Este trabalho fundamenta-se no conceito de máquina de comitê de redes neurais artificiais e tem por objetivo resolver o problema inverso de transferência radiativa em um meio unidimensional, homogêneo, absorvedor e espalhador isotrópico. A máquina de comitê de redes neurais artificiais agrega e combina o conhecimento adquirido por um certo número de especialistas aqui representados, individualmente, por cada uma das redes neurais artificiais (RNA) que compõem a máquina de comitê de redes neurais artificiais. O objetivo é atingir um resultado final melhor do que o obtido por qualquer rede neural artificial separadamente, selecionando-se apenas àquelas redes neurais artificiais que apresentam os melhores resultados na fase de generalização descartando-se as demais, o que foi feito neste trabalho. Aqui são utilizados dois modelos estáticos de máquinas de comitê, usando a média aritmética de conjunto, que se diferenciam entre si apenas na composição do combinador de saída de cada máquina de comitê. São obtidas, usando-se máquinas de comitê de redes neurais artificiais, estimativas para os parâmetros de transferência radiativa, isto é, a espessura óptica do meio, o albedo de espalhamento simples e as refletividades difusas. Finalmente, os resultados obtidos com ambos os modelos de máquina de comitê são comparados entre si e com aqueles encontrados usando-se apenas redes neurais artificiais do tipo perceptrons de múltiplas camadas (MLP), isoladamente. Aqui essas redes neurais artificiais são denominadas redes neurais especialistas, mostrando que a técnica empregada traz melhorias de desempenho e resultados a um custo computacional relativamente baixo. / This work is based on the concept of neural networks committee machine and has the objective to solve the inverse radiative transfer problem in one-dimensional, homogeneous, absorbing and isotropic scattering media. The artificial neural networks committee machine adds and combines the knowledge acquired by an exact number of specialists which are represented, individually, by each one of the artificial neural networks (ANN) that composes the artificial neural network committee machine. The aim is to reach a final result better than the one obtained by any of the artificial neural network separately, selecting only those artificial neural networks that presents the best results during the generalization phase and discarding the others, what was done in this present work. Here are used two static models of committee machines, using the ensemble arithmetic average, that differ between themselves only by the composition of the output combinator by each one of the committee machine. Are obtained, using artificial neural networks committee machines, estimates for the radiative transfer parameters, that is, medium optical thickness, single scattering albedo and diffuse reflectivities. Finally, the results obtained with both models of committee machine are compared between themselves and with those found using artificial neural networks type multi-layer perceptrons (MLP), isolated. Here that artificial neural networks are named as specialists neural networks, showing that the technique employed brings performance and results improvements with relatively low computational cost.

With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predictive performance on this data set, at 90.7\% accuracy. In addition, we have shown that undersampling and varying the discriminator are effective techniques for increasing the performance on the positive class on frequencies where the negative class is prevalent. Finally, we present hybrid algorithms in which the learning algorithm for each model depends on attributes of the training data set. These algorithms perform at a much higher efficiency in terms of memory and run-time without heavily sacrificing predictive performance.

