• Journal of the Society of Naval Architects of Korea
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• v.38 no.3
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• pp.93-106
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• 2001

T-joint and hopper knuckle joint models are typical welded joints in ship structure, which are very susceptible to fatigue damage under service condition. Fatigue test and fracture mechanical analysis were performed on these joints to find out characteristics of fatigue behavior. Unified S-N curve was developed from the test results of these two types of joint using hot spot stress concept, and also propagation life was also estimated using Paris' crack propagation law. Residual stress effect on propagation life was considered in calculating propagation life, as was done with thermo-elasto-plastic FE analysis and residual stress intensity factor calculation. Fatigue life of similar kinds of welded joint could be predicted with this unified S-N curve and fracture mechanical analysis technique.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.147-152
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• 2018

Underwater transient signals are complex, variable and nonlinear, resulting in a difficulty in accurate modeling with reference patterns. We analyze one type of underwater transient signals, in the form of whale sounds, using the MFCC(Mel-Frequency Cepstral Constant) and synthesize them from the MFCC and the weighted $L_2$-norm minimization techniques. The whales in this experiments are Humpback whales, Right whales, Blue whales, Gray whales, Minke whales. The 20th MFCC coefficients are extracted from the original signals using the MATLAB programming and reconstructed using the weighted $L_2$-norm minimization with the inverse MFCC. Finally, we could find the optimum weighted factor, 3~4 for reconstruction of whale sounds.

• Journal of Radiation Protection and Research
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• v.29 no.1
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• pp.65-71
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• 2004

A multi-attribute utility analysis was investigated as a tool for the decision support of countermeasures in early phase of a nuclear accident. The utility function of attributes was assumed to be the second order polynomial expressions, and the weighting constant of attributes was determined using a swing weighting method. Because the main objective of this study focuses on the applicability of a multi-attribute utility analysis as a tool for the decision support of countermeasures in early phase of a nuclear accident, less quantifiable attributes were not included due to lack of information. In postulated accidental scenarios for the application of the designed methodology, the variation of the numerical values of total utility for the considered actions, e.g. sheltering, evacuation and no action, was investigated according to the variation of attributes. As a result, it was shown that the numerical values of total utility for the actions are distinctly different depending on the exposure dose and monetary value of dose. As increasing in both attributes, the rank of the numerical values of total utility increased for evacuation, which is more extreme action than for sheltering, while that of no action decreased. As expected probability of high dose is higher, the break-even values for the monetary value of dose, which are the monetary value of dose when the ranking of actions is changed, were lower. In audition, as aversion psychology for dose is higher, the break-even values for dose were lower.

• Analytical Science and Technology
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• v.31 no.4
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• pp.161-170
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• 2018

The epidemic of disorders associated with synthetic stimulants, such as methamphetamine (MA) and amphetamine (AP), is a health, social, legal, and financial problem. Owing to the high potential of their abuse and addiction, reliable analytical methods are required to detect and identify MA, AP, and their metabolites in biological samples. Thus, a dilute-and-shoot liquid chromatography-tandem mass spectrophotometry (LC-MS/MS) was developed for simultaneous determination of MA, 4-hydroxymethamphetamine (4HMA), AP, and 4-hydroxyamphetamine (4HA) in urine. Urine sample ($100{\mu}L$) was mixed with $50{\mu}L$ of mobile phase consisting of 0.4 % formic acid and methanol and $50{\mu}L$ of working internal-standard solution. Aliquots of $8{\mu}L$ diluted urine was injected into the LC-MS/MS system. For all analytes, chromatographic separation was performed using a C18 reversed-phase column with gradient elution and a total run time of 5 min. The identification and quantification were performed by multiple reaction monitoring (MRM). Linear least-squares regression was conducted to generate a calibration curve, with $1/x^2$ as the weighting factor. The linear ranges were 2.0-200, 1.0-800, and 10-2500 ng/mL for 4HA and 4HMA, AP, and MA, respectively. The inter- and intraday precisions were within 6.6 %, whereas the inter- and intraday accuracies ranged from -14.9 to 11.3 %. The low limits of quantification were 2.0 ng/mL (4HA and 4HMA), 1.0 ng/mL (AP), and 10 ng/mL (MA). The proposed method exhibited satisfactory selectivity, dilution integrity, matrix effect, and stability, which are required for validation. Moreover, the purification efficiency of high-speed centrifugation was clearly higher than 6-15 % for QC samples (n=5), which was higher than that of the membrane-filtration method. The applicability of the proposed method was tested by forensic analysis of urine samples from drug abusers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.41-46
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• 2011

