• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.37-46
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• 2023

This study aims to evaluate the usefulness of Deep Learning Image Reconstruction (TrueFidelity, TF), the image quality of existing Filtered Back Projection (FBP) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) were compared. Noise, CNR, and SSIM were measured by obtaining images with doses fixed at 17.29 mGy and altered to 10.37 mGy, 12.10 mGy, 13.83 mGy, and 15.56 mGy in reconstruction techniques of FBP, ASIR-V 50%, and TF-H. TF-H has superior image quality compared to FBP and ASIR-V when the reconstruction technique change is given at 17.29 mGy. When dose changes were made, Noise, CNR, and SSIM were significantly different when comparing 10.37 mGy TF-H and FBP (p<0.05), and no significant difference when comparing 10.37 mGy TF-H and ASIR-V 50% (p>0.05). TF-H has a dose-reduction effect of 30%, as the highest dose of 15.56 mGy ASIR-V has the same image quality as the lowest dose of 10.37 mGy TF-H. Thus, Deep Learning Reconstruction techniques (TF) were able to reduce dose compared to Iterative Reconstruction techniques (ASIR-V) and Filtered Back Projection (FBP). Therefore, it is considered to reduce the exposure dose of patients.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.435-441
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• 2024

In order for the large amount of collected data sets to be used as deep learning training data, sensitive personal information such as resident registration number and disease information must be changed or encrypted to prevent it from being exposed to hackers, and the data must be reconstructed to match the structure of the built deep learning model. Currently, these tasks are performed manually by experts, which takes a lot of time and money. To solve these problems, this paper proposes a technique that can automatically perform data processing tasks to protect personal information during the deep learning process. In the proposed technique, privacy protection tasks are performed based on data generalization and data reconstruction tasks are performed using circular queues. To verify the validity of the proposed technique, it was directly implemented using C language. As a result of the verification, it was confirmed that data generalization was performed normally and data reconstruction suitable for the deep learning model was performed properly.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.285-295
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• 2023

With the development of technology, efforts to reduce the exposure dose received by patients in CT scans are continuing with the development of new reconstruction techniques. Recently, deep learning reconstruction techniques have been developed to overcome the limitations of repetitive reconstruction techniques. This study aims to evaluate the usefulness of images according to reconstruction techniques in pediatric chest CT images. Patient study conducted a study on 85 pediatric patients who underwent chest CT scan at P-Hospital in Gyeongsangnam-do from January 1, 2021 to December 31, 2022. The phantom used in the Phantom Study is the Pediatrics Whole Body Phantom PBU-70. After the test, the images were reconstructed with FBP, ASIR-V (50%) and DLIR (TF-Medium, High), and the images were evaluated by obtaining SNR and CNR values by setting ROI of the same size. As a result, TF-H of deep learning reconstruction techniques had the lowest noise value compared to ASIR-V (50%) and TF-M in all experiments, and SNR and CNR had the highest values. In pediatric chest CT scans, TF images with deep learning reconstruction techniques were less noisy than ASiR-V images with adaptive statistical iterative reconstruction techniques, CNR and SNR were higher, and the quality of images was improved compared to conventional reconstruction techniques.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.306-309
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• 2017

소프트웨어의 보안에 대한 중요성이 점차 높아짐에 따라, 소스코드 기반의 소프트웨어의 보안약점 분석 기법에서 더 나아가 소스 코드가 존재하지 않는 바이너리 파일을 대상으로 분석을 수행하는 연구가 진행되고 있다. 왜냐하면 소프트웨어 개발의 복잡성 증가에 따른 서드파티 라이브러리 활용과 레거시 코드의 관리 부재, 임베디드 소프트웨어의 특성 등으로 인해 소스 코드가 존재하지 않는 바이너리 코드의 사용이 늘어나고 있기 때문이다. 따라서 최근 바이너리 코드에 내제된 보안약점을 분석하기 위해서 중간코드를 이용하여 정적분석을 수행하는 다양한 연구가 진행되고 있다. 중간언어를 사용함으로 실행환경에 따라 달라지는 바이너리 코드가 중간언어로만 변환이 된다면 동일한 형태의 보안약점 분석기술을 통해 효과적인 수행이 가능하다. 본 논문에서는 이러한 바이너리 코드로부터 중간언어로 변환시 컴파일 과정에서 상실된 복합 자료형을 재구성하기 위해 Word2vec 딥러닝 기법을 이용한 추론기법을 제안한다.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1007-1014
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• 2022

