• 가정과삶의질연구
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• 제8권1호
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• pp.115-127
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• 1990

The Korean have admired and lived with decorums through Jeol, a representative practical formality which expresses the decorums intensively. Jeol is the way of greeting native to Korea and an intangible culture close to Korean life style. To be remained as a culture, the traditional propriety Jeol should reconcile to the present life feflecting our history. On this viewpoint, the purpose of this research is to furnish the authority of the creative inheritance on the settlement of Jeol and other decorums suitable to the present. This research is consisted of 1) the study of the meaning of Jeol and 2) the hidtorical research through records. 'Jurye' is regarded as the oldest literature on the systematic explanation about Jeol and has been very often used down the ages. Jindong in 'Jurye' and Kodu in 'Karyejipram' are the same Jeol which expresses the grief in funeral rites. In 'Seonghosaseol', Abae which stands one knee is explained and investigated as the case of offense by the literature. And it is also interpreated as a Jeol with respect, while many other Jeol is to fall an both of kness, Abae is not performed generally. 'Yeki' has brief explanation about Kongsu Manner (to hold hands), Jeol between the king and minister, host and guest, Buinbae, Jeol in custom, wedding ceremony and funeral ceremony and Jeol in drinking manner. In 'Chunchukongyangjeon'. there is the explanation about Jeol of the minister who comes back after his duty to the king. 'Sunjajiphae' interprets Bae and Kyesang and informs that Kuesu is the most respectful Jeol only to the king, 'Seojeon' proves the Jeol habitually used in all kinds of ceremonies and etiquettes. 'Jeongbomunheonbigo' introduces the custom of Jeol in Shilla, Koguryo, Buyeo, Koryo eras by using literature and shows the commoness of both kneeling down generally. The result of this research has the meaning for the creation of life culture in the basis of the spirit 'Onkojisin'(reviewing the old and learning the new) by establishing the present Jeol with tradition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• 한국멀티미디어학회논문지
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• 제25권1호
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• pp.72-81
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• 2022

Visual Object Tracking is known as the most fundamental problem in the field of computer vision. Object tracking localize the region of target object with bounding box in the video. In this paper, a custom CNN is created to extract object feature that has strong and various information. This network was constructed as a Siamese network for use as a feature extractor. The input images are passed convolution block composed of a bottleneck layers, and features are emphasized. The feature map of the target object and the search area, extracted from the Siamese network, was input as a local proposal network. Estimate the object area using the feature map. The performance of the tracking algorithm was evaluated using the OTB2013 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.611 in Success Plot and 0.831 in Precision Plot were achieved.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.247-256
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• 2022

Visual tracking of selected target objects is fundamental challenging problems in computer vision. Object tracking localize the region of target object with bounding box in the video. We propose a Siam-FPN based custom fully CNN to solve visual tracking problems by regressing the target area in an end-to-end manner. A method of preserving the feature information flow using a feature map connection structure was applied. In this way, information is preserved and emphasized across the network. To regress object region and to classify object, the region proposal network was connected with the Siamese network. The performance of the tracking algorithm was evaluated using the OTB-100 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.621 in Success Plot and 0.838 in Precision Plot were achieved.

• 한국정밀공학회지
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• 제14권7호
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• pp.59-66
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• 1997

A general procedure for a motion capture and mimic system has been delineated. Utilizing sensors operated in the magnetic fields, complicated and optimized movements are easily digitized to analyze and repreduce. The system consists of a motion capture module, a motion visualization module, a motion plan module, a motion mimic module, and a GUI module. Design concepts of the system are modular, open, and user friendly to ensure the overall system performance. Custom-built and/or off-the-shelf modules are ease- ly integrated into the system. With modifications, this procedure can be applied for complicated motion controls. This procedure is implemented on tracking a head and balancing a pole. A neural controller based on this control scheme dtilizing human motions can easily evolve from a small amount of learning data.

• 교육시설 논문지
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• 제22권1호
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• pp.13-23
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• 2015

Currently, the group study becomes the most popular method and common custom for learning in public that is distinctly different from the preferred way of self-study in the last decades. As a result, the college and public facilities have been significantly changed to provide more and more new spaces for discussion, learning and sharing information with others as one of the remarkable improvements and advantages. On the other hands, library is one of the all-important public sites to obtain information and knowledge for students. Moreover, we can split out an individual space from the reading rooms to take part in the group activities such as group meetings and academic exchanges. Recently, several universities begin to recognize the value of group study and try to meet the research needs. Also, needs for students, and the group study rooms are applied into the new buildings and rebuilt ones under this background. In this study, It focuses on analyzing of the 134 group study rooms to investigate the variations in universities and classify the types of buildings with site researches through 14 indicators of indoor environments. To investigate different types of group study rooms, we use the SD method to analyze the findings. So far there is no research focusing on the study rooms, especially for the analysis of their types and indoor environments features. Therefore, this article can provide a theoretical basis and evidence to related researches; also can help us to improve indoor environments to offer a better learning environments for the students in the future.

