• Proceedings of the Korean Radioactive Waste Society Conference
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• 2019.05a
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• pp.275-276
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• 2019

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.7
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• pp.1023-1033
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• 2008

The purpose of this study was to segment home-shopper market by using the demographic characteristics. This study enables a better unders landing of home-shoppers and improving the strategy of marketing. The specific objects of this study are as follow: First, it was to exam market segmentations by demographic factors using mixture model. Second, it was to exam shopping orientations of fashion merchandise according to segmentation groups. The data was collected from 637 subjects who had used the home shopping more than one time in a year. The data was analysised through frequencies, factor analysis, ANOVA, Duncan's mutiple range tests with SPSS 12.0 and Mixture model. The results of data are as follows: 1. The result of market segmentation as demographic factor using Mixture model was extracted to 4 market segments called 20's/ unmarried stage, 30's/ children bearing & rearing stage, 40's/ families with children's education stage, 50's/ aging stage. 2. Shopping orientations were extracted to 5 factors called a pleasure oriented, convenience oriented, off-line oriented, human oriented, thrift oriented.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.23-34
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• 2004

In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.6
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• pp.11-23
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• 2002

Computational models up to now for Korean morphology have been linear in that it deal with only segmentation of morphemes rather than formation of the internal structure of a word. When integrating a linear computational model with syntax analysis, it requires an additional interface component between this model and the syntax to bind morphemes into sentence constituents. Furthermore the linear model is not semantically intuitive. In this paper, based on word-syntactical viewpoint, we propose an integrated computational model that deals with morpheme segmentation, formation of syntactic element (sentence constituent), and even internal structure of word. Formalism of two-level morphology is employed to cope with morpheme segmentation and alternation problems, and functional diacritics are proposed to incorporate categorial context into the two-level formalism. A modified GLR-based algorithm is also proposed to check syntactical constraint of morphemes.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.621-635
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• 2023

The assessment of structural condition is a crucial process for evaluating its usability and determining the diagnostic cycle. The currently employed manpower-based methods suffer from issues related to safety, efficiency, and objectivity. To address these concerns, research based on deep learning using images is being conducted. However, acquiring structural damage data is challenging, making it difficult to construct a substantial amount of training data, thus limiting the effectiveness of deep learning-based condition assessment. In this study, we propose a foundation model-based 2-step structural damage analysis to overcome the lack of training data in image-based structural condition assessments. We subdivided the elements of structural condition assessment into instantiation and quantification. In the quantification step, we applied a foundation model for image segmentation. Our method demonstrated a 10%-point increase in mean intersection over union compared to conventional image segmentation techniques, with a notable 40%-point improvement in the case of rebar exposure. We anticipate that our proposed approach will enhance performance in domains where acquiring training data is challenging.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.513-524
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• 2022

Recently, detecting damages of civil infrastructures from digital images using deep learning technology became a very popular research topic. In order to adapt those methodologies to the field, it is essential to explain robustness of deep learning models. Our research points out that the existing pixel-based deep learning model evaluation metrics are not sufficient for detecting cracks since cracks have linear appearance, and proposes a new evaluation methodology to explain crack segmentation deep learning model more rationally. Specifically, we design, implement and validate a methodology to generate tolerance buffer alongside skeletonized ground truth data and prediction results to consider overall similarity of topology of the ground truth and the prediction rather than pixel-wise accuracy. We could overcome over-estimation or under-estimation problem of crack segmentation model evaluation through using our methodology, and we expect that our methodology can explain crack segmentation deep learning models better.

• 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.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.363-373
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• 2024

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.29-32
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• 2000

This paper proposes a motion segmentation algorithm for layer decomposition of image sequences. The proposed algorithm segments an image into initial regions by using its color and texture and computes a motion model of each initial region. Each pixel assigns one of the motion represented by the models or a motion except them, which segments the image into the motion regions. The proposed algorithm is app]ied image sequences and the segmented motion is shown.

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
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• pp.121-126
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• 1995

A new method for Chinese word segmentation named Conditional F'||'&'||'BMM (Forward and Backward Maximal Matching) which incorporates both bigram statistics (ie., mutual infonllation and difference of t-test between Chinese characters) and linguistic rules for ambiguity resolution is proposed in this paper The key characteristics of this model are the use of: (i) statistics which can be automatically derived from any raw corpus, (ii) a rule base for disambiguation with consistency and controlled size to be built up in a systematic way.