About a third of the energy in Sweden is used for production in industry, where a few energy-intensive industries account for a large proportion of the energy use. When energy efficiency takes place here, the positive environmental effects will be substantial. Therefore, there is often great potential to reduce energy use in these industries. The purpose of this study is to investigate the company's energy use in order to be able to present proposals for cost-effective improvement measures that lead to a reduction in the company's energy consumption, which in turn leads to a reduced environmental impact. The study has used sustainable development, total quality management and Kotter’s 8-step process for leading change as a theoretical background. The study is a qualitative and quantitative case study and the data collection was done using semi-structured and unstructured interviews, observations and document analysis. The study’s results show that the company uses unnecessary energy in three energy consumption areas: compressed air, heating and electricity. To reduce energy use, the company needs to put in place a shut-down management, appoint an energy coordinator, prioritize preventive work, repair broken equipment and introduce preventive maintenance, optimize the ovens, involve employees in continuous improvement and to have a committed leadership. By reducing energy consumption, the company can reduce its impact on the environment, and by implementing the improvement measures, the company can save several million SEK each year. / Ungefär en tredjedel av energin i Sverige används för produktion inom industrin, där ett fåtal energiintensiva branscher står för en stor andel av industrins energianvändning. När energieffektivisering sker här blir de positiva miljöeffekterna stora. Därför finns det ofta stor potential att minska energianvändningen i dessa branscher. Syftet med denna studie är att undersöka företagets energianvändning för att sedan kunna presentera förslag på kostnadseffektiva förbättringsåtgärder som leder till en minskning av företagets energiförbrukning, vilket i sin tur leder till en minskad miljöpåverkan. Studien har använt sig av hållbar utveckling, hörnstensmodellen och Kotters 8-stegsmodell för förändringsledning som teoretisk bakgrund. Studien är en kvalitativ och kvantitativ fallstudie och datainsamlingen gjordes med hjälp av semistrukturerade och ostrukturerade intervjuer, observationer och dokumentstudier. Studiens resultat visar på att företaget använder energi i onödan inom tre energiförbrukningsområden: tryckluft, värme och el. För att minska energianvändningen behöver företaget införa avstängningsrutiner, utse en energikoordinator, prioritera förebyggande arbete, reparera trasig utrustning och införa förebyggande underhåll, optimera ugnarna, involvera medarbetarna i förbättringsarbetet och ha ett engagerat och delaktigt ledarskap. Genom att minska energiförbrukningen kan företaget minska sin påverkan på miljön och genom att implementera förbättringsåtgärder kan företaget spara flera miljoner kronor varje år.

Football analytics is a rapid growing area which utilizes conventional data analysis and computational methods on gathered data from football matches. The results emerging out of this can give insights of performance levels when it comes to individual football players, different teams and clubs. A difficulty football analytics struggles with daily is to translate the analysis results into actual football qualities and knowledge which the wider public can understand. In this master thesis we therefore take on the ball event data collected from football matches and develop a model which classifies individual football player’s playing styles, where the playing styles are well known among football followers. This is carried out by first detecting the playing positions: ’Strikers’, ’Central midfielders’, ’Outer wingers’, ’Full backs’, ’Centre backs’ and ’Goalkeepers’ using K-Means clustering, with an accuracy of 0.89 (for Premier league 2021/2022) and 0.84 (for Allsvenskan 2021). Secondly, we create a simplified binary model which only classifies the player’s playing style as "Offensive"/"Defensive". From the bad results of this model we show that there exist more than just these two playing styles. Finally, we use an unsupervised modelling approach where Principal component analysis (PCA) is applied in an iterative manner. For the playing position ’Striker’ we find the playing styles: ’The Target’, ’The Artist’, ’The Poacher’ and ’The Worker’ which, when comparing with a created validation data set, give a total accuracy of 0.79 (best of all positions and the only one covered in detail in the report due to delimitations).  The playing styles can, for each player, be presented visually where it is seen how well a particular player fits into the different playing styles. Ultimately, the results in the master thesis indicates that it is easier to find playing styles which have clear and obvious on-the-ball-actions that distinguish them from other players within their respective position. Such playing styles, easier to find, are for example "The Poacher" and "The Target", while harder to find playing styles are for example " The Box-to-box" and "The Inverted". Finally, conclusions are that the results will come to good use and the goals of the thesis are met, although there still exist a lot of improvements and future work which can be made.  Developed models can be found in a simplified form on the GitHub repository: https://github.com/Sommarro-Devs/avatar-playing-style. The report can be read stand-alone, but parts of it are highly connected to the models and code in the GitHub repository.

