Study on spectral reconstruction algorithm based on kernel entropy component analysis

2018 ◽  
Author(s):  
Shan Sun ◽  
WeiPing Yang ◽  
Xiaoxiao Zhang ◽  
Yang Zhang ◽  
Dongdong Gong
Keyword(s):  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Spectral Reconstruction ◽  
Entropy Component

scholarly journals A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 644 ◽  
Author(s):  
Shang Zhang ◽  
Yuhan Dong ◽  
Hongyan Fu ◽  
Shao-Lun Huang ◽  
Lin Zhang
Keyword(s):  
Dictionary Learning ◽  
Reconstruction Algorithm ◽  
Sparse Optimization ◽  
Spectral Reconstruction

Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

2020 ◽  
pp. 107754632093203
Author(s):  
Hongdi Zhou ◽  
Fei Zhong ◽  
Tielin Shi ◽  
Wuxing Lai ◽  
Jian Duan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Hebbian Learning ◽  
Component Analysis ◽  
Operating Conditions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Information ◽  
Entropy Component

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

A spectral reconstruction algorithm for two-plane Compton cameras

2020 ◽  
Vol 65 (2) ◽  
pp. 025011
Author(s):  
Enrique Muñoz ◽  
Luis Barrientos ◽  
José Bernabéu ◽  
Marina Borja-Lloret ◽  
Gabriela Llosá ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Spectral Reconstruction

scholarly journals Kernel Entropy Component Analysis for Remote Sensing Image Clustering

2012 ◽  
Vol 9 (2) ◽  
pp. 312-316 ◽  
Author(s):  
Luis Gomez-Chova ◽  
Robert Jenssen ◽  
Gustavo Camps-Valls
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Image Clustering ◽  
Entropy Component

Emission Spectral Reconstruction Based on Principal Component Analysis Applied to Fluorescence Full-Color Prints

2019 ◽  
Vol 11 (10) ◽  
pp. 1349-1356
Author(s):  
Mi Shang ◽  
Ling Yang ◽  
Danfei Liu ◽  
Zijie Cui ◽  
Yunfei Zhong
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Emission ◽  
Emission Spectra ◽  
Principal Component ◽  
Color Difference ◽  
Component Analysis ◽  
Fluorescence Emission Spectrum ◽  
Color Science ◽  
Full Color ◽  
Spectral Reconstruction

Color reproduction of fluorescent full-color prints depends on many factors, such as preparation of luminescent inks, ratio of luminescent inks to each other, printing technology and so on. In order to make color expression more abundant on fluorescent full-color prints, reconstruction of fluorescence emission spectrum is particularly significant. As opposed to custom methods, principal component analysis has been applied to color science permanently. The method was applied to emission spectral reconstruction in this work and the up-conversion luminescent inks were selected. 336 samples were composed of single ink halftone at a quarter, half, 75%, and 100% surface coverages. The samples were firstly superimposed in one ink and two inks on the blank paper. Moreover, their emission spectral was measured and the procedure for principal component analysis was also performed. The emission spectral was reconstructed by using 1 nm interval from 351 nm to 748 nm. Ultimately, the accuracy of recovery spectral was evaluated through CIEDE2000 color difference evaluation. The obtained results indicated that principal component analysis can be used to reconstruct emission spectra. Besides, the method can also be used for color estimation between different printing materials.

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