• The Journal of Engineering Geology
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• v.33 no.3
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• pp.475-487
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• 2023

We surveyed the distribution of soil layers on Dongdo and Seodo of Dokdo and measured the physical properties of the soils. To investigate the distribution of soil layers, the soil depth was measured directly in accessible locations, and visual observations of inaccessible locations were carried out using drones and boats. Soil depths ranged from 3 to 50 cm, and most soil layers had depths of 10~20 cm. Based on these results, a map of the soil layer was drawn using 5 cm intervals for soil depth. To analyze the soil characteristics of Dokdo, soil samples were collected from 13 locations on Dongdo and 13 locations on Seodo, in consideration of various geological settings. According to the results of grain size distribution tests, sand contents were >75%, and soil from Seodo contained more gravel-sized particles than that from Dongdo. Using the unified soil classification system (USCS) and textural classification chart of the United States Department of Agriculture (USDA), most of the soil samples from Dokdo are classified as sand, and some are classified as loamy or clayey sand. In addition, well-graded loamy or clayey sands are more common in Dongdo, and poorly graded sands with gravel are more common in Seodo. These results are expected to be important for studying soil characteristics on Dokdo.

• Land and Housing Review
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• v.14 no.3
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• pp.145-155
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• 2023

Recently, with the widespread adoption of Building Information Modeling (BIM) technology in the construction industry, various object detection algorithms have been used to verify errors between 3D models and actual construction elements. Since the characteristics of objects vary depending on the type of construction facility, such as buildings, bridges, and tunnels, appropriate methods for object detection technology need to be employed. Additionally, for object detection, initial object images are required, and to obtain these, various methods, such as drones and smartphones, can be used for image acquisition. The study uses a 360° camera optimized for internal tunnel imaging to capture initial images of the tunnel structures of railway and road facilities. Various object detection methodologies including the YOLO, SSD, and R-CNN algorithms are applied to detect actual objects from the captured images. And the Faster R-CNN algorithm had a higher recognition rate and mAP value than the SSD and YOLO v5 algorithms, and the difference between the minimum and maximum values of the recognition rates was small, showing equal detection ability. Considering the increasing adoption of BIM in current railway and road construction projects, this research highlights the potential utilization of 360° cameras and object detection methodologies for tunnel facility sections, aiming to expand their application in maintenance.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Atmosphere
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• v.34 no.1
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• pp.35-53
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• 2024

Under the leadership of the National Institute of Meteorological Sciences (NIMS), the first domestic autonomous flight-type weather modification experimental drone for fog and lower-level cloud seeding was developed in 2021. This drone is designed based on a multi-copter configuration with a maximum takeoff weight of approximately 25 kg, enabling the installation of up to four burning flares for seeding materials and facilitating weather observations (temperature, pressure, humidity, and wind) as well as aerosol (PM₁₀, PM_2.5, and PM_1.0) particle measurements. This research aims to introduce the construction of the drone and its recent applications over the past two years, providing insights into the experimental procedures, effectiveness verification, and improvement directions of the weather modification drone-based rain enhancement. In particular, partial confirmation of the experimental effects was obtained through the fog dissipation experiment on December 10, 2021, and the lower-level cloud seeding case study on October 5, 2022. To enhance the scope and rainfall amount of weather modification experiments using drones, various technological approaches, including adjustments to experimental altitude, seeding lines, seeding amount, and verification methods are necessary. Through this research, we aim to propose the development direction for weather modification drone technology, which will serve as foundational technology for practical application of domestic rain enhancement technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.74-91
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• 2023

Several that analyzed the effectiveness of traffic safety facilities a method of comparing changes in the number of accidents, accident severity, speed through traffic accident data before and after installation or speed data collected from vehicle detection systems (VDS). , when traffic accident data is used, it takes a long time to collect because must be collected for at least one year before and after installation. , the road environment may change during this period, such as the addition of other traffic safety facilities in addition to the facilities to be analyzed. , the location of the VDSs for speed data is often different from the location where analysis is required, and there is a problem in that the investigators are exposed to the risk of traffic accident during on-site investigation. Therefore, this study a case study by establishing a methodology to determine effectiveness video images with a drone, extracting data using a program, and comparing vehicle driving speeds before and after speed reduction facilities. Vehicle speed surveys using drones are much safer than observational surveys conducted on highways and have the advantage of tracking speed changes along the vehicle, it is expected that they will be used for various traffic surveys in the future.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.5-18
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• 2023

