• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.39-46
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• 2020

The utilization of artificial intelligence (AI) in the engagement has been a key research topic in the defense field during the last decade. To pursue this utilization, it is imperative to acquire a realistic simulation to train an AI engagement agent with a synthetic, but realistic field. This paper is a case study of training an AI agent to operate with a hardware realism in the air-warfare dog-fighting. Particularly, this paper models the pursuit of an opponent in the dog-fighting setting with a gun-only engagement. In this context, the AI agent requires to make a decision on the pursuit style and intensity. We developed a realistic hardware simulator and trained the agent with a reinforcement learning. Our training shows a success resulting in a lead pursuit with a decreased engagement time and a high reward.

• Fire Science and Engineering
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• v.30 no.3
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• pp.117-123
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• 2016

To accomplish the purpose, the current study drew factors affecting the transfer of education and training through a review of domestic and overseas literature, and aimed to empirically investigate whether these factors actually affect the transfer of education and training of fire officers. The results showed that significant variables affecting the degree of perception on the transfer of education and training were in the order of work relationship, learning culture, peer support, self-efficacy, learning motivation, learning ability, and teaching method.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.405-406
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• 2018

본 논문은 소표본 의료용 영상 분석의 정확도 향상을 위해 전이학습 모델을 feature extractor로 구축하여 학습시키는 방법을 연구하였으며 성능 평가를 위해 선학습모델로 AlexNet, ResNet, DenseNet을 사용하여 fine tuning 기법을 적용하였을 때와의 성능을 비교 분석하였다. 그 결과 실험에 사용된 3개의 모델에서 fine tuning 기법보다 향상된 정확도를 보임을 확인하였고, 또한 ImageNet으로 학습된 AlexNet, ResNet, DenseNet이 소표본 의료용 X-Ray 영상에 적용될 수 있음을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.234-236
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• 2023

Edge AI 환경에서의 DNNs 학습 방법 중 하나인 분할 학습은 모델 전도 공격으로 인해 입력 데이터의 프라이버시가 노출될 수 있다. 본 논문에서는 분할 학습 환경에서의 모델 전도 공격에 대한 기존 방어 기술들의 한계점을 회피할 수 있는 TrapMI 기술을 제안하고, 이를 통해 입력 이미지를 원 본 데이터 세트의 도메인에서 특정 타겟 이미지 도메인으로 이동시킴으로써 이미지 복원의 가능성을 최소화시킨다. 추가적으로, 테스트 과정에서 타겟 이미지의 정보를 알 수 없는 제약을 회피하기 위해 AutoGenerator를 구축한 후 실험을 통해 원본 데이터 보호 성능을 검증한다.

• The Journal of Korean Association of Computer Education
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• v.20 no.6
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• pp.61-69
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• 2017

The Goal of SW education is to improve computational thinking. Especially, non computer majors need to apply computational thinking to their problem solving in their fields after computational thinking class. In this paper, we verified what factors affect the improvement of computational thinking through mixed research method after teaching computational thinking to non major students. Also, we analysed the characteristics of initial learning transfer of computational thinking, and establish the reason about he validity and justification for non major in SW education. The result shows learning satisfaction, learning transfer motivation, and self-CT efficacy affect the perception about improvement of computational thinking. Also, we found that there is application of computational thinking was coming up with problem solving process because the initial learning transfer process of computational thinking has characteristics about concepts and practices of it in programming steps. The effectiveness and learning transfer process of computational thinking for non majors will give the validity and justification to teach SW education for all students.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.639-644
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• 2024

Recent developments in the use of drones on battlefields, extending beyond reconnaissance to firepower support, have greatly increased the importance of technologies for early automatic drone identification. In this study, to identify an effective image classification model that can distinguish drones from other aerial targets of similar size and appearance, such as birds and balloons, we utilized a dataset of 3,600 images collected from the internet. We adopted a transfer learning approach that combines the feature extraction capabilities of three pre-trained convolutional neural network models (VGG16, ResNet50, InceptionV3) with an additional classifier. Specifically, we conducted a comparative analysis of the performance of these three pre-trained models to determine the most effective one. The results showed that the InceptionV3 model achieved the highest accuracy at 99.66%. This research represents a new endeavor in utilizing existing convolutional neural network models and transfer learning for drone identification, which is expected to make a significant contribution to the advancement of drone identification technologies.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.562-564
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• 2003

인터넷 기술의 발달에 따라 교육분야에서도 이를 도입하고 있다. 또한, 웹을 기반으로 하는 문제학습 시스템에서는 피험자의 학습효과를 높이려는 방법으로 학습결과를 반영하는 문항분석 이론이 연구되고 있다. 그러나, 기존의 웹 기반 학습시스템은 대부분 일방적인 학습내용의 제공이나 반복적인 학습에 그치고 있어 학습자의 학습효과를 기대하기 어렵다. 본 연구에서는 문항분석이론 중 문항난이도를 이용한 학습단계별 웹 코스웨어를 설계 및 구현하였다. 학습단계 설정은 문항난이도의 결과에 따라 상.중.하의 단계로 나뉘어 학습자가 학습을 하기 전에 선택 할 수 있도록 하였다.

• Journal of The Korean Association For Science Education
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• v.27 no.4
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• pp.346-353
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• 2007

In order to be efficient in teaching, a teacher should understand the current learner's level through diagnostic evaluation. This study has examined the major issues arising from the noble diagnostic assessment tool based on the theory of knowledge space. The knowledge state analysis method is actualizing the theory of knowledge space for practical use. The knowledge state analysis method is very advantageous when a certain group or individual student's knowledge structure is analyzed especially for strong hierarchical subjects such as mathematics, physics, chemistry, etc. Students' knowledge state helps design an efficient teaching plan by referring their hierarchical knowledge structure. The knowledge state analysis method can be enhanced by computer due to fast data processing. In addition, each student's knowledge can be improved effectively through individualistic feedback depending on individualized knowledge structure. In this study, we have developed a diagnostic assessment test for measuring student's learning outcome which is unattainable from the conventional examination. The diagnostic assessment test was administered to middle school students and analyzed by the knowledge state analysis method. The analyzed results show that students' knowledge structure after learning found to be more structured and well-defined than the knowledge structure before the learning.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.739-743
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• 2008

In this paper, we introduce a method for automatically generating accompaniment music, according to user's input melody. The initial accompaniment chord is generated by analyzing user's input melody. Then next chords are generated continuously based on markov chain probability table in which transition probabilities of each chord are defined. The probability table is learned according to reinforcement learning mechanism using sample data of existing music. Also during playing accompaniment, the probability table is learned and refined using reward values obtained in each status to improve the behavior of playing the chord in real-time. The similarity between user's input melody and each chord is calculated using pitch class histogram. Using our method, accompaniment chords harmonized with user's melody can be generated automatically in real-time.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.377-382
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• 2007

This paper proposes a training algorithm for global optimization of the parameters of radial basis function networks. Since conventional training algorithms usually perform only local optimization, the performance of the network is limited and the final network significantly depends on the initial network parameters. The proposed hybrid simulated annealing algorithm performs global optimization of the network parameters by combining global search capability of simulated annealing and local optimization capability of gradient-based algorithms. Via experiments for function approximation problems, we demonstrate that the proposed algorithm can find networks showing better training and test performance and reduce effects of the initial network parameters on the final results.