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  • Ergano, Wondmagegn
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Heat and Power Technology.
    Optimization of wind turbine loads for maximum power output and low fatigue loading2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis the aerodynamic loads for maximum power output at acceptable fatigue loads on a 1.5MW onshore wind turbine are examined. The objective mainly is to investigate pitch angles where optimal value of maximum power output at an acceptable level of fatigue loading can be achieved while studying the source of fatigue loading and the constraints of increasing the coefficient of performance of wind turbine power output. A total of thirteen hub height mean wind speed profiles, at the same turbulence level, ranging from cut-in wind speed of 3m/s to cut-out wind speed of 27m/s at 2m/s incremental are simulated. The reference wind speed is set at the hub height. For reference wind set below the hub height, the logarithm wind profile is used to determine the hub height mean wind speed, and then the power law follows to determine the mean speed at other height. The speeds are determined on a meshed grid point to examine the change of wind speed and direction in time and space or turbulence which is mainly due to the shape and hostile of the terrains. Wind profile simulation is performed by TurbSim simulation code, and the resulting profile is used as input to analyze the loads at the blade root. The loads are analyzed for the wind speed above the rated wind speed, 11m/s to 27m/s, where the blades are pitched to obtain an even power output. After performing several runs to investigate the relationship of wind speed to power output and fatigue loading, the wind speed, where the load should be analyzed, is narrowed to 21m/s which is close to the cut-out wind speed. The loads at the blade root are examined using the free simulation code, FAST, for different pitch angles ranging from 7.5 degrees to 17 degrees for each hub height mean wind speeds mentioned above. For examination of the loads at the selected locations the blade root is segmented to twelve equal points located 15 degrees away to each other. The points are located in angle between 0 and 180 degrees according to Load Rose approach. The loads at the blade root are FAST output and they are used as input for post-processor MLife to analyze the fatigue load. The fatigue loads are examined in terms of damage equivalent loads of the bending moment out of plane. It is observed that pitching a blade angle has a significant effect on the power output and fatigue load, the power output increases and with undesirable fatigue load while pitching the blade angle to capture as maximum power output as possible. On the other hand, attempting to decrease the fatigue load affects the power output as well, that indicates minimizing the fatigue load cannot be achieved without affecting the power output. Output power and fatigue load relation for different pitch angle ranging from 7.5 to 17 degrees of the selected wind speed 21m/s shows that while pitching the blade the power output increases with undesirable fatigue load. In general, it can be said that expected results are achieved at pitch angle ranging from 15 to 17 degrees. However, the fatigues loads may be not are in acceptable level, hence, it will not be appropriate to conclude that these pitch angles are the optimal angles where the maximum power output and minimum fatigue load can be achieved. Furthermore, looking at only the fatigue loads the minimum fatigue load is achieved at pitch angle of 7.5 at a sacrifice of 0.6MW of the maximum output power, 1.92MW, which is significant compared to the maximum output power that can be achieved.

  • Rana, Sunil
    et al.
    University of Bristol.
    Mouro, João
    University of Bristol.
    Bleiker, Simon J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.
    Reynolds, Jamie D.
    University of Southampton.
    Chong, Harold M. H.
    University of Southampton.
    Niklaus, Frank
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.
    Pamunuwa, Dinesh
    University of Bristol.
    Nanoelectromechanical relay without pull-in instability for high-temperature non-volatile memory2020In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 11, no 1, article id 1181Article in journal (Refereed)
    Abstract [en]

    Emerging applications such as the Internet-of-Things and more-electric aircraft require electronics with integrated data storage that can operate in extreme temperatures with high energy efficiency. As transistor leakage current increases with temperature, nanoelectromechanical relays have emerged as a promising alternative. However, a reliable and scalable non-volatile relay that retains its state when powered off has not been demonstrated. Part of the challenge is electromechanical pull-in instability, causing the beam to snap in after traversing a section of the airgap. Here we demonstrate an electrostatically actuated nanoelectromechanical relay that eliminates electromechanical pull-in instability without restricting the dynamic range of motion. It has several advantages over conventional electrostatic relays, including low actuation voltages without extreme reduction in critical dimensions and near constant actuation airgap while the device moves, for improved electrostatic control. With this nanoelectromechanical relay we demonstrate the first high-temperature non-volatile relay operation, with over 40 non-volatile cycles at 200 °C.

  • Bodell, Victor
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Svenska datautbildningarsrelevans för mjukvaruutveckling inom industri2020Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    International research shows that there is a gap between industrial expectations on Software Development knowledge and methodology compared to the content of education within Computing.

    This report presents qualitative research on whether there is a gap between undergraduate education and the expectations from industry within Software Development specifically in Sweden, and if so, what the gap looks like. To shed further light on the demands and recommendations that Swedish education faces, recommendations from academia and legislation is also investigated.

    The result shows that the international gap between education and industry, differs from the equivalent relation in Sweden. The curricula meet demands from legislation and industrial expectations. The international academical recommendations suggested for the programs are not exactly matched by the content of the curricula. This can partly be explained by the educational focus being broader than towards a specific disciplin within computing. The overall conclusion is that educational programs meet the demands, recommendations and expectations they face. But practical experience and contact with industry should be factored into future adjustments of the curricula.

    Future work includes 1) quantitavely researching academical recommendations and industrial expectations, as well as how these are met by education programs, and 2) creating guidelines for future adjustments of curricula content.

  • Kajak, Kristian
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Impact of Video Compression on the Performance of Object Detection Algorithms in Automotive Applications2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The aim of this study is to generally expose the impact of using video compression methods on the performance accuracy of a neural network-based pedestrian detection system. Moreover, the emphasis is on investigating the consequences of using both compressed training and testing images with such neural network applications.

    This thesis includes a theoretical background into object detection and encoding systems, provides argumentative analysis for the detector, dataset, and evaluation method selection, and furthermore, describes how the selected detector is modified to be compatible with the selected dataset. The presented experiments use a modified MS-CNN pedestrian detection system on the CityScapes/CityPersons dataset. The Caltech benchmark evaluation method is used for comparing the detection performance of the detectors. The HEVC and JPEG 2000 encoding methods are used for data compression.

    This thesis reveals several interesting findings. For one, the results show that a significant amount of compression can be applied to the images before the detector’s performance starts degrading and that the detector is quite robust in dealing with compression artifacts to a certain extent. Secondly, peak signal-to-noise ratio of the data alone does not determine how the detector behaves, and other variables, such as the encoding method also affect the performance. Thirdly, the performance is most of all affected when detecting small-sized pedestrians (or pedestrians at a far distance). Fourthly, in terms of compressing training data, compared to a detector trained on non-compressed data, the detector trained solely on compressed images performs more accurate detections on lower quality data but performs less accurate detections on higher quality data. Furthermore, the results show that a detector trained on data consisting of both low and high quality variants of each frame beholds best detection performance on all quality scales.

  • Stålhandske, Therese
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Incorporating speaker’s role in classification of text-based dialogues2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Dialogues are an interesting type of document, as they contain a speaker role feature not found in other types of texts. Previous work has included incorporating a speaker role dependency in text-generation, but little has been done in the realm of text classification. In this thesis, we incorporate speaker role dependency in a classification model by creating different speaker dependent word representations and simulating a conversation within neural networks.

    The results show a significant improvement in the performance of the binary classification of dialogues, with incorporated speaker role information. Further, by extracting attention weights from the model, we are given an insight into how the speaker’s role affects the interpretation of utterances, giving an intuitive explanation of our model.

