Une utilisation efficace et coordonnée des systèmes installés dans le bâtiment doit permettre d’améliorer le confort des occupants tout en consommant moins d’énergie. Ces objectifs à optimiser sont pourtant antagonistes. Le problème résultant peut être alors vu comme un problème d’optimisation multicritères. Par ailleurs, pour répondre aux enjeux industriels, il devra être résolu non seulement dans une optique d’implémentation simple et peu coûteuse, avec notamment un nombre réduit de capteurs, mais aussi dans un souci de portabilité pour que le contrôleur résultant puisse être implanté dans des bâtiments d’orientation différente et situés dans des lieux géographiques variés.L’approche choisie est de type commande prédictive (MPC, Model Predictive Control) dont l’efficacité pour le contrôle du bâtiment a déjà été illustrée dans de nombreux travaux, elle requiert cependant des efforts de calcul trop important. Cette thèse propose une méthodologie pour la synthèse des contrôleurs, qui doivent apporter une performance satisfaisante en imitant les comportements du MPC, tout en répondant à des contraintes industriels. Elle est divisée deux grandes étapes :1. La première étape consiste à développer un contrôleur MPC. De nombreux défis doivent être relevés tels que la modélisation, le réglage des paramètres et la résolution du problème d’optimisation.2. La deuxième étape applique différents algorithmes d’apprentissage automatique (l’arbre de décision, AdaBoost et SVM) sur une base de données obtenue à partir de simulations utilisant le contrôleur prédictif développé. Les grands points levés sont la construction de la base de données, le choix de l’algorithme de l’apprentissage et le développement du contrôleur logique.La méthodologie est appliquée dans un premier temps à un cas simple pour piloter un volet,puis validée dans un cas plus complexe : le contrôle coordonné du volet, de l’ouvrant et dusystème de ventilation. / An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1. In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2. In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.

Une utilisation efficace et coordonnée des systèmes installés dans le bâtiment doit permettre d’améliorer le confort des occupants tout en consommant moins d’énergie. Ces objectifs à optimiser sont pourtant antagonistes. Le problème résultant peut être alors vu comme un problème d’optimisation multicritères. Par ailleurs, pour répondre aux enjeux industriels, il devra être résolu non seulement dans une optique d’implémentation simple et peu coûteuse, avec notamment un nombre réduit de capteurs, mais aussi dans un souci de portabilité pour que le contrôleur résultant puisse être implanté dans des bâtiments d’orientation différente et situés dans des lieux géographiques variés.L’approche choisie est de type commande prédictive (MPC, Model Predictive Control) dont l’efficacité pour le contrôle du bâtiment a déjà été illustrée dans de nombreux travaux, elle requiert cependant des efforts de calcul trop important. Cette thèse propose une méthodologie pour la synthèse des contrôleurs, qui doivent apporter une performance satisfaisante en imitant les comportements du MPC, tout en répondant à des contraintes industriels. Elle est divisée deux grandes étapes :1. La première étape consiste à développer un contrôleur MPC. De nombreux défis doivent être relevés tels que la modélisation, le réglage des paramètres et la résolution du problème d’optimisation.2. La deuxième étape applique différents algorithmes d’apprentissage automatique (l’arbre de décision, AdaBoost et SVM) sur une base de données obtenue à partir de simulations utilisant le contrôleur prédictif développé. Les grands points levés sont la construction de la base de données, le choix de l’algorithme de l’apprentissage et le développement du contrôleur logique.La méthodologie est appliquée dans un premier temps à un cas simple pour piloter un volet,puis validée dans un cas plus complexe : le contrôle coordonné du volet, de l’ouvrant et dusystème de ventilation. / An efficient and coordinated control of systems in buildings should improve occupant comfort while consuming less energy. However, these objectives are antagonistic. It can then be formulated as a multi-criteria optimization problem. Moreover, it should be solved not only in a simple and cheap implementation perspective, but also for the sake of adaptability of the controller which can be installed in buildings with different orientations and different geographic locations.The MPC (Model Predictive Control) approach is shown well suited for building control in the state of the art but it requires a big computing effort. This thesis presents a methodology to develop logical controllers for equipments in buildings. It helps to get a satisfactory performance by mimicking the MPC behaviors while dealing with industrial constraints. Two keys steps are required :1. In the first step, an optimal controller is developed with hybrid MPC technique. There are challenges in modeling, parameters tuning and solving the optimization problem.2. In the second step, different Machine Learning algorithms (Decision tree, AdaBoost, SVM) are tested on database which is obtained with the simulation with the MPC controller. The main points are the construction of the database, the choice of learning algorithm and the development of logic controller.First, our methodology is tested on a simple case study to control a blind. Then, it is validatedwith a more complex case : development of a coordinated controller for a blind, natural ventilationand mechanical ventilation.

