• Journal of Food Hygiene and Safety
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• v.32 no.6
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• pp.493-499
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• 2017

This study was conducted to monitor the current status of pesticide residues and sulfur dioxide in a total of 91 functional rice products from February to October 2016. Multi class pesticide multiresidue methods of 220 pesticides was performed by the GC/ECD, GC/NPD, GC/TOF/MS, LC/PDA, and LC/FLD. As a result of analysis, the pesticides were detected in 3 samples, representing a detection rate of 3.3%. The kind of pesticides was propiconazole and isoprothiolane used for germicide in agriculture or plant growth regulator purposes. The detected levels were 0.0340~0.0566 mg/kg, which were under the MRL (Maximum Residues Limits). The contents of sulfur dioxide in 91 samples by the Monier-Williams method were not detected. Risk assessment of pesticides evaluated using human health exposure with the ratio of EDI (Estimated daily intake) to ADI (Acceptable daily intake). %ADI (the ratios of EDI to ADI) were 0.24~1.25% with safety level.

• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.119-126
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• 1987

This study was conducted to elucidate the most appropriate method to obtain auxotrophic mutants from Valsa ceratosperma, the causal fungus of apple canker, which may be used as a gene marker in detecting the transfer of the factors of avirulent strains to virulent strains. Among the 3 kinds of synthetic media tested, each have two formula for minimal and complete, the medium which has been used in study of Endothia parasitica (E. P medium) was turned out to be most appropriate for the growth of V. ceratosperma. A medium for single colony formation from pycnidiospore of this fungus was developed by adding 0.5% L - sorbose to the E. P minimal medium. The period of incubation in dark for preventing the photoreactivation after U. V irradiation was estimated as about 60hrs at which most of the spores become binucleate. Largest number of putative auxotrophs were obtained at about 50second of irradiation to the spores smeared on the medium for single colony formation, at which the survival rate of spores was 5 to 6 percent. With these method developed in this experiment, 161 isolates of putative auxotrophs were detected among which the nutrient requirement for 10 isolates were determined. Five out of 10 mutants were still virulent to apple tree and all but one could not sporulate.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.383-389
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• 2009

The main objective of the research is a feasibility study on the intertidal zone using a X-band radar satellite, TerraSAR-X. The TerraSAR-X data have been acquired in the west coast of Korea where large tidal flats, Ganghwa and Yeongjong tidal flats, are developed. Investigations include: 1) waterline and backscattering characteristics of the high resolution X-band images in tidal flats; 2) polarimetric signature of halophytes (or salt marsh plants), specifically Suaeda japonica; and 3) phase and coherence of interferometric pairs. Waterlines from TerraSAR-X data satisfy the requirement of horizontal accuracy of 60 m that corresponds to 20 cm in average height difference while current other spaceborne SAR systems could not meet the requirement. HH-polarization was the best for extraction of waterline, and its geometric position is reliable due to the short wavelength and accurate orbit control of the TerraSAR-X. A halophyte or salt marsh plant, Suaeda japonica, is an indicator of local sea level change. From X-band ground radar measurements, a dual polarization of VV/VH-pol. is anticipated to be the best for detection of the plant with about 9 dB difference at 35 degree incidence angle. However, TerraSAR-X HH/TV dual polarization was turned to be more effective for salt marsh monitoring. The HH-HV value was the maximum of about 7.9 dB at 31.6 degree incidence angle, which is fairly consistent with the results of X-band ground radar measurement. The boundary of salt marsh is effectively traceable specifically by TerraSAR-X cross-polarization data. While interferometric phase is not coherent within normal tidal flat, areas of salt marsh where the landization is preceded show coherent interferometric phases regardless of seasons or tide conditions. Although TerraSAR-X interferometry may not be effective to directly measure height or changes in tidal flat surface, TanDEM-X or other future X-band SAR tandem missions within one-day interval would be useful for mapping tidal flat topography.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.3
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• pp.217-224
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• 2019

