• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1059-1066
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• 2021

In Korea, the importance of marine activities is great, and automatic weather observation facilities are operating on land to investigate abnormal weather phenomena caused by industrialization; however, the number of facilities at sea is insufficient. Marine survey ships are operated to establish marine safety information, but there are many places where marine survey ships are difficult to access and operating costs are high. Therefore, a small, unmanned vessel capable of marine surveys must be developed. The sail has a significant impact on the sailing performance, so much research has been conducted. In this study, the camber effect, which is a design variable of the twin curvy sail known to have higher aerodynamic performance than existing airfoil shapes, was investigated. Flow analysis results for five cases with different camber sizes show that the lift coefficient is highest when the camber size is 9%. Curvy twin sails had the highest lift coefficient at an angle of attack of 23° because of the interaction of the port and starboard sails. The port sail had the highest lift coef icient at an angle of attack of 20°, and the starboard sail had the lowest lift coef icient at an angle of attack of 15°. In addition, the curvy twin sail had a higher lift coefficient than NACA 0018 at all angles of attack.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.186-192
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• 2019

In general, many marine organisms are attached to the bottom of a ship in operation or a ship in construction. Due to this phenomenon, the roughness of the ship surface increases, resulting in loss of ship speed, resulting in economic losses and environmental pollution. This study acquires / utilizes camera images attached to ship's bottom and underwater drones to check the condition of bottom. The acquired image will determine the roughness according to marine life by the administrator's visual confirmation. Therefore, by applying a filter algorithm to correct the image to the original image can help in the correct determination of whether or not attached to marine life. Various correction filters are required for the underwater image correction algorithm, and the lighting suitable for the dark underwater environment has a great influence on the judgment. The results of the research test according to the calibration algorithm and the roughness of each algorithm are considered to be applicable to many fields.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.81-86
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• 2019

Although the localization rate of shipbuilding and marine equipment goods is set to be 70 percent by 2020, but the localization rate of equipment and materials for shipbuilding and marine facilities is currently 10 to 30 percent. For Korea's Big 3 shipbuilders, which build 70 percent of the world's largest shipbuilders, localization of shipbuilding equipment and equipment is an essential factor. In particular, there is a growing need to localize equipment and materials in terms of the number of lead standards and A/S. It is expected that there will be a rapid expansion of LNG carriers in the future, and it is necessary to develop equipment and equipment materials of LNG ships. In this study, the design and manufacture of LNG vessel equipment was conducted. Design and basic performance tests of 4-inch QCDC for LNG bunkering were conducted.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.47-55
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• 2024

The paper presents a docking-type automatic landing system that works in tandem with Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). The system utilizes a pyramid-shaped landing gear and pad for effective landing. In marine environments, a docking device guides the drone to land securely. To test the system, a ship's behavior was simulated using a 3-DoF motion platform, and the successful operation and utility of the docking-type automatic landing system were demonstrated.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.199-215
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• 2020

A seagrass that is marine vascular plants plays an important role in the marine ecosystem, so periodic monitoring ofseagrass habitatsis being performed. Recently, the use of dronesthat can easily acquire very high-resolution imagery is increasing to efficiently monitor seagrass habitats. And deep learning based on a convolutional neural network has shown excellent performance in semantic segmentation. So, studies applied to deep learning models have been actively conducted in remote sensing. However, the segmentation accuracy was different due to the hyperparameter, various deep learning models and imagery. And the normalization of the image and the tile and batch size are also not standardized. So,seagrass habitats were segmented from drone-borne imagery using a deep learning that shows excellent performance in this study. And it compared and analyzed the results focused on normalization and tile size. For comparison of the results according to the normalization, tile and batch size, a grayscale image and grayscale imagery converted to Z-score and Min-Max normalization methods were used. And the tile size isincreased at a specific interval while the batch size is allowed the memory size to be used as much as possible. As a result, IoU was 0.26 ~ 0.4 higher than that of Z-score normalized imagery than other imagery. Also, it wasfound that the difference to 0.09 depending on the tile and batch size. The results were different according to the normalization, tile and batch. Therefore, this experiment found that these factors should have a suitable decision process.

• 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 the Korean earth science society
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• v.41 no.6
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• pp.575-587
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• 2020

Meteorological phenomena are observed by the Korea Meteorological Administration in a variety of ways (e.g., surface, upper-air, marine, ocean, and aviation). However, there are limits to the meteorological observation of the planetary boundary layer (PBL) that greatly affects human life. In particular, observations using a sonde or aircraft require significant observational costs in economic terms. Therefore, the goal of this study was to measure and analyze the meteorological factors of the vertical distribution of the see-land breeze among local meteorological phenomena using meteorological drones. To investigate the spatial distribution of the see-land breeze, a same integrated meteorological sensor was mounted on each drone at three different points (seaside, bottom of mountain, and mountainside), including the Boseong tall tower (BTT) at the Boseong Standard Weather Observatory (BSWO) in the Boseong region. Vertical profile observations for air temperature, relative humidity, wind direction, wind speed, and air pressure were conducted up to 400 m every 30 minutes from 1100 LST to 1800 LST on August 4, 2018. The spatial characteristics of meteorological phenomena for temperature, relative humidity, and atmospheric pressure were not shown at the four points. Strong winds (~8 m s-1) were observed from the midpoint (~100 m) at strong solar radiation hour, and in the afternoon the wind direction changed from the upper layer at the inland area to the west wind. It is expected that the analysis results of the lower atmospheric layer observed using the meteorological drone may help to improve the weather forecast more accurately.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.179-184
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• 2021

Along with the development of the fourth industry, the public sector has increasingly paid more attention to search using drones and real-time monitoring, for various goals. The drones are used and researched to complete a variety of searching and monitoring missions, including search for missing persons, security, coastal patrol and monitoring, speed enforcement, highway and urban traffic monitoring, fire and wildfire monitoring, monitoring of illegal fishing in reservoirs and protest rally monitoring. Police stations, fire departments and military authorities, however, concentrate on the hardware part, so there are little research on efficient communication systems for the real-time monitoring of data collected from high-performance resolution and infrared thermal imagining cameras, and analysis programs suitable for special missions. In order to increase the efficiency of drones with the searching mission, this paper, therefore, attempts to propose an image analysis technique to increase the precision of search by producing image data suitable for searching missions, based on images obtained from drones and provide the foundation for improving relevant policies and establishing proper platforms, based on actual field cases and experiments.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.