Tag Archives: Team Intelligent Information Systems

– REACT

The overall goal of REACT is a systematic, safe and validatable approach to developing, training and use of digital reality with the goal to ensure  safe and reliable acting autonomous systems – especially in critical situations. In order to reach this goal, we use methods and concepts of machine learning – especially
Deep Learning and (Deep) Reinforcement Learning (RL) – to learn lower-dimensional submodels of the real world. From these submodels we compile (semi) automatically complex, high-dimensional models in order to identify and simulate the entire range of critical situations. By means of  digital reality, we virtually synthesize  the otherwise missing sensor data of critical situations and train autonomous systems so that they are able to handle critical situations safe and confident.  The aim of the project is to enhance the capabilities of autonomous systems. Therefore we continuously and  systematically validate and  align  synthetic data with reality and adapt the models where necessary.

Continue reading – REACT

CREMA

Cloud-based Rapid Elastic Manufacturing

The European H2020 research project CREMA (Cloud-based Rapid Elastic Manufacturing) aims at the innovative combination of cloud computing and XaaS (Everything-as-a-Service) for highly flexible, resource-efficient coordination of service-based industrial manufacturing processes in distributed and dynamically changing environments.

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INVERSIV

INVERSIV: Integrated Verification, Simulation and Visualization for Industrial Applications

Industry 4.0 is a main topic in the high-tech strategy of the German  government, aimed at enabling fundamental innovation in industry. The idea behind Industry 4.0 is that “driven by the Internet, the real and virtual worlds are growing closer and closer together to form the Internet of Things. Industrial production of the future will be characterized by the strong individualization of products under the conditions of highly flexible (large series) production, the extensive integration of customers and business partners in business and value-added processes, and the linking of production and high-quality services leading to so-called hybrid products” (BMBF). Together with the increasing requirements of high flexibility, reduced delivery time, and short product life cycles, the Industry 4.0 concept represents the highly dynamic, individualized, and networked environment of modern, digital factories.

There is a large number of challenges on the IT side for realizing Industry 4.0: (i) The high flexibility of production processes requires the ability to quickly redesign and adapt production lines and all supporting processes in a company. (ii) The high variability of products with small batch sizes requires novel, highly adaptable ways to monitor the production line for quality and errors while providing support and training for workers that adapts to the current situation. (iii) To support quick changes we must move from fixed, specialized networks and interfaces to flexible architectures and service interfaces that can easily be reconfigured and support the low-latency, high-volume communication needed in industrial environments.

The main objective of the INVERSIV project is the ability to build fully functional models of systems (such as production lines) and from those models derive the data to monitor, predict, and possibly suggest corrections to the operation of those systems based on live data from real systems (dual reality).

INVERSIV aims at processing and using realtime data streams in production scenarios for visualizing the state of production facilities, detecting failures and problematic situations, and propose and visualize appropriate maintenance and repair actions. In case an error situation has been detected (respectively predicted) actions to resolve the situation have to be planned. We will explore the setup and evaluation of alternative models in terms of hybrid automata and verify their proper functionality with an extended hybrid verification system. The planning stage will also explore maintenance repair actions generated by involving human or intelligent virtual characters, e.g. for installing an alternative model. This highlights again the need to have common data representation and communication mechanism between the modules (here, multi-agent planning and hybrid verification).


The INVERSIV project is funded by the Federal Ministry of Education and Research (FKZ 01IW14004).

Ansprechpartner: Ingo Zinnikus