• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.318-324
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• 2017

We introduced a stereo camera on the aircraft to detect flight objects and to estimate the 3D position of them. The Saliency map algorithm based on PCT was proposed to detect a small object between clouds, and then we processed a stereo matching algorithm to find out the disparity between the left and right camera. In order to extract accurate disparity, cost aggregation region was used as a variable region to adapt to detection object. In this paper, we use the detection result as the cost aggregation region. In order to extract more precise disparity, sub-pixel interpolation is used to extract float type-disparity at sub-pixel level. We also proposed a method to estimate the spatial position of an object by using camera parameters. It is expected that it can be applied to image - based object detection and collision avoidance system of autonomous aircraft in the future.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.419-429
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• 2020

In conducting a mission to explore and track targets using a number of unmanned aerial vehicles(UAVs), performance for that mission may vary significantly depending on the operating conditions of the UAVs such as the number of operations, the altitude, and what future flight paths each aircraft decides based on its current position. However, studies on the number of operations, operating conditions, and flight patterns of unmanned aircraft in these surveillance missions are insufficient. In this study, several types of flight simulations were conducted to detect and determine targets while multiple UAVs were involved in the avoidance of collisions according to various autonomous flight algorithms based by flocking theory, and the results were presented to suggest a more efficient/effective way to control a number of UAVs in target detection missions.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.485-492
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• 2021

Vision-based autonomous precision landing technology for UAVs requires precise position estimation and landing guidance technology. Also, for safe landing, it must be designed to determine the safety of the landing point against ground obstacles and to guide the landing only when the safety is ensured. In this paper, we proposes vision-based navigation, and algorithms for determining the safety of landing point to perform autonomous precision landings. To perform vision-based navigation, CNN technology is used to detect landing pad and the detection information is used to derive an integrated navigation solution. In addition, design and apply Kalman filters to improve position estimation performance. In order to determine the safety of the landing point, we perform the obstacle detection and position estimation in the same manner, and estimate the speed of the obstacle using LSM. The collision or not with the obstacle is determined based on the CPA calculated by using the estimated state of the obstacle. Finally, we perform flight test to verify the proposed algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.1
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• pp.99-107
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• 2014

Abruptly occurred obstacles on highway threaten driving safety. Radar draws the attention to the collision avoidance system because it can be fully operational in all weather, and day and night condition. This paper presents the design, implementation and performance test results of pulsed Doppler radar system for detection and warning of road obstacles. The system is designed to consider highway environment and detection capability about various fixed and moving obstacles. The system consists of 4 subsystems, which include antenna unit, transmitter and receiver unit, radar signal & data processing unit, and controller & display unit. The core technologies include clutter map based change detection for fixed obstacles detection, Doppler estimation for velocity detection of moving targets, and azimuth angle estimation method using monopulse for lane estimation and tracking. The design performance of the developed radar system is verified through experiments using a fixed reference target and moving vehicles in test highway.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.881-890
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• 2022

As individual and group users actively use drones, the risks (Intrusion, Information leakage, and Sircraft crashes and so on) in no-fly zones are also increasing. Therefore, it is necessary to build a system that can detect drones intruding into the no-fly zone. General acoustic drone detection researches do not derive location-independent performance by directly learning drone sound including environmental noise in a deep learning model to overcome environmental noise. In this paper, we propose a drone detection system that collects sounds including environmental noise, and detects drones by removing noise from target sound. After removing environmental noise from the collected sound, the proposed system predicts the drone sound using Mel spectrogram and CNN deep learning. As a result, It is confirmed that the drone detection performance, which was weak due to unstudied environmental noises, can be improved by more than 7%.

• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.82-87
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• 2018

In the field of access control, policy conflicts must be solved and various related solutions are being researched and developed. In order to resolve the policy conflict problem, it is necessary to first identify the cause of the conflict, and as a minimum condition, it is necessary to detect the contextual elements of the policy that have influenced the policy evaluation decision. Although the XACML policy language specification provides a way to define this, the policy creator currently has limitations in not clearly describing the causes of conflicts in every contextual elements. In addition, in order to identify the causes of the policy conflict, it is necessary to acquire additional information such as other policy combination algorithms, in addition to these contextual factors. In this paper, we propose an effective method to identify contextual factors, as well as to locate additional information that cause policy conflicts.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.91-105
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• 2024

Car accidents occur as a result of collisions between vehicles, leading to both vehicle damage and personal and material losses. This study developed a vehicle accident detection model based on 2,550 image frames extracted from car accident videos uploaded to YouTube, captured by CCTV. To preprocess the data, bounding boxes were annotated using roboflow.com, and the dataset was augmented by flipping images at various angles. The You Only Look Once version 8 (YOLOv8) model was employed for training, achieving an average accuracy of 0.954 in accident detection. The proposed model holds practical significance by facilitating prompt alarm transmission in emergency situations. Furthermore, it contributes to the research on developing an effective and efficient mechanism for vehicle accident detection, which can be utilized on devices like smartphones. Future research aims to refine the detection capabilities by integrating additional data including sound.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.4
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• pp.83-90
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• 2017

Unmanned Aerial Vehicles(UAVs) require collision avoidance capabilities equivalent to the capabilities of manned aircraft to enter the airspace of manned aircraft. In the case of Visual Flight Rules of manned aircraft, collision avoidance is performed by 'See-and-Avoid' of pilots. To obtain those capabilities of UAVs named as 'Sense-and-Avoid', sensor-system-based intruder tracking and collision avoidance methods are required. In this study, a multi-sensor-based tracking, data fusion, and collision avoidance algorithm is designed by using a model-based design tool MATLAB/SIMULINK, and validations of the designed model and code using numerical simulations and processor-in-the-loop simulations are performed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.890-893
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• 2014

In this paper, we present a signal processing algorithm for a marine radar system, in which the evaluation of probability of collision as well as target detection and tracking are performed. Moreover, the digital signal processor that implements the algorithm is proposed. As simulation environment, a mechanically scanning antenna utilizing FMCW signal is used, conducting the beamforming operation with 1 degrees intervals. Test board consists of DSP chips and FPGA, which enable the implemented system to operate in real-time.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.499-501
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• 2004

본 논문에서는 네트워크의 구성 정보를 바탕으로 상위 수준에서 하위 수준으로 정책을 변환할 때 나타나는 기존 정책과의 충돌을 탐지하고 순응시키는 메커니즘을 소개한다. 대규모 네트워크는 라우터, 스위치, 방화벽 침임 탐지 시스템, 일반 호스트 등과 같은 다양한 종류의 장비로 구성되어 있으며. 이러한 것들은 각기 다른 접근 일 제어 형식을 가지고 있다. 따라서 트래픽에 대한 일괄적인 통제가 어렵고, 외부의 공격에 대한 신속하고 효과적인 대응이 불가능하다. 또한 대규모 네트워크를 구성하고 있는 장비들을 제어하기 위해서는 그러한 장비들이 포함되어 있는 서브 네트워크의 세부 점보와 각 장비의 고유한 설정 규칙을 필요로 한다. 이러한 점은 대규모 네트워크를 상위 수준의 계층에서 관리를 어렵게 한다. 때문에 하부 계층의 구조나 정보와는 독립적으로 추상화된 고수주의 보안 정책 설정을 위한 도구가 요구된다 이것은 상위 수준의 보안 정책 표현 기법, 하위 수준의 보안 정책 기법, 상위 수준의 보안 정책과 네트워크 구성 정보를 바탕으로 하위 수준의 보안 정책을 도출하는 기법 하위 수준의 보안 정책을 실제 네트워크 구성 요소에 적용하는 기법 등의 네 가지 연구로 구분된다. 본 논문에서는 이 네 가지의 연구와 기법을 바탕으로 관리 네트워크에 새로운 정책이 전달될 때 기존의 단순한 정책 선택을 벗어난 서로의 정책을 변환한 ACL을 최대한 순응시키는 메커니즘을 제안한다