• Journal of fish pathology
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• v.25 no.3
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• pp.263-270
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• 2012

This study surveyed for the prevalence of parasites, bacteria and viruses in four fish species, olive flounder (Paralichthys olivaceus), red sea bream (Pagrus major), black sea bream (Acathopagrus schlegeli) and black rockfish (Sebastes schlegeli) in 2010. The survey was aimed to compare the pathogens detected from wild and cultured fish for an epidemiological study. Anisakis sp. was predominantly detected from wild olive flounder and red sea bream (58.6% and 41.7% respectively), but not from the cultured fishes, suggesting anisakid infection is rare in cultured fish. The wild fish get in contact with the anisakids through their prey such as small fishes or crustaceans which carry the anisakids; whereas the cultured fish are fed with formulated feed, free of anisakids. Bacterial detection rates from the wild fishes examined in the study were lower than those of cultured fishes. Vibrio sp. dominated among detected bacterial population in cultured olive flounder (18%). Since vibriosis is known as a secondary infection caused by other stressful factors such as parasitic infections, handling and chemical treatment, it seems that cultured olive flounder are exposed to stressful environment. Viruses diagnosed in the study showed difference in distribution between wild and cultured fishes; hirame rhabdovirus (HRV) (0.1%) and lymphocystis disease virus (LCDV) (3.9%) were detected in the cultured olive flounder, but not in the wild fish, and marine birnavirus (MBV) (1.7%) and red sea bream iridovirus (RSIV) (3.2%) were detected from the wild and cultured red sea bream, respectively. From the survey conducted, it can be concluded that even though some pathogens (Trichodina sp., Microcotyle sp., etc.) are detected from both the wild and cultured fish, pathogens such as Anisakis sp., Vibrio sp. and LCDV showed difference in distribution in the wild and cultured host of same fish species and this can be attributed to their environmental condition and feeding.

• Korean Journal of Heritage: History & Science
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• v.54 no.1
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• pp.206-219
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• 2021

The warming of the climate is increasing the damage caused by termites to wooden buildings, cultural properties and houses. A group removal system can be installed around the building to detect and remove termite damage; however, if the site is not visited regularly, every one to two months, you cannot observe whether termites have spread within, and it is difficult to take prompt effective action. In addition, since the system is installed and operated in an exposed state for a long period of time, it may be ineffective or damaged, resulting in a loss of function. Furthermore if the system is installed near a cultural site, it may affect the aesthetic environment of the site. In this study, we created a detection system that uses wood, cellulose, magnets, and magnetic sensors to determine whether termites have entered the area. The data was then transferred to a low power LoRa Network which displayed the results without the necessity of visiting the site. The wood was made in the shape of a pile, and holes were made from the top to the bottom to make it easier for termites to enter and produce a cellulose sample. The cellulose sample was made in a cylindrical shape with a magnet wrapped in cellulose and inserted into the top of a hole in the wood. Then, the upper part of the wood pile was covered with a stopper to prevent foreign matter from entering. It also served to block external factors such as light and rainfall, and to create an environment where termites could add cellulose samples. When the cellulose was added by the termites, a space was created around the magnet, causing the magnet to either fall or tilt. The magnetic sensor inside the stopper was fixed on the top of the cellulose sample and measured the change in the distance between the magnet and the sensor according to the movement of the magnet. In outdoor experiments, 11 cellulose samples were inserted into the wood detection system and the termite inflow was confirmed through the movement of the magnet without visiting the site within 5 to 17 days. When making further improvements to the function and operation of the system it in the future, it is possible to confirm that termites have invaded without visiting the site. Then it is also possible to reduce damage and fruiting due to product exposure, and which would improve the condition and appearance of cultural properties.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.483-488
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• 2023