Este trabalho fundamenta-se no conceito de máquina de comitê de redes neurais artificiais e tem por objetivo resolver o problema inverso de transferência radiativa em um meio unidimensional, homogêneo, absorvedor e espalhador isotrópico. A máquina de comitê de redes neurais artificiais agrega e combina o conhecimento adquirido por um certo número de especialistas aqui representados, individualmente, por cada uma das redes neurais artificiais (RNA) que compõem a máquina de comitê de redes neurais artificiais. O objetivo é atingir um resultado final melhor do que o obtido por qualquer rede neural artificial separadamente, selecionando-se apenas àquelas redes neurais artificiais que apresentam os melhores resultados na fase de generalização descartando-se as demais, o que foi feito neste trabalho. Aqui são utilizados dois modelos estáticos de máquinas de comitê, usando a média aritmética de conjunto, que se diferenciam entre si apenas na composição do combinador de saída de cada máquina de comitê. São obtidas, usando-se máquinas de comitê de redes neurais artificiais, estimativas para os parâmetros de transferência radiativa, isto é, a espessura óptica do meio, o albedo de espalhamento simples e as refletividades difusas. Finalmente, os resultados obtidos com ambos os modelos de máquina de comitê são comparados entre si e com aqueles encontrados usando-se apenas redes neurais artificiais do tipo perceptrons de múltiplas camadas (MLP), isoladamente. Aqui essas redes neurais artificiais são denominadas redes neurais especialistas, mostrando que a técnica empregada traz melhorias de desempenho e resultados a um custo computacional relativamente baixo. / This work is based on the concept of neural networks committee machine and has the objective to solve the inverse radiative transfer problem in one-dimensional, homogeneous, absorbing and isotropic scattering media. The artificial neural networks committee machine adds and combines the knowledge acquired by an exact number of specialists which are represented, individually, by each one of the artificial neural networks (ANN) that composes the artificial neural network committee machine. The aim is to reach a final result better than the one obtained by any of the artificial neural network separately, selecting only those artificial neural networks that presents the best results during the generalization phase and discarding the others, what was done in this present work. Here are used two static models of committee machines, using the ensemble arithmetic average, that differ between themselves only by the composition of the output combinator by each one of the committee machine. Are obtained, using artificial neural networks committee machines, estimates for the radiative transfer parameters, that is, medium optical thickness, single scattering albedo and diffuse reflectivities. Finally, the results obtained with both models of committee machine are compared between themselves and with those found using artificial neural networks type multi-layer perceptrons (MLP), isolated. Here that artificial neural networks are named as specialists neural networks, showing that the technique employed brings performance and results improvements with relatively low computational cost.

No presente trabalho duas metodologias da área de inteligência artificial (Redes Neurais e Algoritmos Genéticos) foram utilizadas para realizar um estudo Quimiossistemático da família Asteraceae. A família Asteraceae é uma das maiores famílias entre as Angiospermas, conta com aproximadamente 24.000 espécies. As espécies da família produzem grande diversidade de metabólitos secundários, entre os quais merecem destaque os terpenóides, poliacetilenos, flavonóides e cumarinas. Para um melhor entendimento da diversidade química da família construiu-se um Banco de Dados com as ocorrências de doze classes de metabólitos (monoterpenos, sesquiterpenos, sesquiterpenos lactonizados, diterpenos, triterpenos, cumarinas, flavonóides, poliacetilenos, benzofuranos, benzopiranos, acetofenonas e fenilpropanóides) produzidos pelas espécies da família. A partir desse banco três diferentes estudos foram realizados. No primeiro estudo, utilizando os mapas auto-organizáveis de Kohonen e o banco de dados químico classificado segundo duas das mais recentes filogenias da família foi possível realizar com sucesso separações de tribos e gêneros da família Asteraceae. Também foi possível indicar que a informação química concorda mais com a filogenia de Funk (Funk et al. 2009) do que com a filogenia de Bremer (Bremer 1994, 1996). No estudo seguinte, onde se objetivou a criação de modelos de previsão dos números de ocorrências das doze classes de metabólitos, utilizando o perceptron de múltiplas camadas com algoritmo de retropropagação de erro, o resultado foi insatisfatório. Apesar de em algumas classes de metabólitos a fase de treino da rede apresentar resultados satisfatórios, a fase de teste mostrou que os modelos criados não são capazes de realizar previsão para dados aos quais eles não foram submetidos na fase de treino, e portanto não são modelos adequados para realizar previsões. Finalmente, o terceiro estudo consistiu na criação de modelos de regressão linear utilizando como método de seleção de variáveis os algoritmos genéticos. Nesse estudo foi possível indicar que os monoterpenos e os sesquiterpenos são bastante relacionados biossinteticamente, também foi possível indicar que existem relações biossintéticas entre monoterpenos e diterpenos e entre sesquiterpenos e triterpenos / In this study two methods of artificial intelligence (neural network and genetic algorithms) were used to work out a Chemosystematic study of the Asteraceae family. The family Asteraceae is one of the largest families among the Angiosperms, having about 24,000 species. The species of the family produce a large diversity of secondary metabolites, and some worth mentioning are the terpenoids, polyacetylenes, flavonoids and coumarins. For a better understanding of the chemical diversity of the family a database was built up with the occurrences of twelve classes of metabolites (monoterpenes, sesquiterpenes, lactonizadossesquiterpenes, diterpenes, triterpenes, coumarins, flavonoids, polyacetylenes, Benzofurans, benzopyrans, acetophenones and phenylpropanoids) produced by species of the family. From this database three different studies were conducted. In the first study, using the Kohonen self-organized map and the chemical data classified according to two of the most recent phylogenies of the family, it was possible to successfully separatethe tribes and genera of the Asteraceae family. It was also possible to indicate that the chemical information agrees with the phylogeny of Funk (Funk et al. 2009) than with the phylogeny of Bremer (Bremer 1994, 1996). In the next study, which aims at creating models to predict the number of occurrences of the twelve classes of metabolites using multi-layer perceptron with backpropagation algorithm error, the result was found unsatisfactory. Although in some classes of metabolites the training phase of the network has satisfactory results, the test phase showed that the models created are not able to make prevision for data to which they were submitted in the training phase and thus are not suitable models for predictions. Finally, the third study was the creation of linear regression models using a genetic algorithm method of variable selection. This study could indicate that the monoterpenes and sesquiterpenes are closely related biosynthetically, and was also possible to indicate that there are biosynthetic relations between monoterpenes and diterpenes and between sesquiterpenes and triterpenes

