Titlebook: Machine Learning for Cyber Physical Systems; Selected papers from Jürgen Beyerer,Alexander Maier,Oliver Niggemann Conference proceedings 20

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Prescriptive Maintenance of CPPS by Integrating Multimodal Data with Dynamic Bayesian Networks,nsidering multimodalities and structural heterogeneities of maintenance records, and ii) providing a methodology for integrating the data-model with Dynamic Bayesian Network (DBN) for the purpose of learning cause-effect relations, predicting future events, and providing prescriptions for improving maintenance planning.

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Intelligent edge processing,ata analytics methods. The proposed solution is capable to integrate information from many different sources, by including structured, semi-structured and unstructured data. The key innovation is in IoTization through dynamic, multi-modal, smart data gathering and integration based on the semantic technologies.

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Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensorement set by prediction. Support-vector regression compared to multiple, linear regression model shows only minor improvements. Feature reduction by deep autoencoders was carried out, but failed to achieve further improvements.

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Differential Evolution in Production Process Optimization of Cyber Physical Systems,d and certain properties like manufacturing time or quality are introduced as new fitness criteria for the evolutionary computing algorithm. This is demonstrated in an exemplary use case for injection moulding. Furthermore, a concept for constant production process stabilization is presented for future research.

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Conference proceedings 2020ms are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments..

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2522-8579 n automated machine learning methods.Provides an accessible .The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It  contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was h

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Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis,

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