Today, a large amount of raw data are available within manufacturing industries. Unfortunately, most of it is not further analyzed in search of valuable information regarding the optimization of processes. In the painting process at the Volvo plant in Umeå, adjusted settings on the process equipments (e.g. robots, machines etc.) are mostly based on the experience of the personnel rather than actual facts (i.e. analyzed data). Consequently, time- and cost waste caused by defects is obtained when painting the commercial heavy-duty truck bodies (cabs). Hence, the aim of this masters thesis is to model the quality as a function of available background- and process data. This should be presented in an automated alarm and root-cause system. A variety of supervised learning algorithms were trained in order to estimate the probability of having at least one defect per cab. Even with a small amount of data, results have shown that such algorithms can provide valuable information. Later in this thesis work, one of the algorithms was chosen and used as the underlying model in the prototype of an automated alarm system. When this probability was considered as too high, an intuitive root-cause analysis was presented. Ultimately, this research has demonstrated the importance and possibility of analyzing data with statistical tools in the search of limiting costs- and time waste.

L'apprentissage actif apparaît comme un problème important dans différents contextes de l'apprentissage supervisé pour lesquels obtenir des données est une tâche aisée mais les étiqueter est coûteux. En règle générale, c’est une stratégie de requête, une heuristique gloutonne basée sur un critère de sélection qui recherche les données non étiquetées potentiellement les plus intéressantes pour former ainsi un ensemble d'apprentissage. Une stratégie de requête est donc une procédure d'échantillonnage biaisée puisqu'elle favorise systématiquement certaines observations s'écartant ainsi des modèles d'échantillonnages indépendants et identiquement distribués. L'hypothèse principale de cette thèse s'inscrit dans la réduction du biais introduit par le critère de sélection. La proposition générale consiste à réduire le biais en sélectionnant le sous-ensemble minimal d'apprentissage pour lequel l'estimation de la loi de probabilité est aussi proche que possible de la loi sous-jacente prenant en compte l’intégralité des observations. Pour ce faire, une nouvelle stratégie générale de requête pour l'apprentissage actif a été mise au point utilisant la théorie de l'Information. Les performances de la stratégie de requête proposée ont été évaluées sur des données réelles et simulées. Les résultats obtenus confirment l'hypothèse sur le biais et montrent que l'approche envisagée améliore l'état de l'art sur différents jeux de données. / Active Learning arises as an important issue in several supervised learning scenarios where obtaining data is cheap, but labeling is costly. In general, this consists in a query strategy, a greedy heuristic based on some selection criterion, which searches for the potentially most informative observations to be labeled in order to form a training set. A query strategy is therefore a biased sampling procedure since it systematically favors some observations by generating biased training sets, instead of making independent and identically distributed draws. The main hypothesis of this thesis lies in the reduction of the bias inherited from the selection criterion. The general proposal consists in reducing the bias by selecting the minimal training set from which the estimated probability distribution is as close as possible to the underlying distribution of overall observations. For that, a novel general active learning query strategy has been developed using an Information-Theoretic framework. Several experiments have been performed in order to evaluate the performance of the proposed strategy. The obtained results confirm the hypothesis about the bias, showing that the proposal outperforms the baselines in different datasets.

