Assessing disaster preparedness in a given region is a complex problem. Current methods are often resource intensive and may lack generalizability beyond a specific scenario. Computer-based stochastic simulations may be an additional method but would require systems that are valid, flexible and easy-to-use. Emergo Train System (ETS) is an analogue simulation system used for disaster preparedness assessments. This thesis aimed to digitalize the ETS model and develop a stochastic simulation software for improved disaster preparedness assessments. Simulation software was developed in C#. The simulation model was based on ETS. Preliminary verification and validation (V&amp;V) tests were performed, including unit and integration testing, trace validation, and a comparison to a prior analogue ETS disaster preparedness assessment exercise. The software contains medically validated patients from ETS and is capable of automatically running disaster scenarios with stochastic variations in the injury panorama, available resources, geographical location, and other parameters. It consists of two main programs; an editor where scenarios can be constructed and a simulation system to evaluate the outcome. Initial V&amp;V testing showed that the software is reliable and internally consistent. The comparison to the analogue exercise showed a general high agreement in terms of patient outcome. The analogue exercise featured a train derailment with 397 injured, of which 45 patients suffered preventable death. In comparison, the computer simulation ran 100 iterations of the same scenario and indicated that a median of 41 patients (IQR 31 to 44) would suffer a preventable death. Stochastic simulation methods can be a powerful complement to traditional capability assessments methods. The developed simulation software can be used for both assessing emergency preparedness with some validity and as a complement to analogue capability assessment exercises, both as input and to validate results. Future work includes comparing the simulation to real disaster outcomes. / <p></p><p></p>

In the energy domain today we are seeing an increasing number of energy equipments used and faceing new challenges such as network reliability, distributed renewable energy, increasing network complexity and energy efficiency. The concept of smart grid control systems has recently been seen as an appropriate way to address these new challenges. Today, the IEC 61850 standard is one of the most common standards used for power system automation. One of the services introduced is the so-called Generic Object Oriented Substation Event (GOOSE), which is a protocol to transfer time critical messages between multiple devices in a substation. The 5th generation of mobile networks (5G) are enabling new services and applications requiring lower latency, improved energy efficiency, better reliability and massive connection density. These promises of higher reliability and lower latency could then possibly be used in the future smart grid transmissions. In this work, the main goal was to understand the importance of time-critical messages, such as GOOSE messages, in the IEC61850 standard, and how these possibly could be used in the new 5th generation of mobile network. A proposed experimental setup which can be used for future research within both the GOOSE messaging area itself and the Open5GCore for emulated 5G mobile networks is presented. The intension of the experimental study is to send the GOOSE messages traversing through 5G networks by Open5GCore - an emulated 5G software.

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The security protocol IPsec is used in the LTE network to achieve a securecommunication from prying eyes. However, the use of IPsec is optional bythe LTE standard. Whether or not to use the IPsec thus becomes a securitydecision that each operator has to make after having considered applicablerisks and anticipated costs. It is also important to consider the OperationalExpenditure (OPEX) for deploying, operating, and maintaining the IPsecinstallation. One important factor that can aect OPEX is usability. Forthis reason understanding the usability properties of a system can help toidentify improvements that can reduce OPEX.This study mainly focused on investigating the challenges and also investigateswhether poor usability was a contributing factor for deploymentchallenges of IPsec in the LTE infrastructure. Additionally, this study alsofocused on prerequisite knowledge for an individual in order to ensure thecorrect deployment of IPsec in the LTE network.Cognitive Walkthrough and Heuristic Evaluation usability methods wereused in this study. By using these methods, several usability issues related toIPsec conguring components like documentation, the MO structure, anda used tool were identied. It was also identied that each componenthad rooms for improvements, especially for documentation which can signicantly aid in the deployment of IPsec. Moreover, in order to smoothlydeploy IPsec in the LTE network, it is important to have beforehand knowledgeof conguring components used to deploy IPsec.

The subject of this report is the Command and Control (CaCS) system which is a component whose purpose is to simplify planning, scheduling and surveying work done on a farm in a goal-oriented way. CaCS is part of a larger project, the Aggregate Farming in the Cloud platform (AFarCloud),whose purpose is to simplify the use of contemporary technology to increase the efficiency of farms. AFarCloud is an EU project spanning between 2018 to 2020 and as such, the CaCS is in its infancy. Since the intended users of AFarCloud and CaCS is small to medium sized agricultural businesses,the interface of the CaCS should be constructed in such a way that it is useful and easy to learn. In order to live up to those standards, a combination of live interviews, prototype evaluationsand a comparison with similar software were performed and then compared with the International Standard document on Human-Centered Design for Interactive Systems (ISO 9241-210). The results indicate that a modular interface, where only the information relevant for the unique user’s farm is displayed, is preferable in order to increase the usability of the CaCS. Furthermore, useof icons and explanatory text must be made in consideration of the mental models of the users in order to improve learnability and avoid confusion.

For investigating the large parts of the ocean which have yet to be mapped, there is a need for autonomous underwater vehicles. Current state-of-the-art underwater positioning often relies on external data from other vessels or beacons. Processing seabed image data could potentially improve autonomy for underwater vehicles. In this thesis, image data from a synthetic aperture sonar (SAS) was manually segmented into two classes: sand and gravel. Two different convolutional neural networks (CNN) were trained using different loss functions, and the results were examined. The best performing network, U-Net trained with the IoU loss function, achieved dice coefficient and IoU scores of 0.645 and 0.476, respectively. It was concluded that CNNs are a viable approach for segmenting SAS image data, but there is much room for improvement.

