• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.25-36
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• 2020

Motivation: Fibers as the extracellular filamentous structures determine the shape of the cytoskeletal structures. Their characterization and reconstruction from a 3D cellular image represent very useful quantitative information at the cellular level. In this paper, we presented a novel automatic method to extract fiber diameter distribution through a pipeline to reconstruct fibers from 3D fluorescence confocal images. The pipeline is composed of four steps: segmentation, skeletonization, template fitting and fiber tracking. Segmentation of fiber is achieved by defining an energy based on tensor voting framework. After skeletonizing segmented fibers, we fit a template for each seed point. Then, the fiber tracking step reconstructs fibers by finding the best match of the next fiber segment from the previous template. Thus, we define a fiber as a set of templates, based on which we calculate a diameter distribution of fibers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.877-881
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• 2005

In this paper, a novel method for analyzing a textile fabric structure is proposed to segment each yarn of the textile fabric from voxel data made out of its X-ray computed tomography (CT) images. In order to segment the each yarn, directions of fibers, of which yarn consists, are firstly estimated by correlating the voxel with a fiber model. Second, each fiber is reconstructed by clustering the voxel of the fiber using the estimated fiber direction as a similarity. Then, each yarn is reconstructed by clustering the reconstructed fibers using a distance which is newly defined as a dissimilarity. Consequently, each yarn of the textile fabric is segmented from the voxel data. The effectiveness of the proposed method is confirmed by experimentally applying the method to voxel data of a sample plain woven fabric, which is made of polyester two folded yarn. The each two folded yarn is correctly segmented by the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.12A
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• pp.1285-1290
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• 2004

In this paper, a new algorithm for contention resolution in optical burst switched network is proposed and simulated. The proposed algorithm is made from the all mixing of wavelength conversion, deflection routing, segmentation, and optical fiber delay line buffering. We analysis the performance of the proposed contention resolution algorithm by using ns-2. The application of the algorithm into Korea backbone network shows the superior performance of a low burst loss probability.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.2
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• pp.174-185
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• 2013

This study examines shopping orientation regarding bedclothes and the effect of the segmentation of consumers on the image of Hanji yarn bedclothes derived from mulberry fiber in order to contribute to the development of Hanji bedclothes products and consumer marketing segmentation. Data from 294 questionnaires filled out by female consumers in their 30s to 50s were used for statistical analysis. The shopping orientations for bedclothes are classified into six groups (trend oriented, material oriented, price oriented, convenience oriented, individuality oriented, and assurance oriented). Consumers were subdivided into four consumer segments (show-offish, self-confident, reasonable, and unconcerned case) based on shopping orientations for bedclothes. The images of Hanji bedclothes are categorized into four types (classic, practical, aesthetic, and natural) as related to the shopping orientations of consumers. In terms of consumer segmentation, the 'reasonable' segment is more likely to consider the 'classic' image of Hanji bedclothes as the highest image value; however, the 'show-offish' segment provides the highest value to the 'practical' image as compared to other segments.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.148-156
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• 2007

The fiber dispersion in fiber-reinferced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion in the composite PVA-ECC (polyvinyl alcohol-engineered cementitious composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, a new evaluation method is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a charged couple device (CCD) camera through a microscope, the fiber dispersion is evaluated using an image processing technique and statistical tools. In this image processing technique, the fibers are more accurately detected by employing an enhanced algorithm developed based on a discriminant method and watershed segmentation. The influence of fiber orientation on the fiber dispersion evaluation was also investigated via shape analyses of fiber images.

• Composites Research
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• v.36 no.6
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• pp.395-401
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• 2023

In this study, finite element modeling of unidirectional composite materials of the computed tomography (CT) was conducted using a supervised learning-based segmentation technique. Firstly, Micro-CT scan was performed to obtain the raw volume of unidirectional composite materials, providing microstructure information. From the CT volume images, actual microstructure of the cross-section of unidirectional composite materials was extracted by the labeling process. Then, a U-net deep learning model was trained with a small number of raw images as inputs and their labeled images as outputs to generate a segmentation model. Subsequently, most of remaining images were input to the trained U-net deep learning model to segment all raw volume for identifying complex microstructure, which was used for the generation of finite element model. Finally, the fiber volume fraction of the finite element model was compared with that of experimentally measured volume to validate the appropriateness of the proposed method.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.1
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• pp.103-113
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• 2019

Motivation: Polymerized actin-based cytoskeletal structures are crucial in shape, dynamics, and resilience of a cell. For example, dynamical actin-containing ruffles are located at leading edges of cells and have a significant impact on cell motility. Other filamentous actin (F-actin) bundles, called stress fibers, are essential in cell attachment and detachment. For this reason, their mechanistic understanding provides crucial information to solve practical problems related to cell interactions with materials in tissue engineering. Detecting and counting actin-based structures in a cellular ensemble is a fundamental first step. In this research, we suggest a new method to characterize F-actin wrapping fibers from confocal fluorescence image stacks. As fluorescently labeled F-actin often envelope the fibers, we first propose to segment these fibers by diminishing an energy based on maximum flow and minimum cut algorithm. The actual actin is detected through the use of bilateral filtering followed by a thresholding step. Later, concave actin bundles are detected through a graph-based procedure that actually determines if the considered actin filament is enclosing the fiber.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.1009-1012
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• 2008

The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC(Polyvinyl alcohol-Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device(CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.10
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• pp.1611-1620
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• 2010

This research compares the difference of each preference segments' subjective hands and sensibilities in order to analyze the correlations among preference, subjective hands, and sensibilities. Preference segments were classified into wool, acrylic, and long stitch length-preferred clusters in previous research. To evaluate the subjective hands and sensibilities of knit fabrics, the 20's and 30's women rated twelve knit fabrics by touching, using a questionnaire with a seven-point semantic differential scale. These twelve knit fabrics were differentiated by controlling the mixture ratio and stitch length using a computer-controlled automatic flat knit machine. The difference of each preference segments' subjective hands and sensibilities was determined using the conjoint analysis. The clusters perceived the subjective hands and sensibilities differently according to preferred constituent characteristics. There was no correlation between surface unevenness and preference in wool-preferred cluster, while there were negative correlations in other clusters. The acrylic-preferred cluster had a preference in coolness compared to other clusters; in addition, the long stitch-preferred cluster preferred flexibility/bulkiness and extensibility than the others. All clusters preferred modem and natural sensibilities that were caused by different constituent characteristics of knit fabrics.