• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.425-439
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• 2020

Through the operations of advanced geostationary meteorological satellite such as Himawari-8 and GK2A, higher resolution and frequency of AOD (Aerosol Optical Depth) data have become available. In this study, we analyzed the characteristics of Himawari-8/AHI (Advanced Himawari Imager) aerosol properties using the recent 4 years (2016~2019) of Sun photometer data observed at the five stations(Seoul National University, Yonsei University, Hankuk University of Foreign Studies, Gwangju Institute of Science and Technology, Anmyon island) which is a part of the AERONET (Aerosol Robotic Network). In addition, we analyzed the causes for the AOD differences between Himawari AOD and Sun photometer AOD. The results showed that the two AOD data are very similar regardless of geographic location, in particular, for the clear condition (cloud amount < 3). However, the quality of Himawari AOD data is heavily degraded compared to that of the clear condition, in terms of bias (0.05 : 0.21), correlation (0.74 : 0.64) and RMSE (Root Mean Square Error; 0.21 : 0.51), when cloud amount is increased. In general, the large differences between two AOD data are mainly related to the cloud amount and relative humidity. The Himawari strongly overestimates the AOD at all five stations when cloud amount and relative humidity are large. However, the wind speed, precipitable water, height of cloud base and Angstrom Exponent have been shown to have no effect on the AOD differences irrespective of geographic location and cloud amount. The results suggest that caution is required when using Himawari AOD data in cloudy conditions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.10
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• pp.1393-1401
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• 2010

In this experimental study, we have examined the correlation between a human carpal motion and a surface electromyogram. The carpal motion patterns have been identified and the main muscles involved in the carpal motion have been determined by investigating the anatomical structure of a carpal. The torque acting against the carpal motion has been applied by using a device for carpal rehabilitation training, and the surface electromyogram signal corresponding to the torque at the main muscles has been measured. The root-mean-square (RMS) magnitude of the surface electromyogram signal has been calculated and used to analyze the correlation between the surface electromyogram signal and carpal motion. The experimental results have proved that for carpal torque values below $0.1\;N{\cdot}m$, the RMS magnitude of the surface electromyogram signal is linearly proportional to the carpal torque magnitude and that the carpal torque magnitude is linearly proportional to the cross-sectional area of the carpal muscles. Further, the analysis of the contribution of each muscle to the carpal motion has shown that the contribution of the most dominant muscle is consistently 60%. These three results can be applied to develop more sophisticated devices or robots for carpal rehabilitation training.

• Korean Journal of Soil Science and Fertilizer
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• v.15 no.4
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• pp.226-232
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• 1982

The incubation study of the phosphate Fertilizer Index (P.F.I) fertilizer recommandation method combining two factors-retention capacity of phosphate and available soil phosphate was conducted to test the applicability on both upland and paddy soils. The relationship between added P and the square root of the $NH_4OAc-P$ (for upland) or Bray No.1-P (for paddy) was a straight line for most of soils but was not straight for some soils which are low in phosphate absorption coefficient (P.A.C) However, the relationship between the value of the slop (termed as P.F.I) and the phosphate absorption coefficient was not showed a good correlation. The P.F.I was highly correlated with extractable Al on upland soils. The effect of extractable Al on P.F.I is more pronounced on newly reclaimed soil than cultivated upland. In case of paddy soils the P.F.I showed a high correlation with active iron contents. Also, P.F.I method was compared to NPK field trial on paddy soils to eximaine the applicability of the method in determining phosphate fertilizer recommandation.

• Journal of the Korean association of regional geographers
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• v.12 no.5
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• pp.614-630
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• 2006

Improved estimates of populations at risk for quick and effective response to natural and man-made disasters require spatial disaggregation of zonal population data because of the spatial mismatch problem in areal units between census and impact zones. This paper implements a dasymetric surface model to facilitate spatial disaggregation of the population of a census block group into populations associated with each constituent pixel and evaluates the performance of the surface-based spatial disaggregation model visually and statistically. The surface-based spatial disaggregation model employed geographic information systems (GIS) to enable dasymetric interpolation to be guided by satellite-derived land use and land cover data as additional information about the geographic distributor of population. In the spatial disaggregation, percent cover based empirical sampling and areal weighting techniques were used to objectively determine dasymetric weights for each grid cell. The dasymetric population surface for the Atlanta metropolitan area was generated by the surface-based spatial disaggregation model. The accuracy of the dasymetric population surface was tested on census counts using the root mean square error (RMSE) and an adjusted RMSE. The errors related to each census track and block group were also visualized by percent error maps. Results indicate that the dasymetric population surface provides high-precision estimates of populations as well as the detailed spatial distribution of population within census block groups. The results also demonstrate that the population surface largely tends to overestimate or underestimate population for both the rural and forested and the urban core areas.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.333-339
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• 2015

A thermal time-based phenology model of Kimchi cabbage was developed by using the field observed growth and temperature data for the purpose of accurately predicting heading and harvest dates among diverse cropping systems. In this model the lifecycle of Kimchi cabbage was separated into the growth stage and the heading stage, while the growth amount of each stage was calculated by optimal mathematical functions describing the response curves for different temperature regimes. The parameter for individual functions were derived from the 2012-2014 crop status report collected from seven farms with different cropping systems located in major Kimchi cabbage production area of South Korea (i.e., alpine Gangwon Province for the summer cultivation and coastal plains in Jeonnam Province for the autumn cultivation). For the model validation, we used an independent data set consisting of local temperature data restored by a geospatial correction scheme and observed harvest dates from 17 farms. The results showed that the root mean square error averaged across the location and time period (2012-2014) was 5.3 days for the harvest date. This model is expected to enhance the utilization of the Korea Meteorological Administration's daily temperature data in issuing agrometeorological forecasts for developmental stages of Kimchi cabbage grown widely in South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.281-289
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• 2015