  • Luciani, Cleo
    KTH, School of Electrical Engineering and Computer Science (EECS).
    From MPLS to SD-WAN: Opportunities, Limitations and Best Practices2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis deals with Software-Defined Wide Area Networks (SD-WAN). It defines the term, presents its different variations available on the market and contrasts it to a typical MPLS network, based on criteria such as quality of service, link aggregation capabilities and price.Laboratory tests are conducted to compare the performances of one traditional and two SD-WAN connections to the cloud over redundant links, in terms of resilience to added latency, packet loss, bandwidth aggregation and failover capacities. An example company with a typical network is presented to study whether the current solution is satisfactory and how it could be improved. This thesis finds that the performance of network is no longer satisfactory due to the company’s global presence and its high use of the cloud and internet. The enterprise would benefit greatly from a switch to SD-WAN.Four SD-WAN solutions are then compared based on vendor specifications. The best option is chosen for the considered company and used to design a new network leveraging SD-WAN appliances and cloud security services for local internet access. A best practice is detailed for the design choice and for the transition process.This thesis will be of interest to network professional and employees considering SD-WAN for their company.

  • Kovacs, Tamas
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Performance Ratio and Fault Characterization Methods for Photovoltaic Systems2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This project focuses on investigating performance metrics and fault detectionmethods in photovoltaic systems. The primary goal is to find feasible solutionsproviding high accuracy and reliability in fault detection. The solution shouldbe simple yet robust enough for commercial applicability. For this reason, onlythe minimum essential measurements are utilized for the detection algorithm.These include the output voltage and current of the installation as well asirradiation and temperature from a dedicated sensor. Various existing works inscientific literature are investigated and a viable method is selected for furtheranalysis.The final approach involves a time-series analysis of normalized parameters,providing an overview of the daily performance of a given photovoltaic string.Although the solution is capable of differentiating between three designatedfault types: global shading, line-to-line faults and partial shading, it was foundthat there are numerous limitations that must be overcome before commercialviability, most of which are not directly technical difficulties. The reportends with a conclusive list of shortcomings that need additional thought forfuture works in this area. Overall, the thesis demonstrates the difficulty offault detection for photovoltaic systems when applied outside of controlledlaboratory conditions.

  • Colérus, Lovisa
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Automated visual evaluation of an electrode with neural networks2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This project was done in cooperation with the company Scibase. Scibase producesa product called Nevisense, which is used to detect skin cancer in a noninvasiveway. The measurements are made with electrical impulses and the electrodethat is in contact with the patients’ skin is only used once. When these electrodesare produced, they must pass visual inspections for each step in their assembly.These visual inspections are done by operators using a traditional microscope.This inspection is both time-consuming and uncomfortable for the operators, e.g.,microscopes strain their eyes and the design of the microscopes are not ergonomic.This project is about the automation of these visual inspections to increase the productionof electrodes and to improve working conditions.To automate this, two parts were needed: images of the electrodes and a way toclassify them as pass or fail. The images were taken with a digital microscope andto be able to get several images at once, a programmable XY-table was used. Theimages were processed with OpenCV, a computer vision library. The classificationof the images was done using a neural network and the accuracy that was achievedwas 99.2%, which is a higher accuracy than the conformity that the operators have.

  • Pulluri, Vamshi
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Hardware Power Optimization of Base Station Using Reinforcement Learning2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    5G comes with requirements of much higher data rates for a digitally connected society where billions of new devices will be added in the coming years. From a RAN perspective, these demands will be served by an increasing number of eNB base stations. According to some estimates, eNB base stations consume about 80 percent of the total power of the cellular network. CPU power also accounts for some part of the total power consumption in the eNB base station. To keep the evolution of the 5G cellular network affordable and sustainable, it is imperative to reduce the power consumption in base stations. While system-level power optimization has been extensively studied, there is little research related to robust and intelligent models that adaptively tune the CPU power consumption related to different traffic patterns in the eNB base stations while maintaining the desired quality of service.

    In this thesis, we examine the problem of CPU power optimization in the eNB base station and develop a data-driven solution by employing deep reinforcement learning. We design an RL-agent using Deep Q-learning inside Ericsson’s 3GPP-compliant eNB on x86. We present a detailed explanation of theselectionofthestatespaceandactionspacethatensuresabettersolutionfor solving the RL-problem at hand and an approach to design a reward function. We evaluate our RL-agent on three different traffic patterns, i.e., full-buffered, bursty traffic and assimilated traffic. The performance of our RL-agent is benchmarked against two baseline models, i.e., maximum power of 120 Watts and a rule-based power model.

    The results show that our RL-model can learn the traffic pattern of the eNB base station and dynamically optimize the power consumption. When compared to the maximum power model, the RL-agent saves 33% to 37% CPU power with the degradation of throughput for cell-edge users for bursty traffic and assimilated traffic. However, the impact on median throughput users is little. When compared to the rule-based power model, theRL-agentsaves16%to18% CPU power with high throughput gain for both bursty traffic and assimilated traffic.

  • Chouk, Wissem
    KTH, School of Electrical Engineering and Computer Science (EECS).
    The use of BGP Flowspec in the protection against DDoS attacks2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Flowspec is one of the latest DDoS attacksmitigation tools. It relies on BGPv4 to share itsroute specifications. It presents great advantageswhen it comes to effectively mitigate a (D)DoSattack. However, due to the lack of protection andsecurity of BGP, Flowspec presents somevulnerabilities that can be used against the victimto initiate, enhance or continue an attack. An ISP isinterested to include Flowspec in its mitigationtools. In this thesis, we will evaluate the potentialuse of Flowspec by the ISP after taking intoconsideration 3 uses cases where the protocolwould not be able to act as intended.

  • Chizzali, Lucas
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Deep Learning based Turn Light Detection at Road Intersections for Autonomous Driving2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Autonomous driving promises to revolutionise transportation by making it more efficient, cheaper, safer and climate friendlier. Bringing autonomous vehicles onto roads requires effectively sensing the surrounding environment and integrating derived information into a safe and efficient driving policy. This involves, amongst others, capturing the intent of other traffic participants as signalled by turn lights. Contributing to the development of autonomous systems as part of the publicly funded MEC-View project at Bosch, this thesis aims at detecting turn signals with an emphasis on oncoming vehicles at road intersections. Specifically, using images taken from cameras attached to side mirrors of an autonomous vehicle, a Convolutional LSTM Neural Network detects and classifies turn signals of approaching vehicles during both day and night time. As such, this work differs in important aspects from existing literature. Results obtained from experiments and detailed analyses of test cases indicate that the devised model performs competitively despite data scarcity and strong label imbalance, with a weighted F1 score of 80.8%. Hence, this thesis lays promising ground work in the domain of autonomous driving and identifies potential future improvements.

  • Gao, Yini
    KTH, School of Electrical Engineering and Computer Science (EECS).
    A One-stage Detector for Extremely-small Objects Based on Feature Pyramid Network2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Thanks to the recent development in Graphics Processing Unit (GPU) and deep neural network, outstanding enhancement has been made in real-time and multi-scale object detection. However, most of these detectors ignore the situations where the target needs to be identified is extremely-small corresponding to the size of the image or video. The spatial resolution of feature maps is decreasing and detailed information about extremely-small objects is missing during the process of extracting features with stride and pooling. So how to keep the higher spatial resolution when we extract the richer semantic information and enlarge receptive field becomes the crucial core of this project.

    With the purpose of detecting targets with 30 to 1000 pixels in 1080p videos, we design a one-stage detector that uses DetNet as the backbone and construct the head of detector based on the idea of Feature Pyramid Network (FPN). Taking advantage of the dilated convolutional layer in DetNet, the size of the last three feature maps are not decreasing. By contrast, the receptive field and semantic information are augmented by traversing the backbone of the detector. Besides, with the technique of FPN, feature maps from different stages are combined and assigned to the prediction, making the model more robust and accurate. Additionally, in order to reduce the input size of the image to decrease computational complexity without missing any information of extremely-small objects, we crop the full image based on the distribution of the target’s location in existing data instead of directly resizing the full image.

    We compare the performance of this proposed detector with YOLOv3 on the custom dataset, and it turns out to obtain remarkably good results on extremely small objects, improving mean average precision by 18%.

