• Journal of Korea Water Resources Association
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• v.47 no.11
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• pp.1039-1050
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• 2014

The goal of this study is to demonstrate the diversity of model performance for various climatic elements and indicators. We evaluated the skills of the most advanced 17 General Circulation Models (GCMs) i.e., CMIP5 (Climate Model Inter-comparison project, phase 5) climate models in reproducing retrospective climatology from 1950 to 2000 over the Southeast US for the key climatic elements important in the hydrological and agricultural perspectives (i.e., precipitation, maximum and minimum temperature, and wind speed). The biases of raw CMIP5 GCMs were estimated for 16 different climatic indicators that imply mean climatology, temporal variability, extreme frequency, etc. using a grid-based observational dataset as reference. Based on the error (RMSE) and correlation (R) of GCM outputs, the error-based GCM ranks were assigned on average over the indicators. Overall, the GCMs showed much better accuracy in representing mean climatology of temperature comparing to other elements whereas few GCM showed acceptable skills for precipitation. It was also found that the model skills and ranks would be substantially different by the climatic elements, error statistics applied for evaluation, and indicators as well. This study presents significance of GCM uncertainty and the needs of considering rational strategies for climate model evaluation and selection.

• Restorative Dentistry and Endodontics
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• v.36 no.1
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• pp.59-65
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• 2011

Objectives: The purpose of this study is to compare the apical transportation and working length change in curved root canals created in resin blocks, using 3 geometrically different types of Ni-Ti files, K3, NRT, and Profile. Materials and Methods: The curvature of 30 resin blocks was measured by Schneider technique and each groups of Ni-Ti files were allocated with 10 resin blocks at random. The canals were shaped with Ni-Ti files by Crown-down technique. It was analyzed by Double radiograph superimposition method (Backman CA 1992), and for the accuracy and consistency, specially designed jig, digital X-ray, and CAD/CAM software for measurement of apical transportation were used. The amount of apical transportation was measured at 0, 1, 3, 5 mm from 'apical foramen - 0.5 mm' area, and the alteration of the working length before and after canal shaping was also measured. For statistics, Kruskal-Wallis One Way Analysis was used. Results: There was no significant difference between the groups in the amount of working length change and apical transportation at 0, 1, and 3 mm area (p = 0.027), however, the amount of apical transportation at 5 mm area showed significant difference between K3 and Profile system (p = 0.924). Conclusions: As a result of this study, the 3 geometrically different Ni-Ti files showed no significant difference in apical transportation and working length change and maintained the original root canal shape.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1101-1117
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• 2018

The clear sky radiance (CSR) is one of the baseline products of the Himawari-8 which was launched on October, 2014. The CSR contributes to numerical weather prediction (NWP) accuracy through the data assimilation; especially water vapor channel CSR has good impact on the forecast in high level atmosphere. The focus of this study is the quality analysis of the CSR of the Himawari-8 geostationary satellite. We used the operational CSR (or clear sky brightness temperature) products in JMA (Japan Meteorological Agency) as observation data; for a background field, we employed the CSR simulated using the Radiative Transfer for TOVS (RTTOV) with the atmospheric state from the global model of KMA (Korea Meteorological Administration). We investigated data characteristics and analyzed observation minus background statistics of each channel with respect to regional and seasonal variability. Overall results for the analysis period showed that the water vapor channels (6.2, 6.9, and $7.3{\mu}m$) had a positive mean bias where as the window channels(10.4, 11.2, and $12.4{\mu}m$) had a negative mean bias. The magnitude of biases and Uncertainty result varied with the regional and the seasonal conditions, thus these should be taken into account when using CSR data. This study is helpful for the pre-processing of Himawari-8/Advanced Himawari Imager (AHI) CSR data assimilation. Furthermore, this study also can contribute to preparing for the utilization of products from the Geo-Kompsat-2A (GK-2A), which will be launched in 2018 by the National Meteorological Satellite Center (NMSC) of KMA.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.357-363
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• 2019

This study was conducted to develop a stem volume table for Quercus acutissima in Korea by using Kozak's stem taper equation. In total, 2700 tree samples were collected around the country, and growth performance was investigated through compiling data on diameters by stem height and stem analysis. In order to test the stem taper equation's fitness, the fitness index (FI), bias, and mean absolute deviation (MAD) were analyzed. The fitness of the equation was estimated at 97%, bias as 0.017, and MAD turned out to be 1.118, respectively. Furthermore, there was a statistically significant volume difference between the current volume table and the new volume table (p = 0.0008, <0.005). The result indicates that using the new volume table that reflects the actual forest will reduce the loss when assessing wood resources and will improve the accuracy of forest statistics for national and local governments. A stem volume table, the main result of this research, which is utilized in the estimated stem taper equation, will provide growth information for Quercus acutissima, one of the main broadleaf species in Korea, and will function as a management indicator for rational forest management.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.141-148
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• 2021

