Michel (Ed.) ESANN'2009 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning. (2009)

We propose a “time-biased” and a “space-biased” method for spatiotemporal independent component analysis (ICA). The methods rely on computing an orthogonal approximate joint diagonalizer of a collection ... [more ▼]

We propose a “time-biased” and a “space-biased” method for spatiotemporal independent component analysis (ICA). The methods rely on computing an orthogonal approximate joint diagonalizer of a collection of covariance-like matrices. In the time-biased version, the time signatures of the ICA modes are imposed to be white, whereas the space-biased version imposes the same condition on the space signatures. We apply the two methods to the analysis of gene expression data, where the genes play the role of the space and the cell samples stand for the time. This study is a step towards addressing a question first raised by Liebermeister, on whether ICA methods for gene expression analysis should impose independence across genes or across cell samples. Our preliminary experiment indicates that both approaches have value, and that exploring the continuum between these two extremes can provide useful information about the interactions between genes and their impact on the phenotype. [less ▲]

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