Projects
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Current Research Projects
- AI-CPPS: AI-based Self-Adaptive Cyber-Physical Process Systems
- EASY – Energy-efficient analytics and control processes in the dynamic edge-cloud continuum for industrial manufacturing
- EBLS4Industry – Experience-Based Learning Systems for Industry 4.0 – Adaptive Production Processes and Predictive Maintenance
- myRPA – Experience-based Robotic Process Automation for Knowledge-based Personal Assistants
- SPELL – Semantic Platform for Intelligent Decision-Making and Deployment Support in Control and Situation Centers
myRPA
Experience-based Robotic Process Automation for Knowledge-based Personal Assistants
The goal of the joint project “myRPA” is to reach the next development stage of AI-based assistance systems for the support of knowledge and information workers. Such assistance systems are intended to provide employees with process information on demand through personal semantic support, freeing them from tedious routine tasks by automatically executing workflows across different application systems on the desktop according to need and context. Self-learning mechanisms are used to continuously develop and optimize the workflows. This can improve the efficiency and quality of information and knowledge work and reduce the cognitive load of employees regarding “tedious activities”, thus creating more mental freedom for creative work.
EASY
Energy-efficient analytics and control processes in the dynamic edge-cloud continuum for industrial manufacturing
The EASY project aims at the energy-efficient analysis and execution of manufacturing and control processes in the context of dynamic industrial manufacturing. In an edge-cloud continuum, transitionless and low-threshold industrial manufacturing is monitored in a process-based manner and controlled in a resource-optimized manner. A dynamic interoperable runtime environment incorporates customizable AI value-added services combined with expert knowledge for analysis and in turn uses these for optimization of manufacturing planning and control.
AI-CPPS
AI-based Self-Adaptive Cyber-Physical Process Systems
To this end, we are developing and researching “AI-based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS)“, which, as an extension to classic CPS, focus on the adaptive integration of complex procedural processes. AI has the potential to address the great heterogeneity and dynamic complexity of such systems. AI-CPPS thus become self-learning and resilient and can thus continuously improve with regard to their benefit for humans as well as their sustainable operation.
EBLS4Industry
Experience-based learning systems for Industry 4.0 - Adaptive production processes and predictive maintenance
In this project, experience-based learning systems are explored and combined with methods of process-oriented case-based reasoning, automatic planning and configuration, and machine learning methods such as deep learning. The application of semantic technologies such as ontologies and industry standards is an important part of this project. Developed research prototypes are tested and demonstrated in realistic application scenarios with the help of a factory simulation plant from Fischertechnik in the IoT Laboratory IoT Laboratory of the University of Trier.
SPELL
Semantic Platform for Intelligent Decision-Making and Deployment Support in Control and Situation Centers
The SPELL project represents the idea of a semantic platform for intelligent decision-making and deployment support in control and situation centers. This project aims to enable emergency response, emergency aid, and supply measures for the population to be initiated more quickly and in line with crisis situations (such as major incidents, pandemics, natural disasters or widespread power failures). This is to be achieved with the help of artificial intelligence.
