METHOD OF PATTERN RECOGNITION IN A SIGNAL

• US 20120136813A1
• Filed: 11/30/2010
• Published: 05/31/2012
• Est. Priority Date: 11/30/2010
• Status: Abandoned Application

• Current Assignee: Goodyear Tire & Rubber Company
• Original Assignee: Goodyear Tire & Rubber Company
• Inventors: Caroux, Julien, Grigoryev, Dmitry
• Application Number:
  US12/956,192
• Publication Number:
  US 20120136813A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

The invention is directed to a method for pattern recognition in a signal corresponding for example to the steering angle of a vehicle for testing tires. The method comprises three major steps, namely step a) consisting in identifying phases in the signal by detecting phase changes; step b) consisting in classifying at least some of the identified phases based on their shapes and step c) consisting in detecting the presence of predetermined patterns in the signal where each predetermined pattern corresponds to a specific sequence of classes of phases. The phase changes are determined by extrema of the signal and its first derivative. The classification of the phase is made by means of parameters of the phases, namely the length dL, the amplitude dH, and a form factor S. The definition of the different classes is adjusted in a parameter space by means of manual recognition of maneuvers.

Claims

1. Method of pattern recognition in a signal, comprising the following steps:

a) identifying phases in the signal by detecting phase changes;

b) classifying at least some of the identified phases based on their shapes; and

c) detecting the presence of predetermined patterns in the signal where each predetermined pattern corresponds to a specific sequence of classes of phases.

2. Method of pattern recognition in a signal according to claim 1, wherein step a) is based on the detection of extrema of the signal.

3. Method of pattern recognition in a signal according to claim 1, wherein step a) is based on the detection of extrema of the first derivate of the signal.

4. Method of pattern recognition in a signal according to claim 1, wherein step b) is based on at least two parameters of the phase.

5. Method of pattern recognition in a signal according to claim 4, wherein step b) is based on the amplitude and the length of the phase.

6. Method of pattern recognition in a signal according to claim 5, wherein step b) is based on the ratio amplitude/length or any function thereof of the phase, and the product amplitude by length or any function thereof of the phase.

7. Method of pattern recognition in a signal according to claim 5, wherein step b) is based on a shape factor of the phase signal.

8. Method of pattern recognition in a signal according to claim 7, wherein the shape factor is the integral of the amplitude of the signal on the length of the phase.

9. Method of pattern recognition in a signal according to claim 7, wherein step b) is based on the convexity of the phase.

10. Method of pattern recognition in a signal according to claim 5, wherein step b) comprises classifying sequences of at least two consecutive phases.

11. Method of pattern recognition in a signal according to claim 4, further comprising a prior training step where values and/or ranges of values of the parameters of the signal are associated to each class.

12. Method of pattern recognition in a signal according to claim 11, wherein the values of the phase parameters for each class are delimited in a parameter space by boundaries which move during the training.

13. Method of pattern recognition in a signal according to claim 11, wherein the training step comprises a statistical analysis of the phases of a representative master signal where classes are allocated only to the most frequent phase shapes, the less frequent phase shapes being ignored.

14. Method of pattern recognition in a signal according to claim 13, wherein the training step comprises allocating classes to most frequent sequences of at least two consecutive phases.

15. Method of pattern recognition in a signal according to claim 1, wherein step c) comprises recognizing a predetermined pattern when the specific sequence of classes corresponding to the pattern is present at least to a certain degree in the identified sequence of classes.

16. Method of pattern recognition in a signal according to claim 4, further comprising an evaluation step where the pattern(s) of one or several maneuvers are manually recognized in order to determine the correctness of classification of the different phases according to step b) and, based on this determination, to calculate probabilities of correct detection of the different classes of phases.

17. Method of pattern recognition in a signal according to claim 16, wherein during the manual recognition of the pattern of a maneuver, P_true the probability of correct detection of a phase of a given class representative of the pattern is calculated as follows:

18. Method of pattern recognition in a signal according to claim 17, wherein P_true is calculated for several classes representative of the pattern.

19. Method of pattern recognition in a signal according to claim 17, wherein the parametric definition of classes representative of the manually recognized pattern is adapted in order to maximize P_true for each class.

20. Method of pattern recognition in a signal according to claim 17, wherein the probability of false detection of a phase of the given glass P_false is calculated as follows: