Hierarchical refinement of skills and skill application for autonomous robots

One of the major goals in designing learning robots is to let these robots develop useful skills over time. These skills are not only related to physical actions of the robot, but also to the coordination of activities, communication with humans, and active sensing. Throughout this paper, the interdependency between these different kinds of skills is analyzed. For the case of elementary action skills and coordination skills, methods for inegration of skill application and refinement are developed. It is shown that this integration has the potential to support long-term learning and autonomous experimentation.     

Exploration of Ordinal Data Using Association Rules

The discovery of association rules is a very efficient data mining technique that is especially suitable for large amounts of categorical data. This paper shows how the discovery of association rules can be of benefit for numeric data as well. Based on a review of previous approaches we introduce Q2, a faster algorithm for the discovery of multi-dimensional association rules over ordinal data. We experimentally compare the new algorithm with the previous approach, obtaining performance improvements of more than an order of magnitude on supermarket data. In addition, a new absolute measure for the interestingness of quantitative association rules is introduced. It is based on the view that quantitative association rules have to be interpreted with respect to their Boolean generalizations. This measure has two major benefits compared to the previously used relative interestingness measure; first, it speeds up rule extraction and evaluation and second, it is easier to interpret for a user. Finally we introduce a rule browser which supports the exploration of ordinal data with quantitative association rules.     

Bestimmung der Alkoholergiebigkeit von Getreidearten und polarimetrische St?rkebestimmung

Ohne Zusammenfassung     

Mixed bi-material electrodes based on LiMn2O4 and activated carbon for hybrid electrochemical energy storage devices

The performance of mixed bi-material electrodes composed of the battery material, LiMn₂O₄, and the electrochemical capacitor material, activated carbon, for hybrid electrochemical energy storage devices is investigated by galvanostatic charge/discharge and pulsed discharge experiments. Both, a high and a low conductivity lithium-containing electrolyte are used. The specific charge of the bi-material electrode is the linear combination of the specific charges of LiMn₂O₄ and activated carbon according to the electrode composition at low discharge rates. Thus, the specific charge of the bi-material electrode falls between the specific charge of the activated carbon electrode and the LiMn₂O₄ battery electrode. The bi-material electrodes have better rate capability than the LiMn₂O₄ battery electrode. For high current pulsed applications the bi-material electrodes typically outperform both the battery and the capacitor electrode.     

Efficient Engineering and Production Concepts for Products in Regulated Environments – Dream or Nightmare?

Manufacturing of chemical‐pharmaceutical products is moving increasingly fast on a global scale. Therefore, developing and starting up production facilities fast, with high quality, and at reasonable costs has become extremely challenging. Engineering concepts like modularization, standardization and simultaneous/parallel engineering are discussed as methods for speeding up process design and filing for regulatory approval. Transfer from batch to continuous operation mode of production is pointed out as the key‐issue in such strategies.