In Industry 4.0, production, Information Technology (IT), and the Internet are combined. The nine technologies of Industry 4.0, Artificial Intelligence (AI) and Cyber-Physical System (CPS), are changing machines, strategies, processes, and maintenance. In the first generation of maintenance, machines were run to failure, which is related to Corrective Maintenance. Systems for planning and control were implemented in the second generation, related to Predetermined Maintenance. Condition Based Maintenance (CBM) was presented in the third maintenance generation. Industry 4.0 places new demands on maintenance and different maintenance approaches are presented in previous research, such as Maintenance 4.0, Smart Maintenance and Self-Maintenance. This research focuses on smart maintenance technologies, using the nine technologies of Industry 4.0, such as Industrial Internet of Things (IIoT), and Big Data and Analytics, for machine connection, maintenance data collection, analysis of data, and making decisions using AI. CPS can be used to integrate the physical world, such as manufacturing machines, factory environment, material, people, and executions, with the cyber world, such as data analysis, apps, services, and decision-making. Previous research presents several approaches to smart maintenance technologies. One problem is a lack of research regarding how smart maintenance technologies can be implemented to add benefits to the maintenance organization in line with company’s goal. Furthermore, previous research presents that further research is needed to support the manufacturing industry in what step an organization should take to implement smart maintenance technologies. In this research, four studies have been performed, which include literature reviews to obtain a clear overview of the research area of smart maintenance, as well as collected empirical data. The empirical data is collected from large companies and Small and Medium-sized Enterprises (SMEs), within the manufacturing industry, to obtain a clear overview of the manufacturing industry’ situation. The studies show that the manufacturing industry faces several challenges when implementing smart maintenance technologies, despite the concept of Industry 4.0 has been discussed for more than ten years. In this research, a conceptual implementation process is proposed, including challenges and enablers to consider when implementing smart maintenance technologies, as well as benefits of using smart maintenance technologies.

Det går att hantera nätverksenheter och nätverk på olika sätt. Två av dessa sätt är RESTCONF och NETCONF. Denna undersöknings syfte är att ta reda på vilken av dessa två protokoll som är snabbast och om storlek på nätverket har påverkan på vilket att dessa protokoll som bör användas. Resultatet visar att RESTCONF är snabbare än NETCONF på ett nät med tre routrar och två switchrar. RESTCONF är även snabbare än NETCONF på ett nätverk som består av 17 routrar och fem switchrar. Både NETCONF och RESTCONF tar längre tid i nätverket med fler nätverksenheter än i nätverket med färre nätverksenheter.

This work aims to improve data collection by automating some parts of the manual work involve in managing survey projects with mixed-method data collection, furthermore, this work aims to improve response rate and response inflow by using historical data. Today, response rates have a downward trend and by analyzing previous data we investigate whether there are specific times or days where different groups are more willing to answer a survey. By making predictions on dispatches, the system also aims to be able to achieve a more even inflow of responses spread over several days. In this work, a web-based software is implemented and evaluated through tests on a real project. The tests shows that in the project the system is tested in, an increase in response rates can be achieved and prediction of responses can be made at sufficient level to achieve an even distribution.

In the process of Human-Robot Interaction, improving the intelligibility of robots is crucial. Intelligibility refers to the degree to which humans can understand robot behavior and decision-making. When humans interact with low-intelligibility robots, it can lead to a series of problems, such as misunderstanding and trust issues. For old people, various impairments may occur in their physiological functions. Therefore, in the process of Human-Robot Interaction, when the target group is older adults, it is more critical to improve intelligibility. In this thesis project, we propose a solution to improve the intelligibility of robots when interacting with older adults. Specifically, we design an intelligent dialogue system that combines the Rasa dialogue management assistant, the Pepper robot, and the "Nerve" module to provide cognitive exercises. Among them, the user model is used to expand the Rasa dialogue management assistant to adjust the difficulty of cognitive exercises according to the user's hearing, seeing, and cognitive impairment. Rasa could interact with older adults through the Pepper robot. At the same time, we also consider environmental factors in Human-Robot Interaction, such as lighting and noise. For this purpose, we design a "Nerve" module that could adjust the communication modality and parameters during interaction according to environmental factors and user models. We also use some designed personas to evaluate the intelligent system from the five perspectives of speech recognition accuracy, user experience, system stability, intelligibility, and robustness. Finally, we analyze the limitations of the system properties and give a brief introduction to future work.

