• 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.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.983-993
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• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1298-1311
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• 2022

Purpose To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively. Results The area under the receiver operating characteristic curve, negative predictive value (NPV), positive predictive value, sensitivity, and specificity were 0.856, 0.950, 0.579, 0.985, and 0.285, respectively. Conclusion Our deep learning-based artificial intelligence software in a semi-supervised manner achieved excellent performance in diagnosing scoliosis using the chest PA radiographs of young individuals; thus, it could be used as a screening tool with high NPV and sensitivity and reduce the burden on radiologists for diagnosing scoliosis through health screening chest radiographs.

• International Journal of Contents
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• v.16 no.2
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• pp.8-18
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• 2020

Data is critical in deep learning but the scarcity of data often occurs in research, especially in the preparation of the paired training data. In this paper, document image binarization with unpaired data is studied by introducing adversarial learning, excluding the need for supervised or labeled datasets. However, the simple extension of the previous unpaired training to binarization inevitably leads to poor performance compared to paired data training. Thus, a new deep learning approach is proposed by introducing a multi-diversity of higher quality generated images. In this paper, a two-stage model is proposed that comprises the generative adversarial network (GAN) followed by the U-net network. In the first stage, the GAN uses the unpaired image data to create paired image data. With the second stage, the generated paired image data are passed through the U-net network for binarization. Thus, the trained U-net becomes the binarization model during the testing. The proposed model has been evaluated over the publicly available DIBCO dataset and it outperforms other techniques on unpaired training data. The paper shows the potential of using unpaired data for binarization, for the first time in the literature, which can be further improved to replace paired data training for binarization in the future.

• 대한상한금궤의학회지
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• v.4 no.1
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• pp.13-28
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• 2012

Objective : The purpose of this study is to suggest medical approach to musculoskeletal system disorders using the decoction of Sanghan-Geumgwe. We studied cases of Ling-Gui-Gan-Zao-Tang prescribed patients to evaluate the clinical efficacy in musculoskeletal system disorders Method : We devised medical approach of Sanghan-Geumgwe in musculoskeletal disorders as follows. First, we chose ryeon-je(攣劑) and soo-je(水劑) herb medicine, commonly used in musculoskeletal disorders. In the selected herb group, we designated ryeon-je(攣劑) to be first key herbs, Soo-Je(水劑) as the second key herb, and other herb groups as third key herb. In this sequential selection and exclusion process, herbs were chosen based upon yak-neung-hyo-seon (藥能效選). Combination of those selected herbs drew pre-prescription group, finally prescription were made by the prescription criteria. Results : Based on the medical approach of decoctions of Sanghan-Geumgwe, we chose Ling-Gui-Gan-Zao-Tang to treat many kinds of musculoskeletal system disorders. And we achieved higher results on treatment for musculoskeletal system disorders. Conclusions : The medical approch using the decoctions of Sanghan-Geumgwe is very useful in choosing accurate prescriptions for patients with musculoskeletal system disorders in clinic.

• The Journal of Korean Medical History
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• v.23 no.1
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• pp.43-54
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• 2010

1. "GuGeupGanIBang(救急簡易方)" is a Korean annotation emergency treatment book made by scholars that were learned in medicine such as Naeuiwon(內醫院) head Yoo n Ho(尹壕) Seo Ha gun(西河君) Im Won jun(任元濬) GongJoChamPan(工曹參判) Park An sung(朴安性) Hanseongbu Jwayun(漢城府左尹) Gwon Geon(權健) SungRokDaeBuHaengByeongJoPanSeo(崇祿大夫行兵曹判書) YangCheonGun(陽川君) Heo Jong(許琮) following instructions of King Sungjong. This book was made by supplementing "EiBangRyuChwi(醫方類聚)", "HyangYakJeSengBang(鄕藥濟生方)" and "GuGeupBang(救急方)". When Yoon Ho presented it the king in May 1489(the 20th year of Sungjong), the king made the governors of each province publish it in large numbers, allowing common people to have this book and find the treatment immediately and save lives. 2. "GuGeupGanIBang(救急簡易方)" consists of 8 volumes, 127 chapters. Contents on stroke is the largest section. Separate chapters for gynecology and pediatrics let children that are easily ill and women that cannot get treatment freely be taken care of. It is an first aid medical book covering all ages, fulfilling its original purpose.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5427-5445
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• 2019

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.91-102
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• 2020

Recently, generative adversarial networks (GANs) is a field of research that has rapidly emerged wherein many studies conducted shows overwhelming results. Initially, this was at the level of imitating the training dataset. However, the GAN is currently useful in many fields, such as transformation of data categories, restoration of erased parts of images, copying facial expressions of humans, and creation of artworks depicting a dead painter's style. Although many outstanding research achievements have been attracting attention recently, GANs have encountered many challenges. First, they require a large memory facility for research. Second, there are still technical limitations in processing high-resolution images over 4K. Third, many GAN learning methods have a problem of instability in the training stage. However, recent research results show images that are difficult to distinguish whether they are real or fake, even with the naked eye, and the resolution of 4K and above is being developed. With the increase in image quality and resolution, many applications in the field of design and image and video editing are now available, including those that draw a photorealistic image as a simple sketch or easily modify unnecessary parts of an image or a video. In this paper, we discuss how GANs started, including the base architecture and latest technologies of GANs used in high-resolution, high-quality image creation, image and video editing, style translation, content transfer, and technology.

• Journal of architectural history
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• v.16 no.3
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• pp.115-131
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• 2007

Jeong Woo-tae(?-1809) was a military official who had worked as Byeol-Gan-Yeok in the governmental construction works during the late 18th century through the early 19th century. Byel-Gan-Yeok, literally a special technician, was an official post in the governmental construction works that carries specific technical tasks from the mid 18th century. Over 30 years, Jeong Woo-tae had devoted himself in the construction of various royal tombs, city walls, and palace buildings. He showed superb and various techniques in the works of stone carving and architectural details. After finishing the construction of the tomb of King Jeong-jo's father successfully, he was appointed as a governor of a rural town. Being on duty of the governor, he used to participate in the construction works as a technician. He also made a couple of innovative devices in the field of construction, like a carrying apparatus, Byel-Nok-No. His works secured the setting up of the post of Byel-Gan-Yeok in the governmental construction system in the 19th century. But his technical achievement remained as his own private works rather than developing to the universal growth of the craftsmen's skill. This might be a limitation of the Byel-gan-Yeok's role, whose position was remained in the midway between official and craftsman.

• Korean Journal of Acupuncture
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• v.32 no.2
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• pp.75-79
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• 2015

Objectives : This study is to report one case of the diabetic ophthalmoplegia by acupuncture at Jok-Gwoleum-Gan-Gyeong. Methods : The patient was treated with acupuncture and herbal medicine for about 5 weeks. We evaluated the results of the treatment by observing the patient's symptoms. Results : After acupuncture the patient's symptoms such as strabismus, headache, ptosis, Rt eye pain and discomfort and both shoulder pain were considerably reduced. Conclusions : These results support that acupuncture at Jok-Gwoleum-Gan-Gyeong can have a meaningful effect in improving symptoms of diabetic ophthalmoplegia.