The status of residual pesticides was investigated in four pepper seed oil samples and 36 pepper-flavored oil samples oil distributed on the market from August to December 2022. A total of 179 pesticides were monitored in 40 samples, and 14 pesticides were detected in 39 of the samples, with a detection range of 0.01-2.16 mg/kg. In chili seed oil, 10 pesticides were detected 27 times with a range of 0.11-2.16 mg/kg, and in pepper-flavored oil, 9 pesticides were detected 94 times with a range of 0.01-0.80 mg/kg. The most frequently detected pesticides were tebuconazole, ethion, and difenoconazole, with ethion being detected in large concentrations in products using Chinese raw materials. Ethion, an unregistered pesticide in the Republic of Korea, has not been detected in the Gyeonggi-do area in the past 10 years. It is thought that the detection of ethion can be utilized as an indicator of products made in China. Peppers are a representative agricultural product for which many pesticides are used, and if the pesticides transferred to pepper seeds are not removed, the probability of detecting various types of pesticides in pepper seed oil is very high. Therefore, continuous research is needed to ensure the safety of pepper seed oil.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1309-1320
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• 2023

The technological development and popularization of mobile devices have developed so that users can check their location anywhere and use the Internet. However, in the case of indoors, the Internet can be used smoothly, but the global positioning system (GPS) function is difficult to use. There is an increasing need to provide real-time location information in shaded areas where GPS is not received, such as department stores, museums, conference halls, schools, and tunnels, which are indoor public places. Accordingly, research on the recent indoor positioning technology based on light detection and ranging (LiDAR) equipment is increasing to build a landmark database. Focusing on the accessibility of building a landmark database, this study attempted to develop a technique for estimating the user's location by using a single image taken of a landmark based on a mobile device and the landmark database information constructed in advance. First, a landmark database was constructed. In order to estimate the user's location only with the mobile image photographing the landmark, it is essential to detect the landmark from the mobile image, and to acquire the ground coordinates of the points with fixed characteristics from the detected landmark. In the second step, by applying the bag of words (BoW) image search technology, the landmark photographed by the mobile image among the landmark database was searched up to a similar 4th place. In the third step, one of the four candidate landmarks searched through the scale invariant feature transform (SIFT) feature point extraction technique and Homography random sample consensus(RANSAC) was selected, and at this time, filtering was performed once more based on the number of matching points through threshold setting. In the fourth step, the landmark image was projected onto the mobile image through the Homography matrix between the corresponding landmark and the mobile image to detect the area of the landmark and the corner. Finally, the user's location was estimated through the location estimation technique. As a result of analyzing the performance of the technology, the landmark search performance was measured to be about 86%. As a result of comparing the location estimation result with the user's actual ground coordinate, it was confirmed that it had a horizontal location accuracy of about 0.56 m, and it was confirmed that the user's location could be estimated with a mobile image by constructing a landmark database without separate expensive equipment.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• The Korean Journal of Pesticide Science
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• v.2 no.3
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• pp.90-95
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• 1998

Soil residues of paraquat (1,1-dimethyl-4,4-dipyridinium dichloride) were determined in apple, pear, grape, and peach orchards for which 15 sites each were selected randomly from the corresponding large-scale production area throughout the country. Strong adsorption capacity measured using wheat bioassay (paraquat concentration required to reduce 50% root growth of wheat, SAC-WB) was also investigated on the orchard soils and the paraquat residue level was calculated against total SAC-WB values (SAC-WB value + paraquat residue). Average bound residue of paraquat on the 60 sites was 6.9 ppm, while paraquat residue in apple orchard reached 20.2 ppm which was the highest among the orchards and was almost double as compared with those in the other three orchards. Loosely bound residue of paraquat determined on the bound residue high top five soils occurred less than 0.5 ppm detection limit. Average SAC-WB value was 276.1 ppm and there were no any correlations between the SAC-WB value and clay content, organic matter content, and cation exchange capacity of the orchard soils. Paraquat residue level of the orchard soils against total SAC-WB recorded 2.43%.

• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.1-7
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• 2010

With the widespread use of vision-based surveillance systems, the capability for person identification is now an essential component. However, the CCTV cameras used in surveillance systems tend to produce relatively low-resolution images, making it difficult to use face recognition techniques for person identification. Therefore, an algorithm is proposed for person identification in CCTV camera images based on the clothing. Whenever a person is authenticated at the main entrance of a building, the clothing feature of that person is extracted and added to the database. Using a given image, the clothing area is detected using background subtraction and skin color detection techniques. The clothing feature vector is then composed of textural and color features of the clothing region, where the textural feature is extracted based on a local edge histogram, while the color feature is extracted using octree-based quantization of a color map. When given a query image, the person can then be identified by finding the most similar clothing feature from the database, where the Euclidean distance is used as the similarity measure. Experimental results show an 80% success rate for person identification with the proposed algorithm, and only a 43% success rate when using face recognition.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.11 no.2
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• pp.145-152
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• 2001

