• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.565-574
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• 2024

ChatGPT is causing significant reverberations across all sectors of our society, and this holds true for the field of education as well. However, scholarly and societal discussions regarding ChatGPT in academic settings have primarily focused on issues such as plagiarism, with relatively limited research on the positive effects of utilizing generative AI. Additionally, amidst the educational crisis of the post-COVID era, there is a growing recognition of the need to enhance academic engagement. In light of these concerns, we investigated how academic engagement varies based on students' levels of ChatGPT literacy and examined whether students' academic confidence and perceived academic competence serve as mediators between ChatGPT literacy and academic engagement. An analysis using SPSS was conducted on the data collected from 406 college students. The results showed that ChatGPT literacy had a positive effect on academic engagement, and academic confidence mediated the relationship between ChatGPT literacy and academic engagement. Also, when the mediating effect of perceived academic competence was significant only when it was serially mediated. Based on these findings, we discussed the theoretical contributions of identifying the theoretical mechanism between ChatGPT literacy and academic engagement. In addition, practical implications regarding the importance of ChatGPT literacy education were described.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.279-285
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• 2018

The purpose of this study is to propose the role of future designers and capabilities to be developed in the age of A.I. Active and preliminary designers should prepare themselves to develop necessary capabilities. As a method of study, we investigated the meaning of design and the changing role of designers from the past to present. Additional research was conducted on generative design, design processes, and A.I based design software. Finally, based on the analysis, we proposed the role of future designers and their capabilities in the age of A.I. In conclusion, the role of future designer should lead social innovation through creativity by coworking with artificial intelligence based on understanding and empathy for users. Based on this research, designers are expected to develop unique humanities skills such as empathy and creativity and work with AI in response to $4^{th}$ industrial revolution.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.261-267
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• 1991

다품종소량생산의 특징을 갖고 있는 금형공업에서 컴퓨터통합생산시스템(Computer Integrated Manufacturing System;CIMS)의 실현을 위한 중요한 분야의 하나는 부품설계도면으로부터 최종제품을 생산하는데 필요한 공정계획의 자동화, 즉 컴퓨터를 이용하여 공정계획을 자동적으로 생성하는 자동공정계획시스템(Computer Aided Process Planning;CAPP) 기술의 개발이다. 국내외적으로 CAPP분야의 연구는 컴퓨터 지원에 의한 자동화 기술의 급속한 발전과 더불어 지난 20여년 동안에 기계가공품에 관한 CAPP은 약 150여가지가 개발되었으나, 이는 컴퓨터 지원에 의한 설계의 자동화(Computer Aided Design;CAD)나 컴퓨터 지원에 의한 제조의 자동화(Computer Aided Manufacturing;CAM)분야에 비해 상대적으로 저조한 형편이다. 특히 금형을 대상으로 한 CAPP시스템의 개발은 아직 초기단계에 있기 때문에, 본 연구에서는 사출금형을 대상으로 하여 실용성이 있는 공정설계시스템을 개발함을 목적으로 한다. 일반적으로 공정계획은 "소재로부터 제품을 경제적, 효율적으로 생산하는데 필요한 제조공정의 체계적인 결정"이라고 정의할 수 있다. 공정계획은 제품의 종류와 수량, 재료와 부품의 종류, 보유 생산설비와 제조기술의 수준에 따라 다르나, 공정설계(Process Design)와 작업설계(Operation Design)로 구분할 수 있다. 본 연구에서는 공정계획을 광의의 공정설계로 정의하고, 공정설계와 공정계획을 동의어로 통용토록 한다. 기존의 CAPP시스템의 개발에 관한 기본적인 접근방법은 변성형방법(Variant method), 창성형방법(Generative method) 및 자동화방법(Automatic method)이 있다. 이들 CAPP시스템을 개발할 때 사용하는 기법은 크게 5가지- (1) GT(group Technology) 접근기법, (2) Bottom-up 접근기법, (3) Top-down 접근기법, (4) AI와 전문가시스템(Expert System) 접근기법, (5) 컴퓨터 프로그래밍 언어 - 로 분류할 수 있다. 본 연구에서는 전문가시스템 기법을 도입해서 사출금형 공정계획전문가의 지식과 경험을 획득하여 지식베이스를 구축하고, 전문가시스템 셀(shell)중 CLIPS를 이용하여 자동공정계획시스템인 Mold CAPP을 개발하였다.PP을 개발하였다.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.15-22
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• 2019

