• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.6
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• pp.877-885
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• 2018

The anti-submarine helicopter is the most effective weapon system in anti-submarine warfare. Recently changes in the introduction of the anti-submarine warfare sonar system are expected to operate multi-static sonar equipment of the anti-submarine helicopter. Therefore, it is required to study the operational concept of multi-static of anti-submarine helicopter. This paper studies on the optimal search of multi-static based on anti-submarine helicopter considering Furthest On Circles(FOC). First, the deployment of the sensors of the anti-submarine helicopter is optimized using genetic algorithms. Then, the optimized model is extended to consider FOC. Finally, the proposed model is verified by comparing pattern-deployment the search method in Korean Navy.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.1
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• pp.35-40
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• 2019

The objective of this paper is to identify the fatigue properties of carbon-fiber composite which is widely applied for the development of aircraft structures and obtain data for full-scale fatigue test. The durability and damage tolerance evaluation of composite structures is achieved by fatigue tests and parameters such as fatigue life factor and load enhancement factor. The specimens are made with carbon-fiber/epoxy UD tape and fabric prepreg. Fatigue tests are performed with several stress ratios and lay-up patterns. The Weibull shape parameters are analyzed by Sendeckyj model and individual fatigue lives with Weibull distribution. And the fatigue life factor and load enhancement factor considering reliability are evaluated.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.629-635
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• 2021

This study seeks to examine and analyze the influence of institutional strengthening factors, and capacity building - communication, resources, and training - on the performance of defense policy implementation. This study conducted a quantitative analysis related to the implementation of the institutional strengthening policy. The data used are primary data with a research instrument in the form of a questionnaire. The population in this study were all people in the city of Bandung, Indonesia. The sample of this study consisted of 200 respondents consisting of civilians and soldiers who served in the city of Bandung. Data analysis uses the Structural Equation Model (SEM) measurement model. The results of this study reveals that institutional strengthening (X1) influences positively and significantly capacity building's communication (Y1), resources (Y2), and training (Y3). On the other hand, the performance of defense policy implementation (Y4) is positively and significantly affected by capacity building's communication (Y1), resources (Y2), and training (Y3). The interaction between institutions, consumption support, role of the healthcare sector, and effectiveness are the most important indicators reflecting capacity building (communication, resources, training) and the performance of defense policy implementation. Essentially, this study analyzes the performance of defense policy implementation based on capacity building.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.390-400
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• 2022

Military deception is an action executed to deliberately mislead enemy's decision by deceiving friendly forces intention. In the lessons learned from war history, deception appears to be a critical factor in the battlefield for successful operations. As training using war-game simulation is growing more important, it is become necessary to implement military deception in war-game model. However, there is no logics or rules proven to be effective for CGF(Computer Generated Forces) to conduct deception behavior automatically. In this study, we investigate methodologies for CGF to learn and conduct military deception using Reinforcement Learning. The key idea of the research is to define a new criterion called a "deception index" which defines how agent learn the action of deception considering both their own combat objectives and deception objectives. We choose Korea Marine Corps Amphibious Demonstrations to show applicability of our methods. The study has an unique contribution as the first research that describes method of implementing deception behavior.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.352-360
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• 2023

Recently, AI pilots using reinforcement learning are developing to a level that is more flexible than rule-based methods and can replace human pilots. In this paper, a curriculum was used to help head-on combat with reinforcement learning. It is not easy to learn head-on with a reinforcement learning method without a curriculum, but in this paper, through the two circle-based head-on air combat learning technique, ownship gradually increase the difficulty and become good at head-on combat. On the two-circle, the ATA angle between the ownship and target gradually increased and the AA angle gradually decreased while learning was conducted. By performing reinforcement learning with and w/o curriculum, it was engaged with the rule-based model. And as the win ratio of the curriculum based model increased to close to 100 %, it was confirmed that the performance was superior.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.80-93
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• 2024

This study attempted to analyze the factors that influence the participation of beneficiary companies in the government's defense industry promotion support project. To this end, experimental data were analyzed by constructing a prediction model consisting of highly important variables in beneficiary company decisions among various company information using the decision tree model, one of the data mining techniques. In addition, various rules were derived to determine the beneficiary companies of the government's support project using the analysis results expressed as decision trees. Three policy measures were presented based on the important rules that repeatedly appear in different predictive models to increase the effect of the government's industrial development. Using the analysis methods presented in this study and the determinants of the beneficiary companies of the government support project will help create a sustainable future defense industry growth environment.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.871-879
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• 2020

Perceptual Ad-Blocking is a new advertising blocking technique that detects online advertising by using an artificial intelligence-based advertising image classification model. A recent study has shown that these Perceptual Ad-Blocking models are vulnerable to adversarial attacks using adversarial examples to add noise to images that cause them to be misclassified. In this paper, we prove that existing perceptual Ad-Blocking technique has a weakness for several adversarial example and that Defense-GAN and MagNet who performed well for MNIST dataset and CIFAR-10 dataset are good to advertising dataset. Through this, using Defense-GAN and MagNet techniques, it presents a robust new advertising image classification model for adversarial attacks. According to the results of experiments using various existing adversarial attack techniques, the techniques proposed in this paper were able to secure the accuracy and performance through the robust image classification techniques, and furthermore, they were able to defend a certain level against white-box attacks by attackers who knew the details of defense techniques.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.95-105
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• 2019

The Cooperative Engagement Capability (CEC) System produces a synergy between the sensors and shooters that are used on various platforms by integrating them. Even the US Navy has been recently adopting the CEC system that maximizes the effectiveness of the air defense operations by efficiently coordinating the dispersed air defense assets. The Navy of other countries are conducting research studies on the theory and application methods for the CEC system. The ROK Navy has limited air defense capabilities due to its independent weapons systems on battle ships. Therefore, the ROK Navy is currently going through a phase where research on proving the validity of building the CEC system because it will provide a way to overcome the limit of the platform based air defense capability. In this study, our goal is to propose methods that maximize the air defense capability of ROK Navy, identify the available assets for constructing the CEC system, and estimate effects of the CEC system when it is applied to the naval operations. In addition, we will provide a simple model that was developed to estimate these effects and a case study with virtual data to demonstrate the effects of the system when it is applied to the naval operations. The research result of this study will provide a way for building the basis of the Korean CEC system.

• Structural Engineering and Mechanics
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• v.81 no.6
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.