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See detailApprentissage par renforcement batch fondé sur la reconstruction de trajectoires artificielles
Fonteneau, Raphaël ULg; Murphy, Susan A.; Wehenkel, Louis ULg et al

in Proceedings of the 9èmes Journées Francophones de Planification, Décision et Apprentissage (JFPDA 2014) (2014)

Cet article se situe dans le cadre de l’apprentissage par renforcement en mode batch, dont le problème central est d’apprendre, à partir d’un ensemble de trajectoires, une politique de décision optimisant ... [more ▼]

Cet article se situe dans le cadre de l’apprentissage par renforcement en mode batch, dont le problème central est d’apprendre, à partir d’un ensemble de trajectoires, une politique de décision optimisant un critère donné. On considère plus spécifiquement les problèmes pour lesquels l’espace d’état est continu, problèmes pour lesquels les schémas de résolution classiques se fondent sur l’utilisation d’approxima- teurs de fonctions. Cet article propose une alternative fondée sur la reconstruction de “trajectoires arti- ficielles” permettant d’aborder sous un angle nouveau les problèmes classiques de l’apprentissage par renforcement batch. [less ▲]

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See detailApprentissage par renforcement bayésien versus recherche directe de politique hors-ligne en utilisant une distribution a priori: comparaison empirique
Castronovo, Michaël ULg; Ernst, Damien ULg; Fonteneau, Raphaël ULg

in Proceedings des 9èmes Journée Francophones de Planification, Décision et Apprentissage (2014, May)

Cet article aborde le problème de prise de décision séquentielle dans des processus de déci- sion de Markov (MDPs) finis et inconnus. L’absence de connaissance sur le MDP est modélisée sous la forme ... [more ▼]

Cet article aborde le problème de prise de décision séquentielle dans des processus de déci- sion de Markov (MDPs) finis et inconnus. L’absence de connaissance sur le MDP est modélisée sous la forme d’une distribution de probabilité sur un ensemble de MDPs candidats connue a priori. Le cri- tère de performance utilisé est l’espérance de la somme des récompenses actualisées sur une trajectoire infinie. En parallèle du critère d’optimalité, les contraintes liées au temps de calcul sont formalisées rigoureusement. Tout d’abord, une phase « hors-ligne » précédant l’interaction avec le MDP inconnu offre à l’agent la possibilité d’exploiter la distribution a priori pendant un temps limité. Ensuite, durant la phase d’interaction avec le MDP, à chaque pas de temps, l’agent doit prendre une décision dans un laps de temps contraint déterminé. Dans ce contexte, nous comparons deux stratégies de prise de déci- sion : OPPS, une approche récente exploitant essentiellement la phase hors-ligne pour sélectionner une politique dans un ensemble de politiques candidates et BAMCP, une approche récente de planification en-ligne bayésienne. Nous comparons empiriquement ces approches dans un contexte bayésien, en ce sens que nous évaluons leurs performances sur un large ensemble de problèmes tirés selon une distribution de test. A notre connaissance, il s’agit des premiers tests expérimentaux de ce type en apprentissage par renforcement. Nous étudions plusieurs cas de figure en considérant diverses distributions pouvant être utilisées aussi bien en tant que distribution a priori qu’en tant que distribution de test. Les résultats obtenus suggèrent qu’exploiter une distribution a priori durant une phase d’optimisation hors-ligne est un avantage non- négligeable pour des distributions a priori précises et/ou contraintes à de petits budgets temps en-ligne. [less ▲]

Detailed reference viewed: 53 (19 ULg)
See detailL'apprentissage précoce des langues étrangères par la méthode 'immersive'
Comblain, Annick ULg; Rondal, Jean-Adolphe ULg

in Rééducation Orthophonique (1993), 31

Detailed reference viewed: 111 (17 ULg)
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See detailL'apprentissage sans erreur
Richelle, Marc ULg

in Année Psychologique (L') (1966), 66(2), 535-543

Detailed reference viewed: 27 (2 ULg)
See detailL'apprentissage télématique des langues
Defays, Jean-Marc ULg; Doppagne, Véronique ULg; Thonard, Audrey ULg