Phthalates, endocrine disrupting chemicals, are similar in structure to sex hormones and mainly show reproductive toxicity and developmental toxicity. In this study, we analyzed 11 phthalates, including 3 kinds of phthalates prohibited in cosmetic use and 8 kinds of phthalates regulated in 'Common standards for children's products safety' and EU cosmetic regulation (EC No. 1223/2009). The phthalate analysis was optimized using GC-MS/MS. In analytical method validation, this method was satisfied in specificity, linearity, recovery rate, accuracy and MQL. Therefore, we used this method to analyze 82 products of Nail cosmetics & polish. Although six phthalates such as DBP, BBP, DEHP, DPP, DIBP and DIDP were detected at concentrations of $1.0{\sim}59.8{\mu}g/g$g, they were suitable to Korean cosmetic standards. DIBP and DBP were detected at concentration of $1.1{\sim}2.6{\mu}g/g$ in artificial nail, DBP and DEHP were $1.4{\sim}2.5{\mu}g/g$ in glue for nails, and DIBP, DBP, and DEHP were $2.5{\sim}33.3{\mu}g/g$ in nail stickers. Although substances such as DBP and DEHP in artificial nail, Glue for nails, and nail stickers were detected, they were suitable to 'Common safety standards for children's products. DIBP is not a regulated substance in Korea but showed the third highest detection rate following DBP (84.6%) and DEHP (63.4%). The concentration of phthalates detected in nail products is considered to be safe in current standards but continuous monitoring and research about non-regulated substances are also needed to be considered.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.253-264
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• 2022

In this study, we investigated the occurrence of viral diseases in the early growth stage of soybean to establish management practices. We collected 83 soybean samples showing abnormal symptoms, approximately 3-4 weeks after seeding in the breeding field of the National Institute of Crop Science. Viruses were detected in the collected samples using reverse transcription polymerase chain reaction (RT-PCR) and metatranscriptome analysis of all those samples. The incidence of viral diseases in the field was less than 1% overall and up to 50% in certain cultivars and lines. RT-PCR and metatranscriptome analysis detected Soybean yellow mottle mosaic virus (SYMMV), Soybean mosaic virus (SMV), Soybean yellow common mosaic virus, Peanut stunt virus, and soybean geminivirus A (SGVA). Among these detected viruses, SYMMV and SMV were identified as major viruses causing infection in the early growth stage of soybean, with detection rates of 53.7% and 42.6%, respectively. Soybeans infected with SYMMV showed typical mosaic symptoms, whereas those infected with SMV showed a variety of symptoms such as mosaic, mottle, stunt, and chlorotic spots. Transmission characteristics of these viruses are variable, such that SMV is primarily transmitted by seeds, whereas SYMMV could be transmitted by insects, soil, and seeds. In this study, SGVA was detected in the early growth stage of soybean, and research on the current status and its effects on soybean after the early growth stage should be conducted.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.185-209
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• 2023

With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often converged. In particular, the robotics field is combined with technologies such as big data, artificial intelligence, VR, and digital twins. Accordingly, much research using robotics is being conducted, which is applied to distribution, airports, hotels, restaurants, and transportation fields. In the given situation, research on human-robot interaction is attracting attention, but it has not yet reached the level of user satisfaction. However, research on robots capable of perfect communication is steadily being conducted, and it is expected that it will be able to replace human emotional labor. Therefore, it is necessary to discuss whether the current human-robot interaction technology can be applied to business. To this end, this study first examines the trend of human-robot interaction technology. Second, we compare LDA (Latent Dirichlet Allocation) topic modeling and BERTopic topic modeling methods. As a result, we found that the concept of human-robot interaction and basic interaction was discussed in the studies from 1992 to 2002. From 2003 to 2012, many studies on social expression were conducted, and studies related to judgment such as face detection and recognition were conducted. In the studies from 2013 to 2022, service topics such as elderly nursing, education, and autism treatment appeared, and research on social expression continued. However, it seems that it has not yet reached the level that can be applied to business. As a result of comparing LDA (Latent Dirichlet Allocation) topic modeling and the BERTopic topic modeling method, it was confirmed that BERTopic is a superior method to LDA.

