• The Korean Journal of Physiology
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• v.20 no.1
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• pp.103-124
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• 1986

Since it has been suggested that atrial receptor may be involved in the mechanism of extracellular volume regulation, it was shown that the granularity of atrial cardiocytes can be changed by water and salt depletion, and that an extract of cardiac atrial tissue, when injected intravenously into anesthetized rats, was shown to cause a large and rapid increase in renal excretion of sodium. Various natriuretic peptides were isolated and synthetized, and the effects were investigated by many workers. Most studies, however, have been carried out under anesthesia and there have teen some controversies over direct effect of the factor on the renal function. Therefore, it was attempted in this study to access the effects of an atrial extract and a synthetic natriuretic factor in unanesthetized rabbits. Intrarenal arterial infusion of atrial extract caused a rapid increase of urinary volume and excretion of sodium. Glomerular filtration rate and renal plasma flow were both increased with no change in filtration fraction. The ventricular extract produced no change in urinary excretion of electrolytes, nor in renal hemodynamics. Intrarenal infusion of synthetic atrial natriuretic factor caused increases of renal excretory rate of sodium, chloride and potassium, and $FE_{Na}$. Glomerular filtration rate, renal plasma flow increased. And free water clearance also increased. Accentuated excretory function correlated well with increased glomerular filtration rate and renal plasma flow during infusion and for 10 minutes following the cessation of the infusion. Renin secretion rate decreased during constant infusion of atrial natriuretic factor. However, no correlation was found with the changes in glomerular filtration rate, renal plasma flow, or urinary excretion of sodium. These results suggest that atrial extract or atrial natriuretic factor induces changes in renal hemodynamics, as in excretion of electrolytes either indirectly through hemodynamic changes or directly by inhibiting tubular reabsorption. At the same time, renin secretory function is affected by the factor possibly through an unknown mechanism.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.125-130
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• 2020

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.

• Health Policy and Management
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• v.13 no.3
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• pp.91-103
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• 2003

The purpose of this study was to provide basic reference data for stabilization scheme of health expenditure through forecasting of health expenditure. The authors analyzed the health expenditure from 1985 to 2000 that had been calculated by Korean institute for health and social affair using transfer function model as ARIMA model with input series. They used GDP as the input series for more precise forecasting. The model of error term was identified ARIMA(2,2,0) and Portmanteau statics of residuals was not significant. Forecasting health expenditure as percent of GDP at 2010 was 6.8%, under assumption of 5% GDP increase rate. Moreover that was 7.4%, under assumption of 3% GDP increase rate and that was 6.4%, under assumption of 7% GDP increase rate.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.191-202
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• 2004

The paper considers nonparametric empirical Bayes estimation of residual survival function at age t using a Dirichlet process prior V(a). Empirical Bayes estimators are proposed for the case where both the function ${\alpha}$(0, $\chi$] and the size a(R$\^$+/) are unknown. It is shown that the proposed empirical Bayes estimators are asymptotically optimal at a rate n$\^$-1/, where n is the number of past data available for the present estimation problem. Therefore, the result of Lahiri and Park (1988) in which a(R$\^$+/) is assumed to be known and a rate n$\^$-1/ is achieved, is extended to a(R$\^$+/) unknown case.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.90-96
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• 2000

The technology of preventive maintenance and corrective maintenance is widely applied to ships in order to maintain the good voyageable condition. One of the most important fields of marine engineering is to seek the maximum availability and to solve the stochastic maintenance problem such that the cost for corrective maintenance is minimized. Accordingly, for the purpose of making the most suitable maintenance schedule which minimizes the expected cost function, this paper suggests the method to grasp the failure characteristics by the ship's maintenance data that are collected from the past. And, suggests the method to estimate the optimal maintenance interval by using the dynamic programming and the cumulative hazard rate function attained from the maintenance data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.29-35
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• 2012

This paper introduces an adaptive control method of strong mutation rate and probability for queen-bee genetic algorithms. Although the queen-bee genetic algorithms have shown good performances, it had a critical problem that the strong mutation rate and probability should be selected by a trial and error method empirically. In order to solve this problem, we employed the measure of convergence and used it as a control parameter of those. Experimental results with four function optimization problems showed that our method was similar to or sometimes superior to the best result of empirical selections. This indicates that our method is very useful to practical optimization problems because it does not need time consuming trials.

• ETRI Journal
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• v.22 no.3
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• pp.10-18
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• 2000

Although an n-th order cross-entropy (nCE) error function resolves the incorrect saturation problem of conventional error backpropagation (EBP) algorithm, performance of multilayer perceptrons (MLPs) trained using the nCE function depends heavily on the order of nCE. In this paper, we propose an adaptive learning rate to markedly reduce the sensitivity of MLP performance to the order of nCE. Additionally, we propose to limit error signal values at out-put nodes for stable learning with the adaptive learning rate. Through simulations of handwritten digit recognition and isolated-word recognition tasks, it was verified that the proposed method successfully reduced the performance dependency of MLPs on the nCE order while maintaining advantages of the nCE function.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.503-511
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• 1995

A batch-mode algorithm is proposed to increase the speed of learning in the error backpropagation algorithm with variable learning rate and variable momentum parameters in classification problems. The objective function is normalized with respect to the number of patterns and output nodes. Also the gradient of the objective function is normalized in updating the connection weights to increase the effect of its backpropagated error. The learning rate and momentum parameters are determined from a function of the gradient norm and the number of weights. The learning rate depends on the square rott of the gradient norm while the momentum parameters depend on the gradient norm. In the two typical classification problems, simulation results demonstrate the effectiveness of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.349-350
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• 2006

This paper presents very low bit rate speech coder, ZFE-CELP(ZINC Function Excitation-Code Excited Linear Prediction). The ZFE-CELP speech codec is based on a ZINC function and CELP modeling of the excitation signal respectively according to the frame characteristic such as a voiced speech and an unvoiced speech. And this paper suggest strategies to improve the speech quality of the very low bit rate speech coder.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.990-1006
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• 1997

This paper describes an interface function and its performance assessment for high-bit-rate digital subscriber line (HDSL) under ATM. The interface of HDSL function of ATM system was achieved by HDSL subscriber physical layer board assembly(HSPA) which was modeled as design standard for ATM. We have presented a new worst case of subscriber line conditions from existing results of investigations on impairments such as crosstalk, impulse noise, longitudinal, power line noise and others. We have measured the maximum service loop length available by HDSL, and found that HSPA, at a 2.048Mbps data transmission, is possible within a carrier serving area(CSA) under the worst case loop noise conditions at an error rate or 10$^{-7}$ on a two coordinated unshielded twisted pairs in the presense of impairments. We conclude tht, in terms of a performance-per-lin simulator, the HSPA is an excellent candidate for HDSL implementation under ATM.