• Journal of Radiation Protection and Research
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• v.33 no.4
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• pp.135-141
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• 2008

This paper describes a dynamic compartment model to predict the time-dependent $^{14}C$ activity in a plant as a result of a direct exposure to an amount of $^{14}CO_2$ for a short period of time, and experimental results for the model validation. In the model, the plant consists of two compartments of the body and ears, and five carbon fluxes between the compartments, which are the function of parameters relating to the growth and photosynthesis of a plant, are considered. Model predictions were made for an investigation into the effects of the exposure time, the elapsed exposure time, and the model parameters on the $^{14}C$ radioactivity of a plant. The present model converged to a region where the specific activity model is applicable when the elapsed time of the exposure was extended up to the harvest time of a plant. The $^{14}C$ activity of a plant was predicted to be the greatest when the exposure had happened in the period between the flowering and ears-maturity on account of the most vigorous photosynthesis rate for the period. Comparison of model predictions with the observed 14C radioactivity of rice plants showed that the present model could predict the $^{14}C$ radioactivity of the rice plants reasonably well.

• Journal of the Korean Institute of Landscape Architecture
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• v.31 no.3
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• pp.74-82
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• 2003

Under the assumption that the temperature of one location is closely related to land use characteristics around that location, this study is carried out to assess the impact of urban land use patterns on air temperature. In order to investigate the relationship, GIS techniques and statistical analyses are utilized, after spatially connecting urban land use data in Seoul Metropolitan Area with atmospheric data observed at Automatic Weather Stations (AWS). The research method is as follows: (1) To find out important land use factors on temperature, simple linear regressions for a specific time period (pilot study) are conducted with urban land use characteristics, (2) To make a final model, multiple regressions are carried out with those factors and, (3) To verify that the final model could be appled to explain temperature variations beyond the period, the model is extensively used for 5 different time periods: 1999 as a whole; summer in 1999; 1998 as a whole; summer in 1998; August in 1998. The results of simple linear regression models in the pilot study show that transportation facilities and open space area are very influential on urban air temperature variations, which explain 66 and 61 percent of the variations, respectively. However, the other land use variables (residential, commercial, and mixed land use) are found to have weak or insignificant relationship to the air temperatures. Multiple linear regression with the two important variables in the pilot study is estimated, which shows that the model explains 75 percent of the variability in air temperatures with correct signs of regression coefficients. Thus, it is empirically shown that an increase in open space and a decrease in transportation facilities area can leads to the decrease in air temperature. After the final model is extensively applied to the 5 different time periods, the estimated models explain 68 ∼ 75 percent of the variations in the temperatures is significant regression coefficients for all explanatory variables. This result provides a possibility that one air temperature model for a specific time period could be a good model for other time periods near to the period. The important implications of this result to lessen high air temperature we: (1) to expand and to conserve open space and (2) to control transportation-related factors such as transportation facilities area, road pavement and traffic congestion.

• Journal of Astronomy and Space Sciences
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• v.28 no.3
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• pp.163-172
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• 2011

An intensive analysis of 185 timings of WZ Cep, including our new three timings, was made to understand the dynamical picture of this active W UMa-type binary. It was found that the orbital period of the system has complexly varied in two cyclical components superposed on a secularly downward parabola over about 80y. The downward parabola, corresponding to a secular period decrease of $-9.{^d}97{\times}10^{-8}y^{-1}$, is most probably produced by the action of both angular momentum loss (AML) due to magnetic braking and mass-transfer from the massive primary component to the secondary. The period decrease rate of $-6.^{d}72{\times}10^{-8}y^{-1}$ due to AML contributes about 67% to the observed period decrease. The mass flow of about $5.16{\times}10^{-8}M_{\odot}y^{-1}$ from the primary to the secondary results the remaining 33% period decrease. Two cyclical components have an $11.^{y}8$ period with amplitude of $0.^{d}0054$ and a $41.^{y}3$ period with amplitude of $0.^{d}0178$. It is very interesting that there seems to be exactly in a commensurable 7:2 relation between their mean motions. As the possible causes, two rival interpretations (i.e., light-time effects (LTE) by additional bodies and the Applegate model) were considered. In the LTE interpretation, the minimum masses of $0.30M_{\odot}$ for the shorter period and $0.49M_{\odot}$ for the longer one were calculated. Their contributions to the total light were at most within 2%, if they were assumed to be main-sequence stars. If the LTE explanation is true for the WZ Cep system, the 7:2 relation found between their mean motions would be interpreted as a stable 7:2 orbit resonance produced by a long-term gravitational interaction between two tertiary bodies. In the Applegate model interpretation, the deduced model parameters indicate that the mechanism could work only in the primary star for both of the two period modulations, but could not in the secondary. However, we couldn't find any meaningful relation between the light variation and the period variability from the historical light curve data. At present, we prefer the interpretation of the mechanical perturbation from the third and fourth stars as the possible cause of two cycling period changes.

