Publikationen


Suche nach „[T.] [Gruber]“ hat 14 Publikationen gefunden
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    DigitalS: TC Freyung

    Beitrag (Sammelband oder Tagungsband)

    Peter Hofmann, V. Andrejchenko, P. Lettmayer, M. Schmitzberger, M. Gruber, I. Ozan, M. Belgiu, R. Graf, T. Lampoltshammer, S. Wegenkittl

    Agent based image analysis (ABIA)-preliminary research results from an implemented framework

    Proceedings of GEOBIA 2016: Solutions and Synergies (Enschede, Netherlands; September 14-16, 2016)

    2016

    NachhaltigF: Elektrotechnik und Medientechnik

    Zeitschriftenartikel

    Franz Daiminger, Martin Gruber, Christian Dendorfer, T. Zahner

    Experimental investigations on the offset correction of transient cooling curves of light emitting diodes based on JESD51-14 and simple semi-empirical approximations

    Microelectronics Journal, vol. 46, no. 12/Part A, pp. 1208-1215

    2015

    DOI: 10.1016/j.mejo.2015.10.013

    Abstract anzeigen

    Determination of the thermal resistance of high power light emitting diodes by transient thermal measurements is of rapidly growing interest. Due to electrical disturbances at small delay times, correction algorithms like the offset correction described in JESD51-14 are necessary. A simple model based on a mean temperature is presented which gives insight into the physics of this correction algorithm. It both allows for a rough estimate of time intervals where the correction algorithm is applicable in ideal cases, and it can be used to detect the presence of an interface resistance between the substrate and the package. Measurements and numerical simulations reveal that a significant interface resistance between the epi-layer and the substrate leads to a modification of the thermal transient. Offset correction then leads to an error in the determined thermal resistance in the range of several percent depending on the magnitude of the interface resistance. Additionally some simple semi-empirical approximations for the transient cooling curves are given.

    F: Elektrotechnik und Medientechnik

    Beitrag (Sammelband oder Tagungsband)

    Franz Daiminger, Martin Gruber, Christian Dendorfer, T. Zahner

    Experimental and Theoretical Considerations on the Offset Correction of Transient Cooling Curves of Light Emitting Diodes Based on JESD51-14

    Proceedings of Therminic 2014 - 20th International Workshop on Thermal Investigations of ICs and Systems (Sep 24th-26th 2014, Greenwich, UK)

    2014

    F: Angewandte Gesundheitswissenschaften

    Zeitschriftenartikel

    Bernhard Bleyer, T. Dörfler, H. Gruber, B. Dietl, C.H.R. Wiese, J. Pfirstinger

    Wer über mich verfügt, entscheide ich – und ein Anderer. Die Patientenverfügung und das kommunizierte moralische Urteil

    Zeitschrift für medizinische Ethik, vol. 59, pp. 297-311

    2013

    F: Maschinenbau und Mechatronik

    Zeitschriftenartikel

    D. Fisch, T. Gruber, Bernhard Sick

    SwiftRule: Mining Comprehensible Classication Rules for Time Series Analysis

    IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 5, pp. 774-787

    2011

    DOI: 10.1109/TKDE.2010.161

    Abstract anzeigen

    In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short-term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.

    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 Mechatronik

    Zeitschriftenartikel

    C. Gruber, T. Gruber, S. Krinninger, Bernhard Sick

    On-Line Signature Verification with Support Vector Machines Based on LCSS Kernel Functions

    IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 40, no. 4, pp. 1088-1110

    2010

    DOI: 10.1109/TSMCB.2009.2034382

    Abstract anzeigen

    In this paper, a new technique for online signature verification or identification is proposed. The technique integrates a longest common subsequences (LCSS) detection algorithm which measures the similarity of signature time series into a kernel function for support vector machines (SVM). LCSS offers the possibility to consider the local variability of signals such as the time series of pen-tip coordinates on a graphic tablet, forces on a pen, or inclination angles of a pen measured during a signing process. Consequently, the similarity of two signature time series can be determined in a more reliable way than with other measures. A proprietary database with signatures of 153 test persons and the SVC 2004 benchmark database are used to show the properties of the new SVM-LCSS. We investigate its parameterization and compare it to SVM with other kernel functions such as dynamic time warping (DTW). Our experiments show that SVM with the LCSS kernel authenticate persons very reliably and with a performance which is significantly better than that of the best comparing technique, SVM with DTW kernel.

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    U. Blanke, B. Schiele, M. Kreil, P. Lukowicz, Bernhard Sick, T. Gruber

    All for one or one for all? - Combining Heterogeneous Features for Activity Spotting

    Proceedings of the 7th IEEE Workshop on Context Modeling and Reasoning (CoMoRea) at the 8th IEEE International Conference on Pervasive Computing and Communication (PerCom 2010); Mannheim, 29.03.2010

    2010

    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.

    I: Zentrum für Akademische Weiterbildung

    Vortrag

    D. Festner, Andreas Gegenfurtner, B. Meier, A. Babichenko, J. Huber, T. Koch, B. Morgenthaler, S. Schmid, F. Scheider, H. Gruber

    Transfer of training and its determinants

    A study conducted in the domain of occupational health and safety

    4th EARLI Special Interest Group (SIG) 14 Learning and Professional Development Conference, Jyväskylä, Finnland

    2008

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    K. Glette, J. Torresen, T. Gruber, Bernhard Sick, et al.

    Comparing Evolvable Hardware to Conventional Classiers for Electromyographic Prosthetic Hand Control

    Proceedings of the 3rd NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2008); Noordwijk, Niederlande; 22.-25.06.2008

    2008

    F: Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    T. Gruber, C. Gruber, Bernhard Sick

    Online Signature Verification With New Time Series Kernels for Support Vector Machines

    Advances in biometrics, Berlin; New York, vol. 3832

    2006

    ISBN: 978-3540311119