• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.52-55
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• 1996

Though the increment of using computer vision system in modern industry, there are lots of difficulties to measure precisely because of measurement error distortion phenomenon. Between these reasons, the distortion of edge is dominant reason which is occured by the blurred image. The blurred image is happened when camera can not discriminate its precise focus. To correct and generalize distortion phenomenon is imprrtant. Thus we must fix the discrimination criteria which is collected by image recognition of precise focus. The edge of image means discontinuous point of intensity, and the component of edge is discribed as high frequency component at special domain specturm of image. The good condition of focus means there are much high frequency energy in image. The method of discribing high frequency energy is gradient operater which determines the condition of focus.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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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.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.259-261
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• 2005

수천개의 Gene Expression Measurement를 생성해 내는 DNA Microarray 연구는 조직과 세포의 표본으로부터 진단에 유용한 Gene Expression 정보를 모으게 된다. 이런 종류의 Data를 분석하기 위하여 SVM(Support Vector Machine)을 사용한 새로운 방법이 연구되어왔다. 본 논문에서는 Gene Expression Data에 대한 고유벡터(Eigen Vector)를 이용하여 SVM의 성능을 향상시키고 질병진단에 유용한 Gene을 찾아 내는 알고리즘을 기술한다. 고유벡터를 통하여 Gene을 선택적으로 SVM Learning에 참가 시키고 분류의 결과를 통하여 추가된 Gene이 질병 진단에 미치는 영향력을 알아냄으로써 질병에 대한 Gene 역할을 파악 하는데 활용할 수 있다.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.159-165
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• 2013

ESD damage by Charged Cable Model (CCM) is introduced. Due to its own impedance characteristic unlike Human Body Model (HBM) or Machine Model (MM) electric component can be destroyed even though it is located after typical protection circuit. Possible mechanism of ESD damage to automotive electric control unit (ECU) in vehicle environment by CCM discharge was investigated. Based on investigation, field-returned vehicle whose ECU is expected to be damaged by CCM discharge was tested to reproduce it and similar electric component destruction inside ECU was observed. Suggestions to reduce the possibility of ESD damage by CCM are introduced.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.52-60
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• 2000

In order to design a reliable machine component efficiently, it is necessary to set up the process of durability analysis using computer simulation technique. In this paper, two methods for dynamic stress calculation, which are basis of durability analysis, are reviewed. Then, a user-oriented dynamic stress analysis program is developed from these two algorithms together with a general-purpose flexible body dynamic analysis and structural analysis programs. Finally, a slider-crank mechanism which has a flexible connecting-rod is chosen to show the special characteristics of these two dynamic stress calculation methods.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1248-1259
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• 2016

Classification methods based on dual energy X-ray absorptiometry, ultrasonic waves, and quantitative computed tomography have been proposed. Also, a classification method based on machine learning with bone mineral density and structural indicators extracted from the CT images has been proposed. We propose a method which enhances the performance of existing classification method based on bone mineral density and structural indicators by extending structural indicators and using principal component analysis. Experimental result shows that the proposed method in this paper improves the correctness of osteoporosis classification 2.8% with extended structural indicators only and 4.8% with both extended structural indicators and principal component analysis. In addition, this paper proposes a method of automatic phantom analysis needed to convert the CT values to BMD values. While existing method requires manual operation to mark the bone region within the phantom, the proposed method detects the bone region automatically by detecting circles in the CT image. The proposed method and the existing method gave the same conversion formula for converting CT value to bone mineral density.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.139-147
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• 2003

The paper presents a novel approach of constructing the state matrix of the multi-machine power system for SSR (subsynchronous resonance) analysis using the linearized equations of individual devices including electrical transmission network dynamics. The machine models in the local d-q reference frame are integrated with the network models in the common R-I reference frame by simply transforming their output equations into the R-I frame where the transformed output is used as the input to the network dynamics or vice versa. The salient feature of the formulation is that it allows for modular construction of various component models without rearranging the overall state space formulation. This universal SSR small signal stability program provides a flexible tool for systematic analyses of SSR small-signal stability impacts of both conventional devices such as generation systems and novel devices such as power electronic apparatus and their controllers. The paper also presents its application results to IEEE benchmark models.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2039-2044
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• 2003

The advanced infrastructure for accelerating the development of personal robots is presented. Based on this structure, effective ways for integrating the various commercial components and interfacing among them are studied. The infrastructure includes the technology such as modularization based on independent processing and standardization open to other developers. The infrastructure supports not only that each hardware component of a personal robot can be easily attached to and detached from the whole system mechanically but also that each software of the components can be functionally distributed. As a result, we developed the fully modularized personal robots mechanically, and a virtual machine for the control of these robots. In this paper the proposed infrastructure and its implementations are described.

• Journal of the Korean Society of Food Science and Nutrition
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• v.23 no.5
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• pp.805-810
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• 1994

Relationship between pungency and allicin content of pickled garlic during aging was examined . Degree of pungency of pickled garlic during aging at 20 $^{\circ}C$ was determined by the sensory evaluation. A panel of 10 members evaluated seven samples of pickled garlic which were aged for 0, 10 , 20, 40 , 50 or 60 days by using scoring test (seven point scale). The sensory evaluation results showed that pungency of pickled garlic decreased gradually during aging, and scored at 3.07 on the 40 th day of aging. Content of allicin, which was a major pungent component of garlic homogenate, was quantitatively analyzed by HPLC. The level of allicin in homogenate of pickled garlic was found to decrease gradually, and to 5.9% on the 40 th day of aging compared with that of fresh garlic. Relationship between the pungency score results and the content of allicin demonstrated a highly positive correlation (r=0.9648).

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.