Railway has the potential to become one of the most sustainable mediums for passenger and freight transport. This is possible by continuous updates to the asset management regime supporting Prognostics and Health Management (PHM). Railway tracks and catenaries are linear assets, and their length plays a vital role in maintenance. Railway catenary does not present many failures as compared to the rail track, but the failures that occur do not give enough opportunity for quick recovery. These failures cause extensive time delays disrupting railways operations. Such situations can be handled better by updating the maintenance approach. The domain of maintenance explores possible tools, techniques, and technologies to retain and restore the systems. PHM is dependent on data acquisition and analytics to predict the future state of a system with the least possible divergence. In the case of railway catenary and many other domains, this new technology of data acquisition is Light Detection And Ranging (LiDAR) device-based spatial point cloud collection. Current methods of catenary inspection depend on contact-based methods of inspection of railway catenary and read signals from the pantograph and contact wire while ignoring the rest of the wires and surroundings. Locomotive-mounted LiDAR devices support the collection of spatial data in the form of point-cloud from all the surrounding equipment and environment. This point cloud data holds a large amount of information, waiting for algorithms and technologies to harness it. A Digital Twin (DT) is a virtual representation of a physical system or process, achieved through models and simulations and maintains bidirectional communication for progressive enrichment at both ends. A systems digital twin is exposed to all the same conditions virtually. Such a digital twin can be used to provide prognostics by varying factors such as time, malfunction in components of the system, and conditions in which the system operates. Railways is a multistakeholder domain that depends on many organisations to support smooth function. The development of digital twins depends on the understanding of the system, the availability of sensors to read the state and actuators to affect the system’s state. Enabling a digital twin depends on governance restrictions, business requirements and technological competence. A concrete step towards enablement of the digital twin is designing an architecture to accommodate the technical requirements of content management, processing and infrastructure while addressing railway operations' governance and business aspects.The main objective of this work is to develop and provide architecture and a platform for the enablement of a DT solution based on Artificial Intelligence (AI) and digital technologies aimed at PHM of railway catenary system. The main results of this thesis are i) analysis of content management and processing requirements for railway overhead catenary system ii) methodology for catenary point cloud data processing and information representation iii) architecture and infrastructure requirements for enablement of Digital Twin and iv) roadmap for digital twin enablement for PHM of railway overhead catenary system.

The C11 standard introduced atomic types and operations, with an accompanying memory model, to enable the use of shared variables in concurrent programs. In this thesis, I demonstrate how compilers can be tested, in a way that is deterministic and covers the entire set of atomic operations, to ensure they correctly implement C11 atomics and the C11 memory model.  I use a large set of short concurrent programs (”litmus tests”), generated from a model written in a specification language and based on a formalized C11 memory model. Each test program is compiled and run with a model checker, to determine the possible outcomes; any program with an outcome that is possible after compilation but not allowed by C11 is a failed test case. As an alternative to model checking, I also test a nondeterministic, hardware-based method for running tests, but I find that this method is too inaccurate to be useful.  I test IAR and gcc compilers for Arm and RISC-V; all of these compilers pass all tests. Out of three compilers with purposefully inserted bugs, all are correctly identified as faulty. This testing process thus shows some promise, but further evaluation is needed.

As Industry 4.0 and Internet of Things are established across factories and enterprises, the interest for learning more about these concepts and the possibilities they provide for condition based maintenance is expressed by a factory in Sweden. By addressing the aspects of Internet of Things and Industry 4.0, a system for performing data acquisition from sensors in an industrial environment is developed using Arrowhead Framework. This framework is evaluated around its suitability for this kind of application, and regarding what the framework may provide to the factory compared to other solutions and systems. A solution featuring a system based on Arrowhead Framework is developed, implemented, and briefly tested. The system is successful in performing data acquisition, and Arrowhead Framework is considered a viable option that may be used to provide a system tailored for different purposes, presumed that the factory is prepared to allocate resources on developing a solution around it.

