• Journal of Food Hygiene and Safety
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• v.26 no.2
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• pp.142-149
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• 2011

An isocratic high performance liquid chromatography (HPLC) method for routine analysis of deoxynivalenol in noodles was validated and estimated the measurement uncertainty. Noodles (dried noodle and ramyeon) were analyzed by HPLC-ultraviolet detection using immunoaffinity column for clean-up. The limits of detection (LOD) and quantification (LOQ) were 7.5 ${\mu}g$/kg and 18.8 ${\mu}g$/kg, respectively. The calibration curve showed a good linearity, with correlation coefficients $r^2$ of 0.9999 in the concentration range from 20 to 500 ${\mu}g$/kg. Recoveries and Repeatabilities expressed as coefficients of variation (CV) spiked with 200 and 500 ${\mu}g$/kg were $82{\pm}2.7%$ and $87{\pm}1.3%$% in dried noodle, and $97{\pm}1.6%$ and $91{\pm}12.0%$ in ramyeon, respectively. The uncertainty sources in measurement process were identified as sample weight, final volume, and sample concentration in extraction volume as well as components such as standard stock solution, working standard solution, 5 standard solutions, calibration curve, matrix, and instrument. Deoxynivalenol concentration and expanded uncertainty in two matrixes spiked with 200 ${\mu}g$/kg and 500 ${\mu}g$/kg were estimated to be $163.8{\pm}52.1$ and $435.2{\pm}91.6\;{\mu}g$/kg for dried noodle, and $194.3{\pm}33.0$ and $453.2{\pm}91.1\;{\mu}g$/kg for ramyeon using a coverage factor of two which gives a level of statistical confidence with approximately 95%. The most influential component among uncertainty sources was the recovery of matrix, followed by calibration curve.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Security Journal
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• no.19
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• pp.105-138
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• 2009

This study's purpose is to present the improvement of effectiveness of security activity for international conference among Several Countries which can be held hereafter. On the basis of security activity problems originating in APEC that had been held in Busan in 2005. I made up questions three times to on the members of the police, military, fire figher and national intelligence service who had experienced in Busan APEC and recognition of possible problem and possibility of improvement on each item of questions was analyzed by Delphi Method. Also interviews with 4 security experts selected from each security agency were conducted to present improvement in each part of problem. The results obtained from the face to face interview with four experts of security-enforcement agency about the role and importance of site activity stage for international conference among several countries are as followings ; First, the system that experienced security-enforcement agents can be selected for the next national security event is needed, by data-basing the security-enforcement agents who were experienced in security event as man power management. Second, the middle-term plan for the introduction of high-tech equipment and joint inspection with relevant security agents are needed for the efficient explosive technical detection. Third, high-tech security equipment could be introduced through the international high-tech security equipment exhibition. Fourth, an anti-terrorism plan should be measured by sharing information through the cooperation with domestic and international intelligence agency. Fifth, public relations should be measured systematically by organization rather than agents' individual public relations. Sixth, political consideration to secure integrative coordination with other agency is needed for security activity, through normal cooperation with fire fighting related agency such as an electric, gas, elevator company. Seventh, a definite press guideline is needed for a convenient news coverage and safety during security event.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.