• Korean Journal of Community Nutrition
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• v.23 no.2
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• pp.116-127
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• 2018

Objectives: The purpose of this study was to examine the coverage of the current mandatory nutrition labeling system on the nutrient intake of Koreans. Methods: KNHANES dietary intake data (2013) of 7,242 subjects were used in the analysis. KNHANES dietary intake data were collected by a 24-hour recall method by trained dietitians. For analysis, all food items consumed by the subjects were classified into two groups (foods with mandatory labeling and other foods). In the next step, all food items were reclassified into four groups according to the food type and nutrition labeling regulations: raw material food, processed food of raw material characteristics, processed foods without mandatory labeling, and processed foods with mandatory labeling. The intake of energy and five nutrients (carbohydrate, protein, fat, saturated fat, and sodium) of subjects from each food group were analyzed to determine the coverage of the mandatory nutrition labeling system among the total nutrient intake of Koreans. Results: The average intake of foods with mandatory labeling were 384g/day, which was approximately one quarter of the total daily food intake (1,544 g/day). The proportion of energy and five nutrients intake from foods with mandatory labeling was 18.1%~47.4%. The average food intake from the 4 food groups were 745 g/day (48.3%) for the raw food materials, 54 g/day (3.5%) for the processed food of raw material characteristics, 391 g/day (25.3%) for the processed foods without mandatory labeling, and 354 g/day (22.9%) for the processed foods with mandatory labeling. Conclusions: Although nutrition labeling is a useful tool for providing nutritional information to consumers, the coverage of current mandatory nutrition labeling system on daily nutrient intake of the Korean population is not high. To encourage informed choices and improve healthy eating habits of the Korean population, the nutrition labeling system should be expanded to include more food items and foodservice menus.

• Nutrition Research and Practice
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• v.3 no.4
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• pp.300-306
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• 2009

The goal of this research is to investigate the present situation for front of pack labeling in Korea and the perception of consumers for the new system of labeling, front of pack labeling, based on the consumer survey. We investigated the number of processed foods with front of pack labeling in one retailer in Youngin-si. And we also surveyed 1,019 participants nationwide whose ages were from 20 to 49; the knowledge of nutrition labeling, the knowledge of 'front of pack labeling', and the opinion about the labeling system. The data were analyzed using SAS statistics program. The results were as follows: 13.4% of processed foods had front of pack labeling, and 16.8% of the consumers always checked the nutrition labeling, while 32.7% of the consumers seldom checked it. In addition, 44.3% of the consumers think that 'front of pack labeling' is necessary, and 58.3% of the consumers think it is important to show the percentage of daily value as a way of 'front of pack labeling'. However, 32% of the consumer think the possibility of 'front of pack labeling' is slim. Meanwhile, 58.3% of the consumers think that it is important to have the color difference according to contents. The number of favorite nutrients in the front of pack was four or five. It seems that the recognition of current nutrition labeling has the influence on the willingness of using the future 'front of pack labeling'. Along with our study, the policy for 'front of pack labeling' has to be updated and improved constantly since 'front of pack labeling' helps consumer understand nutrition facts.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.119-125
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• 2024

This paper investigates the effect of an artificial intelligence attention redirection compensation strategy system on the data labeling work attention concentration by individuals with developmental disabilities. Task accuracy and task performance for each session were used as measures of attention concentration. As a result of the study, after the intervention was applied, a significant improvement in attention concentration was observed in all study subjects compared to self-serving task. These results mean that artificial intelligence technology can have a positive effect on improving the attention span of people with developmental disabilities during data labeling tasks. This study shows that the application of artificial intelligence technology can improve the quality of learning data by improving the accuracy of data labeling tasks for people with developmental disabilities, and is expected to provide important implications for vocational training programs related to data labeling for people with developmental disabilities.