Scientific conference (2007, May)

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See detailApprentissage, enseignement: pile ou face?
Defays, Jean-Marc ULg

Scientific conference (2003, June)

Detailed reference viewed: 16 (3 ULg)
See detailL'apprentissage
Richelle, Marc ULg

in Pelicier, Yves (Ed.) L'univers de la psychologie: La vie psychologique normale (1977)

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See detailApprentissages automatiques supervisés pour le monitoring environnemental et énergétique d'une chaudière de régénération
Sainlez, Matthieu ULg

Doctoral thesis (2012)

The broad objective of this thesis is to apply and compare supervised learning techniques for prediction of nitrogen oxide pollutant emission from the recovery boiler of a Kraft pulp mill. In this task ... [more ▼]

The broad objective of this thesis is to apply and compare supervised learning techniques for prediction of nitrogen oxide pollutant emission from the recovery boiler of a Kraft pulp mill. In this task, we want to highlight a technique that is most suited and self-adapted to boiler transient operating conditions. The Kraft process is an alkaline process to produce chemical pulp; cellulose fibers are dissociated from lignin by cooking the chips in a solution of sodium hydroxide (NaOH) and sodium sulfide (Na2S), called white liquor. The residual black liquor is concentrated and burned in a recovery furnace to yield an inorganic smelt of sodium carbonate (Na2CO3) and Na2S. The recovery boiler both regenerates the cooking chemicals and produces high pressure steam to the pulp mill, but the boiler is a major source of atmospheric pollutants in the mill. In particular nitrogen oxide formation is very complex because of several chemicals and dynamic mechanisms: thermal NOx, prompt NOx and fuel NOx. Nowadays, there is an increasing demand in such industries for efficient data analysis tools, especially for pollutant monitoring and/or energy management. Literature reviews refer mainly on numerical solutions where a complete description of the process is needed and where stationary condition is often a working hypothesis. This is the case with the advanced data validation and reconciliation techniques that we evaluate. This technique is based on thermodynamic models, chemical and physical relationships within process parameters and equipment. This is helpful to highlight some lack of information about the process, but this approach failed to model accurately steam and fumes utilities operating points. Indeed, in a Kraft recovery boiler, the total nitrogen oxide emission is dependent on several operating factors and heterogeneous conditions, e.g. operating fuels (black liquor or heavy fuel), furnace load, droplet size, air system operation, retention time, biomass characteristics,... For such a complex problem, machine learning techniques may be used as alternative methods in engineering analysis and predictions. They involve algorithms that improve automatically through experience collected in historical databases. Among supervised learning techniques, we focus mainly on neural networks methods (static and dynamic architectures) and additionally on tree-based (regression tree and random forests) and linear ones. For each method, we evaluate its ability to predict NOx pollutant emission in varying conditions. A random forest is a collection of uncorrelated regression trees, induced from bootstrap samples of the training data. Its internal estimates are also used to measure variable importance and allow us to classify relevant variables for a model inputs selection task. Note that we need some additional a priori knowledge to select the final inputs set. Among static neural network structures, the multilayer perceptron is the most widely used, particularly the two-layer structure in which the input units and the output layer are interconnected with an intermediate hidden layer. The model of each neuron in the network includes a nonlinear activation function that is differentiable; this network can perform static mapping between an input space and an output space. Within dynamic architectures, we distinguish those that have only feed-forward connections and those that have feedback (recurrent) connections. In this work, we focus mainly on NARX network (Nonlinear AutoRegressive model with eXogenous inputs) and additionally on Elman recurrent neural network. This last one incorporates an additional layer, called context layer, the nodes of which are the one-step delay elements embedded into the local feedback paths. Nevertheless, Elman's approach has some drawbacks associated with learning parameters scheme and temporal gradient