• 한국방재안전학회논문집
• /
• 제14권1호
• /
• pp.41-50
• /
• 2021

하천 제방 변위를 사전에 예측하는 방안으로 본 연구에서는 InSAR 기법 중 Differential Interferometry(D-InSAR) 기법을 이용하여 2020년 여름 발생한 남원시 금곡교(섬진강) 인근의 제방 붕괴 지역에서 취약지점을 확인하였다. 2020년 봄과 여름 각각 5장의 sentinel-1 영상과 위성 영상 전처리 도구인 SNAP을 사용하여 2020년 8월 8일 제방 붕괴 전까지의 발생한 변위를 분석한 결과, 붕괴 발생 지역의 변위 변동성지수(Variation Index), V 가 상대적으로 크게 발생하였으며 이를 통해 붕괴 전조증상을 확인할 수 있었다. 향후에 산출한 변위를 분석한 결과와 유역의 지하수위, 기온, 수위, 토양도 및 토양 수분도와 같은 수문기상학적 요인과 상관관계를 분석하여 하천 제방의 모니터링 시스템을 구축할 수 있다면 기존의 하천 제방 유지·보수 점검 시스템의 많은 한계점을 극복하고 초정밀, 자동화된 하천 제방 유지관리 기술 고도화와 국가 재난관리의 향상이 가능할 것으로 기대한다.

• 한국물환경학회지
• /
• 제36권6호
• /
• pp.508-520
• /
• 2020

Dam reservoirs have been reported to contribute significantly to global carbon emissions, but unlike natural lakes, there is considerable uncertainty in calculating carbon emissions due to the complex of emission pathways. In particular, the method of calculating carbon dioxide (CO2) net atmospheric flux (NAF) based on a simple gas exchange theory from sporadic data has limitations in explaining the spatiotemporal variations in the CO2 flux in stratified reservoirs. This study was aimed to analyze the spatial and temporal CO2 distribution and mass balance in Daecheong Reservoir, located in the mid-latitude monsoon climate zone, by applying a two-dimensional hydrodynamic and water quality model (CE-QUAL-W2). Simulation results showed that the Daecheong Reservoir is a heterotrophic system in which CO2 is supersaturated as a whole and releases CO2 to the atmosphere. Spatially, CO2 emissions were greater in the lacustrine zone than in the riverine and transition zones. In terms of time, CO2 emissions changed dynamically according to the temporal stratification structure of the reservoir and temporal variations of algae biomass. CO2 emissions were greater at night than during the day and were seasonally greatest in winter. The CO2 NAF calculated by the CE-QUAL-W2 model and the gas exchange theory showed a similar range, but there was a difference in the point of occurrence of the peak value. The findings provide useful information to improve the quantification of CO2 emissions from reservoirs. In order to reduce the uncertainty in the estimation of reservoir carbon emissions, more precise monitoring in time and space is required.

• 한국산업융합학회 논문집
• /
• 제25권3호
• /
• pp.443-449
• /
• 2022

In this study, in order to improve the disadvantages of the environmental error of the infusion set that performs infusion therapy in the existing clinical practice and to maximize the user's convenience by miniaturizing the existing infusion pump system, the structure of the muscle pump of the human vein was imitated. As a double check valve method, a method for preventing the backflow of fluid and discharging a constant fluid in one direction by external pressure was proposed. The proposed bio-mimic muscle pump uses a check valve that controls the flow of fluid in one direction and a silicone tube with elasticity, and a chamber is constructed. A peristaltic pump for applying intermittent pressure to the tube chamber was constructed using a multi-cam structure roller. In order to verify the performance of the proposed pump, optimization was performed while changing the number of multi-cam rollers and adjusting the speed of the roller driving motor, and the reproducibility of the instantaneous discharge amount and the continuous discharge amount of the pump was compared and tested. The performance of the muscle pump proposed in this study was verified through experiments that it can inject up to 1L of fluid within 12 hours, and that it is possible to inject the fluid with an accuracy of ±0.1ml. Real-time monitoring of the fluid injection volume through the bio-mimic muscle pump proposed in this study not only increases the convenience of the administrator, but also provides a precise fluid administration environment to more patients at a low cost, and additionally applies bubble detection and occlusion detection technology If so, it is believed that a safer medical environment can be provided to patients.

