• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.06a
• /
• pp.98-101
• /
• 2019

At present, deep convolutional neural networks have made a very important contribution in single-image super-resolution. Through the learning of the neural networks, the features of input images are transformed and combined to establish a nonlinear mapping of low-resolution images to high-resolution images. Some previous methods are difficult to train and take up a lot of memory. In this paper, we proposed a simple and compact deep recursive residual network learning the features for single image super resolution. Global residual learning and local residual learning are used to reduce the problems of training deep neural networks. And the recursive structure controls the number of parameters to save memory. Experimental results show that the proposed method improved image qualities that occur in previous methods.

• Journal of the Korean Applied Science and Technology
• /
• v.36 no.3
• /
• pp.797-803
• /
• 2019

Recently, osteosarcopenic obesity (OSO) has been identified and notified world wide. Therefore, this study reviewed OSO related to lifestyle factors such as nutritional intake and exercise. Due to aging, OSO may be initiated by dietary factors and obesity related factors. Reduced muscle mass and increased fat mass may negatively impact bone health causing OSO. The complication of OSO development should be related to dietary imbalance combined with declined exercise and this may contribute to induce OSO by decreasing bone mass, muscle mass, and increasing obesity with aging. To prevent OSO, reaching peak bone mass and building optimal muscle and fat mass through exercise would be recommended. For treating OSO, balanced dietary intake and regular exercise through a whole life would be needed. In addition, sufficient carbohydrate and fat intake for minimizing protein catabolism would be recommended to prevent OSO. The combination of aerobic exercise and resistance training also would be an effective intervention for OSO population.

• PNF and Movement
• /
• v.11 no.1
• /
• pp.1-6
• /
• 2013

Purpose : The purpose of this study was to investigate the effect of sling exercise with PNF basic procedure in patients with chronic low back pain. Methods : This study included 14 patients with chronic low back pain, who were performed sling exercise combined PNF basic procedure. The exercise program comprised 3 sessions of 30 minutes per week for 8 weeks. The VAS(Visual analogue Scale) and BBS(Berg Balance Scale) were evaluated before and after training. All data were analyzed using SPSS 12.0. Results : Significant differences were observed the chronic low back pain patient for VAS, BBS. Chronic low back pain patient improved all test. Conclusion : Sling exercise with PNF basic procedure about chronic low back pain patient is very useful and effective. It is effective in clinical practice.

• Journal of Information Processing Systems
• /
• v.17 no.3
• /
• pp.453-461
• /
• 2021

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch self-supervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.18 no.4
• /
• pp.31-40
• /
• 2022

YOLOv4 can be used for detecting parking vehicles in order to check a vehicle in out-door parking space. YOLOv4 has 9 anchor boxes in each of 13x13 grid cells for detecting a bounding box of object. Because anchor boxes are allocated based on each cell, there can be existed small observational error for detecting real objects due to the distance between neighboring cells. In this paper, we proposed YOLOv4 grid cell shift algorithm for improving the out-door parking vehicle detection accuracy. In order to get more chance for trying to object detection by reducing the errors between anchor boxes and real objects, grid cells over image can be shifted to vertical, horizontal or diagonal directions after YOLOv4 basic detection process. The experimental results show that a combined algorithm of a custom trained YOLOv4 and a cell shift algorithm has 96.6% detection accuracy compare to 94.6% of a custom trained YOLOv4 only for out door parking vehicle images.

• Journal of Advanced Information Technology and Convergence
• /
• v.10 no.1
• /
• pp.111-120
• /
• 2020

Generative adversarial networks (GANs) achieved impressive performance on image generation and visual classification applications. However, adversarial networks meet difficulties in combining the generative model and unstable training process. To overcome the problem, we combined the deep residual network with upsampling convolutional layers to construct the generative network. Moreover, the study shows that image generation and classification performance become more prominent when the residual layers include on the generator. The proposed network empirically shows that the ability to generate images with higher visual accuracy provided certain amounts of additional complexity using proper regularization techniques. Experimental evaluation shows that the proposed method is superior to image generation and classification tasks.

• International journal of advanced smart convergence
• /
• v.11 no.2
• /
• pp.13-21
• /
• 2022

In this paper, we propose a learning based joint demosaicing and super-resolution framework which uses only the mosaiced color filter array(CFA) image as the input. As the proposed method works only on the mosaicied CFA image itself, there is no need for a large dataset. Based on our framework, we proposed two different structures, where the first structure uses one deep image prior network, while the second uses two. Experimental results show that even though we use only the CFA image as the training image, the proposed method can result in better visual quality than other bilinear interpolation combined demosaicing methods, and therefore, opens up a new research area for joint demosaicing and super-resolution on raw images.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.627-630
• /
• 2022

Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

• Journal of information and communication convergence engineering
• /
• v.21 no.2
• /
• pp.167-173
• /
• 2023

Hangul is unique compared to other Asian languages because of its simple letter forms that combine to create syllabic shapes. There are 24 basic letters that can be combined to form 27 additional complex letters. This produces 51 graphemes. Hangul optical character recognition has been a research topic for some time; however, handwritten Hangul recognition continues to be challenging owing to the various writing styles, slants, and cursive-like nature of the handwriting. In this study, a dataset containing thousands of samples of 51 Hangul graphemes was gathered from 110 freshmen university students to create a robust dataset with high variance for training an artificial neural network. The collected dataset included 2200 samples for each consonant grapheme and 1100 samples for each vowel grapheme. The dataset was normalized to the MNIST digits dataset, trained in three neural networks, and the obtained results were compared.

• Journal of Korean Neurosurgical Society
• /
• v.66 no.3
• /
• pp.289-297
• /
• 2023

Technological advances in neonatology led to the improvement of the survival rate in preterm babies with very low birth weights. However, intraventricular hemorrhage (IVH) has been one of the major complications of prematurity. IVH is relevant to neurodevelopmental disorders, such as cerebral palsy, language and cognitive impairments, and neurosensory and psychiatric problems, especially when combined with brain parenchymal injuries. Additionally, severe IVH requiring shunt insertion is associated with a higher risk of adverse neurodevelopmental outcomes. Multidisciplinary and longitudinal rehabilitation should be provided for these children based on the patients' life cycles. During the infantile period, it is essential to detect high-risk infants based on neuromotor examinations and provide early intervention as soon as possible. As babies grow up, close monitoring of language and cognitive development is needed. Moreover, providing continuous rehabilitation with task-specific and intensive repetitive training could improve functional outcomes in children with mild-to-moderate disabilities. After school age, maintaining the level of physical activity and managing complications are also needed.