A Hybrid Model Integrating Principal Component Analysis, Fuzzy C-Means, and Gaussian Process Regression for Dam Deformation Prediction

2020 ◽  
Author(s):  
Yangtao Li ◽  
Tengfei Bao ◽  
Xiaosong Shu ◽  
Zexun Chen ◽  
Zhixin Gao ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Process ◽  
Hybrid Model ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Deformation Prediction ◽  
Fuzzy C Means ◽  
Dam Deformation

Estimation of Alpha-Element Abundance Ratios Based on Gaussian Process Regression

2014 ◽  
Vol 543-547 ◽  
pp. 1685-1688
Author(s):  
Yu De Bu ◽  
Jing Chang Pan ◽  
Jie Wang
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Sloan Digital Sky Survey ◽  
Estimation Accuracy ◽  
Element Abundance ◽  
Sky Survey ◽  
Data Release

In this paper, we present a method to estimate the [α/Fe ]using the spectra from ninth data release of Sloan Digital Sky Survey (SDSS). We first use principal component analysis (PCA) to reduce the dimension of the spectra, and then use Gaussian process regression (GPR) to estimate the [α/Fe ]ratios. The results show that GPR is accurate and efficient in estimating the [α/Fe]ratios. Further analysis shows that using PCA can improve the estimation accuracy of GPR.

Structural performance assessment considering both observed and latent environmental and operational conditions: A Gaussian process and probability principal component analysis method

2022 ◽  
pp. 147592172110620
Author(s):  
Yi-Chen Zhu ◽  
Wen Xiong ◽  
Xiao-Dong Song
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Process ◽  
Performance Assessment ◽  
Structural Changes ◽  
Principal Component ◽  
Component Analysis ◽  
Assessment Model ◽  
Monitoring Data ◽  
Model Parameters ◽  
Structural Responses

Structural faults like damage and degradations will cause changes in structure response data. Performance assessment can be conducted by investigating such changes. In real implementations however, structural responses are affected by environmental and operational variations (EOVs) as well. Such variation should be well captured by the assessment model when detecting structural changes. It should be noted that not all EOVs can be measured by the monitoring system. When both observed and latent EOVs have significant effects on the monitored structural responses, these two effects should be considered properly. Furthermore, uncertainties can be significant for the monitoring data since loads and EOVs cannot be directly controlled under working conditions. To address these problems, this work proposes a performance assessment method considering both observed and latent EOVs. A Gaussian process is used to model the functional behaviour between structural response and observed EOVs whilst principal component analysis is used to eliminate the effect of latent EOVs. These two methods are combined using a Bayesian formulation and the effect of both observed and latent EOVs are modelled. The associated model parameters are inferred through probability density functions to account for the uncertainties. A synthetic data example is presented to validate the proposed method. It is also applied to the monitoring data of a long-span cable-stayed bridge with different damage scenarios considered, illustrating its capability of real implementations in structural health monitoring.

scholarly journals Classifying Hybrids of Energy Cane for Production of Bioethanol and Cogeneration of Biomass-Based Electricity by Principal Component Analysis-Linked Fuzzy C-Means Clustering Algorithm

2019 ◽  
Vol 11 (14) ◽  
pp. 246 ◽  
Author(s):  
B. R. A. Moreira ◽  
R. S. Viana ◽  
L. A. M. Lisboa ◽  
P. R. M. Lopes ◽  
P. A. M. Figueiredo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy Cluster ◽  
Alcoholic Beverages ◽  
Type I ◽  
Energy Cane ◽  
Fuzzy C Means

The biggest challenge facing in sugar-energy plants is to move towards the biorefinery concept, without threatening the environment and health. Energy cane is the state-of-the-art of smart energy crops to provide suitable whole-raw material to produce upgraded biofuels, dehydrated alcohol for transportation, refined sugar, yeast-fermented alcoholic beverages, soft drinks, silage and high-quality fodder, as well as to cogenerate heat and bioelectricity from burnt lignocellulose. We, accordingly, present fuzzy c-means (FCM) clustering algorithm interconnected with principal component analysis (PCA) as powerful exploratory data analysis tool to wisely classify hybrids of energy cane for production of first-generation ethanol and cogeneration of heat and bioelectricity. From the orthogonally-rotated factorial map, fuzzy cluster I aggregated the hybrids VX12-0277, VX12-1191, VX12-1356 and VX12-1658 composed of higher contents of soluble solids and sucrose, and larger productive yields of fermentable sugars. These parameters correlated with the X-axis component referring to technological quality of cane juice. Fuzzy cluster III aggregated the hybrids VX12-0180 and VX12-1022 consisted of higher fiber content. This parameter correlated with the Y-axis component referring to physicochemical quality of lignocellulose. From the PCA-FCM methodology, the conclusion is, therefore, hybrids from fuzzy cluster I prove to be type I energy cane (higher sucrose to fiber ratio) and could serve as energy supply pathways to produce bioethanol, while the hybrids from fuzzy cluster III are type II energy cane (lower sucrose to fiber ratio), denoting potential as higher fiber yield biomass sources to feed cogeneration of heat and bioelectricity in high temperature and pressure furnace-boiler system.

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