In some intersections or busy traffic roads, there are more pedestrians in a specific period of time, and there are many traffic accidents caused by road congestion. Especially at the intersection where there are schools nearby, it is particularly important to protect the traffic safety of students in busy hours. In the past, when designing traffic lights, the safety of pedestrians was seldom taken into account, and the identification of motor vehicles and traffic optimization were mostly studied. How to keep the road smooth as far as possible under the premise of ensuring the safety of pedestrians, especially students, will be the key research direction of this paper. This paper will focus on person, motorcycle, bicycle, car and bus recognition research. Through investigation and comparison, this paper proposes to use YOLO v4 network to identify the location and quantity of objects. YOLO v4 has the characteristics of strong ability of small target recognition, high precision and fast processing speed, and sets the data acquisition object to train and test the image set. Using the statistics of the accuracy rate, error rate and omission rate of the target in the video, the network trained in this paper can accurately and effectively identify persons, motorcycles, bicycles, cars and buses in the moving images.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.461-467
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• 2020

The aim of this study was to utilize Kozak's stem taper model to develop both a stem taper equation and a stem volume table for Criptomeria japonica, a tree species distributed across Korea. A total of 1,000 sample trees were cut and collected across the country to measure their diameters by stem height. The equation was then used to estimate examine their stem shapes. Our results show that the Fitness Index for the equation was 98.7%, the Mean Absolute Deviation (MAD) was -0.0142, and the MAD was 1.1640, thus indicating a high level of fitness. A statistically significant difference (p < 0.05) was also found from the analysis of discrepancies between a current table and the new table used in this study. It is therefore suggested that the new table-with data from actual stands-will contribute to enhancing the accuracy of national and municipal forest statistics and reducing losses caused by imprecise data on available forest resources.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.763-776
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• 2020

Onions show severe volatility in production and price because crop conditions highly depend on the weather. The government has designated onions as a sensitive agricultural product, and prepared various measures to stabilize the supply and demand. First of all, preemptive and reliable information on predicting onion production is essential to implement appropriate and effective measures. This study aims to contribute to improving the accuracy of production forecasting by developing a model to estimate the final weight of onions bulb. For the analysis, multi-level model is used to reflect the hierarchical data characteristics consisting of above-ground growth data in individual units and meteorological data in parcel units. The result shows that as the number of leaf, stem diameter, and plant height in early May increase, the bulb weight increases. The amount of precipitation as well as the number of days beyond a certain temperature inhibiting carbon assimilation have negative effects on bulb weight, However, the daily range of temperature and more precipitation near the harvest season are statistically significant as positive effects. Also, it is confirmed that the fitness and explanatory power of the model is improved by considering the interaction terms between level-1 and level-2 variables.

• Korean Journal of Environmental Agriculture
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• v.41 no.2
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• pp.108-114
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• 2022

BACKGROUND: This study was performed to determine residual characteristics of soil-treated metalaxyl-M and dinotefuran in crown daisy and to evaluate the risks from intake of the residual pesticides in the crop. METHODS AND RESULTS: The pesticide granules were treated in soil on two levels, and the plants samples were collected 51 days after seeding. The analytes were extracted and partitioned using the QuEChERS extraction packet (MgSO₄ 4 g, NaCl 1 g). The quantitative methods for metalaxyl-M and dinotefuran were validated in linearity, accuracy, and precision. Risk assessments of the pesticides were performed using Korea national nutrition statistics 2019. CONCLUSION(S): The residual concentrations of metalaxyl-M in crown daisy were 0.09-0.10 mg/kg (for the treatment at 6 kg/10 a) and 0.17-0.19 mg/kg (12 kg/10 a), respectively. The residual concentrations of dinotefuran in the crop were 0.53-0.75 mg/kg (3 kg/10 a) and 1.17-1.26 mg/kg (6 kg/10 a). The amounts of pesticides were less than MRL (Maximum Residue Limits) according to the Korean MFDS (Ministry of Food and Drug Safety). The HI (Hazard Index) of metalaxyl-M and dinotefuran for consumers was 0.0075% and 0.2250%, respectively. For females in the age between 50-64, the major consumer group, the HIs of the pesticides were <3%. Considering the consumption of crown daisy, they are not considered to be of toxicological concern.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.