• Korean Journal of Veterinary Service
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• v.38 no.3
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• pp.155-162
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• 2015

The incidence of second-grade milk production in 9 dairy farms of South Korea was investigated from May 2011 to March 2012, and the serum composition of cows producing first- and second-grade milk in 14 farms including the 9 farms was analyzed. The incidence rate of second-grade milk production of 402 cows in nine dairy farms located in the central and southwestern regions of Korea was 15.4% with the highest rate being 34.4%. Seasonal morbidity was higher during late winter (February) and early summer (June) with the highest rate observed in February (32.6%) followed by November (33.3%). Second-grade milk was most frequently found within one month postpartum (34.1%) while only 3.5% was found during the first 60~90 days of lactating period (n=785, 5 herds). The morbidity increased thereafter (P<0.05) with the highest observed between 270~300 days of lactation (36.1%). The acidity was not significantly different between second-grade ($0.159{\pm}0.026%$) and first-grade milk ($0.158{\pm}0.027%$). Blood serum analysis of 371 cows in the 14 dairy farms indicated that aspartate aminotransferase (AST) level was significantly higher (P<0.001) in cows producing second-grade milk while albumin was significantly lower (P<0.001) than cows producing first-grade milk. Total protein and triglyceride was also significantly low along with glucose, non-esterified fatty acid and blood urea nitrogen in cows producing second-grade milk. Statistical analysis including sensitivity, specificity and positive/negative prediction values showed that lactating cows with high AST, low albumin, total protein and triglyceride levels in the serum tended to produce second-grade milk. It was concluded that serological parameters, especially live functional and metabolic-related serum compositions (AST, albumin, total protein and triglyceride), were significantly influenced in cows producing second-grade milk.

• Journal of Ecology and Environment
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• v.42 no.2
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• pp.77-84
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• 2018

Background: For understanding and evaluating a more realistic and accurate assessment of ecosystem carbon balance related with environmental change or difference, it is necessary to analyze the various interrelationships between soil respiration and environmental factors. However, the soil temperature is mainly used for gap filling and estimation of soil respiration (Rs) under environmental change. Under the fact that changes in precipitation patterns due to climate change are expected, the effects of soil moisture content (SMC) on soil respiration have not been well studied relative to soil temperature. In this study, we attempt to analyze relationship between precipitation and soil respiration in temperate deciduous broad-leaved forest for 2 years in Gwangneung. Results: The average soil temperature (Ts) measured at a depth of 5 cm during the full study period was $12.0^{\circ}C$. The minimum value for monthly Ts was $-0.4^{\circ}C$ in February 2015 and $2.0^{\circ}C$ in January 2016. The maximum monthly Ts was $23.6^{\circ}C$ in August in both years. In 2015, annual precipitation was 823.4 mm and it was 1003.8 mm in 2016. The amount of precipitation increased by 21.9% in 2016 compared to 2015, but in 2015, it rained for 8 days more than in 2016. In 2015, the pattern of low precipitation was continuously shown, and there was a long dry period as well as a period of concentrated precipitation in 2016. 473.7 mm of precipitation, which accounted for about 51.8% of the precipitation during study period, was concentrated during summer (June to August) in 2016. The maximum values of daily Rs in both years were observed on the day when precipitation of 20 mm or more. From this, the maximum Rs value in 2015 was $784.3mg\;CO_2\;m^{-2}\;h^{-1}$ in July when 26.8 mm of daily precipitation was measured. The maximum was $913.6mg\;CO_2\;m^{-2}\;h^{-1}$ in August in 2016, when 23.8 mm of daily precipitation was measured. Rs on a rainy day was 1.5~1.6 times higher than it without precipitation. Consequently, the annual Rs in 2016 was about 12% higher than it was in 2015. It was shown a result of a 14% increase in summer precipitation from 2015. Conclusions: In this study, it was concluded that the precipitation pattern has a great effect on soil respiration. We confirmed that short-term but intense precipitation suppressed soil respiration due to a rapid increase in soil moisture, while sustained and adequate precipitation activated Rs. In especially, it is very important role on Rs in potential activating period such as summer high temperature season. Therefore, the accuracy of the calculated values by functional equation can be improved by considering the precipitation in addition to the soil temperature applied as the main factor for long-term prediction of soil respiration. In addition to this, we believe that the accuracy can be further improved by introducing an estimation equation based on seasonal temperature and soil moisture.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of Korean Society of Forest Science
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• v.54 no.1
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• pp.36-40
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• 1981

with the chemical equilibrium formula by Hailwood and Horrobin, $$m=a{\cdot}((k_1k_2h)(1+k_1k_2h)^{-1}+(k_2h)_n-k_2h)^{-1})$$, based on absorption theory, monthly equilibrium moisture content(EMC) variations in southern Korea were predicted. The results were as follows: $$k_1=47370272{\cdot}10^{-7}+477345{\cdot}10^{-7}t-502775{\cdot}10^{-8}t^2$$ $$k_2=705940864{\cdot}10^{-9}+16979472{\cdot}10^{-10}t-555336{\cdot}10^{-11}t^2$$ $$w=2233848{\cdot}10^{-4}+694242{\cdot}10^{-6}+185328{\cdot}10^{-7}t^2$$ Here, it is temperature degrees in Celsius, k is the equilibria between hydrate water and dissolved water, k is the equilibria between dissolved water and the water vapour pressure surrounding atmosphere, w is the molecular weight of the polymer unit that forms the hydrate, h is the relative vapour pressure, And the formula was well agreed with the data when the constant values ${\alpha}$ were given to be 2200 in January, February, October, November and December, 1850 in March, April and May, 1920 June, July, August, and September seasonally.