Physical asset management in the electric power sector encompasses the scheduling of the maintenance and replacement of grid components, as well as decisions about investments in new components. Data plays a crucial role in these decisions. The importance of data is increasing with the transformation of the power system and its evolution toward smart grids. This thesis deals with questions related to data management as a way to improve the performance of asset management decisions. Data management is defined as the collection, processing, and storage of data. Here, the focus is on the collection and processing of data. First, the influence of data on the decisions related to assets is explored. In particular, the impacts of data quality on the replacement time of a generic component (a line for example) are quantified using a scenario approach, and failure modeling. In fact, decisions based on data of poor quality are most likely not optimal. In this case, faulty data related to the age of the component leads to a non-optimal scheduling of component replacement. The corresponding costs are calculated for different levels of data quality. A framework has been developed to evaluate the amount of investment needed into data quality improvement, and its profitability. Then, the ways to use available data efficiently are investigated. Especially, the possibility to use machine learning algorithms on real-world datasets is examined. New approaches are developed to use only available data for component ranking and failure prediction, which are two important concepts often used to prioritize components and schedule maintenance and replacement. A large part of the scientific literature assumes that the future of smart grids lies in big data collection, and in developing algorithms to process huge amounts of data. On the contrary, this work contributes to show how automatization and machine learning techniques can actually be used to reduce the need to collect huge amount of data, by using the available data more efficiently. One major challenge is the trade-offs needed between precision of modeling results, and costs of data management. / <p>QC 20210330</p>

<p>The detection of anomalies in real-world networks is applicable in different domains; the application includes, but is not limited to, credit card fraud detection, malware identification and classification, cancer detection from diagnostic reports, abnormal traffic detection, identification of fake media posts, and the like. Many ongoing and current researches are providing tools for analyzing labeled and unlabeled data; however, the challenges of finding anomalies and patterns in large-scale datasets still exist because of rapid changes in the threat landscape. </p><p>In this study, I implemented a novel and robust solution that combines data science and cybersecurity to solve complex network security problems. I used Long Short-Term Memory (LSTM) model, Louvain algorithm, and PageRank algorithm to identify and group anomalies in large-scale real-world networks. The network has billions of packets. The developed model used different visualization techniques to provide further insight into how the anomalies in the network are related. </p><p>Mean absolute error (MAE) and root mean square error (RMSE) was used to validate the anomaly detection models, the results obtained for both are 5.1813e-04 and 1e-03 respectively. The low loss from the training phase confirmed the low RMSE at loss: 5.1812e-04, mean absolute error: 5.1813e-04, validation loss: 3.9858e-04, validation mean absolute error: 3.9858e-04. The result from the community detection shows an overall modularity value of 0.914 which is proof of the existence of very strong communities among the anomalies. The largest sub-community of the anomalies connects 10.42% of the total nodes of the anomalies. </p><p>The broader aim and impact of this study was to provide sophisticated, AI-assisted countermeasures to cyber-threats in large-scale networks. To close the existing gaps created by the shortage of skilled and experienced cybersecurity specialists and analysts in the cybersecurity field, solutions based on out-of-the-box thinking are inevitable; this research was aimed at yielding one of such solutions. It was built to detect specific and collaborating threat actors in large networks and to help speed up how the activities of anomalies in any given large-scale network can be curtailed in time.</p><div><div><div> </div> </div> </div> <br>

