• 한국CDE학회논문집
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• 제10권4호
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• pp.262-273
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• 2005

For CAD data users, few things are as frustrating as receiving CAD data that is unusable due to poor data quality. Users waste time trying to get better data, fixing the data, or even rebuilding the data from scratch from paper drawings or other sources. Most related works and commercial tools handle the boundary representation (B-Rep) shape of CAD models. However, we propose a design history?based approach for healing CAD model errors. Because the design history, which covers the features, the history tree, the parameterization data and constraints, reflects the design intent, CAD model errors can be healed by an interdependency analysis of the feature commands or of the parametric data of each feature command, and by the reconstruction of these feature commands through the rule-based reasoning of an expert system. Unlike other B Rep correction methods, our method automatically heals parametric feature models without translating them to a B-Rep shape, and it also preserves engineering information.

• 한국정밀공학회지
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• 제16권10호
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• pp.194-202
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• 1999

A method for pattern recognition based on wavelet transform is proposed in this paper. The boundary of the object to be recognized includes shape information for object of machine parts. The contour is first represented using a one-dimensional signal and normalized about translation, rotation and scale, then is used to build the wavelet transform representation of the object. Wavelets allow us to decompose a function into multi-resolution hierarchy of localized frequency bands. The recognition of 2-dimensional object based on the wavelet is described to analyze the shape of analysis technique; the discrete wavelet transform(DWT). The feature vectors obtained using wavelet analysis is classified using a multi-layer neural network. The results show that, compared with the use of fourier descriptors, recognition using wavelet is more stable and efficient representation. And particularly the performance for objects corrupted with noise is better than that of other method.

• 정보보호학회논문지
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• 제32권5호
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• pp.975-986
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• 2022

딥러닝은 이미지 처리에 있어 우수한 성능을 보여주며 큰 주목을 받고 있지만, 입력 데이터에 대한 변조를 통해 모델이 오분류하게 만드는 적대적 공격에 매우 취약하다. 적대적 공격을 통해 생성된 적대적 예제는 사람이 식별하기 어려울 정도로 최소한으로 변조가 되며 이미지의 전체적인 시각적 특징은 변하지 않는다. 딥러닝 모델과 달리 사람은 이미지의 여러 특징을 기반으로 판단하기 때문에 적대적 예제에 속지 않는다. 본 논문은 이러한 점에 착안하여 이미지의 색상, 모양과 같은 시각적이고 상징적인 특징인 Symbolic Representation을 활용한 적대적 예제 탐지 방법을 제안한다. 입력 이미지에 대한 분류결과에 대응하는 Symbolic Representation과 입력 이미지로부터 추출한 Symbolic Representation을 비교하여 적대적 예제를 탐지한다. 다양한 방법으로 생성한 적대적 예제를 대상으로 탐지성능을 측정한 결과, 공격 목표 및 방법에 따라 상이하지만 specific target attack에 대하여 최대 99.02%의 탐지율을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4513-4532
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• 2014

Motivated by the Weber's Law, this paper proposes an efficient and robust shape descriptor based on the perceptual stimulus model, called Weber's Law Shape Descriptor (WLSD). It is based on the theory that human perception of a pattern depends not only on the change of stimulus intensity, but also on the original stimulus intensity. Invariant to scale and rotation is the intrinsic properties of WLSD. As a global shape descriptor, WLSD has far lower computation complexity while is as discriminative as state-of-art shape descriptors. Experimental results demonstrate the strong capability of the proposed method in handling shape retrieval.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2000년도 종합학술대회발표논문집
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• pp.99-114
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• 2000

ㆍ General - ISO 10303-202 : Associative draughting (CDS initiatve) ㆍ Building Construction - ISO 10303-225 : Building elements using explicit shape representation - ISO 10303-230 : Building structural frames : steelwork (CIMSTEEL) (중략)

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3804-3819
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• 2018

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

• Journal of Computational Design and Engineering
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• 제3권1호
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• pp.14-23
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• 2016

The problem of fitting B-spline curves to planar point clouds is studied in this paper. A novel method is proposed to deal with the most challenging case where multiple intersecting curves or curves with self-intersection are necessary for shape representation. A method based on Delauney Triangulation of data points is developed to identify connected components which is also capable of removing outliers. A skeleton representation is utilized to represent the topological structure which is further used to create a weighted graph for deciding the merging of curve segments. Different to existing approaches which utilize local shape information near intersections, our method considers shape characteristics of curve segments in a larger scope and is thus capable of giving more satisfactory results. By fitting each group of data points with a B-spline curve, we solve the problems of curve structure reconstruction from point clouds, as well as the vectorization of simple line drawing images by drawing lines reconstruction.

• Journal of information and communication convergence engineering
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• 제11권2호
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• pp.124-131
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• 2013

Extraction and representation of postures and/or gestures from human activities in videos have been a focus of research in this area of action recognition. With various applications cropping up from different fields, this paper seeks to improve the performance of these action recognition machines by proposing a shape-based silhouette-edge descriptor for the human body. Information entropy, a method to measure the randomness of a sequence of symbols, is used to aid the selection of vital key postures from video frames. Morphological operations are applied to extract and stack edges to uniquely represent different actions shape-wise. To classify an action from a new input video, a Hausdorff distance measure is applied between the gallery representations and the query images formed from the proposed procedure. The method is tested on known public databases for its validation. An effective method of human action annotation and description has been effectively achieved.

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• 한국멀티미디어학회논문지
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• 제5권1호
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• pp.18-27
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• 2002

본 논문에서는 평면 형상에 대해 자연스러운 근사화와 효과적인 지역화를 제공하는 새로운 계층적 표현 방법인 MBO-tree를 제안하였다. 곡선 근사화 방법으로 알려진 Douglas-Peucker 알고리즘을 기반으로 곡선 분할점의 근사화 오차를 분할점과 함께 계층적 트리 노드에 저장함으로써 근사화 척도로 활용하였으며, 보다 자연스러운 형상 표현을 위해 오차 조정 알고리즘도 제안하였다. MBO-tree의 오타 조정은 자식 노드의 오차가 부모 노드의 오차보다 크지 않도록 제한하는 것으로 구현하였다. 지역화를 위해서는 MBR(Minimum Bounding Rectangle)을 단순 확장한 MBO(Minimum Bounding Octangle)를 경계 영역으로 사용하였다. MBO는 다른 계층적 표현 체계의 경계 영역들에 비해 대상 객체에 밀착하여 효과적으로 포함할 뿐만 아니라, 계층간 경계 영역 포함 관계도 만족하기 때문에 점 포함 테스트나 형상간 교차 테스트 등과 같은 계층적인 기하학 연산에 매우 유용하다. 실험을 통해서 본 논문에서 제안한 방법이 strip tree, arc tree, HAL tree등과 같은 다른 계층적 표현 체계에 비해 보다 자연스러운 근사화와 효과적인 지역화가 가능함을 확인하였다.