• Journal of Nutrition and Health
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• v.23 no.2
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• pp.93-107
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• 1990

The purposes of this investigation were to determine the validity of various methods (available anthropometric equations and near-infrared light interactance) for estimating body fat and to develop multiple regression equations for the prediction of body fat. Thirty-eight healthy males(age: 20.87$\pm$7.17 yrs) and 12 females(19.58$\pm$2.19 yrs) underwent hydrostatic weighing to determine body fat. Anthropometric measurements were taken of height, weight, nin skinfolds and thirteen circumferences. The results obtained are summarized as follows: 1) Relative body fat determined by underwater weighing was 12.08$\pm$5.21% for the males and 17.97$\pm$5.75% for the females. 2) Circumference and skin fold that had the highest correlation with the body fat were waist girth in males and females(r=0.60, r=0.96, respectively), and subscapular in males(r=0.68) and triceps in females(r=0.96). 3) Corss-validation of 18 selected equations on males revealed total errors ranging from 3.76% to 5.06%. Among these equations, M3(Pollock et al.) demonstrated the least total error. Total error of estimation by near-infrared(NIR) was less than that of available anthropometric measurement equations. The results of the cross-validation of 12 equations on females revealed that F3(Sloan et al.) was clearly superior in accuracy of prediction. 4) Correlational analyses showed that estimation of body fat by NIR measurement seemed to be more closely associated with body fat determined by underwater weighing in women than men, in older subjects than younger ones, and in fatter subjects than leaner ones.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.1-7
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• 2015

As a measure of health, the percentage of body fat has been utilized for many ergonomist, physician, athletic trainers, and work physiologists. Underwater weighing procedure for measuring the percentage of body fat is popular and accurate. However, it is relatively expensive, difficult to perform and requires large space. Anthropometric techniques can be utilized to predict the percentage of body fat in the field setting because they are easy to implement and require little space. In this concern, the purpose of this study was to find a regression model to easily predict the percentage of body fat using the anthropometric circumference measurements as predictor variables. In this study, the data for 10 anthropometric circumference measurements for 252 men were analyzed. A full model with ten predictor variables was constructed based on subjective knowledge and literature. The linear regression modeling consists of variable selection and various assumptions regarding the anticipated model. All possible regression models and the assumptions are evaluated using various statistical methods. Based on the evaluation, a reduced model was selected with five predictor variables to predict the percentage of body fat. The model is : % Body Fat = 2.704-0.601 (Neck Circumference) + 0.974 (Abdominal Circumference) -0.332 (Hip Circumference) + 0.409 (Arm Circumference) - 1.618 (Wrist Circumference) + $\epsilon$. This model can be used to estimate the percentage of body fat using only a tape measure.

• The Korean Journal of Physiology
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• v.4 no.2
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• pp.37-44
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• 1970

Human body volumes were calculated from the measurments of body height and body weight. Equations Suited to express the relations of height, weight, and surface area to show the body volume were derived from the body volume measurements by means of underwater. weighing method. Underwater body weights were corrected for the residual volume of long obtained by the Rahn's three breath method. Underwater weighing was performed on 173 male subjects aged between 13 and 51 years. Subjects were divided into 4 age groups, namely, 13-16 years group of 47 subjects, 16-19 years group of 46 subjects, adult group aged between 22 and 38 years comprising 45 subjects, and middle-aged group (40-51 years) of 35 subjects. The group division was made on .the basis of physical growth and development. The following results were obtained. 1. Body height (H, cm), body weight (W, kg), body surface area $S,\; m^{2})$, and body volume (V, liter.) interrelated closely. V/S showed a high correlation with W/H and the coefficient of correlation was r=0.97 irrespective of age group differences of the subjects. The coefficients of correlation between V/S and W/H in the total mate subjects as a single group was r=1.983. Subsequently the following regression equation was obtained. V = S X (54.84 W/H + 14.08) The agreement of body volume values obtained by the calculation and underwater weighing in the total subject group was better than that of the separate age group division. 2. The calculated values of body volume were: 40.4 l (euiqvalent to the body density value of 1.0562 kg/1) in 13-16 years group; 52.0 l (equivalent to density value of 1.0723 kg/l) in 16-19 years group; 55.3 l (equivalent to density value of 1.0570 kg/l) In the adult group; and 54. 0 l (equivalent to density value of 1.074 kg/l) in the middle-age group. The mean deviation of calculated from the measured volume value ranged between ${\pm}0.55$ and ${\pm}0.81$ liters. 3. The correlation between V/S and mean skinfold thickness of 4 sites (arm, back, iliac and chest) was high, namely, the coefficient of correlation was r=0.656. The coefficients of correlation between V./S and the $R\"{o}hrer$ index ranged between r=0.668 and r=0.810 affected by the difference in group age of the subject. The body volume (V) alone correlated poorly than V/S with mean skinfold thickness (r=0.606) and the $R\"{o}hrer$ index (r ranged between 0.274 and 0.588).

