Navigationshjälpmedel är det som finns i spel för att hjälpa spelaren att navigera. Navigationshjälpmedel kan delas upp i två kategorier: separata navigationshjälpmedel och integrerade navigationshjälpmedel. Det finns väldigt lite forskning om navigationshjälpmedel, speciellt om vilkaattityder spelare har till dem. Den här studien undersöker därför navigationshjälpmedel med frågeställningen: Vad har spelare med olika spelerfarenhet och spelvana för attityder till integrerade navigationshjälpmedel, samt hur speglas dessa attityder i spelupplevelsen? Det undersöks med hjälp av en spelprototyp. Efter att testdeltagarehar spelat prototypen fyller de i en enkät som utvärderar spelupplevelsen och blir efter det intervjuade om navigationshjälpmedel. Resultatet tyder på att spelvana spelare tenderar att ha positiva attityder till integrerade navigationshjälpmedel, medan ovana spelares attityder tenderar att varalite mindre positiva. Detta reflekteras också i spelupplevelsen. På grund av undersökningens låga deltagarantal kan inte lika starka slutsatser dras för ovana spelare, men framtida arbete med fler deltagare kan säkerställa slutsatserna.

För att kunna minska arbetsbelastningen på grafiker och speldesigners används Procedural Content Generation (PCG) för att kunna skapa varierat spelinnehåll med lite ansträngning. Denna studie undersöker algoritmerna Binary Space Partitioning (BSP) och Shortest Path (SP) för generering av grottsystem. Syftet med undersökningen är att ta reda på hur tidseffektivt algoritmerna kan generera ett grottsystem samt hur komplext ett grottsystem genererat av respektive algoritm är. Efter undersökning kan slutsatsen dras att BSP är den mest effektiva algoritmen på att generera grottsystem medan SP bidrar med mest skillnad i komplexitet mellan genereringar.

Manual deployments of software is a tedious, repetitive and non-scaling method of deploying software.Continuous Delivery is a practice that enables automated deployment of software in a rapid fashion at the click of a button.When deciding whether to start using a new practice, software companies need to make an assessment from a cost-benefit perspective.This thesis compares automated deployments through Continuous Delivery with manual deployments from a cost perspective.The comparison is done at a small software company where two tools for Continuous Delivery are chosen based on requirements imposed by the company. The tools, Octopus Deploy and Azure DevOps, are cost efficient to different degrees.Octopus is cost efficient if several deployments per week are necessary, particularly if many deployment targets are involved.Azure DevOps is quickly cost efficient in most cases due to its pricing scheme, only needing roughly one deployment per week for few deployment targets, and a couple of deployments per year for many deployment targets.The initial cost of having a paid employee set up the tool needs to be paid off, but is easily done within a year using weekly deployments with a small number of deployment targets.

Denna studie har som mål att undersöka hur två olika typer av virtuell verklighet (VR) påverkar användbarheten av flygsimulatorer. De två olika typer av VR som jämfördes var icke-immersiv VR och immersiv VR. För att undersöka detta formulerades en forskningsfråga kring ämnet och en undersökning genomfördes i vilken totalt 15 individer deltog. Deltagarna fick använda sig av en artefakt som hade möjligheten att användas i både ett icke-immersivt läge och ett immersivt läge. Samtliga deltagare fick använda båda lägena av artefakten. Ordningen som de använde lägena i fördelades helt slumpmässigt. Efter att deltagarna använt båda lägena av artefakten blev de intervjuade enligt en på förhand bestämd uppsättning frågor. Resultaten från undersökningen sammanställdes därefter, och analyserades enligt tre av de fem aspekterna av användbarhet som undersöktes. Resultaten från undersökningen visade att det immersiva läget hade en högre grad av användbarhet än det icke-immersiva läget.