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas.In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1173-1175
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• 2019

소음이 심하거나 긴급한 상황 등에서 서로 다른 핸드제스처에 대한 인식을 컴퓨터의 입력으로 받고 이를 특정 명령으로 인식하는 등의 연구가 로봇 분야에서 연구되고 있다. 그러나 핸드제스처에 대한 전처리 과정에서 RGB데이터를 활용하거나 또는 스켈레톤을 활용하는 연구들이 다양하게 연구되었지만, 실생활에서의 노이즈가 많아 분류 정확도가 높지 않거나 컴퓨팅 파워의 사용이 과다한 문제가 발생했다. 본 논문에서는 RGBD 이미지를 사용하여 Hand Gesture를 트레이닝 받은 Keras 모델을 통해 입력받은 Hand Gesture을 분류하는 연구를 진행하였다. Depth Camera를 통하여 입력받은 Hand Gesture Raw-Data를 Image로 재구성하여 딥러닝을 진행하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1045-1048
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• 2019

최근 CCTV 사용이 보편화되면서 방범 목적으로 서비스 시설이나 공공시설에 설치되는 CCTV의 수가 급격하게 증가하고 있다. 그에 따라 CCTV를 감시하는 노동력이 부족해지는 문제가 발생하여 이를 대체하기 위해 카메라 영상을 통하여 한번 인식한 사람을 다른 시간이나 장소에서 촬영된 영상에서 다시 인식하는 사람 재인식 기술이 주목받고 있다. 또한, 이러한 사람 재인식 기술은 보안 분야뿐만 아니라 영화나 드라마와 같은 영상 컨텐츠에 적용되어 불법 복제물을 찾는 일에 사용될 수도 있다. 기존의 사람 재인식에는 이미지의 유사도를 계산하는 방법이 사용되었지만, 조명이나 카메라 각도가 달라지면 성능이 급격하게 떨어지는 문제가 있었다. 최근에는 딥러닝 기술이 발달하면서 전반적인 영상처리 분야의 성능이 향상되었고, 사람 재인식 분야 역시 딥러닝을 활용하면서 성능이 향상되었다. 하지만 딥러닝을 활용한 방법의 경우 보통 두 개의 이미지를 입력으로 사용하여 같은지 다른지를 판단하게 되므로 각 이미지의 공통점이나 차이점을 동시에 고려하기는 어려운 점이 있다. 본 논문에서는 이러한 점을 해결하기 위해 세 개의 사람 이미지를 입력으로 사용하여 특징을 추출하고, 특징 맵을 재구성하여 각 이미지의 차이점과 공통점을 동시에 고려하며 학습할 수 있는 모델을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.501-507
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• 2020

This paper proposes a new frame synchronization technique based on convolutional neural network (CNN). The conventional frame synchronizers usually find the matching instance through correlation between the received signal and the preamble. The proposed method converts the 1-dimensional correlator ouput into a 2-dimensional matrix. The 2-dimensional matrix is input to a convolutional neural network, and the convolutional neural network finds the frame arrival time. Specifically, in additive white gaussian noise (AWGN) environments, the received signals are generated with random arrival times and they are used for training data of the CNN. Through computer simulation, the false detection probabilities in various signal-to-noise ratios are investigated and compared between the proposed CNN-based technique and the conventional one. According to the results, the proposed technique shows 2dB better performance than the conventional method.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.333-338
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• 2019

인코더-디코더(Encoder-decoder)는 현대 기계 번역(Machine translation)의 가장 기본이 되는 모델이다. 인코딩은 마치 인간의 뇌가 출발어(Source language) 문장을 읽고 이해를 하는 과정과 유사하고, 디코딩은 뇌가 이해한 의미를 상응하는 도착어(Target language) 문장으로 재구성하는 행위와 비슷하다. 그렇다면 벡터로 된 인코더 표현은 문장을 읽고 이해함으로써 변화된 뇌의 상태에 해당한다고 볼 수 있다. 사람이 어떤 문장을 잘 번역하기 위해서는 그 문장에 대한 이해가 뒷받침되어야 하는 것처럼, 기계 역시 원 문장이 가진 의미를 제대로 인코딩해야 향상된 성능의 번역이 가능할 것이다. 본 논문에서는 뇌과학에서 뇌 동기화(Brain-to-brain coupling)라 일컫는 현상을 모방해, 출발어와 도착어의 공통된 의미를 인코딩하여 기계 번역 성능 향상에 도움을 줄 수 있는 이중 번역 기법을 소개한다.