  • Yates, Anna
    et al.
    University of Newcastle.
    Ceccato, Vania
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Urban and Regional Studies.
    Individual and spatial dimensions of women’s fear of crime: A  Scandinavian study case2020In: International Journal of Comparative and Applied Criminal Justice, ISSN 0192-4036, EISSN 2157-6475Article in journal (Refereed)
    Abstract [en]

    Using insights from criminology and urban geography, this article seeks to investigate individual and spatial dimensions of women’s fear of crime, in particular amongst women who declare to feel the most unsafe. This study is based on three waves of data of the Stockholm Safety Survey using exploratory data analysis and binary logistic regression. Informed by an intersectional framework, the study shows how individual attributes including gender, age, and previous victimisation affect women’s perception of safety. Modelling results indicate how the neighbourhood context affects women’s behaviour in face of fear (functional and dysfunctional fear). Among the most fearful women, poor social contacts in their neighbourhood, rather than fear of crime itself, lead to place avoidance.

  • Moreira, Gustavo
    et al.
    KTH, School of Architecture and the Built Environment (ABE).
    Ceccato, Vania
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Urban and Regional Studies.
    Gendered mobility and violence in the São Paulo metro, Brazil2020In: Urban Studies, ISSN 0042-0980, E-ISSN 1360-063XArticle in journal (Refereed)
    Abstract [en]

    With about 12 million inhabitants, São Paulo, Brazil, is the largest city in South America. As in many other major southern hemisphere cities, this extreme concentration of people imposes a number of mobility and security challenges. The objective of this article was to investigate the space-time patterns of mobility and violent victimisation in São Paulo’s metro stations from a gender perspective. The methodology combines use of a Geographical Information System (GIS), statistical analysis through negative binomial regression modelling and hypothesis testing. Results indicate that mobility and the level of victimisation are gender dependent. Women are at higher risk of victimisation than men in São Paulo’s central metro station, while men run higher risk of violence at end stations – both notably during late night periods. The presence of employees reduces the risk of violence, except during the mornings. The article suggests that crime prevention initiatives need to be gender informed and sensitive to the particular spatial and temporal features of rapid transit environments.

  • Ceccato, Vania
    et al.
    KTH, Superseded Departments (pre-2005), Infrastructure and Planning. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Urban and Regional Studies.
    Näsman, Per
    KTH, Superseded Departments (pre-2005), Infrastructure. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Langefors, Linda
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Sexual violence on the move: An assessment of youth’s victimization in public transportation2020In: Women & Criminal Justice, ISSN 0897-4454, E-ISSN 1541-0323Article in journal (Refereed)
    Abstract [en]

    Informed by principles of environmental criminology, this study assesses patterns of sexual victimization among young riders of rail-bound public transportation using a sample of 1,122 university students in Stockholm, Sweden. Exploratory data analysis and logistic regression models underlie the methodology of the study. Findings indicate that the physical and social characteristics of transit environments have an impact on the likelihood of sexual victimization after controlling for individual factors. The theoretical and practical implications of these results are discussed.

  • Hörnlund, Ewa
    KTH, School of Industrial Engineering and Management (ITM), Learning.
    Utveckling av förslag till en hållbarhetsutbildning för anställda vid ett pappersbruk2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    SCA is one of the biggest private forest owners in Europe and includes different mills in the north of Sweden, where Ortviken Paper mill, a pulp and paper factory, is one of these. Ortviken Paper mill produce publication paper from its own mechanical pulp and have both uncoated qualities and LWC-paper. SCA and Ortviken Paper mill works continuously towards a sustainable development and have lower their impact on the environment considerably over the last decades, just as all forestry in Sweden. The company’s management want to offer their staff a sustainability education with focus on environmental aspects to rise the staff’s competence in sustainability. Rise sustainability competencies is in line with what UNESCO (United Nation Education Science and Culture Organisation) highlights, education is crucial if we want to reach the global sustainable development goals. UNESCO have developed goals for ESD, education for sustainable development, with aim that individuals should develop competences which is considered important to act responsible in a sustainable manner in complex situations.The aim with this work is to design a sustainability education with an environmental focus to Ortviken Paper Mill. To do so, a pre-study and a survey have been done. The pre-study was an observation and created a picture of the mills activities and was a basis for the survey. The survey included interviews and a questionnaire which have been analysed thematically. Since the education is directed to adults and its goal is to rise the staff’s competence in environmental questions, adult education and ESD is chosen as a theoretical basis to the work.It was relieved in the survey that the education should be flexible, preferable designed as an e-learning and the staff learns best through discussion and communications with others. It also relieved that the staff’s experiences of education differ from elementary school to university, and that their knowledge about environmental issues and sustainable development varies. For the content in the education, it relieved in the survey that system thinking, chemicals and emissions impact on nature, and what individuals can do to lower the mills emissions as important or interesting. Since the staff’s knowledge varies and according to the chosen theory, an introduction about environmental issues and sustainable development is highly recommended. The education suggests being divided into six different parts, where the first five are designed as e-learning and the last part is a concluding discussion meeting. The parts in the education suggest focussing on:

    (i) Introduction to environmental issues and sustainable development

    (ii) SCA: s sustainability goals

    (iii) Chemicals and emissions impact on nature

    (iv) Sundsvall and the paper mills history from an environmental view

    (v) Ortviken as a cycle

    (vi) Concluding discussion and Workshop

  • Wangs, Taozhi
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Vehicle Dynamics.
    Analysis on Tyre Wear: Modelling and Simulations2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The tyre is an essential part of a road vehicle. It is in the contact between road and tyre that the forces that create the possibility for the driver to control the vehicle are generated. Tyres, however, wear down, which leads to both unhealthy wear particles and disposal of old tyres, both of which are harmful to the environment. If one could learn more about what causes wear, it might be possible to reduce tyre wear, which would be beneficial from both an economic and an ecological point of view. The aim of this thesis work is to develop a tyre model that can simulate tyre wear and take temperature, pressure and vehicle settings into account. Based on tyre brush theory, a tyre wear model has been developed which includes a thermal model, a pressure model and a friction model. Simulations and analysis of different cases has been performed. From the results, one can conclude the following: the tyre temperature and inflation pressure change with the distance the vehicle travels at the beginning and later become steady; higher external temperature will decrease tyre wear rate since the inflation pressure increases with the external temperature and the sliding friction decreases; higher vehicle speed leads to a higher tyre wear rate; the tyre temperature increases with increasing vehicle speed; the amount of tyre wear increases linearly with the normal load on the tyre; the tyre wear increases with the slip ratio exponentially due to both the siding distance and the sliding friction increasing with the slip ratio; the tyre wear increases exponentially with the slip angle. The complete model can estimate the tyre wear with different vehicle settings and external factors. More experiments are needed in the future to validate the complete model. In addition, since the heat transfer coefficient is changeable with temperature, the thermal model can be improved by introducing dynamic heat transfer coefficients. The Savkoor friction model used in the report can also be improved by tuning its parameters using more experimental data.

  • Public defence: 2020-04-16 09:00 F3, Stockholm
    Johansson, Petter
    KTH, School of Engineering Sciences (SCI), Applied Physics.
    Molecular Processes in Dynamic Wetting2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The spreading of liquids onto and over surfaces is a fundamental process in nature. It is present in all forms and sizes: From rivers carving through landscapes, to our blood stream transporting nutrients to cells, and even single water molecules moving through channels into these cells. We now have a good understanding of how fluid movement works inside the fluid itself. However, we do not fully understand the processes close to the contact line, where the liquid is spreading onto the surface. We are forced to make assumptions about this behaviour and none of these assumptions have yet proven to be universally valid.

    As everything in nature, liquid spreading is a fundamentally molecular process. This thesis summarises my work on applying this lens to the process. By studying molecules we begin at the smallest combined building blocks of nature and do not have to make any prior assumptions of the involved processes. Instead, we simply observe their behaviour. This is accomplished through the use of molecular dynamics simulation, which are an atomistic form of computer experiments. We use a realistic model of water molecules as our base liquid, since this captures realistic effects such as hydrogen bonding which are not present when using simpler models. Combined with large-scale systems which minimise the influence of finite-size effects, we have a realistic treatment of complex liquid systems.

