No description available.

Increasingly flexible and efficient industrial processes and automation systems are developed by integrating computational systems and physical processes, thereby forming large heterogeneous systems of cyber-physical systems. Such systems depend on particular data models and payload formats for communication, and making different entities interoperable is a challenging problem that drives the engineering costs and time to deployment. Interoperability is typically established and maintained manually using domain knowledge and tools for processing and visualization of symbolic metadata, which limits the scalability of the present approach. The vision of next generation automation frameworks, like the Arrowhead Framework, is to provide autonomous interoperability solutions. In this thesis the problem to automatically establish interoperability between cyber-physical systems is reviewed and formulated as a mathematical optimisation problem, where symbolic metadata and message payloads are combined with machine learning methods to enable message translation and improve system of systems utility. An autoencoder based implementation of the model is investigated and simulation results for a heating and ventilation system are presented, where messages are partially translated correctly by semantic interpolation and generalisation of the latent representations. A maximum translation accuracy of 49% is obtained using this unsupervised learning approach. Further work is required to improve the translation accuracy, in particular by further exploiting metadata in the model architecture and autoencoder training protocol, and by considering more advanced regularization methods and utility optimization. / Productive 4.0

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language.

No description available.