En gymnasielärare i teknik i den svenska skolan påverkar och påverkas av samhället utanför skolans väggar. Samhällets digitalisering kan påverka tekniklärarens relativt stora möjligheter att välja det tekniska innehållet i kurserna, i olika grad. För att vara konkurrenskraftig i samhället behöver framtida tekniker ha goda kunskaper om de just för stunden aktuella teknikområdena och tidigare studier har visat att skolans resultat i hög grad beror av vilka kunskaper läraren har. I den här studien undersöktes tre tekniklärare på olika gymnasieskolor i Sverige, med avseende på synen på deras kunskaper i att undervisa om artificiell intelligens (AI), sakernas internet (Internet of Things - IoT) och robotteknik. Vidare studerades vilka undervisningsmetoder de avsåg att använda, samt vilka begränsande ramar de kunde se i detta uppdrag. Via enkät och intervjuer samlades data om frågeställningarna in. Rådata transkriberades och analyserades med utgångspunkt i Shulmans teori om Pedagogical Content Knowledge (PCK – ibland kallat ämnesdidaktisk kunskap på svenska) och även med stöd av ramfaktorteori och läroplansteori. Resultatet visar att de deltagande lärarna har behov av kompetenshöjning inom dessa tre teknikområden, samt att valet av undervisningsmetoder är beroende av de ramar som bland annat i form av tid och ekonomi påverkar undervisningen. Resultatet skiljer sig inte från liknande tidigare studier genomförda på lärare i grundskolan. / A technology teacher in the Swedish upper secondary school acts and is influenced by society outside the school walls. The digitalisation of society more or less influences how the technology teacher will choose the ways of implementing the curriculum, with respect to the technicalcontent of the courses. To be competitive in society, future technicians must have goodknowledge of the current technical areas, and previous studies have shown that the school's results largely depend on what knowledge the teacher has. In this study, three technology teachers at various upper secondary schools in Sweden were examined, regarding their view of their knowledge in teaching about artificial intelligence (AI), the Internet of Things (IoT) and robotics. The study also investigated their intended teaching methods and the framing factors they perceived when trying to perform this task. Through a survey and interviews, data on the issues were collected. Raw data were transcribed and analysed based on the theory of Pedagogical Content Knowledge (PCK), by Shulman and with the support of frame factor theory and curriculum theory. The results show that the participating teachers need more competence in these three technology areas, and that the choice of teaching methods depends on the resources provided, for instance in the form of time and finances. The results do not differ from similar previous studies conducted on primary school teachers.

The interest of drones in all forms has exploded in the recent years. The development of multirotor vehicles such as quadcopters and octocopters, has reached a point where they are cheap and versatile enough to start becoming a part of everyday life. It is clear to say that the future applications seem limitless. This thesis goes through the steps of development, modelling and control design of an octocopter system. The developed octocopter builds on a concept of using the mini computer Raspberry Pi together with the code generation functionality of Matlab/Simulink. The mathematical modelling of the octocopter includes the thrust and torques generated by the propellers, added with gyroscopic torque. These are combined with the aerodynamic effects caused by incoming air. The importance of modelling the later mentioned effects has increased with the demand of precise controlled extreme manoeuvres. A full state feedback based hybrid controller scheme is designed against a linearized model, which makes use of the motor dynamics. The controllers show good performance in simulations and are approved for flight tests, which are conducted on two separate occasions. The octocopter makes two successful flights, proving that the concept can be applied on multirotor vehicles. However, there is a miss-match between the mathematical model and the physical octocopter, leaving questions for future work.

