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Wyszukujesz frazę "Wang, Z. Y." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Visualization investigation on the marine data with multivariate statistical analysis methods
Autorzy:
Li, Y.
Lu, Z.
Wang, M.
Powiązania:
https://bibliotekanauki.pl/articles/260477.pdf
Data publikacji:
2017
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
marine data
factor analysis
cluster analysis
discriminate analysis
visualization
Opis:
Marine information is an important way for us to know and study more about the ocean. Marine data makes the basic of marine information. Because of the huge quantity and diversity of marine data, and at the same time marine data is polyatomic variable, we start with statistical analysis methods to search for the regularity of the marine data. On one hand, we get the aggregate variation functions of the marine data by factor analyzing in aspect of the spatiality. Then we visually describe the marine status of the studied sea area with pre variogram function and post variogram function. On the other hand, we used cluster analysis method to get the verifying rule in time and make visible graphs of the marine data. In this way, we can also supply with the suggestions in classifying the sea seawater quality. The data processing result shows that the suggested methods in this article are both operable and effective. At the same time some reasonable suggestions are given in the article.
Źródło:
Polish Maritime Research; 2017, S 2; 89-94
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using chemometrics to identify water quality in Daya Bay, China
Autorzy:
Wu, M.-L.
Wang, Y.-S.
Sun, C.-C.
Wang, H.
Lou, Z.-P.
Dong, J.-D.
Powiązania:
https://bibliotekanauki.pl/articles/49096.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
robust principal component analysis
water quality
chemometrics
China
cluster analysis
Daya Bay
Opis:
In this paper, chemometric approaches based on cluster analysis, classical and robust principal component analysis were employed to identify water quality in Daya Bay (DYB), China. The results show that these approaches divided water quality in DYB into two groups: stations S3, S8, S10 and S11 belong to cluster A, which lie in Dapeng Cove, Aotou Harbor and the north-eastern part of DYB, where water quality is related mainly to anthropogenic activities. The other stations belong to cluster B, which lie in the southern, central and eastern parts of DYB, where the quality is related mainly to water exchange with the South China Sea. Cluster analysis yields good results as a first exploratory method for evaluating spatial difference, but it fails to demonstrate the relationship between variables and environmental quality on the one hand and the untreated data on the other. However, with the aid of suitable chemometric approaches, the relationship between samples or variables can be investigated. Classical and robust principal component analysis can provide a visual aid for identifying the water environment in DYB, and then extracting specific information about relationships between variables and spatial variation trends in water quality.
Źródło:
Oceanologia; 2009, 51, 2; 217-232
0078-3234
Pojawia się w:
Oceanologia
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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