• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.29-62
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• 2018

We investigate a class of HIV infection models with two kinds of target cells: $CD4^+$ T cells and macrophages. We incorporate three distributed time delays into the models. Moreover, we consider the effect of humoral immunity on the dynamical behavior of the HIV. The viruses are produced from four types of infected cells: short-lived infected $CD4^+$T cells, long-lived chronically infected $CD4^+$T cells, short-lived infected macrophages and long-lived chronically infected macrophages. The drug efficacy is assumed to be different for the two types of target cells. The HIV-target incidence rate is given by bilinear and saturation functional response while, for the third model, both HIV-target incidence rate and neutralization rate of viruses are given by nonlinear general functions. We show that the solutions of the proposed models are nonnegative and ultimately bounded. We derive two threshold parameters which fully determine the positivity and stability of the three steady states of the models. Using Lyapunov functionals, we established the global stability of the steady states of the models. The theoretical results are confirmed by numerical simulations.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.845-852
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• 2003

Forecasting exchange rate movements is a challenging task since exchange rates impact world economy and determine value of international investments. In particular, Korean-U.S. exchange rate behavior is very important because of strong Korean and U.S. trading relationship. Neural networks models have been used for short-term prediction of exchange rate movements. Least squares support vector machine (LS-SVM) is used widely in real-world regression tasks. This paper describes the use of LS-SVM for short-term and long-term prediction of Korean-U.S. exchange rate.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1749-1753
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• 2007

The reliability, that is Long-Term Quality, require an approaching different from Short-Term Quality which is used before. As the electronic components are able to be easily normalized on the reliability testing, various testing standards are used. In this study, we proposed two reliability simulator that is PoF(Physics of Failure)-based and failure rate models-based. PoF-based simulator is introduced based on CalceEP program that is created by University of Maryland. This simulator can be modified by user interface of properties and PoF models and operated on stand alone system. Failure rate models-based simulator introduced according to analyzing reliability prediction documents. Also, unified database including failure data models is built from existing MIL-HDBK-217F N2, PRISM, and Bellcore, and web-based simulator is developed. The developed reliability simulator will service of the PoF model, properties, failure rate model accumulated and its data by web and internet.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.44-55
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• 1994

This paper describes the development of real-time irrigation reservoir operation models that adequately allocate available water resources for paddy rice irrigation. Water requirement deficiency index(WRDI) was proposed as a guide to evaluate the operational performance of release schemes by comparing accumulated differences between daily release requirements for irrigated areas and actual release amounts. Seven reservoir release rules were developed, which are constant release rate method (CRR), mean storage curve method(MSC), frequency analysis method of reservoir storage rate(FAS), storage requirement curve method(SRC), constant optimal storage rate method (COS), ten-day optimal storage rate method(TOS), and release optimization method(ROM). Long-term forecasting reservoir operation model(LFROM) was formulated to find an optimal release scheme which minimizes WRDIs with long-term weather generation. Rainfall sequences, rainfall amount, and evaporation amount throughout the growing season were to be forecasted and the results used as an input for the model. And short-term forecasting reservoir operation model(SFROM) was developed to find an optimal release scheme which minimizes WRDIs with short-term weather forecasts. The model uses rainfall sequences forecasted by the weather service, and uses rainfall and evaporation amounts generated according to rainfall sequences.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.79-116
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• 2008

This paper examines and estimates GARCH-VaR models (RiskMetrics, GARCH, IGARCH, GJR and APARCH) with three different distributions such as Gaussian normal, Student-t, Skewness Student-t Distribution using the daily price data from Korean Stock Market during Jan. 1, 1980-Sept. 30, 2004. It also compares them. In-sample test, this finds that for all confidence level as $90%{\sim}99.9%$, the performance and accuracy of IGARCH with ${\lambda}=0.87$ and skewness Student-t distribution are superior to other models and distributions in long position, but GARCH and GJR with Skewness Student-t distribution in short position. For above 99% confidence level, the performance and accuracy of IGARCH with ${\lambda}=0.87$ in both long and short positions are superior to other models and distributions, but Skewness Student-t distribution for long position and Student-t distribution for short position are more accuracy and superior to other distributions. In-out-of sample test, these results also confirm the evidences that the above findings are consistent as well.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1663-1669
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• 1995

This paper is a study on the recognition of Korean proverb using neural network and Markov model. The neural network uses, at the stage of training neurons, features such as the rate of zero crossing, short-term energy and PLP-Cepstrum, covering a time of 300ms long. Markov models were generated by the recognized phoneme strings. The recognition of words and proverbs using Markov models have been carried out. Experimental results show that phoneme and word recognition rates are 81. 2%, 94.0% respectively for Korean proverb recognition experiments.

