• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.342-342
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• 2020

최근 소형 무인비행장치(UAV; unmaned aerial vehicle)인 드론을 이용한 신산업 육성 및 지원에 관한 관심도가 높아지고 있다. 국외에서는 이미 드론을 이용한 농업관리와 물류배송, 공공부문 모니터링 등 다양한 산업 분야의 드론 이용을 적극 장려하고 있다. 드론 이용에 관한 관심도가 높아짐에 따라 국내외적으로 드론 응용 관련 기술 개발과 연구가 활발하게 진행되고 있지만, 국내에서는 환경모니터링과 시설물 점검 등 일부 제한적으로 활용되고 있다. 국내에서는 2024년까지 드론 응용서비스로 확장되는 산업 변화에 대응, DNA(Data, Network, AI) 기술을 접목한 새로운 개방형 플랫폼 구축을 목표로 기술개발 및 산업 육성을 촉진하고 있다. 이러한 국내 기술 개발 방향에 맞추어 드론과 첨단기술을 이용한 하천조사와 관련해 드론을 연계한 하천관리 플랫폼 개발의 필요성이 높아지고 있다. 본 연구에서는 드론 기반 하천조사 및 모니터링 수행을 위한 하천관리 운영플랫폼 개발을 목표로 국내외 요소기술을 분석하고 기술수준을 조사했다. 특히, 드론 기반 하천관리에 필요한 임무를 영역별로 분리해 요소기술 기반의 플랫폼 서비스를 정의하고 하천관리 부문 개방형 플랫폼 구축을 위한 시스템 구성 및 운영에 필요한 요소기술을 선정했다. 최종적으로 선정된 플랫폼 서비스와 요소기술을 기초로 시스템 적용방안을 검토하고 하천관리 운영플랫폼 구축을 위한 시스템 아키텍처를 정의 및 설계했다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.275-284
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• 2023

Land monitoring involves systematically understanding changes in land use, leveraging spatial information such as satellite imagery and aerial photographs. Recently, the integration of deep learning technologies, notably object detection and semantic segmentation, into land monitoring has spurred active research. This study developed a web service to facilitate such integrations, allowing users to analyze aerial and drone images using CNN models. The web service architecture comprises AI, WEB/WAS, and DB servers and employs three primary deep learning models: DeepLab V3, YOLO, and Rotated Mask R-CNN. Specifically, YOLO offers rapid detection capabilities, Rotated Mask R-CNN excels in detecting rotated objects, while DeepLab V3 provides pixel-wise image classification. The performance of these models fluctuates depending on the quantity and quality of the training data. Anticipated to be integrated into the LX Corporation's operational network and the Land-XI system, this service is expected to enhance the accuracy and efficiency of land monitoring.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.577-585
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• 2024

This study analyzed the relationship between GPS-based physical activity patterns and mental health using Kaggle Student Life data. Data were collected over a 10-week period from 48 students at Dartmouth College through Android smartphones and included GPS, dark, and phone lock data, and measures such as the Patient Health Questionnaire-9 (PHQ-9), Loneliness Scale, the Positive and Negative Affect Schedule (PANAS), and Perceived Stress Scale. Using latitude and longitude data obtained from GPS measurements, various physical activity indicators were calculated, including the total distance traveled, average distance traveled, average distance traveled in the morning, average distance traveled in the afternoon, average distance traveled in the evening, and average distance traveled in the middle of the night. Pearson's correlation analysis was performed to explore the relationship between GPS-based physical activity patterns and mental health. The study results indicated a significant negative correlation between the average distance traveled in the afternoon and PHQ-9 scores. Results indicated that the higher the afternoon activity, the lower the depressive symptoms. There was a positive correlation be-tween the PANAS-Pos score and the average distance traveled in the evening, indicating that positive emotions tended to increase as evening activities increased. This finding suggests a relationship between physical activity at specific times and mental health.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.715-720
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• 2023

The military is driving innovative changes such as AI, cloud computing, and drone operation through the Fourth Industrial Revolution. It is expected that such changes will lead to a rapid increase in the demand for information exchange requirements, reaching all lower-ranking soldiers, as networking based on IoT occurs. The flow of such information must ensure efficient information distribution through various infrastructures such as ground networks, stationary satellites, and low-earth orbit small communication satellites, and the demand for information exchange that is distributed through them must be appropriately dispersed. In this study, we redefined the DSCP, which is closely related to QoS (Quality of Service) in information dissemination, into 11 categories and performed research to map each cluster group identified by cluster analysis to the defense "information exchange requirement list" on a one-to-one basis. The purpose of the research is to ensure efficient information dissemination within a multi-layer integrated network (ground network, stationary satellite network, low-earth orbit small communication satellite network) with limited bandwidth by re-establishing QoS policies that prioritize important information exchange requirements so that they are routed in priority. In this paper, we evaluated how well the information exchange requirement lists classified by cluster analysis were assigned to DSCP through M&S, and confirmed that reclassifying DSCP can lead to more efficient information distribution in a network environment with limited bandwidth.

• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.83-90
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• 2020

Disasters are causing significant damage to the lives and property of our society and are recognized as social problems that need to be solved nationally and globally. The 4th industrial revolution technologies affecting society as a whole such as the Internet of Things(IoT), Artificial Intelligence(AI), Drones(Unmanned Aerial Vehicles), and Big Data are continuously absorbed into the disaster and safety industries as scientific and technological tools for solving social problems. Very soon, twenty-nine domestic UAV-related organizations/companies will complete the construction of a multicopter type small UAV integrated system ('17~'20) that can be operated at disaster and security sites. The current work considers and proposes the mid-to-long term R&D direction of disaster UAV as a strategic asset of the national disaster response system. First, the trends of disaster and safety industry and policy are analyzed. Subsequently, the development status and future plans of small UAV, securing shortage technology, and strengthening competitiveness are analyzed. Finally, step-by-step R&D direction of disaster UAV in terms of development strategy, specialized mission, platform, communication, and control and operation is proposed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.305-313
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• 2019

Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.