• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.93-96
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• 2000

본 논문에서는 SVM의 학습성 개선을 위해 모멘트와 kernel-adatron 기법이 조합된 하이브리드 학습알고리즘을 제안하였다. 제안된 학습알고리즘은 SVM의 학습기법인 기울기상승법에서 발생하는 최적해로의 수렴에 따른 발진을 억제하여 그 수렴속도를 좀 더 개선시키는 모멘트의 장점과 비선형 특징공간에서의 동작과 구현의 용이성을 가진 kernel-adatron 알고리즘의 장점을 그대로 살리는 것이다. 제안된 알고리즘을 비선형 함수 회귀에 적용해 본 결과 학습속도에 있어서 QP와 기존의 kernel-adatron 알고리즘보다 더 우수한 성능이 있음을 확인하였다

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.77-80
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• 2000

본 논문에서는 새로운 학습알고리즘의 비선형 주요성분분석 신경망을 이용한 데이터의 효율적인 특징추출에 대하여 제안하였다. 제안된 학습알고리즘에서는 모멘트와 동적터널링을 조합하여 이용함으로써 최적해로의 수렴에 따른 발진을 억제하고 빠른 수렴속도로 전역최적해에 수렴되도록 학습시킬 수 있다. 제안된 학습알고리즘을 이용하여 128$\times$128 픽셀의 얼굴영상과 256$\times$128 픽셀의 자동차번호판 영상을 대상으로 시뮬레이션 한 결과, 기울기하강의 학습알고리즘을 이용한 기존 비선형 주요성분분석 신경망보다 우수한 수렴성능과 특징추출성능이 있음을 확인 할 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.549-552
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• 2004

본 논문에서는 반도체의 Wafer ID 문자인식을 위해 기존의 오류 역전파 학습알고리즘을 개선하여 최적의 학습 학습 조건에 관해 연구하였다. 결과, 오류 역전파 학습알고리즘의 학습 최적 조건은 은닉층수는 1층, n값은 0.6 이상, 은닉층 노드수는 10개일 때 99%의 높은 인식률을 보였다 본 논문에서 제안하는 최적조건물 사용함으로써 기존의 오류역전파 학습 알고리즘이 가진 문제점을 해결할 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.269-272
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• 2003

웹을 이용한 교수-학습모형인 WBI 학습을 촉진시키기 위해서 교육전문가에 의해 검증된 학습자료를 효율적으로 관리하는 시스템을 구축하고, 사용자가 원하는 문서를 추정하여 제시한다. 사용자가 참조한 문서들에 대하여 에이전트가 추가, 삭제 등을 자율적으로 하며, 참조 문서간의 유사도를 측정한다. 이 유사도를 이용하여 적합한 문서를 추정하고 제시함으로서 사용자의 불명확한 정보요구에도 적합한 문서를 제공할 수 있다. 따라서, 사용자의 정보수집을 지원하고 웹을 통한 학습환경을 개선하여 WBI 학습을 촉진시킬 수 있다.

• Journal of Christian Education in Korea
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• v.77
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• pp.225-249
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• 2024

Research Purpose : Based on artificial intelligence, this study considers learners' characteristics, learning content, and individual learning, and analyzes the collected learning data to develop a model that supports customized learning for individual learners. Research content and method : In order to achieve the research purpose, the literature was analyzed to investigate the structure of customized learning support, learning data analysis, and learning activities, and based on the investigated data, the area and detailed components of the customized learning support model were derived. did. A draft model was constructed through literature analysis, and the first expert Delphi survey was conducted on the draft model with five experts. The model was revised by reflecting the results of the first Delphi, and the validity of the revised model was verified through the second expert Delphi. The model was elaborated through expert Delphi, and the final model was constructed through this. Conclusion and Recommendation : Through research, customized learning support area, class management system area, and learning analysis data area were formed, and detailed elements were derived for each area. The results of this study provide basic data that can be used as a reference for constructing a customized learning support system based on artificial intelligence, taking into account the university's class environment.

