• Korean Journal of Medicinal Crop Science
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• v.25 no.3
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• pp.165-174
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• 2017

Background: Traditional plant drugs, are less toxic and free from side effects compared to general synthetic drugs. They have been used for the treatment of diabetes and associated renal damage. In this study, we evaluated effect of Hachimi-jio-gan against diabetic renal damage in a rat model of type 1 diabetic nephropathy induced by subtotal nephrectomy plus streptozotocin (STZ) injection, and in Otsuka Long-Evans Tokushima Fatty (OLETF) rats and db/db mice as a model of human type 2 diabetes, and its associated complications. To explore the active components of Hachimi-jio-gan, the antidiabetic effect of corni fructus, a consituent of Hachimi-jio-gan, and 7-O-galloyl-${{\small}D}$-sedoheptulose, a phenolic compound isolated from corni fructus, were investigated. Methods and Results: We conducted an extensive literature search, and all required data were collected and systematically organized. The findings were reviewed and categorized based on relevance to the topic. A summary of all the therapeutic effects were reported as figures and tables. Conclusions: Hachimi-jio-gan serves as a potential therapeutic agent to against the development of type 1 and type 2 diabetic nephropathy. From the results of characterization active components of corni fructus, 7-O-galloyl-${\small}D$-sedoheptulose is considered to play an important role in preventing and/or delaying the onset of diabetic renal damage. 7-O-Galloyl-${\small}D$-sedoheptulose is expected to serve as a novel therapeutic agent against the development of diabetic nephropathy.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.9-18
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• 2021

Although Generative Adversarial Networks (GANs) have gained great popularity in computer vision and related fields, generating audio signals independently has yet to be presented. Unlike images, an audio signal is a sampled signal consisting of discrete samples, so it is not easy to learn the signals using CNN architectures, which is widely used in image generation tasks. In order to overcome this difficulty, GAN researchers proposed a strategy of applying time-frequency representations of audio to existing image-generating GANs. Following this strategy, we propose an improved method for increasing the fidelity of synthesized audio signals generated by using GANs. Our method is demonstrated on a public speech dataset, and evaluated by Fréchet Inception Distance (FID). When employing our method, the FID showed 10.504, but 11.973 as for the existing state of the art method (lower FID indicates better fidelity).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.842-848
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• 2020

Music markets have entered the era of streaming. In order to select and propose music that suits the taste of music consumers, there is an active demand and research on an automatic music genre classification system. We propose a method to improve the accuracy of genre unclassified songs, which was a lack of the previous system, by using a generative adversarial network (GAN) to further develop the automatic voting system for deep learning music genre using Softmax proposed in the previous paper. In the previous study, if the spectrogram of the song was ambiguous to grasp the genre of the song, it was forced to leave it as an unclassified song. In this paper, we proposed a system that increases the accuracy of genre classification of unclassified songs by converting the spectrogram of unclassified songs into an easy-to-read spectrogram using GAN. And the result of the experiment was able to derive an excellent result compared to the existing method.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.322-330
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• 2021

In modern aquaculture, mass mortality is a very important issue that determines the success of aquaculture business. If a fish disease is not detected at an early stage in the farm, the disease spreads quickly because the farm is a closed environment. Therefore, early detection of diseases is crucial to prevent mass mortality of fish raised in farms. Recently deep learning-based automatic identification of fish diseases has been widely used, but there are many difficulties in identifying objects due to insufficient images of fish diseases. Therefore, this paper suggests a method to generate a large number of fish disease images by synthesizing normal images and disease images using SinGAN deep learning model in order to to solve the lack of fish disease images. We generate images from the three most frequently occurring Paralichthys Olivaceus diseases such as Scuticociliatida, Vibriosis, and Lymphocytosis and compare them with the original image. In this study, a total of 330 sheets of scutica disease, 110 sheets of vibrioemia, and 110 sheets of limphosis were made by synthesizing 10 disease patterns with 11 normal halibut images, and 1,320 images were produced by quadrupling the images.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.131-138
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• 2023

This paper is related to performance improvement of Image-to-Image translation using Relativistic Average Patch GAN and Residual in Residual Dense Block. The purpose of this paper is to improve performance through technical improvements in three aspects to compensate for the shortcomings of the previous pix2pix, a type of Image-to-Image translation. First, unlike the previous pix2pix constructor, it enables deeper learning by using Residual in Residual Block in the part of encoding the input image. Second, since we use a loss function based on Relativistic Average Patch GAN to predict how real the original image is compared to the generated image, both of these images affect adversarial generative learning. Finally, the generator is pre-trained to prevent the discriminator from being learned prematurely. According to the proposed method, it was possible to generate images superior to the previous pix2pix by more than 13% on average at the aspect of FID.

