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유전자 알고리즘을 활용한 한반도 탄도탄 방어체계 최적 구축방안
장준건,이연수,윤부원,허정행 한국방위산업학회 2018 韓國防衛産業學會誌 Vol.25 No.2
North Korea currently has a variety of ballistic missiles, which pose a direct threat to the Korean Peninsula. Accordingly, it is necessary to establish a " Korea Air and Missile Defense System(KAMD) " that is suitable for the Korean Peninsula environment in an early stage. This requires the establishment of an Anti Ballastic Missile Early Warning system and proper securing of the integrated operational control system and the means of interception. In this study, For creating a multiple layers of missile defense systems, a simulation model is developed and applied to provide the most effective deployment methods and required Forces. In addition, it will analyze the defense capabilities of the each Required Forces operation alternatives using genetic algorithms to come up with an effective alternative to Korea Air and Missile Defense System applied with multiple layers defense concepts. 북한은 현재 다양한 종류의 탄도탄을 보유하고 있으며, 이는 한반도에 직접적인 위협이 되고 있다. 따라서 한반도 환경에 적합한 “한국형 미사일 방어체계”를 조기에 구축하여야 한다. 이를 위해서는 대탄도탄 징후감시체계를 갖추어야 하며, 일원화된 작전통제체제와 요격수단을 적정 확보하여야 한다. 본 연구에서는 미사일 다층방어체계 구축을 위해 모의 분석 모델을 개발하고 이를 적용하여 가장 효과적인 최적배치 방안과 소요전력 대안을 제시한다. 또한 소요전력 운용대안별 방어능력을 유전자 알고리즘을 활용하여 분석하여 다층ㆍ복합적 방어개념을 적용한 효과적인 한국형 전구 탄도탄 방어체계 대안을 도출할 것이다
장준건(Jun-Gun Jang),김경택(Kyeongtaek Kim),최봉완(Bong-Wan Choi) 한국산업경영시스템학회 2015 한국산업경영시스템학회 학술대회 Vol.2015 No.추계
When offense launches missiles at valuable assets of the defense, the defense must assign its weapons to these missiles so as to maximize the total value of surviving assets threatened by them. Recently, a new asset-based linear approximation model was proposed for weapon target assignment problem with shootlook- shoot engagement policy and fixed set-up time between each anti-missile launch from each defense unit. In this paper, we apply the proposed to several ballistic missile defense examples and we show their weapon target assignment results specified with launch order time.
장준건(Jun-Gun Jang),김경택(Kyeongtaek Kim),최봉완(Bong-Wan Choi),서재준(Jae Joon Suh) 한국산업경영시스템학회 2015 한국산업경영시스템학회지 Vol.38 No.3
A missile defense system is composed of radars detecting incoming missiles aiming at defense assets, command control units making the decisions on weapon target assignment, and artillery batteries firing of defensive weapons to the incoming missiles. Although, the technology behind the development of radars and weapons is very important, effective assignment of the weapons against missile threats is much more crucial. When incoming missile targets toward valuable assets in the defense area are detected,the asset-based weapon target assignment model addresses the issue of weapon assignment to these missiles so as to maximize the total value of surviving assets threatened by them. In this paper, we present a model for an asset-based weapon assignment problem with shoot-look-shoot engagement policy and fixed set-up time between each anti-missile launch from each defense unit. Then, we show detailed linear approximation process for nonlinear portions of the model and propose final linear approximation model. After that, the proposed model is applied to several ballistic missile defense scenarios. In each defense scenario, the number of incoming missiles, the speed and the position of each missile, the number of defense artillery battery, the number of anti-missile in each artillery battery, single shot kill probability of each weapon to each target, value of assets, the air defense coverage are given. After running lpSolveAPI package of R language with the given data in each scenario in a personal computer, we summarize its weapon target assignment results specified with launch order time for each artillery battery. We also show computer processing time to get the result for each scenario.
손준형(June-Hyoung Son),장준건(Jun-Gun Jang),최봉완(Bongwan Choi),김경택(Kyeongtaek Kim) 한국산업경영시스템학회 2020 한국산업경영시스템학회지 Vol.43 No.4
Recently there was an incident that military radars, coastal CCTVs and other surveillance equipment captured a small rubber boat smuggling a group of illegal immigrants into South Korea, but guards on duty failed to notice it until after they reached the shore and fled. After that, the detection of such vessels before it reach to the Korean shore has emerged as an important issue to be solved. In the fields of marine navigation, Automatic Identification System (AIS) is widely equipped in vessels, and the vessels incessantly transmits its position information. In this paper, we propose a method of automatically identifying abnormally behaving vessels with AIS using convolutional autoencoder (CAE). Vessel anomaly detection can be referred to as the process of detecting its trajectory that significantly deviated from the majority of the trajectories. In this method, the normal vessel trajectory is gridded as an image, and CAE are trained with images from historical normal vessel trajectories to reconstruct the input image. Features of normal trajectories are captured into weights in CAE. As a result, images of the trajectories of abnormal behaving vessels are poorly reconstructed and end up with large reconstruction errors. We show how correctly the model detects simulated abnormal trajectories shifted a few pixel from normal trajectories. Since the proposed model identifies abnormally behaving ships using actual AIS data, it is expected to contribute to the strengthening of security level when it is applied to various maritime surveillance systems.
SAR 이미지 반전잡음 제거 가속화를 위한 디노이징 CNN 아키텍처 개선
조동희,김태윤,이준우,장준건,김경택 한국산업경영시스템학회 2022 한국산업경영시스템학회 학술대회 Vol.2022 No.추계
Synthetic Aperture Radar (SAR) images are affected by noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. Many different schemes have been proposed for the restoration of SAR images. Among the different possible approaches, methods based on convolutional neural networks(CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. DnCNN(DeNoising Convolutional Neural Network) is one of the most widely used neural network architecture embedded in baseline SAR image despeckling methods. In military applications of SAR satellite image, fast processing is the most critical factor except the precision rate of the recognition. In this paper, we propose an improved DnCNN architecture for faster SAR image despeckling. The experimental results on real-world SAR images show that our proposed method takes faster processing time than the original DnCNN architecture without despeckling performance downgrade. Subjective visual inspection demonstrates that the proposed method has great potential in preserving the image signal details and suppressing speckle noise.