• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.656-661
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• 2010

Lead-alloys are very attractive nuclear coolants due to their thermo-hydraulic, chemical, and neutronic properties. By utilizing the HELIOS (Heavy Eutectic liquid metal Loop for Integral test of Operability and Safety of PEACER$^2$) facility, a thermal hydraulic benchmarking study has been conducted for the prediction of pressure loss in lead-alloy cooled advanced nuclear energy systems (LACANES). The loop has several complex components that cannot be readily characterized with available pressure loss correlations. Among these components is the core, composed of a vessel, a barrel, heaters separated by complex spacers, and the plenum. Due to the complex shape of the core, its pressure loss is comparable to that of the rest of the loop. Detailed CFD simulations employing different CFD codes are used to determine the pressure loss, and it is found that the spacers contribute to nearly 90 percent of the total pressure loss. In the system codes, spacers are usually accounted for; however, due to the lack of correlations for the exact spacer geometry, the accuracy of models relies strongly on assumptions used for modeling spacers. CFD can be used to determine an appropriate correlation. However, application of CFD also requires careful choice of turbulence models and numerical meshes, which are selected based on extensive experience with liquid metal flow simulations for the KALLA lab. In this paper consistent results of CFX and Star-CD are obtained and compared to measured data. Measured data of the pressure loss of the core are obtained with a differential pressure transducer located between the core inlet and outlet at a flow rate of 13.57kg/s.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.37.2-37.2
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• 2021

Turbulence produces the density and velocity fluctuations in molecular clouds, and dense regions within the density fluctuation are the birthplace of stars. Also, turbulence can produce non-thermal pressure against gravity. Thus, turbulence plays a crucial roles in controlling star formation. However, despite many years of study, the detailed relation between turbulence and star formation remain poorly understood. As part of the Taeduk Radio Astronomy Observatory (TRAO) Key Science Program (KSP), "mapping Turbulent properties In star-forming MolEcular clouds down to the Sonic scale (TIMES; PI: Jeong-Eun Lee)", we mapped two star-forming molecular clouds, the Orion A and the ρ Ophiuchus molecular clouds, in six molecular lines (¹³CO 1-0/C¹⁸O 1-0, HCN 1-0/HCO⁺ 1-0, and CS 2-1/N₂H⁺ 1-0) using the TRAO 14-m telescope. We applied the Principal Component Analysis (PCA) to the observed data in two different ways. The first method is analyzing the variation of line intensities in velocity space to evaluate the velocity power spectrum of underlying turbulence. We investigated the relation between the star formation activities and properties of turbulence. The other method is analyzing the variation of the integrated intensities between the molecular lines to find the characteristic correlation between them. We found that the HCN, HCO⁺, and CS lines well correlate with each other in the integral shaped filament in the Orion A cloud, while the HCO⁺ line is anti-correlate with the HCN and CS lines in L1688 of the Ophiuchus cloud.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.230-236
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• 2024

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.117-140
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• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.4 s.95
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• pp.380-388
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• 2005

This paper presents analysis results of the effects of RF filter characteristics on the system performance of impulse radio. The impulse radio system transmits modulated pulses having very short time duration and information can be extracted in receiver side based on cross-correlation between received and transmitted pulses. Accordingly, the pulse distortion due to in-band group delay variation can cause serious system performance degradation. In general, RF bandpass filters inevitably cause group delay difference to the signal passing through the filter which is proportional to its skirt characteristic due to its resonance phenomenon. For time as well as frequency domain analysis, small signal scattering parameter $S_{21}$ and its Fourier transform are used to characterize output pulse waveform under the condition that the input and output ports are matched. The output pulse waveform of the filter is predicted based on convolution integral between input pulse and filter transfer function, and resulting BER performances in the BPM and PPM based impulse radio system are calculated.

• Journal of dental hygiene science
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• v.9 no.1
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• pp.145-151
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• 2009

The purpose of this study was to examine the correlation of oral health behavior, oral health state and PHP in an effort to facilitate the promotion of oral health and improve oral health care. The subjects in this study were patients who got a scaling at the oral hygiene practice lab in the department of dental hygiene in S college dental clinic from September 5 to December 1, 2007. The collected data were analyzed with SPSS WIN 12.0 program. The findings of the study were as follows: 1. The younger patients had a smaller number of FT index, and the older patients excelled the younger ones in oral health behavior involving the use of oral hygiene supplies, regular visit to dental clinics and dietary control. 2. The patients got 2.62 out of possible five points on oral health behavior, which indicated that their oral health behavior wasn't good enough. Those whose oral health behavior was better had a smaller number of DT index and a larger number of FT index. A better oral health behavior led to a lower PHP index. 3. There was a negative(-) interrelationship between oral health behavior and DT index, and oral health behavior had a positive(+) correlation with FT index. The oral health behavior had a negative(-) correlation to PHP index, which showed that a better oral health behavior led to a lower PHP index. The above-mentioned findings seem to suggest that oral health behavior is one of integral factors to affect oral health status and PHP index, and the kind of oral health education that could change oral health care attitude should be provided during scaling practice after grasping the actual oral health behavior of patients.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.4
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• pp.436-445
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• 2014

