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In this paper an approach towards databased fault diagnosis of linear electromagnetic actuators is presented. Time and time-frequency-domain methods were applied to extract fault related features from current and voltage measurements. The resulting features were transformed to enhance class separability using either Principal Component Analysis (PCA) or Optimal Transformation. Feature selection and dimensionality reduction was performed employing a modified Fisher-ratio. Fault detection was carried out using a Support-Vector-Machine classifier trained with randomly selected data subsets. Results showed, that not only the used feature sets (time-domain/time-frequency-domain) are crucial for fault detection and classification, but also feature pre-processing. PCA transformed time-domain features allow fault detection and classification without misclassification, relying on current and voltage measurements making two sensors necessary to generate the data. Optimal transformed time-frequency-domain features allow a misclassification free result as well, but as they are calculated from current measurements only, a dedicated voltage sensor is not necessary. Using those features is a promising alternative even for detecting purely supply voltage related faults.
The improvement of collision avoidance for vessels in close range encounter situations is an important topic for maritime traffic safety. Typical approaches generate evasive trajectories or optimise the trajectories of all involved vessels. Such a collision avoidance system has to produce evasive manoeuvres that do not confuse other navigators. To achieve this behaviour, a probabilistic obstacle handling based on information from a radar sensor with target tracking, that considers measurement and tracking uncertainties is proposed. A grid based path search algorithm, that takes the information from the probabilistic obstacle handling into account, is then used to generate evasive trajectories. The proposed algorithms have been tested and verified in a simulated environment for inland waters.
Die Quadratur der Risse
(2015)
Aktive Solarenergienutzung
(2015)
Excubation
(2015)
This work investigates soft input decoding for generalized concatenated (GC) codes. The GC codes are constructed from inner nested binary Bose-Chaudhuri-Hocquenghem (BCH)codes and outer Reed-Solomon (RS) codes. In order to enable soft input decoding for the inner BCH block codes, a sequential stack decoding algorithm is used. Ordinary stack decoding of binary block codes requires the complete trellis of the code.
In this work a representation of the block codes based on the trellises of supercodes is proposed in order to reduce the memory requirements for the representation of the BCH codes. Results for the decoding performance of the overall GC code are presented.
Furthermore, an efficient hardware implementation of the GC decoder is proposed.
Die Bedeutung immaterieller Rechtsgüter nimmt in der Unternehmensrealität ständig zu. Dieser Bedeutungszuwachs hat einen festen betriebswirtschaftlichen Hintergrund: Zunehmend bestimmen nicht mehr Sachanlagevermögen, Geschäftsbeziehungen oder die Mitarbeiter den Unternehmenswert, sondern überwiegend das Intellectual Property (kurz auch: IP).1 Der Begriff Intellectual Property meint im Folgenden das durch Schutzrechte kodifizierte Wissen, also Patente, Gebrauchsmuster, Marken, Geschmacksmuster und sonstige Schutzrechte (gewerbliche Schutzrechte) sowie Urheberrechte. Diese Schutzrechte vermitteln absolute Verwertungsrechte und damit Monopolrechte, die es den Inhabern ermöglicht, das geschützte IP für eine bestimmte Schutzdauer allein und unter Ausschluss unbefugter Dritter zu verwerten.
Auch die Bedeutung von IT nimmt in der Unternehmenspraxis ständig zu.
