• 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.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.663-672
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• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• The Journal of Bigdata
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• v.5 no.2
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• pp.69-76
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• 2020

This study was conducted with the support of the Information and Communication Technology Promotion Center, funded by the government (Ministry of Science and ICT) in 2019. Highly pathogenic avian influenza (HPAI) is an acute infectious disease of birds caused by highly pathogenic avian influenza virus infection, causing serious damage to poultry such as chickens and ducks. High pathogenic avian influenza (HPAI) is caused by focusing on winter rather than year-round, and sometimes does not occur at all during a certain period of time. Due to these characteristics of HPAI, there is a problem that does not accumulate enough actual data. In this paper study, GAN network was utilized to generate actual similar data containing missing values and the process is introduced. The results of this study can be used to measure risk by generating realistic simulation data for certain times when HPAI did not occur.

• Smart Media Journal
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• v.10 no.1
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• pp.63-69
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• 2021

When a person sees a sentence and understands the sentence, the person understands the sentence by reminiscent of the main word in the sentence as an image. Text-to-image is what allows computers to do this associative process. The previous deep learning-based text-to-image model extracts text features using Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) and bi-directional LSTM, and generates an image by inputting it to the GAN. The previous text-to-image model uses basic embedding in text feature extraction, and it takes a long time to train because images are generated using several modules. Therefore, in this research, we propose a method of extracting features by using the attention mechanism, which has improved performance in the natural language processing field, for sentence embedding, and generating an image by inputting the extracted features into the GAN. As a result of the experiment, the inception score was higher than that of the model used in the previous study, and when judged with the naked eye, an image that expresses the features well in the input sentence was created. In addition, even when a long sentence is input, an image that expresses the sentence well was created.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.82.3-82.3
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• 2019

In this study, we generate future full disk magnetograms in 12, 24, 36 and 48 hours advance from SDO/HMI images using deep learning. To perform this generation, we apply the convolutional generative adversarial network (cGAN) algorithm to a series of SDO/HMI magnetograms. We use SDO/HMI data from 2011 to 2016 for training four models. The models make AI-generated images for 2017 HMI data and compare them with the actual HMI magnetograms for evaluation. The AI-generated images by each model are very similar to the actual images. The average correlation coefficient between the two images for about 600 data sets are about 0.85 for four models. We are examining hundreds of active regions for more detail comparison. In the future we will use pix2pix HD and video2video translation networks for image prediction.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.52.2-52.2
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• 2019

Global map of solar surface magnetic field, such as the synoptic map or daily synchronic frame, does not tell us real-time information about the far side of the Sun. A deep-learning technique based on Conditional Generative Adversarial Network (cGAN) is used to generate farside magnetograms from EUVI $304{\AA}$ of STEREO spacecrafts by training SDO spacecraft's data pairs of HMI and AIA $304{\AA}$. Farside(or backside) data of daily synchronic frames are replaced by the Ai-generated magnetograms. The new type of data is used to calculate the Potential Field Source Surface (PFSS) model. We compare the results of the global field with observations as well as those of the conventional method. We will discuss advantage and disadvantage of the new method and future works.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.439-441
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• 2020

본 논문에서는 국가무형문화재 기록영상의 화질 개선에 관한 연구를 진행한다. 기록영상의 화질 개선을 위해 SRGAN 기반의 초해상화 복원영상 생성 프레임워크의 적용을 제안한다. Image aumentation과 median filter를 적용한 데이터셋과 적대적 신경망인 Generative Adversarial Network (GAN)을 기반으로 딥러닝 네트워크를 구축하여 입력된 Low-Resolution 이미지를 통해 High-Resolution의 복원 영상을 생성한다. 이 연구를 통해 국가무형문화재 기록영상 뿐만 아니라 문화재 전반의 사진 및 영상 기록 자료의 품질 개선 가능성을 제시하고, 영상 기록 자료의 아카이브 구축을 통해 지속적인 활용의 기초연구가 되는 것을 목표로 한다.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.617-619
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• 2018

In this paper, We suggest a face image generating GAN model which is improved by an additive discriminator. This discriminator is trained to be specialized in preventing frequent mistake of generator. To verify the model suggested, we used $^*Inception$ score. We used 155,680 images of $^*celebA$ which is frontal face. We earned average 1.742p at Inception score and it is much better score compare to previous model.

• Journal of KIBIM
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• v.11 no.2
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• pp.54-59
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• 2021

In the process of designing a building, the process of expressing the designer's ideas through images is essential. However, it is expensive and time consuming for a designer to analyze every individual case image to generate a hypothetical design. This study aims to visualize the basic design draft sketch made by the designer as a real image using the Generative Adversarial Network (GAN) based on the continuously accumulated architectural case images. Through this, we proposed a method to build an automated visualization environment using artificial intelligence and to visualize the architectural idea conceived by the designer in the architectural planning stage faster and cheaper than in the past. This study was conducted using approximately 20,000 images. In our study, the GAN algorithm allowed us to represent primary materials and shades within 2 seconds, but lacked accuracy in material and shading representation. We plan to add image data in the future to address this in a follow-up study.