• Preventive Nutrition and Food Science
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• v.5 no.4
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• pp.205-212
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• 2000

The relation of food and supplemental intake of iron, vitamin E and ascorbic acid and other lifestyle variables to packed cell volume (PCV) and serum vitamin levels was studied in urban and rural (71% Amish) communities. Subjects were interviewed (24-h dietary recalls) on three occasions over 18-months, and blood samples were taken (maximum observations = 442). Mean PCV was lower in rural males (43.3) than in urban males (45.4) despite higher man food iron intake (18.7 and 14.4 mg/day, respectively). Mean meal iron availability was higher at lunch and lower at breakfast and dinner for rural than for urban subjects. Smoking was the number one variable in males and females explaining variance in PCV. Supplemental vitamin E and ascorbate intakes explained the most variance in serum vitamin E and ascorbate levels, respectively. Serum vitamin E was also associated with supplemental ascorbate intake (r=0.29). Serum ascorbate was also associated with food ascorbate intake (r=0.28) and body weight (r=-0.24).

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.855-862
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• 1998

This paper investigates the impact of the forecast error on performance of a reservoir system for hydropower production. Forecast error is measured as th Root Mean Square Error (RMSE) and parametrically varied within a Generalized Maintenance Of Variance Extension (GMOVE) procedure. A set of transition probabilities are calculated as a function of the RMSE of the GMOVE procedure and then incorporated into a Bayesian Stochastic Dynamic Programming model which derives monthly operating policies and assesses their performance. As a case study, the proposed methodology is applied to the Skagit Hydropower System (SHS) in Washington state. The results show that the system performance is a nonlinear function of RMSE and therefor suggested that continued improvements in the current forecast accuracy correspond to gradually greater increase in performance of the SHS.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1120-1125
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• 2016

The neutron decay constant, ${\alpha}$, and effective delayed neutron fraction, ${\beta}_{eff}$, are important parameters for the control of the dynamic behavior of nuclear reactors. For the heavy water zero power reactor (HWZPR), this document describes the measurements of the neutron decay constant by noise analysis methods, including variance to mean (VTM) ratio and endogenous pulse source (EPS) methods. The measured ${\alpha}$ is successively used to determine the experimental value of the effective delayed neutron fraction as well. According to the experimental results, ${\beta}_{eff}$ of the HWZPR reactor under study is equal to 7.84e-3. This value is finally used to validate the calculation of the effective delayed neutron fraction by the Monte Carlo methods that are discussed in the document. Using the Monte Carlo N-Particle (MCNP)-4C code, a ${\beta}_{eff}$ value of 7.58e-3 was obtained for the reactor under study. Thus, the relative difference between the ${\beta}_{eff}$ values determined experimentally and by Monte Carlo methods was estimated to be < 4%.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.5
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• pp.331-340
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• 2016

It is important to select proper ground motions for obtaining accurate results from response history analyses. The purpose of this study is to propose an accurate and efficient method that does not require excessive computation for selecting and scaling ground motions to match target response spectrum mean and variance. The proposed method is conceptually simple and straightforward, and it does not use a simulation algorithm that requires a sophisticated subroutine program. In this method, the desired number of ground motions are sequentially scaled and selected from a ground motion library. The proposed method gives the best selection results using Sum of Square Error and has the smallest value(=0.14). Also, The accuracy and consistency of the proposed method are verified by comparing the selection results of the proposed method with those of existing methods.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.3
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• pp.313-321
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• 2012

Purpose: The purpose of the study was to investigate hand washing practice and proper hand cleansing among first and second year nursing students who are prone to be exposed to nosocomial infections, and to identify the influence of perceived behavior control and self-efficacy on hand washing practices and proper hand cleansing. Method: Data for 91 students were collected from a nursing college in a metropolitan city in Korea. Data were analyzed using descriptive, t-test, one way ANOVA, Pearson correlation coefficient, and multiple regression with SPSS/WIN 19.0. Result: The mean score for hand washing practice was 38.35 out of a possible score of 48, and the mean sore for proper hand cleansing was 18.63 out of a possible score of 28. The significant factors affecting student hand washing practice were 'residential type' (p=.016), 'perceived behavior control' (p=.021), and 'self-efficacy' (p=.033) which explained 19.9% of the variance. The significant factors affecting proper hand cleansing by the students were 'perceived behavior control' (p<.001) and 'regular exercise' (p=.026) which explained 29.8% of the variance. Conclusion: These results indicate a need for education programs on hand washing including strategies to improve perceived behavior control and self-efficacy to promote more effective hand washing practices.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.8-18
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• 2008

