• 한국가정과교육학회지
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• 제28권3호
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• pp.65-77
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• 2016

본 연구의 목적은 중학교 기술 가정교과 의복재료 단원에서 실물 교육자료집을 좀 더 적극적으로 활용하여 의복재료에 대한 학습자의 흥미와 이해를 높이고자 물리적 섬유감별방법이 들어간 탐구활동지를 개발하는데 있다. 이를 위해 중학교 2학년 수준에 적합한 물리적 섬유감별 방법을 개발하였으며, 이를 실제 수업에 적용하고 그 효과를 분석하였다. 연구 결과 양모와 아크릴, 견과 폴리에스터를 비교하는 물리적 섬유감별방법을 개발하였으며, 이를 활용하여 탐구활동지를 개발하였다. 탐구활동지를 수업에 활용한 실험 집단과 사용하지 않은 통제 집단의 학습흥미도, 학습수용태도, 학업성취도를 비교 분석한 결과, 탐구활동지를 사용한실험집단이 통제 집단보다 모두 높은 점수를 보였으며, 유의미한 차이가 있음을 알 수 있었다. 따라서 본 연구를 통해 제작된 섬유감별방법과 탐구활동지는 의복재료에 대한 학습자의 이해를 촉진시켜줄 뿐 아니라, 의복재료에 대한 정보를 학습자에게 제공함으로써 학습자가 실생활에 적용할 수 있을 뿐 아니라 학습자의 자기 주도적 학습을 촉진할 수 있는 것으로 생각된다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1991년도 봄 학술발표회 논문집
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• pp.3-7
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• 1991

Considering the relatively large amount of stable flexural teat results available for steel fiber reinforced concrete (SFRC) and their dependency on the constitutive behavior of the material, a technique called “System Identification” is used for interpretating the flexural test data in order to obtain basic information on the tensile constitutive behavior of steel fiber reinforced concrete. “System Identification” was successful in obtaining optimum sets of parameters which provide satisfactory matches between the measured and predicted flexural load-deflection relationships.

• Steel and Composite Structures
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• 제9권5호
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• pp.445-455
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• 2009

This study proposes a system identification technique for a fiber-reinforced polymer deck with neural networks. Neural networks are trained for system identification and the identified structure gives training data in return. This process is repeated until the identified parameters converge. Hence, the proposed algorithm is called an iterative neural network scheme. The proposed algorithm also relies on recent developments in the experimental design of the response surface method. The proposed strategy is verified with known systems and applied to a fiber-reinforced polymer bridge deck with experimental data.

• 펄프종이기술
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• 제47권6호
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• pp.41-48
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• 2015

Fiber identification was attempted for the early twenty century documents that were classified as national archives in Korea, as an initial step for establishing scientific preservation and restoration method. Fiber staining with C stain and a digital microscope were used for the observation. All the documents observed consisted of mostly softwood fibers from fir (Abies) and other minor supplementary fibers, and they were all deteriorated seriously by various damages and aging process. It seemed that at around 1914-1934, fir was used frequently as papermaking raw material.

• Structural Engineering and Mechanics
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• 제54권4호
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• pp.711-723
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• 2015

This study investigates the identification of added mass and its location in the glass fiber reinforced polymer (GFRP) beam structures. The main emphasis of this paper is to ascertain the importance of inclusion of rotational degrees of freedom (dofs) in the introduction of added mass or damage identification. Two identification indices that include the rotational dofs have been introduced in this paper: the modal force index (MFI) and the modal rotational curvature index (MRCI). The MFI amplifies damage signature using undamaged numerical stiffness matrix which is related to changes in the altered mode shapes from the original mode shapes. The MRCI is obtained by using a higher derivative of rotational mode shapes. Experimental and numerical results are compared with the existing methods leading to a conclusion that the contributions of the rotational modes play a key role in the identification of added mass. The authors believe that the similar results are likely in the case of damage identification also.

• 한국가구학회지
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• 제10권1호
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• pp.1-12
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• 1999

Of the vast number of plant taxa in the world, the wood is one of the most useful resources. It is important to identify the fibers of wood and pulp for the plant taxonomy and for the uses, but we do not have enough information on them, on them, especially for the computerizd data. The fiber identification is one of the difficult tasks. In addition to the plant taxonomy and the fiber-using industries, such identification is also important in many other fields, including education. document examiners, etc. For these purpose, the fibers should be exactly distinguished. The TISS system I have programed to identify various woods would also be useful in the identification of fibers by the genus and species in the features of unknown samples and in searching the features of a species based on its scientific name. Such searching programs are being developed in many other countries with a view to searching for the species name by using the features of the cells of the woody materials. With the survey of all the available literature, the features of the fibers of 124 species both of softwood and hardwood were examined under the electron and optical microscopies. Each species were coded and carded by the feature, and the databases were built. The microscopic were inputted into a personal computer program called and by a slide film scanner. The new computer program called TISS 2 was developed using C computer language. Korean language fonts were added to the TISS 2. The TISS 2 can be in adding and searching a image of fiber features both of a known fiber and an unknown fiber. The databases were corded for the DELTA system with was developed by Dallwitz and Paine in Australia, 1986.

• 전산구조공학
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• 제4권3호
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• pp.99-106
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• 1991

강섬유 보강 콘크리트(SFRC)의 휨 거동은 재료의 인장 및 압축 응력-변형도에 의존하며 이때 이들은 휨응력시 작용하는 strain gradient의 영향을 받게 된다. SFRC의 경우, 휨 실험은 직인장 실험과 비교하여 볼 때 상대적으로 간편하며 또한 다수의 실험결과가 확보되어 있다. 따라서 이들 휨 실험 결과로부터 SFRC의 기본적 재료 성질인 인장응력-변형도를 유출하는 것은 중요하다고 하겠다. 본 연구의 목적을 위하여 휨 실험 data를 해석하기 위한 "System Identification"방법론이 사용되었으며 그 결과 휨 응력하에서의 SFRC의 인장거동을 설명하는 주요 변수들이 고찰되었다.

• Smart Structures and Systems
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• 제18권3호
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• pp.389-403
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• 2016

Structural deflection can be identified from measured strains from long gague sensors, but the sensor layout scheme greatly influences on the accuracy of identified resutls. To determine the optimal sensor layout scheme for accurate deflection identification of the tied arch bridge, the method of optimal layout of long-gauge fiber optic sensors is studied, in which the characteristic curve is first developed by using the bending macro-strain curve under multiple target load conditions, then optimal sensor layout scheme with different number of sensors are determined. A tied arch bridge is studied as an example to verify the effectiveness and robustness of the proposed method for static and dynamic deflection identification.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.588-593
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.565-570
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.