• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.1007-1016
• /
• 2011

This article deals with the problem of testing for the correlation coefficient in the bivariate normal distribution. We propose Bayesian hypothesis testing procedures for the bivariate normal correlation coefficient under the noninformative prior. The noninformative priors are usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. A simulation study and an example are provided.

• Nuclear Engineering and Technology
• /
• v.32 no.5
• /
• pp.521-529
• /
• 2000

A modified interfacial heat transfer correlation between a dispersed water droplet and ambient superheated steam is proposed and compared with available experimental data and other correlations. Modified one overcomes the inherent deficiencies of Lee and Ryley's interfacial heat transfer correlation that ignored the effects of steam superheating which can not be neglected especially in the reflood situation of a loss-of-coolant accident. Modified one is represented by (equation omitted) In the present correlation the effect of possible subcooling of a water droplet is not taken into consideration. Comparison of the above correlation with currently available measurement data for a water droplet in high temperature gas flow shows that the proposed one correlates well with the measurement data where the degree of superheating is negligible and considerable.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.2
• /
• pp.21-25
• /
• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• Proceedings of the KSME Conference
• /
• 2000.04b
• /
• pp.83-89
• /
• 2000

This paper presents a pressure drop correlation for evaporation and condensation of alternative refrigerant with oil in micro-fin tubes. The correlation was developed from a data base consisting of oil-free pure and mixed refrigerants in micro-fin tube; Rl25 R134a. R32 R410a(R32/R125 50/50% mass), R22, R407c(R32/R125/R134a, 23/25/52% mass) and R32/R134a(25/75% mass). The micro-fin tube used in this paper had 60 0.2mm high fins with a 18 helix angle. The cross sectional flow area $(A_c)$ was $60.8 mm^2$ giving an equivalent smooth diameter$(D_e)$ of 8.8mm. The hydraulic diameter $(D_h)$ was estimated to the 5.45mm. The new correlation was obtained by replacing the friction factor and the tube-diameter in Bo Pierre correlation by a friction factor derived from pressure drop data for a micro-fin tube and the hydraulic diameter, respectively. This correlation was also used to predict some pressure data with a lubricant after using a mixing viscosity rule of lubricants and refrigerants. As a result, the new correlation was also well predicted to the measured data within a mean deviation of 19.0%.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.11 no.2
• /
• pp.96-115
• /
• 2007

Background and Purpose: Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different origins. Recently, because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to chaos theory, irregular signals of EEG can also result from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze correlation between the correlation dimension of EEG and psychological Test (TCI). Methods: Before and after moxibustion treatment, EEG raw data were measured by moving windows during 15 minutes. The correlation dimension(D2) was calculated from stabilized 40 seconds in 15 minutes data. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results: Correlation analysis of TCI test is calculated with deterministic non-linear data and stochastic non-linear data. 1. Novelty seeking in temperament is positive correlated with D2 of EEG on Fp. 2. reward dependence in temperament is positive correlated with D2 of EEG on T3,T4 and negative correlated with D2 of EEG on P3,P4. 3. self directedness in character is positive correlated with D2 of EEG on F4, P3. 4. Harm avoidance is negative correlated with D2 of EEG on Fp2, T3, P3. Conclusion: These results suggest that nonlinear analysis of EEG can quantify dynamic state of brain abolut psychological Test (TCI).

• Journal of The Korean Society of Integrative Medicine
• /
• v.8 no.4
• /
• pp.231-239
• /
• 2020

Purpose: The purpose of this study is to identify the relationship of between social support, emotional intelligence, depression, and health promotion behaviors of nursing college students, and to establish basic data for the development of a nursing intervention program for health promotion behaviors. Methods: This descriptive correlation study examined the correlation between the social support, emotional intelligence, depression, and health promotion behaviors of nursing students. 203 nursing college students located in J city participated in the study from November to December 2019. The collected data was analyzed used the SPSS WIN 22.0 program. The general characteristics of the subjects were analyzed by frequency and percentage, and health promoting behavior, social support, emotional intelligence, and depression were analyzed using mean and standard deviation. In this study, the correlation between the subjects' social support, emotional intelligence, depression, and health promotion behaviors was analyzed using Pearson correlation coefficient. Results: The study results showed that the subjects' health promotion behaviors averaged 2.22±0.38 points out of 4d social support averaged 3.83±0.59 points out of 5, emotional intelligence averaged 4.53±0.73 out of 7, and depression averaged 0.49±0.42 points out of 2 points. The analysis results of correlation between the subject's health promotion behaviors, social support, emotional intelligence, and depression showed that health promotion behaviors and social support (r=.287, p<.001), health promotion behaviors and emotional intelligence (r=.450, p<.001), and social support and emotional intelligence (r=.450, p<.001) had a positive correlation, but depression and health promotion behaviors (r=-.453, p<.001), depression and social support (r=-.259, p<.001), and depression and emotional intelligence (r=-.322, p<.001) had a negative correlation. Conclusion: This study will provide the basic data for a follow-up researches on the social support, emotional intelligence, depression and health promotion behaviors of nursing college students. It is expected to serve as the basic data for developing nursing intervention programs for health promotion behaviors in the future.

• The Journal of Engineering Geology
• /
• v.21 no.2
• /
• pp.107-116
• /
• 2011

Groundwater level data and flowrate data were collected by considering the distance between the groundwater-level gauge station and flowrate gauge station (< 10 km) in the Geum River watershed, Baseflow was separated from the collected flowrate data using the WHAT SYSTEM, which is a Web- and GIS-based tool developed for hydrological applications, Correlation analysis was performed for the separated baseflow and groundwater-level data collected from a site close to the flowrate gauge station, Twenty regions were selected and data sets were collected from 2002 to 2008. Twelve regions yielded a correlation coefficient of > 0.5, When the data sets were analyzed for each year for all 20 regions, we obtained a correlation coefficient of 0.5-0.6 for 8 cases, 0.6-0.7 for 5 cases, and > 0.7 for 12 cases. For individual regions, the correlation coefficient varied from year to year. There was a tendency toward weak correlation in the case of drought or flood, Therefore, under normal conditions (i.e., neither flood nor severe drought), it is possible to estimate the baseflow from nearby groundwater-level data for regions with a high correlation coefficient.

• Communications for Statistical Applications and Methods
• /
• v.19 no.1
• /
• pp.147-155
• /
• 2012

We propose some properties for a fuzzy correlation test by reduced-spread fuzzy variance for sample fuzzy data. First, we define the condition of fuzzy data for repeatedly observed data or that which includes error term data. By using the average of spreads for fuzzy numbers, we reduce the spread of fuzzy variance and define the agreement index for the degree of acceptance and rejection. Given a non-normal random fuzzy sample, we have bivariate normal distribution by apply Box-Cox power fuzzy transformation and test the fuzzy correlation for independence between the variables provided by the agreement index.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2006.11a
• /
• pp.127-131
• /
• 2006

An optimization algorithm is applied to a calibration task. In this paper, test data correlation, a reverse analysis method, is presented. With this method, flow rate and heat transfer rate, which are difficult to be measured are estimated using measured pressure and temperature data for helium pressurization system of launch vehicle.

• Journal of Advanced Marine Engineering and Technology
• /
• v.30 no.2
• /
• pp.247-252
• /
• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.