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  • Murali, Suhas
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Design (Div.).
    Development and Optimization of Press Fit Model between the Novi Ocean Upper Cylinder Section and Lower Float Body2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    About 3/4th percentage of Earth’s surface is covered with water, the demand for harnessing energy from the ocean is increasing periodically. This form of energy conversion is Wave Energy. This method is practised all around the world, Novi-Ocean by Novige AB is one of its kind where they aim to build a wave energy converter. The main component of the device is the oating platform above the sea level and powertake-o (cylinder) below the sea level. The motion of waves makes the platform to move vertically up and down thus creating a lift force 450 tons. The force is experienced at the interface of platform and cylinder attachment. Therefore, a conceptual design for distributing the force along the length of the shaft is necessary. Also, suitable bearing for the marine application needs to be selected. For the application mentioned relevant research is made on understanding the types of the wave energy converter and their working principles. The product development methodology is carried out to generate a conceptual design. Next, simulations were performed to decide the diameter of the shaft at the interface. A numerical and FEA model analysis of press- t is performed to check the contact pressure.

  • Bizimana, Boumediene
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.
    A hybrid low - temperature heating system in geothermal retrofitting for public buildings in the Mediterranean climate2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    More than 50 % of EU’s yearly energy demand is spent on heating and cooling systems with which most of its source is generated from non-renewable fossil fuel [1]. Furthermore, half of the EU buildings are heated with a non-efficient boiler of about 60% or less efficiency [1]. The report released by EU from 1990 to 2007 revealed that fuel combustion and fugitive emission contribute to about 79.3% of total greenhouse gas emissions in CO2 equivalents [1]. The EU-EBPD long-term renovation strategy is to improve the energy performance of all residential and non-residential buildings in its member countries through supporting the renovation of the existing buildings into highly energy efficient and decarbonised buildings [2]. Despite all these EU policies and efforts to replace these non-efficient heating systems, the main challenge is price comparison of different solutions and their efficiency in retrofitting of the heating old systems together with the lack of information about the functioning of those old systems [1]. Thus, the development of an easy to install heating system in retrofitting with low exergy heat supply is a significant contribution to a sustainable solution in minimizing energy resources depletion and environmental emission. Furthermore, efficient system control of these easy to install heating systems, hybrids combinations solution for retrofitting building could be a sustainable solution for the preservation of the existing building. The main objective of this work was to design an easy to install hybrid low-temperature floor heating system in retrofitting buildings and compare its results on energy performance, thermal comfort and indoor air quality with other conventional heating mainly used in the Mediterranean climate. This study was performed in two existing radiators heated buildings located in Sant Cugat del vallès in Catalonia, Spain.The results showed that the hybrid low-temperature heating system has the highest energy performance and energy saving of 48 % and 52% compared to that of existing radiator heating and all air heating, respectively. However, hybrid low-temperature floor heating showed a slow heating response, and consequently, it showed lower operative temperature compared to others even though it was within the recommended standards limits. The hybrid low-temperature heating system with demand-controlled ventilation also showed a better indoor air quality, while as existing radiator with its natural ventilation showed the worst indoor air quality. All three compared heating systems showed a better coefficient of performance with low-temperature heat supply and were able to operate with low-temperature heat supply.

  • Poujol, Matthieu
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Energy and Climate Studies, ECS.
    Transformation of a policy instrument for energy renovation of housing: Real case application on the French tax credit for energy transition2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The fight against climate change and the reduction of energy precarity are two major challenges of the energy policy of any country. Energy management in the building sector, particularly in the housing sector, is therefore crucial, and the energy renovation of housing makes it possible to reconcile these challenges by reducing households energy expenditure and greenhouse gas emissions in the residential sector. By focusing on the tax credit for energy transition, a French flagship financial policy instrument for the renovation of private housing in France but criticised for its inefficiency and antidistributive nature, we show that a major reform of this policy instrument can lead to achieve these energy and social objectives. The new method of calculating public subsidies for energy renovation proposed in this report represents a major step forward compared to the current situation in France, by creating a tailor-made instrument adapted to the financial situation of households and reflecting the energy performance of the renovation works. We show that it is therefore possible to have an ambitious climate strategy while making a policy aimed at modest households and enhancing the efficiency of public expenditure.