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.9-10
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• 2012

This paper presents an observer to measure the lengths of cylinders of an underwater robot for harbour construction using a pressure sensor. In harbour constructing, we place heavy armour stones weighing over 2~3 tons on the surface of the bank to protect it from storming wave. This work typically done by a diver is difficult and dangerous so that we have developed Stone Diver which is the underwater robot for harbour construction. The robot needs a position sensor to control the hydraulic cylinder. The position sensors mounted outside the cylinders cause poor durability in construction site where shock and dust usually occur. However, the pressure sensor mounted inside a waterproof box improves the durability. Based on the dynamic parameters and the pressures in the cylinder, the observer measures the cylinder's position. This paper presents the positional accuracy of the pressure based observer and the performance of the underwater robot to assemble the armour stones.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.865-871
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• 2012

This paper presents an observer to estimate the displacement of hydraulic cylinders of an underwater robot for harbour construction using a pressure sensor. In harbour constructing, we place heavy armour stones weighing over 2~3 tons on the surface of the bank to protect it from storming wave. This work typically done by a diver is difficult and dangerous so that we have developed Stone Diver which is the underwater robot for harbour construction. The robot needs a displacement sensors to control the position of hydraulic cylinders. The position sensors mounted outside the cylinders cause poor durability in construction site where shock and dust usually occur. However, the pressure sensor mounted inside a waterproof box improves the durability. Based on the dynamic parameters and the pressures in the cylinder, the observer estimates the cylinder's position. This paper presents the positional accuracy of the pressure based observer and the performance of the underwater robot to assemble the armour stones.

• Fisheries and Aquatic Sciences
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• v.27 no.2
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• pp.76-86
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• 2024

The bio-logging method could be a valuable approach to studying the underwater movement of marine fish. We investigated the horizontal and vertical movement patterns of two yellowtails Seriola quinqueradiata weighing 8.7 kg and 9.5 kg with a popup satellite archival tag from October 2020 to January 2021 in the East Sea of Korea. Our results showed that a yellowtail migrated northward in October and November, and then shifted southward in mid-December. The average swimming depth and temperature of the fish monitored over 82 days were 24.9 ± 9.3 m (average ± SD) and 16.5 ± 1.9℃, respectively, and the total traveled distance was 1,172.4 km. The fish swam significantly deeper during the daytime (33.70 ± 14.80 m) than at nighttime (20.65 ± 8.44 m) from November to December (p < 0.05). These results suggest that the horizontal migratory route of yellowtails in accordance with the East Korea Warm Current which is the main branch of Tsushima Warm Current in the fall and early winter seasons, and showed significant diel vertical movement patterns from November to December.

• The Korean Journal of Physiology
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• v.3 no.1
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• pp.1-9
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• 1969

Relationships between red ceil volume $(^{51}Cr-cell)$, total blood volume (red cell volume divided by hematocrit ratio), and extracellular fluid volume (SCN distribution space) and body weight (ranging between 73 and 384 grams) or lean body mass were studied in 59 nembutalized rats. Lean body mass was determined by means of underwater weighing method on rats clipped and eviscerated. There were positive correlations between body weight or lean body mass and the absolute values (in milliliters) of body fluid volumes. Body fluid volumes expressed on the body weight or lean body mass basis, however, showed negative correlations between body weight (grams) or lean body weight (grams) with one exception. Red cell volume expressed as % lean body mass showed a positive correlation with lean body mass. The other results are summarized as follows: 1. Body density of rats was 1.0561 $(range:\;1.0123{\sim}1.0781)$ and 19.8% body weight of total body fat was obtained. The mean value of lean body mass was 80.2% body weight 2. The correlation between body weight and lean body mass was high, namely, coefficient of correlation was r=.99. 3. The correlation between the absolute value of red cell volume (ml) and body weight showed a high correlation, namely, r= 92 and between the lean body mass coefficient of correlation was r=.93. On a weight basis, red cell volume was 2.67 ml/100 gm body weight or 3.48 ml/100 gm lean body mass. The coefficient of correlation between body weight (grams) and red cell volume (% body weight) was r=-. 30. The coefficient of correlation between lean body mass (grams) and red cell volume (% lean body mass) was r=. 50. Thus, the following regression equation was obtained. Red cell volume (% lean body mass)=. 00243 Lean body mass (gm)+3. 12. 4. Total blood volume was 6.06% body weight or 7.83% lean body mass. The correlation between these blood volume values and body weight or lean body mass were negative, namely, r= -.43 and r=-.42 respectively. 5. Extracellular volume (SCN space) was 30.0% body weight or 37.2% lean body mass. These percentage values showed negative correlations between body weight or lean body mass and coefficients of correlation were r=-.40 and r=-.54 respectively. 6. The rate of increase in body weight or lean body mass is accompanied by a smaller rate of increase in blood volume and extracellular fluid volume. The rate of increase in red ceil volume paralled that of lean body mass.