Objectives: Time-activity studies have become an integral part of comprehensive exposure assessment and personal exposure modeling. The aims of this study were to estimate exposure levels to nitrogen dioxide($NO_2$) and volatile organic compounds(VOCs), and to compare estimated exposures by using time-activity patterns and indoor air concentrations. Methods: The major microenvironments for office workers were selected using the Time-Use Survey conducted by the National Statistical Office in Korea in 2009. A total of 9,194 and 6,130 workers were recruited for weekdays and weekends, respectively, from the Time-Use Survey. It appears that workers were spending about 50% of their time in the house and about 30% of their time in other indoor areas during the weekdays. In addition, we analyzed the time-activity patterns of 20 office workers and indoor air concentrations in Daegu using a questionnaire and time-activity diary. Estimated exposures were compared with measured concentrations using the time-weighted average analysis of air pollutants. Conclusions: According to the time-activity pattern for the office workers, time spent in the residence indoors during the summer and winter have been shown as $11.12{\pm}2.20$ hours and $12.48{\pm}1.77$ hours, respectively, which indicates higher hours in the winter. Time spent in the office in the summer has been shown to be 1.5 hours higher than in the winter. The target pollutants demonstrate a positive correlation ($R^2=0.076{\sim}0.553$)in the personal exposure results derived from direct measurement and estimated personal exposure concentrations by applying the time activity pattern, as well as measured concentration of the partial environment to the TWA model. However, these correlations were not statistically significant. This may be explained by the difference being caused by other indoor environments, such as a bar, cafe, or diner.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.769-777
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• 1998

In this study, various time series are analyzed to check nonlinearities of the data. The nonlinearity of a system can be investigated by testing the randomness of the time series data. To test the randomness, four nonparametric test statistics and a new test statistic, called the BDS statistic are used and the results and the results are compared. The Brock, Dechert, and Scheinkman (BDS) statistic is originated from the statistical properties of the correlation integral which is used for searching for chaos and has been shown very effective in distinguishing nonlinear structures in dynamic systems from random structures. As a result of application to linear and nonlinear models which are well known, the BDS statistic is found to be more effective than nonparametric test statistics in identifying nonlinear structure in the time series. Hydrologic time series data are fitted to ARMA type models and the statistics are applied to the residuals. The results show that the BDS statistic can distinguish chaotic nonlinearity from randomness and that the BDS statistic can also be used for verifying the validity of the fitted model.

• Korean Journal of Psychosomatic Medicine
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• v.6 no.1
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• pp.35-45
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• 1998

Objectives : The purpose of this study was to determine correlation between coping strategies to disease and quality of life in chrome viral B hepatitis patients ; to investigate difference of coping strategies to disease and quality in life between chronic viral B hepatitis patients and normal persons ; and to identify major variables related to quality in life of chronic viral B hepatitis patients. Methods: The authors used Weisman coping strategy scale for measuring coping ability and efficacies, and the questionnaire for measuring quality of life including physical, psychological, social and economical aspects and satisfaction of sexual life was made by authors based on related literatures. Data were collected through questionnaire survey over a period from Sep 15, 1994 to Nov 11, 1994. Subjects served for this study consisted of 94 chronic viral B hepatitis patients visited to department of internal medicine at one general hospital and 100 normal persons visited to one general hospital for routine check up of health. The collected data were analyzed by SAS and the statistical methods for analysis were Chisquare, t-test and multiple regression analysis. Results : 1) It was revealed that coping strategies to disease significantly correlated to individual's quality of life. 2) There was no significant difference in coping strategies to disease between chronic viral B hepatitis patients and normal persons. However, chronic viral B hepatitis patients showed the lower scroes of quality of life in physical, psychological and economical aspects. 3) The most important variables which were influenced upon quality of life were coping strategies to disease and satisfaction of sexual life. That is, the more active coping strategies to diseases and the higher satisfaction of sexual life, consequently the higher quality of life was. Especially male patient group or normal persons showed each other the higher scores of quality of life in physical and psychological area than female group or patient group. 4) No statistically significant difference in coping strategies to disease and quality of life was found between HBeAg positive group and HBeAg negative group. Conclusions : The authors suggest that chronic viral B hepatitis patients may show the lower score of quality of life than normal person. Therefore, quality of life assessment should become an integral part of all clinical area that seek to assess the effectiveness of treatment. Also, through the interdisciplinary approach, a comprehensive paradigm that can better account for the effects of chronic disease on the individual' s quality of life should be developed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.