Nowadays, becoming data-driven is the vision of almost all organizations. However, achieving this vision is not as easy as it may look like and there are many factors that affect, enable, support and sustain the data-driven ecosystem in an organization. Among these factors, this study focuses on data democracy which can be defined as the intra-organizational open data that aims to empower the employees getting faster and easier access to data in order to benefit from the business insight they need without the interfere of external help.  In the existing literature, while the importance of becoming data-driven has been widely discussed, when it comes to data democracy within organizations, there is a noticeable gap. As a result, this master’s thesis aims to justify the importance and role of the data democracy in becoming a data-driven organization, focusing on the case of a Swedish bank. Additionally, it intends to provide extra investigation on the role of data analytics tools in achieving data democracy.  The results of the study show that there is a strong connection between the benefits of the empowering different actors of the organization with the needed data knowledge, and the speeding up of the data-driven transformation journey. Based on the study, shared data and the availability of data to a larger number of stakeholders inside an organization result into a better understanding of different aspects of the problems, simplify the data-driven decision making and make the organization more data-driven. In the process of becoming data-driven, the organizations should provide the analytics tools not only to the data specialists but even to the non-data technical people. And by offering the needed support, training and collaboration possibilities between the two groups of employees (data specialists and non-data specialists), it should be attempted to enable the second group to extract the insight from the data, independently from the help of the data scientists.  An organization can succeed in the path of becoming data-driven when they invest on the reusable capabilities of its employees, by discovering the data science skills across various departments and turning their domain experts into citizen data scientists of the organization.

It is commonly agreed among energy experts that smart meters (SMs) are the key component that will facilitate the transition towards the smart grid. Fast-peace innovations in the smart metering infrastructure (AMI) are exposing countless benefits that network operators can obtain when they integrate SMs applications into their daily operations.  Following the amendment in 2017, where the Swedish government dictated that all SMs should now include new features such as remote control, higher time resolution for the energy readings and a friendly interface for customers to access their own data; network operators in Sweden are currently replacing their SMs for a new model, also called the second generation of SMs. While the replacement of meters is in progress, many utilities like Hemab are trying to reveal which technical and financial benefits the new generation of SMs will bring to their operations.    As a first step, this thesis presents the results of a series of interviews carried out with different network operators in Sweden. It is studied which functionalities have the potential to succeed in the near future, as well as those functionalities that are already being tested or fully implemeneted by some utilities in Sweden. Furthermore, this thesis analyses those obstacles and barriers that utilities encounter when trying to implement new applications using the new SMs. In a second stage, an alarm system for power interruptions and voltage-quality events (e.g., overvoltage and undervoltage) using VisionAir software and OMNIPOWER 3-phase meters is evaluated. The results from the evaluation are divided into three sections: a description of the settings and functionalities of the alarm, the outcomes from the test, and a final discussion of potential applications. This study has revealed that alarm functions, data analytics (including several methods such as load forecasting, customer segmentation and non-technical losses analysis), power quality monitoring, dynamic pricing, and load shedding have the biggest potential to succeed in Sweden in the coming years. Furthermore, it can be stated that the lack of time, prioritization of other projects in the grid and the integration of those new applications into the current system seem to be the main barrier for Swedish utilities nowadays. Regarding the alarm system, it was found that the real benefits for network operators arrive when the information coming from an alarm system is combined with a topology interface of the network and a customer notifications server. Both applications could improve customer satisfaction by significantly reducing outage time and providing customers with real-time and precise information about the problems in the grid.