Dans l’industrie de fabrication mécanique et notamment pour l’utilisation des centres d’usinage haute vitesse, la connaissance des propriétés dynamiques du système broche-outil-pièce en opération est d’une grande importance. L’accroissement des performances des machines-outils et des outils de coupe a œuvré au développement de ce procédé compétitif. D’innombrables travaux ont été menés pour accroître les performances et les remarquables avancées dans les matériaux, les revêtements des outils coupants et les lubrifiants ont permis d’accroître considérablement les vitesses de coupe tout en améliorant la qualité de la surface usinée. Cependant, l’utilisation rationnelle de cette technologie est encore fortement pénalisée par les lacunes dans la connaissance de la coupe, que ce soit au niveau microscopique des interactions fines entre l’outil et la matière coupée, aussi bien qu’au niveau macroscopique intégrant le comportement de la cellule élémentaire d’usinage, si bien que le comportement dynamique en coupe garde encore une grande part de questionnement et exige de l’utilisateur un bon niveau de savoir-faire et parfois d’empirisme pour exploiter au mieux les capacités des moyens de production. Le fonctionnement des machines d’usinage engendre des vibrations qui sont souvent la cause des dysfonctionnements et accélère l’usure des composantes mécaniques (roulements) et outils. Ces vibrations sont une image des efforts internes des systèmes, d’où l’intérêt d’analyser les grandeurs mécaniques vibratoires telle que la vitesse ou l’accélération vibratoire. Ces outils sont indispensables pour une maintenance moderne dont l’objectif est de réduire les coûts liés aux pannes / In machining field, chatter phenomenon takes a lot of interest because manufacturing enterprises are turning to the automation system and the development of reliable and robust monitoring system to provide increased productivity, improved part quality and reduced costs. Chatter occurrence has several negatives effects: a) Poor surface quality, b) Unacceptable inaccuracy, c) Excessive noise, d) Machine tool damage, e) Reduced material removal rate, f) Increase costs in terms of production time, g) Waste of material, h) Environmental impact in terms of materials and energy. Moreover, chatter monitoring is not an easy task for various reasons. Firstly, the non linearity of machining processes and the time-varying of systems complicate this task. Secondly, the sensitivity and the dependency of acquired signals from sensors on different factors, such as machining condition, cutting tool geometry and workpiece material. Thirdly, at high rotating speeds, the gyroscopic effects on the spindle dynamics in addition to the centrifugal force on the bearings and thermal effects become more relevant thus affecting the stability of the system. For these reasons, demands for an advanced automatic chatter detection and monitoring system for optimizing and controlling machining processes becomes a topic of enormous interest. Several researches in this field are performed. Advanced monitoring and detection methods are developed mostly relying on time, frequency and time-frequency analysis. In order to detect chatter in milling centers, three new methods are studied and developed using advanced techniques of signal processing and exploiting cyclostationarity property of signals acquired

