• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1215-1227
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• 2021

Surface reflectance, as a product of the absolute atmospheric correction process of low-orbit satellite imagery, is the basic data required for accurate vegetation analysis. The Commission on Earth Observation Satellite (CEOS) has conducted research and guidance to produce analysis-ready data (ARD) on surface reflectance products for immediate use by users. However, this trend is still in the early stages of research dealing with ARD for high-resolution multispectral images such as KOMPSAT-3A and CAS-500, as it targets medium- to low-resolution satellite images. This study first summarizes the types of distribution of ARD data according to existing cases. The link between Open Data Cube (ODC), the cloud-based satellite image application platforms, and ARD data was also explained. As a result, we present practical ARD deployment steps for high-resolution satellite images and several types of application models in the conceptual level for high-resolution satellite images deployed in ODC and cloud environments. In addition, data pricing policies, accuracy quality issue, platform applicability, cloud environment issues, and international cooperation regarding the proposed implementation and application model were discussed. International organizations related to Earth observation satellites, such as Group on Earth Observations (GEO) and Committee on Earth Observation Satellites (CEOS), are continuing to develop system technologies and standards for the spread of ARD and ODC, and these achievements are expanding to the private sector. Therefore, a satellite-holder country looking for worldwide markets for satellite images must develop a strategy to respond to this international trend.

• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.769-776
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• 2018

According IEC 60601-1 ed3.1 and IEC 60601-2-45 regulation, diagnostic X-ray equipment should be display to measured and calculated air kerma area product. On the clinical X ray equipment, air kerma area product dosimeter would like to have an evidence for dosimeter accuracy and energy dependency. This study was performed to indirect calibration and energy dependency test for attached type air kerma area product (KAP) dosimeter by RQR standards beam quality. On the RQR5 beam quality, attached KAP dosimeter error showed -7.5%, respectably. On the RQR9 beam quality, attached KAP dosimeter error showed -10.4%, respectably. All RQR beam quality, average absolute error was $8.30%{\pm}2.85%$, respectably. On this study, attached KAP dosimeter was satisfied to IEC 60580 and AAPM TG 190. This calibration method of KAP dosimeter will help to performance maintain for clinical KAP dosimeter.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.65-74
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• 2020

Continuous and accurate instrument of river water usage is needed for sustainable river water management. Although the instrument methods applicable to each point of use of river water are different, more precise direct instrument methods are required at the point of major open channel. Users of river water should select appropriate direct instrument methods to measure usage, but there is a lack of standards and verification research. In this study, the H-Q rating curve method, ultrasonic method, and microwave method were applied directly to the test basin in the upper basin of Mangyeong river, and the accuracy of measurement data was evaluated by comparing absolute error between discharge data calculated by instrument method. When comparing the calculated discharge of point units, the ultrasonic method showed the best results of the actual measurement. Through continuous instrument, the sum of the daily and monthly units was compared, and the ultrasonic and microwave methods were shown to be highly accurate. Based on the results of this study, it is hoped that the appropriate direct measurement method can be selected according to the importance of the river water use facility, considering that the ultrasonic method and the microwave method are relatively costly compared to the water level-flow relationship method.

• Journal of Food Hygiene and Safety
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• v.36 no.4
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• pp.281-288
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• 2021

This study was conducted to analyze and compare the concentrations of heavy metals in 430 different products of 20 types of herbal medicines available in the domestic market in Korea by Graphite Furnace-Atomic Absorption Spectrometry (GF-AAS) and automated mercury analyzer. The accuracy for lead (Pb), arsenic (As), cadmium (Cd), and mercury (Hg) was in the range 92.67-102.56%, and the precision was 0.21-6.00 relative standard deviation (RSD%), which was in compliance with the Codex acceptable range. Furthermore, the Food Analysis Performance Assessment Scheme (FAPAS) quality control (QC) material showed a recovery range of 96.7-102.0% and 0.33-4.93 RSD%. The average contents (㎍/kg) of Pb, As, Cd, and Hg in herbal medicines were 254.9 (not detected (N.D.)-2,515.2), 171.0 (N.D.-2,465.2), 99.2 (N.D.-797.1), and 6.0 (N.D.-83.6), respectively. Based on the quantitative analysis results, the heavy metal contents of 20 types of herbal medicines distributed in Korea are within the acceptable range according to the standards issued by the Ministry of Food and Drug Safety (MFDS). By using the manufacturer of herbal products as the standard for QC, the Pb, As, Cd, and Hg contents were investigated in the packaging process just before distribution to determine the actual conditions of residual heavy metals in herbal medicines. Thus, these result may contribute to monitoring the QC of herbal medicines distributed in Korea and could provide basic data for supplying safe herbal medicines to the public.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.469-476
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• 2021

The framework plan for the development of an integrated underground space map was established of preventing ground subsidence. The mapping process is expected to be completed to the level of nationwide municipal government standards by end of this year. To facilitate the utilization of the integrated underground space map, paper-based drawings for specialized organizations in underground safety impact assessment have been provided since September 2018, and services for local government officials have been provided in the underground information utilization system since May 2019. However, the map is utilized based on the information at the time of the initial development of the map, without any updates, thereby resulting in a lack of accuracy and latest information. This has led to a decrease in the utilization and reliability of the information. Therefore, in this study, for the underground structures(subway, underground shopping mall, underground passage, underground roadway, underground parking lot, utility tunnel), which are the key components of the integrated underground space map, a standard format for the submission of completed drawings is designed in accordance with Article 42 (2) of the Special Act on Underground Safety Management, which aims at laying the foundation for establishing the updated system of the integrated underground space map. In addition, through the verification of the automatically updated underground structure data based on the standard format, the reliability of the data can be assured. This format is expected to contribute to the improved utilization of the integrated underground space map in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.185-191
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• 2023

Research to analyze the future prediction of value is being conducted in various. However, it was found through the research results of each field that such future value analysis has too many variables according to each field, so the accuracy of the prediction result is low, and it is difficult to find objective key influencing factors that affect the result. In particular, since objective standards for the importance of various influencing factors have not been established, the key influencing factors have been judged and applied based on the researcher's subjectivity. Accordingly, there is a need for a reasonable process model for extracting key influencing factors that affect the prediction of volatility goods value that can be objectively applied in various fields. In this study, process modeling for extracting key influencing factors was conducted in seven steps, and the method for extracting key influencing factors was explained in detail in each step. In addition, as a result of simulation by applying Ni metal among the major variable goods in the field of raw materials using the proposed modeling, the predicted value by the existing method was 0.872% and the predicted value by applying the modeling of this study was 0.864%. conformance was confirmed.