approximation. Particularly, the NARX network is used for input-output modeling of nonlinear dynamical systems. It is a recurrent model: model inputs are applied to a tapped-delay-line memory of n units and outputs are fed back to the input layer through another line of m units. The total model order s=n+m is therefore a key parameter and the method of Lipschitz numbers is a tool for estimating it. An advantage of NARX is that we can use standard backpropagation algorithm for neural network learning scheme. Furthermore, to increase model robustness, we average neural predictions over a set of individual neural predictors, this is helpful for reducing variance prediction across trials. Despite the fact that generalization is done on the worst case configuration possible, we see that ensemble of NARX networks perform well on predicting NOx emissions during transient operations and Lipschitz numbers are very helpful for system orders estimations. We illustrate the potential of a dynamic neural approach compared to the others in the nitrogen oxide prediction task. It is more suited to practical modeling needs and offers a modeling of time and memory. It allows us to monitor NOx pollution and possibly adjusting control variables and performing diagnostics. The thesis is divided into seven chapters covering several publications. Chapter 1 is about the Kraft process and its recovery boiler. We start with a short description of the Kraft pulp mill. Then we describe the Kraft recovery boiler, some chemical reactions in the furnace, the steam production equipments and the atmospheric pollutants. Finally we discuss about nitrogen oxide formation in the furnace, the effects of several operating conditions on its production. Chapter 2 is about data mining, on what it is, on what it is used for and which are the main modeling cultures. This chapter deals with system identification, modeling approaches (white box, grey box, black box), some definitions about learning and modeling, and finally some links between modeling and optimization techniques. Chapter 3 starts with a state-of-the-art about numerical simulation of a Kraft recovery boiler, then we apply and evaluate a data validation scheme for steam and fumes utilities modeling. Finally we discuss the application of artificial intelligence techniques within the framework of a recovery boiler. Chapter 4 aims at selecting model inputs, starting with a supervised selection approach based on random forests. We introduce some methodological insights about tree-based methods, from a simple regression tree to random forests. Random forests internal estimates are used to measure the relative importance of each input variable in predicting a response, i.e. nitrogen oxide emission or high pressure steam production. Finally we discuss about some useful extra knowledge to take in account for the selection of final inputs. Chapter 5 is about neural networks modeling, we introduce the perceptron, the multilayer perceptron, and the associated backpropagation algorithm. We discuss about static and dynamic architectures, especially the Elman recurrent neural network. Finally, we apply a multilayer perceptron and an Elman recurrent neural network for predicting the high pressure steam flow rate from the Kraft recovery boiler. Chapter 6 presents some insights about input-output modeling of nonlinear dynamical systems, especially with NARX network. At the end, we explain the Lipschitz method that is applied for system orders estimation. Chapter 7 summarizes some comparison results about supervised learning techniques applied to predict nitrogen oxide pollutant emission from the recovery boiler. This comparison involves neural network techniques, tree-based methods and multiple linear regression. Finally, some research perspectives are presented and some conclusions are drawn. [less ▲]

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See detailApprentissages élémentaires
Ferrara, André ULg

in Rondal, Jacques (Ed.) Introduction à la psychologie scientifique (1999)

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See detailApprentissages et multimédia
Leclercq, Dieudonné ULg; Denis, Brigitte ULg

Scientific conference (1994, April)

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See detailApprentissages et multimédia
Denis, Brigitte ULg; Leclercq, Dieudonné ULg

in Noirhomme, Monique (Ed.) Actes de la journée d'information sur le Multimédia (1995)

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See detailApprentissages et multimédias
Leclercq, Dieudonné ULg; Denis, Brigitte ULg

in Les centres de ressources en langues : méthodologies, mise en valeur, optimisation de leur utilisation (1994, April)

