Smooth Edge Feature Lines Extraction from Point Clouds of Eroded Fractured Fragments

2013 ◽  
Vol 756-759 ◽  
pp. 4026-4030 ◽  
Author(s):  
Jian Bin Lin ◽  
Ming Quan Zhou ◽  
Zhong Ke Wu
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Point Clouds ◽  
Local Feature ◽  
Feature Points ◽  
Global Feature ◽  
Smooth Edge ◽  
Feature Lines ◽  
Novel Method ◽  
Edge Feature

This paper presents a novel method to extract edge lines from point clouds of these eroded, rough fractured fragments. Firstly, a principal component analysis based method is used to extract feature points, followed by clustering of these feature points. Secondly, a local feature lines fragment is constructed for each cluster and afterwards a smooth and noise pruning process for each local feature lines fragment. Thirdly, these separated local feature lines fragments are connected and bridged in order to eliminate the gaps caused by the eroded regions and construct completed global feature lines. Fourthly, a noise pruning process is performed. The output of this method is completed, smoothed edge feature lines. We illustrate the performance of our method on a number of real-world examples.

Chemical Structure-Odor Correlation in a Series of Synthetic n-Nonen-1-ols

1995 ◽  
Vol 50 (11-12) ◽  
pp. 757-765 ◽  
Author(s):  
Yasunobu Sakoda ◽  
Kenji Matsui ◽  
Tadahiko Kajiwara ◽  
Akikazu Hatanaka
Keyword(s):  
Principal Component Analysis ◽  
Double Bond ◽  
Spatial Structure ◽  
Chemical Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Acetylenic Compound ◽  
Analysis Method ◽  
Entire Series ◽  
Novel Method

In order to elucidate chemical structure-odor correlation in the all isomers of n-nonen-1- ols, an entire series of these alcohols were synthesized stereo-selectively in high purity. For unequivocal syntheses of them, geometrically selective hydrogenation of the respective acetylenic compound was adopted. The synthesized alcohols were converted to their 3,5-dinitrobenzoate derivatives with 3,5-dinitrobenzoyl chloride, and then purified by repeated recrystallization. Chemical structure-odor correlations in all the isomers of n-nonen-1-ols were elucidated by introducing a novel method to evaluate odor characteristics and by treating the obtained data statistically with the principal component analysis method (Cramer et al., 1988). The odor profiles of the tested compounds were attributable largely to the positions of the carbon- double bond. The geometries of compounds had only a little effect. With the principal component analysis, the odor profiles of the series of compounds were successfully integrated into the first and the second principal components. The first component (PC-1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, in which herbal and oily-fatty were conversely correlated each other to the position of double-bond of the tested compounds. Of these, only (6Z)-nonen-1-ol deviated markedly from the correlation, indicative of some special interaction between the spatial structure of this compound and the sensory machinery of human.

scholarly journals A novel method for total chlorine detection using machine learning with electrode arrays

RSC Advances ◽  
2019 ◽  
Vol 9 (59) ◽  
pp. 34196-34206
Author(s):  
Zhe Li ◽  
Shunhao Huang ◽  
Juan Chen
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cyclic Voltammetry ◽  
Support Vector Regression ◽  
Principal Component ◽  
Measurement Model ◽  
Support Vector ◽  
Electrode Arrays ◽  
Soft Measurement ◽  
Novel Method

Establish soft measurement model of total chlorine: cyclic voltammetry curves, principal component analysis and support vector regression.

A novel method for atmospheric correction of cosmic-ray data based on principal component analysis

2019 ◽  
Vol 109 ◽  
pp. 1-11 ◽  
Author(s):  
M. Savić ◽  
A. Dragić ◽  
D. Maletić ◽  
N. Veselinović ◽  
R. Banjanac ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Atmospheric Correction ◽  
Principal Component ◽  
Cosmic Ray ◽  
Component Analysis ◽  
Novel Method

scholarly journals Spectral Feature Extraction Based on the DCPCA Method

2013 ◽  
Vol 30 ◽  
Author(s):  
BU YUDE ◽  
PAN JINGCHANG ◽  
JIANG BIN ◽  
CHEN FUQIANG ◽  
WEI PENG
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Spectral Feature ◽  
Cataclysmic Variables ◽  
Principal Component ◽  
Support Vector ◽  
Sparse Principal Component Analysis ◽  
Astronomical Data ◽  
Feature Lines ◽  
Feature Extraction Technique

AbstractIn this paper, a new sparse principal component analysis (SPCA) method, called DCPCA (sparse PCA using a difference convex program), is introduced as a spectral feature extraction technique in astronomical data processing. Using this method, we successfully derive the feature lines from the spectra of cataclysmic variables. We then apply this algorithm to get the first 11 sparse principal components and use the support vector machine (SVM) to classify. The results show that the proposed method is comparable with traditional methods such as PCA+SVM.

Differentiation between Cerebrospinal Fluid and Serum with Electronic Nose

2005 ◽  
Vol 133 (1) ◽  
pp. 16-19 ◽  
Author(s):  
Anna Aronzon ◽  
C. William Hanson ◽  
Erica R. Thaler
Keyword(s):  
Principal Component Analysis ◽  
Cerebrospinal Fluid ◽  
Electronic Nose ◽  
Clinical Setting ◽  
Sensor Array ◽  
Low Cost ◽  
Principal Component ◽  
Rapid Diagnosis ◽  
Novel Method ◽  
Csf Leaks

OBJECTIVE: The study investigates the ability of the electronic nose to differentiate between cerebrospinal fluid (CSF) and serum and to identify an unknown specimen as CSF or serum. STUDY DESIGN AND SETTING: CSF and serum specimens were heated and tested with an organic semiconductor-based Cyranose 320 electronic nose (Cyrons Sciences, Pasadena, CA). Data from the 32-element sensor array were subjected to principal component analysis to depict differences in odorant patterns. RESULTS: The electronic nose was able to distinguish between CSF and serum isolates with Mahalanobis distance >5. Furthermore, the electronic nose was able to place unknown specimens in the appropriate class of CSF or serum. CONCLUSIONS: The electronic nose is a novel method that may allow rapid, low cost, and reliable distinction between CSF and serum in a clinical setting. SIGNIFICANCE: Because the results are available almost immediately, the electronic nose is a powerful tool that in the future may allow for rapid diagnosis of CSF leaks in the office setting.

scholarly journals A Novel Method for Fault Diagnosis of the Two-Input Two-Output Nonlinear Mass-Spring-Damper System Based on NOFRF and MBPCA

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jialiang Zhang ◽  
Jie Wu ◽  
Xiaoqian Zhang
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Nonlinear System ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Principal Component ◽  
Component Analysis ◽  
Novel Method ◽  
Mass Spring

For fault diagnosis of the two-input two-output mass-spring-damper system, a novel method based on the nonlinear output frequency response function (NOFRF) and multiblock principal component analysis (MBPCA) is proposed. The NOFRF is the extension of the frequency response function of the linear system to the nonlinear system, which can reflect the inherent characteristics of the nonlinear system. Therefore, the NOFRF is used to obtain the original fault feature data. In order to reduce the amount of feature data, a multiblock principal component analysis method is used for fault feature extraction. The least squares support vector machine (LSSVM) is used to construct a multifault classifier. A simplified LSSVM model is adopted to improve the training speed, and the conjugate gradient algorithm is used to reduce the required storage of LSSVM training. A fault diagnosis simulation experiment of a two-input two-output mass-spring-damper system is carried out. The results show that the proposed method has good diagnosis performance, and the training speed of the simplified LSSVM model is significantly higher than the traditional LSSVM.

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