Global Transport ID

The aim of the project is to develop a robust, scalable, and user-oriented technical solution to problems in the logistics. The goal is to detect duplications of the same physical transport that is executed by different companies (through sub-allocation), and to prevent multiple intrusion of the same transport into the transportation database by different customers. An algorithm will be implemented to assign each physical transport a Global Transport ID (GTI) to identify transports beyond company limits.
The project consists of three parts: handle duplication detection and GTI assignment with rule-based systems, with machine learning approaches, and, finally, a hybrid approach of both, rule-based and machine learning.

The aim of the project is the development and implementation of new methods for the automatic, optimized planning of appointments and appointment sequences and the realization of a tool based on this for fast and user-friendly, optimized appointment (sequence) planning across several medical facilities. On the one hand, such a tool saves time and effort for both, patients and medical staff, and allows patient wishes to be taken into account. At the same time, it enables more targeted and successful treatment with optimized use of medical resources. This increases patient satisfaction and the use of resources and time on the part of medical facilities can be improved.

The aim of the project is the modeling and optimization of continuous industrial processes using mathematical methods of symbolic AI. As a continuation of the IntelliInfo project 1, the results, knowledge and findings to date are to be generalized and transferred to other related areas of application.

Intelligent Information Processing 2
(accompanying scientific research)
Part of the WIR! project Co-Innovation Platform Industrial Automation (KOI) 

• The modeling and optimization of continuous industrial processes using mathematical methods of symbolic AI and their combination.
• The implementation of a specific implementation of an industrial partner, here the planning and simulation of a coal storage yard.

Intelligent Information Processing
(accompanying scientific research)
Part of the WIR! project Co-Innovation Platform Industrial Automation (KOI) 

The aim of the project is the modeling and optimization of continuous industrial processes using mathematical methods of symbolic AI. New AI methods are developed that enable powerful modelling, as well as the analysis and processing of the meanings gained from the models. The project consists of three sub-projects: Analysis of Structure and Process Models, Algebraic Semantic Processing, Pattern Recognition.

• The overall goal of the project is the modeling and optimization of continuous industrial processes using mathematical methods of symbolic AI. 
• A hybrid symbolic AI methodology combining quantum information processing, constraint programming and methods for pattern recognition, analysis and intelligent fusion of different types of sensor and process data for dealing with "small data".

Open-Source-Software for rural on-demand transport

On-demand services can help to close gaps in rural public transport. However, their operation requires proprietary software, which usually incurs high license fees - a major hurdle for many municipalities, especially in structurally weak regions. As a result, there is a lack of on-demand offerings precisely where the need is particularly great. Moreover, available software solutions can hardly be integrated into existing rural public transport services: connections to buses and trains cannot be guaranteed and parallel journeys between public transport and on-demand transport cannot always be avoided.
The aim of the OSLO project is an analysis of feasibility of an open-source software solution for the integration of on-demand services into the public transport network in rural areas.
At this, the main task of the BTU research group is the investigation and conception of an intermodal routing algorithm that takes into account, adapts, and combines various routing methods according to the

spur – Modular assistance system to support people with dementia and their caregivers

With the help of this assistance system, dementia patients and their relatives are to be supported in their daily lives. To this end, a modular sensor system is to be developed whose wearables can be integrated into any existing item of clothing and also removed again. In the event of an emergency (e.g. a fall), it will trigger an alarm and notify relatives, caregivers or emergency physicians. It will also be possible to locate dementia patients. The various wearables in the system communicate with a server system via multiple base station.
At the beginning of the project, user needs were identified in a study conducted in cooperation with the Alzheimer Society Berlin. The main focus of the BTU's tasks is on providing analyses for hardware and software, defining the software architecture, and finally developing methods for indoor localization and fall prediction.

• Collection of wearable movement data from different groups (target group and volunteers)
• Survey of the target group (residents of the ASB in Frankfurt / Oder and nursing staff)
• Development of algorithms for fall detection, fall prediction and pressure ulcer prophylaxis
• Development of a system for indoor positioning
• Integration of these algorithms into an existing documentation-solution (Doku2Go)