Semantic Integration in the Context of Cyber-Physical Systems

Industrial systems have been developing into more and more complex systems during last decades. They have changed from centralized solutions to distributed, more robust, and more exible eco-systems comprising a high number of embedded systems. In recent years, we are witnessing the research trend in the area of embedded systems which concerns the very close integration of physical and computing systems. This dissertation thesis deals with the problem of the semantic integration of components (sensors and actuators) of cyber-physical systems within industrial automation domain and presents resulting bene ts. Cyber-physical systems were created based on the aforementioned trend of the close integration of computing systems and physical systems. This tight integration involves infrastructures responsible for control, computation, communication, and sensing. These systems are composed of many subsystems produced by various manufacturers, and the subsystems produce an enormous volume of data. Furthermore, data generated from all of the system parts has di erent dimensions, sampling rates, levels of details, etc. Next, cyber-physical systems form systems which represent building blocks of the fourth industrial revolution (Industry 4.0) for example (Industrial) Internet of Things, Smart Cities, Smart Factories. Thus, the right understanding of data (data meanings, given context, subsystems purposes, and possible ways of subsystems integration) belong to essential requirements for enabling Industry 4.0 visions. In this thesis, the utilization of ontologies was proposed to deal with the semantic heterogeneity for enabling easier cyber-physical system components integration. Moreover, the current widespread e ort to create exible highly customized manufacturing requires novel methods for data handling together with subsequent data utilization. Storing knowledge and data in an ontology o ers a needed solution. For example, an ontology employment brings easy system data model management, increase an e ciency of cyber-physical system components interoperability, advanced data processing, reusability of sensors and actuators, and utilization of ontology matching methods for an integration of other data models. This work concerns the problem, how to describe cyber-physical system components using ontologies to enable e ective integration. Next, the ontology matching system suitable for integration of heterogeneous data models in industrial automation domain is described. The proposed solution of the semantic interoperability is demonstrated on the Plug&Play cyber-physical system components. On the other hand, storing data in an ontology and mainly processing of RDF statements brings one signi cant bottleneck | performance issue. Thus, Big Data technologies are employed for overcoming this issue together with a proposal of suitable storage data models. The overall approach is demonstrated on the proposed and developed prototype named Semantic Big Data Historian. In particular, the main contributions of the dissertation thesis are as follows: 1. The proposal of the solution for CPS low-level semantic integration based on Semantic web Technologies together with a veri cation of a feasibility of proposed approach using Semantic Big Data Historian. 2. The overcoming performance issues of processing shop floor data represented as RDF-triples with the help of Big Data technologies and suitable storage data models | vertical partitioning and hybrid SBDH model. 3. The proposal and implementation of a suitable way how to integrate heterogeneous data models from industrial automation domain where the highest precision and recall are required. The approach is based on similarity measures aggregation using self-organizing maps and user involvement with the help of active learning and visualization of self-organizing map output layer. 4. Enabling reusability of cyber-physical system components together with effortless configuration based on utilization of Semantic Web technologies. This approach was named as Plug&Play cyber-physical system components.Katedra kybernetik

Towards user-friendly and high-performance analytics with big data historian

We are witnessing the trend of increasing data production in various domains including industrial automation. This trend requires means for data capturing, storing, and analyzing. Furthermore, a versatile data model is needed to enable easy knowledge representation as well as change management. In this paper, we utilize Semantic Big Data Historian, which can cope with previously mentioned requirements, for a demonstration of promising analytic approach combining Big Data methods and a user-friendly modular platform. The ap-proach is demonstrated on data from a hydroelectric power station. The station has been dealing with the interesting problem of prediction when to momentari-ly stop their turbine to increase generated power after the restart. In this contri-bution, we discuss several approaches how to process and analyze data from power station sensors for achieving the best results

Ontology learning for facilitating ontology matching in automotive

All manufacturing companies need to monitor a large number of de-vices and from which critical data must be captured and analyzed. The increas-ing complexity of these ecosystems emphasizes the requirement for a flexible and versatile data model architecture. Ontologies may facilitate a proper under-standing of the problem domain as well as the interoperability with surrounding systems using ontology matching approach. However, data models of surround-ing systems are not always ontologies. Thus, concepts and relations among them have to be extracted from the models to enable their integration with the ontology. The definition of concepts, their hierarchy, relations between con-cepts, and properties from a general architecture is a complex task and has to be tailored to an application’s needs. In this paper, we propose an involvement of the ontology learning approach to the process of ontology matching in the au-tomotive

Silymarin Dehydroflavonolignans Chelate Zinc and Partially Inhibit Alcohol Dehydrogenase

Silymarin is known for its hepatoprotective effects. Although there is solid evidence for its protective effects against Amanita phalloides intoxication, only inconclusive data are available for alcoholic liver damage. Since silymarin flavonolignans have metal-chelating activity, we hypothesized that silymarin may influence alcoholic liver damage by inhibiting zinc-containing alcohol dehydrogenase (ADH). Therefore, we tested the zinc-chelating activity of pure silymarin flavonolignans and their effect on yeast and equine ADH. The most active compounds were also tested on bovine glutamate dehydrogenase, an enzyme blocked by zinc ions. Of the six flavonolignans tested, only 2,3-dehydroderivatives (2,3-dehydrosilybin and 2,3-dehydrosilychristin) significantly chelated zinc ions. Their effect on yeast ADH was modest but stronger than that of the clinically used ADH inhibitor fomepizole. In contrast, fomepizole strongly blocked mammalian (equine) ADH. 2,3-Dehydrosilybin at low micromolar concentrations also partially inhibited this enzyme. These results were confirmed by in silico docking of active dehydroflavonolignans with equine ADH. Glutamate dehydrogenase activity was decreased by zinc ions in a concentration-dependent manner, and this inhibition was abolished by a standard zinc chelating agent. In contrast, 2,3-dehydroflavonolignans blocked the enzyme both in the absence and presence of zinc ions. Therefore, 2,3-dehydrosilybin might have a biologically relevant inhibitory effect on ADH and glutamate dehydrogenase