• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.435-440
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• 2020

The application of the unmanned vehicles in various fields has been attracting attention, and the development of a service utilizing unmanned vehicles has been proceeding. As the service market using the unmanned vehicles rapidly increases, the demand for the development of software for performing the mission with unmanned vehicles is increasing. In particular, as the demand for unmanned vehicle utilization services for public missions such as fire detection, mail delivery, and facility management increases, the importance of developing mission software for unmanned vehicle is increasing. To develop common mission software, architecture design should be made so that unmanned vehicle service provider can easily develop software using reusable libraries or functions through analysis commonly required by various public institutions. In this paper, we discuss the research trends of mission software for public mission unmanned vehicles. In addition, the architecture design of developing formal mission software is proposed. Finally, we propose a data transfer architecture between mission software and data platform.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.667-669
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• 2021

Recently, RFID/USN technology has been applied in various fields such as logistics, environment, education, home network, disaster prevention, military, and medical care, but despite the remarkable development of RFID/USN technology, it is difficult to apply it to marine industry due to the characteristics of poor marine environment. Therefore, satellites are mainly used in the marine sector, and existing communication networks are used in the coast, so measures for forming a shelf-only short-range network in the ocean are being considered. In this paper, we consider the use of drones as mobile base stations of USN as a base station role using USN in existing PS-LTE and LTE networks.Since autonomous navigation vessels are aiming for the intelligent system, the number of crew and labor force should be reduced and the function of autonomous network formation in the form of more stable and intelligent ICT convergence technology should be strengthened.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.63-76
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• 2019

UAVs are unmanned, autonomous or remotely piloted aircraft. As UAVs become smaller, lighter and more economical, their applications continue to expand. Researches on UAVs in the field of remote sensing show development methods and purposes similar to those on satellite images, and they are widely used in studies such as 3D image composition and monitoring. In the field of environmental impact assessment(EIA), satellite information and data are mainly used. However, only low-resolution images covering long distances and large-scale data allowing for rough examination are being provided, so their uses are seriously limited. Therefore, in this paper, we construct spatial information of forest area by using unmanned aerial vehicle and seek efficient utilization and policy improvement in the field of environmental impact assessment. As a result, high-resolution images and data from UAVs can be used to identify the location status of SEIA, EIA, and small scale EIA project plans and to evaluate detailed environmental impact analysis. In addition, when provided together with infographics about Post-environmental impact investigation, it was confirmed that the possibility of periodic spatial information construction and evaluation can be used throughout the entire project contents and project post-process.In order to provide sophisticated infographics for the EIA, drone photography and GCP surveying methods were derived.The results of this study will be used as a basis for improving high-resolution monitoring and environmental impact assessment in the forest sector.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.12-21
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• 2019

In the era of the Fourth Industrial Revolution, drones have become a flexible device that can be integrated with new technologies. The drones were originally developed as military unmanned aircraft and are now being used in various fields. In the environment and weather observation area, the atmospheric boundary layer is near the surface where the atmosphere is the most active in the meteorological phenomenon and has a close influence on human activities. In order to carry out the study of these atmospheric boundary layers, it is necessary to observe precisely the lower atmosphere and secure the observation technology. The drones in the meteorological field can be used for meteorological observations at a relatively low maintenance cost compared to existing equipment. When used in conjunction with various sensors, the drones can be widely used in atmospheric boundary layer and local meteorological studies. In this study, the possibility of meteorological observations using drones was confirmed by conducting vertical meteorological (temperature and humidity) observation experiments equipped with a combined meteorological sensor and a radio sonde on drones owned by NIMS.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.201-210
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• 2019

This paper proposes the AMCS(Agricultural Machine Control System that distinguishes farms using satellite photos or drone photos of farms and controls the self-driving and operation of farm drones and tractors. The AMCS consists of the LSM(Local Server Module) which separates farm boundaries from sensor data and video image of drones and tractors, reads remote control commands from the main server, and then delivers remote control commands within the management area through the link with drones and tractor sprinklers and the PSM that sets a path for drones and tractors to move from the farm to the farm and to handle work at low cost and high efficiency inside the farm. As a result of AMCS performance analysis proposed in this paper, the PSM showed a performance improvement of about 100% over Dijkstra algorithm when setting the path from external starting point to the farm and a higher working efficiency about 13% than the existing path when setting the path inside the farm. Therefore, the PSM can control tractors and drones more efficiently than conventional methods.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.401-406
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• 2018

According to the National Security Service of the National Police Agency, intrusion into empty houses increased form 2013 to 2016. Consequentially this statistics seemed that house intrusion, burglary is increasing. Also according to the statistics of Public Prosecutors'Office, a total 203,573 theft crimes occurered in 2016, of which 18.9% were theft after intruding. By reson of this is most frequent case of intrusion and theft, we have been studing the RodSecurityRobot model to enhance security in many factories to manage. In order to care for security to the high place, we have propsed a road guard robot model which controls the ground in cooperation with the robot that manages the ground by using the drones. The robot and the drone move together to autonomy to avoid objects. And they check time interval. they also goes to the charger to charge when there is no battery.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.1
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• pp.73-84
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• 2019

In this research, it is a basic study to investigate the population of birds using UAVs. The research area is Ardeidae species(ASP) habitat and has long-term monitoring. The purpose of the study is to compare the ASP populations which analyzed ground observational survey and UAVs imagery. We used DJI's Mavic pro and Phantom4 for this research. Before investigating the population of ASP, we measured the escape distance by the UAVs, and the escape distances of the two UAVs models were statistically significant. Such a result would be different in UAV size and rotor(rotary wing) noise. The population of ASP who analyzed the ground observation and UAVs imagery count differed greatly. In detail, the population(mean) on the ground observation was 174.9, and the UAVs was 247.1 ~ 249.9. As a result of analyzing the UAVs imagery, These results indicate that the lower the UAVs camera altitude, the higher the ASP population, and the lower the UAVs camera altitude, the higher the resolution of the images and the better the reading of the individual of ASP. And we confirmed analyzed images taken at various altitudes, the individuals of ASP was not statistically significant. This is because the resolution of the phantom was superior to that of mavic pro. Our research is fundamental compared to similar studies. However, long-term monitoring for ASP of South Korea's by ground observation is a barrier of the reliability of the monitoring result. We suggested how to use UAVs which can improve long-term monitoring for ASP habitat.