• Journal of The Korean Society of Integrative Medicine
• /
• v.8 no.2
• /
• pp.53-61
• /
• 2020

Purpose : We investigated the correlation between body composition and lung function in healthy adults. Methods : This study included 204 healthy adults in whom all measurements were obtained once, and all data were analyzed using the SPSS software for Windows, version 22.0. Pearson's correlation analysis was performed to determine the correlation between body composition (represented by the total body water, protein mass, soft lean mass, mineral mass, basal metabolic rate, fat-free mass, skeletal muscle mass, and body fat percentage) and lung function (represented by the forced vital capacity [FVC], forced expiratory volume in 1 second [FEV₁], the FEV₁/FVC ratio, maximum voluntary ventilation [MVV], maximum expiratory pressure [MEP], and the maximum inspiratory pressure [MIP]). All measurements were obtained by two investigators to improve reliability. A significance level of α=.05 was used to verify statistical significance. Results : Among the lung function measurements obtained in both men and women, the FVC, FEV₁, MVV, and MIP were positively correlated with the total body water, protein mass, soft lean mass, mineral mass, basal metabolic rate, fat-free mass, and skeletal muscle mass in men (p<.05). The FEV₁/FVC ratio was negatively correlated with the total body water, soft lean mass, mineral mass, basal metabolic rate, fat-free mass and the body fat percentage (p<.05). Notably, the FVC, FEV₁, and MVV were positively correlated with the total body water, protein mass, soft lean mass, mineral mass, basal metabolic rate, fat-free mass, and skeletal muscle mass in women (p<.05). Conclusion : This study showed a significant correlation between body composition and lung function in healthy adults. In combination with future studies on lung function, our results can provide objective evidence regarding the importance of prevention of lung disease, and our data can be utilized in rehabilitation programs for patients with respiratory diseases.

• Phonetics and Speech Sciences
• /
• v.3 no.3
• /
• pp.91-98
• /
• 2011

In this paper, a multi-channel speech enhancement method using the linearly constrained minimum variance (LCMV) algorithm and a variable learning rate control is proposed. To control the learning rate for adaptive filters of the LCMV algorithm, the direction of arrival (DOA) is measured for each short-time input signal and the likelihood function of the target speech presence is estimated to control the filter learning rate. Using the likelihood measure, the learning rate is increased during the pure noise interval and decreased during the target speech interval. To optimize the parameter of the mapping function between the likelihood value and the corresponding learning rate, an exhaustive search is performed using the Bark's scale distortion (BSD) as the performance index. Experimental results show that the proposed algorithm outperforms the conventional LCMV with fixed learning rate in the BSD by around 1.5 dB.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.3
• /
• pp.623-632
• /
• 2014

We shall consider the estimation for the parameter and the right tail probability in a general exponential distribution. We also shall consider the estimation of the reliability P(X < Y ) and the skewness trends of the density function of the ratio X=(X+Y) for two independent general exponential variables each having different shape parameters and known scale parameter. We then shall consider the estimation of the failure rate average and the hazard function for a general exponential variable having the density function with the unknown shape and known scale parameters, and for a bivariate density induced by the general exponential density.

• Fire Science and Engineering
• /
• v.33 no.6
• /
• pp.20-27
• /
• 2019

In this study, a new function with higher accuracy for fire heat release rate prediction was developed. The 'αt²' curve, which is the major exponential function currently used for fire engineering calculations, must be improved to minimize the prediction gap that causes fire system engineering inefficiency and lower cost-effectiveness. The newly developed prediction function was designed to cover the initial fire stage that features rapid growth based on logistic function theory, which has a more logical background and graphical similarity compared to conventional exponential function methods for 'αt²'. The new function developed in this study showed apparently higher prediction accuracy over wider range of fire growth durations. With the progress of fire growth pattern studies, the results presented herein will contribute towards more effective fire protection engineering.

