• KOREAN JOURNAL OF CROP SCIENCE
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• v.25 no.2
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• pp.6-14
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• 1980

One of the physiological disease, sudden wiliting of Yushin variety suggested that low sunlight, excessive nitrogen application, and highly reduced soil condions either singly or combined, might be possible causes of the disorder. Some visual symptom of sudden wilting are discoloration of leaves, development of nodal roots above the soil surface, total root rot, and lodging. Those observations led to the hypothesis that suffocation of root tissues was a direct cause of the wilting. The oxygen transport characteristics of Yushin, IR262 and Tongil were examined by two methods. First, Soil-cultured plants of the three varieties were subjected to paraffin treatment to decrease the oxygen supply from the air to root tissues through the soil-water system, liquid paraffin was applied to the water surface in the pots at panicle formation stage. In this experiment, sudden wilting was observed of Yushin and IR262 at about a week after the treatment, but Tongil remained green and healthy. Wilting-resistant variety Tongil had higher oxygen release, whereas the susceptible Yushin and IR262 had lower oxygen release. Second, the number and size of the air spaces in each internode were investigated in the 5th internode from the top, all three varieties have a similar number of air spaces, although the air spaces of Thongil were larger. In the 4th internode, Tongil had plenty air spaces, Yushin and one of the Yushin's parents IR262 had scanty or none. The observations indicated that the ability of Yushin and IR262 for oxygen transport is very limited compared with that of Tongil. The limited oxygen supply due to poor development of air space in internode of rice plants may cause suffocation of root tissues, weaken metabolic activity of the tissues, and induce root rot, subsequently inducing sudden wilting and lodging under unfavorable weather, soil and cultural conditions.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.53-68
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• 1998

Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1299-1310
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• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.

• Journal of Trauma and Injury
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• v.23 no.2
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• pp.107-112
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• 2010

Purpose: Recently, the change to a more complex social structure has led to an increased frequency of traumas due to violence, accident and so on. In addition, the severity of the traumas and the frequency of penetrating injuries have also increased. Traumas to cervical and abdominal areas, what are commonly seen by general surgeons, can have mild to fatal consequences because in these areas, various organs that are vital to sustaining life are located. The exact location and characteristics of the injury are vital to treating patients with the trauma to these areas. Thus, with this background in mind, we studied, compared, and analyzed clinical manifestations of patients who were admitted to Wonju Christian hospital for penetrating injuries inflicted by themselves or others. Methods: We selected and performed a retrospective study of 64 patients who had been admitted to Wonju Christian Hospital from January 2005 to December 2009 and who had cervical or abdominal penetrating injuries clearly inflicted by themselves or others. Results: There were 51 male (79.7%) and 13 female (20.3%) patients, and the number of male patients was more dominant in this study, having a sex ratio of 3.9 to 1. The range of ages was between 20 and 86 years, and mean age was 43.2 years. There were 5 self-inflicted cervical injuries, and 19 self-inflicted abdominal injuries, making the total number of self-inflicted injury 24. Cervical and abdominal injuries caused by others were found in 11 and 29 patients, respectively. The most common area involved in self-inflicted injuries to the abdomen was the epigastric area, nine cases, and the right-side zone II was the most commonly involved area. On the other hand, in injuries inflicted by others, the left upper quadrant of the abdomen was the most common site of the injury, 14 cases. In the neck, the left-side zone II was the most injured site. In cases of self-inflicted neck injury, jugular vein damage and cervical muscle damage without deep organ injury were observed in two cases each, making them the most common. In cases with abdominal injuries, seven cases had limited abdominal wall injury, making it the most common injury. The most common deep organ injury was small bowel wounds, five cases. In patients with injuries caused by others, six had cervical muscle damage, making it the most common injury found in that area. In the abdomen, small bowel injury was found to be the most common injury, being evidenced in 13 cases. In self-inflicted injuries, a statistical analysis discovered that the total duration of admission and the number of patients admitted to the intensive care unit were significantly shorter and smaller, retrospectively, than in the patient group that had injuries caused by others. No statistically significant difference was found when the injury sequels were compared between the self-inflicted-injury and the injury-inflicted-by-others groups. Conclusion: This study revealed that, in self-inflicted abdominal injuries, injuries limited to the abdominal wall were found to be the most common, and in injuries to the cervical area inflicted by others, injuries restricted to the cervical muscle were found to be the most common. As a whole, the total duration of admission and the ICU admission time were significantly shorter in cases of self-inflicted injury. Especially, in cases of self inflicted injuries, abdominal injuries generally had a limited degree of injury. Thus, in our consideration, accurate injury assessment and an ideal treatment plan are necessary to treat these patients, and minimally invasive equipment, such as laparoscope, should be used. Also, further studies that persistently utilize aggressive surgical observations, such as abdominal ultrasound and computed tomography, for patients with penetrating injuries are needed.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of the Korean earth science society
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• v.41 no.2
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• pp.95-110
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• 2020