Information on sunshine duration and solar radiation are indispensable to the understanding of crop growth and development. Yet, relevant variables are not available in the Korea Meteorological Administration's (KMA) digital forecast. We proposed the methods of estimating sunshine duration and solar radiation based on the 'sky condition' of digital forecast products and validated using the observed data. The sky condition values (1 for clear, 2 for partly cloudy, 3 for cloudy, and 4 for overcast) were collected from 22 weather stations at 3-hourly intervals from August 2013 to July 2015. According to the observed relationship, these data were converted to the corresponding amount of clouds on the 0 to 10 scale (0 for clear, 4 for partly cloudy, 7 for cloudy, and 10 for overcast). An equation for the cloud amount-sunshine duration conversion was derived from the 3-year observation data at three weather stations with the highest clear day sunshine ratio (i.e., Daegwallyeong, Bukgangneung, and Busan). Then, the estimated sunshine hour data were used to run the Angstrom-Prescott model which was parameterized with the long-term KMA observations, resulting in daily solar radiation for the three weather stations. Comparison of the estimated sunshine duration and solar radiation with the observed at those three stations showed that the root mean square error ranged from 1.5 to 1.7 hours for sunshine duration and from 2.5 to $3.0MJ\;m^{-2}\;day^{-1}$ for solar radiation, respectively.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.138-147
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• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

• The Journal of Engineering Geology
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• v.4 no.3
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• pp.297-309
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• 1994

For a total of 210 city and Kun areas in Korea, a model was developed to predict the amount of groundwater use at each area. At first, the total areas were classified into 3 groups by the characteristics of groundwater use: residential(87), industrial(27) and agricultural (96) areas. Among them, type areas, represented by the dominant groundwater usage for typical purposes, were selected: residential(22), industrial(8) and agricultural(32) areas. Data for the various factors possibly related to the groundwater use were statistically analyzed. The factors include, 1) agricultural area, 2) industrial area, 3) adininistrative unit area(city or Kun), 4) population, 5) groundwater capadty for community water supply, 6) average water supply for a person per day, 7) agricultural water-use, 8) industrial water-use, 9) residential wateruse, 10) rates of community water supply. The data were correlated to the total amount of groundwater use, and the correlations tested at the 95% and 99% significance levels. Influential, significantly related, factors were identified from the tests. Using the multiple regression method with the influential factors, predictive equations were drawn to calculate the amount of groundwater use for residential-industrial and agricultural areas, respectively. The equations were calibrated to minimize the RMS(root mean square) of the differences between predicted and observed groundwater use. After the validation with future data, the model can be utilized in the regional development plans to predict the maximum groundwater demand at each area.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.327-345
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• 2020

The effects of ground vibration on lava tubes during construction were studied to aid design of management and preservation measures for lava tubes. Ground conditions were assessed by RMR (Rock mass rating) and Q-system classifications for the Geomunoreum lava tubes, and vibration velocity was measured during in situ blasting tests in the Manjanggul and Yongcheondonggul lava tubes. Results indicate that the higher the rock quality, the greater the effect of vibration, although there is no clear linear relationship due to ground heterogeneity. A relationship derived between vibration velocity (PPV) and intensity (dB(V)) on the basis of blasting tests indicates that a vibration level of < 0.285 cm/sec meets the regulatory limit of 0.371 cm/sec and 65 dB(V) during daytime, and 0.285 cm/sec and 60 dB(V) during night. For blasting vibrations, square- and cube-root scaled distances are linearly correlated, with R² ≥ 0.76. On the basis of this correlation, explosive-charge weights meeting the 0.2 cm/sec vibration criterion for cultural heritage were estimated to be 2.88 kg at 50 m distance, and 11.52 kg at 100 m.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.28-33
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• 2020

Recently, the overseas construction market has been actively promoted for about 10 years, and overseas dam construction has been continuously performed. For the economic and safe construction of the dam, it is important to prepare the main dam construction plan considering the design frequency of the diversion tunnel and the cofferdam. In this respect, the prediction of river level during the rainy season is significant. Since most of the overseas dam construction sites are located in areas with poor infrastructure, the most efficient and economic method to predict the water level in dam construction is to use the upstream water level. In this study, a linear regression model, which is one of the simplest statistical methods, was proposed and examined to predict the downstream level from the upstream level. The Pyeongchang River basin, which has the characteristics of the upper stream (mountain stream), was selected as the target site and the observed water level in Pyeongchang and Panwoon gaging station were used. A regression equation was developed using the water level data set from August 22th to 27th, 2017, and its applicability was tested using the water level data set from August 28th to September 1st, 2018. The dependent variable was selected as the "level difference between two stations," and the independent variable was selected as "the level of water level in Pyeongchang station two hours ago" and the "water level change rate in Pyeongchang station (m/hr)". In addition, the accuracy of the developed equation was checked by using the regression statistics of Root Mean Square Error (RMSE), Adjusted Coefficient of Determination (ACD), and Nach Sutcliffe efficiency Coefficient (NSEC). As a result, the statistical value of the linear regression model was very high, so the downstream water level prediction using the upstream water level was examined in a highly reliable way. In addition, the results of the application of the water level change rate (m/hr) to the regression equation show that although the increase of the statistical value is not large, it is effective to reduce the water level error in the rapid level rise section. Accordingly, this is a significant advantage in estimating the evacuation water level during main dam construction to secure safety in construction site.