Multiple Feature Selectoin for Pattern Recognition Using ID3 Ensemble System

2012 ◽  
Author(s):  
Koichiro Nakatsu ◽  
Hitoshi Furuta ◽  
Kyousuke Takahashi ◽  
Ken Ishibashi ◽  
Shigeki Yasuda
Keyword(s):  
Pattern Recognition ◽  
Multiple Feature

scholarly journals The on-line detection of engine misfire at low speed using multiple feature fusion with fuzzy pattern recognition

2002 ◽  
Vol 216 (5) ◽  
pp. 391-402 ◽  
Author(s):  
S Liu ◽  
F Gu ◽  
A Ball
Keyword(s):  
Pattern Recognition ◽  
Angular Velocity ◽  
Feature Fusion ◽  
Angular Acceleration ◽  
Angular Frequency ◽  
Diagnostic Reliability ◽  
Fuzzy Pattern Recognition ◽  
Multiple Feature ◽  
Fuzzy Pattern ◽  
Multiple Feature Fusion

This paper proposes a technique for the online detection of incipient engine misfire based on multiple feature fusion and fuzzy pattern recognition. The technique requires the measurement of instantaneous angular velocity signals. By processing the engine dynamics model equation in the angular frequency domain, four dimensionless features for misfire detection are defined, along with fast feature-extracting algorithms. By directly analysing the waveforms of the angular velocity and the angular acceleration, six other dimensionless features are extracted. Via fuzzy pattern recognition, all the features are associated together as a fuzzy vector. This vector identifies whether the engine is healthy or faulty and then locates the position of a misfiring cylinder or cylinders if necessary. The experimental work conducted on a production engine operating at low speeds confirms that such a technique is able to work with the redundant and complementary information of all the features and that it leads to improved diagnostic reliability. It is fully expected that this technique will be simple to implement and will provide a useful practical tool for the online monitoring and realtime diagnosis of engine misfire in individual cylinders.

scholarly journals APPLICATION OF BINARY COMPOSITIONS IN MULTIDIMENSIONAL RECOGNITION TASKS

2001 ◽  
Vol 1 ◽  
pp. 263
Author(s):  
Romans Grekovs
Keyword(s):  
Pattern Recognition ◽  
Pattern Recognition Method ◽  
Fuzzy Relations ◽  
Recognition Method ◽  
Multiple Feature

This paper examines the possibility o f using pattern recognition method, which is based on compositions of fuzzy relations, to work with multiple feature selections such as where the number o f features is greater than 3. Investigation o f problems, which are connected to multidimensional pattern recognition, is also applied. Some practical part concerning proposals on experiments is provided.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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