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  • Presentation: 2019-04-25 13:00 B3, Stockholm
    Ríos Bayona, Francisco
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Analytical and numerical approaches to estimate peak shear strength of rock joints2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In Sweden, there exists a large number of dams. Many of them are founded on rock masses normally affected by the presence of sub-horizontal rock fractures, which makes sliding along rock joints under the dam foundation one of the most critical failure mechanism. Various attempts have been made to relate the peak shear strength of rock joints to measurable parameters. However, the uncertainty in the determination of the shear strength of rock joints is nonetheless still significant.The main aim of this thesis is to investigate, develop and apply analytical and numerical techniques for estimation of peak shear strength of natural and unfilled rock joints. In a first step, the peak shear strength of several natural and unfilled rock joint was calculated by using surface aperture measurements from high-resolution optical scanning and a modified version of the analytical criterion previously developed by Johansson and Stille in 2014. In a second step, PFC2D was utilised to perform numerical shear tests on two-dimensional profiles selected from high-resolution optical scanning on unweathered and perfectly mated tensile induced rock joints.The results from the analytical approach show that the calculated peak shear strengths of the analysed samples are in good agreement compared with the laboratory investigations. Conversely, the obtained results from the numerical approach show lower peak shear strengths in the analysed two-dimensional profiles compared with the conducted laboratory shear tests.The analytical approach together with the advanced techniques to measure surface roughness available today, may be a possible way forward towards a methodology to determine peak shear strength of large-scale natural rock joints in-situ.

  • Presentation: 2019-05-03 10:00 D2, Stockholm
    Müller, Matias
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Regret and Risk Optimal Design in Control2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Engineering sciences deal with the problem of optimal design in the face of uncertainty. In particular, control engineering is concerned about designing policies/laws/algorithms that sequentially take decisions given unreliable data.

    This thesis addresses two particular instances of optimal sequential decision making for two different problems. The first problem is known as the H-norm (or l2-gain, for LTI systems) estimation problem, which is a fundamental quantity in control design through the small gain theorem. Given an unknown system, the goal is to find the maximum l2-gain which, in a model-free approach, involves solving a sequential input design problem. The H-norm estimation problem (or simply "gain estimation problem") is cast as the composition of a multi-armed bandit problem generating data, and an optimal estimation problem given that data. It is shown that the separation of the gain estimation problem into these two sub-problems is optimal in a mean-square sense, as the expected estimation error asymptotically matches the Cramér-Rao lower bound.

    In the second part of the thesis, we address the problem of risk-coherent optimal control design for disturbance rejection under uncertainty, where optimality is studied from an H2 and an H sense. We consider a parametric model for the plant and the noise spectrum, where the modeling error between the model and the real system is uncertain. This uncertainty is condensed in a probability density function over the different realizations of the parameters defining the model. We use this information to design a controller that minimizes the risk of falling into poor closed-loop performance within a financial theory of risk framework. A systematic approach for the design of H2- and H-optimal controllers is proposed in terms of a quadratically-constrained linear program and a semi-definite program, respectively. An interesting application to H2-optimal design under covert attacks is also developed.

  • Presentation: 2019-05-06 13:00 FA31, Stockholm
    Petrou, Georgia
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemistry, Glycoscience.
    Investigating mucin interactions with diverse surfaces for biomedical applications2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Mucous membranes are covered with mucus, a viscoelastic hydrogel that plays an essential role in their protection from shear and pathogens. The viscoelasticity of mucus is owing to mucins, a group of densely glycosylated proteins. Mucins can interact with a wide range of surfaces; thus, there is big interest in exploring and manipulating such interactions for biomedical applications. This thesis presents investigations of mucin interactions with hydrophobic surfaces in order to identify the key features of mucin lubricity, as well as describes the development of materials that are optimized to interact with mucins.

     

    In Paper I we investigated the domains which make mucins outstanding boundary lubricants. The results showed that the hydrophobic terminal domains of mucins play a crucial role in the adsorption and lubrication on hydrophobic surfaces. Specifically, protease digestion of porcine gastric mucins and salivary mucins resulted in the cleavage of these domains and the loss of lubricity and surface adsorption. However, a “rescue” strategy was successfully carried out by grafting hydrophobic phenyl groups to the digested mucins and enhancing their lubricity. This strategy also enhanced the lubricity of polymers which are otherwise bad lubricants.

