• 제목/요약/키워드: Feature Data

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밀링 공정설계의 특징형상 데이터 모델 (A feature data model in milling process planning)

  • 이충수;노형민
    • 대한기계학회논문집A
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    • 제21권2호
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    • pp.209-216
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    • 1997
  • A feature is well known as a medium to integrate CAD, CAPP and CAM systems. For a part drawing including both simple geometry and compound geometry, a process plan such as the selection of process, machine tool, cutting tool etc. normally needs simple geometry data and non-geometry data of the feature as the input. However, a extended process plan such as the generation of process sequence, operation sequence, jig & fixture, NC program etc. necessarily needs the compound geometry data as well as the simple geometry data and non-geometry data. In this paper, we propose a feature data model according to the result of analyzing necessary data, including the compound geometry data, the simple geometry data and the non-geometry data. Also, an example of the feature data model in milling process planning is described.

제조특징인식에 의한 CAD/CAPP 시스템 (CAD/CAPP System based on Manufacturing Feature Recognition)

  • 조규갑;김석재
    • 한국정밀공학회지
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    • 제8권1호
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    • pp.105-115
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    • 1991
  • This paper describes an integrated CAD and CAPP system for prismatic parts of injection mold which generates a complete process plan automatically from CAD data of a part without human intervention. This system employs Auto CAD as a CAD model and GS-CAPP as an automatic process planning system for injection mold. The proposed CAD/CAPP system consists of three modules such as CAD data conversion module, manufacturing feature recognition module, and CAD/CAPP interface module. CAD data conversion module transforms design data of AutoCAD into three dimensional part data. Manufacturing feature recognition module extracts specific manufacturing features of a part using feature recognition rule base. Each feature can be recognized by combining geometry, position and size of the feature. CAD/CAPP interface module links manufacturing feature codes and other head data to automatic process planning system. The CAD/CAPP system can improve the efficiency of process planning activities and reduce the time required for process planning. This system can provide a basis for the development of part feature based design by analyzing manufacturing features.

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CAD 시스템 간의 상호 운용성을 위한 설계 특징형상의 온톨로지 구축 (Building Feature Ontology for CAD System Interoperability)

  • 이윤숙;천상욱;한순흥
    • 한국CDE학회논문집
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    • 제9권2호
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    • pp.167-174
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    • 2004
  • As the networks connect the world, enterprises tend to move manufacturing activities into virtual spaces. Since different applications use different data terminology, it becomes a problem to interoperate, interchange, and manage electronic data among different systems. According to RTI, approximately one billion dollar has been being spent yearly for product data exchange and interoperability. As commercial CAD systems have brought in the concept of design feature for the sake of interoperability, terminologies of design feature need to be harmonized. In order to define design feature terminology for integration, knowledge about feature definitions of different CAD systems should be considered. STEP (Standard for the Exchange of Product model data) have attempted to solve this problem, but it defines only syntactic data representation so that semantic data integration is unattainable. In this paper, we utilize the ontology concept to build a data model of design feature which can be a semantic standard of feature definitions of CAD systems. Using feature ontology, we implement an integrated virtual database and a simple system which searches and edits design features in a semantic way. This paper proposes a methodology for integrating modeling features of CAD systems.

초음파 데이터를 이용한 강인한 형상 검출기 개발 (Development of Robust Feature Detector Using Sonar Data)

  • 이세진;임종환;조동우
    • 한국정밀공학회지
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    • 제25권2호
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    • pp.35-42
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    • 2008
  • This study introduces a robust feature detector for sonar data from a general fixed-type of sonar ring. The detector is composed of a data association filter and a feature extractor. The data association filter removes false returns provided frequently from sonar sensors, and classifies set of data from various objects and robot positions into a group in which all the data are from the same object. The feature extractor calculates the geometries of the feature for the group. We show the possibility of extracting circle feature as well as a line and a point features. The proposed method was applied to a real home environment with a real robot.

Enhancement of CAD Model Interoperability Based on Feature Ontology

  • Lee Yoonsook;Cheon Sang-Uk;Han Sanghung
    • Journal of Ship and Ocean Technology
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    • 제9권3호
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    • pp.33-42
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    • 2005
  • As the networks connect the world, enterprises tend to move manufacturing activities into virtual spaces. Since different software applications use different data terminology, it becomes a problem to interoperate, interchange, and manage electronic data among heterogeneous systems. It is said that approximately one billion dollar has been being spent yearly in USA for product data exchange and interoperability. As commercial CAD systems have brought in the concept of design feature for the sake of interoperability, terminologies of design features need to be harmonized. In order to define design feature terminology for integration, knowledge about feature definitions of different CAD systems should be considered. STEP standard have attempted to solve this problem, but it defines only syntactic data representation so that semantic data integration is not possible. This paper proposes a methodology for integrating modeling features of CAD systems. We utilize the ontology concept to build a data model of design features which can be a semantic standard of feature definitions of CAD systems. Using feature ontology, we implement an integrated virtual database and a simple system which searches and edits design features in a semantic way.

