• The Korean Journal of Pesticide Science
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• v.16 no.3
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• pp.202-208
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• 2012

A high-performance liquid chromatographic (HPLC) method was developed to determine residues of cyromazine, a triazine insecticide, in agricultural commodities. Cyromazine was extracted with 90% aqueous methanol from representative crops which comprised brown rice, oyster mushroom, oriental melon, watermelon, and Chinese cabbage. Following to evaporation of methanol in the extract, the aqueous concentrate was acidified to form the protonated cyromazine. Dichloromethane partition was then applied to remove nonpolar co-extractives in the aqueous phase. Strong cation-exchange chromatography using Dowex 50W-X4 resin was employed for final purification of the extract. Cyromazine was successfully separated on a Zorbax SB-Aq $C_{18}$ column showing high retention for polar compounds. Cyromazine was sensitively quantitated by ultraviolet absorption at 214 nm. Limit of quantitation (LOQ) of the method was 0.04 mg/kg irrespective of sample types. Each crops were fortified at 3 different concentrations of cyromazine for recovery test. Mean recoveries from samples fortified at LOQ~2.0 mg/kg in triplicate ranged 80.2~103.3% in five agricultural commodities. Relative standard deviations in recoveries were all less than 6%. A selected-ion monitoring LC/MS method with electrospray ionization in positive-ion mode was also provided to confirm the suspected residue. The proposed method was reproducible and sensitive enough to routinely determine and inspect the residue of cyromazine in agricultural commodities.

• Journal of Food Hygiene and Safety
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• v.31 no.4
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• pp.227-249
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• 2016

Arsenic and its compounds vary in their toxicity according to the chemical forms. Inorganic arsenic is more toxic and known as carcinogen. The provisional tolerable weekly intake (PTWI) of $15{\mu}g/kg$ b.w./week established by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) has been withdrawn, while the EFSA panel suggested $BMDL_{0.1}$ $0.3{\sim}8{\mu}g/kg\;b.w./day$ for cancers of the lung, skin and bladder, as well as skin lesions. Rice, seaweed and beverages are known as food being rich in inorganic arsenic. As(III) is the major form of inorganic arsenic in rice and anaerobic paddy soils, while most of inorganic arsenic in seaweed is present as As(V). The inorganic arsenic in food was extracted with solvent such as distilled water, methanol, nitric acid and so on in heat-assisted condition or at room temperature. Arsenic speciation analysis was based on ion-exchange chromatography and high-performance liquid chromatography equipped with atomic absorption spectrometry and inductively coupled plasma mass spectrometry. However, there has been no harmonized and standardized method for inorganic arsenic analysis internationally. The inorganic arsenic exposure from food has been estimated to range of $0.13{\sim}0.7{\mu}g/kg$ bw/day for European, American and Australian, and $0.22{\sim}5{\mu}g/kg$ bw/day for Asian. The maximum level (ML) for inorganic arsenic in food has established by EU, China, Australia and New Zealand, but are under review in Korea. Until now, several studies have conducted for reduction of inorganic arsenic in food. Inorganic arsenic levels in rice and seaweed were reduced by more polishing and washing, boiling and washing, respectively. Further research for international harmonization of analytical method, monitoring and risk assessment will be needed to strengthen safety management of inorganic arsenic of foods in Korea.

• Journal of dental hygiene science
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• v.15 no.1
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• pp.24-31
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• 2015

This research was conducted in order to examine the effect of tooth brushing room M elementary school in Changwon-city and to provide foundation data for effective project operation afterwards. The subjects were 347 students at the M elementary school where the tooth brushing room was being taught. The control group is 289 students at J elementary school where the tooth brushing room was not being taught. Research and analysis were carried out with structured survey and examination of decayed, missing, filled teeth (DMFT) index, decayed, missing, filled tooth surface (DMFS) index and O'leary index. The data was analysed by IBM SPSS Statistics ver. 19.0 program and the result is as follows: Depends on the tooth brushing room there was difference in statistical significance in filling teeth, sealant tooth surface, filling tooth surface, missing tooth surface, DMFS, O'leary index between the subject and control group. The less the frequency of brushing, the higher the DMFT index. Negative correlation was statistically significant. With incorrect brushing method, the less the frequency, the higher the DMFS index, Negative correlation was statistically significant. When the tooth brushing room was being implemented, O'leary index became low, negative correlation was statistically significant. As a result, in order to continue the effective operation of tooth brushing room, constant supervision and monitoring on students should be acutely needed by a principal, a school nurse and teachers in charge. Also together with a systemized cooperation between a health center and a nearby university's related majors departments, the research proposes to execute constant oral health education and to expand the implementation project of the tooth brushing room at nearby elementary schools.

