• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.222-227
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• 2022

In this paper, we implemented a Korean text generation and classification model based on a deep learning algorithm that can be applied to various industries. It consists of two implemented GAN-based Korean handwriting generation models and CNN-based Korean handwriting classification models. The GAN model consists of a generator model for generating fake Korean handwriting data and a discriminator model for discriminating fake handwritten data. In the case of the CNN model, the model was trained using the 'PHD08' dataset, and the learning result was 92.45. It was confirmed that Korean handwriting was classified with % accuracy. As a result of evaluating the performance of the classification model by integrating the Korean cursive data generated through the implemented GAN model and the training dataset of the existing CNN model, it was confirmed that the classification performance was 96.86%, which was superior to the existing classification performance.

• Knowledge Management Research
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• v.24 no.3
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• pp.1-15
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• 2023

This paper examines research ethics in using the generative AI ChatGPT for research purposes. After reviewing traditional themes of research ethics and relevant principles, it will be argued to be inappropriate to discuss ChatGPT-related issues only from the perspective of permission, detection, and punishment. We need to consider the fundamental problem that the current rules pose concerning the way ChatGPT works. This leads to the proposal that the usage of ChatGPT should be clearly noted when it is used for research purposes and that some unresolved issues should be recognized. Although the advantages of ChatGPT cannot be denied, consensus on the appropriate scope of use is needed from perspectives of the research community and researcher's social responsibility. As generative artificial intelligence technologies are still in the early stages of development, researchers should pay attention to relevant research ethical issues, while not making hasty conclusions. In the conclusion, it will be also proposed to discuss and make a consensus regarding the definition of research that is premised on existing research ethics, but challenged with the advent of ChatGPT and AI technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.479-480
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• 2023

생성형 인공지능은 학습의 기준을 파악하기 어려운 모델이다. 그 중 DCGAN을 분석하여 판별자를 통해 생성자의 학습 기준을 판단할 수 있는 하나의 방법을 제안하고자 한다. 그 과정에서 XAI 기법인 Grad-CAM을 활용하여 학습 시에 모델이 중요시하는 부분을 분석하여 적합한 학습과 학습에 적합하지 않은 데이터를 분석하는 방법을 소개하고자 한다.

• Proceedings of the KIEE Conference
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• 1999.07a
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• pp.440-442
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• 1999

본 연구에서는 전자연동장치의 확장성과 신뢰성 제고를 위해 전자연동장치의 실시간 연동전략으로 활용될 수 있는 연동 지식베이스를 자동 생성, 구축할 수 있는 지능형 연동지식베이스 자동 구축 소프트웨어(IIKBAGS)를 개발한다. IIKBAGS의 추론부는 주어진 역 모델의 동적탐색하에서 휴리스틱 규칙들의 우선순위에 따라 모든 진로를 탐색함은 물론 각 진로들에 대해 진로상 신호설비들간의 연쇄관계를 확인하여 연동패턴들을 자동생성하는 연동지식 자동생성기능을 가진다. 지식베이스는 전자연동장치상의 실시간 전문가 시스템이 직접적으로 활용할 수 있는 구조로 설계됨으로써 연동도표 입력과정에서 발생할 수 있는 오류를 배제, 연동장치의 정확성과 신뢰성을 높인다.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.291-300
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• 1999

본 논문은 인공신경망과 귀납적 학습방법 등의 인공지능 방법과 선물가격결정에 대한 기존 재무이론을 사용하여 일상어취로 표현되는 파생상품 가격예측 모형을 개발하는데 있다. 모형의 개발은 1단계로 인공신경망이나 기존의 선물가격결정이론(평균보 유비용모형이나 일반균형모형)을 이용하여 선물 가격을 예측한 후, 서로 비교 분석하여 인공신경망 모형의 우수성을 확인하였다. 귀납적 학습방법중 CART 알고리듬을 사용하여 If-Then 규칙을 생성하였다. 특히 실용적 측면에서 선물가격의 일상어휘화를 통한 모형개발을 여러 가지 방법으로 시도하였다. 이러한 선물가격 예측모형의 유용성은 일단 If-Then 규칙으로 표현되어 전문가의 판단에 확실한 이론적인 근거를 제시할 수 있는 장점이 있으며, 특히 의사결정지원시스템으로 활용화 될 경우 매우 유용한 근거자료로 활용될 수 있다. 이러한 선물가격 예측모형의 정확성은 분석표본과 검증표본으로 나누어 검증표본에서 세가지 기본모형(평균보유 비용모형, 일반균형모형, 인공신경망 모형)과 각 모형의 귀납적 학습방법 모형의 다른 3가지 어휘표현방법 3가지를 모형별로 비교 분석하였다. 분석결과 인공신경망모형은 상당한 예측력을 갖고 있는 것으로 판명되었으며, 특히 CART를 기반으로 한 일상어취 기반의 선물가격예측 모형은 예측력이 높은 것으로 나타났다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.479-488
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• 2022