• Korean Journal of Ecology and Environment
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• v.40 no.1
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• pp.155-162
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• 2007

Water balance is the major factor in forest ecosystem, and is closely related to the vegetation and topographic characteristics within a watershed. The hydrologic response of a forest watershed was investigated with the hydrological model. The deterministic, lumped parameter model (BROOK90) was selected and used to evaluate the applicability of the model for simulating daily runoff on the steep, forested watershed. The model was calibrated and validated against the streamflow data measured at the Bukmoongol watershed. The deviation in runoff volume $(D_v)$ was -1.7% for the calibration period, and the $D_v$ value for the validation period was 4.6%. The correlation coefficient (r) and model efficiency (E) on monthly basis were 0.922,0.847, respectively, for the calibration period, while the r- and E-value for the validation period were 0.941, 0.871, respectively. Overall, the simulated streamflows were close to the observations with respect to total runoff volume, seasonal runoff volume, and baseflow index for the simulation period. BROOK90 model was able to reproduce the trend of runoff with higher correlation during the simulation period.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1047-1058
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• 2003

Seasonal occurrence of high ammonia nitrogen(NH3-N) concentrations has hampered chemical treatment processes of a water plant that intakes water at Buyeo site of Geum river. Thus it is often needed to quantify the effect of Daecheong Dam ouflow on the mitigation of $NH_3$-N contamination. In this study, multiple regression models were developed for forecasting daily $NH_3$-N concentrations using 8 years of water quality and dam outflow data, and verified with another 2 years of data set. During model development, the coefficients of determination($R^2$) and model efficiency($E_{m}$) were greater than 0.95. The verification results were also satisfactory although those statistical indices were slightly reduced to 0.84∼0.94 and 0.77∼0.93, respectively. The validated model was applied to assess the effect of different amounts of dam outflow on the reduction of $NH_3$-N concentrations in 2002. The NH3-N concentrations dropped by 0.332∼0.583 mg/L on average during January∼March as outflow increases from 5 to 50cms, and was most significant on February. The results of this research show that the multiple regression approach has potential for efficient cause and effect analysis between dam outflow and downstream water quality.

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.37-44
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• 2011

COMS(Chollian) satellite which was launched on June 26, 2010 has three payloads for Ka-band communications, geostationary ocean color imaging and meteorological imaging. In order to make efficient use of the geostationary satellite, a concept of mission operations has been considered from the beginning of the satellite ground control system development. COMS satellite mission operations are classified by daily, weekly, monthly, and seasonal operations. Daily satellite operations include mission planning, command planning and transmission, telemetry processing and analysis, ranging and orbit determination, ephemeris and event prediction, and wheel off-loading set point parameter calculation. As a weekly operation, North-South station keeping maneuver and East-West station keeping maneuver should be performed on Tuesday and Thursday, respectively. Spacecraft oscillator updating parameter should be calculated and uploaded once a month. Eclipse operations should be performed during a vernal equinox and autumnal equinox season. In this paper, operational validations of the major functions in COMS SGCS are presented for the first three month of in-orbit test operations. All of the major functions have been successfully verified and the COMS SGCS will be used for the mission operations of the COMS satellite for 7 years of mission life time and even more.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.295-303
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• 2017

In this paper, we analyze the trends of the international shipping market and the domestic and foreign factors of the crisis of the domestic shipping market, and identify the characteristics of the recovery of the Busan New Port trade volume which has decreased since the crisis of the domestic shipping market We quantitatively analyzed the future volume of Busan New Port and analyzed the trends of the prediction and recovery trends. As a result of analyzing Busan New Port container cargo volume by using big data analysis tool R, the variation of Busan New Cargo container cargo volume was estimated by ARIMA model (1,0,1) (1,0,1)[12] Estimation error, AICc and BIC were the most optimal ARIMA models. Therefore, we estimated the estimated value of Busan New Port trade for 36 months by using ARIMA (1, 0, 1)[12], which is the optimal model of Busan New Port trade, and estimated 13,157,184 TEU, 13,418,123 TEU, 13,539,884 TEU, and 4,526,406 TEU, respectively, indicating that it increased by about 2%, 2%, and 1%.

• Atmosphere
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• v.25 no.2
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• pp.221-233
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• 2015

Pollen is closely related to health issues such as allergenic rhinitis and asthma as well as intensifying atopic syndrome. Information on current and future spatio-temporal distribution of allergenic pollen is needed to address such issues. In this study, the Community Multiscale Air Quality Modeling (CMAQ) was utilized as a base modeling system to forecast pollen dispersal from oak trees. Pollen emission is one of the most important parts in the dispersal modeling system. Areal emission factor was determined from gridded areal fraction of oak trees, which was produced by the analysis of the tree type maps (1:5000) obtained from the Korea Forest Service. Daily total pollen production was estimated by a robust multiple regression model of weather conditions and pollen concentration. Hourly emission factor was determined from wind speed and friction velocity. Hourly pollen emission was then calculated by multiplying areal emission factor, daily total pollen production, and hourly emission factor. Forecast data from the KMA UM LDAPS (Korea Meteorological Administration Unified Model Local Data Assimilation and Prediction System) was utilized as input. For the verification of the model, daily observed pollen concentration from 12 sites in Korea during the pollen season of 2014. Although the model showed a tendency of over-estimation in terms of the seasonal and daily mean concentrations, overall concentration was similar to the observation. Comparison at the hourly output showed distinctive delay of the peak hours by the model at the 'Pocheon' site. It was speculated that the constant release of hourly number of pollen in the modeling framework caused the delay.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.263-274
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• 2013

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.