• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1-4
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• 2003

Knowledge classification and expression of constructed knowledge have been main research issues in the field of knowledge representation. Constructed design knowledge of the former product loses its utility when new products with different structures are introduced to the market. In order to construct the design knowledge for a new product. designers need to reconstruct the design knowledge with new relationships. The design knowledge has been constructed with level trees, but it is difficult to rearrange the relations. Design DNA is proposed in this work in order to facilitate the rearrangement of design knowledge and give flexibility to knowledge structure. Design DNA is based on Layout-oriented domain knowledge and Function-oriented domain knowledge, which enables to generate new design knowledge that will result in new part geometries for given constraints on the part functions. Design DNA is applied to the design knowledge of lever system of the automatic transmission of passenger cars as an example.

• Coupled systems mechanics
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• 제10권6호
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• pp.521-544
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• 2021

In this work, we present the development of a 3D lattice-type model at microscale based upon the Voronoi-cell representation of material microstructure. This model can capture the coupling between mechanic and electric fields with non-linear constitutive behavior for both. More precisely, for electric part we consider the ferroelectric constitutive behavior with the possibility of domain switching polarization, which can be handled in the same fashion as deformation theory of plasticity. For mechanics part, we introduce the constitutive model of plasticity with the Armstrong-Frederick kinematic hardening. This model is used to simulate a complete coupling of the chosen electric and mechanics behavior with a multiscale approach implemented within the same computational architecture.

• 한국CDE학회논문집
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• 제9권1호
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• pp.51-61
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes.. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the second one of the two companion papers, describes the similarity assessment method using NFD.

• 한국CDE학회논문집
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• 제9권1호
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• pp.44-50
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the first one of the two companion papers, describes the similarity assessment methods using convex decomposition and FFD.

• 한국CDE학회논문집
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• 제8권1호
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• pp.35-40
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• 2003

In this paper, we present the methods to develop a Web-based CAM system that is capable to simulate machining processes in solid model form through internet. The methods include the dynamic representation on Web of machining operation, workpiece definition on the request of users, interpretation technique of NC par programs through CORBA, and algorithm for efficient machining simulation. Based on the methods, a Web-based CAM system it implemented at http://virtual.ulsan.ac.kr. Using the Web-based CAM system, public users tan simulate machining processes by own NC part programs.

• 대한수학교육학회지:학교수학
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• 제13권3호
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• pp.447-468
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• 2011

고등학교 수학교과과정에서 이차곡선에 관련된 단원의 지도는 다른 어떤 단원보다도 연결성이 고려된 지도가 필요한 단원이다. 다시 말해 대수적 접근 방식과 기하적 접근 방식이 동시에 병렬적으로 지도되어야 한다. 특히 대수적 조작력이 미흡한 하위권 학생들에게는 이차곡선에 대한 성질을 역동적으로 표현하는 시각적 표상을 심어주는 기하적 접근 방식이 더욱 중요하다. 이를 위하여 본 연구는 이차곡선의 지도에 있어서 GeoGebra에 기반한 역동적인 시각적 표상의 중요성을 제안하고자 현행 고등학교 '기하와 벡터' 10종의 교과서와 익힘책의 이차곡선 단원 중 포물선에 관련된 부분을 분석하여 시각적 표상을 극대화할 수 있는 지도 방안을 제안하는 실험적 수업을 진행하고 학생들의 표상의 변화를 분석하였다.

• 대한기계학회논문집A
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• 제31권2호
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• pp.260-268
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• 2007

Demand for the use of 3D CAD DMU systems over the Internet environment has been increased. However, transmission of commercial 3D kernels has delayed the communication effectiveness due to the kernel size. Light weight CAD geometric kernel design methodology is required for rapid transmission in the distributed environment. In this paper, an assembly data structure suitable for the top-down and bottom-up assembly models has been constructed. Part features are stored without a hierarchy so that they are created and saved in no particular order. In particular, this paper proposes a new assembly representation model, called multi-level assembly representation (MAR), for the PDM based assembly DMU system. Since the geometric kernel retains assembly hierarchy and topological information, it is applied to the web-viewer for the PDM based DMU system. Effectiveness of the proposed geometric kernel is confirmed through various case studies.

• 한국통신학회논문지
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• 제37권7C호
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• pp.554-564
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• 2012

차량인식은 차량 후보영역 검출단계와 검출된 후보 영역에서 특징을 기반으로 차량을 검증하는 차량 검증단계로 나누어진다. 선형 변환 방식의 특징은 차원 감소 효과와 통계적인 특징을 지니게 되어, 이동이나 회전에 강인한 특성을 갖는다. 선형 변환 방식 중 비음수행렬분해(Non-negative Matrix Factorization, NMF)는 부분 기반 표현 방식으로 차량의 국소적인 특징을 기저벡터로 사용하여 희소성을 갖는 특징을 추출할 수 있기 때문에 도심영상에서 발생하는 차폐 영역에 따른 인식률 저하를 방지할 수 있다. 본 논문에서는 차량 인식에 적합한 NMF 특징 추출 방법을 제안하고, 인식률을 검증하였다. 또한 희소성 제약 조건을 이용하여 기저 벡터에 희소성을 가지는 SNMF(Sparse NMF)와 LVQ2(Learning Vector Quantization) 신경 회로망을 결합하여 차폐 영역에 대한 차량 인식 효율을 기존의 NMF를 이용한 방법과 비교하였다. NMF를 이용하는 특징은 차량이 혼재되어 차폐 영역이 빈번히 발생하는 도심에서도 강건한 특징임을 보였다.

• 한국CDE학회논문집
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• 제5권2호
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• pp.103-112
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• 2000

CAD databases are the core element to the management of product information. A key to the successful use of the databases is a rational method of query to and retrieval from the databases. Although it is parts geometry that users eager to retrieve from the CAD databases, no system yet supports geometry-based query. This paper aims at developing a new method of assessing geometric similarity which can serve as the basis of geometric query for CAD database. The proposed method uses ASVP (Alternating Sums of Volumes with Partitioning) decomposition that is a volumetric representation of a part obtained from its boundary representation. A measure of geometric similarity between two solid models is defined on their ASVP tree representations. The measure can take into account overall shapes of parte, shapes of features and their locations. Several properties that a similarity measure needs to satisfy are discussed. The geometric query developed in this paper can be used in a wide range of applications using CAD databases, which include similarity-based design retrieval, variant process planning, and components selection from part library. An experiment has been carried out to demonstrate the effectiveness of the method, and the results are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권12호
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.