• 대한지역사회영양학회지
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• 제23권2호
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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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• 제3권4호
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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.

• 한국인터넷방송통신학회논문지
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• 제24권2호
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• pp.119-125
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• 2024

본 논문에서는 인공지능 주의환기 보상전략 시스템이 발달장애인의 데이터 라벨링 작업 주의집중력에 미치는 효과를 분석하였다. 주의집중력의 척도로는 세션별 작업 정확도와 작업수행량을 사용하였다. 연구 결과, 중재가 적용된 후 연구대상자 모두 자율작업 대비 주의집중력에서 유의미한 향상이 관찰되었다. 이러한 결과는 인공지능 기술이 발달장애인의 데이터 라벨링 작업 중 주의집중력 향상에 긍정적으로 작용할 수 있음을 의미한다. 본 연구는 인공지능 기술의 적용이 발달장애인의 데이터 라벨링 작업 정확도를 향상하여 학습데이터의 품질을 높일 수 있음을 보여주고 있으며, 발달장애인의 데이터라벨링 관련 직업훈련 프로그램에 중요한 시사점을 제공하리라 본다.

• 유통과학연구
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• 제18권10호
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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.

• 산업융합연구
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• 제22권1호
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• pp.177-183
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• 2024

본 연구는 데이터의 품질이 인공지능(AI) 성능에 미치는 영향을 검토한다. 이를 위해, 데이터 특성변수(Feature)의 유사도와 클래스(Class) 구성의 불균형을 고려한 모의실험(Simulation)을 통해 라벨링 오류 수준이 인공지능의 성능에 미치는 영향을 비교 분석하였다. 그 결과, 특성변수 간 유사성이 높은 데이터에서는 특성 변수 간 유사성이 낮은 데이터에 비해 라벨링 정확도에 더 민감하게 반응하였으며, 클래스 불균형이 증가함에 따라 인공지능 정확도가 급격히 감소되는 경향을 관찰하였다. 이는 인공지능 학습데이터의 품질평가 기준 및 관련 연구를 위한 기초자료가 될 것이다.

• 대한영양사협회학술지
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• 제23권1호
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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.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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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.

• 유통과학연구
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• 제17권6호
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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.

• 대한원격탐사학회지
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• 제20권4호
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• pp.275-288
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• 2004

이 논문은 다중 센서 원격탐사 화상의 분류를 위해 퍼지 논리 융합과 결합된 relaxation labeling 방법을 제안하였다. 다중 센서 원격탐사 화상의 융합에는 퍼지 논리를, 분광정보와 공간정보의 융합에는 반복적인 relaxation labeling 방법을 적용하였다. 특히 반복적 relaxation labeling 방법은 공간정보의 이용에 따른 분류 화소의 변화양상을 얻을 수 있는 장점이 있다. 토지 피복의 감독 분류를 목적으로 광학 화상과 다중 주파수/편광 SAR 화상에 제안 기법을 적용한 결과, 다중 센서 자료를 이용하고 공간정보를 함께 결합하였을 때 향상된 분류 정확도를 얻을 수 있었다.

• 반도체디스플레이기술학회지
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• 제19권4호
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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.