• 대한조선학회논문집
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• 제44권6호
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• pp.572-578
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• 2007

A Rigid Inflatable Boat (RIB) is now widely used for commercial and military purpose. In this paper, it is supposed that seven-meter-class RIB be used as an unmanned target ship for naval training. In order to develop many tactical maneuvering patterns of a target ship, a simple horizontal maneuvering model of a RIB is needed. Therefore, models of speed and yaw rate are constructed as the first-order differential equations based on Lewandowski#s empirical formula for steady turning circle diameter of a conventional planning hull. Some parameters in the models are determined using the results of sea trial tests. Finally, proposed models are validated through the comparison of the simulation result with the sea trial result for a specific scenario. Even though a simple model does not represent the horizontal motion of a RIB precisely, however, it can be used enough to develop tactical trajectory patterns.

• 한국시뮬레이션학회논문지
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• 제26권1호
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• pp.39-46
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• 2017

은밀하게 침투하여 아군의 핵심자산으로 접근하는 표적 잠수함을 상시 감시하기 위하여 수중 해저면 위치에 최적의 고정형 수동탐지소나를 배치하는 것을 고려 할 수 있다. 수동탐지소나 배치 최적화를 위한 효과도 지수는 넓은 탐지영역과 위치추정가능성의 함수로 적용할 수 있는데 계절적인 요인, 해상상태, 표적 잠수함의 침투심도 등의 다양한 확률적 변이를 포함하고 있어서 효과도지수가 배치의 입력에 대하여 확률적으로 나타나는 특성을 갖는다. 본 논문에서는 다양한 파라메타의 입력조건에 대하여 확률적인 출력을 갖는 수동탐지소나의 배치에 대한 최적화 문제를 정의하였으며, DPSO(Discrete binary version of PSO) 방법을 사용하여 최적 배치 안을 도출하기 위한 모의기반의 절차를 제시하고 고찰하였다.

• 한국군사과학기술학회지
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• 제22권6호
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• pp.735-744
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• 2019

One of the key factors for recognition performance in the automatic target recognition for synthetic aperture radar imagery(SAR-ATR) system is reliability of the SAR target database. To achieve optimal performance, the database should be constructed using the images obtained under the same operating condition as the SAR sensor. However, it is impractical to have the extensive set of real-world SAR images, and thus those from the electro magnetic prediction tool with 3-D CAD models are suggested as an alternative where their reliability can be always questionable. In this paper, a method for similarity assessment between target SAR images is presented inspired by the fact that a target SAR image is mainly characterized by the features of scattering centers. The method is demonstrated using a variety of examples and quantitatively measures the similarity related to reliability. Its assessment performance is further compared with that of the existing metric, structural similarity(SSIM).

• 한국군사과학기술학회지
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• 제24권1호
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• pp.12-21
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• 2021

For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application.

• 한국군사과학기술학회지
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• 제25권3호
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

• 한국군사과학기술학회지
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• 제25권5호
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• pp.443-461
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• 2022

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.

• 한국군사과학기술학회지
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• 제25권5호
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• pp.462-475
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• 2022

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.147-155
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• 2024

Based on the recently developed deep learning technology, many studies have been conducted on deep learning networks that simultaneously detect and classify targets of interest in synthetic aperture radar(SAR) images. Although numerous research results have been derived mainly with the open SAR ship datasets, there is a lack of work carried out on the deep learning network aimed at detecting and classifying SAR ground targets and trained with the synthetic dataset generated from electromagnetic scattering simulations. In this respect, this paper presents the deep learning network trained with the synthetic dataset and applies it to detecting and classifying real SAR ground targets. With experiment results, this paper also analyzes the network performance according to the composition ratio between the real measured data and the synthetic data involved in network training. Finally, the summary and limitations are discussed to give information on the future research direction.

• 대한방사선치료학회지
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• 제35권
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• pp.33-39
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• 2023

목 적: 환자 체표면과 고정기구 사이에 발생하는 공기층 두께에 따른 선량 변화를 치료 계획을 통해 알아보고자 한다. 대상 및 방법: 팬텀과 열가소성 고정기구 사이에 5 mm 두께의 Bolus를 0, 1, 2, 3장을 놓아 공기층의 두께를 조절하였고 고정기구를 씌워 총 4가지 조건으로 전산화 모의단층촬영을 시행하였다. 430 cGy (Relative Biological Effectiveness,RBE)씩 6번이 조사 되도록 계획하였으며, 임상표적체적의 95% 부피에 전달된 선량이 2580 cGy (RBE)가 되도록 치료 계획을 수립하였다. 임상표적체적의 선량은 Lateral dose profile의 반치폭값으로 평가하였고 피부 선량은 선량 체적 곡선으로 평가하였다. 결 과: 임상표적체적에서 Lateral dose profile 반치폭 값은 4.89, 4.86, 5.10, 5.10 cm로 나타났다. 피부에서 4가지 조건의 선량의 평균값은 D_95%3.25±1.7 cGy (RBE), D_30%1193.5±10.2 cGy (RBE)의 차이를 보였으며 처방 선량 1%에서의 피부 부피 값 평균은 83.22±4.8% 이내의 차이를 확인하였다. 공기층 두께 변화에 따른 임상표적체적과 피부에서의 선량 모두 큰 변화를 보이지는 않았다. 결론 : 탄소입자 치료를 위해 Solid 형태의 고정기구 제작 시 약간의 공기층은 CTV의 선량 적용 범위를 벗어나지 않는다.

• 한국전자파학회논문지
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• 제26권2호
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• pp.218-225
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• 2015

역합성 개구면 레이더(Inverse Synthetic Aperture Radar: ISAR) 영상은 표적으로부터 반사되어 돌아온 레이더 수신신호들을 코히런트하게 신호처리하여 형성한 표적의 2차원 영상이다. 본 논문에서는 계단 첩 파형(Stepped Chirp Waveform: SCW)을 이용한 ISAR 영상 형성과정에서 펄스 간 움직임(Inter-Pulse Motion: IPM)이 존재하는 경우, 이를 효과적으로 보상하기 위한 알고리즘을 제안한다. 널리 쓰이는 최적화 기법 중 하나인 particle swarm optimization(PSO)를 기반으로 IPM에 관련된 표적의 속도와 가속도를 추정한다. 또한, 개선된 PSO를 통해 기존의 성능을 더욱 향상시켜 실시간 요동보상을 수행한다. 시뮬레이션에서는 Boeing-737의 점 산란원 모델을 이용한 기동 시나리오에서 제안된 알고리즘의 성능을 확인한다.