• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.499-509
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• 2020

This study explores how to build object classification learning data based on artificial intelligence. The data has been investigated recently in image classification fields and, in turn, has a great potential to use. In order to recognize and extract relatively accurate objects using artificial intelligence, a large amount of learning data is required to be used in artificial intelligence algorithms. However, currently, there are not enough datasets for object recognition learning to share and utilize. In addition, generating data requires long hours of work, high expenses and labor. Therefore, in the present study, a small amount of initial aerial image learning data was used in the GAN (Generative Adversarial Network)-based generator network in order to establish image learning data. Moreover, the experiment also evaluated its quality in order to utilize additional learning datasets. The method of oversampling learning data using GAN can complement the amount of learning data, which have a crucial influence on deep learning data. As a result, this method is expected to be effective particularly with insufficient initial datasets.

• Journal of IKEEE
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• v.26 no.2
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• pp.176-185
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• 2022

Data augmentation is general approach to solve overfitting of machine learning models by applying various data transformations and distortions to dataset. However, when data augmentation is applied in GAN-based model, which is deep learning image generation model, data transformation and distortion are reflected in the generated image, then the generated image quality decrease. To prevent this problem called augmentation leak, we propose a scheme that can prevent augmentation leak regardless of the type and number of augmentations. Specifically, we analyze the conditions of augmentation leak occurrence by type and implement auxiliary augmentation task classifier that can prevent augmentation leak. Through experiments, we show that the proposed technique prevents augmentation leak in the GAN model, and as a result improves the quality of the generated image. We also demonstrate the superiority of the proposed scheme through ablation study and comparison with other representative augmentation leak prevention technique.

• Journal of the Korea Computer Graphics Society
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• v.29 no.4
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• pp.7-16
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• 2023

For decades, creating a desired locomotive motion in a goal-oriented manner has been a challenge in character animation. Data-driven methods using generative models have demonstrated efficient ways of predicting long sequences of motions without the need for explicit conditioning. While these methods produce high-quality long-term motions, they can be limited when it comes to synthesizing motion for challenging novel scenarios, such as punching a random target. A state-of-the-art solution to overcome this limitation is by using a GAN Discriminator to imitate motion data clips and incorporating reinforcement learning to compose goal-oriented motions. In this paper, our research aims to create characters performing combat sports such as boxing, using a novel reward design in conjunction with existing GAN-based approaches. We experimentally demonstrate that both the Adversarial Motion Prior [3] and Adversarial Skill Embeddings [4] methods are capable of generating viable motions for a character punching a random target, even in the absence of mocap data that specifically captures the transition between punching and locomotion. Also, with a single learned policy, multiple task controllers can be constructed through the TimeChamber framework.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.222-227
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• 2022

In this paper, we implemented a Korean text generation and classification model based on a deep learning algorithm that can be applied to various industries. It consists of two implemented GAN-based Korean handwriting generation models and CNN-based Korean handwriting classification models. The GAN model consists of a generator model for generating fake Korean handwriting data and a discriminator model for discriminating fake handwritten data. In the case of the CNN model, the model was trained using the 'PHD08' dataset, and the learning result was 92.45. It was confirmed that Korean handwriting was classified with % accuracy. As a result of evaluating the performance of the classification model by integrating the Korean cursive data generated through the implemented GAN model and the training dataset of the existing CNN model, it was confirmed that the classification performance was 96.86%, which was superior to the existing classification performance.

• The Korean Journal of Nuclear Medicine Technology
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• v.24 no.1
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• pp.15-19
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• 2020

Purpose Generative Adversarial Network(GAN) is one of deep learning technologies. This is a way to create a real fake image after learning the real image. In this study, after acquiring artificial intelligence images through GAN, We were compared and evaluated with real scan time images. We want to see if these technologies are potentially useful. Materials and Methods 30 patients who underwent ¹⁸F-FDG Brain PET/CT scanning at Severance Hospital, were acquired in 15-minute List mode and reconstructed into 1,2,3,4,5 and 15minute images, respectively. 25 out of 30 patients were used as learning images for learning of GAN and 5 patients used as verification images for confirming the learning model. The program was implemented using the Python and Tensorflow frameworks. After learning using the Pix2Pix model of GAN technology, this learning model generated artificial intelligence images. The artificial intelligence image generated in this way were evaluated as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR), and Structural Similarity Index(SSIM) with real scan time image. Results The trained model was evaluated with the verification image. As a result, The 15-minute image created by the 5-minute image rather than 1-minute after the start of the scan showed a smaller MSE, and the PSNR and SSIM increased. Conclusion Through this study, it was confirmed that AI imaging technology is applicable. In the future, if these artificial intelligence imaging technologies are applied to nuclear medicine imaging, it will be possible to acquire images even with a short scan time, which can be expected to reduce artifacts caused by patient movement and increase the efficiency of the scanning room.