Bakgrund: Under de senaste två decennierna har artificiell intelligens (AI) blivit en viktig del av teknologisk utveckling och används nu på många håll inom samhället. Organisationer och företag behöver följa med i utvecklingen och därmed överväga integrering av AI i olika arbetsmoment. Idag kan företag integrera AI inom redovisningens repetitivt manuella uppgifter, bland annat, för att bidra till effektivisering, stordriftsfördelar, samt för att reducera risken för mänskliga fel. Däremot påpekas att det krävs ytterligare forskning gällande vilka motiv och attityder det finns för att integrera AI i redovisningen.  Syfte: Studiens syfte är att framhäva centrala aspekter, baserat på Rogers adaptionsteori, som är viktiga för att organisationer skall kunna integrera AI framgångsrikt i redovisningen.  Metod: Studien använder en kvalitativ forskningsmetod med en abduktiv forskningsansats. Forskningen genomfördes genom semistrukturerade intervjuer med nio respondenter för att samla in empiriskt material. Samtliga respondenter, som valdes ut genom målstyrt urval, är verksamma inom redovisnings- och IT-branschen. Slutsats: Det krävs kunskap och en gynnsam attityd för att framgångsrikt integrera AI i redovisningen. Dessutom är det även betydelsefullt att ta hänsyn till risker för att bidra till korrekt och säker användning. Vidare skall nyttan av att integrera AI överstiga kostnaden för investeringen, samt förbättra effektiviteten i redovisningens arbetsmoment. / Backround: Over the past two decades, artificial intelligence (AI) has become a crucial part of technological development and is now widely used in society. Organizations and companies need to keep up with this development and consider integrating AI into various work processes. Today, companies can integrate AI into repetitive manual tasks in accounting, among other areas, to enhance efficiency, achieve economies of scale, and reduce the risk of human error. However, it is noted that further research is needed to understand the motives and attitudes towards integrating AI in accounting. Purpose: The purpose of this study is to highlight key aspects, based on Rogers diffusion of innovations theory, that are important for organizations to successfully implement AI in accounting. Method: The study employs a qualitative research method with an abductive research approach. The research is conducted through semi-structured interviews with nine respondents in order to gather empirical data. All respondents, selected through purposive sampling, are active in the accounting- and IT-industries. Conclusion: It is essential to absorb knowledge and foster a positive attitude in order to successfully integrate AI in accounting. Moreover, it is crucial to consider associated risks to ensure its accurate and secure utilization. Furthermore, the benefits of AI integration should outweigh the investment costs and enhance efficiency in the accounting processes.

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With no online pupillary analysis methods today, both the medical and the research fields are left to carry out a lengthy, manual and often faulty examination. A real-time, intelligent, embedded systems solution to pupillary analysis would help reduce faulty diagnosis, speed-up the analysis procedure by eliminating the human expert operator and in general, provide a versatile and highly adaptable research tool. Therefore, this thesis has sought to investigate, develop and test possible system designs for pupillary analysis, with the aim for caffeine detection. A pair of LED manipulator glasses have been designed to standardize the illumination method across testing. A data analysis method of the raw pupillary data has been established offline and then adapted to a real-time platform. ANN was chosen as classification algorithm. The accuracy of the ANN from the offline analysis was 94% while for the online classification the obtained accuracy was 17%. A realtime data communication and synchronization method has been developed. The resulting system showed reliable and fast execution times. Data analysis and classification took no longer than 2ms, faulty data detection showed consistent results. Data communication suffered no message loss. In conclusion, it is reported that a real-time, intelligent, embedded solution is feasible for pupillary analysis.

Hardware-in-the-loop (HiL) is a concept for testing physical equipment by connecting itto a mathematical representation (model) of the physical process. HiL-testing reduces thecost and saves time before testing the physical equipment (hardware) on the real (physical)process. The physical process chosen for this study is wire+arc additive manufacturing(WAAM), an advanced additive manufacturing (AM) technology that deposits metalbased material layer-by-layer. In this study, simulations of the robot path are carried outwhile the physical robot performs a physical process (additive manufacturing). In robotadditive manufacturing, the desired CAD model is currently sliced down into layers usingslicer software, and the layers are then translated into a path. The robot then moves alongthe path of these pre-defined layers to produce a three-dimensional structure. The heightof the produced structures and desired CAD models have deviations because of processinstabilities and temperature variations among other factors. The robot path should beupdated every time a layer is printed to compensate for the height differences. This isachieved by parametrizing the CAD model, i.e., the CAD model of the structure to beprinted is replaced by a mathematical equation (model). In this study, the mathematicalmodel is updated for each layer in real-time with feedback data from sensors that monitorthe additive manufacturing process. The concept of updating a mathematical model andexecuting it in real-time is called real-time simulation (RTS). In this study, a HiL-basedreal-time simulation setup has been developed, which predicts the required printing layerheight and the number of layers (based upon the latest feedback data from the monitoringsensors), and the required height of the structure. By combining hardware and software,a cyber-physical system has been created, enabling the transition from automation toautonomous robotics and contributing to Industry 4.0.

