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A Fog-Cloud Computing Infrastructure for Condition Monitoring and Distributing Industry 4.0 Services
(2019)
Collaboration in Mixed Homecare – A Study of Care Actors’ Acceptance Towards Supportive Groupware
(2019)
The rise of digital twins in the manufacturing industry is accompanied by new possibilities, like process automation and condition monitoring, real time simulations and quality and maintenance prediction are just a few advantages which can be realized. This paper takes a novel approach by extracting the fundamental knowledge of a data set from a production process and mapping it to an expert fuzzy rule set. Afterwards, new fundamental augmented data is generated by exploring the feature space of the previously generated fuzzy rule set. At the same time, a high number of artificial neural network (ANN)models with different hyperparameter configurations are created.
The best models are chosen, in line with the idea of survival of the fittest, and improved with the additional training data sets, generated by the fuzzy rule simulation. It is shown that ANN models can be improved by adding fundamental knowledge represented by the discovered fuzzy rules. Those models can represent digitized machines as digital twins. The architecture and effectiveness of the digital twin is evaluated within an industry 4.0 use case.
This paper describes current issues regarding regulatory requirements in medical devices with a focus on data-driven / AI based approaches. It shows that the EU Medical Device Regulation (MDR) sets high requirements to assess product performance based on systematically collected data, whereas the collection of data is difficult in the EU. Contrary, it demonstrates that the FDA is currently very active in supporting the development of software based systems in the US with dedicated regulatory programs. In particular, it pursues more dynamic approaches for releasing software devices. The overall situation favors developments in the US. Thus, the paper surveys a program to support local entities on adapting AI technologies.
Data Recording System for Anesthesiology, Patient Monitor and Surgical Devices in Operating Rooms
(2019)
Ein Keim kommt selten allein : wie Mikroben unser Leben bestimmen und wir uns vor ihnen schützen
(2019)
Evaluation eines VR-gestütztes Absaugtraining für professionell Pflegende in Ausbildung und Praxis
(2019)
Flexible piezoresistive PDMS metal-thin-film sensor-concept for stiffness evaluation of soft tissues
(2019)
Formal Description of Use Cases for Industry 4.0 Maintenance Processes Using Blockchain Technology
(2019)
Cylindrical grinding is an important process in the manufacturing industry. During this process, the problem of grinding burn may appear, which can cause the workpiece to be worthless. In this work, a machine learning neural network approach is used to predict grinding burn based on the process parameters to prevent damage. A small dataset of 21 samples was gathered at a specific machine, grinding always the same element type with different process parameters. Each workpiece got a label from 0 to 3 after the process, indicating the severity of grinding burn. To get a robust neural network model, the dataset has been scaled by augmentation controlled by grinding experts, to generate more samples for training a neural network model. As a result, the model is able to predict the severity of grinding burn in a multiclass classification and it turned out that even with little data, the model performed well.