• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.161-169
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• 2023

Due to the Fourth Industrial Revolution, innovation in various technologies such as artificial intelligence (AI), big data (Big Data), and cloud (Cloud) is accelerating, and data is considered an important asset. With the innovation of these technologies, various efforts are being made to lead technological innovation in the field of defense science and technology. In Korea, the government also announced the "Defense Innovation 4.0 Plan," which consists of five key points and 16 tasks to foster advanced science and technology forces in March 2023. The plan also includes the establishment of a Condition-Based Maintenance system (CBM+) to improve the operability and availability of weapons systems and reduce defense costs. Condition Based Maintenance (CBM) aims to secure the reliability and availability of the weapon system and analyze changes in equipment's state information to identify them as signs of failure and defects, and CBM+ is a concept that adds Remaining Useful Life prediction technology to the existing CBM concept [1]. In order to establish a CBM+ system for the weapon system, sensors are installed and sensor data are required to obtain condition information of the weapon system. In this paper, we propose a sensor data metadata schema to efficiently and effectively manage sensor data collected from sensors installed in various weapons systems.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.47-55
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• 2023

This paper presents a method for predicting the threat index of combat systems using Gradient Boosting Regressors and Support Vector Regressors among machine learning models. Currently, combat systems are software that emphasizes safety and reliability, so the application of AI technology that is not guaranteed to be reliable is restricted by policy, and as a result, the electrified domestic combat systems are not equipped with AI technology. However, in order to respond to the policy direction of the Ministry of National Defense, which aims to electrify AI, we conducted a study to secure the basic technology required for the application of machine learning in combat systems. After collecting the data required for threat index evaluation, the study determined the prediction accuracy of the trained model by processing and refining the data, selecting the machine learning model, and selecting the optimal hyper-parameters. As a result, the model score for the test data was over 99 points, confirming the applicability of machine learning models to combat systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.381-389
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• 2022

Detecting partially occluded objects is difficult due to the appearances and shapes of occluders are highly variable. These variabilities lead to challenges of localizing accurate bounding box or classifying objects with visible object parts. To address these problems, we propose a two-stage part-based attention approach for robust object detection under partial occlusion. First, our part attention network(PAN) captures the important object parts and then it is used to generate weighted object features. Based on the weighted features, the re-weighted object features are produced by our reinforced PAN(RPAN). Experiments are performed on our collected military vehicle dataset and synthetic occlusion dataset. Our method outperforms the baselines and demonstrates the robustness of detecting objects under partial occlusion.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.83-86
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• 2020

본 논문에서는 해군의 향후 군사용 드론 활용 가능방안을 제안한다. AI, 자율주행 등의 4차 산업혁명 기술들과 함께 상용분야에서는 이미 다양한 드론 활용방안들이 제시되고 있으며, 육군은 이에 발맞춰 2018년 10월 드론봇 전투단을 창설하여 운용 중에 있다. 하지만 아직 해군의 군사용 드론 운용 및 활용방안 등에 관한 연구는 미진하며, 따라서 현재 해군의 군용 드론 활용현황을 살펴보고 객체인식, 자율주행 등의 최신기술과 상용활용 사례 등을 군에 접목시켜 앞으로의 활용 가능방안에 대해서 제안하고자 한다.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.361-370
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• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.786-787
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• 2024

Large Language Model은 그 기능으로 말미암아 여러 애플리케이션에 통합되고 있다. 특히 OpenAI는 ChatGPT에 여러 세부 사항을 설정함으로써 차별화된 기능을 사용자가 제공할 수 있도록 한다. 하지만 최근 제시되는 프롬프트 연출 공격은 서비스의 핵심 요소를 쉽게 탈취할 수 있는 가능성을 제시한다. 본 연구는 지침 우회 방법론을 통해 기본 대비 공격의 성공률을 10%p 올렸다. 또한 유출공격을 평가할 수 있는 유효성과 성공률을 통해 모델의 방어 성능을 일반화한다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.125-134
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• 2022

Recently, there are an increased the number of reports on the misuse or malicious use of an UAS. Thus, many researchers are studying on defense schemes for UAS by developing or improving C-UAS sensor technology. However, the wrong placement of sensors may lead to a defense failure since the proper placement of sensors is critical for UAS defense. In this study, a multi-object optimization model for C-UAS sensor placement in an air base is proposed. To address the issue, we define two objective functions: the intersection ratio of interested area and the minimum detection range and try to find the optimized placement of sensors that maximizes the two functions. C-UAS placement model is designed using a NSGA-II algorithm, and through experiments and analyses the possibility of its optimization is verified.

• Convergence Security Journal
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• v.21 no.2
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• pp.67-79
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• 2021

Lessons Learned(LL) is a military term defined as all activities that promote future development by finding problems and need improvement in education and reality in the field of warfare development. In this paper, we focus on presenting actual examples and applying AI analysis inference techniques to solve revealed problems in promoting LL activities, such as long-term analysis, budget problems, and necessary expertise. AI legal advice services using cognitive computing-related technologies that have already been practical and in use, were judged to be the best examples to solve the problems of LL. This paper presents intelligent LL inference techniques, which utilize AI. To this end, we want to explore theoretical backgrounds such as LL analysis definitions and examples, evolution of AI into Machine Learning, cognitive computing, and apply it to new technologies in the defense sector using the newly proposed L²-OODA ensemble algorithm to contribute to implementing existing power improvement and optimization.