• Microbiology and Biotechnology Letters
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• v.35 no.2
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• pp.158-162
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• 2007

Methicillin-resistant Staphylococcus aureus (MRSA) is one of a major nosocomial pathogen worldwide and the emergence of this strain has become a major clinical problem. This study was performed for 13 hospitals with more than 400 beds in the country by collecting samples including hands and nasal cavities of doctors, nurses, guardians and patients. Also, additional 320 samples of hands and nasal cavities of 160 community resident in different locations and regions were collected. In all of medical environments and community resident, 625 strains of S. aureus were detected. Among 625 strains of S. aureus, 585 strains(93.6%) showed the resistance to at least one kind of antimicrobial and 112 strains (17.9%) showed multi-drug resistance with the resistance to 4 different types of antimicrobial. Total 152 MRSA strains (24.3%) were isolated from medical environment and community resident. In nasal cavity and hand, 49 MRSA (19.4%) and 103 (27.6%) MRSA were isolated, respectively Minimum inhibitory concentration(MIC) test is used to measure for susceptibility of MRSA isolated to oxacillin. At a concentration $16{\mu}g/ml$ of oxacillin, 11 strains were inhibited. 32 strains at $32{\mu}g/ml$, 41 strains at $64{\mu}g/ml$, 3 strains at $128{\mu}g/ml$, 25 stains at $256{\mu}g/ml$ and 40 strains at over $256{\mu}g/ml$ were inhibited. It was considered that medical environment showed higher than livestock and marine environments in MRSA detection rate.

• Journal of Korea Water Resources Association
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• v.52 no.5
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• pp.349-360
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• 2019

The purpose of this study is to evaluate the stream flow and water quality (SS, T-N, and T-P) behavior of Nakdong river basin ($23,609.3km^2$) by simulating the dam and weir operation scenarios using SWAT (Soil and Water Assessment Tool). The operation senarios are the simultaneous release for all dam and weirs (scenario 1), simultaneous release for all weirs (scenario 2), and sequential release for the weirs with one month interval from upstream weirs (scenario 3). Before evaluation, the SWAT was calibrated and validated using 11 years (2005-2015) daily multi-purpose dam inflow at 5 locations (ADD, IHD, HCD, MKD, and MYD), multi-function weir inflow at 7 locations (SHW, GMW, CGW, GJW, DSW, HCW, and HAW), and monthly water quality monitoring data at 6 locations (AD-4, SJ-2, EG, HC, MK-4, and MG). For the two dam inflow and dam storage, the Nash-Sutcliffe efficiency (NSE) was 0.56~0.79, and the coefficient of determination ($R^2$) was 0.68~0.90. For water quality, the $R^2$ of SS, T-N, and T-P was 0.64~0.79, 0.51~0.74, and 0.53~0.72 respectively. For the three scenarios of dam and weir release combination suggested by the ministry of environment, the scenario 1 and 3 operations were improved the stream water quality (for T-N and T-P) within the 3 months since the time of release, but it showed the negative effect for 3 months after compared to scenario 2.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.30-39
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• 2022

This study aimed to evaluate the awareness of conservation areas and green resources and analyze their characteristics by utilizing accumulated blog data created for specific places and objects. Among all the conservation areas and resources located in the Seoul metropolitan area, places that can be evaluated were classified, and sites were evaluated by dividing them into ten categories based on the number of blog posts written. As a result of the study, the users' awareness of forests was the highest, and the awareness of conservation areas and green resources was higher in urban areas than suburban areas. The result shows that the conservation areas and green resources located around the metropolitan area serve as natural tourist destinations while being the object of conservation for users. In addition, these results are in the same vein as the research results in domestic and foreign studies on the importance of ecosystem services in urban areas. Unlike existing research methods, this study is meaningful in that it identified the level of user awareness through social media analysis and applied it to evaluating conservation areas and green resources. It can be used as basic data to prepare a management plan considering public interest and awareness or to establish a development plan to increase awareness. In addition, the cumulative amount of blog content used in the study is meaningful in that it can identify and monitor users' interest in the space. However, it was not possible to examine the contents of each blog in detail because it was evaluated based on the amount of social media content. In addition, in the case of conservation areas and green resources, it is necessary to review and supplement the evaluation contents by adding keyword analysis and content analysis for the site to be evaluated as content other than the pure viewpoint of users may be mixed with development issues.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.172-186
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• 2022

Benthic macroinvertebrates are important ecological and environmental indicators as primary or secondary consumers, and therefore are widely used in the evaluation of aquatic environments. However, there are no comprehensive river ecosystem monitoring surveys that link the major physicochemical water quality items with benthic macroinvertebrates in urban streams. Therefore, this study investigated the distribution characteristics of benthic macroinvertebrates and physicochemical water quality items (17 items) in Yangjaecheon and Yeouicheon from 2019 to 2020. At the same time, by applying Spearman's rank correlation analysis and nonmetric multidimensional scaling (nMDS) analysis in the water quality data and biotic index, we tried to provide basic data for diagnosing the current status of river ecosystems in major urban rivers in Seoul. Based on the study results, a total of 39 species and 3,787 individuals were identified in Yangjaecheon, the water quality(based on BOD, TOC, and TP) of Yangjaecheon was higher than Grade Ib(good), and the BMI using benthic macroinvertebrates appeared as Grade C(normal) at all the sites. In Yeouicheon, a total of 51 species and 4,199 individuals were identified, the water quality(based on BOD, TOC, TP) was higher than Grade Ib(good) similar to Yangjaecheon, and the BMI of both Upstream and Saewon bridge was Grade B(good), while Yeoui bridge was Grade C(normal). Overall, analysis results for the distribution of benthic macroinvertebrates by a nonmetric multidimensional scaling method showed no significant difference between the two streams (p=0.1491). Also, significant environmental variables related to benthic macroinvertebrates distribution were determined as water temperature and DO. On the other hand, the results of the correlation analysis between biotic index and major water quality items confirmed that R1 and BMI could be used for on-site urban river water quality evaluation.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.