As the development of drones and sensors accelerates, new services and values are created by fusing data acquired from various sensors mounted on drone. However, the construction of spatial information through data fusion is mainly constructed depending on the image, and the quality of data is determined according to the specification and performance of the hardware. In addition, it is difficult to utilize it in the actual field because expensive equipment is required to construct spatial information of high-quality. In this study, super-resolution was performed by applying deep learning to low-resolution images acquired through RGB and THM cameras mounted on a drone, and quantitative evaluation and feature point extraction were performed on the generated high-resolution images. As a result of the experiment, the high-resolution image generated by super-resolution was maintained the characteristics of the original image, and as the resolution was improved, more features could be extracted compared to the original image. Therefore, when generating a high-resolution image by applying a low-resolution image to an super-resolution deep learning model, it is judged to be a new method to construct spatial information of high-quality without being restricted by hardware.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.157-166
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• 2024

The Army TIGER System, which is being deployed to implement a future combat system, is expected to bring innovative changes to the army's combat methods and comabt execution capability such as mobility, networking and intelligence. To this end, the Army will introduce various systems using drones, robots, unmanned vehicles, AI(Artificial Intelligence), etc. and utilize them in combat. The use of various unmanned vehicles and AI is expected to result in the introduction of equipment with new technologies into the army and an increase in various types of transmitted information, i.e. data. However, currently in the military, there is an acceleration in research and combat experimentations on warfigthing options using Army TIGER forces system for specific functions. On the other hand, the current reality is that research on cyber threats measures targeting information systems related to the increasing number of unmanned systems, data production, and transmission from unmanned systems, as well as the establishment of cloud centers and AI command and control center driven by the new force systems, is not being pursued. Accordingly this paper analyzes the structure and characteristics of the Army TIGER force integration system and makes suggestions for necessity of building, available cyber defense solutions and Army TIGER integrated cyber protections system that can respond to cyber threats in the future.

• Journal of Adhesion and Interface
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• v.24 no.3
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• pp.95-99
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• 2023

In this study, LiDAR-detectable black materials are synthesized by coating and reduction of titanium dioxide onto plate-type natural mica, which evaluated practical LiDAR verification. In detail, black TiO₂@Mica materials are fabricated by utilizing a sol-gel reaction to coat titanium dioxide onto natural mica, followed by reduction using sodium tetrahydridoborate. Subsequently, Black TiO₂@Mica materials are dispersed in hydrophilic transparent varnish and sprayed onto the glass substrate to assess applicability as paints. As a result, Black TiO₂@Mica-based paints exhibit true blackness (L^*=12.1) and a higher NIR reflectance (30.2 R%). In addition, it was confirmed that as-synthesized Black TiO₂@Mica materials are successfully recognized by a LiDAR sensor. This phenomenon is attributed to Fresnel's reflection law, in which light reflection occurs at the interface between natural mica and titanium dioxide with different refractive indices. In this regard, the findings of the study are expected to contribute to the potential utilization of LiDAR-detectable materials in various fields such as autonomous vehicles, robotics, and drones.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.125-132
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• 2024

The purpose of this study is to discover a drone utilization model tailored to local characteristics, propose directions for building a drone demonstration city based on demand surveys for drone activation, and suggest ways to utilize and support a drone application system. First, according to the survey results, there was a high understanding of and necessity for drone demonstration projects, particularly in addressing urban issues, which were deemed to have a significant impact. Second, based on the analysis of priorities and short- and long-term approaches, disaster-related tasks were evaluated as a priority, requiring an approach through medium- to long-term strategies. Third, it was noted that budgetary considerations emerged as the most critical issue during project implementation. Practitioners and experts expressed willingness to actively introduce drone-based technologies into their work when budget and technology were ready. Budgetary constraints were identified as the most significant obstacle to proper implementation, emphasizing the need for resolution. Fourth, the necessity of demand surveys during project development was identified in certain areas. Demand surveys were deemed essential for drone-based demonstration city construction, and a survey indicated that public leadership in this regard was also necessary. Fifth, concerning approaches in specific areas, the field of safety and disaster management was highlighted as the most crucial for application.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.275-284
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• 2024

Monitoring crop growth changes and water content is crucial in the agricultural sector. This study utilized drones equipped with Short Wavelength Infrared (SWIR) sensors, sensitive to moisture changes, to observe soybeans' growth and water content variations. We confirmed that as soybeans grow more vigorously, their water content increases and differences in irrigation levels lead to decreases in vegetation and moisture indices. This suggests that waterlogging slows down soybean growth and reduces water content, highlighting the importance of detailed monitoring of vegetation and moisture indices at different growth stages to enhance crop productivity and minimize damage from waterlogging. Such monitoring could also preemptively detect and prevent the adverse effects of moisture changes, such as droughts, on crop growth. By demonstrating the potential for early diagnosis of moisture stress using drone-based SWIR sensors, this research suggests improvements in the efficiency of large-scale crop management and increases in yield, contributing to agricultural production.