Business simulation games are used in educational institutions and various industries in the private and public sector to train students and employees to practice the principles of management and decision-making skills by providing a fail-safe environment and enabling them to reflect on their simulation results. These games are generally advanced multiuser environments where a user, or a group of users, have access to a virtual company for making business decisions. Some of these games are expensive and their licences are time limited; typically, such a licence is only valid during the duration of the course. In general, these games are not available for the public as part of informal instructional courses. In Canada, teaching informal courses to immigrants and refugees, which involve data-driven decision making, to prepare them for future challenges they might encounter as business owners, can be challenging; especially considering language barriers and non-business-related backgrounds obtained outside of Canada. Furthermore, based on their decision-making styles, cognitive limitations, and past experiences, people may have an inaccurate perception of the problem or challenge they face, this could lead to poor decision making of the team they are part of and, therefore, this could reflect in the effectiveness of an organization as a whole. The objective of this research is to enrich current teaching tools in decision-making processes in entrepreneurship courses for newcomers in Canada with a comprehensive and visual representation of operational business problems involved in Business Intelligence and data analytics. More specifically, we designed and developed a 3D Business Simulation Game with randomized scenarios using modern technologies, such as Unreal Engine as the game engine; Adobe Fuse for the character creation, Mixamo for animation of the character, and Substance Painter for textures and materials for the assets. The research was conducted with the participation of the students of the Business Creation and Project Management course at VIRCS (Victoria Immigrant and Refugee Centre Society) where we tested this game on each one of the five units of the course. After designing, developing, and testing the 3D business simulation game, we conducted a comprehensive evaluation to investigate whether the decisions students made while playing were correct or not. We also evaluated whether they felt that the challenges were easier to understand, both as a team and individually, when they used the 3D business simulation game compared to only the written description of the problems. The main results we obtained from our study are the following: After playing the business simulation game, students became more aware of the importance of making correct decisions in different business scenarios. They made sure that the whole team understood the problem, and they felt generally good about their understanding of the course content. We also noticed that when the animation was not part of the business simulation game, they seemed to be confused when following written instructions. This indicate that they depended on the animations for their decision-making. We believe that, in some ways, the course and the 3D business simulation game we created for this research were a great opportunity to observe students becoming more confident in their future in Canada as entrepreneurs. We observed that, once the game has been used, the students become more participatory in class, the discussion of the course material increases, and in general, the students seem to enjoy the course more. / Graduate

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De nos jours, il y a une demande croissante pour fournir de l'information temps réel à partir de l'environnement, e.g. état infectieux de maladies, force du signal, conditions de circulation, qualité de l'air. La prolifération des dispositifs de capteurs et la mobilité des personnes font de la Mobile Collaborative Sensing (MCS) un moyen efficace de détecter et collecter l'information à un faible coût. Dans MCS, au lieu de déployer des capteurs statiques dans une zone, les personnes disposant d'appareils mobiles jouent le rôle de capteurs mobiles. En général, une application MCS exige que l'appareil de chacun ait la capacité d'effectuer la détection et retourne les résultats à un serveur central, mais également de collaborer avec d'autres dispositifs. Pour que les résultats puissent représenter l'information physique d'une région cible et convenir, quel type de données peut être utilisé et quel type d'information doit être inclus dans les données collectées? Les données spatio-temporelles peuvent être utilisées par des applications pour bien représenter la région cible. Dans des applications différentes, l'information de localisation et de temps sont 2 types d'information communes, et en les utilisant la région cible d'une application est sous surveillance complète du temps et de l'espace. Différentes applications nécessitent de l'information différente pour atteindre des objectifs différents. E.g. dans cette thèse: i- MCS-Locating application: l'information de résistance du signal doit être incluse dans les données détectées par des dispositifs mobiles à partir d'émetteurs de signaux ; ii- MCS-Prédiction application : la relation entre les cas d'infection et les cas infectés doit être incluse dans les données par les dispositifs mobiles provenant des zones de flambée de la maladie ; iii- MCS-Routing application : l'information routière en temps réel provenant de différentes routes de circulation doit être incluse dans les données détectées par des dispositifs embarqués. Avec la détection de l'information physique d'une région cible, et la mise en interaction des dispositifs, 3 thèmes d'optimisation basés sur la détection sont étudiés et 4 