Smart contracts are programs that reside and execute on top of blockchains. These programs commonly perform financial transactions and contain the backend logic of several blockchain-supported applications. The presence of errors and bugs in smart contracts poses security threats to the applications they support. This is especially concerning because operations performed by smart contracts are irreversible after deployment due to the immutable nature of blockchains. Thus, ensuring their correctness and security before deployment is important. For this purpose, several program analysis and verification approaches are being actively researched and applied to smart contracts. The volume of research in this area makes it challenging to articulate the state-of-the-art. The first contribution of this thesis is to investigate how predeployment analysis techniques ensure the correctness and security of smart contracts. This investigation factors out the relationship between vulnerabilities in smart contracts and pre-deployment analysis techniques through properties they address. Among the range of issues uncovered by the investigation, one notable set pertains to non-deterministic factors involved in the context of contract execution. For example, transactions (function invocations) dispatched to smart contracts are scheduled in non-deterministic order, and asynchronous calls to external services (known as oracles) return in a non-deterministic order. Consequently, these factors may cause data races and non-deterministic bugs in smart contracts. The second contribution of this thesis is to address such issues by unraveling specific forms of data races in Ethereum smart contracts, denoted as transactional data races. The thesis also presents a static analysis approach to detect issues arising from transactional data races. In addition, this thesis makes a third contribution relating to a design approach for Domain Specific Languages (DSLs). Research on DSL design approaches has the potential to complement the research on smart contracts, as smart contracts are commonly written using DSLs. This thesis proposes an agile approach for designing a DSL for automotive safety test grounds. This approach enables increased communication and learning between different stakeholders involved in DSL development. Finally, this thesis highlights our future research endeavors concerning various forms of concurrency and non-determinism-related issues in smart contracts.

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This project is about automating foam quality control. Its purpose is to construct an automated quality-control station that will characterise foam in line with a production line that non-technical persons can efficiently operate. Foam passes through the station on a purpose-built conveyor. A robot then moves a microscope over the foam to capture detailed close-up images. The characterisation is done in software by examining the foam’s pores and measuring it against a set of tweakable parameters. There were some problems in focusing the microscope, but testing resolved the focusing to be non-essential. A database is used with the quality control station to store and sort test data efficiently. The overall quality control station is a success and can characterise foam according to the project’s predefined goals.

Hand-written text recognition (HTR) is a transformative technology in recent years that significantly assists the study of historical documents, therefore, boosting digital humanity research. Conventional optical character recognition (OCR) technique is sensitive to certain writing styles and thus not adaptive. Our study attempts an adaptive pipeline that enables the HTR of Swedish hand-written documents which contributes to the study of Swedish history including the modern and contemporary democratization process with the Labour’s Memory dataset and Demokrati 100 dataset. This pipeline integrates transfer learning, fine-tuning techniques, and a novel confidence-based ensemble strategy to reduce the transcribing error rate. Our findings demonstrate the efficacy of these strategies in significantly improving performance metrics. Results indicate a substantial reduction in transcribing error rate compared to baseline methods. Notably, our transfer learning model achieves a Character Error Rate (CER) of 6.664%. The introduction of a confidence-based ensemble strategy yields a CER of 5.976%, outperforming any individual model and the baseline significantly. We further propose optimizations in transfer learning by identifying that fine-tuning only recurrent and dense layers balances performance and computational efficiency. This approach enables a more time-efficient training process in case of a large dataset without compromising accuracy, offering practical benefits for real-world applications. Furthermore, our analysis reveals critical insights into the challenges of baseline detection and ground truth accuracy. We identify over-segmentation as a bottleneck in baseline detection and highlight the significance of addressing systematic errors in ground truth data.

During the development of complex embedded systems like controllers, conducting real-world testing can often be impractical due to factors such as cost, safety concerns, or unavailability during certain stages of development. In such scenarios, hardware-in-the-loop testing is a practical alternative. Hardware-in-the-loop testing involves interfacing the device under test with a simulation environment that mimics real-world inputs, enabling comprehensive testing without the associated risks or constraints. This thesis focuses on the transformation of a Matlab model depicting the behavior of a falling missile into VHDL. The purpose of this model is to integrate with an FPGA to facilitate real-time testing of control algorithm and associated hardware. The conversion successfully translated the Matlab model into VHDL, enabling execution within the constraints of the real-time system.  While the VHDL model closely mirrors the original Matlab model, minor deviations exist due to the discretisation process, resulting in slight discrepancies. However, suggestions on how to overcome these are proposed.