• Journal of Aerospace System Engineering
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• v.14 no.5
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• pp.58-65
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• 2020

In this paper, the combat effectiveness of ship guidance rockets to counter the threat of multiple USV(Unmanned Surface Vehicle) was analyzed in three cases. The probability of sinking a number of USVs approaching by distance was compared using various weapon systems and guided rocket systems installed on the naval vessels. As a result of the analysis, it was found that the combat effectiveness of the guided rocket was improved compared to the close defense system of the anti-ship missile installed on the naval vessels.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.197-202
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• 2018

Drone has been continuously studied in the field of geography and remote sensing. The basic researches have been actively carried out before the utilization in the field of photogrammetry. In Korea, it is necessary to study the actual way of research in accordance with the drone utilization environment. In particular, analysis on the characteristics of DSM (Digital Surface Model) generated through drone are needed. In this study, the characteristic of drone DSM as a data acquisition method was analyzed for coastal management. The coastal area was selected as the study area, and data was acquired by using drone. As a result of the study, the terrain model and the ortho image of coastal area were produced. The accuracy of UAV (Unmanned Aerial Vehicle) results were very high about 10cm at check points. However, concavo-convex shapes appeared in very flat areas such as tidal flats and roads. To correct this terrain model distortion, a new terrain model was created through data processing and the results were evaluated. If additional studies are carried out and the construction and analysis of terrain model using drone image is done, drone data for coastal management will be available.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.6
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• pp.731-742
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• 2016

Recent technological advance in UAV (Unmanned Aerial Vehicle) technology offers new opportunities for assessing crop situation using UAV imagery. The objective of this study was to assess if reflectance and NDVI derived from consumer-grade cameras mounted on UAVs are useful for crop condition monitoring. This study was conducted using a fixed-wing UAV(Ebee) with Cannon S110 camera from March 2015 to March 2016 in the experiment field of National Institute of Agricultural Sciences. Results were compared with ground-based recordings obtained from consumer-grade cameras and ground multi-spectral sensors. The relationship between raw digital numbers (DNs) of UAV images and measured calibration tarp reflectance was quadratic. Surface (lawn grass, stairs, and soybean cultivation area) reflectance obtained from UAV images was not similar to reflectance measured by ground-based sensors. But NDVI based on UAV imagery was similar to NDVI calculated by ground-based sensors.

• Smart Structures and Systems
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• v.25 no.6
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• pp.733-749
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• 2020

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

• Smart Media Journal
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• v.8 no.3
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• pp.53-61
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• 2019

In this paper, designing and implementing multi-communication middleware in multi-thread environmet through event-based synchronization method are proposed for stable data transmission of electronic optical equipment, which requires combining camera and various sensors to process multiple high-speed data. To verify the performance of the implemented communication middleware, image data and sensor data were sent to compare differences in reception-based and transmission-based cycles, and the maximum number of communication possibilities to transmit and process multiple was measured and analyzed. In addition, the proposed communication middleware's performance was verified through experiments such as validating the integrity of the transmitted data and measuring the Round Trip Time.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.184-185
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• 2022

