• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.2
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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.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.1
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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.

• Korean Journal of Computational Design and Engineering
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• v.9 no.2
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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.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.2
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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.

• Journal of Ship and Ocean Technology
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• v.9 no.3
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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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.

• Korean Journal of Computational Design and Engineering
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• v.2 no.3
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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.

• Journal of IKEEE
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• v.13 no.3
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• pp.18-23
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• 2009

FEATURE is a computational method to recognize functional and structural sites for automatic protein function prediction. By profiling physicochemical properties around residues, FEATURE can characterize and predict functional and structural sites in 3D protein structures in a high-throughput manner. Despite its effectiveness, it has been challenging to apply FEATURE to novel protein data due to limited customization support. To address this problem, we thoroughly analyze the internal modules of FEATURE and propose a methodology to customize FEATURE so that it can be used for new protein data for automatic functional annotations.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1024-1027
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• 2009

In this letter, we evaluate the classification performance of mixed numeric and categorical data for comparing the efficiency of feature filtering and feature wrapping. Because the mixed data is composed of numeric and categorical features, the feature selection method was applied to data set after discretizing the numeric features in the given data set. In this study, we choose the feature subset for improving the classification performance of the data set after preprocessing. The experimental result of comparing the classification performance show that the feature wrapping method is more reliable than feature filtering method in the aspect of classification accuracy.

• Journal of applied mathematics & informatics
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• v.9 no.2
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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.