• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.161-166
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• 2023

In recent years, new and variant hacking of binary codes has increased, and the limitations of techniques for detecting malicious codes in source programs and defending against attacks are often exposed. Advanced software security vulnerability detection technology using machine learning and deep learning technology for binary code and defense and response capabilities against attacks are required. In this paper, we propose a malware clustering method that groups malware based on the characteristics of the taint information after entering dynamic taint information by tracing the execution path of binary code. Malware vulnerability detection was applied to a three-layered Few-shot learning model, and F1-scores were calculated for each layer's CPU and GPU. We obtained 97~98% performance in the learning process and 80~81% detection performance in the test process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.75-81
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• 2022

Autonomous vehicles, which can dramatically solve the lack of parking spaces, are making great progress through deep reinforcement learning. Activation functions are used for deep reinforcement learning, and various activation functions have been proposed, but their performance deviations were large depending on the application environment. Therefore, finding the optimal activation function depending on the environment is important for effective learning. This paper analyzes 12 functions mainly used in reinforcement learning to compare and evaluate which activation function is most effective when autonomous vehicles use deep reinforcement learning to learn parking. To this end, a performance evaluation environment was established, and the average reward of each activation function was compared with the success rate, episode length, and vehicle speed. As a result, the highest reward was the case of using GELU, and the ELU was the lowest. The reward difference between the two activation functions was 35.2%.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.34 no.3
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• pp.31-34
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• 2016

This article reviews Apache SINGA, a general distributed deep learning (DL) platform. The system components and its architecture are presented, as well as how to configure and run SINGA for different types of distributed training using model/data partitioning. Besides, several features and performance are compared with other popular DL tools.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.263-270
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• 2020

In this paper, In this paper, we describe the UAV system using image processing for autonomous quadcopters, where they can apply logistics, rescue work etc. we propose high-speed hovering height and posture control method based on state feedback control with CNN from camera because we can get image of the information only every 30ms. Finally, we show the advantages of proposed method by simulations and experiments.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.281-288
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• 2020

In this paper, we propose a control method to solve the surface hardness non-uniformity due to flow non-uniformity occurring in the heat treatment process of marine CAM. In the water cooling method including the decarbonization method, an automation device for deformation control has been developed and applied. LSTM was used to estimate the water cooling conditions, and the proposed method was found to be meaningful by improving the prototype results.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.364-366
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• 2018

Visual Question Answering(VQA)은 주어진 이미지와 질문에 대해 알맞은 정답을 찾는 기술이다. VQA는 어린이 학습, 인공지능 비서 등 여러 분야에 활용할 수 있는 중요한 기술이다. 그러나 관련된 한국어 데이터를 확보하기 힘든 이유로 한국어를 이용한 연구는 이루어지지 못하고 있다. 본 논문에서는 기존 영어 VQA 데이터를 한글로 번역하여 한국어 VQA 데이터로 사용하며, 이미지 정보와 질문 정보를 적절히 조절할 수 있는 Gate를 한국어 VQA에 적용한다. 실험 결과, 본 논문에서 제안한 모델이 영어 및 한국어 VQA 데이터에서 다른 모델보다 더 좋은 성능을 보였다.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.183-184
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• 2019

본 논문에서는 로봇과 사람의 자연스러운 감정 소통을 위한 비디오 시퀀스 표정생성 대화 시스템을 제안한다. 제안된 시스템에서는 실시간 비디오 데이터로 판단된 관객의 감정 상태를 반영한 대답을 하며, 딥러닝(Deep Learning)을 활용하여 대화의 맥락에 맞는 로봇의 표정을 실시간 생성한다. 본 논문에서 관객의 표정을 위해 3만여개의 비디오 데이터로 학습한 결과 88%의 학습 정확도로 표정 생성이 가능한 것으로 확인되었다. 본 연구는 로봇 표정 생성에 딥러닝 방식을 적용한 것에 그 의의가 있으며 향후 대화 시스템 자체에도 딥러닝 방식을 확대 적용하기 위한 초석이 될 수 있다는 점에 의의가 있다.

• Proceedings of the Korean Society for Information Management Conference
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• 2017.08a
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• pp.11-14
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• 2017

딥러닝 연구동향에 대한 계량서지적 분석을 자아 중심 주제 인용분석 기법을 활용하여 시도하였다. 이를 위해서 Web of Science에서 'deep learning'으로 검색된 인용빈도 상위 15건의 논문을 핵심 논문으로 삼고, 이들 핵심 논문 15편을 인용한 논문 집합을 자아 문헌집합으로 삼았으며, 자아 문헌집합들이 인용한 주요 문헌들을 인용 정체성 문헌집합으로 설정하였다. 인용 정체성 문헌집합에 대해 동시인용분석을 실시하여 주요 문헌, 주요 연구 주제를 파악하고, 영향을 끼친 주요 선행 연구를 파악해보았다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.153-154
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• 2019

There are many jobs, not actually sleeping. Sleepy driving is one of the biggest problems in modern society. In this paper, we propose a system to control underwater guns by using deep learning (YOLO) to check eyes and to check drowsiness. So let your mind be clear.

• East Asian mathematical journal
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• v.38 no.1
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• pp.95-105
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• 2022

In this paper, we consider a quadratic matrix equation Q(X) = AX² + BX + C = 0 where A, B, C ∈ ℝ^n×n. A new approach is proposed to find solutions of Q(X), using the novel structure of the information processing system. We also present some numerical experimetns with Artificial Neural Network.