    We find that the molecular processes of wetting have an important influence on large-scale wetting. Most importantly, the hydrogen bonding nature of water to realistic substrates yields the no-slip condition often used as a boundary condition for models of wetting. Furthermore, since molecular processes are thermal in nature they create energy barriers which impede contact line advancement. We show how these barriers are created and how they can be diminished, for example in the case of electrowetting. This highlights that understanding the molecular behaviour of fluids remains an important field of study.

  • Stavrou, Fotios
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Asymptotic Reverse Waterfilling Algorithm of NRDF for Certain Classes of Vector Gauss-Markov ProcessesManuscript (preprint) (Other academic)
    Abstract [en]

    In this paper, we revisit the asymptotic reverse-waterfilling characterization of the nonanticipative rate distortion function (NRDF) derived for a time-invariant multidimensional Gauss-Markov processes with mean-squared error (MSE) distortion in \cite{stavrou:2018cdc}. We show that for certain classes of time-invariant multidimensional Gauss-Markov processes, the specific characterization behaves as a reverse-waterfilling algorithm obtained in {\it matrix form} ensuring that the numerical approach of \cite[Algorithm 1]{stavrou:2018cdc} is optimal. In addition, we give an equivalent characterization that utilizes the {\it eigenvalues of the involved matrices} reminiscent of the well-known reverse-waterfilling algorithm in information theory. For the latter, we also propose a novel numerical approach to solve the algorithm optimally. The efficacy of our proposed iterative scheme compared to similar existing schemes is demonstrated via experiments. Finally, we use our new results to derive an analytical solution of the asymptotic NRDF for a correlated time-invariant two-dimensional Gauss-Markov process.

  • Public defence: 2020-04-15 10:00 Stockholm
    Safavi Nick, Arash
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Casting of Metals.
    Pores, inclusions and electromagnetic stirring: Topics from the continuous casting of steel2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis deals with two topics of relevance to the continuous casting of steel,in view of their importance as regards the quality of the final solidified structure.The first concerns the precipitation of gas pores and inclusions in the interden-dritic region of the solidifying metal. Motivated by experimental results thatindicate the formation of pore-inclusion clusters in the final cast structure, a the-oretical model is developed to describe how thus might occur; the model makesuse of the basic principles of fluid mechanics and heat transfer, with asymptoticmethods then being used in order to obtain solutions. In particular, it is foundthat soluto-thermocapillary drift in a direction perpendicular to the direction ofcasting, as a consequence of the dependence of surface tension at the pore-metalinterface on temperature and sulphur concentration, could explain cluster forma-tion. The second is a theoretical study concerning longitudinal electromagneticstirring (EMS), which is often used in the continuous casting of blooms in order toimprove product quality. Via an analysis of the three-dimensional (3D) Maxwellequations for the components of the magnetic flux density, a flaw is found inthe way that the components of the stirring Lorentz force have previously beencalculated; this is corrected and the new results are confirmed by comparison ofsolutions obtained from asymptotic analysis and time-dependent 3D computa-tions using finite-element methods. The analysis identifies the importance of theproduct of the bloom width and the wave vector of the applied field as a keydimensionless parameter.

  • Public defence: 2020-04-22 09:00 Stockholm
    Paschen, Jeannette
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Creating market knowledge from big data: Artificial intelligence and human resources2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The abundance of social media use and the rise of the Internet-of-Things (IoT) has given rise to big data which offer great potential for enhanced market knowledge for marketers. In the literature, market knowledge has been associated with positive marketing performance. The literature also considers market knowledge as an antecedent to insight which in turn is a strategic asset that can yield a sustained competitive advantage. In summary, market knowledge is important due to its relationship with performance and as a pre-requisite to insight.

    Market knowledge (as an outcome) results from market knowledge creation processes which encompasses the activities to create market knowledge. Market knowledge is created by integrating resources, specifically information technology and human resources.

    With respect to information technology, the unique characteristics of big data - volume, variety, veracity, velocity and value (the five V’s) - make traditional information technologies ill-suited to turn big data into information and ultimately market knowledge. Artificial intelligence (AI) has been discussed as one important information technology for creating market knowledge from big data. The literature suggests that AI is having a profound impact on the creation of market knowledge from big data and calls for more research on understanding the value potential of AI.

    Regarding human resources, the primacy of human contributions to the creation of market knowledge has been established in the literature. However, scholars and practitioners alike suggest that AI will change the nature and role of human contributions to creating market knowledge. The literature also suggests that the aspect of AI and human resources in market knowledge has not been adequately studied to date.

    Hence, the research problem in this thesis is formulated as “How do marketers create market knowledge from big data using artificial intelligence and human resources?” This research problem is addressed via five research questions (RQs):

    RQ 1: How does artificial intelligence contribute to creating market knowledge from big data?

    RQ 2: How does artificial intelligence impact the creation of market knowledge from big data and what are the implications for human resources?

    RQ 3: How do artificial intelligence and human resources interact in creating market knowledge from big data?

    RQ 4: What are the mutual contributions of artificial intelligence and human resources in creating market knowledge from big data?

    RQ 5: What are the contributions of artificial intelligence and human resources to different activities in creating market knowledge from big data?

    The research in this thesis encompasses two studies and three papers. The three papers are published or forthcoming in peer-reviewed journals. The research adopts an interpretivist paradigm and follows a qualitative research approach. The findings provide three key contributions to the body of knowledge and to theory. First, this thesis provides a non-technical understanding of what AI is, how it works and its implications for market knowledge, thus addressing a gap in the marketing literature.

    Second, this thesis posits that AI is a resource that meets the criteria of being 'valuable', 'rare', 'in-imitable', and 'organized' (VRIO) postulated by resource-based theory (RBT). The value of AI as a resource occurs in transforming big data into information and also AI transforming information into knowledge. Human resources are an important capability that improve the productivity of AI as a resource. This thesis provides empirical evidence that the nature of contributions offered by AI as a resource and human capabilities differ and explains how they differ.

    Third, this thesis contributes to resource-based theory. It proposes a conceptual model and puts forward five propositions regarding the relationship of AI as a resource, human capabilities and market knowledge. This model and the propositions can be tested in future scholarly work.

    This thesis opens with a chapter providing an introduction to the research area, followed by a literature review, a methodology chapter and a chapter discussing the findings and contributions to theory and practice, and outlining opportunities for future research. The papers and studies underpinning this thesis are presented in the last chapter of this thesis.

  • Public defence: 2020-04-21 15:00 Stockholm
    Dabirian, Amir
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Marketing and Entrepreneurship.
    Unpacking Employer Branding in the Information Technology Industry2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Attracting and retaining the best talent is a concern, particularly for knowledge-based firms in high-turnover industries, which rely on a limited supply of highly qualified individuals (Ewing, Pitt, De Bussy, & Berthon, 2002). In 2014, 36% of global employers criticized talent shortages, and in a 2015 study, 73% of CEOs reported being concerned about the availability of workers with key skills (Mosley, 2015). Employer branding is a key human resource and marketing strategy that contributes to the company’s brand, enhances the firm’s reputation as a great place for employees to work, and attracts a new workforce (Ahmad & Daud, 2016). An employer brand’s and its employer branding value propositions’ (EBV) ability to attract new employees and increase retention will provide benefits for the entire organization.

    EBV defines the employer’s attractiveness (Berthon et al., 2005), is a key aspect of the employer branding process, and provides differentiation for the firm (Alnıaçık & Alnıaçık, 2012; Backhaus & Tikoo, 2004; Berthon et al., 2005; Leekha Chhabra & Sharma, 2014; Moroko & Uncles, 2008) to attract and retain employees. Existing research viewed employer branding and its EBV from one or two views—employee or employer—and lacked multiview approaches to employer branding and employer attractiveness. This research focused on a holistic approach and addressed the question: “How do different EBVs affect the perceptions of employer attractiveness? To answer this question holistically, the following research subquestions emerged:


    RQ1: How do employees perceive the EBV of employer attractiveness?