This dissertation focused in the comprehensive analysis of the capabilities of some non-tested types of Artificial Neural Networks, specifically: the Probabilistic Neural Networks (PNN) and the Multi-Layer Perceptron (MLP) Ensembles. The analysis of the capabilities of these techniques was performed using the native brown trout (Salmo trutta; Linnaeus, 1758), the bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) and the redfin barbel (Barbus haasi; Mertens, 1925) as target species. The analyses focused in the predictive capabilities, the interpretability of the models and the effect of the excess of zeros in the training datasets, which for presence-absence models is directly related to the concept of data prevalence (i.e. proportion of presence instances in the training dataset). Finally, the effect of the spatial scale (i.e. micro-scale or microhabitat scale and meso-scale) in the habitat suitability models and consequently in the e-flow assessment was studied in the last chapter. / Esta tesis se centra en el análisis comprensivo de las capacidades de algunos tipos de Red Neuronal Artificial aún no testados: las Redes Neuronales Probabilísticas (PNN) y los Conjuntos de Perceptrones Multicapa (MLP Ensembles). Los análisis sobre las capacidades de estas técnicas se desarrollaron utilizando la trucha común (Salmo trutta; Linnaeus, 1758), la bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) y el barbo colirrojo (Barbus haasi; Mertens, 1925) como especies nativas objetivo. Los análisis se centraron en la capacidad de predicción, la interpretabilidad de los modelos y el efecto del exceso de ceros en las bases de datos de entrenamiento, la así llamada prevalencia de los datos (i.e. la proporción de casos de presencia sobre el conjunto total). Finalmente, el efecto de la escala (micro-escala o escala de microhábitat y meso-escala) en los modelos de idoneidad del hábitat y consecuentemente en la evaluación de caudales ambientales se estudió en el último capítulo. / Aquesta tesis se centra en l'anàlisi comprensiu de les capacitats d'alguns tipus de Xarxa Neuronal Artificial que encara no han estat testats: les Xarxes Neuronal Probabilístiques (PNN) i els Conjunts de Perceptrons Multicapa (MLP Ensembles). Les anàlisis sobre les capacitats d'aquestes tècniques es varen desenvolupar emprant la truita comuna (Salmo trutta; Linnaeus, 1758), la madrilla roja (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) i el barb cua-roig (Barbus haasi; Mertens, 1925) com a especies objecte d'estudi. Les anàlisi se centraren en la capacitat predictiva, interpretabilitat dels models i en l'efecte de l'excés de zeros a la base de dades d'entrenament, l'anomenada prevalença de les dades (i.e. la proporció de casos de presència sobre el conjunt total). Finalment, l'efecte de la escala (micro-escala o microhàbitat i meso-escala) en els models d'idoneïtat de l'hàbitat i conseqüentment en l'avaluació de cabals ambientals es va estudiar a l'últim capítol. / Muñoz Mas, R. (2016). Multivariate approaches in species distribution modelling: Application to native fish species in Mediterranean Rivers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/76168 / TESIS

Organizations gather enormous amounts of data and analyze these data to extract insights that can be useful for them and help them to make better decisions. Predictive data analytics is a crucial subfield within data analytics that make accurate predictions. Predictive data analytics extracts insights from data by using machine learning algorithms. This thesis presents the supervised learning algorithm to perform predicative data analytics in Embedded Test System at the Nordic Engineering Partner company. Predictive Maintenance is a concept that is often used in manufacturing industries which refers to predicting asset failures before they occur. The machine learning algorithms used in this thesis are support vector machines, multi-layer perceptrons, random forests, and gradient boosting. Both binary and multi-class classifier have been provided to fit the models, and cross-validation, sampling techniques, and a confusion matrix have been provided to accurately measure their performance. In addition to accuracy, recall, precision, f1, kappa, mcc, and roc auc measurements are used as well. The prediction models that are fitted achieve high accuracy.