• Journal of IKEEE
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• v.27 no.4
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• pp.464-471
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• 2023

In this paper, we propose a method to reduce age distortion in facial expression image generation using StyleGAN Encoder. The facial expression image generation process first creates a face image using StyleGAN Encoder, and changes the expression by applying the learned boundary to the latent vector using SVM. However, when learning the boundary of a smiling expression, age distortion occurs due to changes in facial expression. The smile boundary created in SVM learning for smiling expressions includes wrinkles caused by changes in facial expressions as learning elements, and it is determined that age characteristics were also learned. To solve this problem, the proposed method calculates the correlation coefficient between the smile boundary and the age boundary and uses this to introduce a method of adjusting the age boundary at the smile boundary in proportion to the correlation coefficient. To confirm the effectiveness of the proposed method, the results of an experiment using the FFHQ dataset, a publicly available standard face dataset, and measuring the FID score are as follows. In the smile image, compared to the existing method, the FID score of the smile image generated by the ground truth and the proposed method was improved by about 0.46. In addition, compared to the existing method in the smile image, the FID score of the image generated by StyleGAN Encoder and the smile image generated by the proposed method improved by about 1.031. In non-smile images, compared to the existing method, the FID score of the non-smile image generated by the ground truth and the method proposed in this paper was improved by about 2.25. In addition, compared to the existing method in non-smile images, it was confirmed that the FID score of the image generated by StyleGAN Encoder and the non-smile image generated by the proposed method improved by about 1.908. Meanwhile, as a result of estimating the age of each generated facial expression image and measuring the estimated age and MSE of the image generated with StyleGAN Encoder, compared to the existing method, the proposed method has an average age of about 1.5 in smile images and about 1.63 in non-smile images. Performance was improved, proving the effectiveness of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.359-364
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• 2022

The change detection in remote sensing results in errors due to the camera's optical factors, seasonal factors, and land cover characteristics. The inclination of the building in the image was simulated according to the camera angle using the Cycle Generative Adversarial Network method, and the simulated image was used to contribute to the improvement of change detection accuracy. Based on CycleGAN, the inclination of the building was similarly simulated to the building in the other image based on the image of one of the two periods, and the error of the original image and the inclination of the building was compared and analyzed. The experimental data were taken at different times at different angles, and Kompsat-3A high-resolution satellite images including urban areas with dense buildings were used. As a result of the experiment, the number of incorrect detection pixels per building in the two images for the building area in the image was shown to be reduced by approximately 7 times from 12,632 in the original image and 1,730 in the CycleGAN-based simulation image. Therefore, it was confirmed that the proposed method can reduce detection errors due to the inclination of the building.

• The Journal of Bigdata
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• v.7 no.1
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• pp.173-185
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• 2022

Nowadays, Korean webtoons are leading the global digital comic market. Webtoons are being serviced in various languages around the world, and dramas or movies produced with Webtoons' IP (Intellectual Property Rights) have become a big hit, and more and more webtoons are being visualized. However, with the success of these webtoons, the working environment of webtoon creators is emerging as an important issue. According to the 2021 Cartoon User Survey, webtoon creators spend 10.5 hours a day on creative activities on average. Creators have to draw large amount of pictures every week, and competition among webtoons is getting fiercer, and the amount of paintings that creators have to draw per episode is increasing. Therefore, this study proposes to generate webtoon background images using deep learning algorithms and use them for webtoon production. The main character in webtoon is an area that needs much of the originality of the creator, but the background picture is relatively repetitive and does not require originality, so it can be useful for webtoon production if it can create a background picture similar to the creator's drawing style. Background generation uses CycleGAN, which shows good performance in image-to-image translation, and CartoonGAN, which is specialized in the Cartoon style image generation. This deep learning-based image generation is expected to shorten the working hours of creators in an excessive work environment and contribute to the convergence of webtoons and technologies.

• The Journal of Korean Medical History
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• v.26 no.1
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• pp.87-96
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• 2013

This study confirmed that a doctor named Lee, Seok-gan whose name has been widely known but whose real identity has remained unclear, was an active Confucian doctor in the 16th century. In addition, through the newly discovered "Daeyakbu" among his family line, writings, and relics that have been handed down in a family, this study looked into his medical philosophy and medicine culture. The author of "Ieseokgangyeongheombang"(Medical Book by Lee, Seok-gan(李石澗), Seok-gan is the same person as an active famous doctor Lee, Seok-gan(李碩幹, 1509-1574) in the 16th century. Such a fact can be confirmed through "Samuiilheombang", "Sauigyeongheombang" and the newly opened "Ieseokgangyeongheombang". Lee, Seok-gan was born in the 4th ruling year of king Jungjong (1509) and was active as a doctor until the 7th ruling year of king Seonjo(1547); his first name is Jungim with the pen name-Chodang, and he used a doctor name of 'Seokgan.' He was known as a divine doctor, and there have been left lots of anecdotes in relation with Lee, Seok-gan. Legend has it that Seokgan went to China to give treatment to the empress, and a heavenly peach pattern drinking cup and a house, which the emperor bestowed on Seokgan in return for his great services, still have remained up to the present. Usually, Seokgan interacted with Toegye Lee Hwang and his literary persons, and with his excellent medical skills, Seokgan once gave treatment to Toegye at the time of his death free of charge. His medical skills have been handed down in his family, and his descendant Lee, Ui-tae(around 1700) compiled a medical book titled "Gyeongheombangwhipyeon(經驗方彙編)". Out of Lee, Seok-gan's keepsakes which were donated to Sosu museums by his descendant family, 4 sorts of 'Gwabu'(writings of fruit trees) including "Daeyakbu" were discovered. It's rare to find a literary work left by a medical figure like this, so these discoveries have a deep meaning even from a medicine culture level. Particularly, "Daeyakbu" includes the typical "Uigukron". The "Uigukron", which develops its story by contrasting politics with medicine, has a unique writing style as one of the representative explanatory methods of scholars' position during the Joseon Dynasty; in addition, the distinctive feature of "Uigukron" is that it was created in the form of 'Gabu' other than a prose.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.