• 한국군사과학기술학회지
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• 제26권1호
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• pp.102-112
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• 2023

This research mainly focuses on the transport and dispersion of chemical agent plume according to the Lagrangian Puff Model and Lagrangian Particle Model of NBC_RAMS(Nuclear, Biological, Chemical Reporting And Modeling S/W System). NBC_RAMS was developed with the purposes of estimating the fate of Chemical, Biological, and Radioactive(CBR) agent plumes and evaluating damages in the Republic of Korea. First, it calculates the local weather pattern, i.e. wind speed, wind direction, and temperature, by considering the effects of land uses and topography. The plume behaviors are calculated by adopting the Lagrangian Puff Model(LPFM) or Lagrangian Particle Model(LPTM). In this research, we assumed a virtual chemical agent exposure event in a stable atmospheric condition during the summer season. The plume behaviors were estimated by both LPFM and LPTM on the used area(urbanized and dry area) and the agricultural land. The higher heat flux in the used area led to stronger winds and further downward movement moving of the chemical agent than the farmland. The lateral dispersion of the chemical plume was emphasized in the Lagrangian Puff Model because it adopted Gaussian distribution.

• 한국군사과학기술학회지
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• 제9권1호
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• pp.80-88
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• 2006

The purpose of this thesis is to research and propose a practical Information Security Technical Architecture on Primary Defense Information Infrastructure with regard to requirement of information security. The scope of this research is limited to national defense information master plan & security rule, and U.S. DoD's IATF is used to plan a detailed structure. The result of this research can be used as a guide book for providing security for Army IT infrastructure now and in the future as well as to devise a plan for research and development in information protection technology.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.167-176
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• 2024

Lip-reading is the task of inferring the speaker's utterance from silent video based on learning of lip movements. It is very challenging due to the inherent ambiguities present in the lip movement such as different characters that produce the same lip appearances. Recent advances in deep learning models such as Transformer and Temporal Convolutional Network have led to improve the performance of lip-reading. However, most previous works deal with English lip-reading which has limitations in directly applying to Korean lip-reading, and moreover, there is no a large scale Korean lip-reading dataset. In this paper, we introduce the first large-scale Korean lip-reading dataset with more than 120 k utterances collected from TV broadcasts containing news, documentary and drama. We also present a preprocessing method which uniformly extracts a facial region of interest and propose a transformer-based model based on grapheme unit for sentence-level Korean lip-reading. We demonstrate that our dataset and model are appropriate for Korean lip-reading through statistics of the dataset and experimental results.

• 한국산학기술학회논문지
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• 제19권5호
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• pp.117-121
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• 2018

기술발전의 가속화와 기술융합의 시대로 패러다임이 바뀌면서, 국방 분야는 국방과학기술이 세계 최고 수준으로 발전하기 위해 투자 확대와 더불어 선택과 집중에 의한 국방연구개발을 추진하고 있다. 미래전 양상에 부합하는 신개념 첨단 무기 및 핵심기술에 집중적인 투자와 함께 제한된 예산범위 내 투자 전략도 수립 중에 있다. 민간분야에서는 R&D 정책의 성과 극대화와 기획 관리의 전문성 및 책임성 제고를 위하여 PD제도를 도입하여 운영하고 있다. 이러한 추세에 따라, 국방 분야에서도 과제기획역량 강화 및 민간기술 활용 활성화의 목적으로 국방기술기획 고도화 추진계획 수립 하에 전문성을 갖춘 국방기술PD 제도를 추진 중에 있다. 핵심기술 연구개발의 일관된 기술지원과 개방형 기술기획 제도 개선 및 민 군 간기술 연계성 확대의 필요성을 고려한다면, 국방 분야의 PD제도는 반드시 필요한 제도라 할 수 있다. 이에 본 연구에서는 국내 외 유사 사례를 검토하여 국방기술PD 제도 도입 및 운영 방안에 관하여 살펴보고자 한다. 본 연구는 현재 추진되고 있는 국방기술PD 제도에 대한 현황을 바탕으로 작성한 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.805-825
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• 2017

Correlation Filter-based Trackers (CFTs) gained popularity recently for their effectiveness and efficiency. To deal with the size changes of the target which may degenerate the tracking performance, scale estimation has been introduced in existing CFTs. However, the variations of the aspect ratio were usually neglected, which also influence the size of the target. In this paper, Size Aware Correlation Filter Trackers (SACFTs) are proposed to deal with this problem. The SACFTs not only determine the translation and scale variations, but also take the aspect ratio changes into consideration, thus a better estimation of the size of the target can be realized, which improves the overall tracking performance. And competing results can be achieved compared with state-of-the-art methods according to the experiments conducted on two large scale datasets.

• 한국군사과학기술학회지
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• 제25권1호
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• 한국군사과학기술학회지
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• 제26권5호
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• pp.392-399
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• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

• 한국군사과학기술학회지
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• 제18권4호
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• pp.459-468
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• 2015

Although defense standardization is recently becoming more and more active, there are still many obstacles to achieve attainments as planned. On the other hand, national standardization already has been applied as a mandatory rule in the overall industries and R&D programs and is well supported by systematic and specialized national basis. Furthermore, advanced countries, such as U.S.A., England and NATO, are considering defense standardization as a essential element to achieve low cost and highly efficient acquisition system and to enhance interoperability among the allied forces. This study aims to form public opinion in support of importance of defense standardization and to provide the vision and various implementation tasks for improving defense standardization outcome.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2669-2688
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• 2016

An efficient and effective unsupervised single moving object detection framework is presented in this paper. Given the sparsely labelled trajectory points, we adopt a coarse-to-fine strategy to detect and segment the foreground from the background. The superpixel level coarse segmentation reduces the complexity of subsequent processing, and the pixel level refinement improves the segmentation accuracy. A distance measurement is devised in the coarse segmentation stage to measure the similarities between generated superpixels, which can then be used for clustering. Moreover, a Quadmap is introduced to facilitate the refinement in the fine segmentation stage. According to the experiments, our algorithm is effective and efficient, and favorable results can be achieved compared with state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3749-3768
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• 2018

As one of the most potential types of low-density parity-check (LDPC) codes, CPM-QC-LDPC code has considerable advantages but there still exist some limitations in practical application, for example, the existing decoding algorithm has a low convergence rate and a high decoding complexity. According to the structural property of this code, we propose a new method based on a CPM-RID decoding algorithm that decodes block-by-block with weights, which are obtained by neural network training. From the simulation results, we can conclude that our proposed method not only improves the bit error rate and frame error rate performance but also increases the convergence rate, when compared with the original CPM-RID decoding algorithm and scaled MSA algorithm.