• 한국ITS학회 논문지
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• 제21권3호
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• pp.83-95
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• 2022

본 논문은 자율주행 차량 적용을 위한 객체 검출과 거리 추정을 수행하는 시스템을 제안한다. 객체 검출은 최근 활발하게 사용되는 딥러닝 모델 YOLOv4의 특성을 이용해서 입력 이미지 비율에 맞춰 분할 grid를 조정하고 자체 데이터셋으로 전이학습된 네트워크로 수행한다. 검출된 객체까지의 거리는 bounding box와 homography를 이용해 추정한다. 실험 결과 제안하는 방법에서 전반적인 검출 성능 향상과 실시간에 가까운 처리 속도를 보였다. 기존 YOLOv4 대비 전체 mAP는 4.03% 증가했다. 도심로 주행시 빈출하는 보행자, 차량 및 공사장 고깔(cone), PE드럼(drum) 등의 객체 인식 정확도가 향상되었다. 처리 속도는 약 55 FPS이다. 거리 추정 오차는 X 좌표 평균 약 5.25m, Y 좌표 평균 0.97m으로 나타났다.

• 한국조경학회지
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• 제50권6호
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• pp.42-57
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• 2022

본 연구의 목적은 인공지능의 딥러닝을 활용하여 소셜미디어에서 공유되는 도시공원 이용자 활동사진을 분류하는 기초 모델을 만드는 것이다. 소셜미디어 데이터는 네이버 검색을 통해 수집된 도시공원 관련 사진들을 수집하여 분류모델에 활용하였다. 도시공원 특성 평가에 활용할 수 있는 지표인 자연성(naturalness), 잠재적 매력성(potential attraction), 활동(activity)을 기반으로 최종 21개의 분류 항목체계를 만들고, 항목별로 네이버에서 공유되는 실제 도시공원 사진을 수집하여 주석이 달린 데이터 세트를 구축했다. 수집한 사진 데이터 세트에 대해 커스텀(cuntom) CNN 모델과 사전 훈련된 CNN의 전이학습 모델을 설계하고 분석하였다. 연구결과, 가장 우수한 성능을 보였던 Xception 전이학습 모델이 최종적으로 도시공원 이용자 활동 이미지 분류모델로 선정되었으며, 그 외 다양한 평가 지표를 통해 모델을 평가했다. 본 연구는 소셜미디어에 공유되는 이용자 사진을 활용하여 도시공원 특성을 평가할 수 있는 지표로서 AI를 구축한 것에 의의가 있다. 딥러닝을 활용한 분류모델은 수동분류에 대한 한계를 보완하고, 대량의 도시공원 사진을 효율적으로 분류할 수 있어서 향후 도시공원의 모니터링 및 관리에 활용할 수 있는 유용한 방법이라고 할 수 있다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.465-468
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• 2022

교통사고 발생률은 매년 증가하고 있으며 대한민국은 OECD 국가 중에서 상위권에 속한다. 이를 개선하기 위해 다양한 도로교통법이 시행되고 있으며 무인 속도 카메라, 교통단속 카메라 등의 장비를 사용한 다양한 교통단속 방법이 적용되고 있다. 그러나 운전자는 네비게이션을 통해 교통단속 카메라의 위치를 사전 감지하여 단속을 회피함에 따라 불시 단속이 가능한 이동식 단속시스템이 필요하며, 정확한 단속을 위해 도로 측면에서 차량 번호판 인식률을 높일 수 있는 연구가 필요하다. 본 논문에서는 영상처리를 이용한 기울기 보정 알고리즘를 적용하여 도로 측면에서의 차량 번호 인식률을 향상을 위한 방법을 제안한다. 또한 문자 인식 정확도 향상을 위해 CNN 기반의 YOLO 알고리즘을 이용하여 커스텀 데이터 학습을 진행하였다. 해당 알고리즘을 설치 장소에 대한 제약이 없는 이동식 교통단속 카메라 등에 활용 가능할 것으로 기대된다.

• 대한원격탐사학회지
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• 제40권2호
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• pp.151-166
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• 2024

해안쓰레기 문제는 전 세계적으로 환경에 대한 심각한 위협이 되고 있다. 본 연구에서는 딥러닝과 원격탐사 기술을 활용하여 해안쓰레기의 모니터링 방법을 개선하고자 하였다. 이를 위해 You Only Look Once (YOLO)v8 모델을 이용한 객체 탐지 기법을 적용하여 우리나라 주요 해안쓰레기 11종에 대한 대규모 이미지 데이터셋을 구축하고, 실시간으로 쓰레기를 탐지 및 분석할 수 있는 프로토콜(Protocol)을 제안한다. 낙동강 하구에 위치한 신자도를 대상으로 드론 이미지 촬영 및 자체 개발한 YOLOv8 기반의 분석 프로그램을 적용하여 해안쓰레기 성상별 핫스팟을 식별하였다. 이러한 매핑(Mapping) 및 분석 기법의 적용은 해안쓰레기 관리에 효과적으로 활용될 수 있을 것으로 기대된다.