travaux de recherche menés: -Mobile Collaboratif Détection Cadre : un cadre mobile de détection collaborative est conçu pour faciliter la coopérativité de la collecte, du partage et de l'analyse des données. Les données sont collectées à partir de sources et de points temporels différents. Pour le déploiement du cadre dans les applications, les défis clés pertinents et les problèmes ouverts sont discutés. -MCS-Locating : l'algorithme LiCS (Locating in Collaborative Sensing based Data Space) est proposé pour atteindre la localisation de la cible. LiCS utilise la puissance du signal reçu dans tous les périphériques sans fil comme empreintes digitales de localisation pour les différents emplacements. De sorte LiCS peut être directement pris en charge par l'infrastructure sans fil standard. Il utilise des données de trace d'appareils mobiles d'individus, et un modèle d'estimation d'emplacement. Il forme le modèle d'estimation de localisation en utilisant les données de trace pour atteindre la localisation de la cible collaborative. Cette collaboration entre périphériques est au niveau des données et est supportée par un modèle. -MCS-Prédiction: un modèle de reconnaissance est conçu pour acquérir dynamiquement la connaissance de structure de la RCN pertinente pendant la propagation de la maladie. Sur ce modèle, un algorithme de prédiction est proposé pour prédire le paramètre R. i.e. le nombre de reproduction qui est utilisé pour quantifier la dynamique de la maladie pendant sa propagation. -MCS-Routing : un algorithme de navigation écologique ‘eRouting’ est conçu en combinant l'information de trafic temps réel et un modèle d'énergie/émission basé sur des facteurs représentatifs. Sur la base de l'infrastructure standard d'un système de trafic intelligent, l'information sur le trafic est collectée / Nowadays, there is an increasing demand to provide real-time information from the environment, e.g., the infection status of infectious diseases, signal strength, traffic conditions, and air quality, to citizens in urban areas for various purposes. The proliferation of sensor-equipped devices and the mobility of people are making Mobile Collaborative Sensing (MCS) an effective way to sense and collect information at a low deployment cost. In MCS, instead of just deploying static sensors in an interested area, people with mobile devices play the role of mobile sensors to sense the information of their surroundings, and the communication network (3G, WiFi, etc.) is used to transfer data for MCS applications. Typically, a MCS application not only requires each participant's mobile device to possess the capability of performing sensing and returning sensed results to a central server, but also requires to collaborate with other mobile and static devices. In order to make sensed results well represent the physical information of a target region, and well be suitable to a certain application, what kind of data can be used for different applications, and what kind of information needs to be included into the collected sensing data? Spatio-temporal data can be used by different applications to well represent the target region. In different applications, location and time information is two kinds of common information, and by using such information, the target region of an application is under comprehensive monitoring from the view of time and space. Different applications require different information to achieve different sensing purposes. E.g. in this thesis: i- MCS-Locating application: signal strength information needs to be included into the sensed data by mobile devices from signal transmitters; ii- MCS-Prediction application: the relationship between infecting and infected cases needs to be included into the sensed data by mobile devices from disease outbreak areas; iii- MCS-Routing application: real-time traffic and road information from different traffic roads, e.g., traffic velocity and road gradient, needs to be included into the sensed data by road-embedded and vehicle-mounted devices. With sensing the physical information of a target region, and making mobile and static devices collaborate with each other in mind, in this thesis three sensing based optimization applications are studied, and following four research works are conducted: - a MCS Framework is designed to facilitate the cooperativity of data collection, sharing, and analysis among different devices. Data is collected from different sources and time points. For deploying the framework into applications, relevant key challenges and open issues are discussed. - MCS-Locating: an algorithm LiCS (Locating in Collaborative Sensing based Data Space) is proposed to achieve target locating. It uses Received Signal Strength that exists in any wireless devices as location fingerprints to differentiate different locations, so it can be directly supported by off-the-shelf wireless infrastructure. LiCS uses trace data from individuals' mobile devices, and a location estimation model. It trains the location estimation model by using the trace data to achieve collaborative target locating. Such collaboration between different devices is data-level, and model-supported. - MCS-Prediction: a recognition model is designed to dynamically acquire the structure knowledge of the relevant RCN during disease spread. On the basis of this model, a prediction algorithm is proposed to predict the parameter R. R is the reproductive number which is used to quantify the disease dynamics during disease spread. - MCS-Routing: an eco-friendly navigation algorithm, eRouting, is designed by combining real-time traffic information and a representative factor based energy/emission model. Based on the off-the-shelf infrastructure of an intelligent traffic system, the traffic information is collected

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