• Journal of the Korean Society of Clothing and Textiles
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• v.2 no.2
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• pp.227-235
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• 1978

This study was designed to find out consumer's interest in garments label and to help home economists make interest for the further study in relation between producers and consumers as gap bridger. The questionnair method was used to obtained the data which was made by a result of self-administered questionnair. A size of random sample for this research was 364 subjects. The study found the following: (1) Most of consumers are relatively interested in garments label. The most concious age level was woman of fourty. (2) The most interest factor was label of size, price, fiber contents. brand name, directions and precautions on proper use and care. (3) The order of complaining item after washing was change of size, and color, seam pucker. deformation of collar. and button. (4) Most of consumers do not follow the direction when they clean their garments. (5) The respondents seem to be not understand the garment's informative label.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.121-128
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• 2017

In this paper, we propose a multi-label classification method in which multi-label classification estimation techniques are applied to resolving location prediction problem. Most of previous studies related to location prediction have focused on the use of single-label classification by using contextual information such as user's movement paths, demographic information, etc. However, in this paper, we focused on the case where users are free to visit multiple locations, forcing decision-makers to use multi-labeled dataset. By using 2373 contextual dataset which was compiled from college students, we have obtained the best results with classifiers such as bagging, random subspace, and decision tree with the multi-label classification estimation methods like binary relevance(BR), binary pairwise classification (PW).

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.4 no.1
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• pp.85-102
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• 1980

Accepting the necessity for maintaining the objectives of the existing circulation system, the computer-based system could be designed by the system analyst and librarians to gain a variety of improvements in the maintenance, accessibility of circulation records and more meaningful statistical records. If the terminal can be operated on-line, then this circulation data is transmitted directly to the computer, where it may update to the circulation file immediately or alternatively be kept in direct access file for updating in batch mode. on-line system in the circulation operations is "data-collection system" and "Bar-coded label system" Bar-coded label system is simple, quick, and error-free input of data. Attached to CRT terminal is a "light pen" which is hand held and will read a bar-coded label as the pen is passed over the labels (one affixed to the book itself, other carried on the borrower cards). Instantaneously the data concerning transaction is stored in the central mini-computer. It is useful, economical for us to co-operate many libraries in Korea and design borrower's ID code, book no., classification code in the Bar-coded label system by the members of the computer center and the library staff at every stage. As for book loan, the borrowers ID code, book number and classification code are scanned by the bar-code scanner or light pen and the computer decides whether to loan and store the data. The visual display unit shows the present status of a borrowers borrowing and decides whether borrower can borrow.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• Journal of the Korean Home Economics Association
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• v.33 no.2
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• pp.159-166
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• 1995

The purpose of the present study is to investigate how closely care labels comply with the 1984 version of the Care Labeling Rule, as well as the change in degree of compliance prior to and after the 1988 IFI care label campaign. Label information was analyzed on the basis of country of origin. The information was also divided into two sets. The basis for dividing the data into two sets was the beginning of the IFI care label campaign in 1988 The data were obtained from 1147 checklists. The information for 1147 samples in six clothing categories were collected from department, specialty, and discount stores. Chi-square analyses were conducted to test hypotheses. While there was no significant difference in the number of incorrect labels on domestically produced garments compared to imported garments in set 1, there was a significant difference in set 2. Also, there was a significnat differnece in the number of incorrect labels between in set 1 and in set 2.

• ETRI Journal
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• v.46 no.1
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• pp.59-70
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• 2024

We introduce a high-performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta-pseudo-label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human- and pseudo-labeled data. Second, the influence of noisy pseudo-labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human-labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.

• The Korean Journal of Community Living Science
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• v.20 no.1
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• pp.51-59
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• 2009

This study was performed to investigate the effect of food and nutrition label education on the perception and utilization of nutrition labels on food packaging, and to suggest the importance and necessity of food and nutrition label education in the school curriculum. 811 junior-high school students participated in this study and completed self-administered questionnaires regarding general characteristics, and the perception and utilization of nutrition labels. Knowledge of nutrition labels was tested by 13 questions on the questionnaire. Data was analyzed (using SAS package program) based on the educational experience with nutrition labels. Significant differences in each variable were tested using the $X^2$-test and t-test. Students who had learned about the food and nutrition labeling system had more knowledge of nutrition labels and were more likely to check the nutrition label before purchasing food. In addition, students who had been educated about food and nutrition labels in the school curriculum had a significantly higher understanding and recognition of the nutrition label system. These results suggest that education concerning the food and nutrition label system increased the students' interest in nutrition labels and helped them choose healthy food. Therefore, it is necessary to include an education program about food and nutrition labels in the school curriculum to help students use label information and make healthy dietary choices.

• Smart Structures and Systems
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• v.29 no.1
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• Journal of Distribution Science
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• v.22 no.8
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• pp.87-98
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• 2024

Purpose: This study aimed to examine the influence of private label cues, including store image, product design, price promotion, and origin image, on consumers repurchase intention by mediating consumption value from a distribution perspective. Additionally, it explored nationality's moderating role in the relationship between consumption value and repurchase intention. Research design, data and methodology: Drawing on the SOR model, data were collected from 246 consumers who had purchased private-label products in the past month. Structural equation modeling analysis was employed to test hypotheses using AMOS and SPSS. Results: Findings revealed that cues significantly impact consumers' perception of consumption value, influencing repurchase intention. Price promotion directly affected repurchase intention, while other cues indirectly influenced it through consumption value mediation. Nationality moderated the relationship between consumption value and repurchase intention, with Korean consumers showing a higher propensity to repurchase than Chinese consumers. Conclusions: Theoretical implications of the study contributed to understanding consumer behavior by confirming the impact of private label cues, elucidating their differential effects on repurchase intention, and integrating theoretical frameworks. Managerial implications underscored the significance of leveraging cues to enhance consumption value perceptions, tailoring marketing strategies to accommodate cultural nuances, and utilizing cues to bolster consumer repurchase intentions, ultimately enhancing distribution channel effectiveness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.607-610
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• 2021

When learning a model in supervised learning, input data and the label of the data are required. However, labeling is high cost task and if automated, there is no guarantee that the label will always be correct. In the case of supervised learning in such a noisy labels environment, the accuracy of the model increases at the initial stage of learning, but decrease significantly after a certain period of time. There are various methods to solve the noisy label problem. But in most cases, the probability predicted by the model is used as the pseudo label. So, we proposed a method to predict the true label more quickly by refining the probabilities predicted by the model. Result of experiments on the same environment and dataset, it was confirmed that the performance improved and converged faster. Through this, it can be applied to methods that use the probability distribution predicted by the model among existing studies. And it is possible to reduce the time required for learning because it can converge faster in the same environment.