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  • 51.
    Svensson, Magnus
    et al.
    Volvo Technology, Sweden.
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    AASS Örebro University, Sweden.
    Vehicle Diagnostics Method by Anomaly Detection and Fault Identification Software2009In: SAE international journal of passenger cars : electronic and electrical systems, ISSN 1946-4614, Vol. 2, no 1, p. 352-358Article in journal (Refereed)
    Abstract [en]

    A new approach is proposed for fault detection. It builds on using the relationships between sensor values on vehicles to detect deviating sensor readings and trends in the system performance. However, in contrast to previous approaches based on such sensor relations, our approach uses a fleet of vehicles to define the normal conditions and relations. The relationships between the sensors are also determined automatically in a self-organized way on each vehicle, i.e. no off-line modeling is required. The proposed method is the first step in a remote diagnostics and maintenance service where error detection is done automatically, followed by a download of special purpose diagnostics software for the particular subsystem where the possible fault was detected.

  • 52.
    Svensson, Magnus
    et al.
    Volvo Technology.
    Forsberg, Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor2009In: Proceeding Intelligent Systems and Control (ISC 2009) / [ed] M.H. Hamza, Cambridge, Mass.: ACTA Press, 2009Conference paper (Refereed)
    Abstract [en]

    A new approach to improve fault detection is proposed. The method takes benefit of using a population of systems to dynamically define a norm of how the system works. The norm is derived from self-organizing algorithms which generate a low dimensional representation of how selected feature data are correlated. The feature data is selected from the state variables and from the control signals. The self-organizing method and limited number of feature signals enable fast deviation detection and low computational footprint on each system to be analyzed. The comparison analysis between the systems is performed at a service centre, to where the low-dimensional representations of the systems are transmitted. The method is demonstrated on a simulated DC-motor and the results are promising for deviation detection.

  • 53.
    Svensson, Magnus
    et al.
    Volvo Technology.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    West, Martin
    Volvo Technology.
    Andersson, Björn
    Volvo Technology.
    Unsupervised deviation detection by GMM - A simulation study2011In: SDEMPED 2011: 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives : September 5-8, 2011, Bologna, Italy, Piscataway, N.J.: IEEE Press, 2011Conference paper (Refereed)
    Abstract [en]

    A new approach to improve fault detection of electrical machines is proposed. The increased usage of electrical machines and the higher demands on their availability requires new approaches to fault detection. In this paper we demonstrate that it is possible to detect a certain fault on a PMSM (Permanent Magnet Synchronous Machine) by using multiple similar motors, or a single motor, to build a norm of expected behavior by monitoring signal relations. This means that the machine is monitored in an unsupervised way. Four levels of an increased temperature in the rotor magnets have been investigated. The results are based on simulations and the signals used (for relation measurements) are available in a real motor installation. The method shows promising results in detecting two of the temperature faults. © 2011 IEEE.

  • 54.
    Wickström, Nicholas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Hellring, Magnus
    Volvo Technology Corporation, Gothenburg, Sweden.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Neural networks for extracting the pressure peak position from the ion current2004In: Virtual sensing of combustion quality in SI engines using the ion current, Göteborg: Chalmers tekniska högskola , 2004, p. 95-110Chapter in book (Other academic)
  • 55.
    You, Liwen
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Garwicz, Daniel
    Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Comprehensive Bioinformatic Analysis of the Specificity of Human Immunodeficiency Virus Type 1 Protease2005In: Journal of Virology, ISSN 0022-538X, E-ISSN 1098-5514, Vol. 79, no 19, p. 12477-12486Article in journal (Refereed)
    Abstract [en]

    Rapidly developing viral resistance to licensed human immunodeficiency virus type 1 (HIV-1) protease inhibitors is an increasing problem in the treatment of HIV-infected individuals and AIDS patients. A rational design of more effective protease inhibitors and discovery of potential biological substrates for the HIV-1 protease require accurate models for protease cleavage specificity. In this study, several popular bioinformatic machine learning methods, including support vector machines and artificial neural networks, were used to analyze the specificity of the HIV-1 protease. A new, extensive data set (746 peptides that have been experimentally tested for cleavage by the HIV-1 protease) was compiled, and the data were used to construct different classifiers that predicted whether the protease would cleave a given peptide substrate or not. The best predictor was a nonlinear predictor using two physicochemical parameters (hydrophobicity, or alternatively polarity, and size) for the amino acids, indicating that these properties are the key features recognized by the HIV-1 protease. The present in silico study provides new and important insights into the workings of the HIV-1 protease at the molecular level, supporting the recent hypothesis that the protease primarily recognizes a conformation rather than a specific amino acid sequence. Furthermore, we demonstrate that the presence of 1 to 2 lysine residues near the cleavage site of octameric peptide substrates seems to prevent cleavage efficiently, suggesting that this positively charged amino acid plays an important role in hindering the activity of the HIV-1 protease.

  • 56.
    You, Liwen
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Almost Linear Biobasis Function Neural Networks2007In: The 2007 International Joint Conference on Neural Networks: IJCNN 2007 conference proceedings : August 12-17, 2007, Resaissance Orlando Resort, Orlando, Florida, USA, Piscataway, N.J.: IEEE Press, 2007, p. 1774-1778Conference paper (Other academic)
    Abstract [en]

    An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse.

  • 57.
    Åstrand, Björn
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bouguerra, Abdelbaki
    Learning Systems Lab (AASS), Dept. of Technology, Örebro University, Sweden.
    Andreasson, Henrik
    Learning Systems Lab (AASS), Dept. of Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Learning Systems Lab (AASS), Dept. of Technology, Örebro University, Sweden.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    An autonomous robotic system for load transportation2009In: Program and Abstracts, Fourth Swedish Workshop on Autonomous Robotics, SWAR'09 / [ed] Lars Asplund, Västerås: Mälardalen University , 2009, p. 56-57Conference paper (Other academic)
12 51 - 57 of 57
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