• Journal of Distribution Science
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• v.18 no.10
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• pp.111-119
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• 2020

Purpose: The purpose of the current study is to examine the effects of labeling information on the consumers' evaluation, with a focus on the effects of the three types of labeling information on the product quality. Research design, data and methodology: This study conducted a survey of the women respondents living in Gyeonggi province, Korea, during the time period of April 20th through May 30th, 2020. The sample data have been used to run regression analysis, reliability analysis, frequency analysis and factor analysis. Results: The empirical results are summarized as follows: 1) the labeling information on the brand image has a significantly positive effect on the consumers' evaluation about product's functional quality; 2) the labeling information on the product characteristics has a significantly positive effect on the consumers' evaluation about the expressed quality; and 3) the labeling information on the brand image has a significantly positive effect on the consumers' evaluation about the perceived quality. Conclusions: The conclusion is that the labeling information on product characteristics and the brand image is estimated to be statistically significant, therefore the Korean outdoor-wear industry are required to upgrade the information on the brand image and the product characteristics.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.177-183
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• 2024

The study investigates the impact of data quality on the performance of artificial intelligence (AI). To this end, the impact of labeling error levels on the performance of artificial intelligence was compared and analyzed through simulation, taking into account the similarity of data features and the imbalance of class composition. As a result, data with high similarity between characteristic variables were found to be more sensitive to labeling accuracy than data with low similarity between characteristic variables. It was observed that artificial intelligence accuracy tended to decrease rapidly as class imbalance increased. This will serve as the fundamental data for evaluating the quality criteria and conducting related research on artificial intelligence learning data.

• Journal of the Korean Dietetic Association
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• v.23 no.1
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• pp.106-119
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• 2017

The purpose of this study was to examine restaurant customers' use of menu labeling and their perception of menu labeling attributes. Further, the study investigated relations of menu labeling use behavior, and perception of menu labeling attributes with behavioral intentions toward menu labeling. Using a self-administered survey conducted for 2 weeks from the 2nd week of October, 2015, data were collected from restaurant customers who were exposed to menu labeling over 3 months at the time of the survey. A total of 426 respondents completed the survey. Respondents were asked about use of menu labeling, usefulness, ease of understanding, accuracy, and demographic information. There was a difference in menu labeling use behavior according to age, whereas respondents aged 50 years or over showed significantly higher use of menu labeling than those in 20s (P<0.001). Perceptions of menu labeling attributes positively affected behavioral intentions towards menu labeling. While all three menu labeling attributes, 'usefulness', 'ease of understanding', and 'accuracy', were positive factors for behavioral intentions towards menu labeling, usefulness was the biggest attribute explaining behavioral intentions (P<0.001). The study findings offer implications that can be applied to academics, the foodservice industry, and government in an attempt to nurture a healthy eating environment through provision of nutritional information at restaurants.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.267-270
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• 2010

In filtering of urban lidar data, low outliers or opening underground areas may cause errors that some ground points are labelled as non-ground objects. To solve such a problem, this paper proposes an automated method which consists of RANSAC algorithm, one-dimensional labeling, and morphology filter. All processes are conducted along the lidar scan line profile for efficient computation. Lidar data over Dajeon, Korea is used and the final results are evaluated visually. It is shown that the proposed method is quite promising in urban dem generation.

• Journal of Distribution Science
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• v.17 no.6
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• pp.57-63
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• 2019

Purpose - This research is to review the state of standard labeling compliance and identify the factors that are conducive to compliance with the Labeling Standards of the Health Functional Foods Act in internet distribution. Research design, data, and methodology - We checked 9 labels including product name, expiration date, manufacturing date, raw material, ingredient, operative dose, nutritional information, daily intake, and functional effect which are based on Labeling Standards of the Act from 100 health functional foods in the internet shopping malls. These 9 structure & function claims were compared using a Chi-square test. Results - There was a statistically significant difference in the use of standard labeling between domestic product and imported products (p<.001). The related strength between these two variables showed a moderate effect size. Also, there was a statistically significant difference between accredited advertising/unaccredited advertising distinction and use of standard labeling (p<.001). The related strength between these two variables showed a moderate effect size. Conclusions - The Labeling Standards of the Act were not followed and found to be related to imports or unauthorized advertising in internet distribution. The information displayed according to the Labeling Standards was only about 2 on the average, so many labels have been posted unreadably without arrangement.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.275-288
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• 2004

This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.