• Journal of Welding and Joining
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• v.13 no.1
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• pp.51-61
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• 1995

품질보증 활동은 제품의 품질등급을 그 규격기준에 따라 규정하고, 발주자가 지정하는 품질의 제품을 실현하는 것이지만, 압력용기 등의 특정 제품에 대해서는 공적인 제3자가 기관에 의한 검사 인증제도가 요구되는 경우가 있다. 미국에서는 압력용기 등의 규격체계는 민간 Base의 ASME Code를 중심으로 하고, 운용에 있어서의 품질보증제도는 ASME Stamp에 의한 제작공장 인증과 적검사기관을 대행하는 보험회사와의 협조에 의한 개별제품인증이 행해진다. 소위 체제 지향의 품질 관리방식을 채택하고 있다. 미국 외의 구라파제국의 이와는 상대적으로 제품지향의 제품질관리를 하였다고 할 수가 있다. 그러나 87년에 제정되어 세계적으로 확산되고 있는 ISO-9000 시리즈는 미국식의 체제지향의 품질관리로서 양자간 접근방식이 대동소이하다. 그러므로 ASME Code의 품질관리에 대하여 그 본질과 구조 및 활동 내역 등을 고찰해 보는 것은 품질관리의 시대적 조류 파악에 도움이 될 것이다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.3
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• pp.227-231
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• 2004

A total of 219 polymerase chain reaction tests of genetically modified (GM) DNA sequences in soybean seeds and soybean sprouts were conducted during 2000-2001. No CM gene was found in 96 tests of soybean seeds. However, either a functional CP4EPSPS gene or the 355 promoter gene was found three times in 2000 and eight times in 2001, in between 0.01 and 0.17％ of soybean spout products, in 123 tests. Since the amount of GM genes was much less than the threshold limit of 3％, none of the 11 positive soybean-sprout samples needed to be libeled GM crops. Of these, seven sprout samples were from domestic seeds and four were from seeds imported from China. To find the contamination route, the raw materials, seed surface, floor of the storage room, area around the selection machine, surface of the packaging film and corn powder used in the package were tested. The 35S promoter gene was detected in only two samples of the corn powder (0.1％). Although we could not find the cause of the GM contamination, the sprout package film is one possibility. In total,8.9％ of the soybean sprout tests were GM positive, but the amounts were much less than the threshold of 3％. This means that there are frequent false-positives and these would threaten the sprout industry if GMO were decided qualitatively. Food companies should make their safety data available to the public and make an effort to address people's concerns about GM food more openly. In addition, there is a need to establish a quantitative test for GM genes in sampled water and a sampling method for raw materials.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.4
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• pp.308-314
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• 2016

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.463-465
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• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.58-63
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• 2009

The appearance inspection of various electronic products and parts was executed by the eyesight of human. The appearance inspection is applied to the most electronic component of LCD Panel, flexible PCB and remote control. If the appearance of electronic products of small and minute size is inspected by the eyesight of human, we can't expect the stable inspection result because inspection result is changed by condition of physical and spirit of the checker. Therefore currently machine vision systems are used to many appearance inspection fields instead of inspection by human. The many problems of inspection by the checker are not occurred in machine vision circumstance. However, the inspection by automatic machine vision system is mainly influenced by illumination of workplace. In this paper, we propose a histogram transform method for improving accuracy of machine visual inspection.

• The Korea Beekeeping Bulletin
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• s.340
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• pp.28-30
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• 2009

• The Korea Beekeeping Bulletin
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• s.342
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• pp.14-14
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• 2009

• Product Safety
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• s.25
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• pp.22-36
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• 1994

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.727-734
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• 2003

Recently various quality assurance activities have been applied in software industry for the purpose of software qualify improvement, and the ultimate target of those activities are focused on removing defects from its developed applications. We declared "ZERO DEFECT 21" movement on March 1999 whose purpose is to deliver defect-free applications to the customer. In this paper we would like to introduce the followings $\circled1$ Approaching Methods, $\circled2$ Achievements of "ZERO DEFECT 21". After accomplishing first you of "ZERO DEFECT 21" movement which consist of Audits and Software inspections, we could get the following improvement . $\circled1$ due to conducting the "Audits," we could prevent 22 cases of customer claims, enhance 11.7% of design quality and improve 23.3% of deliverable reusability : $\circled2$ also, due to conducting the "Periodic Sampling inspection and Final inspection," we could enhance 123% of defect rate compared with early stage of development and 247% of defect rate compared with previous yew. Based on the survey results, we could conclude that "ZERO DEFECT 21 " movement provides confidence to project team members and reliability to our customers.ce to project team members and reliability to our customers.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.803-814
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• 2008

Product line architectures include variable parts to be selected according to product specific requirements. In order to derive architectures that are valid for a particular product from product line architectures, variabilities of product line architectures must be systematically managed. In this paper, we adopt an approach to variability management of product line architectures through an explicit mapping between a feature model and product line architecture models. If this mapping is incorrect or there exists inconsistency among product line architectural elements, variabilities of product line architectures cannot be managed correctly. Therefore, this paper formally defines product line architectural models in terms of conceptual, process, deployment, and module views, and mapping relationships between the feature model and the architectural models. Consistency rules for correct variability management of product line architectures are defined in terms of consistency in each of product line architecture model, consistency between different architectural view models, and consistency between a feature model and product line architectural models. These consistency rules provide a theoretical foundation for deriving valid product architecture from product line architectures.