• Journal of Korean Home Economics Education Association
• /
• v.28 no.3
• /
• pp.65-77
• /
• 2016

The purpose of this study is to develop an inquiry sheet for the contents of the clothing curriculum of home economics in middle schools using a physical fiber Identification method to increase students' interests in and understanding of clothing materials. Therefore, a physical fiber Identification method suitable to middle school students was developed in actual classrooms and the effects were analyzed. As a result, the physical fiber identification method was developed to distinguish between wool and acrylic knits; moreover, the identification method between silk and polyester fiber was studied. And then the inquiry sheet using fiber identification method was also developed. When interests in learning, attitudes of acceptance toward learning, and learning achievements of the experimental group (used the inquiry sheet) and the control group (did not use the inquiry sheet) were compared, the experimental group scored higher in every category, all of which were meaningful differences. Thus, this study demonstrated that the developed fiber differentiation method and inquiry sheet improved self-directed learning as well as learners' understanding of clothing materials by enabling the application the knowledge to the learners' realities.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1991.04a
• /
• pp.3-7
• /
• 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
• /
• v.9 no.5
• /
• pp.445-455
• /
• 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.

• Journal of Korea Technical Association of The Pulp and Paper Industry
• /
• v.47 no.6
• /
• pp.41-48
• /
• 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
• /
• v.54 no.4
• /
• pp.711-723
• /
• 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.

• Journal of the Korea Furniture Society
• /
• v.10 no.1
• /
• pp.1-12
• /
• 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.

• Computational Structural Engineering
• /
• v.4 no.3
• /
• pp.99-106
• /
• 1991

Flexural load-deflection relationships for steel fiber reinforced concrete(SFRC) are dependent on the tensile and compressive constitutive behaviors of the material, which may be refined in the presence of strain gradients under flexural loads. Considering the relatively large amount of flexural test results available for steel fiber reinforced concrete, and the relative ease of conducting such tests in comparison with direct tension tests, it seems to be important to obtain basic information on the tensile constitutive behavior of SFRC from the result of flexural tests. For this purpose "System Identification" technique was used for interpretating the flexural test data and it was successful in obtaining optimum sets of main parameters which explain the tensile constitutive behavior of SFRC under flexure.

• Smart Structures and Systems
• /
• v.18 no.3
• /
• pp.389-403
• /
• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.05a
• /
• pp.588-593
• /
• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2007.04a
• /
• pp.565-570
• /
• 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.