    RQ2: How do current and former employees perceive the EBV of employer attractiveness?

    RQ3: How do potential employees perceive the EBV of employer attractiveness?

    RQ4: How do employers manage how employees perceive EBV?


    This research consisted of four empirical papers and focused on the information technology (IT) industry context. The first study focused on employee views from all industries, whereas the second study concentrated on the IT industry and compared current and former employees. Study 3 considered potential employees in the IT industry and operationalized the employee attractiveness construct and EBVs. The final study explored EBVs from the employer’s view in an IT firm and compared its employees’ views regarding the psychological contract. The design of this research is a mixed approach with descriptive and exploratory methodologies. IBM Watson’s artificial intelligence content analysis was used in Studies 1, 2, and 4.

    Contributions to the body of knowledge includes operationalizing the employee attractiveness construct as a set of EBVs. This research viewed EBVs holistically and extended the set of EBVs from five to eight value propositions for the IT industry. It also defined employer brand intelligence as a tool for practitioners to develop insights for their employer brand.

    The document is organized with an introductory chapter describing the overall research approach, followed by a literature review chapter, methodology chapter, and summary of findings and contributions. The four papers are included in the final chapter.

  • Ternström, Sten
    KTH, Superseded Departments (pre-2005), Speech Transmission and Music Acoustics. KTH, Superseded Departments (pre-2005), Speech, Music and Hearing. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH, Music Acoustics.
    Choir acoustics: an overview of scientific research published to date2003In: International Journal of Research in Choral Singing, Vol. 1, no 1, p. 3-12Article in journal (Refereed)
    Abstract [en]

    Choir acoustics is but one facet of choir-related research, yet it is one of the most tangible. Several aspects of sound can be measured objectively, and such results can be related to known properties of voices, rooms, ears and musical scores. What follows is essentially an update of the literature overview in my Ph.D. dissertation from 1989 of empirical investigations known to me that deal specifically with the acoustics of choirs, vocal groups, or choir singers. This compilation of sources is no doubt incomplete in certain respects; nevertheless, it will hopefully prove to be useful for researchers and others interested in choir acoustics.

  • Johansson, Petter
    et al.
    KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Hess, Berk
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI).
    Electrowetting diminishes contact line friction in dynamic wettingManuscript (preprint) (Other academic)
    Abstract [en]

    We use large-scale molecular dynamics to study dynamics at the three-phase contact line in electrowetting of water and electrolytes on no-slip substrates. Under the applied electrostatic potential the line friction at the contact line is diminished. The effect is consistent for droplets of different sizes as well as for both pure water and electrolyte solution droplets. We analyze the electric field at the contact line to show how it assists ions and dipolar molecules to advance the contact line. Without an electric field, the interaction between a substrate and a liquid has a very short range, mostly affecting the bottom, immobilized layer of liquid molecules which leads to high friction since mobile molecules are not pulled towards the surface. In electrowetting, the electric field attractscharged and polar molecules over a longer range which diminishes the friction.

  • Malmström, David
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Automatic tag suggestions using a deep learning recommender system2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This study was conducted to investigate how well deep learning can be applied to the field of tag recommender systems. In the context of an image item, tag recommendations can be given based on tags already existing on the item, or on item content information. In the current literature, there are no works which jointly models the tags and the item content information using deep learning. Two tag recommender systems were developed. The first one was a highly optimized hybrid baseline model based on matrix factorization and Bayesian classification. The second one was based on deep learning. The two models were trained and evaluated on a dataset of user-tagged images and videos from Flickr. A percentage of the tags were withheld, and the evaluation consisted of predicting them. The deep learning model attained the same prediction recall as the baseline model in the main evaluation scenario, when half of the tags were withheld. However, the baseline model generalized better to the sparser scenarios, when a larger number of tags were withheld. Furthermore, the computations of the deep learning model were much more time-consuming than the computations of the baseline model. These results led to the conclusion that the baseline model was more practical, but that there is much potential in using deep learning for the purpose of tag recommendation.

  • Liu, Alva
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Descriptive Music Search With Domain-Specific Word Embeddings2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Descriptive search is a type of exploratory search that allows users to search for content by providing descriptors. Instead of having a specific target in mind, the user looks for a recommendation of items that matches the given descriptors. However in the music domain, descriptive words do not necessarily have the same semantic meaning as they have in a generic text corpus. In this study, we investigate if we can train a shallow neural model on playlist data for descriptive music search, and if the model can capture music-specific word semantics. We carry out three experiments to evaluate our model. The first and the second experiments evaluate if the model can predict tracks that are relevant to given search queries, and the third experiment evaluates whether the model successfully captures domain-specific word semantics. From our experiments, we conclude that our model trained on playlist data indeed can capture music-specific word semantics and generate reasonable track predictions. For future work, we suggest to explore possibilities to re-rank the top results retrieved by the model and diversify and/or personalize the ordering of the results.

  • Teye, Mattias
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Predictive Uncertainty Estimates in Batch Normalized Neural Networks2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Recent developments in Bayesian Learning have made the Bayesian view of parameter estimation applicable to a wider range of models, including Neural Networks. In particular, advancements in Approximate Inference have enabled the development of a number of techniques for performing approximate Bayesian Learning. One recent addition to these models is Monte Carlo Dropout (MCDO), a technique that only relies on Neural Networks being trained with Dropout and L2 weight regularization. This technique provides a practical approach to Bayesian Learning, enabling the estimation of valuable predictive distributions from many models already in use today. In recent years however, Batch Normalization has become the go to method to speed up training and improve generalization. This thesis shows that the MCDO technique can be applied to Neural Networks trained with Batch Normalization by a procedure called Monte Carlo Batch Normalization (MCBN) in this work. A quantitative evaluation of the quality of the predictive distributions for different models was performed on nine regression datasets. With no batch size optimization, MCBN is shown to outperform an identical model with constant predictive variance for seven datasets at the 0.05 significance level. Optimizing batch sizes for the remaining datasets resulted in MCBN outperforming the comparative models in one further case. An equivalent evaluation for MCDO showed that MCBN and MCDO yield similar results, suggesting that there is potential to adapt the MCDO technique to the more modern Neural Network architecture provided by Batch Normalization.

  • Miller Ugalde, Patrick
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Temporally Stable Clusters of Movie Series: A Machine Learning Approach to Content Segmentation2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Clustering techniques have been shown to provide insight in various domains and applications. Adaptive evolutionary spectral clustering is a state-of-the-art method to obtain temporally stable clustering results from time-stamped data. This thesis explores the use of adaptive evolutionary spectral clustering to perform a clustering of film series into groups based on video streaming data. The developed method successfully performs a stable segmentation of film series into groups and introduces a number of extensions to the framework within the context of video on demand. We find that the implemented method allows for reasoning about clusters from an evolutionary perspective and that the state-of-the-art can be extended to introduce a dynamic number of clusters without negatively impacting the stability of properties of clusters.

  • Jyu, Yuanping
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Using Gamification and Augmented Reality to Encourage Japanese Second Language Students to Speak English2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Language anxiety is one of the key problems that hinder language learners to speak a target second language. This problem is especially relevant for Japanese second language learners, who tend to have difficulties in English. To alleviate this problem, researchers have succeeded in leveraging modern technologies to help second language learners practice various language skills, including speaking. These research papers introduce modern technologies into the field of education, which is the guide and basis of this paper. This study aims to help Japanese second language students to overcome the barrier of speaking English by designing a game-based language learning tool that incorporates elements of Augmented Reality (AR) technology.

