Partners: Austrian Power Grid AG, HAKOM Time Series GmbH
Quality assessment of time series data; validation and comparison of predictive models
Partners: AVL List GmbH
Validation of simulation data, sensitivity analysis, multi-objective optimization
Partners: RHI Magnesita
Monitoring of production processes and production fault; analysis for process optimization
Partners: Landesgesundheitsagentur Niederösterreich
Quality assessment of accounting data, monitoring and analysis of health system performance indicators
The project REINFORCE researches how reinforcement learning and human-centered visualization methods can be used to solve complex control problems in an efficient, fast, and flexible way.
Training AI algorithms requires a great amount of data. However, raw data often contains sensitive information. Homomorphic encryption offers a solution for secure machine learning - with protected sensitive data.
Renewable energies, such as hydropower, are an essential backbone for a climate-friendly future. To enable better real-time monitoring and thus maintenance of hydropower infrastructure, innovative digital hydropower twins are being developed in this project.
The Rail4Future project is focusing on the design of a digital rail system for the future. To this end, a novel and fully virtual validation platform for large-scale simulations of entire rail lines is being developed to increase the efficiency of existing rail infrastructure.
The primary goal of project INGRESS is to accelerate and improve the process of data scientists working with Industry 4.0 and Internet of Things (IoT) data, by enabling a closer integration of visual analysis into the existing workflows.
The RAILING project deals with the research and development of interactive, scalable and trust-building visualization and analysis tools for the exploration of time-dependent and complex data.
Businesses go smart with Data Science: with the help of visual data analysis, machine learning, deep learning, data mining and visual data processing we help companies to fully exploit the potential of their data.
The goal of the applied research project En2VA (“Visual Analytics for Energy and Engineering Applications”) is to increase the efficiency and the quality of advanced analytics for high-dimensional data from manufacturing, engineering, and the energy sector.
DEXHELPP develops new methods to support analysis, planning and control in health care by combining decision analysis, data security, data management, statistics, mathematical modelling, simulation and visual analysis.
In this project we develop new methods from the field of visual analysis and machine learning to automate the quality control and quality assurance of glass articles.
MINERVA is an integrated framework for planetary scientists allowing members of different instrument teams to cooperate synergistically in virtual workspaces by sharing observations, analyses and annotations of heterogonous mission data.
Novel visual analysis technologies for high-dimensional data in automotive engineering, industrial manufacturing and the energy sector