• Informatization Policy
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• v.25 no.1
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• pp.3-29
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• 2018

The Federal Communications Commission voted to repeal net neutrality protections on December 14, 2017. This is the very opposite decision of the net neutrality rule that the Obama administration has consistently maintained. The ensuing storm from the repeal of net neutrality protections has an extensively effect enough on individuals and businesses to cover the entire spectrum, and the impact is hard to assess in the U. S. content industry, which dominates the worldwide Internet content and platform market. On the other hand, Korea's net neutrality protections have been firmly pursued, and there is no sign of change even after the decision happened in the U. S. Net neutrality is not a simple theme that is associated with the Constitution, such as freedom of expression, as well as the issue of network enhancement to prepare for 5G. Accordingly, this study examines how the net neutrality has been carried out in the U. S. and Korea over the years, and provides the issues of Internet enhancement, perspectives of ISP and ICP, and implications for the Constitution, market economy, fair competition and zero rating. This research delivers future direction and implications of domestic net neutrality policies.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.33-42
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• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.431-439
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• 2007

In the ubiquitous which is rapidly developing with ultra speed these days, constructing the u-Campus which is using the sensor-network as its base is keep developing. Also, any information related equipments like PC and mobile, computing is possible whenever and wherever you want and due to the development of the wireless network, the service environment is continually developing. In this article, I'd like to suggest the u-Campus which is very suitable to the user's environment which had used application of mobile. In u-Campus, various techniques are adopted and applied along with development of info-communication related technidques. Especially, the new type of campus which is constructed by adopting the ubiquitous computing net-work technique to the campus of university is the u-Campus. In this article, by suggesting u-Yuhan designing methods, more effective and advanced school activities of students to make possible.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.326-334
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• 2024

Extra-oral imaging techniques such as Panoramic X-rays (PXs) and Cone Beam Computed Tomography (CBCT) are the most preferred imaging modalities in dental clinics owing to its patient convenience during imaging as well as their ability to visualize entire teeth information. PXs are preferred for routine clinical treatments and CBCTs for complex surgeries and implant treatments. However, PXs are limited by the lack of third dimensional spatial information whereas CBCTs inflict high radiation exposure to patient. When a PX is already available, it is beneficial to reconstruct the 3D oral structure from the PX to avoid further expenses and radiation dose. In this paper, we propose 3DentAI - an U-Net based deep learning framework for 3D reconstruction of oral structure from a PX image. Our framework consists of three module - a reconstruction module based on attention U-Net for estimating depth from a PX image, a realignment module for aligning the predicted flattened volume to the shape of jaw using a predefined focal trough and ray data, and lastly a refinement module based on 3D U-Net for interpolating the missing information to obtain a smooth representation of oral cavity. Synthetic PXs obtained from CBCT by ray tracing and rendering were used to train the networks without the need of paired PX and CBCT datasets. Our method, trained and tested on a diverse datasets of 600 patients, achieved superior performance to GAN-based models even with low computational complexity.

• Smart Media Journal
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• v.13 no.4
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• pp.86-93
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• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• Spatial Information Research
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• v.22 no.1
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• pp.19-26
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• 2014

Recently, ICT environments have been increasingly developed and the pattern of the war also has been changed to NCW. The development of communication and network technology, for example, C4I and TDL(Tactical Data Link), has been prosperous and rapid. But the geospatial intelligence field which is the basis of the network frames relatively has not been developed. The purpose of this paper is to foster the geospatial governance in terms of the defense perspective. In order to do that, this paper deals with the U.S. Intelligence Community(IC) and the U.S. Army Global Information Grid(GIG), LandWarNet and those could be good examples of roles and statuses of geospatial intelligence. IC has been produced essential intelligence which is required for policymakers and military leaders. IC has several stove-piped intelligence process systems which have been separately developed and competed. And so as to complete GIG, the U.S. Army adopted LandWarNet. The U.S. Corps of Engineers organized the Army Geospatial Center(AGC) on 1 October 2009 to support LandWarNet. In order to develop NCW, we should recognize geospatial intelligence as the basis of network framework and make a central leading organization of defense geospatial intelligence. The mission of Korea Defense Geospatial-Intelligence Agency should be changed from producing GEOINT to a strategic GEOINT agency. The Army should organize a laboratory of geospatial intelligence field. The mission of producing GEOINT should be transferred to a geospatial intelligence battalion which is newly organized.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.191-192
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• 2022

본 논문에서는 딥러닝의 주요 기법 중 하나인 GAN 을 활용하여 압축된 영상의 품질을 개선하는 방법을 제안한다. 제안하는 GAN 의 생성자는 U-Net 과 ResNet 을 기반으로 구성되었으며, 판별자는 합성곱층과 전연결층으로 구성하였다. 네트워크의 학습은 HEVC (High Efficiency Video Coding)의 테스트 모델인 HM16.25 를 사용하여 RA (Random Access) 구성하에 양자화 계수 37 로 압축된 영상을 입력으로 하여 수행되었다. 제안하는 네트워크의 성능 확인을 위해 학습 시와 동일한 조건으로 압축된 다른 영상을 입력으로 하여 실험하였다. 실험 결과 영상의 평균 PSNR 은 34.20dB 에서 34.24dB 로 0.04dB 의 품질 향상이 이루어진 것을 확인할 수 있었다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.81-83
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• 2018

Fully Convolutional Network(FCN)은 기존의 방법보다 뛰어난 성능을 보였지만, FCN은 RGB 정보만을 사용하기 때문에 세밀한 예측이 필요한 장면에서는 다소 부족한 성능을 보였다. 이를 해결하기 위해 인코더-디코더 구조를 이용하여 RGB와 깊이의 멀티 모달을 활용하기 위한 FuseNet이 제안되었다. 하지만, FuseNet에서는 RGB와 깊이 브랜치 사이의 융합은 있지만, 인코더와 디코더 사이의 특징 지도를 융합하지 않는다. 본 논문에서는 FCN의 디코더 부분의 업샘플링 과정에서 이전 계층의 결과와 2배 업샘플링한 결과를 융합하는 스킵 레이어를 적용하여 FuseNet의 모달리티를 잘 활용하여 성능을 개선했다. 본 실험에서는 NYUDv2와 SUNRGBD 데이터 셋을 사용했으며, 전체 정확도는 각각 77%, 65%이고, 평균 IoU는 47.4%, 26.9%, 평균 정확도는 67.7%, 41%의 성능을 보였다.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.99-112
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• 2019

Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.