• International Journal of Reliability and Applications
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• v.10 no.1
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• pp.33-42
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• 2009

In most of literatures of age replacement policy, the authors consider the case that a new item starts operating at time zero and is to be replaced by new one at time T. It is, however, often to purchase used items because of the limited budget. In this paper, we consider age replacement policy of a used item whose age is $t_0$. The mathematical formulas of the expected cost rate per unit time are derived for both infinite-horizon case and finite-horizon case. For each case, we show that the optimal replacement age exists and is finite and investigate the effect of the age of the used item.

• Structural Engineering and Mechanics
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• v.72 no.6
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• pp.737-746
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• 2019

Concrete mechanical properties change constantly with age, temperature, humidity and the other environmental factors. This research studies the effects of temperature and age on the development of concrete elastic modulus by a series of prism specimens. Elastic modulus test was conducted at various temperatures and ages in the laboratory to examine the effects of temperature and age on it. The experimental results reveal that the concrete elastic modulus decreases with the rise of temperature but increases with age. Then, a temperature coefficient K is proposed to describe the effects of temperature and validated by existing studies. Finally, on the basis of K, analytical models are proposed to determine the elastic modulus of concrete at a given temperature and age. The proposed models can offer designers an approach to obtain more accurate properties of concrete structures through the elastic modulus modification based on actual age and temperature, rather than using a value merely based on laboratory testing.

• The Journal of the Korean dental association
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• v.13 no.5
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• pp.457-461
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• 1975

The author observed the relationships beween the dental midline and the midsagittal plane by taking 242 cases P-A cephalo-graphy grouped by male and female 2-6 years age group, 7-15 years age group, and adult age group. The following results were obtained by the observation. 1. Generally, the median line almost coincided with dental midline ineach age group. 1. It showed som degree of deviation in each age group. 2. It showed some degree of deviation in each age group. 3. The some degree of deviation shifted in accordance with each age group. 4. In adult age group, the dental milline more coincided with median line in male than in female.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.127-132
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• 2014

Some laws and regulations may require internet service providers to provide services based on the age of users. Age verification in the online environment should be used as a tool to provide service that is appropriate to child based on age. Using the minimum attribute information, processes on age verification provides the proper guidance to the internet services. However, there is a lack of a globally accepted trust framework for age verification process including evaluation factors for age verification information. In this paper the federation model of user attributes were described and evaluation factors for the age verification information were suggested. Also using the suggested evaluation factors, performance evaluation of federation model of user evaluation was performed. To meet the requirements of evaluation factors, framework of federation model should consider the unlinkability pseudonym support, eavesdropping protection and cloning protection.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.792-800
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• 2021

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.

• Journal of Life Science
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• v.19 no.10
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• pp.1403-1409
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• 2009

The purpose of this study was to compare Obesity Age (OA) and chronological age, to calculate Obesity Age (OA), which gauges the state of obesity, and to analyze presented factors of obesity using expectant factors on middle-aged obese women. The subjects were one hundred twenty seven middle-aged obese women ($49.6\pm7.3$ yr, BMI $29.41\pm2.9$, fat $36.8\pm4.6%$) who participated in different weight loss programs three times. The body composition, physical fitness, blood pressure and blood were measured before the weight loss programs. Informed consent was obtained from all subjects before enrollment in the study. The regression equation is as follows: (1) OAS (Obesity Age Score)=$0.106*X_1+0.035*X_2+0.048*X_3+0.041*X_4+0.003*X_5-0.037*X_6-10.667$ ($X_1$: BMI, $X_2$: weight, $X_3$: %fat, $X_4$: WC, $X_5$: TG, $X_6$: $VO_{2max}$), (2) OA (Obesity Age)=7.3*OAS+49.6*(-1), (3) Z (correction factor)=(CA-49.6)(1-0.03), (4) OAc (corrected Obesity Age)=1.03*CA-7.3*OAS+1.47. The comparison of corrected Obesity Age (OAc) and chronological age did not have any differences, and the average of the OAc was close to chronological age. The correlation coefficient between the OAc and chronological age was r=0.724 (p<0.05). The equation can be utilized for middle-aged obese women, because it could evaluate the obesity-related factors by including BMI, body weight, %fat, waist circumference, triglycerides and $VO_{2max}$.

