PhD Thesis (Software Engineering) / Model-Based Development (MBD) is an approach that uses software models to describe the behaviour of embedded software and cyber-physical systems. MBD has become an increasingly prevalent paradigm, with Simulink by MathWorks being the most widely used MBD platform for control software. Unlike textual programming languages, visual languages for MBD such as Simulink use block diagrams as their syntax. Thus, some software engineering principles created for textual languages are not easily adapted to this graphical notation or have not yet been supported. A software engineering principle that is not readily supported in Simulink is the modularization of systems using information hiding. As with all software artifacts, Simulink models must be constantly maintained and are subject to evolution over their lifetime. This principle hides likely changes, thus enabling the design to be robust with respect to future changes. In this thesis, we perform repository mining on an industry change management system of Simulink models to understand how they are likely to change. Then, we explore the various modelling mechanisms available in the Simulink language to see how they could support modular design with information hiding. Next, we propose a module structure, syntactic interface, and modelling conventions for Simulink designs, which are supported by our open-source Simulink Module Tool. Finally, we apply the proposed techniques on case studies from the aerospace and nuclear domains, in order to demonstrate their practicality and validate their effectiveness. Overall, the approach helped support information hiding by encapsulating secrets and facilitating likely changes. It also had a positive effect on interface complexity, cohesion, and coupling. The larger system also exhibited reductions to cyclomatic complexity, testing effort, and execution time, but the smaller case study benefited less in these areas. / Thesis / Doctor of Philosophy (PhD)

Clustering procedures suitable for the analysis of very high-dimensional data are needed for many modern data sets. One model-based clustering approach called high-dimensional data clustering (HDDC) uses a family of Gaussian mixture models to model the sub-populations of the observed data, i.e., to perform cluster analysis. The HDDC approach is based on the idea that high-dimensional data usually exists in lower-dimensional subspaces; as such, the dimension of each subspace, called the intrinsic dimension, can be estimated for each sub-population of the observed data. As a result, each of these Gaussian mixture models can be fitted using only a fraction of the total number of model parameters. This family of models has gained attention due to its superior classification performance compared to other families of mixture models; however, it still suffers from the usual limitations of Gaussian mixture model-based approaches. Herein, a robust analogue of the HDDC approach is proposed. This approach, which extends the HDDC procedure to include the mulitvariate-t distribution, encompasses 28 models that rectify one of the major shortcomings of the HDDC procedure. Our tHDDC procedure is fitted to both simulated and real data sets and is compared to the HDDC procedure using an image reconstruction problem that arose from satellite imagery of Mars' surface. / Thesis / Master of Science (MSc)

yes / We are pleased to welcome you to the 17th edition of the Advances in Model-Based Testing Workshop (A-MOST 2021), collocated with the IEEE International Conference on Software Testing, Verification and Validation (ICST 2021).

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Virtual reality (VR)-based surgical simulators using haptic devices can increase the effectiveness of surgical training for surgeons when performing surgical procedures in hard tissues such as bones or teeth milling. The realism of virtual surgery through a surgical simulator depends largely on the precision and reliability of the haptic device, which reflects the interaction with the virtual model. The quality of perceptiveness (sensation, force/torque) depends on the design of the haptic device, which presents a complex design space due to its multi-criteria and conflicting character of functional and performance requirements. These requirements include high stiffness, large workspace, high manipulability, small inertia, low friction, high transparency, and cost constraints. This thesis proposes a design methodology to improve the realism of force/torque feedback from the VR-based surgical simulator while fulfilling end-user requirements. The main contributions of this thesis are: 1. The development of a model-based and simulation-driven design methodology, where one starts from an abstract, top-level model that is extended via stepwise refinements and design space exploration into a detailed and integrated systems model that can be physically realized. 2. A methodology for creating an analytical and compact model of the quasi-static stiffness of a haptic device, which considers the stiffness of actuation systems, flexible links and passive joints. 3. A robust design optimization approach to find the optimal numerical values for a set of design parameters to maximize the kinematic, dynamic and kinetostatic performances of a 6-degrees of freedom (DOF) haptic device, while minimizing its sensitivity to variations in manufacturing tolerances and cost, and also satisfying the allowed variations in the performance indices. 4. A cost-effective approach for force/torque feedback control using force/torque estimated through a recursive least squares estimation. 5. A model-based control strategy to increase transparency and fidelity of force/torque feedback from the device by compensating for the natural dynamics of the device, friction in joints, gravity of platform, and elastic deformations. / <p>QC 20140415</p>

