Improvement of underwater colour correction using standard deviation ratio

2020 ◽  
Vol 56 (20) ◽  
pp. 1051-1054
Author(s):  
S.W. Moon ◽  
H.S. Lee ◽  
I.K. Eom
Keyword(s):  
Standard Deviation ◽  
Deviation Ratio ◽  
Standard Deviation Ratio

scholarly journals Pengklasteran Kabupaten/Kota di Jawa Tengah berdasarkan Tenaga Kesehatan dengan Menggunakan Metode Ward dan K-Means

2018 ◽  
Vol 7 (2) ◽  
pp. 103-109
Author(s):  
Sri Puji Lestari ◽  
Epha Diana Supandi ◽  
Pipit Pratiwi Rahayu
Keyword(s):  
Cluster Analysis ◽  
Standard Deviation ◽  
Nursing Staff ◽  
Health Workers ◽  
Medical Personnel ◽  
Central Java ◽  
Deviation Ratio ◽  
Standard Deviation Ratio ◽  
Ward Method ◽  
Central Java Province

Analisis klaster merupakan suatu metode yang digunakan untuk mengelompokkan objek (kasus) ke dalam klaster (kelompok) yang relatif sama.  Tujuan penelitian ini untuk mengklasterkan Kabupaten/Kota di Provinsi Jawa Tengah berdasarkan tenaga kesehatan tahun 2015 seperti tenaga medis, tenaga keperawatan, tenaga kebidanan, tenaga kefarmasian dan tenaga kesehatan lainnya dengan menggunakan metode Ward dan K-Means. Hasil penelitian menunjukkan ada tiga klaster terbentuk dimana metode Ward menghasilkan nilai rasio simpangan baku sebesar 0,3019% lebih besar jika dibandingkan dengan nilai rasio simpangan baku pada metode K-Means yaitu 0,2974%. Pada kasus ini, metode K-Means merupakan metode yang lebih baik dibandingkan metode Ward. [Cluster analysis is a method used to group objects (cases) into clusters (groups) that are relatively the same. The purpose of this study is to classify districts/cities in Central Java Province based on health worker in 2015 such as medical personnel, nursing staff, midwifery staff, pharmacy personnel and health workers using the Ward and K-Means methods. The results show that there are three clusters formed where the Ward method produce a standard deviation ratio of 0.3019% greater than the standard deviation ratio in the K-Means method, which is 0.2974%. In this case, the K-Means method is a better method than the Ward method.]

scholarly journals Village Grouping In Southwest Maluku District Based On Poverty Characteristics Using Self Organizing Maps (SOM) Methods

2020 ◽  
Vol 5 (1) ◽  
pp. 16-20
Author(s):  
Monalisa E. Rijoly ◽  
F. L. Lumalessil ◽  
B. P. Tomasouw
Keyword(s):  
Standard Deviation ◽  
Provincial Government ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Network Method ◽  
The Government ◽  
Deviation Ratio ◽  
Standard Deviation Ratio ◽  
Self Organizing

Poverty is one of the fundamental problems that has become the center of attention of the Maluku Provincial government, especially Southwest Maluku Regency. This study aims to provide information to the government about village grouping based on poverty characteristics in Southwest Maluku Regency using the Self Organizing Map network method. In this network, a layer containing neurons will arrange itself based on the input of a certain value in a group known as a cluster. In the grouping process, 3 results were obtained with the best grouping II results because they had the smallest standard deviation ratio value.

scholarly journals A Comprehensive Evaluation of Surface Air Temperature Reanalyses over China against Urbanization Bias–Adjusted Observations

2021 ◽  
Author(s):  
Siqi Zhang ◽  
Guoyu Ren ◽  
Yuyu Ren ◽  
Yingxian Zhang ◽  
Xiaoying Xue
Keyword(s):  
Standard Deviation ◽  
Air Temperature ◽  
Comprehensive Evaluation ◽  
Linear Trend ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Deviation Ratio ◽  
Standard Deviation Ratio ◽  
Linear Trends

Abstract The goal of this study is to compare the differences in surface air temperature (SAT) between observational and reanalysis data in mainland China from 1961–2015 for evaluating the reliability and applicability of the reanalysis datasets, based on an observational dataset of 763 stations which has been adjusted for urbanization bias, and 8 reanalysis datasets. The time series, anomaly correlations, standard deviations, climate state, and linear trends of the reanalysis data are evaluated against the observations. The reanalysis data are consistent with the observational climate characteristics to a large extent. The correlation and standard deviation ratio between the reanalysis data and observations exhibited highly consistent inter–annual variability and dispersion, with the inter–annual SAT variability of JRA55 and ERA5 the closest to the observations for the periods 1961–2015 and 1979–2015, and the dispersions of 20CRV3 and NCEPV1 the most consistent with the observations for the two periods. The annual mean SAT of the reanalyses is generally 0–2.0°C lower than the observations, while the linear trends of all datasets exhibited clear warming. The biases in the SAT climatology of 20CRV3 and CRA40 are lower than other reanalysis datasets, and the linear trends of NCEPV1 and 20CRV3 are closer to the observations. With increasing elevation, the biases of the reanalysis data in terms of correlation, standard deviation, climate state, and linear trend all increased. Overall, in terms of the similarity of multiple measures to the urbanization bias–adjusted observations, CRA40 and JRA55 show the best performance of the products in reproducing various aspects of climatological and climate change features in mainland China for the period 1979–2015 and 1961–2015 respectively.

scholarly journals Pengelompokkan Kabupaten/Kota Di Sulawesi Berdasarkan Indikator Pendidikan Menggunakan Analisis Klaster Average Linkage Dan Median Linkage

2019 ◽  
Vol 8 (3) ◽  
Author(s):  
Eka Oktavianty ◽  
Junaidi ◽  
Lilies Handayani
Keyword(s):  
Cluster Analysis ◽  
Standard Deviation ◽  
Multivariate Technique ◽  
Average Linkage ◽  
Education Indicators ◽  
Measuring Tool ◽  
Educational Indicators ◽  
Deviation Ratio ◽  
Standard Deviation Ratio

Cluster analysis is included in the method of multivariate analysis of interdependence. Cluster analysis is a multivariate technique that classifies objects into different groups between one group and another group. This research is applied to the case of education indicators, education is important for improving the quality of human resources. Educational indicators are a measuring tool used to see how well the quality of education. Educational indicators are classified using average linkage and median linkage. The results of the analysis showed that the median linkage obtained a standard deviation ratio value of 0.061 smaller than the standard deviation ratio average linkage value of 0.078. The method that has the smallest ratio is the method with the best performance. So that grouping City Districts in Sulawesi based on education indicators in 2017 is better to use the median linkage and obtained 5 clusters formed.

The interpretation of ELISA results by means of the standard deviation ratio

1988 ◽  
Vol 115 (1) ◽  
pp. 105-110 ◽  
Author(s):  
R.M. Parkinson ◽  
J.D. Conradie ◽  
L.V. Milner ◽  
T. Marimuthu
Keyword(s):  
Standard Deviation ◽  
Deviation Ratio ◽  
Standard Deviation Ratio
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