• Journal of the Korean earth science society
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• v.38 no.4
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• pp.269-282
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• 2017

In this study, the surface broadband emissivity ($3.0-14.0{\mu}m$) was calculated using the multiple linear regression model with narrow bands (channels 29, 30, and 31) emissivity data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System Terra satellite. The 307 types of spectral emissivity data (123 soil types, 32 vegetation types, 19 types of water bodies, 43 manmade materials, and 90 rock) with MODIS University of California Santa Barbara emissivity library and Advanced Spaceborne Thermal Emission & Reflection Radiometer spectral library were used as the spectral emissivity data for the derivation and verification of the multiple linear regression model. The derived determination coefficient ($R^2$) of multiple linear regression model had a high value of 0.95 (p<0.001) and the root mean square error between these model calculated and theoretical broadband emissivities was 0.0070. The surface broadband emissivity from our multiple linear regression model was comparable with that by Wang et al. (2005). The root mean square error between surface broadband emissivities calculated by models in this study and by Wang et al. (2005) during January was 0.0054 in Asia, Africa, and Oceania regions. The minimum and maximum differences of surface broadband emissivities between two model results were 0.0027 and 0.0067 respectively. The similar statistical results were also derived for August. The surface broadband emissivities by our multiple linear regression model could thus be acceptable. However, the various regression models according to different land covers need be applied for the more accurate calculation of the surface broadband emissivities.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.1
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• pp.25-35
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• 2005

This study analyzed total concentrations and existing forms of heavy metals in soils of Ulsan using a sequential extraction method. Soil samples were collected from 6 categorized areas including green, residential, heavy traffic, petrochemical industrial complex(IC), mechanical and shipbuilding IC, and non-ferrous metal IC areas. which represent different emission characteristics. The highest total concentrations of heavy metals by a sequential extraction analysis were observed in the soils collected from the non-ferrous metal IC area, followed by the mechanical and shipbuilding IC and heavy traffic areas. Dominant(> 50%) existing forms of Cd, Cr and Ni were residual forms followed by Fe and Mn oxides in almost areas. Residual fractions in the non-ferrous metal IC areas were relatively lower than those in other areas. However, the fractions of organic and sulphides in the IC areas were higher. The dominant farms of Cu were much different with the investigated areas. In most areas, the dominant forms of Pb and Zn were Fe and Mn oxides, followed by residual fraction for Pb. The exchangeable and carbonate fractions represent mobility of metallic elements in soils. They are also significantly affected by the environmental renditions, such as pHs of soil and rainfall. In this study the exchangeable and carbonate fractions were lower than other fractions. Because the total concentrations of heavy metals in the soils of the non-ferrous metal IC area were extremely high, however, the mobile fractions of heavy metals in the IC area would be significant. Thus a large amount of heavy metals can be released into plants, water bodies, and soils. Therefore, urgent measures, such as source control for soil remediation of heavy metals, in the non-ferrous metal IC areas are essentially required. Analysis results obtained from the sequential extraction and the aqua regia extraction showed a high correlation, whose determination coefficients(R2) of heavy metals except Cd approximately ranged from 0.7 to 0.9.

• Clinical and Experimental Pediatrics
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• v.46 no.8
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• pp.751-757
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• 2003

Purpose : Obesity is a significant health problem with medical and psychological consequences for children and adolescents. The purpose of this study was to assess the correlation between body fat percent using bioelectrical impedance(BI) and other variable methods. Methods : We measured height, weight, body mass index(BMI) and body fat percent by skinfold thickness(ST) and BI in 1,035(496 male; 539 female) children from seven to 18 years of age. The correlation coefficients between BI and each of the other different methods were obtained. The sensitivity and specificity to predict obesity by these several methods were studied. Results : Fat percent estimated by BI analysis and BMI showed a strong correlation(r=0.749). Fat percent estimated by BI analysis and ST showed a very strong correlation(r=0.835). At the 95th percentile cut-off point for BI, ST showed a sensitivity of 57.7%, and a specificity of 97.6% for estimating body fat. At the 95th percentile cut-off point for BI, BMI showed a sensitivity of 84.9%, and a specificity of 99.3% for estimating body fat. Conclusion : The fat percent estimated by BI analysis correlated strongly with ST or BMI. BI analysis is an objective and accurate method for estimating body fat in childhood obesity.