     

    In Paper II we developed mucoadhesive materials based on genetically engineered partial spider silk proteins. The partial spider silk protein 4RepCT was successfully functionalized with six lysines (pLys-4RepCT), or the Human Galectin-3 Carbohydrate Recognition Domain (hGal3-4RepCT). These strategies were aiming to either non-specific electrostatic interactions between the positive lysines and the negative mucins, or specific binding between the hGal3 and the mucin glycans. Coatings, fibers, meshes and foams were prepared from the new silk proteins, and the adsorption of porcine gastric mucins and bovine submaxillary mucins was measured, demonstrating enhanced adsorption.

     

    The work presented demonstrates how mucin-material interactions can provide us with valuable information for the development of new biomaterials. Specifically, mucin-based and mucin-inspired lubricants could provide desired lubrication to a wide range of surfaces, while our new silk based materials could be valuable tools for the development of mucosal dressings.

  • Presentation: 2019-05-10 09:30 Sal B, Stockholm
    Roozbeh, Amir
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS. Ericsson Research.
    Toward Next-generation Data Centers: Principles of Software-Defined “Hardware” Infrastructures and Resource Disaggregation2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The cloud is evolving due to additional demands introduced by new technological advancements and the wide movement toward digitalization. Therefore, next-generation data centers (DCs) and clouds are expected (and need) to become cheaper, more efficient, and capable of offering more predictable services.

    Aligned with this, we examine the concept of software-defined “hardware” infrastructures (SDHI) based on hardware resource disaggregation as one possible way of realizing next-generation DCs. We start with an overview of the functional architecture of a cloud based on SDHI. Following this, we discuss a series of use-cases and deployment scenarios enabled by SDHI and explore the role of each functional block of SDHI’s architecture, i.e., cloud infrastructure, cloud platforms, cloud execution environments, and applications.

    Next, we propose a framework to evaluate the impact of SDHI on techno-economic efficiency of DCs, specifically focusing on application profiling, hardware dimensioning, and total cost of ownership (TCO). Our study shows that combining resource disaggregation and software-defined capabilities makes DCs less expensive and easier to expand; hence they can rapidly follow the exponential demand growth. Additionally, we elaborate on technologies behind SDHI, its challenges, and its potential future directions.

    Finally, to identify a suitable memory management scheme for SDHI and show its advantages, we focus on the management of Last Level Cache (LLC) in currently available Intel processors. Aligned with this, we investigate how better management of LLC can provide higher performance, more predictable response time, and improved isolation between threads. More specifically, we take advantage of LLC’s non-uniform cache architecture (NUCA) in which the LLC is divided into “slices,” where access by the core to which it closer is faster than access to other slices. Based upon this, we introduce a new memory management scheme, called slice-aware memory management, which carefully maps the allocated memory to LLC slices based on their access time latency rather than the de facto scheme that maps them uniformly. Many applications can benefit from our memory management scheme with relatively small changes. As an example, we show the potential benefits that Key-Value Store (KVS) applications gain by utilizing our memory management scheme. Moreover, we discuss how this scheme could be used to provide explicit CPU slicing – which is one of the expectations of SDHI  and hardware resource disaggregation.

  • Presentation: 2019-05-17 15:50 E2, Stockholm
    Khosrowjerdi, Hojat
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.
    Learning-based Testing for Automotive Embedded Systems: A requirements modeling and Fault injection study2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis concerns applications of learning-based testing (LBT) in the automotive domain. In this domain, LBT is an attractive testing solution, since it offers a highly automated technology to conduct safety critical requirements testing based on machine learning. Furthermore, as a black-box testing technique, LBT can manage the complexity of modern automotive software applications such as advanced driver assistance systems. Within the automotive domain, three relevant software testing questions for LBT are studied namely: effectiveness of requirements modeling, learning efficiency and error discovery capabilities.

    Besides traditional requirements testing, this thesis also considers fault injection testing starting from the perspective of automotive safety standards, such as ISO26262. For fault injection testing, a new methodology is developed based on the integration of LBT technologies with virtualized hardware emulation to implement both test case generation and fault injection. This represents a novel application of machine learning to fault injection testing. Our approach is flexible, non-intrusive and highly automated. It can therefore provide a complement to traditional fault injection methodologies such as hardware-based fault injection.

