• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.24-34
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• 2021

This paper predicts the speed-power-rpm relationship in regular head waves using various indirect methods: load variation, direct powering, resistance and thrust identity, torque and revolution, thrust and revolution, and Taylor expansion methods. The subject ship is KVLCC2. The wave conditions are the regular head waves of λ/LPP = 0.6 and 1.0 with three wave steepness ratios at three ship speeds of 13.5, 14.5 and 15.5 knots (design speed). In the case of λ/LPP = 0.6 at design speed, two more wave steepness ratios have been taken into consideration. The indirect methods have been evaluated through comparing the speed-power-rpm relationships with those obtained from the resistance and self-propulsion tests in calm water and in waves. The load variation method has been applied to predict propulsive performances in waves, and to derive overload factors (ITTC, 2018). The overload factors have been applied to obtain propulsive efficiency and propeller revolution. The thrust and revolution method (ITTC, 2014) has been modified.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.409-412
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• 2001

Identification of systems operating in closed loop has long been of prime interest in industrial applications. The fundamental problem with closed-loop data is the correlation between the unmeasurable noise and the input. This is the reason why several methods that work in open loop fail when applied to closed-loop data. The prediction error based approaches to the closed-loop system are divided to direct method and indirect method. Both of direct and indirect methods are known to be applied to the closed-loop data without critical modification. But the direct method induces the bias error in the experimental frequency response function and this bias error may deteriorates the parameter estimation performance

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.278-291
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• 2021

This paper applies load variation method to predict speed-power-rpm relationship along with propulsive performances in regular head waves, and to derive overload factors (ITTC, 2018). 'Calm-water tests' and 'resistance test in waves' are used. The modified overload factors are proposed taking non-linearity into consideration, and applied to the direct powering, and resistance and thrust identity method. These indirect methods are evaluated through comparing the speed-power-rpm relationships with those obtained from the resistance and self-propulsion tests in calm water and in waves. The objective ship is KVLCC2. The load variation method predicts well the speed-power-rpm relationship and propulsion performances in waves. The direct powering method with modified overload factors also predicts well. The resistance and thrust identity method with modified overload factor predicts with a little difference. The direct powering method with overload factors predicts with a relatively larger difference.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.73-75
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• 1987

The paper proposes a class of discrete-time adaptive controller which may be applicable without sufficient a priori information. Against choices of the Information, GPC algorithm may seem to be more robust than any other methods reported, but it is the method based on Indirect approach. It is, therefore, reasonable to propose an algorithm via the zero-gain prediction, in which the control parameters are directly estimated and calculated.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.19 no.4
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• pp.328-334
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• 2009

There are various methods for welding fume control. These methods can be divided into local exhaust system, general ventilation system and integrated control system. With the general ventilation system, we should have a good prediction tool for testing various appropriate control options. But, until now there are not many studies about how to predict the welding fume concentrations. Especially, the prediction of welding fume concentration is not a very easy task because welding fume is the particulate matters. In this study, we tried to measure $CO_2$ concentrations and welding fume concentrations in a small single room with a small ventilation opening. Using commercially available CFD (Computational Fluid Dynamics) software, we tried to predict $CO_2$ concentrations under the exactly same conditions. Then, we tried to compare the numerical $CO_2concentrations$ with the experimental results to know whether we could predict $CO_2$ concentrations. Then we tried to compare $CO_2$ concentrations with experimental welding fume concentrations to know whether we can use the numerical $CO_2concentrations$ to predict the welding fume concentration indirectly.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• Korean Journal of Community Nutrition
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• v.19 no.6
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• pp.568-580
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• 2014

Objectives: The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers. Methods: Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR. Results: The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow : males RMR = 447.5 + 17.4 Wt, females RMR = 684.5 - 3.5 Ht + 11.8 Wt + 12.4 FFM. Conclusions: This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.

