• Journal of Environmental Health Sciences
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• v.50 no.2
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• pp.125-137
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• 2024

Background: The Korea Risk Information Surveillance System (K-RISS) was developed to enable the early detection of food and drug safety-related issues. Its goal is to deliver real-time risk indicators generated from ongoing food and drug risk monitoring. However, the existing K-RISS system suffers under several limitations. Objectives: This study aims to augment K-RISS with more detailed indicators and establish a severity standard that takes into account structural changes in the daily time series of K-RISS values. Methods: First, a Delphi survey was conducted to derive the required weights. Second, a control chart, commonly used in statistical process controls, was utilized to detect outliers and establish caution, attention, and serious levels for K-RISS values. Furthermore, Bai and Perron's method was employed to determine structural changes in K-RISS time series. Results: The study incorporated 'closeness to life' and 'sustainability' indicators into K-RISS. It obtained the necessary weights through a survey of experts for integrating variables, combining indicators by data source, and aggregating sub K-RISS values. We defined caution, attention, and serious levels for both average and maximum values of daily K-RISS. Furthermore, when structural changes were detected, leading to significant variations in daily K-RISS values according to different periods, the study systematically verified these changes and derived respective severity levels for each period. Conclusions: This study enhances the existing K-RISS system and introduces more advanced indicators. K-RISS is now more comprehensively equipped to serve as a risk warning index. The study has paved the way for an objective determination of whether the food safety risk index surpasses predefined thresholds through the application of severity levels.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.141-150
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• 2018

The purpose of this study is to evaluate the future hydrologic behavior affected by the potential climate and land use changes in upstream of Anseong-cheon watershed ($366.5km^2$) using SWAT. The HadGEM3-RA RCP 4.5 and 8.5 scenarios were used for 2030s (2020-2039) and 2050s (2040-2059) periods as the future climate change scenario. It was shown that maximum changes of precipitation ranged from -5.7% in 2030s to +18.5% in 2050s for RCP 4.5 scenarios and the temperature increased up to $1.8^{\circ}C$ and $2.6^{\circ}C$ in 2030s RCP 4.5 and 2050s 8.5 scenarios respectively based on baseline (1976-2005) period. The future land uses were predicted using the CLUE-s model by establishing logistic regression equation. The 2050 urban area were predicted to increase of 58.6% (29.0 to $46.0km^2$). The SWAT was calibrated and verified using 14 years (2002-2015) of daily streamflow with 0.86 and 0.76 Nash-Sutcliffe model efficiency (NSE) for stream flow (Q) and low flow 1/Q respectively focusing on 2 drought years (2014-2015) calibration. For future climate change only, the stream discharge showed maximum decrease of 24.2% in 2030s RCP 4.5 and turned to maximum increase of 10.9% in 2050s RCP 4.5 scenario compared with the baseline period stream discharge of 601.0 mm by the precipitation variation and gradual temperature increase. While considering both future climate and land use change, the stream discharge showed maximum decrease of 14.9% in 2030s RCP 4.5 and maximum increase of 19.5% in 2050s RCP 4.5 scenario by the urban growth and the related land use changes. The results supported that the future land use factor might be considered especially for having high potential urban growth within a watershed in the future climate change assessment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.26-32
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• 2008

Video compression schemes have been developed and used for many years. Currently, H.264/AVC is the most efficient video coding standard. The H.264/AVC baseline profile adopts CAVLC(Context-Adaptive Variable Length Coding) method as an entropy coding method. CAVLC gives better performance in compression ratios than conventional VLC(Variable Length Coding). However, because CAVLC decoder uses a lot of VLC tables, the CAVLC decoder requires a lot of area in terms of hardware. Conversely, since it must look up the VLC tables, it gives a worse performance in terms of software. In this paper, we propose a new hierarchical grouping method for the VLC tables. We can obtain an index of codes in the reconstructed VLC tables by simple arithmetic operations. In this method, the VLC tables are accessed just once in decoding a symbol. We modeled the proposed algorithm in C language, compiled under ARM ADS1.2 and simulated it with Armulator. Experimental results show that the proposed algorithm reduces execution time by about 80% and 15% compared with the H.264/AVC reference program JM(Joint Model) 10.2 and the arithmetic operation algorithm which is recently proposed, respectively.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.125-145
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• 2005