This study was performed to evaluate the coke oven emissions (COE) and polynuclear aromatic hydrocarbon levels in coke manu-facturing industry, secondary lead smelting industry and glass bottle manufacturing industry. 1. There were no significant difference between the means of personal samples and area samples by the types of industry(p>0.05). The levels of airborne total particulates of the secondary lead smelting industry was the highest($2.30mg/m^3$), and those of the coke manu-facturing industry and glass bottle manu facturing industry were $1.95mg/m^3$ and $1.37mg/m^3$. The concentration of COE was the highest in the glass bottle manufacturing industry($0.79mg/m^3$), and in order of $0.19mg/m^3$ in the coke manufacturing industry and $0.06mg/m^3$ in the secondary lead smelting industry. COE/total particulates(%) was highest in the glass bottle manufacturing industry(58.1%) and in order of 10.3% in the coke manufacturing industry and 3.1% in secondary lead smelting industry. There were significant differences in the total particle concentration and COE by the types of industry(p<0.05). 2. The levels of airborne total particulates was the highest at the smelting process of secondary lead smelting industry($2.30{\pm}0.72mg/m^3$), and the lowest at the smelting process of glass bottle manufacturing industry ($0.99{\pm}1.22mg/m^3$) Concentration of COE was the highest at the casting process of glass bottle manufacturing industry ($1.09{\pm}1.15mg/m^3$), the lowest at the smelting process of secondary lead smelting industry ($0.06{\pm}0.03mg/m^3$). The COE/total particulates(%) was the highest at the casting process of glass bottle manufacturing industry($65.9{\pm}20.5%$), and the lowest at the smelting process of secondary lead smelting indusry($3.1{\pm}2.7%$). 3. There were positive correlations between level of The airborne total particulates and concentration of COE in coke manufacturing industry and glass bottle manufacturing industry (p<0.05), but negative correlation in secondary lead smelting industry. 4. The numbers of case and rates that over the Threshold Limit Values(TLVs) were 24 (77.4%)cases in glass bottle manufacture, 14(23.7%) cases in the coke manufacturing industry and no one case in secondary lead smelting industry. Total numbers of case and rates that over TLVs were 38( 35.5%) cases. 5. The limit of detection(LOD) for PAH was $10{\mu}g/ml$ in standard sample. All PAH levels of the cokes manufacturing industry and the secondary lead smelting industry and the glass bottle manufacturing industry were trace or not to detect.

• Journal of Environmental Impact Assessment
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• v.22 no.2
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• pp.135-145
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• 2013

The recycling of coal bottom ash generated from coal power plants in Korea has been limited due to heterogenous characteristics of the materials. The most common management option for the ash is disposal in landfills (i.e. ash pond) near ocean. The presence of large coarse and fine materials in the ash has prompted the desire to beneficially use it in an application such as fill materials. Prior to reuse application as fill materials, the potential risks to the environment must be assessed with regard to the impacts. In this study, a total of nine test cells with bottom ash samples collected from pretreated bottom ash piles and coal ash pond in a coal-fired power plant were constructed and operated under the field conditions to evaluate the leachability over a period of 210 days. Leachate samples from the test cells were analyzed for a number of chemical parameters (e.g., pH, salinity, electrical conductance, anions, and metals). The concentrations of chemicals detected in the leachate were compared to appropriate standards (drinking water standard) with dilution attenuation factor, if possible, to assess potential leaching risks to the surrounding area. Based on the leachate analysis, most of the samples showed slightly high pH values for the coal ash contained test cells, and contained several ions such as sodium, potassium, calcium, magnesium, chloride, sulfate, and nitrate in relatively large quantities. Three elements (aluminum, boron, and barium) were commonly detected above their respective detection limits in a number of leachate samples, especially in the early leaching period of time. The results of the test cell study indicate that the pollutants in the leachate from the coal ash test cells were not of a major concern in terms of leaching risk to surface water and groundwater under field conditions as fill materials. However, care must be taken in extending these results to actual applications because the results presented in this study are based on the limited field test settings and time frame. Structural characteristics and analysis for coal bottom ash may be warranted to apply the materials to actual field conditions.