국제해사기구(IMO)에서는 2017년 미래선박으로서 자율운항선박(MASS)의 개념을 채택한 바 있으며, 실해역 운항을 위한 국제법규 및 규정 검토를 진행하고 있다. 무인선은 악천후시 유인선이 수행하기 힘든 임무를 대체하거나 지원하기 위하여 원격 혹은 자율적으로 운용되는 일종의 소형 자율운항선박을 의미한다. 선박해양플랜트연구소에서는 2011년부터 해양수산부 연구개발사업을 통하여, 무인선 아라곤호 시리즈를 개발하였으며, 아라곤1호, 2호, 3호 등 총 3척을 운용하고 있다. 해당 선박은 길이 8미터, 배수량 약 3톤급의 활주선형으로 원격운항, 경로추종 및 충돌회피 등 자율운항 기능이 적용되어 있다. 한편, 무인선은 공중 드론과 달리 탑재중량이 크고, 항속시간이 길어 해상에서 감시,첩보, 정찰 등에 효용성이 높으며, 최근 한척보다는 여러 척을 운용하는 것이 효과적이어서 무인선 군집(USV Swarm)으로 해상임무를 수행하려는 연구가 다양하게 진행되고 있다. 선박해양플랜트연구소에서는 2019년부터, 기존의 아라곤호 시리즈 무인선들을 활용하여, 무인선 군집 자율운항 시스템 개발을 위한 "인공지능 기반 무인선 상황인식 및 자율운항 기술 개발" 과제를 진행하고 있다. 해상에서 불법선박이 출현시 이를 효과적으로 단속하기 위하여 추적 기동이 필요한데, 본 연구에서는 무인선 3척을 활용하여 불법선박을 추적하는 해상 감시 실해역 시험을 수행하였다. 본 논문에서는 무인선 군집 자율운항 시스템에 대하여 소개하고, 무인선 군집을 활용한 불법선 추적에 관한 실해역 시험결과에 대해 소개한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.188-188
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• 2022

선박의 운용 효율을 높일 수 있는 방법인 무인 운용체계는 근래에 많은 관심을 받고 연구되어 왔다. 특히 무인수상선과 무인수중체의(USV-AUV)의 복합 운용 분야는 그 동안 어려움이 있었던 심해저 탐사 및 특수 임무 활용에 용이하여 많은 연구가 수행되고 있다. 본 연구에서는 쌍동선 형태인 무인수상선이 모선이 되고 무인수중체가 결합하여 충전하고 다시 진수하여 원거리 및 심해저 조건에서 무인수중체가 운용 가능하도록 하는 시스템의 일부인 USV-AUV의 docking을 위한 DP 시스템을 개발하고 선박해양플랜트연구소 해양공학수조에서 모형시험을 통해 이를 검증하였다. 또한, 실제 제작된 무인쌍동선과 추진 시스템을 활용하여 모형시험을 통해 검증한 DP 알고리즘을 적용하여 화성 제부도 앞바다에서 실선 DP 테스트를 수행하였다. 실 해역에서의 DP 시스템 테스트는 정확한 환경 조건의 계측 및 구현이 어려워 모형시험과 같은 정량적인 평가는 어렵지만, 정성적으로 DP 시스템이 작동하는 것을 확인할 수 있었다.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.6
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• pp.473-481
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• 2023

In this study, a simulation system was developed to verify autonomous navigation algorithm in complex maritime traffic areas. In particular, real-world maritime traffic scenario was applied by considering near real-time maritime traffic information provided by Korean e-Navigation service. For this, a navigation simulation system of Unmanned Surface Vehicle (USV) was integrated with an e-Navigation equipment, called Electronic Chart System (ECS). To verify autonomous navigation algorithm in the simulation system, initial conditions including initial position of an own ship and a set of paths for the ship to follow are assigned by an operator. Then, considering real-world maritime traffic information obtained from the service, the simulation is implemented in which the ship repeatedly travels by avoiding surrounding obstacles (e.g., approaching ships). In this paper, the developed simulation system and its application on verification of the autonomous navigation algorithm in complex maritime traffic areas are introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.779-800
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• 2024

Unmanned Aerial Vehicle (UAV) networks, also known as drone networks, have gained significant attention for their potential in various applications, including communication. UAV networks for communication involve using a fleet of drones to establish wireless connectivity and provide communication services in areas where traditional infrastructure is lacking or disrupted. UAV communication networks need to be highly secured to ensure the technology's security and the users' safety. The proposed survey provides a comprehensive overview of the current state-of-the-art UAV network security solutions. In this paper, we analyze the existing literature on UAV security and identify the various types of attacks and the underlying vulnerabilities they exploit. Detailed mitigation techniques and countermeasures for the protection of UAVs are described in this paper. The survey focuses on the implementation of novel technologies like Q-learning, blockchain, IRS, and mmWave. This paper discusses network simulation tools that range in complexity, features, and programming capabilities. Finally, future research directions and challenges are highlighted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.