Biorthogonal wavelet trees in the classification of embedded signal classes for intelligent sensors using machine learning applications

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

The paper deals with a method of constructing orthonormal bases of coordinates which maximize, through redundant dictionaries (frames) of biorthogonal bases, a class separability index or distances among classes. The method proposes an algorithm which consists of biorthogonal expansions over two redundant dictionaries. Embedded classes are often present in multiclassification problems. It is shown how the biorthogonality of the expansion can really help to construct a coordinate system which characterizes the classes. The algorithm is created for training wavelet networks in order to provide an efficient coordinate system maximizing the Cross Entropy function between two complementary classes. Sine and cosine wavelet packets are basis functions of the network. Thanks to their packet structure, once selected the depth of the tree, an adaptive number of basis functions is automatically chosen. The algorithm is also able to carry out centering and dilation of the basis functions in an adaptive way. The algorithm works with a preliminary extracted feature through shrinkage technique in order to reduce the dimensionality of the problem. In particular, our attention is pointed out for time-frequency monitoring, detection and classification of transients in rail vehicle systems and the outlier problem. In the former case the goal is to distinguish transients as inrush current and no inrush current and a further distinction between the two complementary classes: dangerous inrush current and no dangerous inrush current. The proposed algorithm is used on line in order to recognize the dangerous transients in real time and thus shut-down the vehicle. The algorithm can also be used in a general application of the outlier detection. A similar structure is used in developed algorithms which are currently integrated in the inferential modeling platform of the unit responsible for Advanced Control and Simulation Solutions within ABB's (Asea Brown Boveri) industry division. It is shown how impressive and rapid performances are achieved with a limited number of wavelets and few iterations. Real applications using real measured data are included to illustrate and analyze the effectiveness of the proposed method.

Original languageEnglish
JournalJournal of the Franklin Institute
Volume344
Issue number6
Pages (from-to)813-829
Number of pages17
ISSN0016-0032
DOIs
Publication statusPublished - 01.09.2007
Externally publishedYes

    Research areas

  • Machine learning, Signal classification, Trigonometric bases, Wavelet networks, Wavelet packets, Classification (of information), Computer simulation, Learning systems, Problem solving, Signal processing, Smart sensors, Biorthogonal expansions, Multiclassification problems, Wavelet transforms
  • Engineering

Recently viewed

Publications

  1. Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation
  2. Preventive Emergency Detection Based on the Probabilistic Evaluation of Distributed, Embedded Sensor Networks
  3. Inversion of Fuzzy Neural Networks for the Reduction of Noise in the Control Loop for Automotive Applications
  4. Evaluating OWL 2 reasoners in the context of checking entity-relationship diagrams during software development
  5. A geometric algorithm for the output functional controllability in general manipulation systems and mechanisms
  6. Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal
  7. Reading and Calculating in Word Problem Solving
  8. Microstructural development of as-cast AM50 during Constrained Friction Processing: grain refinement and influence of process parameters
  9. Classical PI Controllers with Anti-Windup Techniques Applied on Level Systems
  10. Constrained Independence for Detecting Interesting Patterns
  11. A localized boundary element method for the floating body problem
  12. Guided discovery learning with computer-based simulation games
  13. Enabling Road Condition Monitoring with an on-board Vehicle Sensor Setup
  14. Some model properties to control a permanent magnet machine using a controlled invariant subspace
  15. A Gait Pattern Generator for Closed-Loop Position Control of a Soft Walking Robot
  16. Holistic and scalable ranking of RDF data
  17. Multi-view discriminative sequential learning
  18. The impact of goal focus, task type and group size on synchronous net-based collaborative learning discourses
  19. Mathematics in Robot Control for Theoretical and Applied Problems
  20. Interaction-Dominant Causation in Mind and Brain, and Its Implication for Questions of Generalization and Replication
  21. Soil conditions modify species diversity effects on tree functional trait expression
  22. On the Inclusion of Parameter Uncertainties into Engineering Design Computations
  23. Soft Optimal Computing to Identify Surface Roughness in Manufacturing Using a Gaussian and a Trigonometric Regressor
  24. A Hybrid Actuator and its Control Using a Cascade Sliding Mode Technique
  25. Enacting migration through data practices
  26. Self-regulation in error management training: emotion control and metacognition as mediators of performance effects
  27. Lyapunov approach for a pi-controller with anti-windup in a permanent magnet synchronous motor using chopper control
  28. Mechanical characterization of as-cast AA7075/6060 and CuSn6/Cu99.5 compounds using an experimental and numerical push-out test
  29. Comparison of EKF and TSO for Health Monitoring of a Textile-Based Heater Structure and its Control
  30. The structure of emotions in learning situations
  31. Crises at Work: Potentials for Change?
  32. An Optimal and Stabilising PI Controller with an Anti-windup Scheme for a Purification Process of Potable Water
  33. Direct parameter specification of an attention shift: Evidence from perceptual latency priming
  34. Automatic generation of periodic representative volume elements for matrix-inclusion composites and their efficiency in multiscaling