Recent technological advances in three-dimensional (3D) sensing devices and machine learning such as deep leaning has enabled data-driven 3D applications. Research on artificial intelligence has developed for the past few years and 3D deep learning has been introduced. This is the result of the availability of high-quality big data, increases in computing power, and development of new algorithms; before the introduction of 3D deep leaning, the main targets for deep learning were one-dimensional (1D) audio files and two-dimensional (2D) images. The research field of deep leaning has extended from discriminative models such as classification/segmentation/reconstruction models to generative models such as those including style transfer and generation of non-existing data. Unlike 2D learning, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become increasingly popular owing to advances in 3D vision technology, the generation/acquisition of 3D data is still very difficult. Even if 3D data can be acquired, post-processing remains a significant problem. Moreover, it is not easy to directly apply existing network models such as convolution networks owing to the various ways in which 3D data is represented. In this paper, we summarize technological trends in AI-based 3D content generation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.77-84
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• 2021

Today, the restoration of cultural properties is done by applying the latest IT technology from relying on existing data and experts. However, there are cases where new data are released and the original restoration is incorrect. Also, sometimes it takes too long to restore. And there is a possibility that the results will be different than expected. Therefore, we aim to quickly restore cultural properties using DeepLearning. Recently, so the algorithm DcGAN made in GANs algorithm, and image creation, restoring sectors are constantly evolving. We try to find the optimal GAN algorithm for the restoration of cultural properties among various GAN algorithms. Because the GAN algorithm is used in various fields. In the field of restoring cultural properties, it will show that it can be applied in practice by obtaining meaningful results. As a result of experimenting with the DCGAN and Style GAN algorithms among the GAN algorithms, it was confirmed that the DCGAN algorithm generates a top image with a low resolution.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.333-335
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• 2022

본 논문에서는 기존에 두 가지 모델이 필요했던 작업을 하나의 모델로 처리할 수 있는 자연어 처리 아키텍처를 제안한다. 단일 모델로 알츠하이머 환자의 언어패턴과 대화맥락을 분석하고 두 가지 결과인 환자분류와 챗봇의 대답을 도출한다. 일상생활에서 챗봇으로 환자의 언어특징을 파악한다면 의사는 조기진단을 위해 더 정밀한 진단과 치료를 계획할 수 있다. 제안된 모델은 전문가가 필요했던 질문지법을 대체하는 챗봇 개발에 활용된다. 모델이 수행하는 자연어 처리 작업은 두 가지이다. 첫 번째는 환자가 병을 가졌는지 여부를 확률로 표시하는 '자연어 분류'이고 두 번째는 환자의 대답에 대한 챗봇의 다음 '대답을 생성'하는 것이다. 전반부에서는 셀프어탠션 신경망을 통해 환자 발화 특징인 맥락벡터(context vector)를 추출한다. 이 맥락벡터와 챗봇(전문가, 진행자)의 질문을 함께 인코더에 입력해 질문자와 환자 사이 상호작용 특징을 담은 행렬을 얻는다. 벡터화된 행렬은 환자분류를 위한 확률값이 된다. 행렬을 챗봇(진행자)의 다음 대답과 함께 디코더에 입력해 다음 발화를 생성한다. 이 구조를 DementiaBank의 쿠키도둑묘사 말뭉치로 학습한 결과 인코더와 디코더의 손실함수 값이 유의미하게 줄어들며 수렴하는 양상을 확인할 수 있었다. 이는 알츠하이머병 환자의 발화 언어패턴을 포착하는 것이 향후 해당 병의 조기진단과 종단연구에 기여할 수 있음을 보여준다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.31-39
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• 2023

The evolution of Natural Language Processing (NLP) and the rise of large language models (LLM) like ChatGPT have paved the way for specialized question-answering (QA) systems tailored to specific domains. This study outlines a system harnessing the power of LLM in conjunction with document search algorithms to interpret and address user inquiries using documents from the Korea Atomic Energy Research Institute (KAERI). Initially, the system refines multiple documents for optimized search and analysis, breaking the content into managable paragraphs suitable for the language model's processing. Each paragraph's content is converted into a vector via an embedding model and archived in a database. Upon receiving a user query, the system matches the extracted vectors from the question with the stored vectors, pinpointing the most pertinent content. The chosen paragraphs, combined with the user's query, are then processed by the language generation model to formulate a response. Tests encompassing a spectrum of questions verified the system's proficiency in discerning question intent, understanding diverse documents, and delivering rapid and precise answers.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.447-452
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• 2024

As the remarkable achievements of generative artificial intelligence technology become increasingly visible, the issue of 'controllability' in artificial intelligence is emerging as a prominent global keyword. This comes at a time when existential threats, such as the possibility of machines dominating humans, are being raised. Accordingly, this study aims to establish the groundwork for shaping a social public sphere by closely examining the concept of control, the current status, and the global landscape of artificial intelligence. It seeks to address the innovative changes anticipated in future society, with artificial intelligence technology at its core. The study aims to derive implications for preparing countermeasures against social problems and unpredictable variables that may arise from the evolution of artificial intelligence technology. It also aims to present guidelines and strategic insights for the establishment of government regulations. Furthermore, the study seeks to uncover implications for the formation of social public discourse.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.