• 대한원격탐사학회지
• /
• 제39권4호
• /
• pp.425-440
• /
• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.344-352
• /
• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.

• 대한원격탐사학회지
• /
• 제40권1호
• /
• pp.9-18
• /
• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.

• 대한의용생체공학회:의공학회지
• /
• 제45권1호
• /
• pp.49-55
• /
• 2024

Accurate measurement of blood pressure is essential for classifying an individual's disease, identifying blood pressure-related risks, and managing health. Due to the environmental and health hazards of mercury sphygmomanometers, automatic sphygmomanometers using the oscillometric method are widely used in hospitals as well as in general homes, and have established themselves as a practical standard sphygmomanometer. In this study, we developed a blood pressure simulator using an actuator that provides simulated pressure to an automatic blood pressure cuff. The developed blood pressure simulator adopts an arm-shaped cylindrical shape similar to the situation in which a person measures blood pressure with an automatic blood pressure monitor, and implements a method of transmitting pressure to the cuff using a pressure plate. Accuracy was evaluated through the mean and standard deviation of the difference with the commercialized blood pressure simulator BP PUMP 2, and reproducibility was confirmed using two automatic blood pressure monitors. The developed blood pressure simulator enables automatic blood pressure monitoring in a simple manner and also meets the evaluation standards for accuracy and reproducibility. In the future, as a standardized blood pressure simulator, it is expected to be of great help in evaluating and verifying the performance of automatic blood pressure monitors by supplementing precise hardware and software and building a blood pressure database.

• Journal of Information Technology Applications and Management
• /
• 제31권1호
• /
• pp.177-188
• /
• 2024

In this research, a cohort of two children, aged 7-8 years, was selected to participate in a specialized three-week training program aimed at enhancing their working memory. The program consisted of three sessions, each lasting approximately 30 minutes. The primary goal was to investigate the impact and developmental trajectory of working memory in school-aged children. Working memory plays a significant role in young children's learning and daily activities. To address the needs of this demographic, products should offer both educational and enjoyable activities that engage working memory. Digital educational tools, known for their flexibility, are suitable for both older individuals and young children. By updating software or modifying content, these tools can be effectively repurposed for young learners without extensive hardware changes, making them both cost-effective and practical. For example, memory training games initially designed for older adults can be adapted for young children by altering images, music, or storylines. Furthermore, incorporating elements familiar to children, like animals, toys, or fairy tales, can increase their engagement in these activities. Historically, working memory capabilities have been assessed predominantly through traditional intelligence tests. However, recent research questions the adequacy of these behavioral measures in accurately detecting changes in working memory. To bridge this gap, the current study utilized electroencephalography (EEG) as a more sophisticated and precise tool for monitoring potential changes in working memory after the training. The research findings were revealing. Participants showed marked improvement in their performance on n-back tasks, a standard measure for evaluating working memory. This improvement post-training strongly supports the effectiveness of the training program. The results indicate that such targeted and structured training programs can significantly enhance the working memory abilities of children in this age group, providing promising implications for educational strategies and cognitive development interventions.

• International Journal of Heart Failure
• /
• 제6권1호
• /
• pp.11-19
• /
• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• 한국수산과학회지
• /
• 제57권4호
• /
• pp.327-335
• /
• 2024

A simultaneous analytical method was developed to quantify antifouling agents and triazine herbicides in seawater using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The target compounds included diuron, irgarol 1051, and prometryn, which are prevalent in marine environments owing to their extensive use in antifouling coatings and agriculture. The analytical procedure involves solid-phase extraction (SPE) using HLB cartridges followed by LC-MS/MS analysis for precise quantification. The method exhibits high recovery rates for diuron (101% ± 1.25), irgarol 1051 (94.7% ± 2.08), and prometryn (93.7% ± 3.06). Seawater samples from 30 coastal sites in Korea were analyzed. Irgarol 1051 was not detected, whereas diuron was consistently detected across all sites, with concentrations from 0.68 to 11.3 ng/L, and prometryn was present at levels between 0.12 and 7.06 ng/L. The highest diuron and prometryn concentrations were recorded along the southeastern and western coasts, respectively. These findings underscore the critical need for continuous monitoring and regulations to manage these contaminants in marine ecosystems, thereby safeguarding ecological integrity and public health. This study establishes a robust analytical framework for the comprehensive assessment of multiple marine contaminants.