    In this study, we try to encourage students to speak English by designing an AR-aided cooperative game. The GOAT (Gamified cOmunicAtion Tool) application was developed with gamification and AR technology. The tool was evaluated with 39 second language students at the different stages of its development during a period of eight months. The results suggest that the GOAT app has a high potential to help students to overcome their language anxiety and ultimately the barrier of speaking English. The findings of this study serve to prove that the use of gamification in the designed tool has a positive influence on second language learners. In particular, the GOAT app was found to promote students’ confidence and to encourage communications in a public setting. The enhanced confidence and more frequent communications ultimately lead Japanese students to be able to converse in English in a more natural and fluent manner. Nonetheless, the evidence with regard to whether the use of AR elements imposes a direct positive influence on second language learners’ confidence is not sufficient enough. Further research along this path is recommended for a more concrete conclusion to be made.

  • Svenningsson, Jakob
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Efficient Enclave Communication through Shared Memory: A case study of Intel SGX enabled Open vSwitch2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Open vSwitch is a virtual network switch commonly used to forward network packages between virtual machines. The switch routes network packets based on a set of flow rules stored in its flow tables. Open vSwitch does not provide confidentiality or integrity protection of its flow tables; therefore, an attacker can exploit software vulnerabilities in Open vSwitch to gain access to the host machine and observe or modify installed flow rules.

    Medina [1] brought integrity and confidentially guarantees to the flow tables of Open vSwitch, even in the presence of untrusted privileged software, by confining them inside of an Intel SGX enclave. However, using an enclave to protect the flow tables has significantly reduced the performance of Open vSwitch. This thesis investigates how and to what extent the performance overhead introduced by Intel SGX in Open vSwitch can be reduced.

    The method consisted of the development of a general-purpose communication library for Intel SGX enclaves, and two optimized SGX enabled Open vSwitch prototypes. The library enables efficient communication between the enclave and the untrusted application through shared memory-based techniques. Integrating the communication library in Open vSwitch, combined with other optimization techniques, resulted in two optimized prototypes that were evaluated on a set of common Open vSwitch use cases.

    The results of this thesis show that it is possible to reduce the overhead introduced by Intel SGX in Open vSwitch with several orders of magnitude, depending on the use case and optimization technique, without compromising its security guarantees.

  • Klobusická, Patricia
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Welcome to KTH: designing a tool for sustainable integration of international students: Case Study2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This study aims to present a design for a tool for sustainable integration of international students at KTH in Stockholm, Sweden. Integration has 3 main parts, social integration which is interaction with natives, structural which is concerned with a civic number, a job, and last but not least cultural integration which deals with customs, traditions, and religion. The tool has two main features, both of which are aiming to create favourable conditions for all three subsets of integration. The tool was developed by conducting 18 interviews, two rounds of prototyping and two rounds of user testing.

    It is made out of two main parts, namely informational and social. The information provided is both structural about institutions and getting around, whereas also information about cultural events, attendance at these by international students has the potential to strengthen social integration as well. The social part is designed as a 1-on-1 randomised chat that aims to encourage forming new friendships between international students and natives. This part allows new students to ask questions about anything, the process will get them randomly assigned to any native who shall answer which will create favourable conditions for forming new friendships between newcomers and natives.

  • Hammarlund, Hampus
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Designing a Software System to Improve Employee Motivation Through Behavior Change2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Due to unique circumstances, Japan is facing world leading rates of employee dissatisfaction. This trend in turn has had a ripple effect causing many other aspects of employee’s lives to be effected. One such area is work motivation, and in turn overall performance has also suffered. With the large impact this problem is having companies now have a financial incentive to help employees, beyond the social wellbeing argument that could be made before.A solution to this problem that the current project explored is a software system to increase employee motivation through behavior change. Using a software system to collect voluntary data on employees, the individual needs of users can be determined. These individual needs can then be addressed through tailored behavior change intervention.Through the course of the current paper the system’s architecture and evaluation will be covered. The current paper will also include the design of the behavior change intervention used during the experiments. Then from the results of these experiments it will be argued why the system designed and developed was a good solution to the problem of low employee motivation.

  • Persson, Emil
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Evaluating tools and techniques for web scraping2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of this thesis is to evaluate state of the art web scraping tools. To support the process, an evaluation framework to compare web scraping tools is developed and utilised, based on previous work and established software comparison metrics. Twelve tools from different programming languages are initially considered. These twelve tools are then reduced to six, based on factors such as similarity and popularity. Nightmare.js, Puppeteer, Selenium, Scrapy, HtmlUnit and rvest are kept and then evaluated. The evaluation framework includes performance, features, reliability and ease of use. Performance is measured in terms of run time, CPU usage and memory usage. The feature evaluation is based on implementing and completing tasks, with each feature in mind. In order to reason about reliability, statistics regarding code quality and GitHub repository statistics are used. The ease of use evaluation considers the installation process, official tutorials and the documentation.While all tools are useful and viable, results showed that Puppeteer is the most complete tool. It had the best ease of use and feature results, while staying among the top in terms of performance and reliability. If speed is of the essence, HtmlUnit is the fastest. It does however use the most overall resources. Selenium with Java is the slowest and uses the most amount of memory, but is the second best performer in terms of features. Selenium with Python uses the least amount of memory and the second least CPU power. If JavaScript pages are to be accessed, Nightmare.js, Puppeteer, Selenium and HtmlUnit can be used.

  • Peterson, Thomas
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Alternating Control Flow Graph Reconstruction by Combining Constant Propagation and Strided Intervals with Directed Symbolic Execution2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis we address the problem of control flow reconstruction in the presence of indirect jumps. We introduce an alternating approach which combines both overand under-approximation to increase the precision of the reconstructed control flow automaton compared to pure over-approximation. More specifically, the abstract interpretation based tool, Jakstab, from earlier work by Kinder, is used for the over-approximation. Furthermore, directed symbolic execution is applied to under-approximate successors of instructions when these can not be over-approximated precisely. The results of our experiments show that our approach can improve the precision of the reconstructed CFA compared to only using Jakstab. However, they reveal that our approach consumes a large amount of memory since it requires extraction and temporary storage of a large number of possible paths to the unresolved locations. As such, its usability is limited to control flow automatas of limited complexity. Further, the results show that strided interval analysis suffers in performance when encountering particularly challenging loops in the control flow automaton.

  • Swords, Michael
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Finding patterns in procurements and tenders using a graph database2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Graph databases are becoming more and more prominent as a result of the increasing amount of connected data. Storing data in a graph database allows for greater insight into the relationships between the data and not just the data itself.An area that has a large focus on relationship is the area of public procurements. Relationships such as who created which procurement and who was the winner. The procurement data today can be very unstructured or inaccessible which means that there is a low amount of analysis available in the area. To make it easier to analyse the procurement market there is a need for a proficient way of storing the data.

    This thesis provides a proof of concept of the combination of public procurements and graph databases. A comparison is made between two models of different granularity, measuring both query speed and storage size. There has also been an exploration of what interesting patterns that can be extrapolated from the public procurement data using centrality and community detection.The result of the model comparison shows a distinct increase in query speed at the cost of storage size. The result of the exploration is several examples of interesting patterns retrieved using a graph database with public procurement data, which show the potential of graph databases.

  • Engerstam, Sviatlana
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Real Estate Economics and Finance.
    Macroeconomic determinants of apartment prices in Swedish and German citiesManuscript (preprint) (Other academic)
    Abstract [en]

    We study the long-term effects of macroeconomic fundamentals on apartment prices in major urban areas in Sweden and Germany. The panel cointegration analysis was chosen as the primary approach due to the limited availability of data for a more extended period and frequency. The dataset consists of 2 countries – Germany and Sweden. The Swedish dataset includes three major cities and a period of  23 years, while the German dataset includes 7 “Big cities” for 29 years. Pooling the observations allows overcoming data restrictions in econometric analysis of long-term time series such as spatial heterogeneity, cross-sectional dependence and non-stationary, but cointegrated data. The results lie in line with previous studies and also allow comparison of single city estimations in an integrated equilibrium framework. The empirical results indicate that apartment prices react much stronger on changes in fundamentals in major Swedish cities than in German ones despite quite similar underlying fundamentals. Comparative analysis of regulations on the rental market, bank lending policies, and approaches to valuation for mortgage purposes in these two countries provide evidence that this overreaction arises due to institutional differences in form bank lending policies, mortgage valuation practices, and regulations on the rental market. Application of the more sustainable value concept such as mortgage lending value in mortgage valuations could make lending for housing less procyclical and stabilize house prices over the long run. Moreover, it will help to keep house prices away from overreaction on changes in macroeconomic fundamentals.