Unmanned Aerial Vehicle (UAV), also called drones, are used for Search And Rescue (SAR) missions, mainly in the form of a pilot manoeuvring a single drone. However, the increase in labour to cover larger areas quickly would result in a very high cost and time spent per rescue operation. Therefore, there is a need for an easy to use, low-cost, and highly autonomous swarm of drones for SAR missions where the detection and rescue times are kept to a minimum. In this thesis, a Subsumption-based architecture is proposed, which combines multiple behaviours to create more complex behaviours. An investigation of (1) what are the critical aspects of controlling a swarm of drones, (2) how can a combination of different behavioural algorithms increase the performance of a swarm of drones, and (3) what benchmarks are necessary when evaluating the fitness of the behavioural algorithms. The proposed architecture was simulated in AirSim using the SimpleFlight flight controller through experiments that evaluated the individual layers and missions that simulated real-life scenarios. The results validate the modularity and reliability of the architecture, where the architecture has the potential for improvements in future iterations. For the search area of 400×400meters, the swarm consistently produced an average area coverage of at least 99.917% and found all the missing people in all missions, with the slowest average being 563 seconds. Compared to related work, the result produced similar or better times when scaled to the same proportions and higher area coverage. As comparisons of results in SAR missions can be difficult, the introduction of Active time can serve as a benchmark for others in future swarm performance measurements.

In this thesis it has been studied how operational and workshop data can be used to improve the handling of field quality (FQ) issues for electronic units. This was done by analysing how failure rates can be predicted, how failure mechanisms can be detected and how data-based lifetime models could be developed. The work has been done on an electronic control unit (ECU) that has been subject to a field quality (FQ) issue, determining thermomechanical stress on the solder joints of the BGAs (Ball Grid Array) on the PCBAs (Printed circuit board assembly) to be the main cause of failure. The project is divided into two parts. Part one, "PCBA" where a laboratory study on the effects of thermomechanical cycling on solder joints for different electrical components of the PCBAs are investigated. The second part, "ECU" is the main part of the project investigating data-driven solutions using operational and workshop history data. The results from part one show that the Weibull distribution commonly used to predict lifetimes of electrical components, work well to describe the laboratory results but also that non parametric methods such as kernel distribution can give good results. In part two when Weibull together with Gamma and Normal distributions were tested on the real ECU (electronic control unit) data, it is shown that none of them describe the data well. However, when random forest is used to develop data-based models most of the ECU lifetimes of a separate test dataset can be correctly predicted within a half a year margin. Further using random survival forest it was possible to produce a model with just 0.06 in (OOB) prediction error. This shows that machine learning methods could potentially be used in the purpose of condition based maintenance for ECUs.

You do not want your drone to crash. Therefore, safety systems should be put in place to prevent such an event, and obstacle avoidance is a major part of this. Today, the most successful techniques use cameras or light detection and ranging (LIDAR) to find and avoid obstacles; but to improve resiliency, multiple systems should be used. This thesis proposes to use microphones, listening to the drone’s own noise, to estimate the distance to surrounding obstacles. An obstacle echolocation solution for multi-rotor aerial vehicles (MAVs) using ego-noise is developed. The MAV’s noise is captured and auto-correlated to detect echoes at different time delays. This signal is whitened to remove structured measurement noise resulting from the narrow-band components of the MAV’s noise. By recording the MAV’s noise using multiple microphones, a time of arrival (TOA) estimate of the obstacle position is achieved. A beamforming-based solution is used to calculate this estimate. A series of simplified proof-of-concept experiments show that ego-noise echolocation is possible and that the developed solution works in a controlled environment. A prototype implementation of a realistic system is also created. Four signal fusion alternatives are compared, though no best alternative is found for all situations. More work is needed to apply the findings of this work in a robust way, but the principle is shown to work.

