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Das Konzept der ‚Industrie 4.0‘ basiert auf neuen, Mechanik, Elektronik und Informatik integrierenden Technologien fur hauptsachlich industrielle Anwendungen. Neben der Produktionstechnik im engeren Sinne ist die Produktionslogistik – oder genereller: die Intralogistik – eines der zentralen Anwendungsfelder.

The paper presents the results of cooperative work done by three different research institutions. The cooperation concluded in a large field experiment with six heterogeneous unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). It is shown, that it is possible to do reconnaissance and surveillance, namely detection of pedestrians and vehicles, with an autonomous multi-robot system under real world conditions. The participants were all using different robotic middlewares and interfaces to the graphical user interface. Hence, it has been agreed to use common interfaces and standards for communication. The Robot Operating System (ROS) is used as a communication layer, supported by commercially available 3G mobile radio communication for large operating distances. A standardized markup language has been used for the description of tasks and feedback from the robots to the control station. Moreover, the applied UGV control strategies and the implemented approach for detection, tracking and classification are presented. The concluding field test showed that it is possible to realize a multi-robot system of dynamic size, and that such teams are well-suited to perform reconnaissance and surveillance without constant observation by a human operator.

Path integration is a primary means of navigation for a number of animals. We present a model which performs path integration with a neural network. This model is based on a neural structure called a sinusoidal array, which allows an efficient representation of vector information with neurons. We show that exact path integration can easily be achieved by a neural network. Thus deviations from the direct home trajectory, found previously in experiments with ants, can not be explained by computational limitations of the nervous system. Instead we suggest that the observed deviations are caused by a strategy to simplify landmark navigation.

Data Mining Algorithms extract patterns from large amounts of data. But these patterns will yield knowledge only if they are interesting, i.e. valid, new, potentially useful, and understandable. Unfortunately, during pattern search most Data Mining Algorithms focus on validity only, which also holds true for Neuro-Fuzzy Systems. In this Paper we introduce a method to enhance the interestingness of a rule base as a whole. In the first step, we aggregate the rule base through amalgamation of adjacent rules and eliminiation of redundant attributes. Supplementing this rather technical approach, we next sort rules with regard to their performance, as measured by their evidence. Finally, we compute reduced evidences, which penalize rules that are very similar to rules with a higher evidence. Rules sorted on reduced evidence are fed into an integrated rulebrowser, to allow for manual rule selection according to personal and situation-dependent preference. This method was applied successfully to two real-life classification problems, the target group selection for a retail bank, and fault diagnosis for a large car manufacturer. Explicit reference is taken to the NEFCLASS algorithm, but the procedure is easily generalized to other systems.

Grosen Datenmengen, die aufgrund moderner Datenbereitstellungstechniken wie Data Warehouses gesammelt werden konnen, stehen oft nur unzureichende Analysewerkzeuge gegenuber. Data-Mining-Methoden, sollen, auf eine Datenbank mit einer bestimmten Fragestellung angewandt, die in den Daten liegenden Zusammenhange aufdecken und dem Benutzer in einer kompakten und fur ihn inhaltsreichen Form prasentieren und somit die oben angesprochene Lucke schliesen. Zu diesen Methoden gehoren neben den „klassischen” multivariaten Analyseverfahren auch neuere Algorithmen, vor allem Methoden der Kunstlichen Intelligenz wie Neuronale Netze und Neuro-Fuzzy-Systeme, die in der Lage sind, aus Datenbestanden fuzzy Regeln abzuleiten. In diesem Beitrag werden verschiedene Neuro-Fuzzy-Ansatze untereinander und mit alternativen Data-Mining-Methoden verglichen. Die Beurteilungskriterien sind die Klassifikationsqualitat, die Robustheit gegenuber Ausreisern und missing values, die Performance bei realistischen Problemgrosen groser Datenbanken, insbesondere Laufzeitverhalten und Datenverarbeitungskapazitat, die Einbringbarkeit von a-priori Wissenselementen und die Interpretierbarkeit der erhaltenen Ergebnisse.

Den im Marketing gesammelten Mengen an Daten stehen oft nur unzureichende Auswertungswerkzeuge gegenuber. Ein noch junger Ansatz zur Losung dieser Problematik sind Neuro-Fuzzy-Systeme, die in der Lage sind, aus Datenbestanden interpretierbare Regeln abzuleiten. In diesem Beitrag werden verschiedene Neuro-Fuzzy-Ansatze untereinander und mit alternativen Verfahren des Data Mining verglichen. Grundlage der Untersuchungen bildet dabei das aus dem Database Marketing bekannte Problem der Klassifikation von Kunden nach deren Antwort auf eine Direktmarketing-Aktion.

We present simple upper and lower bounds for the ground-state energy of the Emery model. These bounds are derived by partitioning the full Hamiltonian into a sum of sub-Hamiltonians that may be treated with the Lanczos algorithm. The energy bounds are used to derive bounds on the magnetization as a function of magnetic field and the hole density as a function of chemical potential. We improve upon the recent variational calculation of Carstensen and Dichtel [Phys. Rev. B 46, 11 040 (1992)], which suggested the existence of phase separation. Our results contain no evidence for phase separation.