Detailed reference viewed: 29 (4 ULg)
See detailApprentissages et multimédias
Leclercq, Dieudonné ULg; Denis, Brigitte ULg

in Slangen, L. (Ed.) Actes du colloque 'Les centres de ressources en langues. Méthodologies, Mises en valeur, optimisation de leur utilisation' (1995)

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See detailApprentissages et scolarité des primo-arrivants
Defays, Jean-Marc ULg

Conference given outside the academic context (2014)

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See detailL'apprivoisement de la méthode en Belgique, exploration par une équipe Sos-enfants.
Bullens, Quentin ULg

in Alföldi, Françis (Ed.) 18 cas pratiques d'évaluation en action sociale et médico-sociale. (2008)

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See detailApprivoiser les génies littéraires
Saint-Amand, Denis ULg

Article for general public (2014)

Compte rendu de Vincent Laisney, Sept génies. Voyage au centre de la littérature, Bruxelles, Les Impressions Nouvelles, 2014

Detailed reference viewed: 7 (0 ULg)
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See detailAn approach for dividing models of biological reaction networks into functional units
Ederer, Michael; Sauter, Thomas; Bullinger, Eric ULg et al

in Simulation: Trans. Society for Modeling and Simulation International (2003), 79(12), 703-716

Biological reaction networks consist of many substances and reactions between them. Like many other biological systems, they have a modular structure. Therefore, a division of a biological reaction ... [more ▼]

Biological reaction networks consist of many substances and reactions between them. Like many other biological systems, they have a modular structure. Therefore, a division of a biological reaction network into smaller units highly facilitates its investigation. The authors propose an algorithm to divide an ordinary differential equation (ODE) model of a biological reaction network hierarchically into functional units. For every compound, an activity function dependent on concentration or concentration change rate is defined. After performing suitable simulations, distances between the compounds are computed by comparing the activities along the trajectories of the simulation. The distance information is used to generate a dendrogram revealing the internal structure of the reaction network. The algorithm identifies functional units in two models of different networks: catabolite repression in Escherichia coli and epidermal growth factor (EGF) signal transduction in mammalian cells. [less ▲]

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See detailApproach of patients with lower tract disease
Clercx, Cécile ULg

in Proceedings of the 24thProgram - Topical Symposium on diseases of small animals - Portoroz - Slovenia (2011, April)

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See detailApproach of regionalization of low flow of the Walloon Region
Gailliez, Sébastien; Degre, Aurore ULg

Poster (2010, April)

The walloon part of the Meuse’s watershed represents 12283 km² and 17694 km of river. The anthropogenic pressure is important (population density is more or less 200 inhabitants/km²). In low flow period ... [more ▼]

The walloon part of the Meuse’s watershed represents 12283 km² and 17694 km of river. The anthropogenic pressure is important (population density is more or less 200 inhabitants/km²). In low flow period, water scarcity can touch both the water users (producer of drinking water and hydropower, tourism and pleasure activities and kayaking) and the river itself affecting the ‘good environmental state’ (context of Water Framework Directive 2000/60/CE). The operational management of rivers during low flow periods needs a deep knowledge of this drought phenomenon including an analysis of low flow severity and its occurrence probability. It also needs the computation of low flow discharge at any point of a river based on available hydrologic variables. The aim of this study is the low flows’ regionalization in the Walloon Region. First of all, the time series of flow data are filled in and validated. The quality is then controlled. The different tests are the determination of the minimum year requirement for a monitoring site, homogeneity tests, verification of presence or absence of summer alga and the proportion of extrapolation of the discharge rating curve Secondly, homogeneous regions will be defined and regression equations will be build. These equations will establish the relation between low flow and physical parameters (watershed area, pedology, slope), climate ones and/or ground water ones. The regression model will permit the computation of low flow discharge at any point of an ungauged river. [less ▲]

Detailed reference viewed: 29 (9 ULg)