• Korean Journal of Food Science and Technology
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• v.49 no.3
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• pp.274-278
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• 2017

In this study, predictive mathematical models were developed to estimate the kinetics of Staphylococcus aureus growth in processed meat product galbitang. Processed meat product galbitang was inoculated with 0.1 mL of S. aureus culture and stored at 4, 10, 20, $37^{\circ}C$. The ${\mu}_{max}$ (maximum specific growth rate) and LPD (lag phase duration) values were calculated. The primary model was used to develop a response surface secondary model. The growth parameters were analyzed using the square root model as a function of storage temperature. The developed model was confirmed by calculating RMSE (Root Mean Square Error) values as statistic parameters. The LPD decreased, but ${\mu}_{max}$ increased with an increase in the storage temperature. At 4, 10, 20 and $37^{\circ}C$, $R^2$ was 0.99, 0.98, 0.99 and 0.99, respectively; RMSE was 0.39. The developed predictive growth model can be used to predict the risk of S. aureus contamination in processed meat product galbitang; hence, it has potential as an input model for the risk assessment.

• Progress in Medical Physics
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• v.21 no.2
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• pp.201-208
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• 2010

Recently, along with technology development of endoscopic equipment, a stent has been developed for the convenience of operation, shortening of recovery times, and reduction of patient's pain. To this end, optimal factors are simulated for the stent structure and mechanical reaction and verified using finite element analysis. In order to compare to present commercialized product such as Zilver (Cook, Bloomington, Indiana, USA) and S.M.A.R.T (Cordis, Bridgewater Towsnhip, New Jersey, USA), mechanical impact factors were determined through Taguchi factor analysis, and flexibility and expandability of all the products including ours were tested using finite element analysis. Also, important factors were sought that fulfill the optimal condition using central composition method of response surface analysis, and optimal design were carried out based on the important factors. From the centra composition method of Response surface analysis, it is found that importat factors for flexibility is stent thickness (T) and unit area (W) and those for expandability is stent thickness (T). In results, important factors for optimum condition are 0.17 mm for stent thickness (T) and $0.09\;mm^2$ for unit area (W). Determined and verified by finite element analysis in out research institute, a stent was manufactured and tested with the results of better flexibility and expandability in optimal condition compared to other products. Recently, As Finite element analysis stent mechanical property assessment for research much proceed. But time and reduce expenses research rarely stent of optimum coditions. In this research, Important factor as mechanical impact factor stent Taguchi factor analysis arrangement to find flexibility with expansibility as Finite element analysis. Also, Using to Center composition method of Response surface method appropriate optimized condition searching for important factor, these considering had design optimized. Production stent time and reduce expenses was able to do the more coincide with optimum conditions. These kind of things as application plan industry of stent development period of time and reduce expenses etc. be of help to many economic development.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.3
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• pp.261-269
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• 2015

The purpose of this research is to identify the significance of climate factors related to the significance of change of dry matter yield (DMY) of whole crop maize (WCM) by year through the exploratory data analysis. The data (124 varieties; n=993 in 7 provinces) was prepared after deletion and modification of the insufficient and repetitive data from the results (124 varieties; n=1027 in 7 provinces) of import adaptation experiment done by National Agricultural Cooperation Federation. WCM was classified into early-maturity (25 varieties, n=200), mid-maturity (40 varieties, n=409), late-maturity (27 varieties, n=234) and others (32 varieties, n=150) based on relative maturity and days to silking. For determining climate factors, 6 weather variables were generated using weather data. For detecting DMY and climate factors, SPSS21.0 was used for operating descriptive statistics and Shapiro-Wilk test. Mean DMY by year was classified into upper and lower groups, and a statistically significant difference in DMY was found between two groups (p<0.05). To find the reasons of significant difference between two groups, after statistics analysis of the climate variables, it was found that Seeding-Harvesting Accumulated Growing Degree Days (SHAGDD), Seeding-Harvesting Precipitation (SHP) and Seeding-Harvesting Hour of sunshine (SHH) were significantly different between two groups (p<0.05), whereas Seeding-Harvesting number of Days with Precipitation (SHDP) had no significant effects on DMY (p>0.05). These results indicate that the SHAGDD, SHP and SHH are related to DMY of WCM, but the comparison of R2 among three variables (SHAGDD, SHP and SHH) couldn't be obtained which is needed to be done by regression analysis as well as the prediction model of DMY in the future study.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1985-1999
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• 2021

