• Korean Journal of Ichthyology
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• v.34 no.3
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• pp.208-217
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• 2022

We wanted to develop a real-time PCR assay capable of detecting Liobagrus obesus in environmental DNA (eDNA) extracted from freshwater samples using a pair of species-specific primers and probe for the endangered fish, L. obesus. The species-specific primers and probe were designed in consideration of single nucleotide polymorphisms between 65 species of freshwater fish living in the Republic of Korea within the cytochrome b (cytb) gene of mitochondrial DNA. The species-specific primers and probe, in the real-time PCR assay, showed high specificity as only the L. obesus genomic DNA (gDNA) was found to be positive in the specificity verification using 65 species gDNA of freshwater fish in the Republic of Korea. In addition, in the detection limit analysis using the serial dilution concentrations of L. obesus gDNA, it was found that it was possible to detect up to 0.2 pg, showing high sensitivity. Afterwards, using the species-specific primers and probe, real-time PCR assay was performed on freshwater samples obtained from 8 stations in the mid-upper basin of Geum River. As a result, the cytb gene of L. obesus was detected in total 5 stations including all 3 stations where this species was collected at the time of field survey. Therefore, the species-specific primers and probe developed in present study, and the real-time PCR assay using them, can accurately detect the cytb gene of L. obesus from eDNA samples, which can be utilized to monitor the existing habitats of this species and to discover potential new habitats.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.30-38
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• 2023

As the number of old buildings subject to safety inspection increases, the burden on designated institutions and management entities that are responsible for safety management is increasing. Accordingly, when selecting buildings subject to safety inspection, appropriate safety inspection standards and appropriate technology are essential. The current safety inspection standards for old buildings give low scores when it is difficult to confirm damage such as cracks in structural members due to finishing materials. This causes the evaluation results to be underestimated regardless of the actual safety status of the structure, resulting in an increase in the number of aging buildings subject to safety inspection. Accordingly, this study proposed a thermal imaging technique, a non-destructive and non-contact inspection, to detect cracks inside finishing materials. A concrete specimen was produced to observe cracks inside the finishing material using a thermal imaging camera, and thermal image data was measured by exciting a heat source on the concrete surface and cracked area. As a result of the measurement, it was confirmed that it was possible to observe cracks inside the finishing material with a width of 0.3mm, 0.5mm, and 0.7mm, but it was difficult to determine the cracks due to uneven temperature distribution due to surface peeling and peeling of the wallpaper. Accordingly, as a result of performing data analysis by deriving the amplitude and phase difference of the thermal image data, clear crack measurement was possible for 0.5mm and 0.7mm cracks. Based on this study, we hope to increase the efficiency of field application and analysis through the development of technology using big data-based deep learning in the diagnosis of internal crack damage in finishing materials.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.263-275
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• 2023

The Sun-Earth Lagrange point L4 is considered as one of the unique places where the solar activity and heliospheric environment can be observed in a continuous and comprehensive manner. The L4 mission affords a clear and wide-angle view of the Sun-Earth line for the study of the Sun-Earth and Sun-Moon connections from he perspective of remote-sensing observations. In-situ measurements of the solar radiation, solar wind, and heliospheric magnetic field are critical components necessary for monitoring and forecasting the radiation environment as it relates to the issue of safe human exploration of the Moon and Mars. A dust detector on the ram side of the spacecraft allows for an unprecedented detection of local dust and its interactions with the heliosphere. The purpose of the present paper is to emphasize the importance of L4 observations as well as to outline a strategy for the planned L4 mission with remote and in-situ payloads onboard a Korean spacecraft. It is expected that the Korean L4 mission can significantly contribute to improving the space weather forecasting capability by enhancing the understanding of heliosphere through comprehensive and coordinated observations of the heliosphere at multi-points with other existing or planned L1 and L5 missions.

• Research in Plant Disease
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• v.29 no.4
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• pp.414-419
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• 2023

In 2023, a number of seeds suspected to be discolored black oat seeds on oat farms in Gangjin were observed and examined for fungal infection. The species Alternaria alternata was predominant, accounting for 84% of all fungal infections. The appearance and quality of seeds harvested in 2022 were compared with seeds harvested in 2023 from the same field. The lightness value was lower in the seeds harvested in 2023, while the electrical conductivity was higher in the seeds harvested in 2023. The content of avenanthramide was found to be 10 times higher in the 2023 seeds than in those harvested in 2022. The accumulated precipitation in Gangjin in May 2023 was 230 times higher than that in May 2022, and the average relative humidity was high. These conditions created an environment suitable for infection of A. alternata, which were thought to have caused discolored black oat seeds.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.410-418
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• 2023

Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.102-108
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• 2024