This paper presents a semantic vector space retrieval model incorporating a word sense disambiguation algorithm in an attempt to improve retrieval effectiveness. Nine Korean homonyms are selected for the sense disambiguation and retrieval experiments. The total of approximately 120,000 news articles comprise the raw test collection and 18 queries including homonyms as query words are used for the retrieval experiments. A Naive Bayes classifier and EM algorithm representing supervised and unsupervised learning algorithms respectively are used for the disambiguation process. The Naive Bayes classifier achieved $92\%$ disambiguation accuracy. while the clustering performance of the EM algorithm is $67\%$ on the average. The retrieval effectiveness of the semantic vector space model incorporating the Naive Bayes classifier showed $39.6\%$ precision achieving about $7.4\%$ improvement. However, the retrieval effectiveness of the EM algorithm-based semantic retrieval is $3\%$ lower than the baseline retrieval without disambiguation. It is worth noting that the performances of disambiguation and retrieval depend on the distribution patterns of homonyms to be disambiguated as well as the characteristics of queries.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.33-49
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• 2015

The objective of this study was to predict land use change based on the land use change scenarios for the Hwangguji river watershed, South Korea. The land use change scenario was derived from the representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The CLUE (conversion of land use and its effects) model was used to simulate the land use change. The CLUE is the modeling framework to simulate land use change considering empirically quantified relations between land use types and socioeconomic and biophysical driving factors through dynamical modeling. The Hwangguji river watershed, South Korea was selected as study area. Future land use changes in 2040, 2070, and 2100 were analyzed relative to baseline (2010) under the RCP4.5 and 8.5 scenarios. Binary logistic regressions were carried out to identify the relation between land uses and its driving factors. CN (Curve number) and impervious area based on the RCP4.5 and 8.5 scenarios were calculated and analyzed using the results of future land use changes. The land use change simulation of the RCP4.5 scenario resulted that the area of urban was forecast to increase by 12% and the area of forest was estimated to decrease by 16% between 2010 and 2100. The land use change simulation of the RCP8.5 scenario resulted that the area of urban was forecast to increase by 16% and the area of forest was estimated to decrease by 18% between 2010 and 2100. The values of Kappa and multiple resolution procedure were calculated as 0.61 and 74.03%. CN (III) and impervious area were increased by 0-1 and 0-8% from 2010 to 2100, respectively. The study findings may provide a useful tool for estimating the future land use change, which is an important factor for the future extreme flood.

• Imaging Science in Dentistry
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• v.50 no.3
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• pp.199-208
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• 2020

Purpose: This study was performed to introduce an in vivo hybrid multimodality technique involving the coregistration of micro-computed tomography (micro-CT) and high-resolution magnetic resonance imaging (HR-MRI) to concomitantly visualize and quantify mineralization and vascularization at follow-up in a rat model. Materials and Methods: Three adult female rats were randomly assigned as test subjects, with 1 rat serving as a control subject. For 20 weeks, the test rats received a weekly intravenous injection of 30 ㎍/kg zoledronic acid, and the control rat was administered a similar dose of normal saline. Bilateral extraction of the lower first and second molars was performed after 10 weeks. All rats were scanned once every 4 weeks with both micro-CT and HR-MRI. Micro-CT and HR-MRI images were registered and fused in the same 3-dimensional region to quantify blood flow velocity and trabecular bone thickness at T0 (baseline), T4 (4 weeks), T8 (8 weeks), T12 (12 weeks), T16 (16 weeks), and T20 (20 weeks). Histological assessment was the gold standard with which the findings were compared. Results: The histomorphometric images at T20 aligned with the HR-MRI findings, with both test and control rats demonstrating reduced trabecular bone vasculature and blood vessel density. The micro-CT findings were also consistent with the histomorphometric changes, which revealed that the test rats had thicker trabecular bone and smaller marrow spaces than the control rat. Conclusion: The combination of micro-CT and HR-MRI may be considered a powerful non-invasive novel technique for the longitudinal quantification of localized mineralization and vascularization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.11-21
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• 2020

