• Transactions of the Korean hydrogen and new energy society
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• v.27 no.1
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• pp.127-134
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• 2016

Most of reconstruction algorithms for the calculation of temperature distributions in CT (computed tomography)-TDLAS (tunable diode laser absorption spectroscopy) are based upon two-line thermometry method. This method gives unstable calculation convergence due to signal noise, bias error, and signal mis-matches. In this study, a new reconstruction algorithm based on cross-correlation for temperature calculation is proposed. The patterns of the optical signals at all wave lengths were used to reconstruct the temperature distribution. Numerical test has been made using phantom temperature distributions. Using these phantom temperature data, absorption spectra for all wave lengths were constructed, and these spectra were regarded as the signals that would be obtained in an actual experiments. Using these virtually generated experimental signals, temperature distribution was once again reconstructed, and was compared with those of the original phantom data. Calculation errors obtained by the newly proposed algorithm were slightly large at high temperatures with small errors at low temperature.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.46-54
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• 2019

Even though the fire performance-based design concept has been introduced for various structures and buildings, which have their own specific fire performance level, the uncertainties of input parameters always exist and, then, could reduce significantly the reliability of the fire modeling. Sensitivity analysis was performed with three limited input parameters, HRRPUA, type of combustible materials, and mesh size, which are significantly important for fire modeling. The output variables are limited to the maximum HRR, the time reaching the reference temperature($60^{\circ}C$), and that to reach limited visible distance(5 m). In addition, correlation coefficient analysis was attempted to analyze qualitatively and quantitatively the degree of relation between input and output variables above. Finally, the relationship among the three variables is also analyzed by the principal component analysis (PCA) to systematically analyze the input data bias. Sensitivity analysis showed that the type of combustible materials is more sensitive to maximum HRR than the ignition source and mesh size. However, the heat release parameter of the ignition source(HRR) is shown to be much more sensitive than the combustible material types and mesh size to both time to reach the reference temperature and that to reach the critical visible distance. Since the derived results can not exclude the possibility that there is a dependency on the fire model applied in this study, it is necessary to generalize and standardize the results of this study for the fire models such as various buildings and structures.

• Food Science and Biotechnology
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• v.15 no.5
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• pp.715-720
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• 2006

This study was performed to develop a predictive model for the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) using a response surface model with a combination of potassium lactate (PL), temperature, and pH. The growth parameters, specific growth rate (SGR), and lag time (LT) were obtained by fitting the data into the Gompertz equation and showed high fitness with a correlation coefficient of $R^2{\geq}0.9192$. The polynomial model was identified as an appropriate secondary model for SGR and LT based on the coefficient of determination for the developed model ($R^2\;=\;0.97$ for SGR and $R^2\;=\;0.86$ for LT). The induced values that were calculated using the developed secondary model indicated that the growth kinetics of L. monocytogenes were dependent on storage temperature, pH, and PL. Finally, the predicted model was validated using statistical indicators, such as coefficient of determination, mean square error, bias factor, and accuracy factor. Validation of the model demonstrates that the overall prediction agreed well with the observed data. However, the model developed for SGR showed better predictive ability than the model developed for LT, which can be seen from its statistical validation indices, with the exception of the bias factor ($B_f$ was 0.6 for SGR and 0.97 for LT).

• Journal of the Korean Statistical Society
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• v.35 no.4
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• pp.377-395
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• 2006

In this paper we have suggested a family of chain estimators of the population mean $\bar{Y}$ of a study variate y using two auxiliary variates in two phase (double) sampling assuming that the coefficient of variation of the second auxiliary variable is known. It is well known that chain estimators are traditionally formulated when the population mean $\bar{X}_1$ of one of the two auxiliary variables, say $x_1$, is not known but the population mean $\bar{X}_2$ of the other auxiliary variate $x_2$ is available and $x_1$ has higher degree of positive correlation with the study variate y than $x_2$ has with y, $x_2$ being closely related to $x_1$. Here the classes are constructed when the population mean $\bar{X}_1\;of\;X_1$ is not known and the coefficient of variation $C_{x2}\;of\;X_2$ is known instead of population mean $\bar{X}_2$. Asymptotic expressions for the bias and mean square error (MSE) of the suggested family have been obtained. An asymptotic optimum estimator (AOE) is also identified with its MSE formula. The optimum sample sizes of the preliminary and final samples have been derived under a linear cost function. An empirical study has been carried out to show the superiority of the constructed estimator over others.

• Journal of the Korean Society of Physical Medicine
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• v.11 no.3
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• pp.81-88
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• 2016

PURPOSE: This study was aimed at investigating the types of gender roles and the relationship between gender roles and job satisfaction among neurological physical therapists. METHODS: The study subjects were 169 (male 74, female 95) neurological physical therapists working at general hospitals or rehabilitation centers in the Daejeon Metropolitan City area, South Korea. To identify job satisfaction scores, the subjects completed a questionnaire, and a vernier caliper was used by a trained examiner to measure the lengths of the subjects' index and ring fingers (i.e., digitus secundus manus and digitus annularis, respectively) to examine gender roles. The index to ring finger length ratio (i.e., 2D:4D ratio) was calculated using a personal computer. An independent t-test was performed to compare the finger length ratio and job satisfaction of male group with that of the female group and a correlation analysis was performed to examine job satisfaction by gender roles. RESULTS: Finger length ratio is lower in males than in females. However, there was no significant difference statistically (p>.05). Regarding job satisfaction by gender, males were more satisfied with their jobs than females (p<.05). However, there were no significant correlations between job satisfaction and gender roles (p>.05). CONCLUSION: It cannot be concluded that bias against gender roles is a contributing factor for neurological physical therapists being satisfied with their job, and thus bias against gender roles among neurological physical therapists should be removed.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.653-662
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• 2011

Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 ~ 280K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm.

