• 대한지역사회영양학회지
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• 제27권2호
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• pp.132-145
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• 2022

Objectives: This study examined trends in the health status and dietary behavior changes by region using the raw data from the 2008 ~ 2019 Community Health Survey. Methods: This study analyzed the data of 2,738,572 people among the raw data of the Community Health Survey from 2008 to 2019. The regional differences in health status and dietary behavior were examined by classifying the regions into capital and non-capital regions, and the non-capital regions were classified into metropolitan cities and provinces. A chi-square test was conducted on the body mass index (BMI), diagnosis of diabetes and hypertension, frequency of eating breakfast, salty taste in usual diet, recognition of nutrition labeling, reading of nutrition labeling, and utilization of nutrition labeling. Results: In determining obesity using the BMI, the normal weight by year decreased, and the obesity rate by year was 34.6% in 2019, which increased by 13% compared to 2008. In addition, the diabetes diagnosis rate and hypertension diagnosis rate continued to increase with the year. Both diabetes and hypertension diagnosis rates were higher in the non-capital regions than in the capital region. Eating breakfast five to seven times per week was most common and showed a significant decreasing trend by year (P < 0.001). The percentage of respondents who said they eat slightly bland foods increased from 19.5% in 2008 to 19.9% in 2010 and then to 22.1% in 2013. The percentage then decreased to 19.9% in 2019, but showed an overall increasing trend (P < 0.001). According to the region, the capital region had a higher percentage than the non-capital region. The nutrition labeling's recognition rate and utilization rate increased yearly, whereas the reading rate decreased. Conclusions: The study results presented the primary data necessary to develop nutrition education programs and establish strategies for local nutrition management projects to improve disease prevention and dietary problems.

• 한국컴퓨터정보학회논문지
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• 제20권10호
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• pp.31-38
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• 2015

In this paper, we propose the ensemble-based classification model which extracts just new data patterns from the streaming-data by using clustering and generates new classification models to be added to the ensemble in order to reduce the number of data labeling while it keeps the accuracy of the existing system. The proposed technique performs clustering of similar patterned data from streaming data. It performs the data labeling to each cluster at the point when a certain amount of data has been gathered. The proposed technique applies the K-NN technique to the classification model unit in order to keep the accuracy of the existing system while it uses a small amount of data. The proposed technique is efficient as using about 3% less data comparing with the existing technique as shown the simulation results for benchmarks, thereby using clustering.

• ETRI Journal
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• 제36권3호
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• pp.429-438
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• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• 한국유기농업학회지
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• 제28권2호
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• pp.139-154
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• 2020

The purpose of this study is to improve some problems of the country-of-origin labeling based on the perception of consumers. For this, we surveyed 636 people. The questions of the survey are largely divided into three categories; 1) criteria and subject for imposition of fine, 2) the possibility of getting consumers confused with the products using domestic regional names as domestic products, 3) criteria for the country-of-origin transplantation of agricultural products and forestry products. According to the results, more than 30.0% of consumers preferred that it is adequate for imposing fine as much as its total sales, regardless of the type of business. In addition, in the case of products using domestic regional names, consumers can be confused about the products with domestic ones, even though there is a standard for confusing country-of-origin labeling. Standard for changing the country-of-origin of agricultural, forestry products and livestock, fisheries products are not balanced. The results of this study can be used as basis data for revising the country-of-origin labeling.

• Asian Journal of Business Environment
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• 제14권3호
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• pp.13-21
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• 2024

Purpose: This study explores the potential use of food e-labels for restaurants to solve the current inadequacies in food labeling within the restaurant sector. Additionally, the study examines the feasibility and scalability of implementing e-labels for food labeling purposes, investigates consumers' perceptions of e-labels for restaurant offerings, and assesses the value of implementing e-labels. Research design, data and methodology: The value of food e-labels was estimated using the contingent valuation method. Samples were selected from the survey, considering the distribution of population, using stratified sampling method. In the survey, respondents were provided with information explaining the food e-label and were asked whether they would accept the proposed amount for food e-labeling. Results: Estimation results revealed that the individual demographic factors of the respondents significantly influenced their willingness to pay (WTP), along with their food purchasing behavior and the degree of food labeling checking. Based on the estimated results, WTP was calculated to be 2,624 KRW. Conclusions: The study findings can serve as a reference for related businesses and policies, suggesting the need for further research and detailed discussions. To activate food e-labeling, promotion and education are essential complements to mere regulatory implementation.