Reliable information on the surface solar radiation is indispensable for rebuilding food production system in the famine plagued North Korea. However, transfer of the related modeling technology of South Korea is not possible simply because raw data such as solar radiation or sunshine duration are not available. The objective of this study is restoring solar radiation data at 27 synoptic stations in North Korea by using satellite remote sensing data. We derived relationships between MODIS radiation estimates and the observed solar radiation at 18 locations in South Korea. The relationships were used to adjust the MODIS based radiation data and to restore solar radiation data at those pixels corresponding to the 27 North Korean synoptic stations. Inverse distance weighted averaging of the restored solar radiation data resulted in gridded surfaces of monthly solar radiation for 4 decadal periods (1983-1990, 1991-2000 and 2001-2010), respectively. For a direct application of these products, we produced solar irradiance estimates for each sub-grid cell with a 30 m spacing based on a sun-slope geometry. These products are expected to assist planning of the North Korean agriculture and, if combined with the already prepared South Korean data, can be used for climate change impact assessment across the whole Peninsula.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.94-104
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• 2019

In this study, we investigated the spatial variation in land use and topographic effects on water quality at the Geum river watershed in South Korea, using the ordinary least squares(OLS) and geographically weighted regression (GWR) models. Understanding the complex interactions between land use, slope, elevation, and water quality is essential for water pollution control and watershed management. We monitored four water quality indicators -total phosphorus, total nitrogen, biochemical oxygen demand, and dissolved oxygen levels - across three land use types (urban, agricultural, and forested) and two topographic features (elevation and mean slope). Results from GWR modeling revealed that land use and topography did not affect water quality consistently through space, but instead exhibited substantial spatial non-stationarity. The GWR model performed better than the OLS model as it produced a higher adjusted $R^2$ value. Spatial variation in interactions among variables could be visualized by mapping $R^2$ values from the GWR model at fine spatial resolution. Using the GWR model, we were able to identify local pollution sources, determine habitat status, and recommend appropriate land-use planning policies for watershed management.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.366-373
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• 2021

This study attempted to establish a field data-based write analysis standard by analyzing field observation data, which is non-linear data of southern garlic. Five regions, including Goheung, Namhae, Sinan, Changnyeong, and Haenam, were selected for analysis. Observation values for each observation station were extracted from the temperature data of farmland in the region through inverse distance weighted. Southern-type garlic production and temperature data were collected for 10 years, from 2010 to 2019. Local regression analysis (Kernel) of the obtained data was performed, and growth temperatures were analyzed, such as 0.8 (18.781℃), 0.9 (18.930℃), 1.0 (19.542℃), 1.1 (20.165℃), and 1.2 (21.042℃) depending on the bandwidth. The analyzed optimum temperature and the grown temperature (4℃/25℃) were applied to extract the growth temperature for each temperature by using the temperature response model analysis. Regression analysis and correlation analysis were performed between the analyzed growth temperature and production data. The coefficient of determination(R²) was analyzed as 0.325 to 0.438, and in the correlation analysis, the correlation coefficient of 0.57 to 0.66 was analyzed at the significance probability 0.001 level. Overall, as the bandwidth increased, the coefficient of determination was higher. However, in all analyses except bandwidth 1.0, it was analyzed that all variables were not used due to bias. The purpose of this study is to accommodate all data through non-linear data. It was analyzed that bandwidth 1.0 with a high coefficient of determination while accepting modeling as a whole is the most suitable.