Bakgrund: Den omänskliga faktorn kan vara en riskfaktor för verksamhetsnyttan, goda arbetsmiljön, säkerheten och hållbarheten i hälso- och sjukvården. Några av anledningarna till att Artificiell Intelligens (AI) har börjat bli realistiskt inom hälso- och sjukvården är datorernas ökade prestanda och tillgång till stora mängder data. AI har potential att fungera som ett verktyg för att implementera digitaliseringen inom hälso- och sjukvården, samtidigt som den mänskliga närvaron kan bevaras. Syfte: Undersöka hur vårdpersonalen förhåller sig till användningen av AI-system och deras upplevelse av kontroll och ansvar. Metod: Kvalitativ design används och semistrukturerade intervjuer genomfördes. Inledningsvis användes allmänna frågor för att fånga informanternas tidigare och nuvarande erfarenhet av AI-system i hälso- och sjukvården. Därefter utformades fortsatta frågor enligt vinjettmetoden. Kvalitativ analysmetod användes för att tolka intervjuerna. Resultat: Analysen resulterade i sex kategorier som belyser olika aspekter av vårdpersonalens samarbete med AI-system inom hälso- och sjukvården. Dessa kategorier inkluderar kontrollbehov, samstämmighet, prestation, övervakning, kunskap och prioritering. Diskussion: Motsats till AI kan människor hållas ansvariga för sina beslut och för de beslut som fattas av AI-system. Inom sjukvården upplever vårdpersonalen ett gemensamt helhetsansvar för patientvården, oavsett lagar och regler. Vetskapen om att behöva möta patienter eller anhöriga och framföra dåliga nyheter, är det som verkligen sätter ansvaret i perspektiv. Vid felaktigheter påverkas hela vårdteamet, även om de inte själva orsakat felet. I framtiden kommer vårdpersonalens roll att gå från att vara bedömare till att vara övervakare. Denna förändring kan dock orsaka problem, eftersom vårdpersonalens kontrollbehov för AI-system minskar i akuta situationer. I dessa situationer fokuserar vårdpersonalen på att normalisera situationen och prioriterar bort allt annat. Beslut som fattas görs med medvetenheten om att de sannolikt behöver omprövas när situationen har stabiliserats. Detta problematiserar den nya rollen som övervakare, eftersom vårdpersonalen i dessa situationer egentligen borde ha större fokus på att se till att AI-systemet fungerar korrekt. Slutsats: Det är osannolikt att vårdpersonalen kommer att kunna hantera de snabba och komplexa beslut som AI-systemet gör. Det kommer kräva ökad kunskap från vårdpersonalen för att identifiera situationer där AI-systemet kan göra fel och i händelse av en akut situation kommer personalen att prioritera detta över andra åtaganden. För att etablera ett samarbete mellan människa och maskin inom vården krävs utbildning och förståelse för det skifte som sker för vårdpersonalens roll, att gå från bedömning till övervakning av AI-systemen. / Background: The inhumane factor can be a risk factor for operational benefit, good working environment, safety and sustainability in healthcare. Some of the reasons why Artificial Intelligence (AI) has started to become realistic in healthcare are the increased performance of computers and access to large amounts of data. AI has the potential to act as a tool to implement digitization in healthcare, while preserving the human presence. Purpose: Investigate how healthcare professionals relate to the use of AI systems and their experience of control and responsibility. Method: Qualitative design is used and semi-structured interviews were conducted. Initially, general questions were used to capture the informants' previous and current experience with AI-systems in healthcare. Further questions were then designed according to the vignette method. Qualitative analysis method was used to interpret the interviews. Results: The analysis resulted in six categories that highlight different aspects of healthcare professionals collaboration with AI-systems in healthcare. These categories include control needs, compliance, performance, monitoring, knowledge, and prioritization. Discussion: Unlike AI, humans can be held accountable for their decisions and for the decisions made by AI-systems. In healthcare, the healthcare professionals experience a shared overall responsibility for patient care, regardless of laws and regulations. The knowledge of having to meet patients or relatives and deliver bad news is what really puts the responsibility into perspective. In the event of errors, the entire healthcare team is affected, even if they did not cause the error themselves. In the future, the role of healthcare professionals will change from being an assessor to instead monitor the AI-system. However, this change can cause problems, as healthcare professionals control needs for AI-systems are reduced in emergency situations. In these situations, the healthcare professionals focus on normalizing the situation and deprioritises everything else. Decisions that are made are made with the awareness that they will likely need to be reconsidered once the situation has stabilized. This problematizes the new role of supervisor, because in these situations the healthcare professionals should really be more focused on making sure the AI-system is working correctly. Conclusion: It is unlikely that healthcare professionals will be able to handle the rapid and complex decisions made by the AI-system. It will require increased knowledge from the healthcare professional, to identify situations where the AI system can make mistakes and in the event of an emergency, the staff will prioritize this over other commitments. In order to establish a collaboration between humans and machines in healthcare, training and understanding of the shift that is taking place for the role of the healthcare professionals, to go from assessment to monitoring of the AI systems, is required.