• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.464-469
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• 2019

A remote monitoring panel and control system was developed to control various valves and access control chambers, including gas shutoff valves used in CBR(Chemical, Biological and Radiological) facilities. The remote monitoring panel consisted of a main panel installed in the NBC (Nuclear, Biological and Chemical) control room and auxiliary panel installed in the clean room, and the size was divided into pure control and control including CCTV. This system can be monitored and controlled remotely according to the situation where an explosion door and gas barrier door can occur during war and during normal times. This system is divided into normal mode and war mode. In particular, it periodically senses the operation status of various valves, sensors, and filters in the CBR facilities to determine if each apparatus and equipment is in normal operation, and remotely alerts situation workers when repair or replacement is necessary. Damage due to the abnormal operation of each device in the situation can be prevented. This enables control of the blower, supply and exhaust damper, emergency generator, and coolant pump according to the state of shutoff valve and positive pressure valve in the occurrence of NBC, and prevents damage caused by abrupt inflow of conventional weapons and nuclear explosions.

• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.13-19
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• 2019

This paper analyzed the SCMS and pilot policy, which is pursued by the U.S. government in connected vehicles. SCMS ensures authentication, integrity, privacy and interoperability. The SCMS Support Committee of U.S. government has established the National Unit SCMS and is responsible for system-wide control. Of course, it introduces security policy, procedures and training programs making. In this paper, the need for SCMS to be applied to C-ITS was discussed. The structure of the SCMS was analyzed and the U.S. government's filot policy for connected vehicles was discussed. The discussion of the need for SCMS highlighted the importance of the role and responsibilities of SCMS between vehicles and vehicles. The security certificate management system looked at the structure and analyzed the type of certificate used in the vehicle or road side unit (RSU). The functions and characteristics of the certificates were reviewed. In addition, the functions of basic safety messages were analyzed with consideration of the detection and warning functions of abnormal behavior in SCMS. Finally, the status of the pilot project for connected vehicles currently being pursued by the U.S. government was analyzed. In addition to the environment used for the test, the relevant messages were also discussed. We also looked at some of the issues that arise in the course of the pilot project.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.535-549
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• 2022

Damage to forests, such as broken or falling trees, has increased due to the increased intensity and frequency of abnormal climate events, such as strong winds and heavy rains. However, it is difficult to respond to them in advance based on prediction since structural defects such as cavities and bumps inside trees are difficult to identify with a visual inspection. Non-invasive sonic tomography (SoT) is a method of estimating internal defects while minimizing physical damage to trees. Although SoT is effective in diagnosing internal defects, its accuracy varies depending on the species. Therefore, it is necessary to analyze the reliability of its measurement results before applying it in the field. In this study, we measured internal defects in wood by cross-applying destructive resistance micro drilling on old Pinus densifloraSiebold & Zucc. and Ginkgo bilobaL., which are representative tree species in Korea, to verify the reliability of SoT and compared the evaluation results. The t-test for the mean values of the defect measurement between the two groups showed no statistically significant difference in pine trees and some difference in ginkgo trees. Linear regression analysis results showed a positive correlation with an increase in defects in SoT images when the defects in the drill resistance graph increased in both species.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.191-202
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• 2024

Global warming is causing abnormal climates worldwide due to the increase in greenhouse gas concentrations in the atmosphere, negatively affecting ecosystems and humanity. In response, various countries are attempting to reduce greenhouse gas emissions in numerous ways, and interest in blue carbon, carbon absorbed by coastal ecosystems, is increasing. Known to absorb carbon up to 50 times faster than green carbon, blue carbon plays a vital role in responding to climate change. Particularly, the tidal flats of South Korea, one of the world's five largest tidal flats, are valued for their rich biodiversity and exceptional carbon absorption capabilities. While previous studies on blue carbon have focused on the carbon storage and annual carbon absorption rates of tidal flats, there is a lack of research linking tidal flat area changes detected using satellite data to carbon storage. This study applied the direct difference water index to high-resolution satellite data from PlanetScope and RapidEye to analyze the area and changes of the Nakdong River estuary tidal flats over six periods between 2013 and 2023, estimating the carbon storage for each period. The analysis showed that excluding the period in 2013 with a different tidal condition, the tidal flat area changed by up to approximately 5.4% annually, ranging from about 9.38 km² (in 2022) to about 9.89 km² (in 2021), with carbon storage estimated between approximately 30,230.0 Mg C and 31,893.7 Mg C.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.274-283
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• 2023