Several sustainable development indicators have been used to monitor and measure the progress of various countries. Similarly, reports and data available about countries progress prove that development has not been equal in all regions.  On the brighter side, the data can be used to inform decision making in areas that are experiencing deficiencies. In this research, a decision support system(DSS) is built to help governments and NGOs to properly choose projects that align with the needs of the people. We approached this research by utilizing Abraham Maslow’s proven psychological framework on the hierarchy of needs as the main criteria for choosing projects for sustainable development. The system ranks development projects based on the needs priority and how much it has been fulfilled. It ranks projects that meet an urgent need that is also lacking fulfillment higher than other project alternatives. The social progress index (SPI), a comprehensive open data that measures the social progress of counties were correlated to the needs indicated by Maslow’s Hierarchy. The needs were then used as criteria in the AHP decision analysis model to build a classic DSS to aid in selecting the most appropriate development project.

Background : The heat load has an emergent role in the energy consumption of the heating system in buildings. The industry experts also have been constantly focusing on the heat load optimization techniques and in the recent years, numerous Machine Learning (ML) techniques have come into picture to resolve various tasks. Objectives : This study is mainly focused on to analyze the time-series hourly data and choose suitable Supervised Machine Learning approach among Multivariate Linear Regression (MLR), Support Vector Regression, and Multi-layer Perceptron (MLP) Regressor so as to predict heat demand for identifying the deviating behaviors and potentially faults. Methods : An experiment is performed and the method consists of imputing the missing values, extreme values and selection of six different feature sets. Cross validation on Multivariate Linear Regression, Support Vector Regression, and Multi-layer Perceptron Regressor was performed to find the best suitable algorithm. Finally the residuals of the best algorithm and the best feature set was used to find the fault using the calculation of studentized residuals. Because of the time-series based data in data set, regression based algorithms was the best suitable choice to work with such type of data that is continuous. The faults in the system were identified based on the studentized residuals that exceeds the threshold value of 3 are classified as fault. Results : Among the regression based algorithms, Multi-layer Perceptron Regressor resulted in Mean Absolute Error (MAE) of 1.77 and Mean Absolute Percentage Error (MAPE) 0.29% on the feature set 1. Multivariate Linear Regression shown Mean Absolute Error 1.83 and Mean Absolute Percentage Error 0.31% on feature set 1 that has relatively higher error for the metrics of Mean Absolute Error and Mean Absolute Percentage Error as comparing to Multi-layer Perceptron Regressor. Support Vector Regression (SVR) shown Mean Absolute Error 2.54 that is higher than that of both Multivariate Linear Regression and Multi-layer Perceptron Regressor, while theMean Absolute Percentage Error 0.24% that is similar to Multivariate Linear Regression and Multi-layer Perceptron Regressor on the feature set 1. So the best performing algorithm is Multi-layer Perceptron Regressor. The feature sets 4,5 and 6 which are super-sets of 1, 2 and 3 feature sets along with addition of outdoor temperature. These feature sets 4, 5 and 6 did not show much impact even after considering the outdoor temperature. From, the Table 5.1 the feature sets 1, 2 and 3 are comparitively better than feature sets 4, 5 and 6 for the metrics Mean Absolute Error and Mean Absolute Percentage Error.Finally on comparing the first three feature sets, the feature set 1 resulted in less error for all three algorithms as comparing to feature set 2 and feature set 3 that can be seen in Table 5.1. So the feature set 1 is the best feature set. Conclusions : Multi-layer Perceptron Regressor perfomed well on six different feature sets comparing with Multivariate Linear Regression and Support Vector Regression. The feature set 1 had shown Mean Absolute Error and Mean Absolute Percentage values relatively low than other feature sets. Therefore the feature set 1 was the best performing and the best suited algorithm was Multi-layer Perceptron Regressor. The Figure A.3 represents the flow of work done in the thesis.