• Journal of Korean Academy of Nursing
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• v.50 no.1
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• pp.1-13
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• 2020

Purpose: The purpose of this study was to construct and test a structural equation model on nursing work outcomes based on Youssef and Luthans' positive psychological capital and integrated conceptual framework of work performance. Methods: This study used a structured questionnaire administered to 340 nurses. Data were analyzed using structural equation modeling. Results: Positive psychological capital showed indirect and direct effects on job satisfaction, retention intention, organizational citizenship behavior, and nursing performance. While, the nursing work environment had direct and indirect effects on job satisfaction and nursing performance, it only had indirect effects on intention to work and organizational citizenship behavior. Additionally, a mediating effect on retention intention and organizational citizenship behavior was found between job satisfaction and nursing performance variables. Conclusion: The nursing organization needs to build a supportive work environment and reinforce positive psychological capital to improve nursing performance. Additionally, it needs to actively manage the necessary parameters involved in the stages of job satisfaction, retention intention, nursing performance, and organizational citizenship behavior of nurses. The findings propose the continuous management of nursing personnel based on nurses' attitude outcome, behavioral intention, behavioral outcome, and stage of role performance.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.9
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• pp.1481-1490
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• 2018

Objective: The objective of this experiment was to determine the net energy (NE) content of full-fat rice bran (FFRB), corn germ meal (CGM), corn gluten feed (CGF), solvent-extracted peanut meal (PNM), and dehulled sunflower meal (SFM) fed to growing pigs using indirect calorimetry or published prediction equations. Methods: Twelve growing barrows with an average initial body weight (BW) of $32.4{\pm}3.3kg$ were allotted to a replicated $3{\times}6$ Youden square design with 3 successive periods and 6 diets. During each period, pigs were individually housed in metabolism crates for 16 d, which included 7 days for adaptation. On d 8, the pigs were transferred to the respiration chambers and fed one of the 6 diets at 2.0 MJ metabolizable energy (ME)/$kg\;BW^{0.6}/d$. Total feces and urine were collected and daily heat production was measured from d 9 to d 13. On d 14 and d15, pigs were fed at their maintenance energy requirement level. On the last day pigs were fasted and fasting heat production was measured. Results: The NE of FFRB, CGM, CGF, PNM, and SFM measured by indirect calorimetry method was 12.33, 8.75, 7.51, 10.79, and 6.49 MJ/kg dry matter (DM), respectively. The NE/ME ratios ranged from 67.2% (SFM) to 78.5% (CGF). The NE values for the 5 ingredients calculated according to the prediction equations were 12.22, 8.55, 6.79, 10.51, and 6.17 MJ/kg DM, respectively. Conclusion: The NE values were the highest for FFRB and PNM and the lowest in the corn co-products and SFM. The average NE of the 5 ingredients measured by indirect calorimetry method in the current study was greater than values predicted from NE prediction equations (0.32 MJ/kg DM).

• Korean Journal of Community Nutrition
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• v.23 no.5
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• pp.424-430
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• 2018

Objectives: The purpose of this study was to compare predictions and measurements of the resting energy expenditure (REE) of overweight and obese adult women in Korea. Methods: The subjects included 65 overweight or obese adult women ranging in age from 20~60 with a recorded body mass index (BMI) of 23 or higher. Their height, weight, waist-hip ratio, and blood pressure were measured. The investigator also measured their body fat, body fat percentage, and body composition of total weight without fat using Dual energy X-ray absorptiometry (DXA) and measured resting energy expenditure by indirect calorimetry. Measured resting energy expenditures were compared with predictions from six methods: Harris-Benedict, Mifflin, Owen, WHO-WH, Henry-WH, and KDRI. Results: Harris-Benedict predictions showed the smallest differences from measured resting energy expenditure at an accurate prediction rate of 70%. The study analyzed regression between measured resting energy expenditure and body measurements including height, weight and age. The formula proposed by this research is as follows: Proposed REE equation for overweight and obese Korean women = $721-(1.5{\times}age)+(0.4{\times}height)+(9.9{\times}weight)$. Conclusions: These findings suggest that age is a significant variable when predicting resting energy expenditure in overweight and obese women. Therefore, prediction of resting energy expenditure should consider age when determining energy requirements in overweight and obese women.