• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.158-169
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• 2019

Ergot alkaloids are mycotoxin produced by fungi of the Claviceps genus, mainly by Claviceps purpurea in EU. Recently obtained informations indicates necessity for control the ergot in imported grains. Recent occurrence data of ergot alkaloids from EU countries indicate the necessities of management and control these toxins from the imported grains like rye, wheat, oat etc. The aim of this study is to optimize the liquid chromatography-tandem mass spectrometry method for determination of ergot alkaloids (ergometrine, ergosine, ergotamine, ergocornine, ergocryptine, ergocristine and their epimers (-inines) from grain and grain-based food. The test method was optimized by extracting the sample with acetonitrile containing 2 mM ammonium carbonate, purification with Mycosep cartridge, and instrumental analysis by LC-MS/MS using Syncronis C18 column. The standard calibration curves showed linearity with correlation coefficents; $R^2$ >0.99. Mean recoveries ranged from 72.0 to 111.3% at three different fortified levels (20, 50, and $100{\mu}g/kg$). The correlation coefficient expressed as precision was within the range of 1.9-12.9%. The limit or quantifications (LOQ) ranged from 0.012 to $0.058{\mu}g/kg$. The developed analytical method met the criteria of AOAC Int. and CAC validation parameters like accuracy and sensitivity. As a result, it was confirmed that the test method developed in this study is suitable for the simultaneous analysis of six species of ergot alkaloid from grains and grain products.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.465-486
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• 2021

Purpose: Since most disaster information systems are centered on non-disabled people, the reality is that there is a lack of disaster information delivery systems for the vulnerable, such as the disabled, the elderly, and children, who are relatively vulnerable to disasters. The purpose of the service is to improve the safety of the disabled and the elderly by eliminating blind spots of informatization and establishing customized disaster information services to respond to disasters through IoT-based integrated control technology. Method: The model at the core of this study is the disaster alert propagation model and evacuation support model, and it shall be developed by reflecting the behavioral characteristics of the disabled and the elderly in the event of a disaster. The disaster alert propagation model spreads disaster situations collected using IoT technology, and the evacuation support model uses geomagnetic field-based measuring technology to identify the user's indoor location and help the disabled and the elderly evacuate safely. Results: Demonstration model demonstration resulted in an efficient qualitative evaluation of indoor location accuracy, such as the suitability of evacuation route guidance and satisfaction of services from the user's perspective. Conclusion: Disaster information and evacuation support services were established for the safety vulnerable groups of mobile app for model verification. The disaster situation was demonstrated through experts in the related fields and the disabled by limiting it to the fire situation. It was evaluated as "satisfaction" in the adequacy of disaster information delivery and evacuation support, and its functional satisfaction and user UI were evaluated as "normal" due to the nature of the pilot model. Through this, the disaster information and evacuation support services presented in this study were evaluated to support the safety vulnerable groups to a faster disaster evacuation without missing the golden time of disaster evacuation.

• Journal of radiological science and technology
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• v.40 no.1
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• pp.27-34
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• 2017

The author investigated interventional radiology patient doses in several other countries, assessed accuracy of DAP meters embedded in intervention equipments in domestic country, conducted measurement of patient doses for 13 major interventional procedures with use of Dose Area Product(DAP) meters from 23 hospitals in Korea, and referred to 8,415 cases of domestic data related to interventional procedures by radiation exposure after evaluation the actual effectives of dose reduction variables through phantom test. Finally, dose reference level for major interventional procedures was suggested. In this study, guidelines for patient doses were $237.7Gy{\cdot}cm^2$ in TACE, $17.3Gy{\cdot}cm^2$ in AVF, $114.1Gy{\cdot}cm^2$ in LE PTA & STENT, $188.5Gy{\cdot}cm^2$ in TFCA, $383.5Gy{\cdot}cm^2$ in Aneurysm Coil, $64.6Gy{\cdot}cm^2$ in PTBD, $64.6Gy{\cdot}cm^2$ in Biliary Stent, $22.4Gy{\cdot}cm^2$ in PCN, $4.3Gy{\cdot}cm^2$ in Hickman, $2.8Gy{\cdot}cm^2$ in Chemo-port, $4.4Gy{\cdot}cm^2$ in Perm-Cather, $17.1Gy{\cdot}cm^2$ in PCD, and $357.9Gy{\cdot}cm^2$ in Vis, EMB. Dose referenece level acquired in this study is considered to be able to use as minimal guidelines for reducing patient dose in the interventional radiology procedures. For the changes and advances of materials and development of equipments and procedures in the interventional radiology procedures, further studies and monitorings are needed on dose reference level Korean DAP dose conversion factor for the domestic procedures.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.30 no.4
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• pp.342-352
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• 2020

