• The Journal of the Korea Contents Association
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• v.6 no.4
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• pp.19-28
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• 2006

XML instances for purpose of information exchange are normally stored in the legacy relational database. Therefore, integrations with relational database are required for effective XML applications. To support these requirements, virtual decomposition storage or decomposition storage methods which save separates structures of instances to relational database have researched. However, these storage methods contain different information of schema structure and layers which has caused difficulties to process query during search operation as well as increased overheads due to duplicate savings for separate storages. Therefore, in this research, additional field of 'Eltype' has introduced to previous database schema structure to instance and schema structure, provide consistent level information and propose storage structure to map each field to schema field of relational database. As results, XML instance and structures can be stored together to minimize overheads and required storage-space. Also, synchronized storage layer structure provides easier processing of search query.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.1-11
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• 2010

The success of semantic web depends largely on the semantic annotation which systematizes knowledge for the construction and production of ontology. Therefore, the efficiency of semantic annotation is very important in order to change many knowledge expressions and generate into ontology instances. In this paper, we presents a generation system of rule-based ontology instances which are produced accurately and efficiently via semantic annotation in conventional web sites. In conventional studies, the manual process is necessary for finding relevant information, comparing it with ontology, and entering information. We propose a new method that manages keyword data regarding extracted information and rule information separately. Thus, it is quite practical to extract information efficiently from various web documents by adding a small number of keywords and rules. The proposed method shows the possibility of ontology instance generation which reuses the rules and keywords from the various websites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.381-388
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• 2022

In instance segmentation, Mask-RCNN is mostly used as a base model. Increasing the performance of Mask-RCNN is meaningful because it affects the performance of the derived model. Mask-RCNN has a transform module for unifying size of input images. In this paper, to improve the Mask-RCNN, we apply deep-learning-based ASSR to the resizing part in the transform module and inject calculated scale information into the model using IM(Integration Module). The proposed IM improves instance segmentation performance by 2.5 AP higher than Mask-RCNN in the COCO dataset, and in the periment for optimizing the IM location, the best performance was shown when it was located in the 'Top' before FPN and backbone were combined. Therefore, the proposed method can improve the performance of models using Mask-RCNN as a base model.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.7-8
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• 2012

본 논문에서는 클라우드 플랫폼인 Windows Azure를 사용하여 N-스크린 서비스 구현 방법을 제안한다. 이 방법은 클라우드 컴퓨팅 환경에서 Map/Reduce 기법을 사용하여 대용량 동영상 콘텐츠를 분할하고, 인코딩한다. Windows Azure의 Web Role에서는 사용자가 요청한 인코딩 작업을 수신하고, Worker Role에서는 요청받은 인코딩 작업을 처리한다. Windows Azure의 많은 가상머신에 인스턴스를 할당하여 처리함으로써 인코딩 소요시간을 단축할 수 있다.

• 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.

• Journal of KIISE
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• v.42 no.7
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• pp.852-859
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• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.127-132
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• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• 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.

• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.55-56
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• 2020

The construction industry has the highest occupational fatality and injury rates related to accidents of any industry. Accordingly, safety managers closely monitor to prevent accidents in real-time by installing surveillance cameras at construction sites. However, due to human cognitive ability limitations, it is impossible to monitor many videos simultaneously, and the fatigue of the person monitoring surveillance cameras is also very high. Thus, to help safety managers monitor work and reduce the occupational accident rate, a study on object recognition in construction sites was conducted through surveillance cameras. In this study, we applied to the instance segmentation to identify the classification and location of objects and extract the size and shape of objects in construction sites. This research considers ways in which deep learning-based computer vision technology can be applied to safety management on a construction site.