scholarly journals Rapid multivariate analysis of 3D ToF-SIMS data: graphical processor units (GPUs) and low-discrepancy subsampling for large-scale principal component analysis

2016 ◽  
Vol 48 (12) ◽  
pp. 1328-1336 ◽  
Author(s):  
Peter J Cumpson ◽  
Ian W Fletcher ◽  
Naoko Sano ◽  
Anders J Barlow
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Tof Sims ◽  
Graphical Processor

Application of principal component analysis and tof-sims to mineral recognition, surface chemistry and separation by flotation

2006 ◽  
Vol 70 (18) ◽  
pp. A597 ◽  
Author(s):  
Roger St. C. Smart ◽  
Brian Hart ◽  
Mark Biesinger ◽  
James Francis ◽  
Tesfaye Negeri
Keyword(s):  
Principal Component Analysis ◽  
Surface Chemistry ◽  
Principal Component ◽  
Component Analysis ◽  
Tof Sims

Principal Component Analysis (PCA) of Surface Contamination by TOF-SIMS

2021 ◽  
Author(s):  
Kei Lin Sek ◽  
Pei Lin Lee ◽  
Khin Yin Pang ◽  
Younan Hua ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Surface Contamination ◽  
Tof Sims

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.

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