scholarly journals Insights from principal component analysis applied to Py-GCMS study of Indian coals and their solvent extracted clean coal products

2021 ◽  
Author(s):  
Abyansh Roy ◽  
Heena Dhawan ◽  
Sreedevi Upadhyayula ◽  
Hariprasad Kodamana
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Clean Coal ◽  
Pyrolysis Products ◽  
Dimensionality Reduction Technique ◽  
Input Variables ◽  
Indian Coals ◽  
Structural Insights

AbstractThe present work aims at studying five Indian coals and their solvent extracted clean coal products using Py-GCMS analysis and correlating the characterization data using theoretical principal component analysis. The pyrolysis products of the original coals and the super clean coals were classified as mono-, di- and tri-aromatics, while other prominent products that were obtained included cycloalkanes, n-alkanes, and alkenes ranging from C10–C29. The principal component analysis is a dimensionality reduction technique that reduced the number of input variables in the characterization dataset and gave inferences on the relative composition of constituent compounds and functional groups and structural insights based on scores and loading plots which were consistent with the experimental observations. ATR-FTIR studies confirmed the reduced concentration of ash in the super clean coals and the presence of aromatics. The Py-GCMS data and the ATR-FTIR spectra led to the conclusion that the super clean coals behaved similarly for both coking and non-coking coals with high aromatic concentrations as compared to the raw coal. Neyveli lignite super clean coal was found to show some structural similarity with the original coals, whereas the other super clean coals showed structural similarity within themselves but not with their original coal samples confirming the selective action of the e,N solvent in solubilizing the polycondensed aromatic structures in the coal samples.

scholarly journals Insights From Principal Component Analysis Applied to Py-GCMS Study of Indian Coals and Their Solvent Extracted Clean Coal Products

2020 ◽  
Author(s):  
Abyansh Roy ◽  
Heena Dhawan ◽  
Sreedevi Upadhyayula ◽  
Hariprasad Kondamana
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Solvent System ◽  
Clean Coal ◽  
Pyrolysis Products ◽  
Ftir Studies ◽  
Indian Coals ◽  
Structural Insights

Abstract The present work aims at studying five Indian coals and their solvent extracted clean coal products using Py-GCMS analysis and correlating these characterizations with results from theoretical a principal component analysis. The pyrolysis products of the original coals and the super clean coals were classified as mono-, di and tri- aromatics while other prominent products that were obtained included cycloalkanes, n-alkanes and alkenes ranging from C10-C29. The Py-GCMS results for the samples were studied using Principal Component Analysis. Inferences on relative composition of constituent compounds and functional groups and structural insights based on scores and loading plots of the PCA analysis were consistent with the experimental observations. ATR-FTIR studies confirmed the reduced concentration of ash in the super clean coals and the presence of aromatics. The Py-GCMS data and the ATR-FTIR spectra led to the conclusion that the super clean coals behaved similarly for both coking and non-coking coals with high aromatic concentrations as compared to the raw coal. Neyveli lignite super clean coal was found to show some structural similarity with the original coals, whereas, the other super clean coal showed structural similarity among them but not with their original coals indicative of the selective action of the e,N solvent system on the polycondensed aromatic structures in coal.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

scholarly journals Demixed principal component analysis of neural population data

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dmitry Kobak ◽  
Wieland Brendel ◽  
Christos Constantinidis ◽  
Claudia E Feierstein ◽  
Adam Kepecs ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Population Data ◽  
Component Analysis ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Cortical Areas ◽  
Dimensionality Reduction Technique ◽  
Single Figure

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Study of the Fouling Characteristics of Arc Pipe Based on Principal Component Analysis and Partial Least Squares

2012 ◽  
Vol 443-444 ◽  
pp. 731-737 ◽  
Author(s):  
Li Jing ◽  
Xiao Qiang Wen
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Fouling Resistance ◽  
Experimental Platform ◽  
Input And Output ◽  
Input Variables ◽  
Fouling Characteristics

An experimental platform of arc pipe was built to measure the corresponding parameters of fouling and the equation of fouling characteristics was built based on principal component analysis and partial least squares, in which there are several input variables such as input and output temperature, wall temperature, flow rate and so on, and there is only one output variables—fouling resistance. In order to compare with the five-input-variables equation, the six-input-variables equation was also built. The prediction results show the model built in this paper is reasonable and feasible.