Objectives: The characteristics of research workers are different from those working in the manufacturing industry. Furthermore, the reagents used change according to the research due to the characteristics of the laboratory, and the amounts used vary. In addition, since the working time changes almost every day, it is difficult to adjust the time according to exposure standards. There are also difficulties in setting standards as in the manufacturing industry since laboratory environments and the types of experiments performed are all different. For these reasons, the measurement of the working environment of research workers is not realistically carried out within the legal framework, there is a concern that the accuracy of measurement results may be degraded, and there are difficulties in securing data. The exposure evaluation based on an eight-hour time-weighted average used for measuring the working environment to be studied in this study may not be appropriate, but it was judged and consequently applied as the most suitable method among the recognized test methods. Methods: The investigation of the use of chemical substances in the research laboratory, which is the subject of this study, was conducted in the order of carrying out work environment measurement, sample analysis, and result analysis. In the case of the use of chemical substances, after organizing the substances to be measured in the working environment, the research workers were asked to write down the status, frequency, and period of use. Work environment measurement and sample analysis were conducted by a recognized test method, and the results were compared with the exposure standards (TWA: time weighted average value) for chemical substances and physical factors. Results: For the substances subject to work environment measurement, the department of chemical engineering was the most exposed, followed by the department of chemistry. This can lead to exposure to a variety of chemicals in departmental laboratories that primarily deal with chemicals, including acetone, hydrogen peroxide, nitric acid, sodium hydroxide, and normal hexane. Hydrogen chloride was measured higher than the average level of domestic work environment measurements. This can suggest that researchers in research activities should also be managed within the work environment measurement system. As a result of a comparison between the professional science and technology service industry and the education service industry, which are the most similar business types to university research laboratories among the domestic work environment measurements provided by the Korea Safety and Health Agency, acetone, dichloromethane, hydrogen peroxide, sodium hydroxide, nitric acid, normal hexane, and hydrogen chloride are items that appear higher than the average level. This can also be expressed as a basis for supporting management within the work environment measurement system. Conclusions: In the case of research activity workers' work environment measurement and management, specific details can be presented as follows. When changing projects and research, work environment measurement is carried out, and work environment measurement targets and methods are determined by the measurement and analysis method determined by the Ministry of Employment and Labor. The measurement results and exposure standards apply exposure standards for chemical substances and physical factors by the Ministry of Employment and Labor. Implementation costs include safety management expenses and submission of improvement plans when exposure standards are exceeded. The results of this study were presented only for the measurement of the working environment among the minimum health management measures for research workers, but it is necessary to prepare a system to improve the level of safety and health.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.67-75
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• 2022

The aim of this study is to establish a new QC method that can simultaneously evaluate the resolution of the x/y plane and the z-axis by producing a phantom that can reflect exposure and reconstruction parameter of MDCT system. It was used with Aquilion ONE(Cannon Medical System, Otawara, Japan), and the examination was scanned using of 120 kV, 260 mA, and the D-FOV of 300 mm². It produced new SSP phantom modules in which two aluminum plates inclined at 45° to a vertical axis and a transverse axis to evaluate high contrast resolution of x/y plane and z axis. And it changed factors such as the algorithm, distance from gantry iso-center. All images were reconstructed in five steps from 0.6 mm to 10.0 mm slice thickness to measure resolution of x/y plane and z-axis. The image data measured FWHM and FWTM using Profile tool of Aquarius iNtusion Edition ver. 4.4.13 P6 software(Terarecon, California, USA), and analysed SPQI and signal intensity by ImageJ program(v1.53n, National Institutes of Health, USA). It decreased by 4.09~11.99%, 4.12~35.52%, and 4.70~37.64% in slice thickness of 2.5 mm, 5.0 mm, and 10.0 mm for evaluating the high contrast resolution of x/y plane according to distance from gantry iso-center. Therefore, the high contrast resolution of the x/y plane decreased when the distance from the iso-center increased or the slice thickness increased. Additionally, the slice thicknesses of 2.5 mm, 5.0 mm, and 10.0 mm with a high algorithm increased 74.83, 15.18 and 81.25%. The FWHM was almost constant on the measured SSP graph for evaluating the accuracy of slice thickness which represents the resolution of x/y plane and z-axis, but it was measured to be higher than the nominal slice thickness set by user. The FWHM and FWTM of z-axis with axial scan mode tended to increase significantly as the distance increased from gantry iso-center than the helical mode. Particularly, the thinner slice thickness that increased error range compare with the nominal slice thickness. The SPQI increased with thick slice thickness, and that was closer to 90% in the helical scan than the axial scan. In conclusion, by producing a phantom suitable for MDCT detectors and capable of quantitative resolution evaluation, it can be used as a specific method in the management of research quality and management of outdated equipment. Thus, it is expected to contribute greatly to the discrimination of lesions in the field of CT imaging.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.591-603
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• 2022

The color and optical properties of seawater are determined by the interaction between dissolved organic and inorganic substances and plankton contained in it. The Ieodo - Ocean Research Institute (I-ORS), located in the East China Sea, is affected by the low salinity of the Yangtze River in the west and the Tsushima Warm Current in the south. Thus, it is a suitable site for analyzing the fluctuations in circulation and optical properties around the Korean Peninsula. In this study, seawater surrounding the I-ORS was classified according to its optical characteristics using the satellite remote reflectance observed with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua and National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) from January 2016 to December 2020. Additionally, the variation characteristics of optical water types (OWTs) from different seasons were presented. A total of 59,532 satellite match-up data (d ≤ 10 km) collected from seawater surrounding the I-ORS were classified into 23 types using the spectral angle mapper. The OWTs appearing in relatively clear waters surrounding the I-ORS were observed to be greater than 50% of the total. The maximum OWTs frequency in summer and winter was opposite according to season. In particular, the OWTs corresponding to optically clear seawater were primarily present in the summer. However, the same OWTs were lower than overall 1% rate in winter. Considering the OWTs fluctuations in the East China Sea, the I-ORS is inferred to be located in the transition zone of seawater. This study contributes in understanding the optical characteristics of seawater and improving the accuracy of satellite ocean color variables.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.