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Cloud Computing an der HFU
(2010)
Understanding Cloud Audits
(2012)
Towards a Domain Specific Security Policy Language for Automatic Audit of Virtual Machine Images
(2012)
Cloud Utility Price Models
(2013)
Cloud Resource Price System
(2014)
Software Defined Privacy
(2016)
Testing applications for SmartHome environments is quite complicated, since a real environment is not accessible or the conditions are not controllable during development time. The need to set up the whole hardware environment, increase the complexity of these systems enormously. Therefore, it is helpful to simulate the SmartHome hardware components and environment conditions (e.g. rain, heat, etc.). This paper contains an approach to improve the test and demonstration process of Internet of Things scenarios. A prototype (ScnSim: Scenario Simulator) was developed to set up scenarios. The user of the ScnSim can create her/his own scenario using items (sensors/actuators) and rules, which control the sensors and actors building the IoT enviornment. This simulator is supposed to support the user testing IoT applications or configurations of SmartHome platforms like openHAB. In addition, the ScnSim is supposed to help demonstrating showcases, for example, often demonstrated on a trade fair or as a proof of concept for a customer.
Delegated Audit of Cloud Provider Chains Using Provider Provisioned Mobile Evidence Collection
(2017)
Software Defined Privacy
(2017)
Towards an Ontological Representation of Condition Monitoring Knowledge in the Manufacturing Domain
(2018)
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.