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Suche nach „[T.] [Fuchs]“ hat 9 Publikationen gefunden
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    F: Maschinenbau und MechatronikI: Fraunhofer AWZ CTMT

    Zeitschriftenartikel

    Jochen Hiller, T. Fuchs, S. Kasperl, L. Reindl

    Einfluss der Bildqualität röntgentomographischer Abbildungen auf Koordinatenmessungen

    Grundlagen, Messungen und Simulationen

    tm - Technisches Messen, no. 7/8, pp. 334-347

    2011

    DOI: 10.1524/teme.2011.0137

    Abstract anzeigen

    Die industrielle Computertomographie (CT) findet in der dimensionellen Messtechnik zunehmend Akzeptanz. Allerdings ist noch wenig über den konkreten Einfluss verschiedener Systemkomponenten, physikalischer Störgrößen oder Bildverarbeitungsschritte auf dimensionelle CT-Messungen bekannt. In diesem Beitrag werden physikalische Bildgütemerkmale zur Beurteilung der Bildqualität in der CT vorgestellt und mittels Computersimulationen der Einfluss der Rekonstruktionsbildgüte auf Koordinatenmessungen untersucht.

    F: Elektrotechnik und Medientechnik

    Vortrag

    M. Fuchs, P. Scheer, T. Liebetrau, Andreas Grzemba

    Selektiver Teilnetzbetrieb im Fahrzeug: Eine Realisierung für den CAN-Bus und Übertragungsmöglichkeiten auf andere Bussysteme

    AME 2011, Dortmund

    2011

    S: TC Freyung

    Beitrag (Sammelband oder Tagungsband)

    Andreas Grzemba, T. Liebetrau, S. Edenharter, P. Scheer, M. Fuchs, Alexander Faschingbauer

    Partial Networking – Selektiver Teilnetzbetrieb

    AmE 2011 - Automotive meets Electronics, Berlin [u.a.], vol. 69, CD-ROM

    2011

    ISBN: 978-3-8007-3345-3

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    T. Gruber, E. Fuchs, H. Pree, Bernhard Sick

    Temporal Data Mining Using Shape Space Representations of Time Series

    Neurocomputing, vol. 74, no. 1-3, pp. 379-393

    2010

    DOI: 10.1016/j.neucom.2010.03.022)

    Abstract anzeigen

    Subspace representations that preserve essential information of high-dimensional data may be advantageous for many reasons such as improved interpretability, overfitting avoidance, acceleration of machine learning techniques. In this article, we describe a new subspace representation of time series which we call polynomial shape space representation. This representation consists of optimal (in a least-squares sense) estimators of trend aspects of a time series such as average, slope, curve, change of curve, etc. The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure. Depending on the application, time series segmentation techniques can be applied to obtain a piecewise shape space representation of the time series in subsequent segments. In this article, we investigate the properties of the polynomial shape space representation and the shape space distance measure by means of some benchmark time series and discuss possible application scenarios in the field of temporal data mining.

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    E. Fuchs, T. Gruber, Bernhard Sick

    On-line Segmentation of Time Series Based on Polynomial Least-Squares Approximations

    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2232-2245

    2010

    DOI: 10.1109/TPAMI.2010.44

    Abstract anzeigen

    The paper presents SwiftSeg, a novel technique for online time series segmentation and piecewise polynomial representation. The segmentation approach is based on a least-squares approximation of time series in sliding and/or growing time windows utilizing a basis of orthogonal polynomials. This allows the definition of fast update steps for the approximating polynomial, where the computational effort depends only on the degree of the approximating polynomial and not on the length of the time window. The coefficients of the orthogonal expansion of the approximating polynomial-obtained by means of the update steps-can be interpreted as optimal (in the least-squares sense) estimators for average, slope, curvature, change of curvature, etc., of the signal in the time window considered. These coefficients, as well as the approximation error, may be used in a very intuitive way to define segmentation criteria. The properties of SwiftSeg are evaluated by means of some artificial and real benchmark time series. It is compared to three different offline and online techniques to assess its accuracy and runtime. It is shown that SwiftSeg-which is suitable for many data streaming applications-offers high accuracy at very low computational costs

    F: Maschinenbau und MechatronikI: Fraunhofer AWZ CTMT

    Beitrag (Sammelband oder Tagungsband)

    T. Schön, I. Bauscher, T. Fuchs, U. Hassler, Jochen Hiller, S. Kasperl, S. Oeckl, S. Schröpfer, V. Weizel

    Dimensionelles Messen mit Helix-Computertomographie

    Berichtsband BB-115 zur DGZfP-Jahrestagung (Münster)

    2009

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    E. Fuchs, C. Gruber, T. Reitmaier, Bernhard Sick

    Processing Short-Term and Long-Term Information With a Combination of Polynomial Approximation Techniques and Time-Delay Neural Networks

    IEEE Transactions on Neural Networks, vol. 20, no. 9, pp. 1450-1462

    2009

    Abstract anzeigen

    Neural networks are often used to process temporal information, i.e., any kind of information related to time series. In many cases, time series contain short-term and long-term trends or behavior. This paper presents a new approach to capture temporal information with various reference periods simultaneously. A least squares approximation of the time series with orthogonal polynomials will be used to describe short-term trends contained in a signal (average, increase, curvature, etc.). Long-term behavior will be modeled with the tapped delay lines of a time-delay neural network (TDNN). This network takes the coefficients of the orthogonal expansion of the approximating polynomial as inputs such considering short-term and long-term information efficiently. The advantages of the method will be demonstrated by means of artificial data and two real-world application examples, the prediction of the user number in a computer network and online tool wear classification in turning.

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    E. Fuchs, T. Gruber, J. Nitschke, Bernhard Sick

    On-line Motif Detection in Time Series With SwiftMotif

    Pattern Recognition, vol. 42, no. 11, pp. 3015-3031

    2009

    Abstract anzeigen

    This article presents SwiftMotif, a novel technique for on-line motif detection in time series. With this technique, frequently occurring temporal patterns or anomalies can be discovered, for instance. The motif detection is based on a fusion of methods from two worlds: probabilistic modeling and similarity measurement techniques are combined with extremely fast polynomial least-squares approximation techniques. A time series is segmented with a data stream segmentation method, the segments are modeled by means of normal distributions with time-dependent means and constant variances, and these models are compared using a divergence measure for probability densities. Then, using suitable clustering algorithms based on these similarity measures, motifs may be defined. The fast time series segmentation and modeling techniques then allow for an on-line detection of previously defined motifs in new time series with very low run-times. SwiftMotif is suitable for real-time applications, accounts for the uncertainty associated with the occurrence of certain motifs, e.g., due to noise, and considers local variability (i.e., uniform scaling) in the time domain. This article focuses on the mathematical foundations and the demonstration of properties of SwiftMotif—in particular accuracy and run-time—using some artificial and real benchmark time series.

    F: Maschinenbau und MechatronikI: Fraunhofer AWZ CTMT

    Beitrag (Sammelband oder Tagungsband)

    Jochen Hiller, S. Schindler, T. Fuchs, F. Porsch

    Bestimmung des lokalen räumlichen Auflösungsvermögens eines industriellen Mikro-Computertomographie-Systems (μCT) mit Hilfe einer Modulationsübertragungsfunktion

    VDI-Berichte 2011 - Sensoren und Messsysteme (Ludwigsburg)

    2008