• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.581-586
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• 1996

This paper presents a feature recognition system for recognizing and extracting feature information needed for machining from design data contained in the CAD database of AutoCAD system. The developed system carries out feature recognition from an orthographic view of a press mold containing not only atomic features such as holes, pockets, and slots, but also compound features. Based on the result of feature recognition, it generates a 3-D modeling of the press mold. Especially, The feature recognition part is designed for detecting feature styles according to feature definition and classification, extracting parameters for various atomic features, and constructing necessary data structures for the recognized features.

• 한국CDE학회논문집
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• 제5권1호
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• pp.12-22
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• 2000

Network and Internet technology opens up another domain for building future CAD/CAM environment. The environment will be global, network-centric, and spatially distributed. In this paper, we present an approach for network-centric feature-based modeling in a distributed design environment. The presented approach combines the current feature-based modeling technique with distributed computing and communication technology for supporting product modeling and collaborative design activities over the network. The approach is implemented in a client/server architecture, in which Web-enabled feature modeling clients, neutral feature model server, and other applications communicate with one another via a standard communication protocol. The paper discusses how the neutral feature model supports multiple views and maintains naming consistency between geometric entities of the server and clients. Moreover, it explains how to minimize the network delay between the server and client according to incremental feature modeling operations.

• 한국인터넷방송통신학회논문지
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• 제14권3호
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• pp.105-110
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• 2014

휘처 지향 소프트웨어 프로덕트 라인 공학 방법은 휘처 단위로 프로덕트 라인의 핵심 자산을 개발하고, 이를 조합하여 쉽게 다양한 제품을 개발한다. 하지만 휘처를 조합하여 제품을 개발하는 동안 휘처상호작용문제를 효과적으로 대응하지 못하면, 개발된 제품이 원하는 대로 동작하지 않을 수 있다. 본 논문에서 휘처 간에 발생될 수 있는 원하지 않는 상호작용을 검출하는 기법과 이를 효과적으로 모듈화하는 방법을 제안한다. 제안된 방법의 적용가능성을 평가하기 위해서 공학용 계산기 프로덕트 라인에 적용하였다.

• 한국CDE학회논문집
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• 제12권5호
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• pp.395-404
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• 2007

Feature-based modeling and history-based modeling are the two main paradigms that are used in most of current CAD systems. Although these modeling paradigms make it easier for designers to create solid model, it may pose dependency constraints on features that are interacting one with another. When editing such features, these constraints often cause unpredictable and unacceptable results. For example, when a parent feature is deleted, the child features of the parent feature are also deleted. This entails re-generations of the deleted features, which requires additional modeling time. In order to complement this situation, we propose a method to delete only the features of interest by disconnecting the dependency constraints. This method can provide designers with more efficient way of model modification.

• 전기전자학회논문지
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• 제19권4호
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• pp.526-534
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• 2015

In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by local shrinking and expanding operations, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where the concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our scheme is tested by recognition experiments on the handwritten numeral database CENPARMI, where SVM classifier with RBF kernel is used. The experimental results show the usefulness of our scheme and recognition rate of 99.10% is achieved.

• 반도체디스플레이기술학회지
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• 제12권3호
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• pp.59-64
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• 2013

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

• 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.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• 한국정밀공학회지
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• 제28권10호
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• pp.1174-1180
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• 2011

Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.