• Analytical Science and Technology
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• v.20 no.4
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• pp.308-314
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• 2007

The analytical method of HPLC with the radial-flow electrochemical cell (RFEC) has been developed to determine doxorubicin, epirubicin, nogalamycin, daunorubicin and idarubicin simultaneously by employing a reversed-phase chromatography. Anthracyclines were detected at -0.74 V vs. a Ag/AgCl (0.01 M NaCl) reference electrode, a potential of diffusion current plateau in the mobile phase. At a $V_f$ of 1.0 mL/min doxorubicin, epirubicin, daunorubicin and idarubicin appeared at a retention time ($t_r$) of 6.4 min, 7.4 min, 12.7 min and 18.4 min, respectively, while at a $V_f$ of 0.6 mL/min, doxorubicin, epirubicin, nogalamycin, daunorubicin and idarubicin appeared at a $t_r$ of 9.9 min, 11.5 min, 13.5 min, 19.6 min and 28.7 min, respectively. The linearity between each anthracycline injected ($2.40{\times}10^{-7}M{\sim}1.42{\times}10^{-5}M$) and peak area (charge) was excellent with the square of the correlation coefficient ($R^2$) higher than 0.999. The detection limits were $1.0{\times}10^{-8}M{\sim}1.5{\times}10^{-7}M$ for the five anthracyclines. Within-day precision for the five anthracyclines were in reasonable relative standard deviations less than 3 % ($1.00{\times}10^{-6}M{\sim}1.42{\times}10^{-5}M$) except the lower concentrations less than $0.7{\mu}M$. Solid phase extractions of $1.00{\times}10^{-5}M$ epirubicin, $0.48{\times}10^{-5}M$ nogalamycin and $1.52{\times}10^{-5}M$ daunorubicin from human serum with a $C_{18}$ cartridge resulted in 97 %, 100 % and 90 % of recoveries, respectively.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.881-887
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• 2023

This study aims to analyze the development and current trends of AI-based medical imaging devices commercialized in South Korea. As of September 30, 2023, there were a total of 186 AI-based medical devices licensed, certified, and reported to the Korean Ministry of Food and Drug Safety, of which 138 were related to imaging. The study comprehensively examined the yearly approval trends, equipment types, application areas, and key functions from 2018 to 2023. The study found that the number of AI medical devices started from four products in 2018 and grew steadily until 2023, with a sharp increase after 2020. This can be attributed to the interaction between the advancement of AI technology and the increasing demand in the medical field. By equipment, AI medical devices were developed in the order of CT, X-ray, and MR, which reflects the characteristics and clinical importance of the images of each equipment. This study found that the development of AI medical devices for specific areas such as the thorax, cranial nerves, and musculoskeletal system is active, and the main functions are medical image analysis, detection and diagnosis assistance, and image transmission. These results suggest that AI's pattern recognition and data analysis capabilities are playing an important role in the medical imaging field. In addition, this study examined the number of Korean products that have received international certifications, particularly the US FDA and European CE. The results show that many products have been certified by both organizations, indicating that Korean AI medical devices are in line with international standards and are competitive in the global market. By analyzing the impact of AI technology on medical imaging and its potential for development, this study provides important implications for future research and development directions. However, challenges such as regulatory aspects, data quality and accessibility, and clinical validity are also pointed out, requiring continued research and improvement on these issues.

• Analytical Science and Technology
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• v.36 no.6
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• pp.291-299
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• 2023

The benzo[a]pyrene in edible oils is extracted using methods such as Liquid-liquid, soxhlet and ultrasound-assisted extraction. However these extraction methods have significant drawbacks, such as long extraction time and large amount of solvent usage. To overcome these drawbacks, this study attempted to improve the current complex benzo[a]pyrene analysis method by applying the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method that can be analyzed in a simple and short time. The QuEChERS method applied in this study includes extraction of benzo[a]pyrene into n-hexane saturated acetonitrile and n-hexane. After extraction and distribution using magnesium sulfate and sodium chloride, benzo[a]pyrene is analyzed by liquid chromatography with fluorescence detector (LC/FLR). As a result of method validation of the new method, the limit of detection (LOD) and quantification (LOQ) were 0.02 ㎍/kg and 0.05 ㎍/kg, respectively. The calibration curves were constructed using five levels (0.1~10 ㎍/kg) and coefficient (R²) was above 0.99. Mean recovery ratio was ranged from 74.5 to 79.3 % with a relative standard deviation (RSD) between 0.52 to 1.58 %. The accuracy and precision were 72.6~79.4 % and 0.14~7.20 %, respectively. All results satisfied the criteria ranges requested in the Food Safety Evaluation Department guidelines (2016) and AOAC official method of analysis (2023). Therefore, the analysis method presented in this study was a relatively simple pretreatment method compared to the existing analysis method, which reduced the analysis time and solvent use to 92 % and 96 %, respectively.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.