• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.166-171
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• 2023

With significant advancements in the field of artificial intelligence, many novel algorithms and technologies have emerged. Currently, AI painting can generate high-quality images based on textual descriptions. However, it is often challenging to control details when generating images, even with complex textual inputs. Therefore, there is a need to implement additional control mechanisms beyond textual descriptions. Based on ControlNet, this passage describes a combined utilization of various local controls (such as edge maps and depth maps) and global control within a single model. It provides a comprehensive exposition of the fundamental concepts of ControlNet, elucidating its theoretical foundation and relevant technological features. Furthermore, combining methods and applications, understanding the technical characteristics involves analyzing distinct advantages and image differences. This further explores insights into the development of image generation patterns.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.476-486
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• 2022

Synthetic dynamic infrared image generation from the given virtual environment is being the primary goal to simulate the output of the infra-red(IR) camera installed on a vehicle to evaluate the control algorithm for various search & reconnaissance missions. Due to the difficulty to obtain actual IR data in complex environments, Artificial intelligence(AI) has been used recently in the field of image data generation. In this paper, CycleGAN technique is applied to obtain a more realistic synthetic IR image. We added the Structural Similarity Index Measure(SSIM) loss function to the L1 loss function to generate a more realistic synthetic IR image when the CycleGAN image is generated. From the simulation, it is applicable to the guided-missile flight simulation tests by using the synthetic infrared image generated by the proposed technique.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.544-546
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• 2020

의료 데이터를 이용하여 인공지능 기계학습 연구를 수행할 때 자주 마주하는 문제는 데이터 불균형, 데이터 부족 등이며 특히 정제된 충분한 데이터를 구하기 힘들다는 것이 큰 문제이다. 본 연구에서는 이를 해결하기 위해 GAN(Generative Adversarial Network) 기반 고해상도 의료 영상을 생성하는 프레임워크를 개발하고자 한다. 각 해상도 마다 Scale 의 Gradient 를 동시에 학습하여 빠르게 고해상도 이미지를 생성해낼 수 있도록 했다. 고해상도 이미지를 생성하는 Neural Network 를 고안하였으며, PGGAN, Style-GAN 과의 성능 비교를 통해 제안된 모델이 양질의 고해상도 의료영상 이미지를 더 빠르게 생성할 수 있음을 확인하였다. 이를 통해 인공지능 기계학습 연구에 있어서 의료 영상의 데이터 부족, 데이터 불균형 문제를 해결할 수 있는 Data augmentation 이나, Anomaly detection 등의 연구에 적용할 수 있다.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.81.2-81.2
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• 2019

We develop an image-to-image translation model, which is a popular deep learning method based on conditional Generative Adversarial Networks (cGANs), to generate solar magnetograms and EUV images from sunspot drawings. For this, we train the model using pairs of sunspot drawings from Mount Wilson Observatory (MWO) and their corresponding SDO/HMI magnetograms and SDO/AIA EUV images (512 by 512) from January 2012 to September 2014. We test the model by comparing pairs of actual SDO images (magnetogram and EUV images) and the corresponding AI-generated ones from October to December in 2014. Our results show that bipolar structures and coronal loop structures of AI-generated images are consistent with those of the original ones. We find that their unsigned magnetic fluxes well correlate with those of the original ones with a good correlation coefficient of 0.86. We also obtain pixel-to-pixel correlations EUV images and AI-generated ones. The average correlations of 92 test samples for several SDO lines are very good: 0.88 for AIA 211, 0.87 for AIA 1600 and 0.93 for AIA 1700. These facts imply that AI-generated EUV images quite similar to AIA ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. This application will be used to generate solar images using historical sunspot drawings.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.904-905
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• 2023

본 논문에서는 의료 영상 생성을 위한 Med-StyleGAN2를 제안한다. 생성적 적대 신경망은 이미지 생성에는 효과적이지만, 의료 영상 생성에는 한계점을 가지고 있다. 따라서 본 연구에서는 의료 영상 생성에 특화된 StyleGAN 기반 학습 모델을 제안한다. 이는 다양한 의료 영상 어플리케이션에 활용할 수 있으며, 생성된 의료 영상에 대한 정량적, 정성적 평가를 수행함으로써 의료 영상 생성 분야의 발전 가능성에 대해 연구한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.292-300
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• 2023

This paper proposes a patent discovery method and strategy for Generative AI-related patents by utilizing qualitative evaluation indicators established based on the core components of the technology. Currently, the evaluation of patent quality relies on quantitative indicators, but existing quantitative indicators cannot represent the characteristics of Generative AI technology, making it difficult to accurately evaluate. Therefore, there is a need for additional qualitative indicators that consider technical characteristics based on patent claims, which can reveal the actual strength of the patent. In this paper, we propose a new evaluation index considering the technical characteristics of Generative AI. Core patents were selected using the proposed evaluation index, and the appropriateness of the proposed index was verified through the existing quantitative evaluation method for the selected core patents.

• Journal of Fashion Business
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• v.26 no.2
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• pp.28-47
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• 2022

Rapid development of information and communication technology is leading the digital transformation (hereinafter, DT) of various industries. At this point in rapid online transition, fashion manufacturers operating offline-oriented businesses have become highly interested in DT and artificial intelligence (hereinafter AI), which leads DT. The purpose of this study is to examine the development status and application case of AI-based digital technology developed for the fashion industry, and to examine the DT stage and AI application status of domestic fashion manufacturers. Hence, in-depth interviews were conducted with five domestic IT companies developing AI technology for the fashion industry and six domestic fashion manufacturers applying AI technology. After analyzing interviews, study results were as follows: The seven major AI technologies leading the DT of the fashion industry were fashion image recognition, trend analysis, prediction & visualization, automated fashion design generation, demand forecast & optimizing inventory, optimizing logistics, curation, and ad-tech. It was found that domestic fashion manufacturers were striving for innovative changes through DT although the DT stage varied from company to company. This study is of academic significance as it organized technologies specialized in fashion business by analyzing AI-based digitization element technologies that lead DT in the fashion industry. It is also expected to serve as basic study when DT and AI technology development are applied to the fashion field so that traditional domestic fashion manufacturers showing low growth can rise again.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.465-470
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• 2024

The advancement of AI-based image generation technology has resulted in the creation of various images, emphasizing the need for technology capable of accurately discerning them. The amount of generated image data is limited, and to achieve high performance with a limited dataset, this study proposes a model for discriminating generated images using transfer learning. Applying pre-trained models from the ImageNet dataset directly to the CIFAKE input dataset, we reduce training time cost followed by adding three hidden layers and one output layer to fine-tune the model. The modeling results revealed an improvement in the performance of the model when adjusting the final layer. Using transfer learning and then adjusting layers close to the output layer, small image data-related accuracy issues can be reduced and generated images can be classified.