• Korean Journal of Health Education and Promotion
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• v.13 no.1
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• pp.128-162
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• 1996

Using Random Sampling, the authors measured the body heights and weights of 31,151 persons - 17,102 in males and 14,049 in females from metropolitan, urban and rural areas between 6 to over 80 year old - for the purpose of investigating the type and the actual condition of the Korean's growth and development. At first, on the basis of the results, the authors measured the growth and development, various kinds of physiques, nutritional index of the 6 to 20s age group. Second, the authors presented the standard body weight of males and females by their body height, who were in the end of their growth (20-29 age group). Third, the authors calculated and presented the normal adapted body weight of the age group who were over 30 age after the growth had been completed. Forth, the author presented the obesity rate of the adults over 20 years old by body mass index. Finally, the authors compared chronological change of the Koreans' body heights and body weights with the results of other researchers. 1. Body Measurement Rapid growth, in terms of body height, which is described by a straight line on a growth curve has been observed among males in the ages 6-13 and among females 6-14. That growth curve turned out to be slower among the people of higher ages by both sexes. The cross-over occurred in both sexes at 11-14. The highest growth rate for a year is at 13-16 for males and 11-13 for females. This indicates that females enter a rapidly growing stage 2 years earlier than males. 2. Various Physiques and Nutritional Index Rapid growth, in terms of Relative Body Weight Index, which is described by a straight line, has been observed among males in the ages 6-16 and females in the ages 6-14. The cross-over occurred in both sexes 12.5-14.5 age in the adolescencent period. Whereupon females outgrow males. The Roher Index displayed more good value in case of females than male and in the adolescent period, the level of fullness is lower than after the completion of development. The Kaup Indices of both sexes increase with age. The index is less than 2.0 for males in 6-14 age group and for females in 6-13 age group and with this, it appeared that development of horizontal axis to long axis is poor. The index is more than 2.0 after 15 age group in males and 14 age group in females and developmental state4 age group and for females in 6-13 age group and with this, it appeared that development of horizontal axis to long axis is poor. The index is more than 2.0 after 15 age group in males and 14 age group in females and developmental state Body Mass Index is less than 20 for males 6-14 age group and for females in 6-13 age group. In the case of the higher age group, that index maintains a normal state.

• Journal of the Korean association of regional geographers
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• v.3 no.1
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• pp.35-50
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• 1997

The purpose of this research seeks to analyze the spatial variations in the sex age structure which have been shown to exist within the study atrea, the Kyonggi province in Korea. In this study it is desired to use the Age Structure Index developed by Coulson in order to describe thi sex age structure of each of 186 tracts that comprise the tracted portion of the Kyonggi province. The mechanics of computing the Age Structure Index are found in the equation describing a linear least squares trend line: y=a+bx. For each census tract, the percentage of the population in each age group(y) was plotted against the middle age of each age group(x). The a is a constant representing the value of y, when x equals zero. The b is the regression coefficient and is a measure of the angle of the slope of the least squares trend line. Thus the value of b is the Age Structure Index for each census tract. The major results of this investigation can be summarized as follows: The spatial distributions of sex age structures in the Kyonggi province are far from random. They have exhibited great regularity with the yonger sex age structures near Seoul and a sharp decline to the older sex age structures out in all derections towards rural region. The results of this investigation should have important general significance for the study of the Kyonggi province Age Structure Index is a flexible, operational definition shich allows sex age structure to be measured, mapped, and incorporated in a wide variety of methods of statistical analysis. Futurer, it has been demonstrated that sex age structure varies spatially within Seoul metropolitan finge and that this variation is relagfed to many other attributes of the population. Especially, Age Structure Index is strongly related to the variables-rate of population growth rate. density, rate of numbers of manufacturing, land price. At the same time, considerably more research is needed before a genmeral body of knowlege concerning sex age structure can be developed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.331-343
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• 2019

The purpose of this study is to observe the trends of heart age of Koreans by using the predictor of heart age of the Framingham Heart Study. The subjects were 20,012 adults aged 30~74 years who were enrolled in the Korean National Health and Nutrition Examination Survey from 2005~2013. They filled in the determinants data and they had no history of cardiovascular disease (CVD). The heart age was calculated using a non-laboratory based model of prediction. The difference of heart age and chronological age, and the rate of excessive heart age over 10 years were calculated. The annual trend, the difference according to gender, the age bracket and geographic region, the heart age were all evaluated. Data analysis performed using the SAS program (version 9.3). Complex designed analysis was done. The heart age showed differences according to gender, age bracket and geographic region. The heart age is a useful comprehensive indicator for predicting the CVD events in the near future. So, it could be used for the purposes of exercising caution and guidance on CVD for administering medical care. It is strongly recommended to use heart age as an indicator for customized medical management to focus efforts on relatively vulnerable subjects and their factors for CVD. Further study on Koreans' customized heart age is needed.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.