Electricity spot prices are difficult to predict since they depend on different unstable and erratic parameters, and also due to the fact that electricity is a commodity that cannot be stored efficiently. This results in a volatile, highly fluctuating behavior of the prices, with many peaks. Machine learning algorithms have outperformed traditional methods in various areas due to their ability to learn complex patterns. In the last decade, deep learning approaches have been introduced in electricity spot price prediction problems, often exceeding their predecessors. In this thesis, several deep learning models were built and evaluated for their ability to predict the spot prices 10-days ahead. Several conclusions were made. Firstly, it was concluded that rather simple neural network architectures can predict prices with high accuracy, except for the most extreme sudden peaks. Secondly, all the deep networks outperformed the benchmark statistical model. Lastly, the proposed LSTM and CNN provided forecasts which were statistically, significantly superior and had the lowest errors, suggesting they are the most suitable for the prediction task. / Elspotspriser är svåra att förutsäga eftersom de beror på olika instabila och oregelbundna faktorer, och också på grund av att elektricitet är en vara som inte kan lagras effektivt. Detta leder till ett volatilt, fluktuerande beteende hos priserna, med många plötsliga toppar. Maskininlärningsalgoritmer har överträffat traditionella metoder inom olika områden på grund av deras förmåga att lära sig komplexa mönster. Under det senaste decenniet har djupinlärningsmetoder introducerats till problem inom elprisprognostisering och ofta visat sig överlägsna sina föregångare. I denna avhandling konstruerades och utvärderades flera djupinlärningsmodeller på deras förmåga att förutsäga spotpriserna 10 dagar framåt. Den första slutsatsen är att relativt simpla nätverksarkitekturer kan förutsäga priser med hög noggrannhet, förutom för fallen med de mest extrema, plötsliga topparna. Vidare, så övertränade alla djupa neurala nätverken den statistiska modellen som användes som riktmärke. Slutligen, så gav de föreslagna LSTM- och CNN-modellerna prognoser som var statistiskt, signifikant överlägsna de andra och hade de lägsta felen, vilket tyder på att de är bäst lämpade för prognostiseringsuppgiften.

Computer simulation is a commonly applied technique for studying electronic warfare duels. This thesis aims to apply machine learning techniques to convert simulation output data into knowledge and insights regarding defensive actions for a ship facing multiple hostile missiles. The analysis may support tactical decision-making, hence the interpretability aspect of predictions is necessary to allow for human evaluation and understanding of impacts from the explanatory variables. The final distance for the threats to the target and the probability of the threats hitting the target was modeled using a multi-layer perceptron model with a multi-task approach, including custom loss functions. The results generated in this study show that the selected methodology is more successful than a baseline using regression models. Modeling the outcome with artificial neural networks results in a black box for decision making. Therefore the concept of interpretable machine learning was applied using a post-hoc approach. Given the learned model, the features considered, and the multiple threats, the feature contributions to the model were interpreted using Kernel SHapley Additive exPlanations (SHAP). The method consists of local linear surrogate models for approximating Shapley values. The analysis primarily showed that an increased seeker activation distance was important, and the increased time for defensive actions improved the outcomes. Further, predicting the final distance to the ship at the beginning of a simulation is important and, in general, a guidance of the actual outcome. The action of firing chaff grenades in the tracking gate also had importance. More chaff grenades influenced the missiles' tracking and provided a preferable outcome from the defended ship's point of view.

Cette thèse s’intéresse à la problématique de la prédiction dans le cadre du design de systèmes énergétiques et des problèmes d’opération, et en particulier, à l’évaluation de l’adéquation de systèmes de production d’énergie renouvelables. L’objectif général est de développer une approche empirique pour générer des prédictions avec les incertitudes associées. En ce qui concerne cette direction de la recherche, une approche non paramétrique et empirique pour estimer les intervalles de prédiction (PIs) basés sur les réseaux de neurones (NNs) a été développée, quantifiant l’incertitude dans les prédictions due à la variabilité des données d’entrée et du comportement du système (i.e. due au comportement stochastique des sources renouvelables et de la demande d'énergie électrique), et des erreurs liées aux approximations faites pour établir le modèle de prédiction. Une nouvelle méthode basée sur l'optimisation multi-objectif pour estimer les PIs basée sur les réseaux de neurones et optimale à la fois en termes de précision (probabilité de couverture) et d’information (largeur d’intervalle) est proposée. L’ensemble de NN individuels par deux nouvelles approches est enfin présenté comme un moyen d’augmenter la performance des modèles. Des applications sur des études de cas réels démontrent la puissance de la méthode développée. / This Ph.D. work addresses the problem of prediction within energy systems design and operation problems, and particularly the adequacy assessment of renewable power generation systems. The general aim is to develop an empirical modeling framework for providing predictions with the associated uncertainties. Along this research direction, a non-parametric, empirical approach to estimate neural network (NN)-based prediction intervals (PIs) has been developed, accounting for the uncertainty in the predictions due to the variability in the input data and the system behavior (e.g. due to the stochastic behavior of the renewable sources and of the energy demand by the loads), and to model approximation errors. A novel multi-objective framework for estimating NN-based PIs, optimal in terms of both accuracy (coverage probability) and informativeness (interval width) is proposed. Ensembling of individual NNs via two novel approaches is proposed as a way to increase the performance of the models. Applications on real case studies demonstrate the power of the proposed framework.