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ILNAS-SnT Research Programme 2021-2024

Technical Standardisation for Trustworthy ICT, Aerospace and Construction

PARTNERS

The title Technical Standardisation is shown inside a blue and white circle in the centre of the image. The circle is divided into three segments representing the three focus topics of the programme, with each segment connected to one of three blocks titled Aerospace, Construction, and Information and Communications Technology. The Aerospace block is subtitled Space Commerce, Exploration, and Mining and contains a stylised image of a rocket and a satellite orbiting a ringed planet among three stars. The Information and Communications Technology block is subtitled Tools, Techniques and Algorithms to Assess, Analyze and Incorporate Security and Privacy Aspects of Artificial Intelligence Systems. It contains an illustration of artificial intelligence, represented by a stylised head, connected to many applications in everyday life including a lightbulb, a power plant and a data interface. The Construction block is subtitled Devices for Smart Homes, Smart Buildings and Building Information Modeling. It is illustrated with a stylised image of a modern skyline drawn out of blocks of numbers representing data.

RESEARCH

Core research pillars

- ICT.
- Aerospace.
- Construction.

With a special focus on

Core scientific research areas, technical standardisation needs, and identification of gaps between research and standardisation.

Objectives

Deepen data and AI capabilities and continuously drive innovation in 3 major economic sectors of Luxembourg.

ICT

Nowadays the space sector is no longer an exclusive market for governments and military applications. As access to space has become cheaper, more private companies have entered the space business, increasing its popularity. The increase in the amount of satellite data, as well as the increase of satellite data providers, have made necessary the use of satellite data marketplaces to facilitate access to satellite data. In our research, we center on problems related to these satellite data marketplaces. In particular, we focus on the NP-hard combinatorial problem of selecting the cheapest set of images that meet certain quality criteria, such as cloud coverage percentage, incident angle, resolution, etc, such that these images may be used to form a bigger image to study extensive regions. We are also developing machine learning techniques to forecast the demand for satellite images of a region after certain events, for example, floods, earthquakes, etc.

Satellite data marketplaces

Combinatorial problems

Machine-learning to forecast demand

Aerospace

With our research, we are extending the machine learning capabilities of distributed systems to make truly autonomous swarms of satellites a reality. These will give unprecedented flexibility and resilience to future space missions while also reducing the cost of missions in the long term. By simultaneously identifying related standardisation needs in this field, we prepare stakeholders to be ready for liftoff when the time comes.

Innovative satellite swarm techniques

Novel machine-learning strategies

New space mission frontiers

Construction

The development of the construction industry is one of the manifestations of the growth and development of communities. The highest share of fixed capital accumulation and the highest share of employment is in this industry sector. Improving the quality in the production of materials, design and implementation methods, speeding up the construction process, competition in the advancement of technology, optimal use of labor and the use of new construction technologies are among the influential components in this industry.
With our research, we aim to increase the efficiency and quality control of the construction sector through the optimisation of some fundamental variables, which carry important roles in this industry.

Enhancement of construction quality

Reducing macroeconomic costs

Process automation

Cutting-edge research

Research at the forefront of three fast-developing domains

Identification of standardisation gaps

Identifying standardisation needs to prepare for the future of industry

Maximum impact

Encouraging uptake of research output by stakeholders