  • Våge, William
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Using machine learning for resource provisioning to run workflow applications in IaaS Cloud2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The rapid advancements of cloud computing has made it possible to execute large computations such as scientific workflow applications faster than ever before. Executing workflow applications in cloud computing consists of choosing instances (resource provisioning) and then scheduling (resource scheduling) the tasks to execute on the chosen instances. Due to the fact that finding the fastest execution time (makespan) of a scientific workflow within a specified budget is a NP-hard problem, it is common to use heuristics or metaheuristics to solve the problem.

    This thesis investigates the possibility of using machine learning as an alternative way of finding resource provisioning solutions for the problem of scientific workflow execution in the cloud. To investigate this, it is evaluated if a trained machine learning model can predict provisioning instances with solution quality close to that of a state-of-the-art algorithm (PACSA) but in a significantly shorter time. The machine learning models are trained for the scientific workflows Cybershake and Montage using workflow properties as features and solution instances given by the PACSA algorithm as labels. The predicted provisioning instances are scheduled utilizing an independent HEFT scheduler to get a makespan.

    It is concluded from the project that it is possible to train a machine learning model to achieve solution quality close to what the PACSA algorithm reports in a significantly shorter computation time and that the best performing models in the thesis were the Decision Tree Regressor (DTR) and the Support Vector Regressor (SVR). This is shown by the fact that the DTR and the SVR on average are able to be only 4.97 % (Cybershake) and 2.43 % (Montage) slower than the PACSA algorithm in terms of makespan while imposing only on average 0.64 % (Cybershake) and 0.82 % (Montage) budget violations. For large workflows (1000 tasks), the models showed an average execution time of 0.0165 seconds for Cybershake and 0.0205 seconds for Montage compared to the PACSA algorithm’s execution times of 57.138 seconds for Cybershake and 44.215 seconds for Montage. It was also found that the models are able to come up with a better makespan than the PACSA algorithm for some problem instances and solve some problem instances that the PACSA algorithm failed to solve. Surprisingly, the ML models are able to even outperform PACSA in 11.5 % of the cases for the Cybershake workflow and 19.5 % of the cases for the Montage workflow.

  • Li, Yingyu
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Object Detection and Instance Segmentation of Cables2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis introduces an innovative method to detect and do segmentation of cables for visual inspection. Cables lack significant features and fixed structures, which are difficult to capture with a cluttered background. This method is based on cable color and cable width in a specific scenario. It takes a splitand-merge approach to do detection and segmentation. This method can be used to inspect the status of cables on radio towers for maintenance and damage assessment by analyzing photos captured by unmanned aerial vehicles (UAV). This method to detect cables may also be beneficial to fields of navigation of UAV and navigation of autonomous underwater vehicles. With a loose metric with IoU of 30%, the mean precision reaches 50.79%, and the mean recall reaches 55.96%.

  • Huang, Mengdi
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Consistent Outdoor Environment Mapping with a 3D Laser Scanner2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Simultaneous Localization and Mapping(SLAM) is an essential prerequisite for various automated systems, such as self-driving cars, and planetary rovers. These autonomous machines acquire the knowledge of the environment through building a map while exploring an unknown area. Without this knowledge, they are not able to make right decisions.

    We used a 3D laser scanner with 16 channels, and encoders to collect the internal and external information. Then we estimate the trajectory that the robot has been to and build a consistent map upon sensor data. In this project, we studied and proposed several ways to improve the existing registration and optimization methods. By adding odometry information to predict the initial estimation as the input for Normal Distribution Transform(NDT), the performance is proved to be boosted. And the result witnessed a satisfying change after we distribute the error before inputing the registration result into g2o optimization framework. Besides, we proposed a method to increase the weight of certain voxels to improve the performance of NDT. We also experimented with different configurations of NDT and g2o to see how they impact the results.

    We conducted and analyzed the experiment on two datasets, urban dataset and forest dataset. The mapping is considered successful in the urban dataset.

  • Yeramian, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Autonomous testing of web forms2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A web form requires filling it with correct information in order to access pages behind it. As a result web forms tend to hinder automatic navigation through web sites. In order to fill a web form, we are going to extract relevant information contained in the HTML. Difficulty arises when taking into account the fact that that visual web pages are designed to be read by humans and not by robots. A human user can easily extract the information contained in a web form that is necessary to fill it. Extraction of visual information for automatic filling of web forms is an ongoing topic of research, which has already provided interesting results. However the task of indexing web sites continues to require some human intervention. The following thesis exposes a novel method of extracting visual as well as hidden information and automatically label each field composing a web form. The classification step boils down to finding keywords and then associating them with a label by using the mechanism validation and submission of web forms. These labeled data are then used to train machine learning models that aim at classifying text from given fields of a web form. A comparison between two different methods of classification illustrates the poor results obtained by the machine learning models when compared to the new methods based on keywords.

  • Androulakaki, Theofronia
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Probing User Perceptions on Machine Learning2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Machine Learning is a technology that has risen in popularity in the last decade. Designers face difficulties in working with Machine Learning as a design material. In order to help designers to cope with this material, many different approaches have been suggested, from books to insights of experienced designers with Machine Learning. In this research, the focus is on the users’ perceptions on Machine Learning and how these could contribute to better design. For this purpose, 10 participants deployed probes to investigate the term Machine Learning. Probes consisted of simple tasks that provoked participants to recognize Machine Learning elements in applications they already use and were deployed with the use of their smart phones. Participants formed personalized perceptions on Machine Learning which varied from creativity in Machine Learning to preoccupations about data use. Based on these findings, suggestions to designers were proposed. Moreover, a secondary research question that emerged was the difficulties the researcher faced while working with probing on Machine Learning user experiences for the specific research.

  • Karlsson, Viktor
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Introducing a Hierarchical Attention Transformer for document embeddings: Utilizing state-of-the-art word embeddings to generate numerical representations of text documents for classification2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The field of Natural Language Processing has produced a plethora of algorithms for creating numerical representations of words or subsets thereof. These representations encode the semantics of each unit which for word level tasks enable immediate utilization. Document level tasks on the other hand require special treatment in order for fixed length representations to be generated from varying length documents.

    We develop the Hierarchical Attention Transformer (HAT), a neural network model which utilizes the hierarchical nature of written text for creating document representations. The network rely entirely on attention which enables interpretability of its inferences and context to be attended from anywhere within the sequence.

    We compare our proposed model to current state-of-the-art algorithms in three scenarios: Datasets of documents with an average length (1) less than three paragraphs, (2) greater than an entire page and (3) greater than an entire page with a limited amount of training documents. HAT outperforms its competition in case 1 and 2, reducing the relative error up to 33% and 32.5% for case 1 and 2 respectively. HAT becomes increasingly difficult to optimize in case 3 where it did not perform better than its competitors.

  • Tatarakis, Nikolaos
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Differentially Private Federated Learning2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Federated Learning is a way of training neural network models in a decentralized manner; It utilizes several participating devices (that hold the same model architecture) to learn, independently, a model on their local data partition. These local models are then aggregated (in parameter domain), achieving equivalent performance as if the model was trained centrally. On the other hand, Differential Privacy is a well-established notion of data privacy preservation that can provide formal privacy guarantees based on rigorous mathematical and statistical properties. The majority of the current literature, at the intersection of these two fields, only considers privacy from a client’s point of view (i.e., the presence or absence of a client during decentralized training should not affect the distribution over the parameters of the final (central) model). However, it disregards privacy at a single (training) data-point level (i.e., if an adversary has partial, or even full access to the remaining training data-points, they should be severely limited in inferring sensitive information about that single data-point, as long as it is bounded by a differential privacy guarantee). In this thesis, we propose a method for end-to-end privacy guarantees with minimal loss of utility. We show, both empirically and theoretically, that privacy bounds at a data-point level can be achieved within the proposed framework. As a consequence of this, satisfactory client-level privacy bounds can be realized without making the system noisier overall, while obtaining state-of-the-art results.

