Monitoring and predicting machine breakdowns are of vital importance in the manufacturing industry. Machine Learning models could be used to improve these breakdown predictions. However, the operators responsible for the machines need to trust and understand the predictions in order to base their decisions on the information. For this reason, Explainable Artificial Intelligence, XAIs, was introduced. It is defined as the set of Artificial Intelligence systems that can provide predictions in an intelligible and trustful form. Hence, the purpose of this research is to study different techniques of Explainable Artificial Intelligence XAIs in order to discover the most suitable methodology for allowing people without a machine learning background, employed in a manufacturing environment, to understand and trust predictions. Four XAI interfaces have been tested: three integrated XAI techniques were identified through a literature review, and one was presenting an experimental XAIs facility based on a machine learning model for outliers identification. In order to predict future machines’ states, classifiers based on Random Forest were built, while for identifying anomalies a model based on Isolation Forest was built. In addition, a user study was carried out in order to discern end-users perspectives about the four XAI interfaces. Final results showed that the XAI interface based on anomalous production values gained high approval among users with no or basic machine learning knowledge. / Övervakning och förutsägelse av maskinhaverier är av avgörande betydelse inom tillverkningsindustrin. Machine Learning-modeller kan användas för att förbättra dessa förutsägelser om sammanbrott. De operatörer som ansvarar för maskinerna måste dock lita på och förstå förutsägelserna för att kunna basera sina beslut på informationen. Av denna anledning introducerades Explainable Artificial Intelligence, XAIs. Det definieras som en uppsättning artificiell intelligenssystem som kan ge förutsägelser i en begriplig och pålitlig form. Därför är syftet med denna forskning att studera olika tekniker för Explainable Artificiell Intelligens XAIs för att upptäcka den mest lämpliga metoden för att låta människor utan maskininlärningsbakgrund, anställda i en tillverkningsmiljö, förstå och lita på förutsägelser. Fyra XAIgränssnitt har testats: tre integrerade XAI-tekniker identifierade genom en litteraturgenomgång, och en presenterade en experimentell XAI-anläggning baserad på en maskininlärningsmodell för identifiering av extremvärden. För att förutsäga framtida maskiners tillstånd byggdes klassificerare baserade på Random Forest, medan för att identifiera anomalier byggdes en modell baserad på Isolation Forest. Dessutom genomfördes en användarstudie för att urskilja slutanvändarnas perspektiv på de fyra XAI-gränssnitten. Slutresultaten visade att XAI-gränssnittet baserat på onormala produktionsvärden fick högt godkännande bland användare utan någon eller grundläggande kunskap om maskininlärning.

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The general trend of efficiency increase, weight and noise reduction has derived in the design of more slender, loaded, and 3D shaped blades. This has a significant impact on the stability of fan, and low pressure turbine blades, which are more prone to aeroelastic phenomena such as flutter. The flutter phenomenon is a self-excited, self-sustained unstable vibration produced by the interaction of flow and structure. These working conditions will induce either blade overload, or High Cycle Fatigue (HCF) produced by Limited Cycle Oscillation (LCO). The main objectives of the present work are on the investigation of the aeroelastic properties of a high-lift low-pressure in the light of the local flow features present in such profiles, in nominal and extreme off-design conditions both in high and low subsonic Mach number, for three dif-ferent rigid body modes. In addition, the validity of the linearity assump-tion of the influence coefficient technique has also been investigated, in order to expand the understanding of the physical limits of this assumption. This work has been designed as experimental investigation in the influence coefficient domain focused on a high-lift low-pressure turbine designed by ITP within the framework of the European FP7 project FU-TURE. These experiments have been carried out in the Aeroelastic test rig (AETR), at KTH Stockholm, which consist of an instrumented annular sector cascade with a single oscillating blade. The results acquired have been supported by numerical results provided by a non-propietary commercial software package (ANSYS CFX). The results suggest that the typical three-dimensional effects associated secondary flow features and tip leakage flows have a significant influence on the aeroelastic performance and the cascade stability. However the major influence appears as a consequence of the separation surface on the pressure side which appears at extreme off-design operating conditions. The contribution to stability of this local feature depend on the oscillation mode showing for the axial and torsion mode a neutral stability contribution, which is directly associated with the geometrical properties of the cascade. However, on the circumferential mode this separation surface has a stabilizing effect much more independent of the blade geometry. The study of the linearity assumption of the influence coefficient domain has revealed, that an apparent linear relation between the integrated unsteady response and the vibrational amplitude, does not necessary imply that the local unsteady response is linear with respect to the oscillation amplitude. The results also suggest that the validity of the linearity as-sumption is more sensitive to high oscillation amplitudes at high Mach conditions. / <p>QC 20140609</p>