• International Journal of Reliability and Applications
• /
• v.2 no.1
• /
• pp.49-56
• /
• 2001

In this paper we consider empirical Bayes estimation of the hazard rate and survival probabilities with right censored data under the assumption that the hazard function is constant over the period of observation and the prior distribution is gamma. We provide an estimator of the first derivative of the prior moment generating function that converges at each point to the true value in $L_2$ and use it to obtain, easy to compute, asymptotically optimal estimators under the squared error loss function.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.05a
• /
• pp.972-975
• /
• 2013

This work developed a code analysis & extraction tool named Function Network Analyzer (FNA) to reduce the cost of software safety analysis. FNA analyzes functions and variables which a given function depends on, and extracts subset of code that can be compiled of automotive operating system, final resulting a well-ordered code sequence that can be compiled for model checking technique. And the experimental result illustrates that FNA can get 100% accurate rate and over 96% reduction rate by testing API functions from trampoline system.

• International Journal of Reliability and Applications
• /
• v.17 no.2
• /
• pp.149-158
• /
• 2016

We are enjoying a very comfortable life thanks to modern civilization, however, comfort is not guaranteed to us. Development of software system is a difficult and complex process. Therefore, the main focus of software development is on improving the reliability and stability of a software system. We have become aware of the importance of developing software reliability models and have begun to develop software reliability models. NHPP software reliability models have been developed through the fault intensity rate function and the mean value functions within a controlled testing environment to estimate reliability metrics such as the number of residual faults, failure rate, and reliability of the software. In this paper, we present a new NHPP software reliability model with Burr Type III fault detection rate, and present the goodness-of-fit of the fault detection rate software reliability model and other NHPP models based on two datasets of software testing data. The results show that the proposed model fits significantly better than other NHPP software reliability models.

• Korea Trade Review
• /
• v.46 no.6
• /
• pp.191-209
• /
• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• Journal of Environmental Science International
• /
• v.13 no.3
• /
• pp.263-277
• /
• 2004

The purpose of this study is to analyze the characteristics of riverbed variation due to the sediment protection weir located on the estuary of the main stream of Taehwa river using I-D finite difference model, HEC-6 model, and the followings are the results of estimating sediment transport rate, amount of scour or deposition, and accumulated amount of deposit according to before and after of the sediment protection weir removal with various flow rates in the channel. Ackers-White transport function produced the greatest sediment transport rate while Meyer-Peter showed the smallest sediment transport rate at the most down stream area of the watershed through the sediment transport rate analyses for various flow rates according to the existence or nonexistence of the sediment protection weir. Toffaleti's and Colby transport function were closest to the average value, and the difference among the results of the sediment transport functions showed up to 8～9 times. Duboy's transport function produced the greatest riverbed variation while Toffaleti's showed the smallest variation through the riverbed variation analyses according to the existence or nonexistence of the sediment protection weir. Yang's was closest to the average value, and the difference among the results of the riverbed variation analyses ranged from 1.4 times to 11 times. It is thought that a sediment transport function must be selected very carefully with respect to the criteria of sediment yield estimation because the analysis results of the sediment transport rate and riverbed variation according to flow rates showed significant differences among the sediment transport functions, and the differences of sediment transport rate and riverbed variation according to the various sediment transport functions decreased as the flow rate increased.

• KSBB Journal
• /
• v.13 no.6
• /
• pp.730-734
• /
• 1998

The mathematical model of specific growth rate of Saccharomyces cerevisiae ATCC 24858 is proposed as a function of sugar and ethanol concentrations by the combination of Andrew's equation and Aiba's equation. The maximum concentration of sugar Sm, which was the highest concentration of sugar not having any effect on the growth inhibition, was 150 g/L and the substrate inhibition was expressed as a function of (S-Sm). The maximum specific growth inhibition, was 150 g/L and the substrate inhibition was expressed as a function of (S-Sm). The maximum specific growth rate ${\mu}m$, Monod's constant Ks, and Andrew's inhibition constant KI were 0.49 hr-1, 19 g/L, and 139 g/L, respectively. The maximum ethanol concentration, Pm, which did not show any inhibition effect on the specific growth rate was found to be 2 g/L. Therefore, the ethanol inhibition was represented as a function of (P-Pm). The final mathematical model for the specific growth rate of the microorganism in this work is proposed as the following. And the average percent of errors between the calculated specific growth rate and the experimental values was 5.96%.