  • Barbette, Tom
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Tang, Chen
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Yao, Haoran
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Kostic, Dejan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Maguire Jr., Gerald Q.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.
    Papadimitratos, Panagiotis
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.
    Chiesa, Marco
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    A High-Speed Load-Balancer Design with Guaranteed Per-Connection-Consistency2020In: 17th USENIX Symposium on Networked Systems Design and Implementation / [ed] USENIX Association, Santa Clara, CA, USA, 2020, p. 667-683Conference paper (Refereed)
    Abstract [en]

    Large service providers use load balancers to dispatch millions of incoming connections per second towards thousands of servers. There are two basic yet critical requirements for a load balancer: uniform load distribution of the incoming connections across the servers and per-connection-consistency (PCC), i.e., the ability to map packets belonging to the same connection to the same server even in the presence of changes in the number of active servers and load balancers. Yet, meeting both these requirements at the same time has been an elusive goal. Today's load balancers minimize PCC violations at the price of non-uniform load distribution.

    This paper presents Cheetah, a load balancer that supports uniform load distribution and PCC while being scalable, memory efficient, resilient to clogging attacks, and fast at processing packets. The Cheetah LB design guarantees PCC for any realizable server selection load balancing mechanism and can be deployed in both a stateless and stateful manner, depending on the operational needs. We implemented Cheetah on both a software and a Tofino-based hardware switch. Our evaluation shows that a stateless version of Cheetah guarantees PCC, has negligible packet processing overheads, and can support load balancing mechanisms that reduce the flow completion time by a factor of 2–3×.

  • Rosa, Anna De
    et al.
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Kulkarni, Rohan
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Qazizadeh, Alireza
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Berg, Mats
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.
    Gialleonardo, Egidio Di
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Faccinetti, Alan
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Bruni, Stefano
    Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
    Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms2020In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017Article in journal (Refereed)
    Abstract [en]

    In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics.

  • Ekener, Elisabeth
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Sustainability Assessment and Management.
    Katzeff, Cecilia
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Strategic Sustainability Studies.
    Gunnarsson-Östling, Ulrika
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Sustainability Assessment and Management.
    Skånberg, Kristian
    Ömsesidiga beroenden mellan olika hållbarhetsperspektiv: Del II: Möjligheter att genom kunskaper om synergier och trade-offs mellan olika globala hållbarhetsmål förbättra förutsättningarna att nå Agenda 2030 i sin helhet- RAPPORT 69032019Report (Other academic)
  • Engström, Karl
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.
    Skoglund Lartell, Maximilian
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.
    Evaluating locations for subsurface dams: Case study on Storsudret, Gotland2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Due to precipitation poor winters and springs and over-exploitation of groundwater reserves during the summer months as a consequence of tourism, the island of Gotland has experienced fresh water shortages during recent years which has led to harsh restrictions in the use of municipal water. In order to find a solution to the islands fresh water problems, the region of Gotland and the Swedish Environmental Institute (IVL) have initiated a project in which the southernmost part of Gotland, Storsudret, will be used as a test site for new methods of sustainable freshwater storage and extraction methods.

    A, for Sweden, new method currently being investigated is the use of subsurface dams in order to increase the storage capacity of soil groundwater, thus increasing the possible extractions. Methods for finding suitable sites for subsurface dams has been investigated by Imran Jamali, 2016, and Ludvig Almqvist, 2017. There is however a need in further investigating and developing methods for subsurface dam location. This master thesis has focused on performing on-site data collection and on the use of groundwater flow model to evaluate the possibility of placing a subsurface dam on Storsudret, as steps in a method to localize areas suitable for subsurface dams.