The purpose of this study is to apply urban heat island reduction techniques(green roof, cool roof, and cool pavements using heat insulation paint or blocks) recommended by the Environmental Protection Agency (EPA) to our study area and determine their actual effects through a comparative analysis between land cover objects. To this end, the area of Mugye-ri, Jangyu-myeon, Gimhae, Gyeongsangnam-do was selected as a study area, and measurements were taken using a drone DJI Matrice 300 RTK, which was equipped with a thermal infrared sensor FLIR Vue Pro R and a visible spectrum sensor H20T 1/2.3" CMOS, 12 MP. A total of nine heat maps, land cover objects (711) as a control group, and heat island reduction technique-applied land covering objects (180) were extracted every 1 hour and 30 minutes from 7:15 am to 7:15 pm on July 27. After calculating the effect values for each of the 180 objects extracted, the effects of each technique were integrated. Through the analysis based on daytime hours, the effect of reducing heat islands was found to be 4.71℃ for cool roof; 3.40℃ for green roof; and 0.43℃ and -0.85℃ for cool pavements using heat insulation paint and blocks, respectively. Comparing the effect by time period, it was found that the heat island reduction effect of the techniques was highest at 13:00, which is near the culmination hour, on the imaging date. Between 13:00 and 14:30, the efficiency of temperature reduction changed, with -8.19℃ for cool roof, -5.56℃ for green roof, and -1.78℃ and -1.57℃ for cool pavements using heat insulation paint and blocks, respectively. This study was a case study that verified the effects of urban heat island reduction techniques through the use of high-resolution images taken with drones. In the future, it is considered that it will be possible to present case studies that directly utilize micro-satellites with high-precision spatial resolution.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1607-1622
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• 2022

The purpose of this study is to determine the actual performance of cool roof in preventing absorbed solar radiation. The spatial correlation between surface temperature and absorbed solar radiation is the method by which the performance of a cool roof can be understood and evaluated. The research area of this study is the vicinity of Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where an actual cool roof is applied. FLIR Vue Pro R thermal infrared sensor, Micasense Red-Edge multi-spectral sensor and DJI H20T visible spectral sensor was used for aerial photography, with attached to the drone DJI Matrice 300 RTK. To perform the spatial correlation analysis, thermal infrared orthomosaics, absorbed solar radiation distribution maps were constructed, and land cover features of roof were extracted based on the drone aerial photographs. The temporal scope of this research ranged over 9 points of time at intervals of about 1 hour and 30 minutes from 7:15 to 19:15 on July 27, 2021. The correlation coefficient values of 0.550 for the normal roof and 0.387 for the cool roof were obtained on a daily average basis. However, at 11:30 and 13:00, when the Solar altitude was high on the date of analysis, the difference in correlation coefficient values between the normal roof and the cool roof was 0.022, 0.024, showing similar correlations. In other time series, the values of the correlation coefficient of the normal roof are about 0.1 higher than that of the cool roof. This study assessed and evaluated the potential of an actual cool roof to prevent solar radiation heating a rooftop through correlation comparison with a normal roof, which serves as a control group, by using high-resolution drone images. The results of this research can be used as reference data when local governments or communities seek to adopt strategies to eliminate the phenomenon of urban heat islands.

• Journal of fish pathology
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• v.36 no.2
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• pp.263-275
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• 2023

Chum salmon (Oncorhynchus keta) is a species which returns to Korea for spawning and was produced as seed production at the Fisheries Resources Agency located in Uljin-gun, Gyeongsangbuk-do to preserve the species. However, farmed chum salmon showed symptoms of bacterial infection. Therefore, in this study, bacteria were isolated to identify the causative agent from chum salmon in October 2021. The isolated bacteria were identified based on the sequences of 16S rDNA, rpoD (RNA polymerase sigma factor σ⁷⁰), and vapA (A-layer) genes. Also, salinity-growth curve, biochemical characterization, antibiotic susceptibility test, and pathogenicity analysis were performed in four strains. As a result, four isolated strains were identified as Aeromonas salmonicida subsp. salmonicida. Additionally, the bacterial strains showed a decrease in growth as the salt concentration increased in the medium. All of the isolated strains exhibited γ-hemolysis, and the same biochemical properties. In the antimicrobial susceptibility test, all strains showed an inhibition zone of 40 to 44 mm for oxolinic acid, flumequine, and florfenicol. Pathogenic factors were assessed by RT-PCR at the mRNA level, and found that the four strains expresses the outer membrane ring of T3SS (ascV), inner membrane ring of T3SS (ascC), vapA, enterotoxin (act), and lipase (lip) genes which are well known to significantly contribute to the pathogenicity of A. salmonicida. The results of this study can be used as basic data to prevent A. salmonicida subsp. salmonicida occurring in sea-chum salmon in the future.