scholarly journals Fibre attributes and mapping the cultivar influence of different industrial cellulosic crops (cotton, hemp, flax, and canola) on textile properties

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ikra Iftekhar Shuvo
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Textile Fibre ◽  
Two Dimensional ◽  
Production Engineering ◽  
Hemp Fibres ◽  
Likelihood Analysis ◽  
Microscopic Studies ◽  
And Function

Abstract Natural lignocellulosic fibres (NLF) extracted from different industrial crops (like cotton, hemp, flax, and canola) have taken a growing share of the overall global use of natural fibres required for manufacturing consumer apparels and textile substrate. The attributes of these constituent NLF determine the end product (textiles) performance and function. Structural and microscopic studies have highlighted the key behaviors of these NLF and understanding these behaviors is essential to regulate their industrial production, engineering applications, and harness their benefits. Breakthrough scientific successes have demonstrated textile fibre properties and significantly different mechanical and structural behavioral patterns related to different cultivars of NLF, but a broader agenda is needed to study these behaviors. Influence of key fibre attributes of NLF and properties of different cultivars on the performance of textiles are defined in this review. A likelihood analysis using scattergram and Pearson’s correlation followed by a two-dimensional principal component analysis (PCA) to single-out key properties explain the variations and investigate the probabilities of any cluster of similar fibre profiles. Finally, a Weibull distribution determined probabilistic breaking tenacities of different fibres after statistical analysis of more than 60 (N > 60) cultivars of cotton, canola, flax, and hemp fibres.

scholarly journals The Principal Component Analysis of Tribological Tests of Surface Layers Modified with IF-WS2 Nanoparticles

2015 ◽  
Vol 235 ◽  
pp. 9-15
Author(s):  
Jacek Pietraszek ◽  
Joanna Korzekwa ◽  
Andrii Goroshko
Keyword(s):  
Principal Component Analysis ◽  
Inverse Problem ◽  
Surface Layer ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Technological Parameters ◽  
Surface Layers ◽  
Hyper Plane

The investigation described in this paper resulted in some complicated statistical analysis. The first level was an experimental design with technological parameters as factorials input and geometrical surface layer properties as quantitative outputs. The second level was an analysis generally leading to an optimization inverse problem: what parameters result in desired surface layer properties. The principal component analysis was made to identify possibility of a dimensionality reduction and simplify the optimization. Obtained results showed that the experimental dataset is practically two-dimensional but PCA projection involves all factors into the skewed hyper-plane. This paper contains a description of the problem, obtained results, analysis and conclusions.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

Sign in / Sign up

Export Citation Format

Share Document