scholarly journals Case of spectral emission measurement. 3-4. Spectral emission characteristics of upland field crop. Classification of crops by main component analysis of spectral emission.

1985 ◽  
pp. 36-39
Author(s):  
MICHIO SHIBAYAMA
Keyword(s):  
Component Analysis ◽  
Emission Characteristics ◽  
Emission Measurement ◽  
Field Crop ◽  
Spectral Emission ◽  
Main Component Analysis ◽  
Crop Classification ◽  
Upland Field ◽  
Main Component

scholarly journals The Main Component Analysis of the Longitudinal Distribution of Solar Activity

1991 ◽  
Vol 130 ◽  
pp. 275-276
Author(s):  
Ladislav Hejna ◽  
Hubertus Wöhl
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Sunspot Group ◽  
Component Analysis ◽  
Longitudinal Distribution ◽  
Sunspot Activity ◽  
Significant Feature ◽  
Solar Cycles ◽  
Main Component Analysis ◽  
Main Component

Abstract In this contribution, preliminary results of the main component analysis of Bartels diagram of time series of daily values of sunspot group numbers for solar cycles 18, 19 and 20 are presented. The results obtained suggest that the most significant feature in the longitudinal distribution of sunspot activity is the existence of preferred solar hemispheres alternating with a mean period of 2.5 Bartels rotations.

scholarly journals Youth spare time: Typical patterns of behavior

Sociologija ◽  
2009 ◽  
Vol 51 (3) ◽  
pp. 247-261
Author(s):  
Ivana Stepanovic ◽  
Marina Videnovic ◽  
Dijana Plut
Keyword(s):  
Secondary School ◽  
Secondary School Students ◽  
Component Analysis ◽  
Free Time ◽  
School Students ◽  
Academic Orientation ◽  
Spare Time ◽  
Patterns Of Behavior ◽  
Main Component Analysis ◽  
Main Component

In this research we have tried to identify typical patterns of young people's behavior in their spare time and to use these patterns in order to group our subjects regarding their interests and preferences. Main-component analysis showed that it was possible to find different patterns of secondary school students' behavior in spare time as well as that the identified models could be the criteria for grouping them. Five patterns have been identified describing youth orientations towards their free time: academic orientation, orientation towards sports, orientation towards entertainment, orientation towards spending time going out and orientation towards music and computers.

scholarly journals KELIMPAHAN BIVALVIA DI EKOSISTEM LAMUN PANTAI PUDING KABUPATEN BANGKA SELATAN

2018 ◽  
Vol 12 (2) ◽  
pp. 64-75
Author(s):  
Mutiara Febrina ◽  
Wahyu Adi ◽  
Arief Febrianto
Keyword(s):  
Environmental Parameters ◽  
Component Analysis ◽  
Habitat Characteristics ◽  
Distribution Analysis ◽  
Seagrass Species ◽  
Total Percentage ◽  
Main Component Analysis ◽  
Main Components ◽  
Main Component ◽  
Abundance And Distribution

Puding Beach is administratively located in Pasir Putih Village, Tukak Sadai District, South Bangka Regency. As a coastal area, Puding Beach holds natural resource potential in both marine tourism and biota diversity. This study aims to analyze the abundance and distribution of bivalves and the seagrass beach community structure. This research was conducted in March 2018. The research method used was purposive sampling. Whereas for analysis of water quality characteristics and bivalve abundance, use main component analysis (Principle component analysis / PCA). The results showed that there were 6 species of seagrass and 7 species of bivalves at all stations. Each station I, II and III is dominated by the bivalve type Gafrarium tumidium with different seagrass species density. The highest density of seagrass species at stations I, II and III were Halodule uninervis (141 ind / m), Enhalus acoroides (7 ind / m) and Cymodocea serrulata (38 ind / m). Based on the analysis of main components can be seen the description of the condition to research location and can be seen the correlation between the existing bivalve abundance and the condition of the waters in all the research stations described in the F1-F2 factorial field. Important information on the main component axis is centered on the 2 main axes F1 (82.38%) and F2 (4.62%) of the total percentage. Bivalvia distribution analysis at Puding Beach is uniform and clustered based on habitat characteristics and environmental parameters that influence the life of bivalves

Image Tracking and Analysis Algorithm by Independent Component Analysis

2010 ◽  
Vol 44-47 ◽  
pp. 1622-1627
Author(s):  
Cheng Yi Yu ◽  
Yi Ying Chang ◽  
Yen Chieh Ouyang ◽  
Shen Chuan Tai ◽  
Tzu Wei Yu
Keyword(s):  
Independent Component Analysis ◽  
Natural Environment ◽  
Color Space ◽  
Component Analysis ◽  
Brightness Distribution ◽  
Target Object ◽  
Independent Component ◽  
Image Capture ◽  
Main Component Analysis ◽  
Main Component

Along with digitizing and multimedia era, the image has not changed from the original entity into any changes can be dealt with digital preservation methods. Although the digital image capture technology means more and more developed, but there are still many variables affect the quality of an image. An image quality usually depends on the user's usage or changes in the natural environment. Due to the natural environment of the most common factors that influence is light, so an image of the brightness distribution over the target object caused by extreme hardly recognizable condition common. Therefore, we will use the independent component analysis of an input color images Red, Green, and Blue three Color Space to the main component analysis, in order to achieve the target tracking and analysis.

scholarly journals Characterization, classification and analysis of the main properties of the Southwestern Goias soil

2016 ◽  
Vol 37 (3) ◽  
pp. 1135 ◽  
Author(s):  
Kathleen Lourenço Fernandes ◽  
Adriana Aparecida Ribon ◽  
José Marques Junior ◽  
Angélica Santos Rabelo de Souza Bahia ◽  
João Tavares Filho
Keyword(s):  
Management Practices ◽  
Principal Component ◽  
Soil Classification ◽  
Component Analysis ◽  
Minor Amount ◽  
Native Forest ◽  
Field Methods ◽  
Physical Chemical ◽  
Main Component Analysis ◽  
Main Component

Characterization of the soil and the grouping of its properties through main component analysis can assist in the classification and observation of the consequences of the conversion of native forest areas in pastures. Such data can aid in management practices. Considering the lack of studies in Cerrado soils of south-western Goias, developed metagranites of Jurubatuba suite, the objective was to classify and study the physical, chemical and mineralogical soil (under native forest and pasture) Southwest of Goias. Soil sampling and classification followed the Field Methods Manual and the Brazilian System of Soil Classification, respectively. The physical, chemical and mineralogical soil were studied, as well as main component analysis made for these attributes. The profile 1 under native forest was classified as “Cambissolo Háplico Tb eutrófico latossólico”. The profile 2 under degraded pasture was rated “Latossolo Amarelo eutrófico típico”. In addition, the profile 3 under rotated pasture was rated as “Latossolo Vermelho eutrófico chernossólico”. It was observed that the “Latossolos” have higher bulk density and soil penetration resistance, indicating greater soil compaction. They showed high amounts of iron oxides and minor amount of nutrients such as calcium and magnesium. The principal component analysis allowed the grouping of profiles into two groups. Group 1 brought together the properties related to “Latossolos”, and the second was related to “Cambissolo” properties. Thus, the principal components analysis aids the understanding of soil properties and the grouping of soils with similar characteristics, the level of order and management.

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