• Transactions of the Korean Society of Mechanical Engineers
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• v.8 no.3
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• pp.257-263
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• 1984

저항 점 용접시 발생하는 어려운 문제점의 하나는 아무리 동일한 용접조건 하에서 용접을 한다하 더라도 균일한 용접품질을 얻을 수 없다는 점이다. 이는 주로 용접중의 전원에서의 변화로 인한 용접전류 및 전압변동, 용접모재의 표면상태 및 두께의 불균일, 두 전극의 마멸 및 맞춤상태 등의 인자 때문에 기인한다고 볼 수 있다. 이러한 문제점을 해결하기 위해서는 용접하는 도중에 수시 로 인자 때문에 기인한다고 볼 수 있다. 이러한 문제점을 해결하기 위해서는 용접하는 도중에 수 시로 용접 상태를 점검하여 용접조건을 그때 그때에 적합하게 변화시켜 주는 것이 바람직한데 우 선 용접상태를 판별하기 위해서는 용접품질(weld quality)을 잘 대변할 수 있는 용접법 변수를 찾 아내는 것이 중요한 일이다. 따라서 본 논문에서는 용접품질에 관련되는 변수중, 전기동저항과 전 극분리현상을 측정해서 마이크로 컴퓨터를 사용하여 관련되는 특정값들을 구했고 이들과 인장실 험에서 얻은 용접강도와의 상관관계를 얻음으로써 과연 어떠한 공정변수가 용접품질을 잘 대변해 줄 수 있는가를 실험적으로 조사하였다.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.221-223
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• 2002

최근 정보통신산업의 발전 및 생활수준의 향상에 따라 정보통신기기, 정밀제어기기 등의 보급이 확산되면서 안정적이면서도 신뢰성을 갖춘 양질의 전력에 대한 수용가 측의 관심 및 요구가 급격히 증가하고 있고, 이에 파라 전력품질 모니터링. 진단 향상 시스템에 대한 연구 및 개발이 활발히 이루어지고 있다. 본 논문에서는 LG산전과 서울대학교가 공동 개발한 전력품질 모니터링 시스템에 연계되어 전력품질 오염위치 추정, 오염원 판별 통계적 추이 분석, 전력품질 인덱싱, 확률론적 분석을 수행하는 전력품질 진단시스템에 대해 소개한다. 전 세계적으로 상용화되어 있는 전력품질 진단시스템을 살펴보고, 현재 LG산전과 서울대학교가 공동 개발하고 있는 전력품질 진단시스템의 구조 및 기능에 대해 소개한다.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.215-221
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• 2014

The Fingerprint recognition system is a system which identify the user's identify by verifying user's fingerprint and prepared data. The performance of fingerprint recognition system is dependent on 'fingerprint recognition time' and 'fingerprint recognition accuracy' and so on. In this paper, we developed a evaluation model about efficiency based on ISO quality evaluation standard for evaluating of quality level of fingerprint recognition system. We expect to contribute to construct and use of evaluation criteria based on quality evaluation standard by this study.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1197-1205
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• 2018

In this paper, we perform Single Image Super Resolution(SISR) for acquired images of EOTS or IRST from naval combat system. In order to conduct super resolution, we use Generative Adversarial Networks(GANs), which consists of a generative model to create a super-resolution image from the given low-resolution image and a discriminative model to determine whether the generated super-resolution image is qualified as a high-resolution image by adjusting various learning parameters. The learning parameters consist of a crop size of input image, the depth of sub-pixel layer, and the types of training images. Regarding evaluation method, we apply not only general image quality metrics, but feature descriptor methods. As a result, a larger crop size, a deeper sub-pixel layer, and high-resolution training images yield good performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.933-937
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• 2005

FCBGA가 마더보드에 실장된 후 솔더 조인트에 균열이 생기면 단선이 발생한다. 솔더 조인트의 신뢰성을 평가하기 위한 방법 중 전단강도시험(shear test)은 약한 솔더 조인트를 판별하기 어려워 양품 로트와 불량 로트를 구별할 수 없으며, 인장강도시험(pull test)은 솔더 볼의 위치별 산포가 크고, peel test는 품질을 정량적으로 나타낼 수 없는 등의 문제가 있다. 새로운 시험 방법은 Area Pull Test(이하 APT)라 명명했으며 peel test와 pull test를 합한 개념으로서, 시험 샘플을 만드는 과정은 peel test와 동일하다. 솔더 조인트의 인장강도 측정은 지그를 만들어서 FCBGA 전체를 당겨서 측정하였다. 샘플은 Ni도금 두께를 3, 5, 8 ${\mu}m$로 제작하여 불량(3${\mu}m$), 양품으로 구분하였고, 양품 또한 품질 수준을 두가지(5,8${\mu}m$)로 나누었다. 그 결과 peel test 기준에 의거한 불량, 양품을 정량적인 수치(인장강도)로 판별할 수 있었으며, 솔더 조인트의 파괴모드별 인장강도를 구분 할 수 있었다.

• Journal of Biosystems Engineering
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• v.24 no.3
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• pp.225-234
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• 1999

This study was performed as a basic research for establishing an objective quality evaluation method on whole bulbs of garlic. The size of a whole bulb of garlic, the number and the uniformity of complete individual garlics, and the existence of bad individual garlics in the whole bulb of garlic were selected as quality grading factors. Quality discrimination algorithms with machine vision techniques were developed and verified for the four factors based on morphological and physical features of whole bulbs of garlic. Based on the results, the size discrimination by the projected area of a whole bulbs of garlic suggested four grading levels and the algorithm for predicting the number of complete individual garlics based on the peaks on its projected boundary showed ${\pm}$0.78 prediction error. In addition, the uniformity represented by coefficient of variation could be divided into four levels, but the algorithm for discriminating the existence of bad individual garlics in a whole bulb of garlic was not effective.

• Journal of Korea Multimedia Society
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• v.11 no.1
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• pp.13-22
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• 2008

This paper proposes an efficient ROI code block discrimination algorithm for dynamic ROI coding. The proposed algorithm calculates the girth of the ROI only with some mask information in consideration of the characteristics of the shape of the ROI for reducing a ROI code block discrimination time, and this proposed algorithm discriminates whether there is a ROI code block by the girth and the critical value of the ROI. Also, this discrimination algorithm is capable of treating the coefficients of the background within a ROI code block preferentially and controlling a loss by controlling the threshold value of the ROI. In order to demonstrate the utility of the proposed method, this paper conducted a comparative experiment of the proposed method with the existing methods. As a result of this experiment, it was confirmed that the proposed method was superior to the conventional methods in terms of quality and speed.

• Journal of Ginseng Research
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• v.26 no.1
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• pp.6-9
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• 2002

This study was undertaken to evaluate the automatic decision-making on the rusty root of fresh ginseng (Panax ginseng C.A. Meyer) by an image analyzer. Critical value of rusty root of ginseng by image analyzing was the percentage of grey value 0∼148 area (G 148) to the total area of grey value 0∼255. And the discriminant formula of rusty root of ginseng as follows; rusty root of ginseng : 6.68$\times$G(148) +3.74, normal ginseng : 2.86$\times$G(148) +9.96, and fitness rates of this formula were 89.8%. Also, we developed the automatic rusty root of decision-making program. As the result of this study, the automatic decision-making on the rusty root of fresh ginseng by an image analyzer seems to have high possibility.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.55-64
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• 2013

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.