Brain-computer interfaces (BCIs) enables direct communication between a brain and a computer by recording and analyzing a subject’s neural activity in real-time. Research in BCI that classifies motor imagery (MI) activities are common in the literature due to its importance and applicability, e.g., stroke rehabilitation. Electroencephalography (EEG) is often used as the recording technique because its non-invasive, portable and have a relatively low cost. However, an EEG recording returns a vast number of features which must be reduced to decrease the computational time and complexity of the classifier. For this purpose, feature selection is often applied. In this study, a multiobjective evolutionary algorithm (MOEA) was used as feature selection in a high spatial and temporal feature set to (1) compare pairwise combinations of different objectives, (2) evaluate the relationship between the specific objective pair and their relation to model prediction accuracy, (3) compare multiobjective optimization versus a linear combination of the individual objectives. The results show that correlation feature selection (CFS) obtained the best performance between the evaluated objectives which were also more optimized than a linear combination of the individual objectives when classified with support vector machine (SVM).

In today’s digitization scenario, drivers such as the Internet of Things (IoT), cloud computing and big data lead to many initiatives such as Industry 4.0 or smart manufacturing. Large mining organizations are witnessing the emergence of big data not only through IoT but also through legacy systems and internal processes. Addressing big data is a challenging and time-demanding task that requires an extensive computational infrastructure to ensure successful data processing and analysis. Though most organizations have adopted a wide variety of powerful analytics, visualization tools, and storage options, efficient data usage, and sharing is taxing and may lead to data isolation. The thesis proposes, develops and validates a data catalog system called CATALYST: A Cloud-Based Data Catalog System for a Swedish Mining Company to address the data isolation, access and sharing challenges faced in a large organization. The prototype implementation and the evaluation of our system show that the average query time  was  reduced  from  59.813  milliseconds  to  11.009  milliseconds, as well as the average data count was reduced from 12,691 to 5721.7, which is almost less than 50 per cent, and solving data isolation challenges within Boliden, a large Swedish mining company. Finally, Boliden has confirmed the value of CATALYST in general and finds it beneficial for data management within their organization

Siemens produce a variety of different products that provide innovative solutions within different areas such as electrification, automation and digitalization, some of which are turbine machines. During the process of creating or modifying a machine, it is vital that the documentation used as reference is trustworthy and complete. If the documentation is incomplete during the process, the risk of delivering faulty machines to customers drastically increases, causing potential harm to Siemens. This thesis aims to explore the possibility of finding anomalies in Bill of Material structures, in order to determine the completeness of a given machine structure. A prototype that determines the completeness of a given machine structure by utilizing anomaly detection, was created. Three different anomaly detection algorithms where tested in the prototype: DBSCAN, LOF and Isolation Forest. From the tests, we could see indications of DBSCAN generally performing the best, making it the algorithm of choice for the prototype. In order to achieve more accurate results, more tests needs to be performed.

Tjänstgöringstid och dygnsvila är för ambulanshelikopterpiloter strikt reglerat. Avgångar och ankomster styrs av inkommande larm, vilket kan göra beräkning av tjänstgöringstid och dygnsvila komplicerat. I dagsläget görs beräkning och loggning manuellt, vilket kan generera osäkerhet med onödig larmfrånsägning som resultat. I detta arbete utförs en förstudie till ett tidsloggningssystem för ambulanshelikopterpiloter, med huvudsyfte att minska onödig larmfrånsägning. Ett IT-system skapas där en smarttelefon samlar in tidsdata som sedan presenteras i en webbapplikation. Som del i systemet utforskas möjligheten att automatiskt kunna logga flygtid med smarttelefon. En mobilapplikation ämnad för automatisk flygtidsloggning skapas med underlag och idéer hämtade från tidigare forskning och datainsamling. Resultatet visar att applikationen under testning loggar samma flygtid som helikoptern i 5 av 6 fall, med en minuts noggrannhet. För presentation av insamlade tidsdata tas en visualiseringsmodell fram i form av en webbapplikation baserad på tidigare forskning och vedertagna designprinciper. Testning av visualiseringsmodellen visar att det, för piloterna, är lättare att fatta rätt beslut under tidspress med framtagen visualiseringsmodell än med numeriskt format av samma data. Resultatet analyseras med bland annat t-test som visar att skillnaden mellan graf- och tabellpresentation är statistiskt signifikant. / Active duty time is strictly regulated for ambulance helicopter pilots. Departures and arrivals are determined by incoming alarms, which can make active duty time calculations complicated. These calculations are currently done manually, which can generate uncertainty and unnecessary denial of incoming alarms as a result. In this work, a pilot study is made for a time management system for ambulance helicopter pilots, with the main purpose to decrease unnecessary denial of incoming alarms. An IT-system is created where a smartphone collects time data which in turn is presented in a web application. As a part of the system, the ability to automatically log flight time with a smartphone is explored. A smartphone application with the purpose of automatically logging flight time is made with its basis and ideas retrieved from previous research and data collection. The test result shows that the application logs the same flight time as the helicopter in 5 times out of 6, with the accuracy of one minute. For presenting the collected time data, a visualization model is made in the form of a web application based on previous research and established design principles. Testing of the visualization model shows, for the pilots, that it is easier to make an accurate decision under time pressure with the developed visualization model than with a numerical format of the same data. The result is analyzed with a t-test which concludes that the difference between the graphical and table presentation is statistically significant.