• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.571-586
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• 2014

Moderate Resolution Imaging Spectroradiometer (MODIS) provides air temperature (Tair) and dew point temperature (Tdew) profiles at a spatial resolution of 5 km. New Collection 6 (C006) MODIS07_L2 atmospheric profile product has been produced since 2012. The Collection 6 algorithm has several modifications from the previous Collection 5 (C005) algorithm. This study evaluated reliabilities of two alternative datasets of surface-level Tair and Tdew derived from C005 and C006 Aqua MODIS07_L2 (MYD07_L2) products using ground measured temperatures from 77 National Weather Stations (NWS). Saturated and actual vapor pressures were calculated using MYD07_L2 Tair and Tdew. The C006 Tair showed lower mean error (ME, -0.76 K) and root mean square error (RMSE, 3.34 K) than the C005 Tair (ME = -1.89 K, RMSE = 4.06 K). In contrasts, ME and RMSE of C006 Tdew were higher than those (ME = -0.39 K, RMSE = 5.65 K) of C005 product. Application of ambient lapse rate for Tair showed appreciable improvements of estimation accuracy for both of C005 and C006, though this modification slightly increased errors in C006 Tdew. The C006 products provided better estimation of vapor pressure datasets than the C005-derived vapor pressure. Our results indicate that, except for Tdew, C006 MYD07_L2 product showed better reliability for the region of South Korea than the C005 products.

• Communications of Mathematical Education
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• v.31 no.1
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• pp.49-70
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• 2017

The purpose of this study was to investigate the relative influence of multiple error sources and to find optimal measurement conditions that obtain a desired level of reliability of a creative attitude test in mathematical creativity. This study analyzed the scores of the Creative Attitude Scale-Korea allowed to access publicly of 125 general students and 109 mathematically gifted students by performing a multivariate generalizability analysis. The main results were as follows. First, based on reliability, the Creative Attitude Scale-Korea was measured less precisely for mathematically gifted students. On the contrary, based on the conditional standard error of measurement, it was measured less precisely for general students. However, the Creative Attitude Scale-Korea showed strong reliability in both groups. Second, the optimal weights should adjust to .3, .3, .4 in mathematically gifted students and .4, .4, .2 in general students with three scoring components of divergent attitude, problem solving attitude, and convergent attitude based on the maximum reliability. Third, to approach desirable reliability, it is possible to use one component of divergent attitude in general students but three components of divergent attitude, problem solving attitude, and convergent attitude in mathematically gifted students. Finally this study proposed application plans for the Creative Attitude Scale-Korea and future directions of research.

• Journal of Korean Neurosurgical Society
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• v.55 no.3
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• pp.136-141
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• 2014

Objective : The aims of this study are to identify interpersonal differences in defining coordinates and to figure out the degree of distortion of the MRI and compare the accuracy between CT, 1.5-tesla (T) and 3.0T MRI. Methods : We compared coordinates in the CT images defined by 2 neurosurgeons. We also calculated the errors of 1.5T MRI and those of 3.0T. We compared the errors of the 1.5T with those of the 3.0T. In addition, we compared the errors in each sequence and in each axis. Results : The mean difference in the CT images between the two neurosurgeons was $0.48{\pm}0.22mm$. The mean errors of the 1.5T were $1.55{\pm}0.48mm$ (T1), $0.75{\pm}0.38$ (T2), and $1.07{\pm}0.57$ (FLAIR) and those of the 3.0T were $2.35{\pm}0.53$ (T1), $2.18{\pm}0.76$ (T2), and $2.16{\pm}0.77$ (FLAIR). The smallest mean errors out of all the axes were in the x axis : 0.28-0.34 (1.5T) and 0.31-0.52 (3.0T). The smallest errors out of all the MRI sequences were in the T2 : 0.29-0.58 (1.5T) and 0.31-1.85 (3.0T). Conclusion : There was no interpersonal difference in running the Gamma $Plan^{(R)}$ to define coordinates. The errors of the 3.0T were greater than those of the 1.5T, and these errors were not of an acceptable level. The x coordinate error was the smallest and the z coordinate error was the greatest regardless of the MRI sequence. The T2 sequence was the most accurate sequence.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.861-870
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• 2021

In this study, fused Aerosol Optical Depth (AOD) data were produced using AOD products from the Geostationary Ocean Color Imager (GOCI) onboard Communication, Oceanography and Meteorology Satellite (COMS)satellite and the Advanced Himawari Imager (AHI) onboard Himawari-8. Since the spatial resolution and the coordinate system between the satellite sensors are different, a preprocessing was first preceded. After that, using the level 1.5 AOD dataset of AErosol RObotic NETwork (AERONET), which is ground-based observation, correlations and trends between each satellite AOD and AERONET AOD were utilized to produce more accurate satellite AOD data than the originalsatellite AODs. The fused AOD were found to be more accurate than the originalsatellite AODs. Root Mean Square Error (RMSE) and mean bias of the fused AODs were calculated to be 0.13 and 0.05, respectively. We also compared errors of the fused AODs against those of the original GOCI AOD (RMSE: 0.15, mean bias: 0.11) and the original AHI AOD (RMSE: 0.15, mean bias: 0.05). It was confirmed that the fused AODs have betterspatial coverage than the original AODsin areas where there are no observations due to the presence of cloud from a single satellite.

