• Journal of Digital Convergence
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• v.18 no.8
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• pp.371-376
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• 2020

This study is about convenience store lunchbox labels for efficient information transmission. Its purpose is to identify the importance of the information that buyers want and to establish labels that can help buyers when purchasing lunchbox. The literature research and survey identified the factors that buyers consider important when purchasing lunchbox, analyzed the information identified on the label and the order of importance of the information. The study found that label information that lunchbox buyers think is important was in order of price, expiration date, calories, lunchbox components, and date of manufacture. Also, buyers who do not check labels pointed the less readable labels. This study expects that the information that buyers think is important will be newly established on labels so that buyers can easily check and purchase information through labels.

• YAKHAK HOEJI
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• v.52 no.3
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• pp.201-211
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• 2008

All pharmacists must provide the drug consultation whenever dispense drugs to patients by the Korean Pharmacy Law. Drug consultation is very important procedure for increasing pharmacotherapy. Because it maximizes the therapeutic effects or/and minimizes adverse drug reaction during the drug therapy. However, it is not easy to do because of the dynamic and hectic pharmacy environment. Especially, if someone has a disabling body function, they required more time and efforts to perform consultation by pharmacist. Currently several auxiliary labels for helping drug consultation are using in pharmacy practice but not for disabling patients. Therefore we developed the total 53 auxiliary labels with size of 0.7 cm (width) and 1 cm (length) by Braillewriter letters for blind patients. This research has been performed for total 12 months (Mar. 15ts, 2007$\sim$Feb. 25th, 2008) and the developing methods are consisted of 4 steps: 1) selection of essential informations, 2) simplification of information, 3) changing for Braillewriter letters, 4) application and revising by blindness patients. Also the labels are consisted of 12 for adverse reactions and precautions, 8 for directions, 2 for storages, 9 for duration, 9 for dosage forms, and 12 for common names. After developed those labels, we revised those labels by discussion with 2 blind people. In conclusion, the new auxiliary labels for blind patients can increase therapeutic effects and decrease risks from pharmacotherapy besides decreasing of pharmacist's work load in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1053-1059
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• 2012

Barcode labels are being widely used for identifying products since they are cheap and easy to use. As the barcode labels are massively produced by seal printing, some labels have defects such as poor printing quality or data mismatch between barcode and the text. Barcode read errors and business errors caused by defected barcodes result in delay in logistics and increased processing costs. In this paper, we propose an on-line barcode verification system that uses image processing technique to verify the quality of labels at the production stage. The proposed system captures label images through the vision camera and then checks the print quality and verifies the combination of barcodes and texts in a label. If any defected label is found, the proposed system gives alarm signals and marks the defected labels so that they are removed at early stage of the production.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.6
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• pp.716-724
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• 1998

The purpose of this study is to investigate the washing methods specified on care labels of blue jeans and to examine the appropriateness of the specification. In the study, the present condition of care labels on 100% cotton blue jeans was investigated and the consumers' washing methods of blue jeans were surveyed. Also, the shrinkage of blue jeans after washing was measured. The major results were as follows: 1. In spite of the fact that same materials of 100% cotton denim were used in all cases, washing signs on care labels showed very differently and the symbols of hand wash, using light duty detergent, no wring, drying in shade, and warm ironing with a covering cloth were demanding too high level of care for the protection of blue jeans. 2. Almost all consumers didn't follow instructions proposed on care labels because they didn't anticipate problem. In reality, most consumers washed blue jeans by machine in cold water with heavy duty detergent at standard course, dried under the sun, and didn't iron. The 40.8% of consumers didn't have problems even if they didn't follow instructions. Most problems happened after washing were shrinkage in length, but in shrinkage test after 15 times washings, it was found that there was no serious shrinkage problem. 3. For ideal care of blue jean, it is necessary for manufacturers to recognize the importance of care label and to stick correct appropriate care label. Also, consumers have to trust and follow instructions on care label.

• Bulletin of the Korean Mathematical Society
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• v.59 no.1
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• pp.119-139
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• 2022

This paper deals with the λ-labeling and L(2, 1)-coloring of simple graphs. A λ-labeling of a graph G is any labeling of the vertices of G with different labels such that any two adjacent vertices receive labels which differ at least two. Also an L(2, 1)-coloring of G is any labeling of the vertices of G such that any two adjacent vertices receive labels which differ at least two and any two vertices with distance two receive distinct labels. Assume that a partial λ-labeling f is given in a graph G. A general question is whether f can be extended to a λ-labeling of G. We show that the extension is feasible if and only if a Hamiltonian path consistent with some distance constraints exists in the complement of G. Then we consider line graph of bipartite multigraphs and determine the minimum number of labels in L(2, 1)-coloring and λ-labeling of these graphs. In fact we obtain easily computable formulas for the path covering number and the maximum path of the complement of these graphs. We obtain a polynomial time algorithm which generates all Hamiltonian paths in the related graphs. A special case is the Cartesian product graph K_n☐K_n and the generation of λ-squares.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.55-66
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• 2023

Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including "reader community-opinion leader-text content" was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-of-the-art approaches, which indicates that the UIT has better performance in the smooth time dimension.

• Journal of Distribution Science
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• v.22 no.3
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• pp.11-19
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• 2024

Purpose: Thisstudy aimsto explore the mechanism by which poverty alleviation labelsinfluence purchase intentionsin the e-commerce environment, as well as the role of mediating factors within this process. Research Design, Data, and Methodology: The research employs a questionnaire survey method, targeting a sample of 1668 consumers in Anhui Province, China. It focuses on understanding the impact of poverty alleviation labels on e-commerce platforms and the involvement of mediating factors like capability trust, benevolence trust, and honesty trust in this relationship. Results: The results indicate that poverty alleviation labels on e-commerce platforms significantly enhance consumers' purchase intentions. Regression analysis validates the positive impact of poverty alleviation labels on purchase intentions, capability trust, benevolence trust, and honesty trust, revealing the existence of mediating effects. Conclusions: The study provides empirical support for e-commerce poverty alleviation marketing, emphasizing the importance of focusing on and strengthening consumer trust in products. It offers profound insights for both academic research and practical operations in related fields. However, the research is limited to Anhui Province, and future studies could consider expanding the sample size to gain a deeper understanding of regional and cultural variations. Additionally, further research is encouraged to explore the applicability of the findings to other sales channels.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.1-7
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• 2018

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

• Speech Sciences
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• v.10 no.1
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• pp.27-39
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• 2003

This study attempts to verify the reliability of automatically generated segment labels as compared to those obtained by conventional labelling by hand. First of all, an autolabeller is constructed using the standard HMM speech recognition technique. For evaluation, we compare the automatically generated labels with manually annotated labels for the same speech data. The comparison is performed by calculating the temporal difference between an autolabel boundary and its corresponding hand label boundary. When the mismatched duration between two labels falls within 10 msec, we consider the autolabel as correct. The results suggest that overall 78% of autolabels are correctly obtained. It is found that the boundary of obstruents is better aligned than that of sonorants and vowels. In case of stop sound classes, strong stops in manner-of-articulation wise and velar stops in place-of-articulation wise show better performance in boundary alignment. The result suggests that more phone-specific consideration is necessary to improve autosegmentation performance.