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  • Presentation: 2019-10-17 13:15 Delta, Västerås
    Mehmed, Ayhan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Runtime Monitoring of Automated Driving Systems2019Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    It is the period of the World's history, where the technological progress reached a level that enables the first steps towards the development of vehicles with automated driving capabilities. The swift response from the significant portion of the industry resulted in a race, the final line set at the introduction of vehicles with full automated driving capabilities.

    Vehicles with automated driving capabilities target making driving safer, more comfortable, and economically more efficient by assisting the driver or by taking responsibilities for different driving tasks. While vehicles with assistance and partial automation capabilities are already in series production, the ultimate goal is in the introduction of vehicles with full automated driving capabilities. Reaching this level of automation will require shifting all responsibilities, including the responsibility for the overall vehicle safety, from the human to the computer-based system responsible for the automated driving functionality (i.e., the Automated Driving System (ADS)). Such a shift makes the ADS highly safe-critical, requiring a safety level comparable to an aircraft system.

    It is paramount to understand that ensuring such a level of safety is a complex interdisciplinary challenge. Traditional approaches for ensuring safety require the use of fault-tolerance techniques that are unproven when it comes to the automated driving domain. Moreover, existing safety assurance methods (e.g., ISO 26262) suffer from requirements incompleteness in the automated driving context. The use of artificial intelligence-based components in the ADS further complicate the matter due to their non-deterministic behavior. At present, there is no single straightforward solution for these challenges. Instead, the consensus of cross-domain experts is to use a set of complementary safety methods that together are sufficient to ensure the required level of safety.

    In the context of that, runtime monitors that verify the safe operation of the ADS during execution, are a promising complementary approach for ensuring safety. However, to develop a runtime monitoring solution for ADS, one has to handle a wide range of challenges. On a conceptual level, the complex and opaque technology used in ADS often make researchers ask the question ``how should ADS be verified in order to judge it is operating safely?".

    Once the initial Runtime Verification (RV) concept is developed, researchers and practitioners have to deal with research and engineering challenges encountered during the realization of the RV approaches into an actual runtime monitoring solution for ADS. These challenges range from, estimating different safety parameters of the runtime monitors, finding solutions for different technical problems, to meeting scalability and efficiency requirements.

    The focus of this thesis is to propose novel runtime monitoring solutions for verifying the safe operation of ADS. This encompasses (i) defining novel RV approaches explicitly tailored for automated driving, and (ii) developing concepts, methods, and architectures for realizing the RV approaches into an actual runtime monitoring solution for ADS. Contributions to the former include defining two runtime RV approaches, namely the Computer Vision Monitor (CVM) and the Safe Driving Envelope Verification. Contributions to the latter include (i) estimating the sufficient diagnostic test interval of the runtime verification approaches (in particular the CVM), (ii) addressing the out-of-sequence measurement problem in sensor fusion-based ADS, and (iii) developing an architectural solution for improving the scalability and efficiency of the runtime monitoring solution.

  • Presentation: 2019-10-30 13:00 Pi, Västerås
    Rahman, Moksadur
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Towards a learning system for process and energy industry: Enabling optimal control, diagnostics and decision support2019Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Driven by intense competition, increasing operational cost and strict environmental regulations, the modern process and energy industry needs to find the best possible way to adapt to maintain profitability. Optimization of control and operation of the industrial systems is essential to satisfy the contradicting objectives of improving product quality and process efficiency while reducing production cost and plant downtime. Use of optimization not only improves the control and monitoring of assets but also offers better coordination among different assets. Thus, it can lead to considerable savings in energy and resource consumption, and consequently offer a reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks that can be integrated with the existing industrial automation platforms to benefit from optimal control and operation. The main focus of this dissertation is the use of different process models, soft sensors and optimization techniques to improve the control, diagnostics and decision support for the process and energy industry. A generic architecture for an optimal control, diagnostics and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies within the energy-intensive pulp and paper industry and the promising micro-combined heat and power (CHP) industry are selected to demonstrate the learning system. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a micro-CHP fleet. The main argument behind the selection of these two case studies is that if the proposed learning system architecture can be adapted for these significantly different cases, it can be adapted for many other energy and process industrial cases. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation and diagnostics techniques are explored for the micro-CHP fleet.

