• 한국농공학회논문집
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• 제56권1호
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• pp.11-20
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• 2014

Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detecting the communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of the probabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For the probabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communities of nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparing with Monte-carlo Simulation and other community-detecting methods.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.810-813
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• 2000

Burr makes trobles on manufacturing process due to deburring cost, quality of products and productivity. This paper described the results of experimental study on the influence of the cutting parameters on the formation of exit burrs in face milling. The cutting parameters were investigated changing exit angle, rake nagle , lead angle in tool geometry as well as feed per tooth. Also we carried out experimets on several materials. Using the result of experimental study, burr types are classified according to appearance and formation mechanism in exit burr and we are considered the burr formation in each type of burr.

• International Journal of Advanced Culture Technology
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• 제6권3호
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1545-1546
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• 2008

In this study, an image processing method with FRANSAC(Fuzzy RANSAC) is presented and discussed for the development of a microscopic gap measuring algorithm. Many problems in edge detection processing are mainly occurred by the illumination system. A serious problem is that the edge set of gap could include the error elements that have relatively larger error than normal. This problem leads to a incorrect measurement of gap. We present a gap measuring algorithm using FRANSAC[1] that is a representative robust estimation algorithm. FRANSAC is peformed by first categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification and then sampling in only good sample set. Experimental results show that the presented gap measuring algorithm gives a higher accurate value of gap especially for the more noisy image data.

• 대한수학회보
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• 제57권3호
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• pp.739-750
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• 2020

A simple graph is called semisymmetric if it is regular and edge transitive but not vertex transitive. Let p be a prime. Folkman proved [J. Folkman, Regular line-symmetric graphs, Journal of Combinatorial Theory 3 (1967), no. 3, 215-232] that no semisymmetric graph of order 2p or 2p² exists. In this paper an extension of his result in the case of cubic graphs of order 34p³, p ≠ 17, is obtained.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.345-350
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• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권3호
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• pp.148-155
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• 2003

In this paper, a nonlinear anisotropic filtering method without the loss of important information happened due to the repeated filtering in magnetic resonance images is proposed. First of all original images are divided into four regions, e.g., SPR(Strong Plain Region), EPR(Easy Plain Region), SER(Strong Edge Region), and EER(Easy Edge Region). An optimal template among multiple templates is selected, then the nonlinear anisotropic filtering based on the template is applied in pixel by pixel basis. In the proposed algorithm, filtering strength of EER containing important information is adjusted very weak and filtering strength for remaining regions is also adjusted according to the degree of the importance. In spite of repeated filtering, resulting images by the proposed method could still preserve anatomy information of original images without any degradation. Compared to the existing nonlinear anisotropic filtering, the proposed filtering method with multiple templates provides higher reliability for filtered images.

• 한국컴퓨터정보학회논문지
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• 제21권12호
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• pp.125-130
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• 2016

Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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• pp.721-726
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• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.1247-1249
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• 2023

스마트 컨트랙트는 블록체인 상에서 실행되는 프로그램으로 복잡한 비즈니스 논리를 처리할 수 있다. 그러나 블록체인의 무결성과 조건에 따라 실행되는 특성을 이용한 악의적 사용으로 인하여 블록체인 보안에서 시급한 문제가 되고있다. 따라서 스마트 컨트랙트 취약성 탐지문제는 최근 많은 연구가 이루어지고 있다. 그러나 기존 연구의 대부분이 단일 유형의 취약성 여부에 대한 탐지에만 초점이 맞춰져 있어 여러 유형의 취약성에 대한 동시 식별이 어렵다. 이 문제를 해결하고자 본 연구에서는 스마트 컨트랙트 소스코드 제어 흐름 그래프를 기반으로 그래프의 forward edge와 backward edge를 고려한 신경망으로 그래프 구조를 학습한 후 그래프 multi-label classification을 진행하여 다중 취약성을 탐지할 수 있는 모델을 제안한다.