In this paper, we propose an efficient algorithm using wavelet transform for still image database retrieval. Especially, it uses only the lowest frequency sub-band in multi-level wavelet transform so that a retrieval system uses a smaller quantity of memory and takes a faster processing time. We extract different textured features, statistical information such as mean, variance and histogram, from low frequency sub-band. Then we measure the distances between the query image and the images in a database in terms of these features. To obtain good retrieval performance, we use the first feature (mean and variance of wavelet coefficients) to filter out most of the unlikely images. The rest of the images are considered to be candidate images. Then we apply the second feature (histogram of wavelet coefficient) to rank all the candidate images. To evaluate the algorithm, we create various distorted image databases using MIT VisTex texture images and PICS natural images. Through simulations, we demonstrate that our method can achieve performance satisfactorily in terms of the retrieval accuracy as well as the both memory requirement and computational complexity. Therefore it is expected to provide good retrieval solution for JPEG-2000 using wavelet transform.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.32 no.5
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• pp.14-25
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• 2000

In this investigation, a wavelet transform analysis was used to decompose beta-radiographic formation images into spectral and spatial components. Conventional formation analysis may use spectral analysis, based on Fourier transformation or variance vs. zone size, to describe the grammage distribution of features such as flocs, streaks and mean fiber orientation. However, these methods have limited utility for the analysis of statistically stationary data sets where variance is not uniform with position, e.g. paper machine CD profiles (especially those that contain streaks). A continuous wavelet transform was used to analyze formation data arrays obtained from radiographic imaging of handsheets and cross machine paper samples. The response of the analytical method to grammage, floc size distribution, mean fiber orientation an sensitivity to feature localization were assessed. From wavelet analysis, the change in scale of grammage variation as a function of position was used to demonstrate regular and isolated differences in the formed structure.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.1-10
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• 2021

This paper compares the performance of Fama-French three-factor and five-factor models using a dataset of 20 Pakistani commercial banks for the period 2011 to 2020. We focus on an emerging economy as the findings from earlier studies on developed countries cannot be generalized in emerging markets. For empirical analysis, twelve portfolios were developed based on size, market capitalization, investment strategy, and growth. Subsequently, we constructed five Fama-French factors namely, R_M, SMB, HML, RMW, and CMA. The OLS regression technique with robust standard errors was applied to compare the predictive power of both the Fama-French models. Further, we also compared the mean-variance efficiency of the Fama-French models through the GRS test. Our empirical analysis provides three unique and interesting findings. First, both asset pricing models have similar predictive power to explain the expected portfolio returns in most cases. Second, our results from the GRS test suggest that there is no noticeable difference in the mean-variance efficiency of one asset pricing model over the other. Third, we find that all factors of both Fama-French models are statistically significant and are important for explaining the volatility of expected commercial bank returns in the context of Pakistan.

• Journal of Korean Society of Rural Planning
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• v.25 no.3
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• pp.1-9
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• 2019

Population is important as a fundamental element of local industry and economy, and census data is essential to regional planning and policy making. Although there have been many researches on population and regional planning, there are few studies on population considering spatial unit. In this study, the population of three spatial scales were compared in order to establish the spatial unit suitable for the rural planning. The study area is Gangwon, Chungcheong-Nam, Chungcheong-Buk, Jeolla-Nam, Jeolla-Buk, Gyeonsang-Nam, Gyeonsang-Buk and Jeju province. Population were compared using statistical data analysis, GIS visualization, and spatial statistics. The mean, maximum, minimum, and variance of population were calculated and the coefficient of variation according to spatial unit was compared. The mean, maximum, minimum, and variance of population were calculated and the coefficient of variation according to spatial unit was compared. As the results, the census output area unit is difficult to interpret spatial analysis results. Administrative district unit has the limit that includes areas where the population does not live. The grid unit is well suited to the geographical characteristics but has many disadvantages of the grid with small population. Therefore, It is necessary to complement the limits of the Eup and Myeon-dong administrative district through the grid unit data.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.785-793
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.