  • Presentation: 2019-10-31 10:15 Futurum, Eskilstuna
    Simola, Hanna
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Styrning som stöttning: en etnografisk fallstudie om en högstadielärares didaktiska ledarskap som stödstruktur för språk- och kunskapsutveckling2019Licentiatavhandling, monografi (Övrigt vetenskapligt)
    Abstract [sv]

    I den här licentiatuppsatsen undersöks en lärares arbete med språk- och kunskapsutveckling i ett högstadieklassrum. Syftet är att belysa betydelsen av det didaktiska ledarskapet i undervisningen av nyanlända och flerspråkiga elever. Genom att beskriva det didaktiska ledarskapet kan uppsatsen också bidra med ytterligare kunskap om språk- och kunskapsutvecklande arbetssätt.  Två forskningsfrågor har använts för att nå syftet. Den första frågan rör kännetecken för ett didaktiskt ledarskap. Den andra frågan handlar om aspekter i det didaktiska ledarskapet som kan utgöra en stödstruktur för nyanlända och flerspråkiga elevers språk- och kunskapsutveckling. Studien har genomförts som en etnografisk fallstudie, där jag  genom deltagande observationer följt en lärare över tid i ämnena SO, hemkunskap och matematik i hens mentorsklass bestående av omkring 28 elever, varav nio nyanlända och flerspråkiga elever. Jag har också intervjuat läraren. Centralt för studien är att kunskaper skapas genom interaktion mellan människor. Licentiatuppsatsen utgår från ett sociokulturellt perspektiv på undervisning och lärande där begreppet stöttning i den närmaste utvecklingszonen ingår. Vidare diskuteras även begreppet didaktiskt ledarskap. Sociokulturellt perspektiv, stöttning i den närmaste utvecklingszonen och didaktiskt ledarskap är således betydelsefulla för studien, men det är inte givet hur relationen mellan dem ser ut. Det finns ingen tidigare studie som kombinerar dessa tre.

    Resultaten visar att studiens teman tydlighet och struktur, tillgänglighet och reflexivitet samt höga krav och kognitiva utmaningar är betydelsefulla faktorer i det didaktiska ledarskapet som har en positiv inverkan på lärandeklimatet, där elevernas tillgång till olika stödstrukturer i undervisningen för att vidareutveckla nya kunskaper och sitt lärande är en självklarhet. Genom att i en modell sammanfoga den didaktiska triangeln med förhållandet mellan studiens olika teman åskådliggörs det didaktiska ledarskapet i det didaktiska rummet, det vill säga i och utanför klassrummet, där stöttning på olika nivåer är betydelsefull i undervisningen och där interaktion utgör den sammanhållande länken mellan didaktiskt ledarskap och stöttning.  Stöttning kan både vara en förutsättning för didaktiskt ledarskap och en effekt av det. Fokus i studien har varit på flerspråkiga och nyanlända elever, men jag ser det som självklart att ett didaktiskt ledarskap som jag har beskrivit det är betydelsefullt för alla elever, i det didaktiska rummet, oavsett språk och bakgrund, grundskola eller gymnasiet.

  • Presentation: 2019-11-27 13:15 Gamma, Västerås
    Khanfar, Husni
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Demand-Driven Static Backward Program Slicing Based on Predicated Code Block Graphs2019Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Static backward program slicing is a technique to compute the set of program statements, predicates and inputs that might affect the value of a particular variable at a program location. The importance of this technique comes from being an essential part of many critical areas such as program maintenance, testing, verification, debugging, among others. The state-of-art slicing approach collects all the data- and control-flow information in the source code before the slicing, but not all the collected information are used for computing the slice. Thus, this approach causes a significant amount of unnecessary computations, particularly for slicing large industrial systems, where unnecessary computations lead to wastage of a considerable amount of processing time and memory. Moreover, this approach often suffers from scalability issues.

    The demand-driven slicing approaches aim at solving this problem by avoiding unnecessary computations. However, some of these approaches trade precision for performance, whereas others are not entirely demand-driven, particularly for addressing unstructured programs, pointer analysis, or inter-procedural cases.

    This thesis presents a new demand-driven slicing approach that addresses well-structured, unstructured, and inter-procedural programs. This approach has four distinct features, each of which prevents a special type of unnececessary computations. The effectiveness and correctness of the proposed approach are verified using experimental evaluation. In addition, the thesis proposes an approach that can compute on the fly the control dependencies in unstructured programs.

    Publikationen är tillgänglig i fulltext från 2019-11-06 08:00