• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.107-117
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• 2014

The importance of a network security to protect infringement of corporate assets has been issued due to the increasing various threats such as warm virus, vicious codes, and hacking. Thus, the goal of this research is to discover the procedure and methods for a NAC system implementation. In this case study, we suggest that the critical management issues during the implementing a NAC system as well as measure its performance in qualitative and quantitative perspective. The contribution of this paper is both to lead to the further research in this network security field and to provide a guideline for companies willing to introduce a NAC system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1588-1594
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• 2020

In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.4
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• pp.628-641
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• 2011

Focusing on the photonics industry as a new one and the photonics industry agglomeration in Gwangju, this study investigated the regional characteristics of the photonics industry's growth. To analyze the location and business network of photonics firms as foundation of industrial growth, in-depth interviews were conducted. It also aimed to review the meanings of regional policy to promote a strategic industry. As a result, it founded the differential growth of the photonics industry in the Capital region(with a market advantage) and Gwangju(with a policy advantage). Gwangju's regional policy had an importance that it formed a regional consensus and increased the momentum and variety to drive regional industrial development.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.503-510
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• 2018

Restoring of damaged images is a fundamental problem that was attempted before digital image processing technology appeared. Various algorithms for reconstructing damaged images have been introduced. However, the results show inferior restoration results compared with manual restoration. Recent developments of DNN (Deep Neural Network) have introduced various studies that apply it to image restoration. However, if the wide area is damaged, it can not be solved by a general interpolation method. In this case, it is necessary to reconstruct the damaged area through contextual information of surrounding images. In this paper, we propose an image restoration network using a generative adversarial network (GAN). The proposed system consists of image generation network and discriminator network. The proposed network is verified through experiments that it is possible to recover not only the natural image but also the texture of the original image through the inference of the damaged area in restoring various types of images.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.124-131
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• 2023

Object detection techniques are increasingly used to obtain information on physical characteristics or situations of a specific area from remote images. The accuracy of object detection is decreased in remote sensing images with low resolution because the low resolution reduces the amount of detail that can be captured in an image. A single neural network is proposed to joint the super-resolution method and object detection method. The proposed method constructs a deep residual-based network to restore object features in low-resolution images. Moreover, the proposed method is used to improve the performance of object detection by jointing a single network with YOLOv5. The proposed method is experimentally tested using VEDAI data for low-resolution images. The results show that vehicle detection performance improved by 81.38% on mAP@0.5 for VISIBLE data.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1109-1116
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• 2023

Object tracking is used to track a goal in a video sequence by using coordinate information provided as annotation in the first frame of the video. In this paper, we propose a tracking algorithm that combines deep features and region inference modules to improve object tracking accuracy. In order to obtain sufficient object information, a convolution neural network was designed with a Siamese network structure. For object region inference, the region proposal network and overlapping confidence module were applied and used for tracking. The performance of the proposed tracking algorithm was evaluated using the Object Tracking Benchmark dataset, and it achieved 69.1% in the Success index and 89.3% in the Precision Metrics.

• Review of KIISC
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• v.29 no.6
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• pp.59-66
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• 2019

사물인터넷 환경은 무수히 많은 이기종의 기기가 연결되는 초연결 네트워크 구성을 갖는 특성이 있다. 본 논문에서는 이러한 특성을 갖는 사물인터넷 환경에 적합한 보안 기술로 네트워크를 통해 침입하는 위협의 효율적인 탐지 기술을 제안한다. 사물인터넷 환경에서의 대표적인 위협 행위를 분석하고 관련하여 공격 데이터를 수집하고 이를 토대로 특성 연구를 진행하였다. 이를 기반으로 인공신경망 기반의 오토인코더 알고리즘을 활용하여 심층학습 탐지 모델을 구축하였다. 본 논문에서 제안하는 탐지 모델은 비지도 학습 방식의 오토인코더를 지도학습 기반의 분류기로 확장하여 사물인터넷 환경에서의 대표적인 위협 유형을 식별할 수 있었다. 본 논문은 1. 서론을 통해 현재 사물인터넷 환경과 보안 기술 연구 동향을 소개하고 2. 관련연구를 통하여 머신러닝 기술과 위협 탐지 기술에 대해 소개한다. 3. 제안기술에서는 본 논문에서 제안하는 인공신경망 알고리즘 기반의 사물인터넷 위협 탐지 기술에 대해 설명하고, 4. 향후연구계획을 통해 추후 활용 방안 및 고도화에 대한 내용을 작성하였다. 마지막으로 5. 결론을 통하여 제안기술의 평가와 소회에 대해 설명하였다.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.733-743
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• 2006

블로그는 현재 광범위하게 대중성을 얻고 있는 온라인 커뮤니케이션의 최근 형태이며, 블로그 사용이 빠른 속도로 주류를 형성해 나가고 있다. 초기 HCI 분야에서의 온라인 커뮤니케이션(CMC)에 대한 연구는 사용자가 시스템의 소프트웨어나 하드웨어의 인터페이스를 어떻게 대하는가 하는 점에 초점이 맞춰졌었지만, 최근에는 온라인 커뮤니티, 블로그처럼 사용자들이 인터넷을 통한 사회화가 어느 때보다 활발하게 이루어지고 있기 때문에, 시스템의 사용성에 대한 고려만으로는 충분하지가 않다. 즉, 시스템이 어떻게 사용자들 간에 사회적 상호 교류를 지원할 수 있고, 이러한 사회적 교류가 원활하게 이루어지기 위해서 시스템이 어떻게 설계되어야 하는 지에 대한 이해가 필요하다. 따라서 본 연구에서는 실제 블로그 이용자들을 대상으로 이들과 상호 연결된 블로거들과의 온라인에서의 관계적 행위를 살펴보고, 또한 이러한 행위가 네트워크 구조적 특성과는 어떠한 상관 관계를 가지는지를 심층 인터뷰와 사회 네트워크 분석법을 이용하여 탐색적으로 알아보고자 한다. 본 연구 결과를 통해서 얻은 사용자들의 온라인 교류 시 보이는 행위적 특성을 바탕으로 온라인 개인 미디어 서비스 설계에 대한 가이드라인을 제안해 보고자 한다.