Ce travail a pour objet le développement d’un système combinant un algorithme génétique d’optimisation multiobjectifs avec des outils de thermodynamique prédictive de type calphad (calcul des diagrammes de phases) et de fouille de données permettant l’estimation des propriétés thermochimiques et thermomécaniques d’alliages multicomposants. L’intégration de ces techniques permet l’optimisation quasi-autonome de la composition d’alliages complexes vis-à-vis de plusieurs critères antagonistes telles les résistances mécaniques et chimiques, la stabilité microstructurelle à haute température et le coût. La méthode est complétée d’une technique d’analyse décisionnelle multicritères pour assister la sélection d’alliages. L’approche est illustrée par l’optimisation de la chimie de deux familles d’alliages multicomposants. Le premier cas d’étude porte sur les superalliages à base de nickel polycristallins corroyés renforcés par précipitation de la phase 0 destinés à la fabrication de disques de turbines dans l’aéronautique ou de tuyauteries de centrales thermiques. L’optimisation résulte en la conception d’alliages moins onéreux et prédits plus résistants que l’Inconel 740H et le Haynes 282, deux superalliages de dernière génération. Le second cas d’étude concerne les alliages dits « à forte entropie » dont la métallurgie singulière est emblématique des problèmes combinatoires. À l’issue de l’optimisation, quelques alliages à forte entropie ont été sélectionnés et fabriqués ; leur caractérisation expérimentale préliminaire met en évidence des propriétés attrayantes tel un ratio dureté sur masse volumique inédit. / The present work revolves around the development of an integrated system combining a multi-objective genetic algorithm with calphad-type computational thermodynamics (calculations of phase diagrams) and data mining techniques enabling the estimation of thermochemical and thermomechanical properties of multicomponent alloys. This integration allows the quasiautonomous chemistry optimisation of complex alloys against antagonistic criteria such as mechanical and chemical resistance, high-temperature microstructural stability, and cost. Further alloy selection capability is provided by a multi-criteria decision analysis technique. The proposed design methodology is illustrated on two multicomponent alloy families. The first case study relates to the design of wrought, polycrystalline 0-hardened nickel-base superalloys intended for aerospace turbine disks or tubing applications in the energy industry. The optimisation leads to the discovery of novel superalloys featuring lower costs and higher predicted strength than Inconel 740H and Haynes 282, two state-of-the-art superalloys. The second case study concerns the so-called “high-entropy alloys” whose singular metallurgy embodies typical combinatorial issues. Following the optimisation, several high-entropy alloys are produced; preliminary experimental characterisation highlights attractive properties such as an unprecedented hardness to density ratio.

The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them. In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name. In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase. Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities. The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set. The software that has been implemented and used in this project has been implemented in C.

Artificial intelligence has made a big impact on the world in recent years, and more knowledge inthe subject seems to be of vital importance as the possibilities seems endless. Is it possible to teacha computer to drive a car in a virtual environment, by training a neural network to act intelligentlythrough the usage of supervised machine learning? With less than 2 hours of data collected whenpersonally driving the car, I show that yes, it is indeed possible. This is done by applying thetechniques of supervised machine learning combined in conjunction with a deep convolutional neuralnetwork. This were applied through software developed to interact between the network and the agentinside the virtual environment. I believe the dataset could have been cut down to about 10 percentof the size and still achieve the research goal. This shows not just the possibility of teaching aneural network a good policy in stochastic environments with supervised machine learning, but alsothat it can draw accurate (enough) conclusions to imitate human behavior when driving a car.

This master thesis is a collaboration with Jeppesen, a Boeing company to attempt applying machine learning techniques to predict “When does Operator manually solve the disruption? If he chooses to use Optimiser, then which option would he choose? And why?”. Through the course of this project, various techniques are employed to study, analyze and understand the historical labeled data of airline consisting of alerts during disruptions and tries to classify each data point into one of the categories: manual or optimizer option. This is done using various supervised machine learning classification methods.

abstract: Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately. Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin. Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore. / Dissertation/Thesis / Masters Thesis Computer Engineering 2019

abstract: Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data. In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks. / Dissertation/Thesis / Masters Thesis Computer Science 2019

There are many challenges facing society today, both socially and industrially. Whether it is to improve productivity in factories or with the intention of improving the quality of life of people in their homes, technological advances in robotics and computing have led to solutions to many problems in modern society. These areas are of great interest and are in constant development, especially in societies with a relatively ageing population. In this thesis, we address different challenges in which robotics, artificial intelligence and computer vision are used as tools to propose solutions oriented to home assistance. These tools can be organised into three main groups: “Grasping Challenges”, where we have addressed the problem of performing robot grasping in domestic environments; “Hand Interaction Challenges”, where we have addressed the detection of static and dynamic hand gestures, using approaches based on DeepLearning and GeometricLearning; and finally, “Human Behaviour Recognition”, where using a machine learning model based on hyperbolic geometry, we seek to group the actions that performed in a video sequence.

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