• 한국융합학회논문지
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• 제9권8호
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• pp.1-8
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• 2018

얼굴인식, 홍채인식과 같은 생체보안 분야에서 눈, 코, 입술 등 얼굴특징을 추출하는 과정은 필수적이다. 본 논문은 초고속(faster) R-CNN을 이용하여 얼굴영상에서 눈 및 입술영역을 검출하는 방법을 연구하였다. 초고속 R-CNN은 딥러닝을 이용한 물체검출 방법으로 기존의 특징기반 방법에 비해 성능이 우수한 것으로 알려져 있다. 본 논문에서는 얼굴영상에 콘볼루션, 선형정류과정, max pooling과정을 차례로 적용하여 특징맵을 추출하고 이로부터 제안영역(region proposal)을 검출하는 RPN(region proposal network)을 학습한다. 그리고 제안영역과 특징맵을 이용하여 눈 및 입술 검출기(detector)를 학습한다. 제안방법의 성능을 검토하기 위해 남녀한국인 얼굴영상 800장으로 실험하였다. 학습을 위해 480장을 이용했으며 테스트용으로 320장을 사용하였다. 컴퓨터모의 실험결과 눈 및 입술영역 검출의 평균정확도는 50 에포치일 때 각각 97.7%, 91.0%를 얻을 수 있었다.

• 융합신호처리학회논문지
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• 제10권4호
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• pp.233-238
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• 2009

본 논문에서는 얼굴을 자동 초점(Auto-focus) 디지털 카메라의 관심영역(ROI : Region Of Interest)으로 이용하는 시스템에서 요구되는 검출 영역의 안정적인 선택을 위한 방법을 제안한다. 이 방법은 디지털 카메라와 모바일 카메라에 포함되는 ISP(Image Signal Processor)에서 실시간으로 처리되는 프로그레시브 입력 영상에서 얼굴 영역을 관심영역으로 간주하고 자동으로 초점을 맞추는 방법이다. 얼굴 영역 검출을 위하여 사용한 학습 알고리즘은 에이다부스트 알고리즘을 이용하였다. 학습에 포함되지 않은 기울어진 얼굴에 대한 검출방법과 검출 결과에 대한 후처리 방법, 관심영역이 흔들리지 않고 일정한 영역을 유지하도록 하기 위한 안정화 대책을 제안한다. 제안된 ROI 영역 안정화 알고리즘에 대한 성능을 평가하기 위하여 움직임이 있는 얼굴에 대하여 기준 궤적과의 차이를 보이고, 각 궤적의 회귀곡선과의 RMS 오차를 안정화 성능평가의 척도로 이용하였다.

• Journal of applied mathematics & informatics
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• 제37권3_4호
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• pp.271-279
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• 2019

We investigate how a feedforward neural network works to separate a geometric region from its complement. Our investigations are restricted to regions in ${\mathbb{R}}$ or ${\mathbb{R}}^2$ including an interval, a triangular region, a disk and the union of two disjoint disks. We also examine what happens at each layer of the network.

• 한국멀티미디어학회논문지
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• 제20권5호
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• International Journal of Advanced Culture Technology
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• 제6권2호
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• pp.23-29
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• 2018

The purpose of the present study was to investigate the influence of smartphone addiction on learning immersion, sociality and morality of college students and the present study is a descriptive survey research to investigate the influence of smartphone addiction on learning immersion, sociality and morality of college students. The subjects were 145 freshman, sophomore and junior students in the Department of Health at a college located in the Gyeongbuk region, and the data were collected from November 15 to 18, 2017. upon analyzing the correlations between the variables, a negative correlation was found between learning immersion and sociality and between learning immersion and morality, indicating that the learning immersion decreased as the sociality or morality increased. A positive correlation was found between sociality and morality, indicating that those who have a higher degree of sociality have a higher level of morality.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.160-169
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• 2022

This qualitative study aimed to investigate ways to improve effective cooperative learning from students' perspective by understanding and analyzing the learning experiences of nursing students who participated in a collaborative problem-solving learning model. Data were collected through focus group interviews and reflection journals of six second-year nursing students from G-university in J-city who participated in a collaborative problem-solving learning model course. The interview data were analyzed and divided into 3 categories and 10 subcategories according to the six-step thematic analysis method proposed by Braun and Clarke. The results of analyzing the interviews were considered based on three areas: preparation before learning, the process of collaborating as a cooperative learning experience, and solutions and expectations after learning. The participants felt frustrated because collaborative problem-solving took more time for individual learning than traditional methods did and would not allow them to check the correct answers immediately. However, they gained new experiences by solving problems and engaging in discussions within their learning community. The participants' expectations included material that could help their learning, measures to prevent free-riders, and consideration of the learning process in evaluation factors. Although this study has sample limitations by targeting nursing students in only one region, it can be used to help operate collaborative problem-solving classes, as it reflects the real experiences and opinions of students.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.468-475
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• 2020