Syftet med studien är att undersöka hur processor-, minnesanvändning och responstid påverkas vid en Distributed Denial-of-Service (DDoS) attack av typen TCP SYN-Flood. För att testa detta används metoderna Baseline (utan SYN-Cookies), Mod_Evasive, Suricata samt SYN-Cookies. Delar av resultatet går att jämföra med tidigare forskning vad beträffar metoden SYN-Cookies med processoranvändning och responstid, för övriga metoder har det ej gått att hitta någon forskning som påvisar resursanvändning och responstid över tid. Studien kan hjälpa organisationer och myndigheter att göra ett informerat val av skydd mot en TCP SYN-Flood-attack beträffande processor-, minnesanvändning och responstid. Resultaten av studien visar att Mod_Evasive använder lägst processor-, minnesanvändning och har lägst responstid av skyddsmetoderna.

Ever since the first industrial revolution industries have had the goal to increase their production. With new technology such as CPS, AI and IoT industries today are going through the fourth industrial revolution denoted as industry 4.0. The new technology not only revolutionises production, but also maintenance, making predictive maintenance possible. Predictive maintenance seeks to predict when failure would occur, instead of having scheduled maintenance or maintenance after failure already occurred. In this report a convolutional neural network (CNN) will analyse data from an ultrasound machine scanning a weld joint. The data from the ultrasound machine will be transformed by the short time Fourier transform in order to create an image for the CNN. Since the data from the ultrasound is not complete, simulated data will be created and investigated as another option for training the network. The results are promising, however the lack of data makes it hard to show any concrete proof.

För en arkivarie är autenticitet en viktig del i arkivet, att kunna bevisa olika typer av informations äkthet. I takt med att tekniken utvecklas kommer också nya möjligheter. Möjligheterna kommer tillsammans med frågor och diskussioner om hur detta ska gå att bevara den dagen informationen ska arkiveras. Att kunna signera en handling på distans öppnar upp möjligheter för ett snabbare och mer flexibelt arbetssätt. I vissa fall är det även säkrare. Det kan spara tid, kostnader och ge medborgare, företag och myndigheter alternativ till vad som krävs för en handskriven signatur. Frågan är hur långt myndigheter har kommit i användningen av dessa signaturer och hur de hanteras när de når arkivet. Elektroniska signaturer kan ge verksamheter en chans till ett smidigare sätt att arbeta, men de innebär också vissa utmaningar när de ska hanteras. Den här uppsatsen är en studie av olika verksamheters arbete kring ämnet elektroniska signaturer. Resultatet kommer från intervjuer med fyra olika myndigheter och totalt sex respondenter där jag frågat om deras hantering av elektroniska signaturer i dagsläget, vad de tror om utvecklingen samt vilka utmaningar de ser. Resultatet visar på en variation i arbetet kring dessa frågor och även olikheter i vilka behov man har av utveckling och projekt angående dessa signaturer.

Detta arbete handlar om hur indiska musikskalor påverkar en spelares prestation i ett spel i förhållande till hur spelare presterar till skalor från västvärlden. Rapporten innehåller den ve-tenskapliga bakgrunden till arbetet, förberedelserna och resultat ifrån en undersökning där tre musikskalor (dur, frygisk och den indiska skalan ”thaat marwa”) spelades upp under ett spel-test. Tiden det tog för deltagare i testet att slutföra en spelnivå mättes för alla tre skalor. Resultatet visade att ingen skillnad hittades i deltagarnas prestation mellan de olika musikaliska skalorna. Denna forskning har gett underlag till att i framtiden undersöka om instrumenten och rytmerna i indisk musik skulle påverka spelares prestationer annorlunda, jämfört med hur enbart skalorna i detta arbete påverkade prestationerna.

