Spectral data compression using weighted principal component analysis with consideration of human visual system and light sources

2016 ◽  
Vol 23 (5) ◽  
pp. 753-764 ◽  
Author(s):  
Qian Cao ◽  
Xiaoxia Wan ◽  
Junfeng Li ◽  
Qiang Liu ◽  
Jingxing Liang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Visual System ◽  
Data Compression ◽  
Spectral Data ◽  
Human Visual System ◽  
Principal Component ◽  
Component Analysis ◽  
Light Sources ◽  
Weighted Principal Component

Optimization of Process Parameters for Submerged Arc Welding by Weighted Principal Component Analysis Based Taguchi Method

2012 ◽  
Vol 622-623 ◽  
pp. 45-50 ◽  
Author(s):  
Joydeep Roy ◽  
Bishop D. Barma ◽  
J. Deb Barma ◽  
S.C. Saha
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Arc Welding ◽  
Principal Component ◽  
Traverse Speed ◽  
Component Analysis ◽  
Submerged Arc Welding ◽  
Bead Geometry ◽  
Weighted Principal Component ◽  
Submerged Arc

In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, traverse speed, arc voltage and stickout since they are closely related to weld joint. The joint quality can be defined in terms of properties such as weld bead geometry and mechanical properties. There are several control parameters which directly or indirectly affect the response parameters. In the present study, an attempt has been made to search an optimal parametric combination, capable of producing desired high quality joint in submerged arc weldment by Taguchi method coupled with weighted principal component analysis. In the present investigation three process variables viz. Wire feed rate (Wf), stick out (So) and traverse speed (Tr) have been considered and the response parameters are hardness, tensile strength (Ts), toughness (IS).

scholarly journals Hyperspectral data compression based upon the principal component analysis

2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov
Keyword(s):  
Principal Component Analysis ◽  
Data Compression ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Gas Absorption ◽  
Absorption Bands ◽  
Spectral Bands ◽  
The Mean ◽  
Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

scholarly journals On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

2016 ◽  
Vol 38 (3) ◽  
pp. 1208-1223 ◽  
Author(s):  
Francisco Jesús Martinez-Murcia ◽  
Meng-Chuan Lai ◽  
Juan Manuel Górriz ◽  
Javier Ramírez ◽  
Adam M. H. Young ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Brain Structure ◽  
Site Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Weighted Principal Component ◽  
Structure Heterogeneity ◽  
The Brain
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