Finished research projects: Skövde Artificial Intelligence Lab

2015-2020 An unprecedented growth of data, fed by novel technologies, user behaviour and business models, is one of the most dramatic and important developments in both ICT and society at large. Most of this is Big Data, characterised by vast volume, high velocity, large variety and unknown veracity. These properties pose significant challenges when collecting, managing and processing data.

2015-2019 Big data has gained much interesting in recent years due to the rapid expansion of the massive amount of data that is available for solving different types of tasks within many different application domains. However, today's big data is still on a fairly low level of abstraction when it comes to complex decision support tasks, subject to e.g. high dimensionality and significant portions of uncertainty regarding which patterns to look for in the data.

2017-2021 Companies keep store a large quantities of data about us, as e.g. our electronic transactions, traces of our internet usage, phone calls and social network posts. This information is valuable for the companies and also for the users as data permits personalization in services. Nevertheless, some of this information is confidential.

The research directly contributes to Saab's technology and product development by creating a new method with unique properties for detecting anomalies in movement patterns, such as those of ships.

2021 Artificial intelligence and advanced data analysis are enhancing Swedish elite golf.

2018-2020 Fusarium attacks on Swedish cereals in recent years have attracted increasing attention. While these attacks can lead to reduced harvests, the most serious effect is that this fungus can produce toxins. EU legislation has defined limit values for the content of Fusarium toxins because they are harmful to both humans and animals. Our project aims to develop a farm-based decision support system that will assess the risk of Fusarium attacks and elevated levels of deoxynivalenol (DON) in cereals. It can be used by both advisers/consultancies and farmers.

2021-2024 The iron and steel industry needs to be streamlined and the amount of scrap reduced to increase returns. What is required is a faster and more reliable production with stricter tolerances – then better products are created that reach the customer faster. This project, INSITE-X, will develop an AI-based prototype for detailed simulation of dynamic production-behaviour of critical machines, to increase process understanding and control.

2019-2022 Intention recognition is the task of inferring an agent´s intention based on its previous actions. It is crucial for human social intelligence which in turn enables understanding of, and for the ability to predict, other humans´ behaviours, such as for example other drivers´ intent to overtake, stop, turn, or switch lanes. For making situation-based decisions, both autonomous and human drivers need to take the intentions of surrounding vehicles into account. This is especially true in a mix of autonomous and human drivers.

This project focuses on reducing costs and risks in algorithmic trading while maintaining profitability through machine learning.

2009-2013 The project studied how information fusion, i.e. synergetic integration of available data and information, could be used to offer advanced decision support for tactical air unit cooperation in future aircraft platforms. The project addressed two main issues: Human System Interaction and Tactical support with situation analysis. The project involved two senior researchers and two PhD students at SAIL. The Swedish Governmental Agency for Innovation Systems (VINNOVA), Saab Aeronautics (coordinator) and the University of Skövde funded the project with 17.7 million SEK from 2009–2013.

2019-2022 Predictive Maintenance (PdM) using Artificial Intelligence (AI) and Machine Learning (ML) is the top-ranked use case in terms of business value of industrial digitalization. Not surprising since the annual maintenance cost in Swedish manufacturing industry is over 100 billion SEK and 60% of all maintenance activities are reactive.

2021-2023 This project aims to identify, adjust and implement AI methods to improve and create a more resilient drinking water production and distribution, where the operators at the drinking water plants are supported in their decision making processes.

2023-2024 The driver's ability to understand a current traffic situation, and anticipate future maneuvers of other traffic participants are key safety enablers. How can we help drivers observe the traffic safety of different traffic situations via information and warnings via a safety system?

2018-2021 The project will create new values for the Swedish metal industry. This will be done through the use and development of recent data analysis techniques. By doing so, one will find currently hidden relationships between known and unknown process parameters and the resulting product quality.

2019-2022 The project will study design elements of future air systems that offer cognitive support for increased team effectiveness. The project will investigate how the design of complex systems can benefit from taking the teamwork perspective.

Industrial production can be complex, with many factors at play, such as machinery breakdowns. However, an improved decision-support system that incorporates multiple information sources can make a difference when costs are totalled. In this project, Bernard Schmidt has studied a model that could lead to reduced maintenance costs.

2019-2020 The complex interactions among socio-demographic, climatic, economic environments, and possibly legal/institutional situations involve many factors that can influence patterns of population dynamics. In this project, we plan to resolve the limitation of machine learning by providing an explainable predictive model that allows users to try out ‘what-if’ scenarios by adding interactive input at a specific point in the time-series.