Navigation is the fundamental capability of mobile robots which allows them to move fromone point to another without any human interference. The autonomous operation of theserobots is depended on reliable, robust, and intelligent navigation system. With the recenttechnological progress, autonomous mobile robots are being deployed and used in differentareas and scenarios. Conventional navigation approaches depend on predefined accurateobstacle maps and costly high-end precise laser sensors. These maps are difficult and expensiveto acquire and degrade due changes in the environment. This limits the overall use of mobilerobots in dynamic settings. In this research, we investigate the end-to-end learning-basedapproach using vision and ranging sensors while using Deep Reinforcement Learning formobile robot navigation for indoor environments. Different state-of-the-art DRL algorithms were trained and compared in 3D-simulation in termsof sample efficiency and cumulative reward. Next, extensive experiments were carried outusing 10-dimensional sparse distance data from vision and ranging sensor. The trained modelswere evaluated in different environments of varying complexity to analyze the strength andgeneralizability of the learnt policies. Our results showed that ranging sensor approach was able to learn a robust navigation policywhich was able to generalize in unseen virtual environments without any additional trainingwith a high success rate. Whereas vision-based approach performed poorly due to insufficientinformation and hardware constraints. Moreover, all the experiment were carried out only insimulation. However, they should be directly transferable to an actual robot since abstractobservation space was used.

Augmented Reality (AR) and Mixed Reality (MR) have increased in attention recently and there are several implementations in video games and entertainment but also work-related applications. The technology can be used to guide workers in order to do the work faster and reduce human error while performing their tasks.  The potential of this kind of technology is evaluated in this thesis through a proof-of-concept prototype which guides a novice in the kitchen in following a recipe and completing a dish. The thesis shows a comparison between five different object detection algorithms, selecting the best in terms of time performance, energy performance and detection accuracy. Then the selected object detection algorithm is implemented in the prototype application.

Many websites and products rely heavily on consumers’ usage habits to maximize profit. Therefore, this project aims to aid product development by analyzing server traffic logs. The proposed solution is a monitoring system focusing on user analytics. The website for this monitoring system contains graphical presentations of statistics on user activity, browser usage, OS usage, browser version usage and OS version usage, most common requests, and the slowest requests over a chosen time interval. Statistical calculations are made in the backend code through connections to two databases. One database contains server traffic logs and is connected to the second database through indexing. Testing on the application proved proper functionality and fulfilled the requirements. The performance testing on the application showed effectiveness with relatively low latency for most statistics methods. This latency was further reduced by 50% using indexing. / Många webbplatser och produkter är mycket beroende av konsumenter och deras användningsvanor för att maximera vinsten. Därför syftar detta projekt till att hjälpa produktutvecklingen genom att analysera servertrafikloggar. Den föreslagna lösningen är ett övervakningssystem med fokus på användaranalys. Hemsidan för detta övervakningssystem innehåller grafiska presentationer av statistik över användaraktivitet, webbläsaranvändning, OS-användning, webbläsarversionsanvändning och OS-versionsanvändning, vanligaste förfrågningar och de långsammaste förfrågningarna under ett valt tidsintervall. Statistiska beräkningar görs i backend-koden genom kopplingar till två databaser. En databas innehåller servertrafikloggar och är kopplad till den andra databasen genom indexering. Testning på applikationen visade att den fungerade korrekt och uppfyllde kraven. Prestandatestningen på applikationen visade effektivitet med relativt låg latens för de flesta statistikberäknande metoder. Denna latens reducerades ytterligare med 50 % med hjälp av indexering.

The usage of a graphical user interface (GUI) in software often make up for a greatexperience for the user and is often not an issue, until the only way to run a programis through a GUI. Such a dependency will make development of a project very hardas the only way to perform tests is to execute them manually. This is the case for asimulator that the company Creone uses and it is where we will perform our work.Creone works with smart key management systems and cabinets that allow for a safeand convenient way to store and handle keys. Registered users can open the cabinetswith a pin code that is entered on the dial on the cabinet door. Keys are assigned tousers and what keys that a user can take from a cabinet is seen on the display abovethe dialpad. We are to create a new core implementation that will remove the GUIdependency and allow the simulator to perform automated tests to some extent.