This study was performed to establish the epidemiological features of a food poisoning outbreak that occurred in the cafeteria of a company in Chungcheongnam-do Province, Korea, in October 2020, and to recommend measures to prevent similar outbreaks. Twenty-one patients with acute gastroenteritis, three food handlers, seven cooking utensils, and 12 preserved food samples were subjected to viral and bacterial analyses based on procedures described in the "Manual for Detection of Foodborne Pathogens at Outbreaks". Among 135 individuals who had been served the meals, 21 (15.6%) showed symptoms of nausea and vomiting within an hour of consuming the food. Bacillus cereus were isolated from 11 (52.4%) of the 21 patients, one food service employee, one item of cooking ware, and 12 preserved food samples. In addition, we confirmed the toxin genes CER, nheA, and entFM from the isolated B. cereus strains. Pulsed-field gel electrophoresis results indicated that all of the isolated B. cereus strains were closely related, with the exception of strains obtained from one patient and one sample of preserved food. These findings provide evidence to indicate that the isolated B. cereus originated from preserved foods and an unhygienic eating environment. This outbreak highlights that the provision of food in non-commercial food systems must be thoroughly managed. In addition, it emphasizes the necessity for the correct and timely identification of causal pathogens for tracing the cause of food poisoning outbreaks, and the need to preserve food under appropriate conditions. To prevent similar cases of food poisoning, it is necessary to investigate cases based on an epidemiological approach and share the findings.

• Korean Journal of Radiology
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• v.20 no.1
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• pp.58-68
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• 2019

Objective: To compare digital breast tomosynthesis (DBT) and conventional full-field digital mammography (FFDM) in the detectability of breast cancers in patients with dense breast tissue, and to determine the influencing factors in the detection of breast cancers using the two techniques. Materials and Methods: Three blinded radiologists independently graded cancer detectability of 300 breast cancers (288 women with dense breasts) on DBT and conventional FFDM images, retrospectively. Hormone status, histologic grade, T stage, and breast cancer subtype were recorded to identify factors affecting cancer detectability. The Wilcoxon signed-rank test was used to compare cancer detectability by DBT and conventional FFDM. Fisher's exact tests were used to determine differences in cancer characteristics between detectability groups. Kruskal-Wallis tests were used to determine whether the detectability score differed according to cancer characteristics. Results: Forty breast cancers (13.3%) were detectable only with DBT; 191 (63.7%) breast cancers were detected with both FFDM and DBT, and 69 (23%) were not detected with either. Cancer detectability scores were significantly higher for DBT than for conventional FFDM (median score, 6; range, 0-6; p < 0.001). The DBT-only cancer group had more invasive lobular-type breast cancers (22.5%) than the other two groups (i.e., cancer detected on both types of image [both-detected group], 5.2%; cancer not detected on either type of image [both-non-detected group], 7.3%), and less detectability of ductal carcinoma in situ (5% vs. 16.8% [both-detected group] vs. 27.5% [both-non-detected group]). Low-grade cancers were more often detected in the DBT-only group than in the both-detected group (22.5% vs. 10%, p = 0.026). Human epidermal growth factor receptor-2 (HER-2)-negative cancers were more often detected in the DBT-only group than in the both-detected group (92.3% vs. 70.5%, p = 0.004). Cancers surrounded by mostly glandular tissue were detected less often in the DBT only group than in the both-non-detected group (10% vs. 31.9%, p = 0.016). DBT cancer detectability scores were significantly associated with cancer type (p = 0.012), histologic grade (p = 0.013), T and N stage (p = 0.001, p = 0.024), proportion of glandular tissue surrounding lesions (p = 0.013), and lesion type (p < 0.001). Conclusion: Invasive lobular, low-grade, or HER-2-negative cancer is more detectable with DBT than with conventional FFDM in patients with dense breasts, but cancers surrounded by mostly glandular tissue might be missed with both techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.77-85
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• 2024

In this dissertation, a we designed and implemented a patrol robot that integrates a thermal imaging camera, speed dome camera, PTZ camera, radar, lidar sensor, and smartphone. This robot has the ability to monitor and respond efficiently even in complex environments, and is especially designed to demonstrate high performance even at night or in low visibility conditions. An orbital movement system was selected for the robot's mobility, and a smartphone-based control system was developed for real-time data processing and decision-making. The combination of various sensors allows the robot to comprehensively perceive the environment and quickly detect hazards. Thermal imaging cameras are used for night surveillance, speed domes and PTZ cameras are used for wide-area monitoring, and radar and LIDAR are used for obstacle detection and avoidance. The smartphone-based control system provides a user-friendly interface. The proposed robot system can be used in various fields such as security, surveillance, and disaster response. Future research should include improving the robot's autonomous patrol algorithm, developing a multi-robot collaboration system, and long-term testing in a real environment. This study is expected to contribute to the development of the field of intelligent surveillance robots.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.99-114
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• 2024

Domestic coastal debris monitoring encounters challenges due to labor-intensive methods and limited survey scope. Consequently, research is utilizing remote sensing techniques to enhance efficiency in data collection. However, standards for domestic remote sensing based monitoring methods remain insufficient. In this study, we conducted a meta-analysis of 19 coastal debris monitoring studies utilizing drones and other remote sensing devices. We analyzed data collection methods, collected data information, monitoring target details, monitoring status, detection targets, and utilization models. Based on our meta-analysis results, we proposed monitoring criteria, recommended items, and performance standards for monitoring coastal debris using drones. Our findings define necessary conditions and standards for establishing operational guidelines for coastal debris monitoring using drones. Furthermore, we anticipate that incorporating foreign case analyses and field application results will enable the development of national-level guidelines for coastal debris monitoring utilizing remote sensing devices.