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Suche nach „[Sick] [Bernhard]“ hat 115 Publikationen gefunden
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    DigitalMaschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    Roswitha Giedl-Wagner, Thomas Miller, Bernhard Sick

    Determination of optimal ct scanning parameters using radial basic function neural networks

    4th Conference on Industrial Computed Tomography (iCT) 2012

    2012

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

    T. Gruber, Bernhard Sick, D. Fisch

    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.

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

    A. Hofmann, Bernhard Sick

    On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling

    IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 2, pp. 282-294

    2011

    DOI: 10.1109/TDSC.2009.36

    Abstract anzeigen

    Alert aggregation is an important subtask of intrusion detection. The goal is to identify and to cluster different alerts—produced by low-level intrusion detection systems, firewalls, etc.—belonging to a specific attack instance which has been initiated by an attacker at a certain point in time. Thus, meta-alerts can be generated for the clusters that contain all the relevant information whereas the amount of data (i.e., alerts) can be reduced substantially. Meta-alerts may then be the basis for reporting to security experts or for communication within a distributed intrusion detection system. We propose a novel technique for online alert aggregation which is based on a dynamic, probabilistic model of the current attack situation. Basically, it can be regarded as a data stream version of a maximum likelihood approach for the estimation of the model parameters. With three benchmark data sets, we demonstrate that it is possible to achieve reduction rates of up to 99.96 percent while the number of missing meta-alerts is extremely low. In addition, meta-alerts are generated with a delay of typically only a few seconds after observing the first alert belonging to a new attack instance.

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

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

    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

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

    Bernhard Sick, D. Fisch, et al., B. Kuehlbeck

    So Near And Yet So Far: New Insight Into Properties Of Some Well-Known Classier Paradigms

    Information Sciences, vol. 180, no. 18, pp. 3381-3401

    2010

    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.

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

    A. Hofmann, Bernhard Sick, D. Fisch

    On the Versatility of Radial Basis Function Neural Networks: A Case Study in the Field of Intrusion Detection

    Information Sciences, vol. 180, no. 12, pp. 2421-2439

    2010

    Abstract anzeigen

    Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.

    Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

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

    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

    Maschinenbau und Mechatronik

    Vortrag

    Bernhard Sick, D. Fisch, F. Kastl

    Novelty-Aware Attack Recognition - Intrusion Detection With Organic Computing Techniques

    3rd IFIP Conference on Biologically-Inspired Collaborative Computing (BICC 2010) at the World Computer Congress (WCC 2010), Brisbane, Australien

    2010

    Maschinenbau und Mechatronik

    Vortrag

    C. Müller-Schloer, M. Jänicke, Bernhard Sick, D. Fisch

    Quantitative Emergence - A Refined Approach Based on Divergence Measures

    Fourth IEEE International Conference on Self- Adaptive and Self-Organizing Systems (SASO 2010), Budapest, Ungarn

    2010

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

    Bernhard Sick, et al., S. Ovaska

    Periodical Switching Between Related Goals for Improving Evolvability to a Fixed Goal in Multi-Objective Problems

    Information Sciences, vol. 179, no. 23, pp. 4046-4056

    2009

    Abstract anzeigen

    Evolutionary computation plays a principal role in intelligent design automation. Evolutionary approaches have discovered novel and patentable designs. Nonetheless, evolutionary techniques may lead to designs that lack robustness. This critical issue is strongly connected to the concept of evolvability. In nature, highly evolvable species tend to be found in rapidly changing environments. Such species can be considered robust against environmental changes. Consequently, to create robust engineering designs it could be beneficial to use variable, rather than fixed, fitness criteria. In this paper, we study the performance of an evolutionary programming algorithm with periodical switching between goals, which are selected randomly from a set of related goals. It is shown by a dual-objective filter optimization example that altering goals may improve evolvability to a fixed goal by enhancing the dynamics of solution population, and guiding the search to areas where improved solutions are likely to be found. Our reference algorithm with a single goal is able to find solutions with competitive fitness, but these solutions are results of premature convergence, because they are poorly evolvable. By using the same algorithm with switching goals, we can extend the productive search length considerably; both the fitness and robustness of such designs are improved.

    Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    Bernhard Sick, D. Fisch

    Training of Radial Basis Function Classifiers With Resilient Propagation and Variational Bayesian Inference

    Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009); Atlanta, GA, USA 14.-19.06.2009

    2009

    Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    M. Jaenicke, E. Kalkowski, Bernhard Sick, D. Fisch

    Learning by Teaching Versus Learning by Doing: Knowledge Exchange in Organic Agent Systems

    Proceedings of the IEEE Symposium on Intelligent Agents (IA 2009); Nashville, TN, USA, 30.03.-02.04.2009

    2009

    Angewandte WirtschaftswissenschaftenMaschinenbau und Mechatronik

    Zeitschriftenartikel

    R. Kern, O. Buchtala, M. Bauer, T. Horeis, Bernhard Sick, et al.

    Technical Data Mining with Evolutionary Radial Basis Function Classiers

    Applied Soft Computing, vol. 9, no. 2, pp. 765-774

    2009

    Abstract anzeigen

    This article deals with two key problems of data mining, the automation of the data mining process and the integration of human domain experts. We show how an evolutionary algorithm (EA) can be used to optimize radial basis function (RBF) neural networks used for classification tasks. First, input features will be chosen from a set of possible input features (feature selection). Second, the number of hidden neurons is adapted (model selection). It is known that interpretable (fuzzy-type) rule sets may be extracted from RBF networks. We show how appropriate training algorithms for RBF networks and penalty terms in the fitness function of the EA may improve the understandability of the extracted rules. The properties of our approach are set out by means of two industrial application examples (process identification and quality control).

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

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

    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.

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

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

    Maschinenbau und Mechatronik

    Vortrag

    D. Andrade, Bernhard Sick

    Lower Bound Bayesian Networks - An Ecient Inference of Lower Bounds on Probability Distributions in Bayesian Networks

    Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI 2009);, Montreal, Kanada

    2009

    Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    T. Hackl, T. Rabl, A. Lang, Bernhard Sick, et al.

    Generating Shifting Workloads to Benchmark Adaptability in Relational Database Systems

    Performance Evaluation and Benchmarking; Lecture Notes in Computer Science 5895, First TPC Technology Conference (TPCTC); Lyon, 24.08.2009

    2009

    Maschinenbau und Mechatronik

    Beitrag (Sammelband oder Tagungsband)

    D. Andrade, T. Horeis, Bernhard Sick

    Knowledge Fusion Using Dempster-Shafer Theory and the Imprecise Dirichlet Model

    Proceedings of the IEEE Conference on Soft Computing in Industrial Applications (SMCia/08); Muroran, Japan; 25.-27.06.2008

    2008