• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.321-322
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• 2023

현재 우리 군은 다가오는 인구절벽에 대비하기 위해 인공지능(AI) 기반의 과학기술강군 육성을 목표 국방혁신 4.0을 추진중에 있다. 특히 북한의 도발위협이 높아지는 현시점에 우리 군은 첨단기술을 활용한 과학화경계시스템 도입을 통해 병력절감을 도모하고 있다. 하지만 우리 군의 통합 전투능력을 보장하기 위한 핵심 기반통신체계인 전술정보통신체계(TICN)의 경우 전송 대역폭이 좁아 영상정보 송수신이 원활하지 않을뿐더러 보안 및 난청지역 발생 등의 이유로 평시 과학화경계시스템의 기반 네트워크로 활용하기에는 제한적이다. 이러한 문제를 해결하기 위해 본 논문은 2017년부터 국내에서 무료로 활용할 수 있게 된 TVWS 기반의 무선네트워크 구축 기술을 활용해 TVWS 기반 과학화경계시스템 구축방안을 제안한다. 본 논문에서 제안하는 TVWS 기반 과학화경계시스템의 경우 기존의 유선네트워크 기반의 과학화경계시스템과 비교해 작전공백 최소화, 구축비용 절감, 설치 및 운용의 탄력성 측면에서 다양한 장점을 가진다.

• Convergence Security Journal
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• v.22 no.5
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• pp.135-143
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• 2022

The defense environment is rapidly changing, such as nuclear and missile threats of North Korea, changes in war patterns, and a decrease in military service resources due to low birth rate. In order to actively respond to these changes, the Korean military is promoting Defense Innovation 4.0 and is trying to foster an army armed with high technology such as artificial intelligence (AI), big data analysis, etc. In this regard, we analyze the effectiveness of the radar-based AI scientific guard system applied by high technology for guard operations using Analytic Hierarchy Process (AHP). We first select evaluation factors that can assess the effectiveness of the scientific guard system, and analyze its relative importance. Each evaluation factor was selected by deriving a significant concept from operating principle and how they work, and by consulting experts on the correlation between each factor and effectiveness of the scientific guard system. We examine the relative effects of the radar-based AI scientific guard system and existing scientific guard system based on the importance of the evaluation factors.

• Convergence Security Journal
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• v.23 no.4
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• pp.81-92
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• 2023

In recent years, the ROK military is promoting Defense Innovation 4.0 with the goal of fostering strong military based on science and technology equipped with artificial intelligence(AI) to prepare for the upcoming population cliff. In particular, at the present time of increased threats of North Korea, the South Korean military is seeking to deal with a decrease in military service resources through the introduction of a Scientific Guard System using advanced technology. TICN which is a core basic communication system to ensure the integrated combat capability of the ROK military is, however, limited to use as a based network for the emerging Scientific Guard System due to the narrow transmission bandwidth with widely spread poor reception area. To deal with this problem, this paper proposes TVWS-based Scientific Guard Systems with TVWS-based wireless network construction technology that has been available for free in Korea since 2017. The TVWS-based Scientific Guard System proposed in this paper, when compared to the existing wired network-based Scientific Guard Systems, has various advantages in terms of minimizing operational gaps, reducing construction costs, and flexibility in installation and operation.

• Maritime Security
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• v.4 no.1
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• pp.115-138
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• 2022

The Korean military is preparing for successful manpower reduction using advanced science and technology, in addition to carrying out the initiative of the Defense Innovation 4.0. Accordingly, studies on core technologies related to defense reform have been conducted both internally and externally in the military, and the corresponding results have also been applied. Nevertheless, compared to the development of such technologies, it is considered necessary to have more preparation for the policies related to the operation of the newly introduced equipment. As for the placement of personnel and the organization of time in service (TIS) with respect to the operation of surveillance equipment, there has been a tendency to sustain the conventional practice. Therefore, this study intends to suggest the schemes for facilitating policy improvements in the operation of manpower and security regulations in the field of information for the purpose of introducing a successful next-generation coastal guard system. To do this, the approach of this study was focused on the policies for the operation of the guard system. This is in contrast to previous studies that centered on its equipment and technologies. In addition, how to efficiently operate the guard system was also studied in view of cognitive science by deriving the most efficient time for a person to execute surveillance through the monitor based on the previous studies.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.133-145
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• 2022

As a result of the study, the poorest keywords were information, big data, management, policy, government, law, and smart. In addition, as a result of network analysis, related research was being conducted on topics such as data industry policy, data governance performance, defense, governance, and data public. The four topics derived through topic modeling were "DG policy," "DG platform," "DG in laws," and "DG implementation," of which research related to "DG platform" showed an increasing trend, and "DG implementation" tended to shrink. This study comprehensively summarized data governance-related studies. Data governance needs to expand research areas from various perspectives and related fields such as data management and data integration policies at the organizational level, and related technologies. In the future, we can expand the analysis targets for overseas data governance and expect follow-up studies on research directions and policy directions in industries that require data-based future industries such as Industry 4.0, artificial intelligence, and Metaverse.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.