In this paper, we proposed a three-dimensional visualization system for medical images in augmented reality based on deep learning. In the proposed system, the artificial neural network model performed fully automatic segmentation of the region of lung and pulmonary nodule from chest CT images. After applying the three-dimensional volume rendering method to the segmented images, it was visualized in augmented reality devices. As a result of the experiment, when nodules were present in the region of lung, it could be easily distinguished with the naked eye. Also, the location and shape of the lesions were intuitively confirmed. The evaluation was accomplished by comparing automated segmentation results of the test dataset to the manual segmented image. Through the evaluation of the segmentation model, we obtained the region of lung DSC (Dice Similarity Coefficient) of 98.77%, precision of 98.45%, recall of 99.10%. And the region of pulmonary nodule DSC of 91.88%, precision of 93.05%, recall of 90.94%. If this proposed system will be applied in medical fields such as medical practice and medical education, it is expected that it can contribute to custom organ modeling, lesion analysis, and surgical education and training of patients.

• Structural Engineering and Mechanics
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• 제82권3호
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• 대한토목학회논문집
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• 제39권3호
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• pp.399-407
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• 2019

대한민국에서 건설업은 타 업종들과 비교하여 안전사고의 위험성이 가장 높게 나타난다. 따라서 건설업 내 안전성 향상을 도모하기 위해 여러 연구가 예전부터 진행이 되어 왔고, 본 연구에선 건설현장 영상 데이터를 기반으로 물체 탐지 및 분류 알고리즘을 이용해서 효과적인 안전모 자동탐지 시스템을 구축하여 건설현장 노동자들의 안전성 향상에 기여하고자 한다. 본 연구에서 사용된 알고리즘은 Convolutional Neural Network (CNN) 기반의 물체 탐지 및 분류 알고리즘인 Region-based Fully Convolutional Networks (R-FCN)이고 이를 Transfer Learning 기법을 사용하여 딥러닝을 실시하였다. ImageNet에서 수집한 1089장의 사람과 안전모가 포함된 영상으로 학습을 시행하였고 그 결과, 사람과 안전모의 mean Average Precision (mAP)은 각각 0.86, 0.83로 측정되었다.

• 한국산학기술학회논문지
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• 제19권3호
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• pp.693-698
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• 2018

본 논문은 최근 물체탐지 분야에서 실시간 물체 탐지 알고리즘으로 주목을 받고 있는 YOLOv2(You Only Look Once) 알고리즘을 이용하여 밀집 영역에 주차되어 있는 자동차 탐지 방법을 제안한다. YOLO의 컨볼루션 네트워크는 전체 이미지에서 한 번의 평가를 통해서 직접적으로 경계박스들을 예측하고 각 클래스의 확률을 계산하고 물체 탐지 과정이 단일 네트워크이기 때문에 탐지 성능이 최적화 되며 빠르다는 장점을 가지고 있다. 기존의 슬라이딩 윈도우 접근법과 R-CNN 계열의 탐지 방법은 region proposal 방법을 사용하여 이미지 안에 가능성이 많은 경계박스를 생성하고 각 요소들을 따로 학습하기 때문에 최적화 및 실시간 적용에 어려움을 가지고 있다. 제안하는 연구는 YOLOv2 알고리즘을 적용하여 기존의 알고리즘이 가지고 있는 물체 탐지의 실시간 처리 문제점을 해결하여 실시간으로 지상에 있는 자동차를 탐지하는 방법을 제안한다. 제안하는 연구 방법의 실험을 위하여 오픈소스로 제공되는 Darknet을 사용하였으며 GTX-1080ti 4개를 탑재한 Deep learning 서버를 이용하여 실험하였다. 실험결과 YOLO를 활용한 자동차 탐지 방법은 기존의 알고리즘 보다 물체탐지에 대한 오버헤드를 감소 할 수 있었으며 실시간으로 지상에 존재하는 자동차를 탐지할 수 있었다.