<p>The history of oil well engineering applications has revealed that the frequent operational problems are still common in oil well practice. Well blowouts, stuck pipes, well leakages are examples of the repeated problems in the oil well engineering industry. The main reason why these unwanted problems are unavoidable can be the complexity and uncertainties of the oil well processes. Unforeseen problems happen again and again, because they are not fully predictable, which could be due to lack of sufficient data or improper modelling to simulate the real conditions in the process. Traditional mathematical models have not been able to totally eliminate unwanted oil well problems because of the many involved simplifications, uncertainties, and incomplete information. This research work proposes a new approach and breakthrough for overcoming these challenges. The main objective of this study is merging two scientific fields; artificial intelligence and petroleum engineering in order to implement a new methodology.</p><p>Case-Based Reasoning (CBR) and Model-Based Reasoning (MBR), two branches of the artificial intelligence science, are applied for solving complex oil well problems. There are many CBR and MBR modelling tools which are generally used for different applications for implementing and demonstrating CBR and MBR methodologies; however, in this study, the Creek system which combines CBR and MBR has been utilized as a framework. One specific challenging task related to oil well engineering has been selected to exemplify and examine the methodology. To select a correct candidate for this application was a challenging step by itself. After testing many different issues in the oil well engineering, a well integrity issue has been chosen for the context. Thus, 18 leaking wells, production and injection wells, from three different oil fields have been analysed in depth. Then, they have been encoded and stored as cases in an ontology model given the name Wellogy.</p><p>The challenges related to well integrity issues are a growing concern. Many oil wells have been reported with annulus gas leaks (called internal leaks) on the Norwegian Continental Shelf (NCS) area. Interventions to repair the leaking wells or closing and abandoning wells have led to: high operating cost, low overall oil recovery, and in some cases unsafe operation. The reasons why leakages occur can be different, and finding the causes is a very complex task. For gas lift and gas injection wells the integrity of the well is often compromised. As the pressure of the hydrocarbon reserves decreases, particularly in mature fields, the need for boosting increases. Gas is injected into the well either to lift the oil in the production well or to maintain pressure in the reservoir from the injection well. The challenge is that this gas can lead to breakdown of the well integrity and cause leakages. However, as there are many types of leakages that can occur and due to their complexity it can be hard to find the cause or causal relationships. For this purpose, a new methodology, the Creek tool, which combines CBR and MBR is applied to investigate the reasons for the leakages. Creek is basically a CBR system, but it also includes MBR methods.</p><p>In addition to the well integrity cases, two complex cases (knowledge-rich cases) within oil well engineering have also been studied and analysed through the research work which is part of the PhD. The goal here is to show how the knowledge stored in two cases can be extracted for the CBR application.</p><p>A model comprising general knowledge (well-known rules and theories) and specific knowledge (stored in cases) has been developed. The results of the Wellogy model show that the CBR methodology can automate reasoning in addition to human reasoning through solving complex and repeated oil well problems. Moreover, the methodology showed that the valuable knowledge gained through the solved cases can be sustained and whenever it is needed, it can be retrieved and reused. The model has been verified for unsolved cases by evaluating case-matching results. The model gives elaborated explanations of the unsolved cases through the building of causal relationships. The model also facilitates knowledge acquisition and learning curves through its growing case base.</p><p>The study showed that building a CBR model is a rather time-consuming process due to four reasons:</p><p>1. Finding appropriate cases for the CBR application is not straightforward</p><p>2. Challenges related to constructing cases when transforming reported information to symbolic entities</p><p>3. Lack of defined criteria for amount of information (number of findings) for cases</p><p>4. Incomplete data and information to fully describe problems of the cases at the knowledge level</p><p>In this study only 12 solved cases (knowledge-rich cases) have been built in the Wellogy model. More cases (typically hundreds for knowledge-lean cases and around 50 for knowledge-rich cases) would be required to have a robust and efficient CBR model. As the CBR methodology is a new approach for solving complex oil well problems (research and development phase), additional research work is necessary for both areas, i.e. developing CBR frameworks (user interfaces) and building CBR models (core of CBR). Feasibility studies should be performed for implemented CBR models in order to use them in real oil field operations. So far, the existing Wellogy model has showed some benefits in terms of; representing the knowledge of leaking well cases in the form of an ontology, retrieving solved cases, and reusing pervious cases.</p>