This paper deals with the impact of a GNSS (Global Navigation Satellite System) antenna radome on the PCV (Phase Center Variations) and the estimated kinematic coordinates. For the Trimble and Leica antennas, specially set up CORS (Continuously Operation Reference Stations) in Korea, the PCC (Phase Center Corrections) were calculated and compared for NONE, SCIS, SCIT, and TZGD radome from the PCV model published by the IGS (International GNSS Services). The results revealed that the PCC differences compared to the NONE were limited to about 1mm in the horizontal component while those of the vertical direction ranged from a few millimeters to a maximum of 7mm. Among the radomes of which PCV were compared, the SCIT had the most significant influence on the vertical component, and its GPS (Global Positioning System) L2 and L2 PCC (Phase Center Corrections) had opposite direction. As a result of comparing the kinematic coordinates estimated by the baseline processing of 7 CORSs with an application of the PCV models of the various radomes, the SCIS which was actually installed at CORS in Korea showed 3.4mm bias, the most substantial impact on the ellipsoidal height estimation whereas the SCIT model resulted in relatively small biases.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.85-92
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• 2018

In this study investigated the factors for the career preparation behaviors of social welfare major students based on Lent et al. (1994)'s Social-cognitive Career Theory so as to provide essential baseline data for establishing proper career support strategies that suit the distinctive nature of social welfare studies. The participants of this study are 132 social welfare major students from three colleges who have completed social welfare field education. This study analyzed the relationship between cognitive factor (outcome expectation), vocational interest factor (major selection satisfaction), goal factor (career decision level), and work performance factor (career preparation behavior). For analysis, SPSS 24.0 and AMOS 24.0 were used. The analysis results are as follows. First, the model's goodness of fit was found to be at a statistically ideal level with CFI=.904, TLI=.887, and RMSEA=.068. Second, the result of analyzing the correlation between the primary variables is as follows: as outcome expectation increased, major selection satisfaction grew, which then increased the career decision level and led to the improvement in career preparation behavior. These results indicate the importance of developing a customized route support program considering the perceived and interesting factors of individual students to improve their career preparation behavior for social welfare majors.

• Journal of Veterinary Clinics
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• v.29 no.5
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• pp.372-376
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• 2012

Acute kidney injury (AKI) is a serious problem associated with high morbidity and mortality. Ischemia-reperfusion is an important cause of acute kidney injury. This study was performed to ascertain clinically useful biomarkers for the diagnosis of AKI. In three miniature pigs, AKI were induced by 60 minutes of bilateral renal ischemia by the clamping renal artery. Blood and urine samples were collected from the pigs prior to clamping (baseline) and 0, 1, 3 and 5 days post-clamping. Serum blood urea nitrogen (BUN), creatinine, sodium and uric acid were measured in serum and urine samples. Fractional excretion of sodium ($FE_{Na}$) and fractional excretion of uric acid ($FE_{UA}$) were calculated. Also, interleukin (IL)-6, IL-18, liver type fatty acid binding protein (L-FABP) and glutathione-S-transferase (GST) were detected by Western immunoblotting. Serum BUN and creatinine levels were increased significantly at day 1 post-clamping in all three miniature pigs. However, $FE_{Na}$ and $FE_{UA}$ showed marked individual differences. Western immunoblotting revealed significantly increased levels of IL-6, IL-18, L-FABP and GST in post-ischemic urine, compared to pre-clamping. While more research concerning the variance of $FE_{Na}$ and $FE_{UA}$ is needed, serum BUN, creatinine, IL-6, IL-18, L-FABP and GST may be sensitive urine biomarkers for diagnosis of AKI together with other biomarkers in the porcine ischemia-reperfusion model.