• Atmosphere
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• v.28 no.1
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• pp.15-24
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• 2018

Sea ice plays an important role in modulating surface conditions at high and mid-latitudes. It reacts rapidly to climate change, therefore, it is a good indicator for capturing these changes from the Arctic climate. While many models have been used to study the predictability of climate variables, their performance in predicting sea ice was not well assessed. This study examines the predictability of the Arctic sea ice extent from ensemble prediction systems. The analysis is focused on verification of predictability in each model compared to the observation and prediction in particular, on lead time in Sub-seasonal to Seasonal (S2S) scales. The S2S database now provides quasi-real time ensemble forecasts and hindcasts up to about 60 days from 11 centers: BoM, CMA, ECCC, ECMWF, HMCR, ISAC-CNR, JMA, KMA, Meteo France, NCEP and UKMO. For multi model comparison, only models coupled with sea ice model were selected. Predictability is quantified by the climatology, bias, trends and correlation skill score computed from hindcasts over the period 1999 to 2009. Most of models are able to reproduce characteristics of the sea ice, but they have bias with seasonal dependence and lead time. All models show decreasing sea ice extent trends with a maximum magnitude in warm season. The Arctic sea ice extent can be skillfully predicted up 6 weeks ahead in S2S scales. But trend-independent skill is small and statistically significant for lead time over 6 weeks only in summer.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3233-3239
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• 2015

Background: Whether depression causes increased risk of the development of breast cancer has long been debated. We conducted an updated meta-analysis of cohort studies to assess the association between depression and risk of breast cancer. Materials and Methods: Relevant literature was searched from Medline, Embase, Web of Science (up to April 2014) as well as manual searches of reference lists of selected publications. Cohort studies on the association between depression and breast cancer were included. Data abstraction and quality assessment were conducted independently by two authors. Random-effect model was used to compute the pooled risk estimate. Visual inspection of a funnel plot, Begg rank correlation test and Egger linear regression test were used to evaluate the publication bias. Results: We identified eleven cohort studies (182,241 participants, 2,353 cases) with a follow-up duration ranging from 5 to 38 years. The pooled adjusted RR was 1.13(95% CI: 0.94 to 1.36; $I^2=67.2%$, p=0.001). The association between the risk of breast cancer and depression was consistent across subgroups. Visual inspection of funnel plot and Begg's and Egger's tests indicated no evidence of publication bias. Regarding limitations, a one-time assessment of depression with no measure of duration weakens the test of hypothesis. In addition, 8 different scales were used for the measurement of depression, potentially adding to the multiple conceptual problems concerned with the definition of depression. Conclusions: Available epidemiological evidence is insufficient to support a positive association between depression and breast cancer.

• Atmosphere
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• v.20 no.2
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• pp.111-130
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• 2010

An uncertainty assessment for precipitation datasets simulated by Atmosphere-Ocean Coupled General Circulation Model (AOGCM) is conducted to provide reliable climate scenario over East Asia. Most of results overestimate precipitation compared to the observational data (wet bias) in spring-fall-winter, while they underestimate precipitation (dry bias) in summer in East Asia. Higher spatial resolution model shows better performances in simulation of precipitation. To assess the uncertainty of spatiotemporal precipitation in East Asia, the cyclostationary empirical orthogonal function (CSEOF) analysis is applied. An annual cycle of precipitation obtained from the CSEOF analysis accounts for the biggest variability in its total variability. A comparison between annual cycles of observed and modeled precipitation anomalies shows distinct differences: 1) positive precipitation anomalies of the multi-model ensemble (MME) for 20 models (thereafter MME20) in summer locate toward the north compared to the observational data so that it cannot explain summer monsoon rainfalls across Korea and Japan. 2) The onset of summer monsoon in MME20 in Korean peninsula starts earlier than observed one. These differences show the uncertainty of modeled precipitation. Also the comparison provides the criteria of annual cycle and correlation between modeled and observational data which helps to select best models and generate a new MME, which is better than the MME20. The spatiotemporal deviation of precipitation is significantly associated with lower-level circulations. In particular, lower-level moisture transports from the warm pool of the western Pacific and corresponding moisture convergence significantly are strongly associated with summer rainfalls. These lower-level circulations physically consistent with precipitation give insight into description of the reason in the monsoon of East Asia why behaviors of individually modeled precipitation differ from that of observation.

• Fashion & Textile Research Journal
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• v.13 no.3
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• pp.418-424
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• 2011

The purpose of this study was to reveal the effects of the seam type on the fabric drape to provide the basic knowledge for proper seam type according to the design of sewing products. Seven kinds of specimens were constructed with seam (no seam, welt seam with over-edged finish, welt seam with bias bound finish, plain seam with over-edged finish, plain seam with bias bound finish, french seam, and flat fell seam) in wrap direction of the fabric. Using a drape measurement system involving two 18 cm diameter supporting disks, and a digital camera, the images of draped specimens were captured and processed. Drape behavior was evaluated in terms of drape coefficient, node number, and drape profile. Significant differences were found in drape coefficient by the seam types. The specimens with french seam and flat fell seam showed higher drape coefficients compared to those with welt seam and plain seam. Node numbers in the drape profiles showed positive correlation with the weight of the specimens, however, no significant differences were observed in node numbers by the seam types. Significant differences were found in the length of the seamed part by the seam types. The specimens with french seam and flat fell seam showed longer length of the seamed part compared to those with welt seam and plain seam. The ratio of the maximum length in the seam direction to the maximum length perpendicular to the seam direction showed significant differences by the seam types.