Discrete Wavelet Transform on Near-Infrared Spectra Analysis for Moisture Determination in Lignitic Coal Samples

2014 ◽  
Vol 513-517 ◽  
pp. 319-322
Author(s):  
Xiao Li Yang ◽  
Neng Bang Hou ◽  
Jun You Shi ◽  
Yan Fang Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Near Infrared ◽  
Processing Method ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Least Squares Regression ◽  
Spectra Analysis ◽  
Near Infrared Spectra ◽  
Compressed Data

We studied moisture determination in lignitic coal samples through near-infrared (NIR) technique. This research was developed by applying partical least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Three type DWTs were investigated.Determination performance at different resolution scales was studied. The results show that DWT is a very efficient pre-processing method for NIR spectra analysis.

DWT-PLS Regression on Near-Infrared Spectra for Moisture Determination of Coal

2013 ◽  
Vol 827 ◽  
pp. 209-212
Author(s):  
Xiao Li Yang ◽  
Fan Wang ◽  
Wen Chao Wang ◽  
Yun Xiu Chen ◽  
Ji Shu Chen
Keyword(s):  
Bituminous Coal ◽  
Near Infrared ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Least Squares Regression ◽  
Compression Performance ◽  
Moisture Analysis ◽  
Near Infrared Spectra ◽  
Compressed Data

We studied moisture determination in bituminous coal and lignitic coal samples using near-infrared (NIR) spectra. This research was developed by applying partial least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Compression performance at different resolution scales was investigated. Using the compressed data, PLS can obtain more accurate result than using raw spectra. The number of principal component in PLS model was investigated too. The results show DWT-PLS can obtain satisfactory determination performance for moisture analysis in bituminous coal and lignitic coal.

Support Vector Regression on Near-Infrared Spectra Analysis for Moisture Determination in Lignitic Coal Samples

2014 ◽  
Vol 494-495 ◽  
pp. 964-967
Author(s):  
Xiao Li Yang ◽  
Yan Fang Li ◽  
Xing Wang Zhang ◽  
Shi Qiang Hu
Keyword(s):  
Support Vector Regression ◽  
Near Infrared ◽  
Parameters Optimization ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Moisture Determination ◽  
Support Vector Regression Model ◽  
Spectra Analysis ◽  
Near Infrared Spectra ◽  
Spectrometry Technique

We studied rapid moisture determination in lignitic coal samples using near-infrared (NIR) spectrometry technique. This research applied support vector regression (SVR) and discrete wavelet transform (DWT) to analyze NIR spectra. Firstly, NIR spectra were pre-processed by DWT for fitting and compression. Then, DWT coefficients were used to build support vector regression model. Through parameters optimization, the results show that DWT-SVR can obtain satisfactory performance for moisture determination in lignitic coal samples.

Pre-Processing Influence on Identification of Optimal Path Length for NIR Analysis of Tea Polyphenols

2013 ◽  
Vol 791-793 ◽  
pp. 265-268
Author(s):  
Xiao Li Yang ◽  
Qiong He ◽  
Li Liu ◽  
Tong Yang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Path Length ◽  
Near Infrared ◽  
Optimal Path ◽  
Tea Polyphenols ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Least Squares Regression ◽  
Path Lengths

We investigated the optical path length to tea polyphenols (TP) determination in Puer tea by near infrared (NIR) spectroscopy. The NIR spectra samples include three path lengths (1mm, 2mm and 5mm). Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. To study the influence of pre-processing on identification of optimal path for NIR analysis of tea polyphenols, we applied five techniques to pre-process spectra, including normalization, standardization, centralization, derivative and discrete wavelet transform. Comparison of the mean absolute percentage error (MAPE) of the models with different path lengths show that the models constructed with spectra collected in 2mm path length gave the best results. 1mm path length gained the uncorrected determination results. Normalization, centralization and derivative are better than standardization or discrete wavelet transform for pre-processing.

Pre-Processing Influence on Near-Infrared Spectra for Determination of Volatile in Lignite Coal Samples

2014 ◽  
Vol 898 ◽  
pp. 831-834 ◽  
Author(s):  
Xiao Li Yang ◽  
Fan Wang
Keyword(s):  
Wavelet Transform ◽  
Least Squares ◽  
Discrete Wavelet Transform ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Discrete Wavelet ◽  
Least Squares Regression ◽  
Lignite Coal

We studied volatile determination in lignite coal samples using near-infrared (NIR) spectra. Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. To study the influence of pre-processing on determination of volatile for NIR analysis of lignite coal samples, we applied four techniques to pre-process spectra, including normalization, standardization, centralization, derivative and discrete wavelet transform. Comparison of the mean absolute percentage error (MAPE) and root mean square error of prediction (RMSEP) of the models show that the models constructed with spectra pre-processed by discrete wavelet transform gave the best results. Through parameters optimization, the results show that discrete wavelet transform and partial least squares regression can obtain satisfactory performance for moisture and volatile determination in coal samples.

DWT-PLS Regression on Near-Infrared Spectra for Moisture and Volatile Determination of Coal

2013 ◽  
Vol 848 ◽  
pp. 313-316
Author(s):  
Xiao Li Yang ◽  
Fan Wang ◽  
Ji Shu Chen ◽  
Gong Zhe Ma
Keyword(s):  
Partial Least Squares Regression ◽  
Bituminous Coal ◽  
Near Infrared ◽  
Parameters Optimization ◽  
Discrete Wavelet ◽  
Pls Regression ◽  
Least Squares Regression ◽  
Satisfactory Performance ◽  
Near Infrared Spectra

We studied moisture and volatile determination in bituminous coal samples using near-infrared (NIR) spectra. This research was developted by applying partial least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, NIR spectra were pre-processed by DWT for fitting and compression. Then, DWT coefficients were used to build regression model with PLS. We used NIR spectra to determination moisture and volatile determination in coal samples seperately and simultaneously. Through parameters optimization, the results show that DWT-PLS can obtain satisfactory performance for separate and simultanous determination.

Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

2014 ◽  
Vol 31 ◽  
pp. 13-27 ◽  
Author(s):  
Sajad Farokhi ◽  
Siti Mariyam Shamsuddin ◽  
U.U. Sheikh ◽  
Jan Flusser ◽  
Mohammad Khansari ◽  
...  
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Near Infrared ◽  
Zernike Moments ◽  
Discrete Wavelet
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