Madshus is the oldest, still operating, producer of cross-country skis. The factory delivers advanced racing and entry level skis with regards to material quality, design, weight and defined ski properties.
The primary objective of SAM is to optimize demanding industrial processes by developing advanced physical models and machine learning algorithms, and integrating new online sensors where real time data is currently limited or lacking.
The primary objectives of the project are to explore health risks among firefighters, and to study the dual challenge of exposure to heat and carcinogenic compounds exposure and related outcomes, and how this knowledge can be used to establish preventive measures.
The aim of TAPI (Towards Autonomy in Process Industries) is to move Norwegian land-based process industries towards more autonomous operations by exploring the intersection between machine learning (ML) and more traditional model-based control methods.
The project will develop specifications that simplify and automate design and operation of safety systems in the petroleum industry. The industry partners will, based on the R&D activities in the project, develop new solutions for collecting, analyzing and sharing failure data of safety systems during design and operation.
At SINTEF we have started a strategic project to investigate the possibilities and application areas of quantum computers to solve practically relevant problems.
As many European countries, Norway has still a significant length of non-electrified railways. New technologies like hydrogen fuel cells and batteries can be a better option than expensive catenary lines.
Warped cardboard is a major quality issue in the production of corrugated cardboard. It reduces the productivity in the converting units, causes reduced quality on the final product, and increases waste. In UnWarp we are using machine learning to understand when the cardboard becomes warped in and provide guidelines for minimising the warp.
The Digital Worker ERA will focus on exposure, physical and organisational factors for workers in the petroleum industry. We will build new knowledge on real-time exposure assessment of chemical, physiological strain and the human factors elements of using such assessments in-situ.
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