scholarly journals Principal component analysis: The path ahead for aircrew-aircraft compatibility at the Institute of Aerospace Medicine

2019 ◽  
Vol 63 ◽  
pp. 8-15
Author(s):  
P Biswal ◽  
YS Dahiya
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Principal Components ◽  
Computer Aided Design ◽  
Principal Component ◽  
Component Analysis ◽  
Critical Parameters ◽  
Design Stage ◽  
Aviation Industry ◽  
Aerospace Medicine

Introduction: The Institute of Aerospace Medicine provides design consultancy on aircraft-aircrew compatibility on a number of fixed-wing as well as rotary-wing aircrafts during various stages of development. Till date, the cockpit compatibility of aircrew has been determined based on the percentile concept. Percentiles, though useful when dealing with a single parameter, pose major design and fitment problems when considering multiple parameters simultaneously as in aircraft cockpit design. The concept of multivariate analysis has been the solution which the aviation industry the world over has accepted in overcoming this problem. This paper presents the Institute of Aerospace Medicine initial foray into the field of multivariate analysis, specifically principal component analysis (PCA) to achieve desired aircrew fitment in the aircraft cockpit right from the design stage. Materials and Methods: The fighter aircraft of the near future is being designed using the anthropometric parameters available in the IAF aircrew anthropometry survey 2013. Of the 57 parameters available, six parameters critical to the design of the cockpit were subject to PCA to derive three principal components. About 96% confidence ellipse was drawn on the plot of the principal components. From this, along the different axes, 21 boundary individuals were identified defining the extreme individuals in various combinations of the six parameters. Discussion: The use of more than 2 parameters is not amenable to sequential use of percentiles. As the number of parameters considered increases, it leads to reduced fitment percentage. The use of PCA allows consideration of critical parameters together at one go. The design aim is changed from the 3rd to 97th percentile to an overall aim of fitting 96% of the target population in the cockpit. The boundary individual’s entire anthropometry data are used to create boundary manikins for use in computer-aided design models. The fitment of these boundary individuals ensures that if these individuals fit, all others would fit in the cockpit. This concept brings about a paradigm shift in the aircrew-aircraft compatibility in the aviation industry in India.

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.

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

scholarly journals Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira
Keyword(s):  
Principal Component Analysis ◽  
Disease Severity ◽  
Principal Components ◽  
Leaf Spot ◽  
Oil Content ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Gge Biplot ◽  
Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

scholarly journals Principal component analysis for evaluating a ranking method used in the performance testing in sheep of Morada Nova breed

2015 ◽  
Vol 36 (6) ◽  
pp. 3909
Author(s):  
Michelle Santos da Silva ◽  
Luciana Shiotsuki ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Correlation Coefficients ◽  
Performance Testing ◽  
Principal Component ◽  
Component Analysis ◽  
Ranking Method ◽  
Daily Weight Gain ◽  
Meat Production ◽  
Body Depth

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.

scholarly journals Inferência Exploratória de Dados Espaço-Temporal da Precipitação Pluviométrica no Nordeste Brasileiro

2020 ◽  
Vol 13 (5) ◽  
pp. 2019
Author(s):  
Jhon Lennon Bezerra da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
Marcos Vinícios Da Silva ◽  
Roni Valter De Souza Guedes ◽  
Pabrício Marcos Oliveira Lopes ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Temporal Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Northeastern Brazil ◽  
Annual Rainfall ◽  
Intermittent Rivers ◽  
Rainfall Patterns ◽  
Brazilian Northeast

A gestão eficiente dos recursos hídricos no Nordeste brasileiro torna-se fundamental diante do regime hidrológico dos rios intermitentes, dos quais muitos são extremamente críticos. Todavia estes dependem de um regime pluviométrico irregular, tanto em escala de tempo mensal quanto anual. Objetivou-se determinar a variabilidade espaço-temporal da precipitação pluviométrica total anual, averiguando-se, também, as regiões com padrões de precipitação semelhantes por técnicas de análise multivariada (clusters e componentes principais) no Nordeste do Brasil. Foram analisados dados de precipitação pluviométrica total anual, entre os anos de 1995 e 2016, de 37 diferentes estações meteorológicas do INMET, estas situadas nos limites territoriais dos nove estados do Nordeste brasileiro. A análise de clusters verificou a formação de quatro grupos distintos, com padrões semelhantes de precipitação nas regiões dentro dos grupos, conforme também observado na análise de componentes principais. A padronização e/ou variabilidade espaço-temporal da precipitação pluviométrica dos municípios analisados mostrou-se está intimamente associada as condições das estações do ano e anomalias climatológicas, aos fatores de uso e ocupação do solo, condições de altitude e relevo, tais quais favorecem na formação e estabilidade de chuvas menores ou maiores no Nordeste brasileiro. A análise multivariada de cluster e componentes principal identificaram padrões e semelhanças pluviométricas de grupos, nos diferentes estados do Nordeste do Brasil entre os anos de 1995 e 2016. Exploratory Inference of Spatial-Temporal Data of Rainfall in the Brazilian Northeast ABSTRACTThe efficient management of water resources in the Northeast of Brazil is essential in view of the hydrological regime of intermittent rivers, of which many are extremely critical, as they depend on an irregular rainfall regime, both on a monthly and annual time scale. The objective of this study was to determine the spatial and temporal variability of the annual total rainfall, also investigating the regions with similar rainfall patterns by multivariate analysis techniques (clusters and principal components) in Brazilian Northeast. Data from total annual rainfall between the years 1995 and 2016, of 37 different INMET weather stations were analyzed, located within the territorial limit of the nine states of Brazilian Northeast. Cluster analysis verified the formation of four distinct groups, with similar precipitation patterns in the regions within the groups as also observed in the principal component analysis. The pattern and/or spatial-temporal variability of rainfall in the municipalities analyzed was shown to be intimately associated with the conditions of the year and climatic anomalies stations, and the factors of land use and occupation, altitude and relief conditions, such as favoring the formation and stability of minor or major rain in the Brazilian Northeast. Multivariate cluster and principal component analysis identified rainfall patterns and similarities of groups, in the different states of Northeastern Brazil between the years 1995 and 2016.Keywords: multivariate analysis, climate change, semiarid, regional climate patterns.

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

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