• Journal of Industrial Convergence
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• v.20 no.11
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• pp.19-25
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• 2022

In the development of existing games, it is judged that virtual world production was done by arranging game elements one by one. What is noteworthy here is the question of whether quality virtual worlds were efficiently produced in preparation for investment. In this study, we propose a methodology that can build an efficient virtual world based on the concept of modularization in an unreal engine. First, precedents were analyzed and five reference elements for modularization were extracted. In addition, the concept of an instance production pipeline was proposed by dividing it into four stages, and the minimum-unit instance modules for urban virtual world production were compressed into four. Finally, an urban virtual world constructed based on the minimum unit module and reference elements was implemented and presented. In conclusion, research on the production method centered on this efficiency is thought to be able to focus the time that designers or artists had to spend on production only on ideas and creativity. The limitations of the research are that the basic minimum module is limited to the city, and the derived reference elements and production pipelines have not been verified when implementing them with an unreal engine. Therefore, it is expected that various virtual world creation plans will be derived through more advanced modular research.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.44-55
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• 2022

In order to automatically provide information on fruits in agricultural related broadcasting contents, instance image segmentation of target fruits is required. In addition, the information on the 3D pose of the corresponding fruit may be meaningfully used. This paper represents research that provides information about tomatoes in video content. A large amount of data is required to learn the instance segmentation, but it is difficult to obtain sufficient training data. Therefore, the training data is generated through a data augmentation technique based on a small amount of real images. Compared to the result using only the real images, it is shown that the detection performance is improved as a result of learning through the synthesized image created by separating the foreground and background. As a result of learning augmented images using images created using conventional image pre-processing techniques, it was shown that higher performance was obtained than synthetic images in which foreground and background were separated. To estimate the pose from the result of object detection, a point cloud was obtained using an RGB-D camera. Then, cylinder fitting based on least square minimization is performed, and the tomato pose is estimated through the axial direction of the cylinder. We show that the results of detection, instance image segmentation, and cylinder fitting of a target object effectively through various experiments.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.175-177
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• 2004

최근, 이미지 검색기법에서는 객체추출 방법이나 관심영역 추출방법에 관한 연구가 활발히 이루어지고 있다. 그러나, 컬러 이미지의 경우 색상을 고려한 관심영역 특징추출 방법이나 인덱스 기법은 많이 연구되지 못하고 있다. 따라서, 본 논문에서는 컬러 이미지의 색상을 기반으로 하는 사분트리 분할 인덱스 기법을 제안한다. 사분트리 분할 인덱스 구조는 컬러 이미지의 공간 영역을 계층적인 영역으로 분할하여 각 공간 영역의 평균 색상 갓을 데이터베이스에 저장한다 저장되어진 각 영역의 평균 색상은 검색의 효율성을 높이기 위해 사분트리 인스턴스(Quad-tree distance)를 퍼지 값으로 계산하여 인덱스를 생성한다. 생성된 사분트리 분할 인덱스는 컬러 이미지의 관심영역(Region of Interest)의 색상을 검색할 때 유용하게 사용되며. 검색속도의 향상에 도움을 준다.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.582-587
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• 2021

한국어 통합 지식베이스를 생성하기 위해 필수적인 분류체계(taxonomy)를 구축하는 방식을 제안한다. 위키데이터를 기반으로 분류 후보군을 추출하고, 상하위 관계를 통해 방향 비순환 그래프(Directed Acyclic Graph)를 구성한 뒤, 국부적 도달 중심성(local reaching centrality) 등의 정보를 활용하여 정제함으로써 246 개의 분류와 314 개의 상하위 관계를 갖는 분류체계를 생성한다. 워드넷(WordNet), 디비피디아(DBpedia) 등 기존 링크드 오픈 데이터의 분류체계 대비 깊이 있는 계층 구조를 나타내며, 다중 상위 분류를 지닐 수 있는 비트리(non-tree) 구조를 지닌다. 또한, 위키데이터 속성에 기반하여 위키데이터 정보가 있는 인스턴스(instance)에 자동으로 분류를 부여할 수 있으며, 해당 방식으로 실험한 결과 99.83%의 분류 할당 커버리지(coverage) 및 99.81%의 분류 예측 정확도(accuracy)를 나타냈다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1227-1234
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• 2006