  • Presentation: 2019-05-22 10:00 643, Stockholm
    Darwish, Rami
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management.
    The Missing link: Business Models Lock-in in Sociotechnical Transitions2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Societal and environmental challenges present challenges for our current industrial systems. In order to respond to these difficulties, various alternative systems have been suggested, as they promise sustainability and increased societal quality of life enabled by innovative technologies. These future solutions hold the capacity to solve problems by unlocking considerable business potential. However, the journey to the forthcoming era will bring dramatic changes, not only to the success of incumbent industrial stakeholders but also to their very existence. The upcoming changes are impregnated with hazards to current business models of successful organizations. So, one may ask what impact future technologies may have on the realms that industrial actors live in? To disentangle the complexity of the unknowns, multitudes of collaborative efforts in protected spaces have emerged globally to experiment with potential systems on the road to sociotechnical transitions.

    The transport sector is undergoing efforts towards transitions to future sustainable systems. This sector has a special focus when it comes to sustainability challenges due to its substantial economic and environmental impact.  Bus systems are at the heart of this challenge due to the central role they play in urban mobility. Hence, different fuels and charging technologies for buses have been tested in pilot projects to facilitate the march towards sustainability; electric charging is one of the promising technologies, which achieve this aim. However, current business models of incumbent transport stakeholders seem to be problematic, and changes to facilitate the transitions seem to be complex.

    Extant literature indicates a critical role of business models under sociotechnical transitions. Theoretically, there is an underlying need for incumbents to change their business models to reap the benefits of innovative technologies. However, this change is difficult and potential business models are far from clear. With that, the dynamics of business models under transition remains as an underexplored area, and the challenge to incumbent business models poses itself as an interesting area to gauge. Under this umbrella, a question arises regarding how the pressure on incumbent business models interacts with systemic innovations.

    This thesis is a case study of an incumbent bus operator participating in a pilot project on a future bus system. The case study is in-depth in nature and investigates the potential business model of a bus operator in a multiple stakeholder pilot project, which tests an inductive electric hybrid bus. With a strong empirical exploratory nature, this thesis is built on an “insider” single case study that occurred in the year 2016-2017. The focus of the study is on the pressure on bus operator business model in the face of systemic innovation. The findings reveal positive future value proposition, disrupted value creation, and unclear value capture in the potential business model of the operator. Moreover, the findings show lock-in and resource dependence situation of the operator’s current business model. The lock-in of the business model hinder the transition to future sociotechnical bus system and makes it difficult to commercialize the new technology.

    The outcome of this thesis speaks to a significant influence of history and the regulator, manifested by rules on the future of business models of commercial incumbent stakeholders. This demonstrates lock-in may prove to be a major impediment, and that unchained and flexible business model of incumbents is critical for further continuation of successful shifts. Given these findings, this thesis suggests applying the business model lens to pilot projects for sustainability. This would aid in better comprehending how current business models may facilitate or hinder favorable transitions. This knowledge informs both managerial decisions and policy making, especially when it comes to resource optimization and investment decisions.

  • Presentation: 2019-06-13 09:30 Sal B, Kista
    Farshin, Alireza
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Realizing Low-Latency Internet Services via Low-Level Optimization of NFV Service Chains: Every nanosecond counts!2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    By virtue of the recent technological developments in cloud computing, more applications are deployed in a cloud. Among these modern cloud-based applications, some require bounded and predictable low-latency responses. However, the current cloud infrastructure is unsuitable as it cannot satisfy these requirements, due to many limitations in both hardware and software.

    This licentiate thesis describes attempts to reduce the latency of Internet services by carefully studying the currently available infrastructure, optimizing it, and improving its performance. The focus is to optimize the performance of network functions deployed on commodity hardware, known as network function virtualization (NFV). The performance of NFV is one of the major sources of latency for Internet services.

    The first contribution is related to optimizing the software. This project began by investigating the possibility of superoptimizing virtualized network functions(VNFs). This began with a literature review of available superoptimization techniques, then one of the state-of-the-art superoptimization tools was selected to analyze the crucial metrics affecting application performance. The result of our analysis demonstrated that having better cache metrics could potentially improve the performance of all applications.

    The second contribution of this thesis employs the results of the first part by taking a step toward optimizing cache performance of time-critical NFV service chains. By doing so, we reduced the tail latencies of such systems running at 100Gbps. This is an important achievement as it increases the probability of realizing bounded and predictable latency for Internet services.