• 대한상한금궤의학회지
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• v.4 no.1
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• pp.1-12
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• 2012

Objective : The purpose of this study is to find out the effectiveness of Ling-Gui-Gan-Zao-Tang for patients of panic disorder. Method : To achieve the purpose of this study, Ling-Gui-Gan-Zao-Tang was prescribed for three months to two different patients of panic disorder. They were diagnosed as panic disorder in department of neuropsychiatry, and had no other prescribed decoction or psychotherapy. Results : 1. The BAI score for anxiety were decreased in both patients, and they got improved overall symptoms. 2. In panic attack, patients are in dominant state of sympathetic nerve, so they have palpitaion and get nervous. Fu-Ling(茯笭) can treate this kind of situation. 3. Based on and , urgent situation, over-tension of muscles, hot flash can be treated Gancao(甘草), Dazao(大棗), Guizhi(桂枝) respectively. Conclusions : When panic disorder attaks, the sympathetic nerves are dominant in patient's body. So they feel palpitating, sweating, suffocating. Ling-Gui-Gan-Zao-Tang can treat this series of symptoms.

• The Journal of Pediatrics of Korean Medicine
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• v.27 no.4
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• pp.1-9
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• 2013

Objectives The purpose of this study is to report the cases of two pediatric patients diagnosed as Sik-gan (食癎), a kind of epileptic seizure thought to be caused by uncontrolled consumption of food, treated by formula variation of Samchulkunbi-tang (蔘朮健脾湯). Methods Two pediatric patients diagnosed as Sik-gan (食癎) according to traditional Korean medical terms were administered by variety of Samchulkunbi-tangs (蔘朮健脾湯) while correcting unhealthy eating habit. To measure the degree of the patients' process, the number and exact symptoms of seizure events, and gastrointestinal symptoms were recorded. Results The treatment of various Samchulkunbi-tang (蔘朮健脾湯) was not only extended remission period of seizure, but also improved gastrointestinal symptoms on both of the patients. Conclusions Pediatric patients who have past medical conditions for epileptic seizure as well as unhealthy eating habits or gastrointestinal malfunctions are prone to have a specific form of seizure called the Sik-gan (食癎). In this report, we have proven that variety of Samchulkunbi-tang (蔘朮健脾湯) can considerably be effective in improving the patients' gastrointestinal symptoms and preventing recurring seizure events.

• Journal of Korean Medical classics
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• v.20 no.2
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• pp.195-200
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• 2007

Lee Je-Ma's theory claimed Xin(心) as Qi(氣). But he also described Xin as Taiji(太極) in the center as if Xin combines LI(理) and Qi. Taiji is meant to be a residence of the body, but it does not mean that Xin equals Li. The relations between Xin and the remaining four viscera are similar to the relations between RenXin(人心) and DaeXin(道心) and Zhuzi's(朱子) theory in many respects. If the theory that Xin equals Daoxan and FeiPiGanShen(肺脾肝腎) equals RenXin is acceptable, the vertical relations between Xin and FeiPiGanShen can be explained. That is, Xin is explained as the controller of the body, and FeiPiGanShen acts as a subordinate serving Xin. In other words, the relations associated with Xin can explain physiological states of the body. When the Xin does not function normally, FeiPiGanShen cannot perform its roles and will have a negative impact on physiological functions of the body.

• The Journal of Pediatrics of Korean Medicine
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• v.30 no.3
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• pp.61-68
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• 2016

Objectives The purpose of this study is to report a case of one infant patient diagnosed as Sik-Gan (食癎) who was treated by Korean medical treatment. Methods We diagnosed an infant patient as Sik-Gan (食癎) and treated him with herbal medicine, acupuncture, moxa and chuna therapy while correcting his eating habit. To measure the degree of the patient's progress, the frequency and exact symptoms of seizure events, and gastrointestinal symptoms were recorded. Results Korean medical treatment reduced the patient's the frequency of seizure, and improved gastrointestinal symptoms. Conclusions A patient who has past medical history of epileptic seizures, unhealthy eating habits and gastrointestinal malfunctions is prone to have a specific form of seizure called the Sik-Gan (食癎). In this report, we have proven that variety of Korean medical treatment can considerably be effective in preventing recurring seizure events and improving the patients' gastrointestinal symptoms.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.799-802
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• 2017

We attempt to combines concepts of Morphological Operator(MO) and Convolutional Neural Networks(CNN) to improve image-to-image translation. To do this, we propose an operation that approximates morphological operations. Also we propose S-Convolution, an operation that extends the operation to use multiple filters like CNN. The experiment result shows that it can learn MO with big filter using multiple S-convolution layer of small filter. To validate effectiveness of the proposed layer in image-to-image translation we experiment with GAN with S-convolution applied. The result showed that GAN with S-convolution can achieve distinct result from that of GAN with CNN.