• Annual Conference of KIPS
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• 2009.11a
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• pp.267-268
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• 2009

많은 컴퓨터들이 기존의 단일코어 컴퓨터에서 벗어나 멀티코어를 장착한 컴퓨터로 변화하는 과정에서 기존의 단일 스레드 프로그래밍에서 다중처리가 중요해지는 세상이 되었다. 이러한 다중처리는 지능로봇 시뮬레이션 교육에 창의적 학습방법을 아주 잘 지원하고 있다. 지능로봇의 형태나 모형 그리고 센서 융합분야에서 동시처리는 중요한 역할을 하고 있다. 본 논문에서는 다중처리 기반 지능로봇 시뮬레이션 환경을 통해 창의적 학습방법에 관한 연구를 제안한다. 무한한 상상력과 창의성을 발휘하여 지능로봇의 모형 설계부터 직접 인공지능 프로그램까지 구현할 수 있는 방법을 소개한다.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.213-221
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• 2021

With the advent of the 4th industrial revolution, the need for artificial intelligence education has increased. The online learning environment caused by COVID-19 made it possible to use variety of artificial intelligence platforms. In this study, an aritificial intelligence class plan was developed and proposed to achieve the goal of artificial intelligence education using an AI platform. The AI platform used is AI for Oceans, With the theme of creating a program for the environment, designed a 6-hour project class using Novel Engineering-based on STEAM model. Students experience AI for Oceans enough time and learn supervised learning by experience. Based on understanding of supervised learning, students design their own programs for the environment using Entry's AI blocks. In this study, for AI convergence education, this lesson was developed and presented with the goal of acquiring the creative problem solving ability and integrated thinking ability by using the principles of artificial intelligence to solve problems.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.4
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• pp.283-292
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• 2020

Artificial intelligence, which is one of the representative images of the 4th industrial revolution, has been highly recognized since 2016. This paper analyzed domestic paper trends for 'Artificial Intelligence', 'Machine Learning', and 'Deep Learning' among the domestic papers provided by the Korea Academic Education and Information Service. There are approximately 10,000 searched papers, and word count analysis, topic modeling and semantic network is used to analyze paper's trends. As a result of analyzing the extracted papers, compared to 2015, in 2016, it increased 600% in the field of artificial intelligence, 176% in machine learning, and 316% in the field of deep learning. In machine learning, a support vector machine model has been studied, and in deep learning, convolutional neural networks using TensorFlow are widely used in deep learning. This paper can provide help in setting future research directions in the fields of 'artificial intelligence', 'machine learning', and 'deep learning'.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.277-297
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• 2023

In mathematics, the concept of convolution is widely used. The convolution operation is required for understanding computer vision and deep learning in artificial intelligence. Therefore, it is vital for this concept to be explained in college mathematics education. In this paper, we present our new teaching and learning materials on convolution available for engineering mathematics. We provide the knowledge and applications on convolution with Python-based code, and introduce Convolutional Neural Network (CNN) used for image classification as an example. These materials can be utilized in class for the teaching of convolution and help students have a good understanding of the related knowledge in artificial intelligence.

• Convergence Security Journal
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• v.23 no.2
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• pp.55-66
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• 2023

Recently, there is an increasing movement to increase the value of AI learning data and to secure high-quality data based on previous research on AI learning data in all areas of society. Therefore, quality management is very important in construction projects to secure high-quality data. In this paper, quality management to secure high-quality data when building AI learning data and improvement plans for each construction process are presented. In particular, more than 80% of the data quality of unstructured data built for AI learning is determined during the construction process. In this paper, we performed quality inspection of image/video data. In addition, we identified inspection procedures and problem elements that occurred in the construction phases of acquisition, data cleaning, labeling, and models, and suggested ways to secure high-quality data by solving them. Through this, it is expected that it will be an alternative to overcome the quality deviation of data for research groups and operators participating in the construction of AI learning data.