KI-Einsatz in KMU: Einstiegshürden ausräumen [Clearing entry hurdles for AI deployment in SMEs – Artificial intelligence for German SMEs]

Anwendungen künstlicher Intelligenz (KI) bieten für die Produktionstechnik enorme Potenziale. Diese sind vor allem in kleinen und mittelständischen Unternehmen (KMU) nicht umfassend ausgeschöpft. Ein Grund dafür ist, dass die Umsetzung von KI-Projekten Ressourcen benötigt, welche die KMU oft nicht eigenständig bereitstellen können. Vorgestellt wird ein Konzept, um das Deployment von KI-Modellen im Produktionsumfeld zu begleite. Es ist einsetzbar unter verschiedensten Randbedingungen und mit geringem Eigenentwicklungsanteil

From the Smart City to the Smart Community, Model and Architecture of a Real Project: SensorNet

This article presents a conceptual, architectural and organizational model for the realization of a smart city based on a holistic paradigm as its cornerstone and on the new technologies as its enabling tools. The model is based on the concept of integration of the data belonging to different systems, through the development of a middleware, which allows the retrieval of data from various sources and their storage in a standard format in a new centralized database. The article also illustrates a real project concerning the integration of different sensor networks for the environmental monitoring that exemplifies and implements the main topics discussed. The issues related to its "governance" are also highlighted, not only from a strategic point of view, but also, and above all, from the perspective of its maintenance, which is an important and crucial feature for its "survival" over time

A service orientated architecture and wireless sensor network approach applied to the measurement and visualisation of a micro injection moulding process. Design, development and testing of an ESB based micro injection moulding platform using Google Gadgets and business processes for the integration of disparate hardware systems on the factory shop floor

Factory shop floors of the future will see a significant increase in interconnected devices for monitoring and control. However, if a Service Orientated Architecture (SOA) is implemented on all such devices then this will result in a large number of permutations of services and composite services. These services combined with other business level components can pose a huge challenge to manage as it is often difficult to keep an overview of all the devices, equipment and services. This thesis proposes an SOA based novel assimilation architecture for integrating disparate industrial hardware based processes and business processes of an enterprise in particular the plastics machinery environment. The key benefits of the proposed architecture are the reduction of complexity when integrating disparate hardware platforms; managing the associated services as well as allowing the Micro Injection Moulding (µIM) process to be monitored on the web through service and data integration. An Enterprise Service Bus (ESB) based middleware layer integrates the Wireless Sensor Network (WSN) based environmental and simulated machine process systems with frontend Google Gadgets (GGs) based web visualisation applications. A business process framework is proposed to manage and orchestrate the resulting services from the architecture. Results from the analysis of the WSN kits in terms of their usability and reliability showed that the Jennic WSN was easy to setup and had a reliable communication link in the polymer industrial environment with the PER being below 0.5%. The prototype Jennic WSN based µIM process monitoring system had limitations when monitoring high-resolution machine data, therefore a novel hybrid integration architecture was proposed. The assimilation architecture was implemented on a distributed server based test bed. Results from test scenarios showed that the architecture was highly scalable and could potentially allow a large number of disparate sensor based hardware systems and services to be hosted, managed, visualised and linked to form a cohesive business process