Objective: To compare the outcomes of digital breast tomosynthesis (DBT) screening combined with ultrasound (US) with those of digital mammography (DM) combined with US in women with dense breasts. Materials and Methods: A retrospective database search identified consecutive asymptomatic women with dense breasts who underwent breast cancer screening with DBT or DM and whole-breast US simultaneously between June 2016 and July 2019. Women who underwent DBT + US (DBT cohort) and DM + US (DM cohort) were matched using 1:2 ratio according to mammographic density, age, menopausal status, hormone replacement therapy, and a family history of breast cancer. The cancer detection rate (CDR) per 1000 screening examinations, abnormal interpretation rate (AIR), sensitivity, and specificity were compared. Results: A total of 863 women in the DBT cohort were matched with 1726 women in the DM cohort (median age, 53 years; interquartile range, 40-78 years) and 26 breast cancers (9 in the DBT cohort and 17 in the DM cohort) were identified. The DBT and DM cohorts showed comparable CDR (10.4 [9 of 863; 95% confidence interval {CI}: 4.8-19.7] vs. 9.8 [17 of 1726; 95% CI: 5.7-15.7] per 1000 examinations, respectively; P = 0.889). DBT cohort showed a higher AIR than the DM cohort (31.6% [273 of 863; 95% CI: 28.5%-34.9%] vs. 22.4% [387 of 1726; 95% CI: 20.5%-24.5%]; P < 0.001). The sensitivity for both cohorts was 100%. In women with negative findings on DBT or DM, supplemental US yielded similar CDRs in both DBT and DM cohorts (4.0 vs. 3.3 per 1000 examinations, respectively; P = 0.803) and higher AIR in the DBT cohort (24.8% [188 of 758; 95% CI: 21.8%-28.0%] vs. 16.9% [257 of 1516; 95% CI: 15.1%-18.9%; P < 0.001). Conclusion: DBT screening combined with US showed comparable CDR but lower specificity than DM screening combined with US in women with dense breasts.

• Korean Journal of Radiology
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• v.25 no.8
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• pp.742-748
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• 2024

Objective: ¹⁸F-N-(2-(Diethylamino)ethyl)-5-(2-(2-(2-fluoroethoxy)ethoxy)ethoxy) picolinamide (¹⁸F-PFPN) is a novel positron emission tomography (PET) probe designed to specifically targets melanin. This study aimed to evaluate the diagnostic feasibility of ¹⁸F-PFPN in patients with ocular or orbital melanoma. Materials and Methods: Three patients with pathologically confirmed ocular or orbital melanoma (one male, two females; age 41-59 years) were retrospectively reviewed. Each patient underwent comprehensive ¹⁸F-PFPN and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET scans. The maximum standardized uptake value (SUV_max) of the lesion and the interference caused by background tissue were compared between ¹⁸F-PFPN and ¹⁸F-FDG PET imaging. In addition, the effect of intrinsic pigments in the uvea and retina on the interpretation of the results was examined. The contralateral non-tumorous eye of each patient served as a control. Results: All primary tumors (3/3) were detected using ¹⁸F-PFPN PET, while only two primary tumors were detected using ¹⁸F-FDG PET. Within each lesion, the SUV_max of ¹⁸F-PFPN was 2.6 to 8.3 times higher than that of ¹⁸F-FDG. Regarding the quality of PET imaging, the physiological uptake of ¹⁸F-FDG PET in the brain and periocular tissues limited the imaging of tumors. However, ¹⁸F-PFPN PET minimized this interference. Notably, intrinsic pigments in the uvea and retina did not cause abnormal concentrations of ¹⁸F-PFPN, as no anomalous uptake of ¹⁸F-PFPN was detected in the healthy contralateral eyes. Conclusion: Compared to ¹⁸F-FDG, ¹⁸F-PFPN demonstrated higher detection rates for ocular and orbital melanomas with minimal interference from surrounding tissues. This suggests that ¹⁸F-PFPN could be a promising clinical diagnostic tool for distinguishing malignant melanoma from benign pigmentation in ocular and orbital melanomas.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.