In recent years, with the rapid development of machine learning technology, research utilizing machine learning has been actively conducted in fields such as cognition, reasoning and judgment, and action among various technologies constituting intelligent systems. In order to utilize this machine learning, it is indispensable to collect data for learning. However, the types of data generated vary according to the environment in which the data is generated, and the types and forms of data required are different depending on the learning model to be used for machine learning. Due to this, there is a problem that the existing data collection method cannot be reused in a new environment, and a specialized data collection module must be developed each time. In this paper, we propose a specification-based methology for data collection in artificial intelligence system to solve the above problems, ensure the reusability of the data collection method according to the data collection environment, and automate the implementation of the data collection function.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.109-133
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• 2024

This study investigated the impact of the technical characteristics of Generative AI (GAI) and librarians' personal traits on their intention to use GAI. Personalization, interaction, and context awareness were considered as technical characteristics of GAI that influence the intention to use GAI, while innovativeness and frequency of GAI use were considered as librarians' personal traits. The study targeted 187 librarians working in libraries, and 165 questionnaires were collected and analyzed. The results showed that the technical characteristics of GAI had a statistically significant impact on the intention to use GAI. Additionally, librarians' personal traits, namely innovativeness and frequency of GAI use, were also found to have a significant impact on the intention to use GAI. The findings of this study can be used as valuable information to help librarians increase their intention to use GAI and improve the quality and satisfaction of library services.

• Journal of Korea Game Society
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• v.18 no.4
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• pp.75-82
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• 2018

With the development of information and communication technology, various methods have been tried to provide learners with a fun educational environment through fun and interest. It is a good example to utilize technologies such as games and robots in education for edutainment and game-based learning. In this study, we propose an intelligent rhythm education system using user data collection and analysis for humanoid robot rhythm generation. To do this, the user selects music and inputs rhythm information according to the selected music. The robot utilization data of this user extracts patterns through collection and analysis. Patterns are based on frequency, and FFT similarity comparison method is applied when past data is insufficient. The proposed method is validated through experiments of kindergarten children.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.493-498
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• 2024

With the rapid advancement of generative artificial intelligence technology, there is growing interest in how to utilize it in practical applications. Additionally, the importance of prompt engineering to generate results that meet user demands is being newly highlighted. Exploring the new possibilities of generative AI can hold significant value. This study aims to utilize ChatGPT 4.0, a leading generative AI, to propose an effective method for evaluating user experience through the analysis of online customer review data. The user experience evaluation method was based on the six-layer elements of user experience: 'functionality', 'reliability', 'usability', 'convenience', 'emotion', and 'significance'. For this study, a literature review was conducted to enhance the understanding of prompt engineering and to grasp the clear concept of the user experience hierarchy. Based on this, prompts were crafted, and experiments for the user experience evaluation method were carried out using the analysis of collected online customer review data. In this study, we reveal that when provided with accurate definitions and descriptions of the classification processes for user experience factors, ChatGPT demonstrated excellent performance in evaluating user experience. However, it was also found that due to time constraints, there were limitations in analyzing large volumes of data. By introducing and proposing a method to utilize ChatGPT 4.0 for user experience evaluation, we expect to contribute to the advancement of the UX field.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.22-25
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• 2022

본 연구는 대화형 인공지능이 비윤리적 표현을 학습하거나 생성하는 것을 방지하기 위한 기초적 연구로, 메신저 대화에 나타나는 단어 단위, 구 단위 이상의 비윤리적 표현을 수집하고 그 특성을 분석하였다. 비윤리적 표현은 '욕설, 혐오 및 차별 표현, 공격적 표현, 성적 표현'이 해당된다. 메신저 대화에 나타난 비윤리적 표현은 욕설이 가장 많은 비중을 차지했는데, 욕설에서는 비표준형뿐만 아니라 '존-', '미치다' 등과 같이 맥락을 고려하여 판단해야 하는 경우가 있다. 가장 높은 빈도로 나타난 욕설 '존나류, 씨발류, 새끼류'의 타입-토큰 비율(TTR)을 확인한 결과 '새끼류'의 TTR이 가장 높게 나타났다. 다음으로 메신저 대화에서는 공격적 표현이나 성적인 표현에 비해 혐오 및 차별 표현의 비중이 높았는데, '국적/인종'과 '젠더' 관련된 혐오 및 차별 표현이 특히 높게 나타났다. 혐오 및 차별 표현은 단어 단위보다는 구 단위 이상의 표현의 비중이 높았고 문장 단위로 떨어지기 보다는 대화 전체에 걸쳐 나타나는 것을 확인하였다. 따라서 혐오 및 차별 표현을 탐지하기 위해서는 단어 단위보다는 구 단위 이상 표현의 탐지에 대한 필요성이 있음을 학인하였다.