Comparison of Feature Reduction Approaches and Classification Approaches for Pattern Recognition

2016 ◽  
Author(s):  
Xiaoyang Li
Keyword(s):  
Pattern Recognition ◽  
Feature Reduction

scholarly journals Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 541 ◽  
Author(s):  
Moh Arozi ◽  
Wahyu Caesarendra ◽  
Mochammad Ariyanto ◽  
M. Munadi ◽  
Joga D. Setiawan ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Principal Components ◽  
Single Channel ◽  
Feature Reduction ◽  
Hand Movements ◽  
Hand Gesture ◽  
Signal Acquisition ◽  
Finger Movements ◽  
Semg Signal

A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classification by employing single channel sEMG signal acquisition is quite challenging, especially for low-rate sampling frequency. In this paper, a study on the pattern recognition method for sEMG signals of nine finger movements is presented. Common surface single channel electromyography (sEMG) was used to measure five different subjects with no neurological or muscular disorder by having nine hand movements. This research had several sequential processes (i.e., feature extraction, feature reduction, and feature classification). Sixteen time-domain features were employed for feature extraction. The features were then reduced using principal component analysis (PCA) into two and three-dimensional feature space. The artificial neural network (ANN) classifier was tested on two different feature sets: (1) using all principal components obtained from PCA (PC1–PC3) and (2) using selected principal components (PC2 and PC3). The third best principal components were then used for classification using ANN. The average accuracy using all subject signals was 86.7% to discriminate the nine finger movements.

An image-based pattern recognition approach to condition monitoring of reciprocating compressor valves

2017 ◽  
Vol 24 (19) ◽  
pp. 4433-4448 ◽  
Author(s):  
Jason R Kolodziej ◽  
John N Trout
Keyword(s):  
Pattern Recognition ◽  
Condition Monitoring ◽  
Principal Component ◽  
Feature Reduction ◽  
Image Texture ◽  
Monitoring Method ◽  
Time Frequency ◽  
Use Of Time ◽  
Combined Use ◽  
Frequency Domain Approach

This work presents the development of a vibration-based condition monitoring method for early detection and classification of valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis with image-based pattern recognition techniques. Two common valve related fault conditions are spring fatigue and valve seat wear and are seeded on the crank-side discharge valves of a Dresser-Rand ESH-1 industrial compressor. Operational data including vibration, cylinder pressure, and crank shaft position are collected and processed using a transformed time-frequency domain approach. The results are processed as images with features extracted using 1st and 2nd order image texture statistics and binary shape properties. Feature reduction is accomplished by principal component analysis and a Bayesian classification strategy is employed with accuracy rates greater than 90%.

scholarly journals Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

2021 ◽  
Author(s):  
Wei Dong ◽  
shuqing zhang ◽  
Mengfei Hu ◽  
Liguo Zhang ◽  
Haitao Liu
Keyword(s):  
Pattern Recognition ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Feature Reduction ◽  
Maintenance Cost ◽  
Joint Entropy ◽  
Wind Turbine Gearbox ◽  
Multi Scale ◽  
Diagnosis Method

Abstract The fault diagnosis of gearbox and bearing in wind turbine is crucial to improve service life and reduce maintenance cost. This paper proposes a novel fault diagnosis method based on refined generalized composite multi-scale state joint entropy (RGCMSJE), robust spectral learning framework for unsupervised feature selection (RSFS) and extreme learning machine (ELM) to identify the different health conditions of gearboxes, including feature extraction, feature reduction and pattern recognition. In this method, MAED is firstly adopted to assist RGCMSJE in parameter selection. Second, RGCMSJE is utilized to extract the multi-scale features of gearbox vibration signal and construct high-dimension feature set. Thirdly, RSFS method is used to reduce the dimension of high-dimensional RGCMSJE feature set. In the end, the obtained low-dimensional features are input to the ELM classifier to realize fault pattern recognition. Through two gearbox fault diagnosis experiments, the effectiveness of the fault diagnosis method is verified. The analysis results show that this method can effectively and accurately identify different fault types of wind turbine gearbox.

scholarly journals Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 2045
Author(s):  
Ida Nurhaida ◽  
Vina Ayumi ◽  
Devi Fitrianah ◽  
Remmy A. M. Zen ◽  
Handrie Noprisson ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Time Complexity ◽  
Science Research ◽  
Research Area ◽  
Feature Reduction ◽  
Classification Model ◽  
Computational Time ◽  
Computer Science Research ◽  
New Research ◽  
Improved Accuracy

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.

Feature reduction by Fourier transform in pattern recognition of NIR data

1996 ◽  
Vol 331 (1-2) ◽  
pp. 75-83 ◽  
Author(s):  
W. Wu ◽  
B. Walczak ◽  
W. Penninckx ◽  
D.L. Massart
Keyword(s):  
Pattern Recognition ◽  
Fourier Transform ◽  
Feature Reduction

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.

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