Markvärme används för att hålla bland annat gångvägar och parkeringshus fria från snö och is. Dessa system styrs oftast baserat på momentan data hämtat direkt från anläggningen. Dessa markvärmesystem förbrukar stora mängder energi och syftet med detta projekt var att baserat på framtida väderprognoser optimalt och individuellt styra driften för att minimera energiförbrukningen.   Baserat på momentan marktemperatur och framtida väderprognoser går det att skapa en modell över markens framtida temperatur. Med hjälp av den framtida marktemperaturen samt tidigare och framtida nederbördsintensiteten går det att identifiera potentiellt farliga förhållanden som kommer att kräva markvärme. När detta behov är identifierat beräknas den tid det kommer ta att värma upp marken till en bestämd temperatur och smälta all nederbörd som befinner sig på marken.   När all nederbörd har smält börjar processen att torka marken. Med en ytavrinningsfaktor på 0.9 antas det att 90% av all nederbörd kommer att rinna av från vägen. Baserat på marktemperaturen och diverse olika väderparametrar beräknas avdunstningstiden för de resterande 10% av nederbörd som fortfarande är kvar på marken. När all nederbörd har avdunstat och ingen ny nederbörd är väntad inom tre timmar så anses marken vara torr och systemet går ur drift.   Systemet testades med data från februari 2023 och jämfördes med driften av markvärmen hos en av GateIBS anläggningar. Logiken för start av drift testades separat från logiken för stopp av drift. Det nya systemet skulle starta driften två gånger under februari med en nederbördsgräns satt till 3mm och 5mm och tre gånger med nederbördsgränsen satt till 1mm. Detta är en drastisk minskning jämfört med det faktiska markvärmesystemet som gick i drift totalt tio gånger under samma månad. Logiken för stopp av drift testades enbart då det faktiska markvärmesystemet gick i drift och under hela månaden så skulle det nya systemet sammanlagt stoppat driften 50 timmar tidigare vilken är en minskning i tid av total drift med 29.2% och en minskning i förbrukad energi med 28.5%. / Ground heating is used to keep walkways and parking lots free from snow and ice. These systems are usually controlled based on real-time data obtained directly from the facility. These ground heating systems consume large amounts of energy, and the purpose of this project was to optimally and individually control the operation based on future weather forecasts in order to minimize energy consumption.   Based on the current ground temperature and future weather forecasts, it is possible to create a model of the future temperature of the ground. Using the future ground temperature, as well as past and future precipitation intensity, it is possible to identify potentially hazardous conditions that will require ground heating. Once this need is identified, the time it will take to heat the ground to a specific temperature and melt all the precipitation on the ground is calculated.   After all the precipitation has melted, the process of drying the ground begins. With a surface runoff factor of 0.9, it is assumed that 90% of all precipitation will run off from the road. Based on the ground temperature and various weather parameters, the evaporation time for the remaining 10% of precipitation still on the ground is calculated. When all the precipitation has evaporated and no new precipitation is expected within three hours, the ground is considered dry, and the system stops operating.   The system was tested using data from February 2023 and compared with the operation of the ground heating system at one of GateIBS facilities. The logic for starting the operation was tested separately from the logic for stopping the operation. The new system would initiate the operation twice in February with a precipitation threshold set at 3mm and 5mm, and three times with the threshold set at 1mm. This is a drastic reduction compared to the actual ground heating system, which started a total of ten times during the same month. The logic for stopping the operation was tested only when the actual ground heating system was in operation, and throughout the month, the new system would have stopped the operation 50 hours earlier, resulting in a total reduction in operating time of 29.2% and a reduction in energy use by 28.5%.