    On site data was collected through resistivity measurements and water level measurements. This was used as input data for the flow model, MIKE SHE, together with more general GIS-data available. Flow modelling was performed during the period 2015-2018, which included the initially dry years of 2015-16 and the summer of 2017, and the more precipitation rich second half of 2017 and spring of 2018. Subsurface dams were modelled to investigate the results on the surroundings.

    The result did not show any obvious locations for the placement of a subsurface dam within the modelled area. The site considered to be most suitable for dam placement was modelled but showed only a rather small additional stored volume. However, the model result indicated that large possibilities for freshwater extraction already could be present in an existing geological formation in the area, even without the presence of a subsurface dam.

    As a tool for finding the specific location of groundwater dams, it was concluded that MIKE SHE gives a good overview over the general hydrogeological features and flow paths. Thus, it is a valuable tool when it comes to finding interesting sites for further investigations. However, due to problems in obtaining detailed enough input data, the model is considered to be less suitable for finding specific locations for dam placement when investigating a larger domain.

  • Lind, Jonas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Strategic Sustainability Studies.
    Malmqvist, Tove
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.
    Granth, Anna
    SGBC.
    Fauré, Eléonore
    KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Strategic Sustainability Studies.
    Walve, Sigrid
    SGBC.
    Wangel, Josefin
    SLU.
    Bakgrund och motiveringar i utvecklingen av Citylab manual för certifiering av en stadsdels hållbarhet2020Report (Other academic)
  • Fay, Dominik
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Membership Privacy in Neural Networks for Medical Image Segmentation2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Neural networks are known to memorize parts of their training set. Therefore, whenever sensitive information is involved, releasing a trained network may constitute a privacy breach. In this thesis, we use differential privacy to train neural networks that provably protect the identity of participants. In particular, we address the problems that arise in the domain of image segmentation. Here, previous methods needed to add unreasonably high noise to protect privacy, due to the high output dimensionality. We use dimensionality reduction to lower the required noise level, resulting in a better privacy-utility trade-off. We prove the privacy guarantee formally and evaluate predictive performance empirically on a synthetic dataset.

  • Paschen, Jeannette
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Paschen, Ulrich
    Pala, Erol
    Kietzmann, Jan
    Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resourcesManuscript (preprint) (Other academic)
    Abstract [en]

    Artificial intelligence (AI) allows business actors to exchange resources, particularly information and knowledge, to strengthen their businesses. These AI-enabled value co-creation processes are playing a substantial role in the business-to-business (B2B) sales context. However, little is known about the mechanisms and the process of value co-creation enabled by AI. On this basis, this study addresses this gap by employing Service-Dominant Logic to understand value co-creation with AI. This study identifies the value co-creation process and provides an understanding of the actors, activities and resources during the usage of AI to create value in B2B sales. The study also identifies several limitations of AI, such as value co-creation is heavily dependent on human activities and resources. Lastly, we suggest that managers continue to manage customer expectations when using AI for value co- creation and highlight the necessity of human actors and resources in the value co-creation process.

  • Paschen, Jeannette
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Kietzmann, Jan
    Kietzmann, Tim C.
    Unpacking artificial intelligence – How the building blocks of artificial intelligence (AI) contribute to creating market knowledge from big dataManuscript (preprint) (Other academic)
    Abstract [en]

    Purpose:

    This study explains artificial intelligence (AI) and its contributions to creating market knowledge from big data. Specifically, this study describes the foundational building blocks of any AI technology, their interrelationships and the implications of different building blocks with respect to creating market knowledge, along with illustrative examples.

     

    Design/methodology/approach:

    The study is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a model of how AI contributes to creating market knowledge from big data.

     

    Findings:

    The study explains AI from an input–processes–output lens and explicates the six foundational building blocks of AI. It discusses how the use of different building blocks transforms data into information and knowledge. It proposes a conceptual model to explicate the role of AI in creating market knowledge and suggests avenues for future research.

     

    Practical implications:

    This study explains the phenomenon artificial intelligence, how it works and its relevance for creating market knowledge for B2B firms.

     

    Originality/value:

    The study contributes to the literature on market knowledge and addresses calls for more scholarly research to understand AI and its implication for creating market knowledge.