• Journal of Platform Technology
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• v.9 no.2
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• pp.38-45
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• 2021

A frequently used bioimpedance analytical method in Korea is the segmental multi-frequency BIA (SMF-BIA) method, but it is not directly determined at a segmented impedance. This study was to compare SMF-BIA determinations with direct segmented determinations for accuracy and appropriateness of segment parameters. This study is to compare the segment parameters, accuracy and appropriateness of the multi-frequency segmental bioimpedance analysis. To this end, 108 elderly individuals were measured. Segmented bioelectrical measurements obtained from a SMF-BIA (Inbody S10) at 50 kHz and measured with a phase sensitive single frequency device (SF-BIA, bia-101, RJL / akern systems) were compared. The significant difference (%) was demonstrated between single - and multiple frequency determinations of the right upper limb (R = 35.5 ± 6.2%, P < 0.001; Xc = 2.7 ± 7.6%, P < 0.01), left upper limb difference (R= 33. 9 ± 6.0%, P < 0.001; Xc = 2.8 ± 8.3%, P < 0.01), right lower limb difference (R = 18.6 ± 4.3%, P < 0.001; Xc = 25.8 ± 10.0%, P < 0.001), left lower limb difference (R = 18.0 ± 4.7%, P < 0.001; Xc = 31.8%). Of the results determined with the two BIA methods, the impedance measurements of the limbs and whole body showed a high correlation (RA: R = 0. 950, LA: R = 0. 949, RL: R = 0.899, LL: R = 0.88), and in the agreement test, the impedance values of the upper limbs and whole body also showed strong agreement (ICC > 0.9), but in the Xc, the correlation was weak. In conclusion, it was found that although bioimpedance devices had significantly different characteristics and inconsistent cross sectionally, there was a high population level agreement in the upper and lower extremities in determining segmental resistance value changes. But a large error was found on the trunk. Further studies were needed for reducing the error.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.457-464
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• 2021

The advent of the big data era has enabled the rapid development of deep learning that learns rules by itself from data. In particular, the performance of CNN algorithms has reached the level of self-adjusting the source data itself. However, the existing image processing method only deals with the image data itself, and does not sufficiently consider the heterogeneous environment in which the image is generated. Images generated in a heterogeneous environment may have the same information, but their features may be expressed differently depending on the photographing environment. This means that not only the different environmental information of each image but also the same information are represented by different features, which may degrade the performance of the image analysis model. Therefore, in this paper, we propose a method to improve the performance of the image color constancy model based on Adversarial Learning that uses image data generated in a heterogeneous environment simultaneously. Specifically, the proposed methodology operates with the interaction of the 'Domain Discriminator' that predicts the environment in which the image was taken and the 'Illumination Estimator' that predicts the lighting value. As a result of conducting an experiment on 7,022 images taken in heterogeneous environments to evaluate the performance of the proposed methodology, the proposed methodology showed superior performance in terms of Angular Error compared to the existing methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.395-402
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• 2010

The seriousness of a traffic accident appears relatively higher on the curve sections compared with the straight sections due to a change in speed caused by a change in the driver's sight. In particular, the visual distortion phenomenon, one of the dangerous factors taking place on the curve sections, appears different according to the road's geometric design. Although it is a genuinely principal design factor which should be necessarily considered in designing a road, the previous researches on establishing the design standards for it have been insufficiently conducted. As a result, the establishment of the road design standards for the curve sections considering the sight distortion phenomenon is desperately required. This research examined the previous researches on the driver's behaviors, the driver's sight characteristics and the perceived curve radius on the curve sections, and developed the theoretical model of perceived curve radius to which a mathematical technique is applied in consideration of the visual distortion phenomenon on the two-lane curve sections in a local area. In addition, after the theoretical visual distortion was calculated on the basis of the theoretical model of perceived curve radius, the range of error on the theoretical recognition radius model formula was verified through comparing it with the previous researches' experiential visual distortion level and analyzing both of them. As a result, it was observed that as the curve radius practically increases in the theoretical recognition curve radius, the range of error tends to go down, which reflects well the characteristics of the curve sections on the road. Based on this research, it is expected that this research will be helpful to eliminate the safety defects when designing the curve sections and contribute to develop the road design standards considering human factors in the future.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.467-479
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• 2023

Satellite altimeters have continuously observed sea surface height (SSH) in the global ocean for the past 30 years, providing clear evidence of the rise in global mean sea level based on observational data. Accurate altimeter-observed SSH is essential to study the spatial and temporal variability of SSH in regional seas. In this study, we used measurements from the Ieodo Ocean Research Station (IORS) and validate SSHs observed by satellite altimeters (Envisat, Jason-1, Jason-2, SARAL, Jason-3, and Sentinel-3A/B). Bias and root mean square error of SSH for each satellite ranged from 1.58 to 4.69 cm and 6.33 to 9.67 cm, respectively. As the matchup distance between satellite ground tracks and the IORS increased, the error of satellite SSHs significantly amplified. In order to validate the correction of the tide and atmospheric effect of the satellite data, the tide was estimated using harmonic analysis, and inverse barometer effect was calculated using atmospheric pressure data at the IORS. To achieve accurate tidal corrections for satellite SSH data in the seas around the Korean Peninsula, it was confirmed that improving the accuracy of tide data used in satellites is necessary.