최근 의료 서비스의 질적 향상을 위해 지식 기반의 의사결정지원 시스템 (Decision Support System)의 도입이 지속적으로 이루어지고 있으며, 이의 대표적 예로 임상실행지침(CPG : Clinical Practice Guideline) 중심의 진료 시스템이 있다. 임상실행지침은 환자가 병원에서 거치는 프로세스를 표현한 것으로, 질환에 대한 환자의 표준화된 진료 프로세스 지식이다. 본 연구에서는 임상실행지침, 의료 지식, 환자의 실시간 데이터를 연결시켜 환자가 병원에서 받아야할 진료 과정을 동적으로 생성하는 의사결정지원 시스템을 제시한다. 본 시스템은 임상실행지침과 의료지식을 바탕으로 추상화된 진료 프로세스 템플릿을 생성하고, 이 템플릿의 인스턴스에 해당하는 환자의 실시간 데이터를 반영하여 이후의 진료 프로세스를 동적으로 생성한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.1457-1460
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• 2005

본 논문은 지능형 로봇을 통하여 사용자가 원하는 서비스를 제공 받기 위해 외부의 웹 리소스를 이용하여 최적의 서비스 컴포지션 과정을 거쳐 실행 가능한 형태의 언어로 생성하는 방법을 제시한다. 온톨로지 형태로 정의된 템플릿을 서비스 컴포지션을 통해 플랜 인스턴스를 생성하고, 구축된 웹 서비스와 온톨로지를 이용하여 서비스 플랜에 맞게 실행 가능한 형태의 언어인 BPEL4WS 를 생성 한다. 이를 통하여 기존에 제공되었던 제한적이고 수동적인 서비스를 외부의 웹 서비스를 이용하여 보다 많은 정보를 지능형 로봇을 통해 제공 할 수 있다.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1213-1218
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• 2018

Recently introduced Mask R - CNN presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation mask of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask R - CNN is an algorithm that extends Faster R - CNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. The mask R - CNN is added to the high - speed R - CNN which training is easy and fast to execute. Also, it is easy to generalize the mask R - CNN to other tasks. In this research, we propose an infrared image detection algorithm based on R - CNN and detect heating elements which can not be distinguished by RGB images. As a result of the experiment, a heat-generating object which can not be discriminated from Mask R-CNN was detected normally.

• The Journal of Information Technology and Database
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• v.2 no.2
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• pp.25-34
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• 1995

In this paper, we present design philosophy and implementation issues of a functionality testing tool for object-oriented database systems. A testing tool has been developed to validate UniSQL/X functionalities with C++ interface. A testing tool is designed under consideration of scaleability, simplicity and extendibility. The schema is deliberately constructed to verify the object-oriented functionalities such as abstraction, inheritance and aggregation. Each test item has been derived under various black box techniques such as equivalent partitioning and boundary-value analysis. The testing tool consists of six phases, namely, database creation, database population, construction of testindex, compilation and link, execution and result reporting, and final cleanup. The prototype provides more than 140 test items at 90 programs.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.3
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• pp.157-177
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• 1998

The 21st century will be the advanced Information society era. The management of very large quantity of electronic documents is important because new applications such as Digital Libraries, CSCW (Computer-Supported Cooperative Work) in Intranet, CALS (Commerce At the Light Speed) are emerging, which require the functionalities of efficient storing, searching and managing a bulk of electronic documents. SGML(Standard Generalized Markup Language) is an ISO Standard for representing structure information of electronic documents. This paper proposes an effective data model for storing and managing SGML documents. We also describe the design and implementation details of SGML document management system, which has capabilities of storing SGML instances, generating schema dynamically, and retrieving structure elements efficiently.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.