A Principal Component Analysis Control Chart Method for Catenary Status Evaluation and Diagnosis by Shan Lin, Liping Liu, Meiwan Rao, Shu Deng, Jiaxin Wang, Wenfan Zhong, Li Lun

“…To make accurate and comprehensive evaluation of the catenary and diagnose the causes of the catenary fault, a method of catenary state evaluation and diagnosis based on the principal component analysis control chart was proposed, which can make full use of the multidimensional detection parameters of the catenary. …”
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Study of Seasonal Variation in Groundwater Quality of Sagar City (India) by Principal Component Analysis by Hemant Pathak, S. N. Limaye

“…Results of principal component analysis evinced that all the parameters equally and significantly contribute to groundwater quality variations. …”
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A PRINCIPAL COMPONENT ANALYSIS OF KNOWLEDGE MANAGEMENT SUCCESS FACTORS IN CONSTRUCTION FIRMS IN NIGERIA by Taiwo Adegbembo, Oluwaseyi Awodele, Ayodeji Oke

Subjects: “…Critical success factors, Knowledge management, Principal component analysis, Nigeria…”
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Usage of Principal Component Analysis in the Spatial Distribution of Mangrove Vegetation in North Coast of Pemalang by Erny Poedjirahajoe, Djoko Marsono, Frita Kusuma Wardhani

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An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow by Xuehui Wang, Yong Zhang, Hao Liu, Yang Wang, Lichun Wang, Baocai Yin

“…In this method, an improved robust principal component analysis model is presented to detect anomalies; then ST-DBSCAN algorithm is used to group the station-level anomaly data on space-time dimensions to reveal the propagation law and potential impact of different anomaly events. …”
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Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis by Zihan Wang, Chenglin Wen, Xiaoming Xu, Siyu Ji

“…Principal component analysis (PCA) is widely used in fault diagnosis. …”
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App-DDoS detection method based on K-means multiple principal component analysis by Hong-yu YANG, Yuan CHANG

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Subspace Method Aided Data-Driven Fault Detection Based on Principal Component Analysis by Lingling Ma, Xiangshun Li

“…In this paper, subspace method aided data-driven fault detection based on principal component analysis (PCA) is proposed. The basic idea is to use PCA to identify the system observability matrices from input and output data and construct residual generators. …”
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Supercritical fluid extraction and encapsulation of Rivas (Rheum ribes) flower: Principal component analysis (PCA) by Seyyed Ali Hoseini, Mohsen Vazifedoost, Bahareh Hajirostamloo, Zohreh Didar, Mohamad Mehdi Nematshahi

“…The current study aims to extract Rivas (Rheum ribes) flower using the ethanol modified supercritical CO2 (SCO2-EOH) method; analyze its chemical compounds and bioactivity, encapsulate the extract in maltodextrin, gum-Arabic (GA), and their combination (GA + MD) using the spray drying method and investigate the differences among microparticles using Principal Component Analysis (PCA). The Rivas extract obtained by the SCO2-EOH method was a rich source of unsaturated fatty acids (mainly linoleic acid: 57.58 %), phytosterols (mainly sitosterol: 197.02 and campesterol: 144.47 mg/100g), terpenoids (mainly camphor: 17.52 %; and 1,8-cineol: 10.91 %) and phenolics (mainly m-coumaric acid: 48.22; luteolin: 38.07 and gallic acid: 26.25 mg/g). …”
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Research on parallel coordinate visualization technology based on principal component analysis and K-means clustering by Guo-jun MA, Shui-bo WANG, Qing-qi PEI, Yang ZHAN

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A principal component analysis of regulatory environment features for sustainable building construction in South Africa by Chijioke Emere, Clinton Aigbavboa, Didibhuku Thwala

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Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants by Van Patiluna, James Owen, Joe Mari Maja, Jyoti Neupane, Jan Behmann, David Bohnenkamp, Irene Borra-Serrano, José M. Peña, James Robbins, Ana de Castro

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Hydrogen bond interactions of coumarin-153 in molecular solvents: molecular dynamics and principal component analysis by Goloviznina, Kateryna, Dudariev, Dmytro, Miannay, François-Alexandre,  Kalugin, Oleg, Koverga, Volodymyr, Takamuku, Toshiyuki, Vitale, Raffaele, Idrissi, Abdenacer

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Genetic diversity studies for yield and physiological traits using principal component analysis in little millet by T. Venkata Ratnam1 and L. Madhavi Latha2*

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Research on Rehabilitation Training Based on Principal Component Analysis of Three Dimensional Motion Data of Taijiquan by Xiang LI, Jian WANG, Tianjiao LI, Xiangbin WANG

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RECOGNITION METHOD OF METAL FRACTURE IMAGES BASED ON GROUPLET TRANSFORM AND KERNEL PRINCIPAL COMPONENT ANALYSIS by LI ZhiNong, CHEN Kang, YAN JingWen, YANG YanChun

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The Use of Principal Component Analysis for Reduction in Sleep Quality and Quantity Data in Female Professional Soccer by Eider Barba, David Casamichana, Pedro Figueiredo, Fábio Yuzo Nakamura, Julen Castellano

“…The main aim of the present study was to uncover multivariate relationships between sleep quantity and quality using principal component analysis (PCA) in professional female soccer players. …”
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Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis by Renfu Jia, Shibiao Fang, Wenrong Tu, Zhilin Sun

“…It combines multiple stepwise regression (MSR) and principal component analysis (PCA) to obtain more realistic results. …”
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Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size. by Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho

“…The aim of this study was to evaluate the performance of alternative multivariate procedures for principal component analysis (PCA), using the Nonlinear Iterative Partial Least Squares (NIPALS) and Expectation-Maximization (EM) algorithms, for imputing missing data in time series of meteorological variables. …”
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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence by Lesia Mochurad, Viktoriia Babii, Yuliia Boliubash, Yulianna Mochurad

“…To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in particular XGBoost and optimized principal component analysis (PCA), which provide data structuring and increase processing speed, especially for large datasets. …”
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