The primary objective of fusion research is to realize a thermonuclear fusion power plant. The main method to confine the hot plasma is by using a magnetic field. The reversed-field pinch is a type of magnetic confinement device which suffers from variety of magnetohy- drodynamic (MHD) instabilities. A particular unstable mode that is treated in this work is the resistive wall mode (RWM), which occurs due to the current gradient in the RFP and has growth rates of the order of the magnetic diffusion time of the wall. Application of control engineering tools appears to allow a robust and stable RFP operation.A model-based approach to stabilize the RWMs is pursued in this thesis. The approach consists of empirical modeling of RWMs using a class of subspace identification methodology. The obtained model is then used as a basis for a model based controller. In particular the first experimental results of using a predictive control for RWM stabilization are obtained. It is shown that the formulation of the model based controller allows the user to incorporate several physics relevant phenomena along with the stabilization of RWM. Another use of the model is shown to estimate and compensate the inherent error field. The results are encouraging, and the methods appear to be generically useful as research tools in controlled magnetic confinement fusion. / Fusionsforskningens primära mål är att förverkliga en ny typ av kraftverk baserade på termonukleär fusion. Den viktigaste metoden för att innesluta det heta plasmat är användandet av  magnetfält. ”Reverserat-fält pinch” (RFP) är en typ av anläggning för magnetisk inneslutning av fusionsplasma som uppvisar ett flertal magneto-hydrodynamiska instabiliteter. En specifik instabil mod som behandlas i detta arbete är”resistiv-vägg” moden (RWM). Den orsakas av strömgradienten i RFPn och tillväxer med en tidskonstant som är av samma storleksordning som magnetfältets diffusionstid i det omgivande metallskalet.  Tillämpning av verktyg från reglerteknikområdet förefaller tillåta en robust och stabil RFP drift. I detta arbete används ett modell-baserat tillvägagångssätt för kompensering av RWM. Det innefattar empirisk modellering av RWM med användning av ”subspace” system-identifieringsmetoder. Den erhållna modellen används sedan som grund för en modell-baserad regulator. De första experimentella resultaten från modell-prediktiv kompensering av RWM har erhållits.  I detta arbete har också visats att formuleringen av den modellbaserade regulatorn tillåter användaren att integrera flera relevanta fysikaliska aspekter förutom RWM. Ytterligare en användning av modellen är för att göra uppskattning och kompensering av avvikelser i anläggningens magnetfält, så kallade fält-fel. Resultaten är uppmuntrande, och det förefaller som om de undersökta metoderna är allmänt användbara som verktyg för forskning om magnetisk inneslutning av fusionsplasma. / <p>QC 20170202</p>

<p>Throughout recent years, legislations concerning emission levels for vehicles have become more restrictive and will be even more restrictive in the future. In the recent European environmental standards, EURO 4 (2006) and EURO 5 (2008), further requirements have been added on top of low emission demands. All heavy duty trucks have to be equipped with an OBD-system. Scania CV AB has today an existing OBD-system that consists of several tests. Typically, a test is designed to check if a signal is inside specified limits or thresholds. To improve the system, Scania CV AB and Vehicular Systems at Linköping University have developed a method to design diagnosis systems in an automatic way, implemented in a toolbox called DSAME.</p><p>In this thesis, an automatic designed OBD-system has been created with DSAME and the corresponding parts in a manually designed OBD-system have been identified. The two systems have been compared. The result shows that both systems are equally at detecting faults but the automatic designed OBD-system is a lot better to isolate the faults than the existing OBD-system.</p>

In model-based recognition, ad hoc techniques are used to decide if a match of data to model is correct. Generally an empirically determined threshold is placed on the fraction of model features that must be matched. We rigorously derive conditions under which to accept a match, relating the probability of a random match to the fraction of model features accounted for, as a function of the number of model features, number of image features and the sensor noise. We analyze some existing recognition systems and show that our method yields results comparable with experimental data.

This thesis addresses the problem of recognizing solid objects in the three-dimensional world, using two-dimensional shape information extracted from a single image. Objects can be partly occluded and can occur in cluttered scenes. A model based approach is taken, where stored models are matched to an image. The matching problem is separated into two stages, which employ different representations of objects. The first stage uses the smallest possible number of local features to find transformations from a model to an image. This minimizes the amount of search required in recognition. The second stage uses the entire edge contour of an object to verify each transformation. This reduces the chance of finding false matches.