Comparisons between two different surface temperatures from high-resolution ECMWF ReAnalysis 5 (ERA5) and Automated Synoptic Observing System (ASOS) observations were performed to investigate the reliability of the new reanalysis data over South Korea. As ERA5 has been recently produced and provided to the public, it will be highly used in various research fields. The analysis period in this study is limited to 1999-2018 because regularly recorded hourly data have been provided for 61 ASOS stations since 1999. Topographic characteristics of the 61 ASOS locations are classified as inland, coastal, and mountain based on Digital Elevation Model (DEM) data. The spatial distributions of whole period time-averaged temperatures for ASOS and ERA5 were similar without significant differences in their values. Scatter plots between ASOS and ERA5 for three different periods of yearlong, summer, and winter confirmed the characteristics of seasonal variability, also shown in the time-series of monthly error probability density functions (PDFs). Statistical indices NMB, RMSE, R, and IOA were adopted to quantify the temperature differences, which showed no significant differences in all indices, as R and IOA were all close to 0.99. In particular, the daily mean temperature differences based on 1-hour-averaged temperature had a smaller error than the classical daily mean temperature differences, showing a higher correlation between the two data. To check if the complex topography inside one ERA5 grid cell is related to the temperature differences, the kurtosis and skewness values of 90-m DEM PDFs in a ERA5 grid cell were compared to the one-year period amplitude among those of the power spectrum in the time-series of monthly temperature error PDFs at each station, showing positive correlations. The results account for the topographic effect as one of the largest possible drivers of the difference between ASOS and ERA5.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.11-19
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• 2012

Air-borne pollen, biogenically created aerosol particle, influences Earth's radiative balance, visibility impairment, and human health. The importance of pollens has resulted in numerous experimental studies aimed at characterizing their dispersion and transport, as well as health effects. There is, however, limited scientific information concerning the optical properties of airborne pollen particles contributing to total ambient aerosols. In this study, for the first time, optical characteristics of pollen such as aerosol backscattering coefficient, aerosol extinction coefficient, and depolarization ratio at 532 nm and their effect to the atmospheric aerosol were studied by lidar remotes sensing technique. Dual-Lidar observations were carried out at the Gwangju Institute of Science & Technology (GIST) located in Gwagnju, Korea ($35.15^{\circ}E$, $126.53^{\circ}N$) for a spring pollen event from 5 to 7 May 2009. The pollen concentration was measured at the rooftop of Gwangju Bohoon hospital where the building is located 1.0 km apart from lidar site by using Burkard trap sampler. During intensive observation period, high pollen concentration was detected as 1360, 2696, and $1952m^{-3}$ in 5, 6, and 7 May, and increased lidar return signal below 1.5km altitude. Pollen optical depth retrieved from depolarization ratio was 0.036, 0.021, and 0.019 in 5, 6, and 7 May, respectively. Pollen particles mainly detected in daytime resulting increased aerosol optical depth and decrease of Angstrom exponent.

• Journal of the Korean association of regional geographers
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• v.23 no.1
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• pp.151-167
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• 2017

The main objective of this study was to investigate the climatic impact of $PM_{10}$ concentration on the temperature change pattern in Busan Metropolitan City(BMC), Korea during 2001~2015. Mean $PM_{10}$ concentration of BMC has gradually declined over the past 15 years. While the highest $PM_{10}$ concentration was observed in spring followed by winter, summer, and fall on average, the seasonal variations of $PM_{10}$ concentration differed from place to place within the city. Frequency analysis showed that the most frequently observed $PM_{10}$ concentration ranged from $20{\mu}g/m^3$ to $60{\mu}g/m^3$, which accounted for 64.6% of all daily observations. Overall, the west-high and east-low pattern of $PM_{10}$ concentration was relatively strong during the winter when the effect of yellow-dust events on the air quality was weak. Comparative analyses between $PM_{10}$ concentration and monthly temperature slope derived from generalized temperature curves indicated that the decreasing trend of $PM_{10}$ concentration was associated with increases of annual temperature range, and $PM_{10}$ concentration had a negative relationship with the temperature slope of warming months. Overall, $PM_{10}$ concentration had a weak correlation with the annual mean temperature, but it had a significant, positive correlation with the winter season, which had a dominant influence on the annual mean temperature. In terms of energy budget, it has been known that the change in $PM_{10}$ concentration contributes to the warming or cooling effect by affecting the radiative forcing due to the reflection and absorption of radiant energy. The correlation between $PM_{10}$ concentration and temperature changes in the study area was not seasonally and spatially consistent, and its significance was statistically limited partly due to the number of observations and the lack of potential socioeconomic factors relevant to urban air quality.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.447-460
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• 2022

It is well known that satellite synthetic aperture radar interferometry (InSAR) has been widely used for the observation of surface displacement owing to earthquakes, volcanoes, and subsidence very precisely. In wetlands where vegetation exists on the surface of the water, it is possible to create a water level change map with high spatial resolution over a wide area using the InSAR technique. Currently, a number of imaging radar satellites are in operation, and most of them support a ScanSAR mode observation to gather information over a large area at once. The Cienaga Grande de Santa Marta (CGSM) wetland, located in northern Colombia, is a vast wetland developed along the Caribbean coast. The CGSM wetlands face serious environmental threats from human activities such as reclamation for agricultural uses and residential purposes as well as natural causes such as sea level rise owing to climate change. Various restoration and protection plans have been conducted to conserve these invaluable environments in recognition of the ecological importance of the CGSM wetlands. Monitoring of water level changes in wetland is very important resources to understand the hydrologic characteristics and the in-situ water level gauge stations are usually utilized to measure the water level. Although it can provide very good temporal resolution of water level information, it is limited to fully understand flow pattern owing to its very coarse spatial resolution. In this study, we evaluate the L-band ALOS-2 PALSAR-2 ScanSAR mode to observe the water level change over the wide wetland area using the radar interferometric technique. In order to assess the quality of the interferometric product in the aspect of spatial resolution and coherence, we also utilized ALOS-2 PALSAR-2 stripmap high-resolution mode observations.