  • Yu, Shi
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Exploring Use Cases for an Artificial Intelligence Poet2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    I report on the iterative process of designing a mobile AI poetry system, along with a series of broad scale use cases in which different variants of the system has been tested in the wild. The project has so far resulted in the generation of about 20 million individual poems, co-created by the system together with millions of users. Apart from the design of the technical side of the system, my focus has been on how the system could be adapted to and deployed in different commercial settings. I discuss my insights related to systems support for creative processes, and how findings from these use cases could be applicable also to other AI content generation systems.

  • Hallgrímsson, Guðmundur
    KTH, School of Electrical Engineering and Computer Science (EECS).
    An Embedded System for Classification and Dirt Detection on Surgical Instruments2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The need for automation in healthcare has been rising steadily in recent years, both to increase efficiency and for freeing educated workers from repetitive, menial, or even dangerous tasks. This thesis investigates the implementation of two pre-determined and pre-trained convolutional neural networks on an FPGA for the classification and dirt detection of surgical instruments in a robotics application. A good background on the inner workings and history of artificial neural networks is given and expanded on in the context of convolutional neural networks. The Winograd algorithm for computing convolutional operations is presented as a method for increasing the computational performance of convolutional neural networks. A selection of development platform and toolchains is then made. A high-level design of the overall system is explained, before details of the high-level synthesis implementation of the dirt detection convolutional neural network are shown. Measurements are then made on the performance of the high-level synthesis implementation of the various blocks needed for convolutional neural networks. The main convolutional kernel is implemented both by using the Winograd algorithm and the naive convolution algorithm and comparisons are made. Finally, measurements on the overall performance of the end-to-end system are made and conclusions are drawn. The final product of the project gives a good basis for further work in implementing a complete system to handle this functionality in a manner that is both efficient in power and low in latency. Such a system would utilize the different strengths of general-purpose sequential processing and the parallelism of an FPGA and tie those together in a single system.

  • Belo, Pedro
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Heterogeneous 3D Exploration with UAVs2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Multi-robot exploration algorithms usually focus on exploration time minimization while ignoring map accuracy. In this thesis, it is presented a new heterogeneous multi-robot exploration strategy that finds a balance between time consumption and map accuracy. By ranking UAVs based on their sensor accuracy, it is possible to coordinate them and pick strategic points to explore rather than the most rewarding ones. In particular, with a function (in this case a Gaussian) that maps a voxel’s uncertainty to a score, it is possible to tailor a UAV’s preference (by tuning expected value and variance) towards certain features and not only unexplored space. This algorithm was compared with AEPlanner for several environments, achieving better accuracy towards complete map exploration.

  • Wei, Xiao
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Deep Active Learning for 3D Object Detection for Autonomous Driving2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    3D object detection is vital for autonomous driving. However, to train a 3D detector often requires a huge amount of labeled data which are extremely expensive and tedious to obtain. In order to alleviate the annotation effort while maintaining detection performance, we aim to adopt active learning framework for training a 3D object detector with the least amount of labeled data. In contrast with the conventional passive learning that a machine learning model is trained on a pre-determined training dataset, active learning allows the model to actively select the most informative samples for labeling and add them to the training set. Therefore, only a fraction of data need to be labeled. To the best of our knowledge, this thesis is the first that studies active learning for 3D detection.

    We take progressive steps towards the goal. There are three stages with increasingly complex models and learning tasks. First, we start with active learning for image classification which can be viewed as a sub-problem of object detection. Second, we investigate and build a multi-task active learning framework with a deep refinement network for multi-modal 3D object detection. Lastly, we further analyze multi-task active learning with a more complicated two-stage 3D LiDAR vehicle detector. In our experiments, we study the fundamental and important aspects of an active learning framework with an emphasis on evaluating several popular data selection strategies based on prediction uncertainty. Without bells and whistles, we successfully propose an active learning framework for 3D object detection using 3D LiDAR point clouds and accurate 2D image proposals that saves up to 60% of labeled data on a public dataset. In the end, we also discuss some underlying challenges on this topic from both academic and industrial perspectives.

  • Ma, Yanwen
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Exploiting mobile technology affordances to support second language students using affective learning2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Self-regulated learning (SRL) which relates to challenges concerning both cognitive and affective learning domains is directly associated with students’ academic performance. It is especially critical for second language learners who need to employ SRL strategies and skills to be able to acquire the target language effectively. However, these students need help to develop their SRL, since the majority of them are not capable to make accurate judgments about their learning processes.

    This study aims at facilitating second language learners to develop their affective learning skills and strategies needed for their successful acquisition of a studied second language. In this design-oriented case study, a special mobile tool, ATLAS (AffecTive LeArning Srl) was designed and evaluated with 13 second language students through semi-structured interviews. All the interviews were carried out by the author of this thesis. Written informed consent was obtained from all participants for their issues to be utilized for this work. The interview data was later anonymized. The results showed that 85% of the study participants exhibited positive attitudes towards the use of affective learning activities in the tool to support their development of SRL during their second language studies. In particular, the ATLAS tool was perceived to be able to increase student motivation for SRL and to increase their awareness of their SRL progress.

    All in all, this study stresses that it is beneOicial to use technology-supported affective learning in order to assist students in their development of SRL skills, strategies, and knowledge needed for their successful second language acquisition. From a practical perspective, this study also provides a tool and several design guidelines that should be considered by designers when designing similar tools.

  • Persson, Alexander
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Redesigning a graphical user interface for usage in challenging environments with a user-centered design process2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Different possible interactions with computers is an ever-evolving topic and the usage of computers are more ubiquitous than ever. To design with the user in mind is not an easy task with regular use cases and interactions at hand. Designing for users in a military context can be even more difficult as the working environment of said users is demanding. This thesis sets out to investigate how a redesign of an existing GUI can reduce the impact of the contextual challenging environment of operating a software in terrain vehicles and in outdoor weather. For the redesign of the GUI a user-centered design process was performed. The process was initiated by using the method of contextual interviews and affinity diagram for data gathering and analysis,

    which gave a deeper understanding of the user’s issues and needs. After defining the different key elements for the redesign, a prototype was developed. The first prototype was evaluated by experienced users of the software out of military context. With the feedback from the users another developed version of the software was created and evaluated by current users of the software with an interview in military context. The evaluation showed that the users believed that the redesign of the GUI would help mitigate problems caused by the challenging context the software is used in, as well as improve quality of work.

  • Bergström, Philip
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Multimodal Relation Extraction of Product Categories in Market Research Data2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Nowadays, large amounts of unstructured data are constantly being generated and made available through websites and documents. Relation extraction, the task of automatically extracting semantic relationships between entities from such data, is therefore considered to have high commercial value today. However, many websites and documents are richly formatted, i.e., they communicate information through non-textual expressions such as tabular or visual elements. This thesis proposes a framework for relation extraction from such data, in particular, documents from the market research area. The framework performs relation extraction by applying supervised learning using both textual and visual features from PDF documents. Moreover, it allows the user to train a model without any manually labeled data by implementing labeling functions.We evaluate our framework by extracting relations from a corpus of market research documents on consumer goods. The extracted relations associate categories to products of different brands. We find that our framework outperforms a simple baseline model, although we are unable to show the effectiveness of incorporating visual features on our test set. We conclude that our framework can serve as a prototype for relation extraction from richly format-ted data, although more advanced techniques are necessary to make use of non-textual features.