• Journal of The Korean Association For Science Education
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• v.35 no.5
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• pp.877-884
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• 2015

Technology is an important means to enhance students' understanding about scientific concepts. In particular, newly introduced 3D printing technology has great potential to help students learn scientific concepts better. 3D printing is a process for a creating physical object with a three dimensional model. In this study, we explored two types of learners' (students vs. adults) motivation and satisfaction with 3D printing technology. With regard to motivation, student learners showed higher task value, self-efficacy for learning, and satisfaction than adult learners. The result implied that 3D printing technology is more effective to student learners than adult learners. In addition, for adult learner group, negative relationship between technology and satisfaction was found. Therefore, support for reducing the technology anxiety for adult learners is necessary. Further discussions are provided for the research and application of 3D printing technology in science classroom.

• The Optical Journal
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• s.144
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• pp.28-28
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• 2013

광학 및 특수 유리 전문 기업 쇼트(SChoTT)가 지난 2월 19일 전라남도, 한국광기술원, 소모홀딩스엔테크놀러지와 적외선 기술 및 투자 협력을 위한 업무협약(MoU)을 체결했다. 이날 행사에는 쇼트 어드벤스드 옵틱스 사업부(SChoTT Advanced optics Business Segment)의 마리타 파쉬(Marita Paasch) 사장, 전라남도 박준영 도지사, 소모그룹 신준수 회장, 한국광기술원 김선호 원장 등이 참석해 적외선 광학 사업을 추진하기로 했다.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.705-718
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• 1996

A similarity coefficient based algorithm is proposed to solve the machine cells and part families formation problem in group technology. Similarity coefficients are newly designed from the machine-part incidence matrix. Machine cells are formed using a recurrent neural network in which the similarity coefficients are used as connection weights between processing units. Then parts are assigned to complete the cell composition. The proposed algorithm is applied to 30 different kinds of problems appeared in the literature. The results are compared to those by the GRAFICS algorithm in terms of the grouping efficiency and efficacy.

• IE interfaces
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• v.15 no.3
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• pp.308-315
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• 2002

This study presents the web based cutting parameter selection system using Group Technology (GT). The GT is basically applied to classify and code the work material and cutting process which are main factors to affect cutting parameter selection. The proposed system has been designed to electronically select proper cutting conditions based on the stored GT database. The existing approaches used in most small and medium sized companies are basically to use manufacturing engineer's experience or to find the recommended values from the manufacturing engineers handbook. These processes are often time consuming and inconsistent, especially when a new engineer is involved. Consequently, the proposed system could automatically and consistently generate the proper cutting conditions (feed, depth of cut, and cutting speed) as soon as relatively simple data input is given thanks to the classified GT database.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.780-783
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• 2009

기존 정보보호 콘텐츠에 대한 공유는 웹이나 메일 등을 통하여 수동적으로 배포되고, 운영관리자의 판단을 거친 후 보안등급에 맞게 제공되었다. 하지만 사이버 공간의 침해사고는 급속히 확산되어 끊임없이 보안 환경을 위협하는데 그에 대한 확산방지 대응은 즉각적이지 못한 문제점을 가지고 있다. 이러한 침해사고의 빠른 확산을 방지하기 위해서는 실시간 보안 콘텐츠 공유를 통해 각 시스템에서 콘텐츠의 추가 및 변경이 발생할 경우 자동으로 인지 또는 배포할 수 있는 정보보호 시스템을 개발할 필요가 있다. 따라서 본 논문에서는 보안등급에 따른 가상 정보공유 그룹을 구성하기 위해 P2P방식인 JXTA 플랫폼을 적용하였다. 또한 JXTA CMS의 확장을 통해 정보공유 시스템 간 연동할 수 있는 실시간 보안 콘텐츠 공유 시스템을 설계하였다. 이를 통하여 지리적으로 분산된 정보보호 콘텐츠를 실시간 자동인지와 보안등급에 맞는 실시간 공유 방식으로 배포하는 정보보호 시스템을 구현하고자 한다.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.1
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• pp.27-35
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• 2019

The existing middle-long span railway bridge has been mainly applied to steel box girder bridges. However, the steel box girder bridges have disadvantages in securing the space under the bridge, and the main girder is made of a thin plate box shape, resulting in a ringing noise due to the vibration. Many complaints about noise have been raised. For this reason, there is a need for the development of long railway bridges that can replace steel box girder bridges. In this paper, the characteristics of the steel composite railway bridge currently developed were introduced and a time history analysis was conducted using MIDAS Civil reflecting the speed of KTX load for 40m and 50m bridges. In addition, from the analysis results, the dynamic behavior of target bridges were verified and it was examined whether they meet the dynamic performance criteria proposed in the railway design standards. As a result, all of the bridges under review satisfied the dynamic safety criteria, however, in case of 40m of span, the vertical acceleration value was very large. In order to solve this problem, authors proposed the improvement plan and corrected the cross section to confirm that the vertical acceleration decreased.