• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.125-126
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• 1996

Beijing-Arizona-Taipei-Connecticut (BATC) survey is a long term project to map the spectral energy distribution of various objects using 15 intermediate band filters and aims to cover about 450 sq degrees of northern sky. The SED information, combined with image structure information, is used to classify objects into several stellar and galaxy categories as well as QSO candidates. In this paper, we present a preliminary setup of robust data reduction procedure recently developed at NCU and also briefly discuss general classification scheme: redshift estimate, and automatic detection of variable objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6043-6062
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• 2019

Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.

• Journal of Fashion Business
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• v.15 no.4
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• pp.155-166
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• 2011

The purposes of this study was to examine Chinese consumers's perception toward Korean fashion brands. Especially, this study aimed to compare the Chinese consumers in three local groups (Beijing, Shanghai, & Yangji). The subjects used for this study were one hundred ninety-six participants(male; 110, female; 86) in 20s age who live in China. For data analysis, descriptive statistics, Cronbach's alpha, and t-test were used. Cronbach's alpha test revealed that all instruments which were used for this study had over 0.85. As the results, first, 67.9% of Chinese consumers perceived Korean brands correctly as Korean brands. Also, 42.5% of Chinese participants had purchased Korean fashion brand products such as Teenie Weenie or E-land. Second, there were not significant differences in brand attitudes among three group participants. However, there was a significant difference in 'brand preference' factor, one of three brand attitudes, between two ethnic groups. Finally, there were not signifiant differences in brand image, while there was a significant difference in intelligent brand image, one of 4 brand image factors, between two ethnic groups. These results of this study would be very useful for Korean fashion brand marketers in order to understand Chinese fashion consumers more details, and provide more efficient fashion marketing strategies.

• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.770-777
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• 2016

It has been a challenge for researchers to accurately measure high temperature creep strain online without damaging the mechanical properties of the pipe surface. To this end, a noncontact method for measuring high temperature strain of a main steam pipe based on digital image correlation was proposed, and a system for monitoring of high temperature strain was designed and developed. Wavelet thresholding was used for denoising measurement data. The sub-pixel displacement search algorithm with curved surface fitting was improved to increase measurement accuracy. A field test was carried out to investigate the designed monitoring system of high temperature strain. The measuring error was less than $0.4ppm/^{\circ}C$, which meets actual measurement requirements for engineering. Our findings provide a new way to monitor creep damage of the main steam pipe of a boiler of an ultra-supercritical power plant in service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2480-2495
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• 2014

This paper describes a hierarchical method for image-based age estimation that combines age group classification and age value estimation. The proposed method uses a coarse-to-fine strategy with different appearance features to describe facial shape and texture. Considering the damage to continuity between neighboring groups caused by fixed divisions during age group classification, a dynamic grouping technique is employed to allow non-fixed groups. Based on the given group, an ordinal hyperplane ranking (OHRank) model is employed to transform age estimation into a series of binary enquiry problems that can take advantage of the intrinsic correlation and ordinal information of age. A set of experiments on FG-NET are presented and the results demonstrate the validity of our solution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5583-5604
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• 2016

In this paper, we present a novel image operator to extract textual information in natural scene images. First, a powerful refiner called the Stroke Color Extension, which extends the widely used Stroke Width Transform by incorporating color information of strokes, is proposed to achieve significantly enhanced performance on intra-character connection and non-character removal. Second, a character classifier is trained by using gradient features. The classifier not only eliminates non-character components but also remains a large number of characters. Third, an effective extractor called the Character Color Transform combines color information of characters and geometry features. It is used to extract potential characters which are not correctly extracted in previous steps. Fourth, a Convolutional Neural Network model is used to verify text candidates, improving the performance of text detection. The proposed technique is tested on two public datasets, i.e., ICDAR2011 dataset and ICDAR2013 dataset. The experimental results show that our approach achieves state-of-the-art performance.

• Structural Engineering and Mechanics
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• v.71 no.6
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• pp.751-761
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• 2019

Lamb wave technology is a promising technology in the field of structural health monitoring and can be applied in the detection and monitoring of defects in plate structures. Based on the reconstruction algorithm for the probabilistic inspection of damage (RAPID), a Lamb-based detection and evaluation method of through-thickness rectangular notches in metal plates was proposed in this study. The influences of through-thickness rectangular notch length and the angle between sensing path and notch length direction on signals were further explored through simulations and experiments. Then a damage index calculation method which focuses on both phase and amplitude difference between detected signals and baseline signals was proposed. Based on the damage index difference between two vertically crossed sensing paths which pass through the notch in a sensor network, the notch direction identification method was proposed. In addition, the notch length was determined based on the damage index distribution along sensing paths. The experimental results showed that the image reconstructed with the proposed method could reflect the information for the evaluation of notches.

• Korean Journal of Agricultural Science
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• v.40 no.4
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• pp.417-423
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• 2013

Recently, there is the huge interest to promote the export of Korean agro-food to Chinese market. However, small number of papers analyze the Chinese consumer to find the strategy for launching Korean agro-food in Chinese market. The purpose of this paper is to analyze Chinese consumer for Korean agro-food in Chine. Survey analysis was conducted in 4 big city (Beijing, Shanghai, Guangzhou, and Tsingtao) for this research. The results of studies present a few findings: First, many Chinese consumers prefer Korean agro-food. Second, among big cities, Beijing shows the highest level of preference for Korean agro-food, Third, Chinese consumers can pay higher price for Korean agro-food, Fourth, Chinese consumer usually buy small amount of agro-food. Fifth, the image of Korea is also important to promote the exportation of Korean agro-food to Chinese market.