The project was developed in collaboration with voxelJet. The company specialises in the production of 3D printers for industrial 3D printing and also offers 3D printing as a service.
Like most people, you have probably never experienced a fire. As a result, many people are unaware of the correct behaviour to adopt in the event of a fire. With the right behaviour, you can drastically reduce the risk to yourself, your own family or your fellow human beings.
You need to know this: It is not only the fire that is dangerous, but above all the toxic smoke from the fire. Just a few breaths are enough for you to lose consciousness and die of smoke inhalation. →
The project is part of the CARE REGIO joint project. As part of the sub-project "Digitisation of the care transition report (PÜB)", the student project is pursuing the application of deep learning techniques to advance the standardisation of PÜBs. →
Every great innovation needs a place where it can be created. →
Augsburg car-free! The task is the integral planning of an above-ground modular parking system, which is at the same time an electricity power plant and offers co-working spaces. →
As part of the Basic Subjects project, eleven students from the 4th semester of the Civil Engineering course became aware of the wastewater treatment plant in Otzing, Lower Bavaria, through their client Prof. Dr.-Ing. Rita Hilliges. →
Master Civil Engineering is accredited →
Predictive maintenance optimizes the maintenance of machines by preventing unplanned downtime and unnecessary repairs. The implementation is made possible by modern machine learning methods and a well-developed data infrastructure. In the research project PAS4PCM – in cooperation with Rational AG and on the basis of collected device data – the research group HSA_dsg of the Faculty of Computer Science at the Technical University of Augsburg is developing a concept and an implementation of such a system. →
Modern Industry 4.0 applications can be implemented with the Local-First Cooperation design principle. Proven methods of artificial intelligence (AI) can be used to solve problems. Distributed software agents communicate directly, coordinate themselves with others, and cooperate to solve local problems. Symbolic and sub-symbolic AI complement each other in problem solving. In this way, complex planning and optimization tasks can be implemented using resilient systems. These systems of the Industrial Internet of Things increasingly show self-x properties of organic computing. →
In collaboration with XITASO, we have developed a trade fair showcase that presents object recognition and segmentation using neural networks. By using RGB and infrared cameras, road users such as cyclists and pedestrians are recognised under different environmental conditions. Pre-trained models such as YOLOv8 and Mask R-CNN are used, which have been subsequently fine-tuned to infrared data to enable reliable detection even in poor lighting conditions. →