The master thesis presents an overview of face detection task in color, static images. Face detection term is posed in the context of various branches. Main concepts of face detection and also their relationships are described. Individual approaches are divided into groups and then define in turn. In the thesis is in detail described algorithm AdaBoost, which is selected on the basis of its properties. Especially speed of computation and good detection results are key features. In the scope of this work Viola-Jones detector was implemented. This detector was trained with face pictures from public accessible database. Combination of Viola-Jones detector with simple color detector is described. In the thesis is also presented experiment approach to facial features detection.

Train delays occur on a daily basis in the commuter rail of Stockholm. This means that the travellers might become delayed themselves for their particular destination. To find the most accurate method for predicting train delays, the machine learning methods decision tree with and without AdaBoost and neural network were compared with different settings. Neural network achieved the best result when used with 3 layers and 22 neurons in each layer. Its delay predictions had an average error of 122 seconds, compared to the actual delay. It might therefore be the best method for predicting train delays. However the study was very limited in time and more train departure data would need to be collected. / Tågförseningar inträffar dagligen i Stockholms pendeltågstrafik. Det orsakar att resenärerna själva kan bli försenade till deras destinationer. För att hitta den mest träffsäkra metoden för att förutspå tågförseningar jämfördes maskininlärningsmetoderna beslutsträd, med och utan AdaBoost, och artificiella neuronnät med olika inställningar. Det artificiella neuronnätet gav det bästa resultatet när det användes med 3 lager och 22 neuroner i varje lager. Dess förseningsförutsägelse hade ett genomsnittligt fel på 122 sekunder jämfört med den verkliga förseningen. Det kan därför vara den bästa metoden för att förutspå tågförseningar. Den här studien hade dock väldigt begränsat med tid och mer information om tågavgångar hade behövts samlas in.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Abstract Drug misuses result in medical resource wastes and significant society costs. Due to the narrow therapeutic range of vancomycin, appropriate vancomycin dosage is difficult to determine. When inappropriate dosage is used, such side effects as poisoning reaction or drug resistance may occur. Clinically, medical professionals adjust drug protocols of vancomycin based on the Therapeutic Drug Monitoring (TDM) results. TDM is usually defined as the clinical use of drug blood concentration measurements as an aid in dosage finding and adjustment. However, TDM cannot be applied to first-time treatments and, in case, dosage decisions need to reply on medical professionals¡¦ clinical experiences and judgments. Data mining has been applied in various medical and healthcare applications. In this study, we will employ a decision-tree induction (specifically, C4.5) and a backpropagation neural network technique for predicting the appropriateness of vancomycin usage for patients with renal insufficiency and impairment. In addition, we will evaluate whether the use of the boosting and bagging algorithms will improve predictive accuracy. Our empirical evaluation results suggest that use of the boosting and bagging algorithms could improve predictive accuracy. Specifically, use of C4.5 in conjunction with the AdaBoost algorithm achieves an overall accuracy of 79.65%, which significantly improves that of the existing practice, recording an accuracy rate at 41.38%. With respect to the appropriateness category (¡§Y¡¨) and the inappropriateness category (¡§N¡¨), C4.5 in conjunction with the AdaBoost algorithm can achieve a recall rate at 78.75% and 80.25%, respectively. Hence, the incorporation of data mining techniques to decision support would enhance the drug safety, which in turn, would improve patient safety and reduce subsequent medical resource wastes.

Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing satellites, have urged the development of automatic target detection systems. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this thesis, a detection method is proposed for airport runways, which is the most distinguishing element of an airport. This method, which operates on large optical satellite images, is composed of a segmentation process based on textural properties, and a runway shape detection stage. In the segmentation process, several local textural features are extracted including not only low level features such as mean, standard deviation of image intensity and gradient, but also Zernike Moments, Circular-Mellin Features, Haralick Features, as well as features involving Gabor Filters, Wavelets and Fourier Power Spectrum Analysis. Since the subset of the mentioned features, which have a role in the discrimination of airport runways from other structures and landforms, cannot be predicted, Adaboost learning algorithm is employed for both classification and determining the feature subset, due to its feature selector nature. By means of the features chosen in this way, a coarse representation of possible runway locations is obtained, as a result of the segmentation operation. Subsequently, the runway shape detection stage, based on a novel form of Hough Transform, is performed over the possible runway locations, in order to obtain final runway positions. The proposed algorithm is examined with experimental work using a comprehensive data set consisting of large and high resolution satellite images and successful results are achieved.

In this study, an automated building extraction system, which is capable of detecting buildings from satellite images using only RGB color band is implemented. The approach used in this work has four main steps: local feature extraction, feature selection, classification and post processing. There are many studies in literature that deal with the same problem. The main issue is to find the most suitable features to distinguish a building. This work presents a feature selection scheme that is connected with the classification framework of Adaboost. As well as Adaboost, four SVM kernels are used for classification. Detailed analysis regarding window type and size, feature type, feature selection, feature count and training set is done for determining the optimal parameters for the classifiers. A detailed comparison of SVM and Adaboost is done based on pixel and object performances and the results obtained are presented both numerically and visually. It is observed that SVM performs better if quadratic kernel is used than the cases using linear, RBF or polynomial kernels. SVM performance is better if features are selected either by Adaboost or by considering errors obtained on histograms of features. The performance obtained by quadratic kernel SVM operated on Adaboost selected features is found to be 38% in terms of pixel based performance criteria quality percentage and 48% in terms object based performance criteria correct detection with building detection threshold 0.4. Adaboost performed better than SVM resulting in 43% quality percentage and 67% correct detection with the same threshold.

Hydrocarbon (HC) microseepages can be indicator of possible reservoirs. For that reason, mapping the microseepages has potential to be used in petroleum exploration. This study presents a methodology for mapping HC microseepages and related clay mineral alteration in Gemrik Anticline, Adiyaman. For this purpose samples were collected from the potential seepage zones and tested by geochemical analysis. All samples were found to contain some HC. Then, an ASTER image of the region was obtained and a band combination was generated to map this particular region. To map related clay mineral alteration, firstly reflectance spectra of samples were measured using field spectrometer. Secondly, spectrally-known samples were analyzed in USGS Library to determine the reflectance spectra of the constitutional clay minerals in the samples. Lastly, the reflectance characteristics of selected end v members were represented as ASTER band combinations based on their spectral absorption characteristics and literature information. Crosta Technique was used to determine required principal components to map HC microseepage and related clay mineral alteration. Then, this methodology is applied to the whole ASTER image. Ground truth study showed that more than 65% of the revisited anomalies show similar prospects to that of the referenced anticline regardless of their geochemical content. In order to certify the ASTER band combination for mapping HC microseepages, anomalous and non-anomalous pixels were selected from the resultant HC map and given as training data samples to AdaBoost loop which is an image processing algorithm. It has been found that ASTER band combination offered for mapping HC microseepages is similar to that of AdaBoost Algorithm output.

We propose a new ensemble algorithm: the meta-boosting algorithm. This algorithm enables the original Adaboost algorithm to improve the decisions made by different WeakLearners utilizing the meta-learning approach. Better accuracy results are achieved since this algorithm reduces both bias and variance. However, higher accuracy also brings higher computational complexity, especially on big data. We then propose the parallelized meta-boosting algorithm: Parallelized-Meta-Learning (PML) using the MapReduce programming paradigm on Hadoop. The experimental results on the Amazon EC2 cloud computing infrastructure show that PML reduces the computation complexity enormously while retaining lower error rates than the results on a single computer. As we know MapReduce has its inherent weakness that it cannot directly support iterations in an algorithm, our approach is a win-win method, since it not only overcomes this weakness, but also secures good accuracy performance. The comparison between this approach and a contemporary algorithm AdaBoost.PL is also performed.