• Journal of KIISE
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• v.44 no.4
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• pp.417-422
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• 2017

Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building's HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.160-160
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• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.39-47
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• 2024

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman's rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.

• Journal of Environmental Impact Assessment
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• v.33 no.4
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• pp.155-163
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• 2024

The purpose is to explore the meaning of the open space land use space pattern from the perspective of urban heat reduction using the land-use scenario. The employed methods are as follows: (1) to calculate the cooling capacity Index for each of five land use scenarios, using the InVEST Urban Cooling Model, (2) to calculate open space entropy & morphological spatial pattern for each land use scenario, using the Guidos Spatial Pattern Toolbox, and (3) to perform a Spearman rank correlation analysis between the InVEST and Guidos results. It is found that the rank correlation is moderate between the cooling capacity Index and the open space area ratio (rho=0.50). However, other relations are low. It is observed that only the total amount of open space is likely to have a meaning from the perspective of urban heat reduction, and that other open space location spatial patterns may not have much meaning from the perspective of urban thermal environment management.

• Korean Journal of Community Nutrition
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• v.3 no.2
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• pp.273-280
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• 1998

This study was designed to estimate the improvement of Validity for food frequency questionnaire(FFQ) by offering multiple choice portion size in developing a questionnaire. Validity of the two methods(food frequency questionnaire I=FFQ I & Food frequency questionnaire II=FFQ II) was tested in comparison with reference method of the 7-day weighed record(7DWR). Dietary consumption data of the three methods(FFQ I, FFQ II & 7DWR) were collected from 101 female university students for the analysis. Validity was measured in two categories : One was the nutrient intake value from the three methods, the other was the identification of between individual variation within the group. Spearman's rank order correlation test and distribution graphs were used for the analysis. The result showed that individual intake value of the FFQII was closer to that of the 7DWR than that of the FFQ I.Spearman's rank order correlation between the FFQII and the 7DWR did not show any improved correlation. The distribution graphs of nutrient intake derived from both the FFQ I and the FFQII were different from that of the 7DWR. Therefore, it could be suggested that single one portion size food frequency questionnaire is an equally efficient method as a multiple choice food frequency questionnaire to be adopted in epidemiologic studies.

• The Korean Journal of Health Service Management
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• v.8 no.2
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• pp.61-72
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• 2014

The purpose of this study was to identify the effect nursing organizational culture and happiness index on turnover intention among nurses. The subjects of this study were 377 nurses who were working at 3 general hospitals in B city. The data were collected by structured questionnaire from July 1 to August 28 of 2013. The data collected were analyzed with SPSS Win 20.0 using descriptive methods, t-test, ANOVA, Pearson correlation coefficient and Stepwise multiple regression. Turnover intention were significant negative correlation for affiliative oriented organizational culture(r=-.137, p=.008), happiness index(r=-.290, p<.001). There were significant positive correlation for innovative oriented organizational culture(r=.123, p=.017), rank oriented organizational culture(r=.126, p=.015), task oriented organizational culture(r=.218, p<.001). Factors affecting for turnover intention were happiness index(${\beta}$=-.297, p<.001), rank oriented organizational culture(${\beta}$=.266, p<.001), nursing experience(${\beta}$=.199, p=.009), affiliative oriented organizational culture(${\beta}$=-.142, p=.034). The explained variances for were turnover intention among nurses 17.2%. Based on the study consider to development and education program of happiness index and affiliative oriented organizational culture for nurses in the hospital setting.

• Bulletin of the Korean Chemical Society
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• v.24 no.9
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• pp.1345-1350
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• 2003

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra.

• Restorative Dentistry and Endodontics
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• v.49 no.2
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• pp.21.1-21.8
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• 2024

Objectives: This paper aims to serve as a useful guide for sample size determination for various correlation analyses that are based on effect sizes and confidence interval width. Materials and Methods: Sample size determinations are calculated for Pearson's correlation, Spearman's rank correlation, and Kendall's Tau-b correlation. Examples of sample size statements and their justification are also included. Results: Using the same effect sizes, there are differences between the sample size determination of the 3 statistical tests. Based on an empirical calculation, a minimum sample size of 149 is usually adequate for performing both parametric and non-parametric correlation analysis to determine at least a moderate to an excellent degree of correlation with acceptable confidence interval width. Conclusions: Determining data assumption(s) is one of the challenges to offering a valid technique to estimate the required sample size for correlation analyses. Sample size tables are provided and these will help researchers to estimate a minimum sample size requirement based on correlation analyses.

• Journal of the Korean Society of Combustion
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• v.15 no.4
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• pp.58-64
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• 2010

Spontaneous ignition tests of five different coals with non-iso-thermal and iso-thermal test method based on the standard test procedure of NF T20-036 were carried. These five coals included the 2 low rank coals and 3 bituminous coals. Test results showed that the ignition temperatures of all coals at the iso-thermal conditions were higher than that of non-isothermal condition, and those of low rank SM and BR coal in both nonisothermal and isothermal conditions were lower than bituminous AN and CN coals. The chemical species of coals such as oxygen and hematite also plays an important role in enhancing the ignition rate that the ignition temperature of SM coal was lowered. The heat accumulation tendency of five coals inside outdoor stack pile was monitored with emphasis on the change in the temperature of the coal depth in stack pile. In case of low rank BR coal, its temperature inside coal stack pile due to the rate of high heat accumulation and oxidation was $59^{\circ}C$ compared to $51^{\circ}C$ for other SW bituminous coal. And the heat accumulation rate inside coal stack piles was increased with increased the Cp value which it was defined as the specific heat of coal at constant pressure, whereas other factors such as thermal diffusivity and conductivity of coal relatively had less effect on heat accumulation.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.189-196
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• 2011

In this study, we perform a simulation study to compare frequently used standardization methods for interview scores based on trimmed mean, rank mean, and z-score mean. In this simulation study we assume that interviewer's score is influenced by a weighted average of true interviewee's true score and independent noise whose weight is determined by the professionality of the interviewer. In other words, as interviewer's professionality increases, the observed score becomes closer to the true score and if interviewer's professionality decreases, the observed score becomes closer to the noise instead of the true score. By adding interviewer's tendency bias to the weighed average, final interviewee's score is assumed to be observed. In this simulation, the interviewers's cores for each method are computed and then the method is considered best whose rank correlation between the method's scores and the true scores is highest. Simulation results show that when the true score is from normal distributions, z-score mean is best in general and when the true score is from Laplace distributions, z-score mean is better than rank mean in full interview system, where all interviewers meet all interviewees, and rank mean is better than z-score mean in half split interview system, where the interviewers meet only half of the interviewees. Trimmed mean is worst in general.