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북미 법규 강화를 고려한 국내 자동차의 천정강도 시험특성에 관한 연구
김은희,이재광,이문구,홍민성,Kim, Eun-Hee,Lee, Jae-Kwang,Lee, Moon-Gu,Hong, Min-Sung 한국생산제조학회 2009 한국생산제조학회지 Vol.26 No.3
In order to reduce the risk of roll over crash, one of the greatest risk events, National Highway Traffic Safety Administration(NHTSA) issued Notice of Proposed Rulemaking(NPRM) enhancing the safety standard on roof crush resistance, FMVSS No. 216 and changing some part of the test procedure. According to this NPRM, the boundary Gross Vehicle Weight Rating(GVWR) of the vehicles applied by this standard is extended from 2,722kg(6000 lb) to 4,536 kg(10000 lb) and the applied test force is increased from 1.5 times to 2.5 times of Unloaded Vehicle Weight (UVW). Also the current limit on the amount of roof crush, 127mm(5 inch), is replaced with a new requirement of maintaining enough headroom without touching the head of a seated 50% male dummy. In this paper, we carried out the rollover crash test on some domestic cars and investigated their safety due to the KMVSS No. 92 and the enhanced safety standard, FMVSS No. 216, respectively. The result shows that most of them can satisfy the new standards but further tests will be necessary, especially for heavier cars.
분산 / 병렬 시스템을 위한 최소화의 오류-허용 방사형 그래프 설계
전문석(Jun Moon Seog),이문구(Lee Moon Gu) 한국정보처리학회 1998 정보처리학회논문지 Vol.5 No.12
The arrangement graph, which is a viable interconnection scheme for parallel and distributed systems, has been proposed as an attractive alternative to th n-cube. However. A fault -tolerant design model which is well suitable for the arrangement graph doesn't has been proposed until recently, but fault-tolerant design models for many schemes have been proposed in a large number of paper. So, our paper presents a new fault-tolerant design technique suited for the arrangement graph. To maintains the previous structures when it occurs a fault in the current processing, the scheme properly substitutes a fault-component into the existing structures by adding a spare component. The first of all, it converts arrangement graph into a circulant graph using the hamiltonian property and then uses automorphism of circulant graph to tolerate faults. Also, We optimize the cost of rate fault-tolerant architectures by adding exactly k spare processors(while tolerating up to k processor) and minimizing the maximum number of links per processor. Specially, we proposes a new technique to minimize the maximum number of links.
Defect Classification of Cross-section of Additive Manufacturing Using Image-Labeling
Jeong-Seong Lee(이정성),Byung-Joo Choi(최병주),Moon-Gu Lee(이문구),Jung-Sub Kim(김정섭),Sang-Won Lee(이상원),Yong-Ho Jeon(전용호) 한국기계가공학회 2020 한국기계가공학회지 Vol.19 No.7
Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.