• Journal of Biosystems Engineering
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• v.19 no.2
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• pp.112-123
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• 1994

At present, no appropriate drying conditions can be found for the heated-air drying of mushroom in Korea. Usually, mushroom is being dried at the temperature range of 40 to $50^{\circ}C$ until the moisture content reaches 10~13% (wb). However, drying characteristics of the mushroom should be investigated for quality improvement and efficient drying operation of the mushroom. The results of this study may be summarized as follows ; 1. The effect of air temperature on the rate of drying was greater than that of relative humidity for drying of mushroom, and the rate of drying was increased with increase in the air temperature. 2. Drying rate for Shiitake mushroom showed falling-rate period of drying without constant-rate period of drying. Drying rate for Oyster mushroom showed a short constant-rate period at the initial stage of drying process, and followed by falling-rate period of drying. 3. Exponential and App.-Diffusion models were found to describe well the drying process of Shiitake mushroom. Exponential and Thompson models for Oyster mushroom in which Thompson model was the most suitable for Oyster mushroom. 4. The equilibrium moisture content of the mushroom decreased with decrease in the air temperature and increase in the relative humidity. In room condition($20^{\circ}C$, 54% RH), the calculated values of the equilibrium moisture content showed 11.17% for Shiitake mushroom and 13.19% for Oyster mushroom, respectively.

• Computers and Concrete
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• v.8 no.4
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• pp.371-388
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• 2011

In this paper, time-continuous constitutive equations for strain rate-dependent materials are presented first, among which those for the overstress and the consistency viscoplastic models are considered. By allowing the stress states to be outside the yield surface, the overstress viscoplastic model directly defines the flow rule for viscoplastic strain rate. In comparison, a rate-dependent yield surface is defined in the consistency viscoplastic model, so that the standard Kuhn-Tucker loading/unloading condition still remains true for rate-dependent plasticity. Based on the formulation of the consistency viscoplasticity, a computational elasto-viscoplastic constitutive model is proposed for the short fiber-reinforced fresh cementitious paste for extrusion purpose. The proposed constitutive model adopts the von-Mises yield criterion, the associated flow rule and nonlinear strain rate-hardening law. It is found that the predicted flow stresses of the extrudable fresh cementitious paste agree well with experimental results. The rate-form constitutive equations are then integrated into an incremental formulation, which is implemented into a numerical framework based on ANSYS/LS-DYNA finite element code. Then, a series of upsetting and ram extrusion processes are simulated. It is found that the predicted forming load-time data are in good agreement with experimental results, suggesting that the proposed constitutive model could describe the elasto-viscoplastic behavior of the short fiber-reinforced extrudable fresh cementitious paste.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.195-202
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• 2011

The study interprets each of three classification models based on Bath-Tub Failure Rate (BTFR), Extreme Value Distribution (EVD) and Conjugate Bayesian Distribution (CBD). The classification model based on BTFR is analyzed by three failure patterns of decreasing, constant, or increasing which utilize systematic management strategies for reliability of time. Distribution model based on BTFR is identified using individual factors for each of three corresponding cases. First, in case of using shape parameter, the distribution based on BTFR is analyzed with a factor of component or part number. In case of using scale parameter, the distribution model based on BTFR is analyzed with a factor of time precision. Meanwhile, in case of using location parameter, the distribution model based on BTFR is analyzed with a factor of guarantee time. The classification model based on EVD is assorted into long-tailed distribution, medium-tailed distribution, and short-tailed distribution by the length of right-tail in distribution, and depended on asymptotic reliability property which signifies skewness and kurtosis of distribution curve. Furthermore, the classification model based on CBD is relied upon conjugate distribution relations between prior function, likelihood function and posterior function for dimension reduction and easy tractability under the occasion of Bayesian posterior updating.