Comparative Analysis of Building Models to Develop a Generic Indoor Feature Model

  • Kim, Misun;Choi, Hyun-Sang;Lee, Jiyeong
    • 한국측량학회지
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    • 제39권5호
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    • pp.297-311
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    • 2021
  • Around the world, there is an increasing interest in Digital Twin cities. Although geospatial data is critical for building a digital twin city, currently-established spatial data cannot be used directly for its implementation. Integration of geospatial data is vital in order to construct and simulate the virtual space. Existing studies for data integration have focused on data transformation. The conversion method is fundamental and convenient, but the information loss during this process remains a limitation. With this, standardization of the data model is an approach to solve the integration problem while hurdling conversion limitations. However, the standardization within indoor space data models is still insufficient compared to 3D building and city models. Therefore, in this study, we present a comparative analysis of data models commonly used in indoor space modeling as a basis for establishing a generic indoor space feature model. By comparing five models of IFC (Industry Foundation Classes), CityGML (City Geographic Markup Language), AIIM (ArcGIS Indoors Information Model), IMDF (Indoor Mapping Data Format), and OmniClass, we identify essential elements for modeling indoor space and the feature classes commonly included in the models. The proposed generic model can serve as a basis for developing further indoor feature models through specifying minimum required structure and feature classes.

특징 형상의 간섭 표현에 대한 연구 (A Study on the Expression of Features Interaction)

  • 김경영;이수홍;고희동;김현석
    • 한국CDE학회논문집
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    • 제2권3호
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    • pp.142-149
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    • 1997
  • This study is intended to develop a Feature based modeler. It is difficult to integrate CAD and CAM/CAPP with information that is given only by a conventional CAD system. Therefore a lot of studies have concentrated on a Feature based CAD system. But conventional Feature based modelers have had limitation on providing sufficient information related to Feature interaction. If a Feature based modeler is to be used in assembly simulation, a new Feature-based modeling method needs to be developed. Also to support collision detection between parts, we have to handle Feature interaction systematically. Therefore we suggest Cell data structure which handles interaction of Features by volume. The volume created by Feature interaction is saved as a Cell. With the Cell structure we solve problems involved with Feature interaction. This study shows how the Cell data structure can manage Feature interaction and give enough information in assembly simulation.

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단백질 구조 및 기능 분석을 위한 FEATURE 시스템 개선 (Deciphering FEATURE for Novel Protein Data Analysis and Functional Annotation)

  • 유승학;윤성로
    • 전기전자학회논문지
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    • 제13권3호
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    • pp.18-23
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    • 2009
  • FEATURE는 단백질 내에서 특정 기능이나 구조를 가지고 있는 site의 미세환경분포를 이용하여 다른 단백질 내에서 이와 유사한 미세환경을 가지고 있는 부분을 찾아 그 분분이 site일 확률을 수치적으로 제시해 줌으로써 사용자로 하여금 site의 존재 유무와 그 위치를 판단하는데 기준을 제공해주는 유용한 툴이다. 하지만 기존의 FEATURE에서 사용된 데이터 이외의 새로운 단백질 구조 데이터를 FEATURE에 적용하기 위해서는 FEATURE 내부의 module을 입력 데이터 구조에 맞게 수정해야 한다. 그러나 FEATURE 내부의 module 구조를 수정하는 방식이 직관적이지 않기 때문에 많은 연구자들이 FEATURE를 원활하게 사용하지 못하였다. 따라서 본 논문에서는 FEATURE의 내부 구조를 분석하고 FEATURE를 새로운 단백질 데이터에 적용하기 위한 방법을 제시한다.

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특징 래핑을 통한 숫자형 특징과 범주형 특징이 혼합된 데이터의 클래스 분류 성능 향상 기법 (Improving Classification Performance for Data with Numeric and Categorical Attributes Using Feature Wrapping)

  • 이재성;김대원
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제36권12호
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    • pp.1024-1027
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    • 2009
  • 본 논문에서는 혼합형 데이터에 대한 특징 선별 기법의 효율성을 비교하기 위해 특징 필터링과 특징 래핑을 통한 특징 선별 후, 클래스 분류 성능을 측정하였다. 혼합형 데이터는 숫자형 특징과 범주형 특징이 함께 혼합되어 있으므로, 숫자형 특징을 범주형 특징으로 이산화를 하여 단일형 데이터로 변환한 뒤 특징 선별 기법 등을 적용할 수 있다. 본 연구에서는 혼합형 데이터를 전처리하여 단일형 데이터로 변환하고, 널리 활용되는 특징 필터링 기법과 특징 래핑 기법을 통해 클래스 분류 성능을 높일 수 있는 특징 집합을 선별하였다. 선별된 특징 집합을 통한 클래스 분류 성능을 비교한 결과, 특징 필터링에 비해 특징 래핑을 통해 선별한 특징 집합을 활용하여 클래스 분류를 하였을 때 분류 정확도가 높은 것을 확인할 수 있었다.

A METHOD OF IMAGE DATA RETRIEVAL BASED ON SELF-ORGANIZING MAPS

  • Lee, Mal-Rey;Oh, Jong-Chul
    • Journal of applied mathematics & informatics
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    • 제9권2호
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    • pp.793-806
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    • 2002
  • Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.