• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.86-88
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• 2005

An autonomous semantic-map building method is proposed, with the robot localized in the semantic-map. Our semantic-map is organized by objects represented as SIFT features and vision-based relative localization is employed as a process model to implement extended Kalman filters. Thus, we expect that robust SLAM performance can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based SLAM

• 로봇학회논문지
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• 제7권4호
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• pp.252-258
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• 2012

This paper describes hierarchical semantic map building using the classified area information in home environments. The hierarchical semantic map consists of a grid, CAIG (Classified Area Information in Grid), and topological map. The grid and CAIG maps are used for navigation and motion selection, respectively. The topological map provides the intuitive information on the environment, which can be used for the communication between robots and users. The proposed semantic map building algorithm can greatly improve the capabilities of a mobile robot in various domains, including localization, path-planning and HRI (Human-Robot Interaction). In the home environment, a door can be used to divide an area into various sections, such as a room, a kitchen, and so on. Therefore, we used not only the grid map of the home environment, but also the door information as a main clue to classify the area and to build the hierarchical semantic map. The proposed method was verified through various experiments and it was found that the algorithm guarantees autonomous map building in the home environment.

• 제어로봇시스템학회논문지
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• 제17권3호
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• pp.277-282
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• 2011

This paper describes a sensor fusion-based semantic map building which can improve the capabilities of a mobile robot in various domains including localization, path-planning and mapping. To build a semantic map, various environmental information, such as doors and cliff areas, should be extracted autonomously. Therefore, we propose a method to detect doors, cliff areas and robust visual features using a laser scanner and a vision sensor. The GHT (General Hough Transform) based recognition of door handles and the geometrical features of a door are used to detect doors. To detect the cliff area and robust visual features, the tilting laser scanner and SIFT features are used, respectively. The proposed method was verified by various experiments and showed that the robot could build a semantic map autonomously in various indoor environments.

• 한국정보시스템학회지:정보시스템연구
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• 제18권3호
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• pp.327-349
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• 2009

Traditional keyword-based searching methods suffer from low accuracy and high complexity due to the rapid growth in the amount of information. Accordingly, many researchers attempt to implement a so-called semantic search which is based on the semantics of the user's query. Semantic information can be described using a semantic modeling language, such as Topic Map. In this paper, we propose a new method to map a topic map to a traditional Relational Database (RDB) without any information loss. Although there have been a few attempts to map topic maps to RDB, they have paid scant attention to handling multi-role topics. In this paper, we propose a new storage structure to map multi-role topics to traditional RDB. The proposed structure consists of a mapping table, role tables, and content tables. Additionally, we devise a query translator to convert a user's query to one appropriate to the proposed structure.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제4권10호
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• pp.439-446
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• 2015

최근 위치기반서비스의 확장을 위해 GPS 위치정보에 관심지점(POI: Point of Interest) 정보를 결합한 시맨틱 궤적(Semantic Trajectory)이 주목받고 있다. 기존 연구의 경우 GPS 궤적과 POI의 면적정보(polygon)가 겹치는 경우를 찾아내어 시맨틱 궤적을 생성하였다. 하지만 구글 지도, 네이버 지도, OpenStreetMap 등과 같은 공개된 지리 정보 시스템에서는 POI의 면적정보를 제공하지 않기 때문에 기존 방법으로는 시맨틱궤적을 생성하지 못하는 문제가 있다. 본 논문에서는 POI의 면적정보가 없는 제한적인 상황에서도 GPS 위치정보와 POI의 좌표값(points)만을 이용하여 시맨틱 궤적을 생성할 수 있는 기법을 제안한다.

• 전기전자학회논문지
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• 제12권4호
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• pp.217-224
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• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.171-174
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• 2000

In this paper, We try to design combined source-channel coder that is compatible with video coding standards. This MAP decoder is proposed by adding semantic structure and semantic constraint of video coding standards to the method using redundnacy of the MAP decoders proposed previously. Then, We get the better performance than usual channel coder＇s.

• Genomics & Informatics
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• 제12권1호
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• pp.35-41
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• 2014

Single-nucleotide polymorphisms (SNPs) have been emerging out of the efforts to research human diseases and ethnic disparities. A semantic network is needed for in-depth understanding of the impacts of SNPs, because phenotypes are modulated by complex networks, including biochemical and physiological pathways. We identified ethnicity-specific SNPs by eliminating overlapped SNPs from HapMap samples, and the ethnicity-specific SNPs were mapped to the UCSC RefGene lists. Ethnicity-specific genes were identified as follows: 22 genes in the USA (CEU) individuals, 25 genes in the Japanese (JPT) individuals, and 332 genes in the African (YRI) individuals. To analyze the biologically functional implications for ethnicity-specific SNPs, we focused on constructing a semantic network model. Entities for the network represented by "Gene," "Pathway," "Disease," "Chemical," "Drug," "ClinicalTrials," "SNP," and relationships between entity-entity were obtained through curation. Our semantic modeling for ethnicity-specific SNPs showed interesting results in the three categories, including three diseases ("AIDS-associated nephropathy," "Hypertension," and "Pelvic infection"), one drug ("Methylphenidate"), and five pathways ("Hemostasis," "Systemic lupus erythematosus," "Prostate cancer," "Hepatitis C virus," and "Rheumatoid arthritis"). We found ethnicity-specific genes using the semantic modeling, and the majority of our findings was consistent with the previous studies - that an understanding of genetic variability explained ethnicity-specific disparities.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(C)
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• pp.255-257
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• 2011

본 연구에서는 각종 웹사이트와 이메일을 통해 전달되는 회의공지에 포함된 회의장소를 나타내는 문자열로부터 실제 위치를 추정하는 시스템을 설계 및 구현하였다. 직접 구현한 NER과 Relation-type Classification 모듈을 사용하였으며, 장소에 대한 모델은 기존의 지리정보시스템들과의 상호 운용성을 위하여 OpenStreetMap[6]과 Geonames[7]의 데이터 구조를 참조하여 설계되었고, 실제 위치를 구하기 위하여 내부자원 외에도, 각종 오픈API들을 외부자원으로 활용하였다.

• 한국콘텐츠학회논문지
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• 제21권3호
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• pp.23-32
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• 2021

컴퓨터 비전 분야의 의미론적 영상 분할(Semantic Image Segmentation) 기술은 이미지를 픽셀 단위로 분할 하여 클래스를 나누는 기술이다. 이 기술도 기계 학습을 이용한 방법으로 성능이 빠르게 향상되는 중이며, 픽셀 단위의 정보를 활용할 수 있는 높은 활용성이 주목받는 기술이다. 그러나 이 기술은 초기부터 최근까지도 계속 '세밀하지 못한 분할'에 대한 문제가 제기되어 왔다. 이 문제는 레이블 맵의 크기를 계속 늘리면서 발생한 문제이기 때문에, 자세한 에지 정보가 있는 원본 영상의 에지 맵을 이용해 레이블 맵을 수정하여 개선할 수 있을 것으로 예상할 수 있었다. 따라서 본 논문은 기존 방법대로 학습 기반의 의미론적 영상 분할을 유지하되, 그 결과인 레이블 맵을 원본 영상의 에지 맵 기반으로 수정하는 후처리 알고리즘을 제안한다. 기존의 방법에 알고리즘의 적용 한 뒤 전후의 정확도를 비교했을 때 평균적으로 약 1.74% 픽셀 정확도와 1.35%의 IoU(Intersection of Union) 정확도가 향상되었으며, 결과를 분석했을 때 성공적으로 본래 목표한 세밀한 분할 기능을 개선했음을 보였다.