The history of oil well engineering applications has revealed that the frequent operational problems are still common in oil well practice. Well blowouts, stuck pipes, well leakages are examples of the repeated problems in the oil well engineering industry. The main reason why these unwanted problems are unavoidable can be the complexity and uncertainties of the oil well processes. Unforeseen problems happen again and again, because they are not fully predictable, which could be due to lack of sufficient data or improper modelling to simulate the real conditions in the process. Traditional mathematical models have not been able to totally eliminate unwanted oil well problems because of the many involved simplifications, uncertainties, and incomplete information. This research work proposes a new approach and breakthrough for overcoming these challenges. The main objective of this study is merging two scientific fields; artificial intelligence and petroleum engineering in order to implement a new methodology. Case-Based Reasoning (CBR) and Model-Based Reasoning (MBR), two branches of the artificial intelligence science, are applied for solving complex oil well problems. There are many CBR and MBR modelling tools which are generally used for different applications for implementing and demonstrating CBR and MBR methodologies; however, in this study, the Creek system which combines CBR and MBR has been utilized as a framework. One specific challenging task related to oil well engineering has been selected to exemplify and examine the methodology. To select a correct candidate for this application was a challenging step by itself. After testing many different issues in the oil well engineering, a well integrity issue has been chosen for the context. Thus, 18 leaking wells, production and injection wells, from three different oil fields have been analysed in depth. Then, they have been encoded and stored as cases in an ontology model given the name Wellogy. The challenges related to well integrity issues are a growing concern. Many oil wells have been reported with annulus gas leaks (called internal leaks) on the Norwegian Continental Shelf (NCS) area. Interventions to repair the leaking wells or closing and abandoning wells have led to: high operating cost, low overall oil recovery, and in some cases unsafe operation. The reasons why leakages occur can be different, and finding the causes is a very complex task. For gas lift and gas injection wells the integrity of the well is often compromised. As the pressure of the hydrocarbon reserves decreases, particularly in mature fields, the need for boosting increases. Gas is injected into the well either to lift the oil in the production well or to maintain pressure in the reservoir from the injection well. The challenge is that this gas can lead to breakdown of the well integrity and cause leakages. However, as there are many types of leakages that can occur and due to their complexity it can be hard to find the cause or causal relationships. For this purpose, a new methodology, the Creek tool, which combines CBR and MBR is applied to investigate the reasons for the leakages. Creek is basically a CBR system, but it also includes MBR methods. In addition to the well integrity cases, two complex cases (knowledge-rich cases) within oil well engineering have also been studied and analysed through the research work which is part of the PhD. The goal here is to show how the knowledge stored in two cases can be extracted for the CBR application. A model comprising general knowledge (well-known rules and theories) and specific knowledge (stored in cases) has been developed. The results of the Wellogy model show that the CBR methodology can automate reasoning in addition to human reasoning through solving complex and repeated oil well problems. Moreover, the methodology showed that the valuable knowledge gained through the solved cases can be sustained and whenever it is needed, it can be retrieved and reused. The model has been verified for unsolved cases by evaluating case-matching results. The model gives elaborated explanations of the unsolved cases through the building of causal relationships. The model also facilitates knowledge acquisition and learning curves through its growing case base. The study showed that building a CBR model is a rather time-consuming process due to four reasons: 1. Finding appropriate cases for the CBR application is not straightforward 2. Challenges related to constructing cases when transforming reported information to symbolic entities 3. Lack of defined criteria for amount of information (number of findings) for cases 4. Incomplete data and information to fully describe problems of the cases at the knowledge level In this study only 12 solved cases (knowledge-rich cases) have been built in the Wellogy model. More cases (typically hundreds for knowledge-lean cases and around 50 for knowledge-rich cases) would be required to have a robust and efficient CBR model. As the CBR methodology is a new approach for solving complex oil well problems (research and development phase), additional research work is necessary for both areas, i.e. developing CBR frameworks (user interfaces) and building CBR models (core of CBR). Feasibility studies should be performed for implemented CBR models in order to use them in real oil field operations. So far, the existing Wellogy model has showed some benefits in terms of; representing the knowledge of leaking well cases in the form of an ontology, retrieving solved cases, and reusing pervious cases.