Systems like autonomous vehicles may require real time embedded image processing under hardware constraints. This paper provides directions to design time and resource efficient Haar cascade detection algorithms. It also reviews some software architecture and hardware aspects. The considered algorithms were meant to be run on platforms equipped with a CPU and a GPU under power consumption limitations. The main aim of the project was to design and develop real time underwater object detection algorithms. However the concepts that are presented in this paper are generic and can be applied to other domains where object detection is required, face detection for instance. The results show how the solutions outperform OpenCV cascade detector in terms of execution time while having the same accuracy. / System så som autonoma vehiklar kan kräva inbyggd bildbehandling i realtid under hårdvarubegränsningar. Denna uppsats tillhandahåller anvisningar för att designa tidsoch resurseffektiva Haar-kasad detekterande algoritmer. Dessutom granskas en del mjukvaruarkitektur och hårdvaruaspekter. De avsedda algoritmerna är menade att användas på plattformar försedda med en CPU och en GPU under begränsad energitillgång. Det huvudsakliga målet med projektet var att designa och utveckla realtidsalgoritmer för detektering av objekt under vatten. Dock är koncepten som presenteras i arbetet generiska och kan appliceras på andra domäner där objektdetektering kan behövas, till exempel vid detektering av ansikten. Resultaten visar hur lösningarna överträffar OpenCVs kaskaddetektor beträffande exekutionstid och med samtidig lika stor träffsäkerhet.

Robot mapping is a major field of research in robotics. Its basic task is to combine (register) spatial data, usually gained from range devices, to a single data set. This data set is called global map and represents the environment, observed from different locations, usually without knowledge of their positions. Various approaches can be classified into groups based on the type of sensor, e.g. Lasers, Microsoft Kinect, Stereo Image Pair. A major disadvantage of current methods is the fact, that they are derived from hardly scalable 2D approaches that use a small amount of data. However, 3D sensing yields a large amount of data in each 3D scan. Autonomous mobile robots have limited computational power, which makes it harder to run 3D robot mapping algorithms in real-time. To remedy this limitation, the proposed research uses mid-level geometric features (lines and ellipses) to construct 3D geometric primitives (planar patches, cylinders, spheres and cones) from 3D point data. Such 3D primitives can serve as distinct features for faster registration, allowing real-time performance on a mobile robot. This approach works in real-time, e.g. using a Microsoft Kinect to detect planes with 30 frames per second. While previous approaches show insufficient performance, the proposed method operates in real-time. In its core, the algorithm performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model using a three stage approach. The first step inspects 1.5D sub spaces, to find lines and ellipses. The next stage uses these lines and ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidates are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. The real-time performance suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping. In this thesis, I will show how these features are derived and used for scene registration. Optimal registration is determined by finding plane-feature correspondence based on mutual similarity and geometric constraints. Our approach determines the plane correspondence in three steps. First step computes the distance between all pairs of planes from the first scan to all pair of planes from the second scan. The distance function captures angular, distance and co-planarity differences. The resulting distances are accumulated in a distance matrix. The next step uses the distance matrix to compute the correlation matrix between planes from the first and second scan. Finally plane correspondence is found by finding the global optimal assignment from the correlation matrix. After finding the plane correspondence, an optimal pose registration is computed. In addition to that, I will provide a comparison to existing state-of-the-art algorithms. This work is part of an industry collaboration effort sponsored by the National Institute of Standards and Technology (NIST), aiming at performance evaluation and modeling of autonomous navigation in unstructured and dynamic environments. Additional field work, in the form of evaluation of real robotic systems in a robot test arena was performed. / Computer and Information Science / Accompanied by two .mp4 files.

Development of state-of-the-art medical imaging modalities such as Magnetic Resonance Imaging, Computed Tomography, Galactography, MR Diffusion Tensor Imaging, and Tomosynthesis plays an important role for visualization and assessment of anatomical structures. Included among these structures are structures of branching topology such as the bronchial tree in chest computed tomography images, the blood vessels in retinal images and the breast ductal network in x-ray galactograms and the tubular bone patterns in dental radiography. Analysis of such images could help reveal abnormalities, assist in estimating a risk of diseases such as breast cancer and COPD, and aid in the development of realistic anatomy phantoms. This thesis aims at the development of a set of automated methods for the analysis of anatomical structures of tree and network topology. More specifically, the two main objectives include (i) the development of analysis framework to explore the association between topology and texture patterns of anatomical branching structures and (ii) the development of the image processing methods for enhanced visualization of regions of interest in anatomical branching structures such as branching nodes. / Computer and Information Science