PREDICTING Ms TEMPERATURE APPLYING PRINCIPAL COMPONENT ANALYSIS-ARTIFICIAL NEURAL NETWORKS

2009 ◽  
Vol 23 (06n07) ◽  
pp. 1099-1104 ◽  
Author(s):  
XUEXIA XU ◽  
BINGZHE BAI ◽  
WEI YOU
Keyword(s):  
Principal Component Analysis ◽  
Martensite Transformation ◽  
Prediction Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Scatter Diagram ◽  
Ann Model ◽  
Training Samples ◽  
Artificial Neural ◽  
Input Variables

The principal component analysis-artificial neural network (PCA-ANN) model was developed to predict martensite transformation start temperature ( Ms ) of steels. Training samples were processed by principal component analysis and the number of input variables was reduced from 6 to 4, then the scores of principal components were used to establish new sample database to train the ANN model. Ms of steels were predicted by the PCA-ANN model. The predicted and measured Ms distribute along the 0-45° diagonal in the scatter diagram and the statistical errors are MSE-16.0256, MSRE-4.49% and VOF-1.97790 respectively. Comparing the prediction results of different models it is shown that the accuracy of the PCA-ANN model was the highest, which indicated that the principal component analysis was helpful to improve the prediction accuracy of ANN model.

Predicting the Amount of Municipal Solid Waste via Hybrid Principal Component Analysis-Artificial Neural Network Approach

2015 ◽  
Vol 768 ◽  
pp. 722-727 ◽  
Author(s):  
Salman Safavi ◽  
S. Mohyeddin Bateni ◽  
Tong Ren Xu
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Principal Component ◽  
Component Analysis ◽  
Ann Model ◽  
Input Variables ◽  
Overfitting Problem

Accurate prediction of the amount of municipal solid waste (MSW) is crucial for designing and programming MSW management systems. Reliable estimation of MSW is difficult since many variables such as socio-economic characteristics, climatic factors and standard of living affect it. A number of studies used artificial neural network (ANN) to predict MSW. However, due to the large number of input variables to the ANN, it could not not perform well and generally encountered overfitting. This study takes advantage of the principal component analysis (PCA) technique to reduce the number of input variables to the ANN model in order to overcome the overfitting problem. The proposed PCA-ANN approach is used to predict the weight of MSW in the province of Mashhad, Iran. The utilized experimental data in this study are obtained from the Recycling Organization of Mashhad Municipality archive (http://www.wmo.mashhad.ir). It is found that the PCA approach can successfully decrease the number of input variables from thirteen to eight. The PCA-ANN model (with eight input variables) outperforms ANN (with thirteen input variables) and provides more accurate estimates of MSW as it mitigates the overfitting problem associated with ANN. The root-mean-square-error (RMSE) of MSW estimates reduces from 499000 Kg to 448000 Kg by using the PCA-ANN model instead of ANN.

scholarly journals A sparse principal component analysis of Class III malocclusions

2019 ◽  
Vol 89 (5) ◽  
pp. 768-774 ◽  
Author(s):  
Tae-Joo Kang ◽  
Soo-Heang Eo ◽  
HyungJun Cho ◽  
Richard E. Donatelli ◽  
Shin-Jae Lee
Keyword(s):  
Principal Component Analysis ◽  
Soft Tissue ◽  
Principal Components ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Ct Images ◽  
Class Iii ◽  
Hard Tissue ◽  
Input Variables

ABSTRACT Objectives: To identify the most characteristic variables out of a large number of anatomic landmark variables on three-dimensional computed tomography (CT) images. A modified principal component analysis (PCA) was used to identify which anatomic structures would demonstrate the major variabilities that would most characterize the patient. Materials and Methods: Data were collected from 217 patients with severe skeletal Class III malocclusions who had undergone orthognathic surgery. The input variables were composed of a total of 740 variables consisting of three-dimensional Cartesian coordinates and their Euclidean distances of 104 soft tissue and 81 hard tissue landmarks identified on the CT images. A statistical method, a modified PCA based on the penalized matrix decomposition, was performed to extract the principal components. Results: The first 10 (8 soft tissue, 2 hard tissue) principal components from the 740 input variables explained 63% of the total variance. The most conspicuous principal components indicated that groups of soft tissue variables on the nose, lips, and eyes explained more variability than skeletal variables did. In other words, these soft tissue components were most representative of the differences among the Class III patients. Conclusions: On three-dimensional images, soft tissues had more variability than the skeletal anatomic structures. In the assessment of three-dimensional facial variability, a limited number of anatomic landmarks being used today did not seem sufficient. Nevertheless, this modified PCA may be used to analyze orthodontic three-dimensional images in the future, but it may not fully express the variability of the patients.

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