• Fisheries and Aquatic Sciences
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• v.16 no.3
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• pp.211-220
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• 2013

A shaking motion could be used to improve fish escapement from a cod-end net by creating a sieving effect over the swept volume or by disturbing the optomotor response of the fish. In this study, a perpendicular shaking motion was generated in a towed cod-end net by a circular canvas attached to the end of the codend, which formed a biased cap-like shape. This concept was tested by using a model in a flow tank and by towing a prototype cod-end during sea trials. For the model tests, the amplitude of the shaking motion was $0.6{\pm}0.1$ times the rear diameter of the cod-end, and the period of the shaking motion was $2.6{\pm}0.1$ s at a flow velocity of 0.6 or 0.8 m/s. In the sea trials, the amplitude was $0.5{\pm}0.2$ times the rear diameter of the cod-end, and the period of the shaking motion was $7{\pm}4$ s at towing speeds of 1.2 or 1.7 m/s. Thus, the shaking amplitude during the sea trials was equal to or less than that observed in the tank tests, and the shaking period was twice as long. The shaking motion described by the amplitude and period could be an effective means to stimulate fish escapement from cod-end during fishing operations considering the response of the fish.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.7 no.1
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• pp.46-56
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• 1995

Three Pressure type wave gauges were installed for about 10 days for the analysis of long wave agitations in Mukho Harbor. Helmholtz and second resonant periods of seiche in Mukho Harbor are shown to be approximately 10.0-14.3 and 3.3 minutes from the spectral analysis of measured wave data. Amplification ratio at Helmholtz period reaches about 6.8 and the wave amplitudes in the harbor were in the range of 5-10 cm during the measurement period. Helmholtz and second resonant periods of seiche in Mukho Harbor agree very well with those computed using Jeong dt al. (1993b)'s model. The model gives rise to the first and third resonant peaks at 7.5 and 1.9 minutes, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.51-57
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• 2007

The expected busy period for the controllable M/G/1 queueing model operating under the triadic policy is derived by using the pseudo probability density function which is totally different from the actual probability density function. In order to justify the approach using the pseudo probability density function to derive the expected busy period for the triadic policy, well-known expected busy periods for the dyadic policies are derived from the obtained result as special cases.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.95-115
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• 2013

In traditional inventory models, purchase prices of raw materials are assumed to be fixed and have no effect on the optimal choice of inventory policies. However, when purchase prices fluctuate continuously over time, inventory costs are heavily affected by purchasing prices. Risk-averse inventory model decides order quantity and ordering time by considering not just purchase prices but also the risk from the discrepancy between estimated prices and realized prices. In this paper, we propose a myopic inventory policy which incorporates price risk into deciding ordering time and quantities. While the existing risk-averse model has no mechanism to reallocate inventories already purchased for a specific future period, the revised one reallocates initial inventories of each period to other future periods so that it can avoid purchasing raw materials at high prices. Experimental results demonstrate that the revised model outperforms the existing one in respect of total cost and variability.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1-13
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• 2021

Previous studies have found the significant adverse effects of coronavirus disease 2019 (COVID-19) on stock returns and volatility. The effects varied with the confirmed cases and deaths. However, the extent of the effects have never been measured exactly. This study proposes a measurement model for the COVID-19 effects. In the proposed model, stock returns in the COVID-19 period are weighted averages of pre-COVID-19 normal returns and COVID-19-induced returns. The effects are measured by the contributing weights of the COVID-19-induced returns. Kalman filtering is used to estimate the model for the world and Chinese markets, in combination with 10 markets - five most affected countries (United States, India, Brazil, Russia, and France) and five best recovering countries (Hong Kong, Australia, Singapore, Thailand, and South Korea). The sample returns are daily, obtained from the closing Morgan Stanley global investable market indexes. The full period is from September 24, 2018, to October 30, 2020, whereas the COVID-19 period is from November 18, 2019, to October 30, 2020. The contributing weights are significant and close to 100% for all markets. The COVID-19-induced returns replace the pre-COVID-19 normal returns; they are negatively auto-correlated and highly volatile. The COVID-19-induced returns are new normal returns in the COVID-19 period.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• Journal of Biosystems Engineering
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• v.26 no.5
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• pp.449-460
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• 2001

This study was carried out to develop a simulation model with EES(Engineering equation solver) for analyzing the performance of a grain cooler. In order to validate the developed simulation model, several main factors which have affected on the performance of the gain cooler were investigated through experiments. A simulation model was developed in the standard vapor compression cycle, and then this model was modified considering irreversibe factors so that the developed alternate model could predict the actual cycle of a grain cooler. The compressor efficiency in vapor compression cycle considering irreversibility much affected on the coefficient of performance(COP). The COP in the standard vapor compression cycle model was greatly as high as about 6.50, but the COP in an alternative model considering irreversibility was as low as about 3.27. As a result of comparison between the actual cycle and the vapor compression cycle considering irreversibility, the difference of pressure at compressor outlet(inlet) was a little by about 48kPa (8.8kPa), the temperatures of refrigerant at main parts of the grain cooler were similar. and the temperature of chilled air was about 8$\^{C}$ in both. The model considering irreversibility could predict performance of the grain cooler. The theoretical period required to chill grain of 1,383kg from the initial temperature 24$\^{C}$ to below 11$\^{C}$ was about 55 hours 30 minutes, and the actual period required in a grain bin was about 58 hours. The difference between the predicted and an actual period was about 2 hours 30 minutes. The cooling performance predicted by the developed model could well estimate the cooling period required to chill the grain.