• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.281-286
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• 2023

더욱 사람같은 대화 모델을 실현하기 위해, 페르소나 메모리를 활용하여 응답을 생성하는 연구들이 활발히 진행되고 있다. 다수의 기존 연구들에서는 메모리로부터 관련된 페르소나를 찾기 위해 별도의 검색 모델을 이용한다. 그러나 이는 전체 시스템에 속도 저하를 일으키고 시스템을 무겁게 만드는 문제가 있다. 또한, 기존 연구들은 페르소나를 잘 반영해 응답하는 능력에만 초점을 두는데, 그 전에 페르소나 참조의 필요성 여부를 판별하는 능력이 선행되어야 한다. 따라서, 우리의 제안 모델은 검색 모델을 활용하지 않고 생성 모델의 내부적인 연산을 통해 페르소나 메모리의 참조가 필요한지를 판별한다. 참조가 필요하다고 판단한 경우에는 관련된 페르소나를 반영하여 응답하며, 그렇지 않은 경우에는 대화 컨텍스트에 집중하여 응답을 생성한다. 실험 결과를 통해 제안 모델이 장기적인 대화에서 효과적으로 동작함을 확인하였다.

• Electronics and Telecommunications Trends
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• v.34 no.3
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• pp.34-42
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• 2019

This study introduces artificial intelligence (AI) techniques for video generation. For an effective illustration, techniques for video generation are classified as either semi-automatic or automatic. First, we discuss some recent achievements in semi-automatic video generation, and explain which types of AI techniques can be applied to produce films and improve film quality. Additionally, we provide an example of video content that has been generated by using AI techniques. Then, two automatic video-generation techniques are introduced with technical details. As there is currently no feasible automatic video-generation technique that can generate commercial videos, in this study, we explain their technical details, and suggest the future direction for researchers. Finally, we discuss several considerations for more practical automatic video-generation techniques.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.450-450
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• 2023

인공지능 기법의 급격한 발달에 따라 다양한 분야에서 인공지능 기법을 활용하기 위한 노력이 이루어지고 있다. 재난은 발생하기 전에 다양한 전조 현상을 나타내나 수많은 정보 속에서 전조 증상을 정확히 인지하는 것은 매우 어렵다. 따라서 인공지능은 방대한 사전 정보의 해석을 통해 재난 발생의 전조를 신속 정확하게 감지하는데 최적의 기술이다. 최근 OpenAI의 딥러닝 기반의 언어모델인 GPT(Generative Pre-trained Transformer)의 성능이 기대 이상을 나타내면서 많은 분야에서 GPT에 대한 관심과 실험이 시작되고 있다. 본 실험에서는 GPT를 이용하여 재난 검색 및 해석의 특징을 검토하여 보았다. 정확한 재난 기록은 정확한 재난 예측을 위해 반드시 필요한 자료이나 부정확한 재난 기록은 그 기록이 비록 방대하더라도 오히려 예측의 신뢰도를 크게 떨어뜨린 수 있다. 따라서 비지도학습 기반의 대화형 인공지능을 재난 검색에 활용하기 위해서는 인공지능 기법의 인지 특성을 반드시 가늠해 봐야 한다. 향후 보다 많은 연구자가 이에 관심을 가진다면 보다 정확한 인공지능 기반의 재난 탐지 기술의 개발이 가능할 것으로 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.201-203
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• 2022

Over the past five years, the number of patients treated for diabetes has increased by 27.7% to 3.22 million, and since blood sugar is still checked through finger blood collection, continuous blood glucose measurement and blood sugar peak confirmation are difficult and painful. To solve this problem, based on blood sugar data measured for 14 days, three months of blood sugar prediction data are provided to diabetics using artificial intelligence technology.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.239-240
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• 2023

Construction site management involves overseeing tasks from the construction phase to the maintenance stage, and digitalization of construction sites is necessary for digital construction site management. In this study, we aim to conduct research on object recognition at construction sites using drones. Images of construction sites captured by drones are reconstructed into BIM (Building Information Modeling) models, and objects are recognized after partially rendering the models using artificial intelligence. For the photorealistic rendering of the BIM models, both traditional filtering techniques and the generative adversarial network (GAN) model were used, while the YOLO (You Only Look Once) model was employed for object recognition. This study is expected to provide insights into the research direction of digital construction site management and help assess the potential and future value of introducing artificial intelligence in the construction industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4326-4344
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• 2021

The SinGAN is one of generative adversarial networks that can be trained on a single nature image. It has poor ability to learn more global features from nature image, and losses much local detail information when it generates arbitrary size image sample. To solve the problem, a non-linear function is firstly proposed to control downsampling ratio that is ratio between the size of current image and the size of next downsampled image, to increase the ratio with increase of the number of downsampling. This makes the low-resolution images obtained by downsampling have higher proportion in all downsampled images. The low-resolution images usually contain much global information. Therefore, it can help the model to learn more global feature information from downsampled images. Secondly, the attention mechanism is introduced to the generative network to increase the weight of effective image information. This can make the network learn more local details. Besides, in order to make the output image more natural, the TVLoss function is introduced to the loss function of SinGAN, to reduce the difference between adjacent pixels and smear phenomenon for the output image. A large number of experimental results show that our proposed model has better performance than other methods in generating random samples with fixed size and arbitrary size, image harmonization and editing.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.357-364
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• 2023

Due to the rapid advancement of AI technology, generative artificial intelligence platforms are experiencing innovative applications in various fields. In this paper, it examines research cases involving the utilization of AI in education, explore instances where generative AI platforms are applied in the realm of creative endeavors, and discuss the direction of utilizing generative AI in educational contexts. In the field of computer graphics, this study introduced generative AI platforms that are applicable for image creation, editing, and video editing. It also proposed platforms that can be utilized in the video editing production process. These generative AI platforms not only offer advantages in terms of efficiency, by reducing the efforts of creators and saving time in the production process, but they also present positive aspects in enhancing individual capabilities. It is advocated that their swift integration into education is necessary, considering these benefits. This study aims to provide direction for the expansion of creative education utilizing generative AI platforms.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.195-204
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• 2023

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.

• Information Systems Review
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• v.25 no.2
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• pp.125-144
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• 2023

Generative Artificial Intelligence (AI) technology is gaining global attention as it can automatically generate sentences, images, and voices that humans previously generated. In particular, ChatGPT, a representative generative AI service, shows proactivity and accuracy differentiated from existing chatbot services, and the number of users is rapidly increasing in a short period of time. Despite this growing interest in generative AI services, most preceding studies are still in their infancy. Therefore, this study utilized LDA topic modeling and keyword network diagrams to derive success factors for generative AI services and to propose successful business strategies based on them. In addition, using ChatGPT, a new research methodology that complements the existing text-mining method, was presented. This study overcomes the limitations of previous research that relied on qualitative methods and makes academic and practical contributions to the future development of generative AI services.