The increasing use of AI components in vehicles and transportation is leading us down a path where the computer makes decisions and we humans have to blindly trust the AI and live with those decisions. Explainable AI is increasingly becoming an area of great interest to various user groups, such as citizens who want to trust the systems they use, legal entities who are responsible for liability and accountability, and researchers who want to understand the limitations of AI and improve the models. The goal of AITHENA is to research and develop methods to create transparency and interpretability of AI models for use in CCAM solutions. 

Further information can be found here.

In this project, we design, develop and evaluate AI algorithms for the semi-automated damage assessment of freight cars in train formation. The goal is to bring in the state of the art in the use of artificial intelligence for the localization and classification of defects in image data, to adapt and extend established methods from the academic environment for the present use case.

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The central goal of CC5G.NRW is to contribute to the development of NRW as a lead market for 5G. The focus is on new solutions for companies and authorities in the application of 5G, as well as research into social issues relating to security, social inclusion and sustainability. In this context, innovation potentials and obstacles are identified, systemic competencies are developed and expanded, and potentials of 5G are made accessible.

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The DACE (Data Competence Center for Circular Economy) explores the necessary competencies for the collection, analysis, and utilization of Circular Economy (CE) data by the scientific community and for the CE itself. The goal is to empower researchers, in particular, in handling CE data and to connect stakeholders in data-driven Circular Economy initiatives.

Further information can be found here.

The aim of the project is to increase the resilience of critical infrastructures in the health sector, using the Malteser network as an example, and to create a sustainable security culture. To this end, holistic information security concepts will be developed that integrate technical, organizational and personnel development measures. 

Further information can be found here.

In the DigiData project, the TMDT will explore and establish a new form of interdisciplinary knowledge and method transfer within universities. Starting from the people who drive digitization, data science and artificial intelligence, the project aims at a horizontal and intra-university transfer to people from other disciplines who do not yet fully use the achievements of these fields.

Further information can be found here.

Automated extension of the digital twin of the city of Wuppertal with visual data using semantic technologies

The digital twin serves as a basis for the digital recording of data collected in the municipal environment and is to be used in future for the simulation and planning of developments in the Wuppertal area.

Further information can be found here.

The Flex4Green project aims to automate and dynamically plan the strictly regulated work processes in the assembly of aircraft shells using AI. Using reinforcement learning and interactions with a simulation, the AI learns to create robust plans for error-prone production sequences, thereby saving time, money, energy and resources in reality. Flex4Green thus contributes to more environmentally friendly and sustainable aircraft production.

Further information can be found here.

Advancing digitalisation and the growing demand for realistic visual representations have led to increased interest in the use of three-dimensional (3D) objects in recent years. The ability to transform the physical world into virtual 3D models offers a wide range of possible applications in various fields.

Further information can be found here.

The goal of the project is to use AI methods to prevent flood damage to protect the property of the local economy by developing an early warning system for the temporal and spatial prediction of flood events. The early warning system is based on existing information systems of the Wupperverband and uses state-of-the-art AI methods for data-driven prediction of water levels and flood hazards, taking into account current weather conditions and other environmental factors detected by sensors.

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As part of the BMBF program "Innovations for the Production, Service and Work of Tomorrow", the Institute for Technologies and Management of Digital Transformation (TMDT) will enable craft businesses to explore the potential of the Internet of Things (IoT) for themselves and use it profitably within the three-year project.

Further information can be found here.

The aim of the project is to innovate the configuration and operation of modern filling systems through the use of digital technologies and generative AI processes. With the help of these methods, it should be possible to generate configurations without the need for extensive simulations. The trained models will be able to use data from laboratory tests of the materials to be filled to suggest a configuration that requires only minor adjustments by the operating personnel for an optimal filling process. Through continuous learning, these models will be able to adapt to the conditions of the process and learn continuously.

Further information can be found here.

The "Artificial Intelligence and Citizens' Councils (KIB)" project is dedicated to analysing digital solutions - in particular artificial intelligence (AI) - and their significance for public participation in political processes. Citizens are becoming increasingly actively involved in issues that directly affect their everyday lives, be it in the areas of energy, transport or environmental policy. The use of digital tools should make it easier to obtain information and form opinions and support deliberative knowledge production.

Further information can be found here.

The KI-Makerspace at the University of Wuppertal is an innovative concept that aims to systematically promote the AI competence of students and teachers. By combining training, project support and the freedom to develop one's own ideas, it enables the direct and effective integration of AI methods into teaching.

Further information  can be found here.

Within the framework of the in-house project KoLBi-BK, a quantitative and qualitative profiling of industrial and technical schooling at vocational colleges is to be carried out. The focus of the TMDT is on the design of industrial and technical classes with virtual reality. Student teachers will be taught how to create virtual reality lessons and the development of teaching concepts with virtual reality will be examined.

Further information can be found here.

The aim of the TOP.KI project is to develop, test and evaluate a translation tool that uses artificial intelligence to facilitate the creation of exam questions in plain language. While maintaining the same content and level of difficulty, the texts created in this way should be better understood by examinees. This should help to ensure that exam results primarily reflect the trainees' technical and application knowledge rather than their text comprehension skills.

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The upcoming traffic shift confronts the automotive supply industry with comprehensive transformation requirements. Answers for the Bergische Region are being developed by the TrAIBeR.NRW project. The project, led by TMDT/SiKom, was created on the joint initiative of the regional IG Metall branch offices and employers' associations.

Further information can be found here.

In collaboration with the Chair of Electrical Power Supply Engineering, Energieforen Leipzig GmbH, and Horstmann GmbH, supported by network operators and associated partners, TMDT is working on the development of a virtual energy system.

Further information can be found here.

As part of STOTIC, the TMDT is responsible for the design, development and evaluation of learning methods for assessing the sustainability of configurable supply chains. In particular, the TMDT makes a major contribution to modelling, taking into account modern neural network architectures and their applicability for processing sustainability information.

Further information can be found here.