• 한국조사연구학회지:조사연구
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• 제11권3호
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• pp.57-79
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• 2010

본 연구는 정책평가방법에 따라 표시 광고규제의 정책성과에 대한 평가결과를 비교분석하였다. 이를 위해 우리나라 표시 광고규제의 주요정책인 중요정보고시제도, 표시광고실증제도, 임시중지명령제도, 정정광고제도를 평가대상으로 선정하였다. 그리고 소비자 관점의 전문가와 기업의 표시 광고업무 실무자, 공정거래위원회에서 소비자정책을 담당하는 전 현직 공무원 등 총 76명을 직접 방문하여 구조화된 설문지와 병행하여 면접법으로 자료를 수집하였다. 본 연구의 결과 정책성과에 대한 평가방법에 따라 기업의 표시 광고업무 실무자들과 정책담당자들의 정책별 평가순위에 소폭의 변동이 있었으며, 기존의 일반적인 단순평가 결과는 '문항의 중요도'를 반영한 가중평가와 '판단에 대한 확신성'을 고려한 퍼지평가에 비해 상향된 평가점수가 산출되었다. 이는 정책성과 평가에 있어 어떠한 평가방법을 사용하는가에 따라 평가결과가 다르게 나타날 수 있음을 시사한다.

• 한국식품영양과학회지
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• 제38권12호
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• pp.1801-1810
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• 2009

This study was carried out to investigate food-nutrition labeling perception and utilization classified by age in Busan. The survey was conducted from March 26 to April 30, 2008 by questionnaires and data analyzed by SPSS program. The results are summarized as follows: reasons for purchase of the processed food was 'delicious' in elementary school children and middle & high school students, but was 'easy to eat and cook' in the adults groups (p<0.001). The criteria for choice of the processed foods was 'taste' in all of the subjects. Eighty seven point five percent of the over 60's do not know about food labeling and 70.1% of them did not check the food label. The first confirmed items for buying the processed foods was 'expiration date' in all of the subjects (71.1%). In elementary school children, middle & high school students, 20's & 30's group, the ratio of awareness of nutrition label was higher than the 40's & 50's and over 60's group. For reading of nutrition label, all of the subjects except elementary group replied 'often' (p<0.001). For the experience of education and publicity on food-nutrition labeling, 54.3% of the subjects replied 'often', and there was a significant difference by age. For the necessity of education and publicity on food-nutrition labeling, 49.5% of the subjects replied 'necessary'. There was significant positive correlation between degree of checking of nutrition label and degree of checking of food label, accuracy of knowledge of processed food, necessity of education and publicity. Therefore, education and publicity on food-nutrition labeling for the subjects are required to encourage them to choose more nutritious food and have healthier dietary pattern.

• 한국정보과학회논문지:데이타베이스
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• 제33권1호
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• pp.129-137
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• 2006

XML 레이블링 기법은 엘리먼트 간의 조상-자손 관계 및 형제들 간의 순서 둥을 쉽게 결정할 수 있도록 하는 색인을 위한 인코딩(encoding)이라고 할 수 있다. 특히 근래에는 Web Services 및 AXML (Active XML)과 같은 기술에 동적 XML 문서가 등장하게 되었고 이로 인해 동적 XML 레이블링 기법이 필요하게 되었다. 대표적인 동적 레이블링 기법인 프라임 넘버 레이블링(prime number labeling)기법은 XML 문서의 엘리먼트 간의 부모-자식간의 관계를 소수의 특성을 이용하여 결정할 수 있도록 하는 기법이다. 이 기법은 새로운 엘리먼트가 삽입이 될 때 부여되는 레이블이 기존의 레이블 정보를 변화시키지 않는다는 장점이 있으나 형제간의 순서를 결정하는 순서 값(Order number)을 갱신하기 위해 추가의 연산 및 자료구조를 유지하는 비용을 갖는 단점을 가지고 있다. 본 논문에서는 이러한 비용을 줄이기 위해 요소의 순서정보를 나타내는 오더 값을 공유하는 기법과 삽입되는 위치에 따라 레이블의 값 또는 오더 값을 이용하여 형제간의 순서를 결정할 수는 방법을 제안하여 기존방법보다 적은 비용으로 처리할 수 있도록 하였다.

• 한국기계가공학회지
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• 제19권7호
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• pp.7-15
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• 2020

Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.

• 정보교육학회논문지
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• 제25권2호
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• pp.327-335
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• 2021

본 연구는 초등학생의 컴퓨팅 사고력을 향상시키기 위한 교육 방법으로 데이터 라벨링 중심의 머신러닝 교육 프로그램을 개발하여 적용한 후 그 효과를 검증하였다. 교육 프로그램은 현직 초등학교 교사 100명을 대상으로 실시한 사전 요구분석 결과를 바탕으로 설계 및 개발을 진행하였다. 개발한 교육 프로그램의 효과를 검증하기 위하여 K 초등학교에 재학 중인 6학년 학생 17명을 대상으로 1일 2차시씩 총 6주간 12차시의 교육을 진행하였다. 해당 교육이 컴퓨팅 사고력 향상에 미친 효과를 측정하기 위해 ' 버챌린지(Bebras Challenge)'를 활용하여 사전 사후 검사를 진행하여 교육적 효과를 분석하였다. 분석 결과 데이터 라벨링 중심의 머신러닝 교육이 초등학생의 컴퓨팅 사고력 향상에 기여한 것으로 나타났다.