@misc{12785, abstract = {{Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation.}}, author = {{Wunderlich, Paul and Wiegräbe, Frauke and Dörksen, Helene}}, booktitle = {{INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH}}, issn = {{1660-4601}}, keywords = {{machine learning, healthcare, case management, caring, multi-label classification}}, number = {{2}}, publisher = {{MDPI}}, title = {{{Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance}}}, doi = {{10.3390/ijerph20021215}}, volume = {{20}}, year = {{2023}}, } @misc{13015, abstract = {{food are discarded annually, with a worldwide total exceeding 1.3 billion tonnes. A significant contributor to this issue are consumers throwing away still edible food due to the expiration of its best-before date. Best-before dates currently include large safety margins, but more precise and cost effective prediction techniques are required. To address this challenge, research was conducted on low-cost sensors and machine learning techniques were developed to predict the spoilage of fresh pizza. The findings indicate that combining a gas sensor, such as volatile organic compounds or carbon dioxide, with a random forest or extreme gradient boosting regressor can accurately predict the day of spoilage. This provides a more accurate and cost-efficient alternative to current best-before date determination methods, reducing food waste, saving resources, and improving food safety by reducing the risk of consumers consuming spoiled food.}}, author = {{Wunderlich, Paul and Pauli, Daniel and Neumaier, Michael and Wisser, Stephanie and Danneel, Hans-Jürgen and Lohweg, Volker and Dörksen, Helene}}, booktitle = {{Foods}}, issn = {{2304-8158}}, keywords = {{Plant Science, Health Professions (miscellaneous), Health (social science), Microbiology, Food Science}}, number = {{6}}, publisher = {{MDPI }}, title = {{{Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors}}}, doi = {{10.3390/foods12061347}}, volume = {{12}}, year = {{2023}}, } @inproceedings{4780, author = {{Bunte, Andreas and Wunderlich, Paul and Moriz, Natalia and Li, Peng and Mankowski, Andre and Rogalla, Antje and Niggemann, Oliver}}, booktitle = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, title = {{{Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS}}}, year = {{2019}}, } @misc{12803, abstract = {{The increasing amount of alarms and information for an operator in a modern plant becomes a significant safety risk. Although the notifications are a valuable support, they also lead to the curse of overloading with information for the operator. Due to the huge amount of alarms it is almost impossible to separate the crucial information from the insignificant ones. Therefore, new procedures are required to reduce these alarm floods and support the operator to minimize the safety risk. One approach is based on learning a causal model that represents the relationships between the alarms. This allows alarm sequences that are causally implied to be reduced to the root cause alarm. Fundamental element of this approach is the causal model. Therefore in this work, different probabilistic graphical models are considered and evaluated on the basis of appropriate criteria. A real use case of a bottle filling module serves as a benchmark for how well they are suitable as a causal model for the application in alarm flood reduction.}}, author = {{Wunderlich, Paul and Hranisavljevic, Nemanja}}, booktitle = {{2019 IEEE 17th International Conference on Industrial Informatics (INDIN)}}, isbn = {{978-1-7281-2928-0}}, keywords = {{probabilistic graphical model, causal model, alarm flood reduction, Bayesian network, Markov chain, restricted boltzmann machine, automata}}, location = {{Helsinki, Finland }}, pages = {{1285--1290}}, publisher = {{IEEE}}, title = {{{Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction}}}, doi = {{10.1109/indin41052.2019.8972251}}, year = {{2019}}, } @inproceedings{4327, abstract = {{In ever changing world, the industrial systems become more and more complex. Machine feedback in the form of alarms and notifications, due to its growing volume, becomes overwhelming for the operator. In addition, expectations in relation to system availability are growing as well. Therefore, there exists strong need for new solutions guaranteeing fast troubleshooting of problems that arise during system operation. The approach proposed in this study uses advantages of the Asset Administration Shell, machine learning, and human-machine interaction in order to create the assistance system which holistically addresses the issue of troubleshooting complex industrial systems.}}, author = {{Lang, Dorota and Wunderlich, Paul and Heinz, Mario and Wisniewski, Lukasz and Jasperneite, Jürgen and Niggemann, Oliver and Röcker, Carsten}}, booktitle = {{14th IEEE International Workshop on Factory Communication Systems (WFCS)}}, keywords = {{Maintenance engineering, Adaptation models, Machine learning, Data models, Standards, Software, Bayes methods}}, location = {{Imperia, Italy }}, publisher = {{IEEE}}, title = {{{Assistance System to Support Troubleshooting of Complex Industrial Systems}}}, doi = {{10.1109/WFCS.2018.8402380}}, year = {{2018}}, } @inproceedings{4254, abstract = {{The current trend of integrating machines and factories into cyber-physical systems (CPS) creates an enormous complexity for operators of such systems. Especially the search for the root cause of cascading failures becomes highly time-consuming. Within this paper, we address the question on how to help human users to better and faster understand root causes of such situations. We propose a concept of interactive alarm flood reduction and present the implementation of a first vertical prototype for such a system. We consider this prototype as a first artifact to be discussed by the research community and aim towards an incremental further development of the system in order to support humans in complex error situations.}}, author = {{Büttner, Sebastian and Wunderlich, Paul and Heinz, Mario and Niggemann, Oliver and Röcker, Carsten}}, booktitle = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}}, editor = {{Holzinger, Andreas}}, isbn = {{978-3-319-66807-9}}, keywords = {{Alarm flood reduction, Machine learning, Assistive system}}, location = {{Reggio, Italy}}, pages = {{69--82}}, publisher = {{Springer}}, title = {{{Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction}}}, volume = {{10410}}, year = {{2017}}, }