Focus projects

At the ADA Lovelace Center, research and industry join forces to take methods and procedures in the field of artificial intelligence beyond the boundaries of what is currently possible.

The key feature of autonomous systems is that they use sensors to map their environment and can interact with it independently using actuators. For example, this paves the way for self-driving cars, robots that perform tasks autonomously, and systems that regulate themselves adaptively. Autonomous systems are made up of sensors for mapping the environment and components for the aggregation, analysis, and interpretation of data, as well as situation assessment, action planning, and actuators. A method known as deep reinforcement learning (DRL) is used to implement decision-making in autonomous systems or agents.

Traction energy consumption is the primary cost factor in the electricity bills of rail transport companies, and is largely determined by how the trains are driven. Energy-efficient speed profiles can therefore lead to significant reductions in power consumption. This includes making the greatest possible use of coasting phases, during which the train consumes no energy. However, it is also vital to coordinate rail traffic with an eye to energy efficiency – for instance by avoiding excessive numbers of simultaneous departures, which result in high peak loads on the electricity grid and therefore additional charges. Moreover, it is important to synchronize arriving and departing trains so that energy recovered while one train is braking can be used to accelerate another.

The availability of land is the most important factor when it comes to developing major new business locations. In reality, however, municipalities and companies wishing to locate here are struggling with an increasing shortage of land, especially in metropolitan areas. In addition, for reasons of sustainability, the net new sealing of commercial areas is to be reduced, so that in many cases new settlements can only be realized by reactivating commercial areas that are no longer used, so-called brownfields. These have further advantages: they are often already connected to local supply networks, have good infrastructure links and are easily accessible for employees. But where are these brownfields located and how many do they actually have? And what legacy issues can be expected on site? While some municipalities have a detailed insight here, other regions up to federal states have only sporadic and decentralized information on this.

As progressive automation brings us closer to autonomous systems, machine learning methods for mapping and processing complex and unknown situations have become indispensable. Autonomous systems with an advanced degree of automation – i.e., highly automatic to fully autonomous systems – often employ neural networks for context recognition. Early results in the field of deep learning appear highly promising for this task, enabling driverless vehicles to recognize objects, interpret traffic events and issue driving instructions. These techniques, which are based on machine learning, can either be used system-wide, that is from the sensor to the actuator, or merely to solve individual aspects of autonomous driving.

In this project, an advanced neural network has been developed, specialized in predicting and minimizing casting defects in production processes. The main strength of the system lies in analyzing production data to detect errors early and generate optimization recommendations. This results in a significant improvement in production quality and efficiency by reducing scrap and material waste.

In this project, an advanced neural network has been developed, specialized in predicting and minimizing casting defects in production processes. The main strength of the system lies in analyzing production data to detect errors early and generate optimization recommendations. This results in a significant improvement in production quality and efficiency by reducing scrap and material waste

There are numerous commercial and open source tools on the market for ML solution development in the industry. However, the choice is complex due to application-specific constraints such as license agreements, data security and system compatibility. The integration of multiple tools for a specific functionality leads to challenges in usability, operability, maintenance and integration into the existing infrastructure. Fraunhofer IIS develops customized MLOps solutions for the supply chain that take into account the specific requirements of our industry partners. These solutions optimize the strengths of different ML tools while being easy to use and maintain.