• 대한조선학회지
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• 제56권3호
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• pp.20-28
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• 2019

• 한국군사과학기술학회지
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• 제25권6호
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• pp.637-647
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• 2022

Since the concept of Manned-UnManned Teaming(MUM-T) and Unmanned Aircraft System(UAS) can efficiently respond to rapidly changing battle space, many studies are being conducted as key components of the mosaic warfare environment. In this paper, we propose a rule-based AI engagement model based on Basic Fighter Maneuver(BFM) capable of Within-Visual-Range(WVR) air-to-air combat and a simulation environment in which human pilots can participate. In order to develop a rule-based AI engagement model that can pilot a fighter with a 6-DOF dynamics model, tactical manuals and human pilot experience were configured as knowledge specifications and modeled as a behavior tree structure. Based on this, we improved the shortcomings of existing air combat models. The proposed model not only showed a 100 % winning rate in engagement with human pilots, but also visualized decision-making processes such as tactical situations and maneuvering behaviors in real time. We expect that the results of this research will serve as a basis for development of various AI-based engagement models and simulators for human pilot training and embedded software test platform for fighter.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.58-61
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• 2022

데이터가 디지털 전환을 위한 핵심자산으로 평가됨에 따라, 국방분야에서도 데이터의 효율적·안정적 관리와 원활한 공유, 그리고 데이터를 활용한 서비스 제공이 요구되고 있다. 이를 지원하기 위해 우리군은 국방데이터 관리 기반체계인 국방 지능형 플랫폼 구축을 위한 초석을 마련하고 있다. 본고에서는 우리나라 및 주요 선진군의 데이터전략과 데이터 플랫폼 추진방향을 살펴보고, 주요 시사점과 함께 우리군의 국방 지능형 플랫폼 발전방향을 제시하였다. 이를 통해 안전하고 효율적인 우리 군의 국방데이터 관리 기반체계 구축에 기여할 수 있기를 기대한다.

• 한국군사과학기술학회지
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• 제26권1호
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• pp.44-53
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• 2023

Adversarial attacks have received great attentions for their capacity to distract state-of-the-art neural networks by modifying objects in physical domain. Patch-based attack especially have got much attention for its optimization effectiveness and feasible adaptation to any objects to attack neural network-based object detectors. However, despite their strong attack performance, generated patches are strongly perceptible for humans, violating the fundamental assumption of adversarial examples. In this paper, we propose a camouflaged adversarial patch optimization method using military camouflage assessment metrics for naturalistic patch attacks. We also investigate camouflaged attack loss functions, applications of various camouflaged patches on army tank images, and validate the proposed approach with extensive experiments attacking Yolov5 detection model. Our methods produce more natural and realistic looking camouflaged patches while achieving competitive performance.

• 한국군사과학기술학회지
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• 제26권5호
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• pp.400-408
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• 2023

The tracking accuracy of buoy-based LBL(Long Base Line) systems can be significantly influenced by sea environmental conditions. Particularly, the position of buoys that may have drifted due to sea currents. Therefore it is necessary to predict and optimize the drifted-buoy positions in the deploying step. This research introduces a free-drift simulation model using ocean data from the European CMEMS. The simulation model's predictions are validated by comparing them to actual sea buoy drift tracks, showing a substantial match in averaged drift speed and direction. Using this drift model, we optimize the initial buoy layout and compare the tracking performance between the center hexagonal layout and close track layout. Our results verify that the optimized layout achieves lower tracking errors compared to the other two layout.

• 한국군사과학기술학회지
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• 제27권3호
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• pp.375-386
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• 2024

Autonomous weapon systems act according to artificial intelligence-based judgement based on recognition through various sensors. Test and evaluation for various scenarios is required depending on the characteristics that artificial intelligence-based judgement is made. As a part of this approach, this paper proposed a LiDAR point cloud augmentation method for mixed-reality based test and evaluation. The augmentation process is achieved by mixing real and virtual LiDAR signals based on the virtual LiDAR synchronized with the pose of the autonomous weapon system. For realistic augmentation of test and evaluation purposes, appropriate intensity values were inserted when generating a point cloud of a virtual object and its validity was verified. In addition, when mixing the generated point cloud of the virtual object with the real point cloud, the proposed method enhances realism by considering the occlusion phenomenon caused by the insertion of the virtual object.

• 한국군사과학기술학회지
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• 제24권6호
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• pp.591-601
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• 2021

Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.

• 출판저널
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• 통권235호
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• pp.30-30
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• 1998

기자들이 쓴 "신한국군 리포트"에는 한국군 장군들의 모습이 여과없이 적나라하게 묘사돼 있어 마치 현장을 보는 것과 같은 느낌이 들게 한다. 국방연구원에서 재직한 학자들의 저술인 "선진국방의 지평"에는 입문서 성격의 책으로 구체적인 대안제시가 아쉽다.

• KDI북한경제리뷰
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• 제21권2호
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• pp.79-87
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• 2019

• KDI북한경제리뷰
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• 제21권3호
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• pp.86-88
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• 2019