• 대한영양사협회학술지
• /
• 제22권4호
• /
• pp.310-319
• /
• 2016

Limitation of a questionnaire survey which is widely used is time and money, limited numbers of participants, biased confidence interval and unreliable results. To overcome these, we performed tendency and network analysis of diet using big Data in Koreans. The keyword on diet were collected from the portal site Naver from January 1, 2015 until December 31, 2015 and collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis and seasonality analysis. The results showed that diet menu appeared most frequently by N-gram analysis, even though exercise had the highest frequency by simple frequency analysis. In addition, keyword network analysis were categorized into four groups: diet group, exercise group, commercial diet program company group and commercial diet food group. The analysis of seasonality showed that subjects' interests in diet had increased steadily since February, 2015, although subjects were most interested indiet in July, these results suggest that the best strategies for weight loss are based on diet menu and starting diet before July. As people are especially sensitive to diet trends, researches are needed about annual analysis of big data.

• International Journal of Computer Science & Network Security
• /
• 제22권10호
• /
• pp.51-56
• /
• 2022

The main purpose of the article is to analyze the main approaches to the formation of the food system in modern conditions. The constant growth of the population, the increase in the level of use of natural resources against the background of a decrease in their reserves causes a number of risks for the food security of both a person and the country as a whole. The problem of the formation of food security has always remained at the center of scientific interests of both domestic and foreign researchers. In the context of globalization, this issue is considered as one of the key global problems in the system. Theoretical and metodological basis studia systematic approach to study of fundamental provisions of economic science regarding the formation of the food system. Based on the results of the study, the main approaches to the formation of the food system in modern conditions were characterized. The study has limitations associated with the lack of the ability to analyze the food system on the practical activities of a single socio-economic system.

• 식품기술
• /
• 제23권1호
• /
• pp.96-103
• /
• 2010

생산, 가공, 유통, 그리고 소매로 이어지는 식품 공급의 흐름은 분업체제에서 복잡하게 뒤얽혀 있는 통합적인 형태로 변화해오고 있다. 그 변화를 뒤돌아보고 금후의 협동적 시스템 구축을 위한 과제를 찾아본다.

• Journal of Nutrition and Health
• /
• 제30권7호
• /
• pp.870-878
• /
• 1997

• 한국식생활문화학회지
• /
• 제35권1호
• /
• pp.65-78
• /
• 2020

The objective of this study was to review and evaluate the growing subject of food tourism research, and thus identify the trend of food tourism research. Using a Text mining technique, this paper discovered the trends of the literature on food tourism that was published from 2004 to 2018. The study reviewed 201 articles that include the words 'food' and 'tourism' in their abstracts in the KCI database. The Wordscloud analysis results presented that the research subjects were predominantly 'Festival', 'Region', 'Culture', 'Tourist', but there was a slight difference in frequency according to the time period. Based on the main path analysis, we extracted the meaningful paths between the cited references published domestically, resulting in a total of 12 networks from 2004 to 2018. The Text network analysis indicated that the words with high centrality showed similarities and differences in the food tourism literature according to the time period, displaying them in a sociogram, a visualization tool. This study has implications that it offers a new perspective of comprehending the overall flow of relevant research.

• 생태와환경
• /
• 제54권2호
• /
• pp.130-141
• /
• 2021

국내 멸종위기 어류 25종의 먹이원을 문헌 조사한 결과, 먹이원은 20문, 31강, 58목, 116과, 154속으로 나타났다. 먹이원 중 가장 많은 어류가 섭식한 먹이원은 분류군에 따라 절지동물문, 곤충강, 파리목, 깔따구과로 조사되었으며, 식물류 먹이원은 돌말문, 윷돌말강, 반달돌말목, 반달돌말과로 조사되었다. 계층적 군집분석과 NMDS를 이용하여 멸종위기 어류 20종의 먹이원 유형화 결과, 어류를 주로 포식하는 충식성 어류와 식물플랑크톤을 섭식하는 어류 2가지 유형으로 나타났다. 네트워크 분석의 허브 점수가 높은 먹이원은 파리목, 하루살이목, 날도래목, 강도래목, 딱정벌래목으로 나타났으며 식물류 먹이원 중 허브 점수가 높은 쪽배돌말목과 반달돌말목, 김발돌말목으로 조사되었다. 먹이원 폭이 큰 어류는 연준모치(PP)와 열목어, 좀수수치, 가는돌고기, 꼬치동자개, 퉁사리, 묵잡자루, 미호종개로 Bi 지수 값이 0.3 이상으로 조사되어 다양한 먹이를 먹는 것으로 조사되었다. 반면, 금강모치, 부안종개, 감돌고기, 흰수마자, 다묵장어, 돌상어, 얼룩새코미꾸리, 북방종개는 Bi 지수 값이 0.1 이하로 조사되어 먹이원 다양성이 낮게 조사되었다.

• International Journal of Computer Science & Network Security
• /
• 제21권12spc호
• /
• pp.503-512
• /
• 2021

The article proposes a methodical approach to assessing the innovative capacity of agri-food enterprises. This approach is based on the calculation of personnel, investment, technical and technological, information components of the ability of agri-food enterprises to innovate. The algorithm of search of production, intellectual, financial, information resources reserves, which are necessary for functioning of the enterprises of agro-food sphere, is defined. The approach developed by the authors, in contrast to the existing ones in the scientific world, allows the tools of mathematical modeling to identify shortcomings in the development of agri-food enterprises, to forecast the development of these enterprises and on this basis to form different models of market stakeholders. The method proposed by the authors to assess the innovative capacity of agri-food enterprises allows market participants to assess the current state of agri-food enterprises and form the necessary management levers to influence its activities to eliminate market failures and pitfalls.

• 한국식품과학회지
• /
• 제20권2호
• /
• pp.148-156
• /
• 1988

밀가루의 제면특성을 평가하는 방법을 수립하기 위하여 열변성 글루텐의 점탄성 측정 방법을 연구하였다. 열변성 글루텐의 인장력 완화시험에서 얻어진 완화곡선을 6개의 점탄성 요소를 포함하는 일반화 맥스웰 모델로 표현될 수 있었다. 열변성 글루텐의 인장력은 열처리 시간이 경과 할수록 증가하였으며 전체 완화분의 70-74%를 차지하는 제1차 지수항에서의 탄성과 점성은 열처리 시간 19분 동안 계속 증가하였다. 글루텐의 강화제로 알려져 있는 $KBrO_3$를 1000pm수준으로 첨가할 경우 탄성과 점성은 감소하였으나 그루텐 약화제인 L-시스텐은 이들을 증가시켰다. 두 경우 모두 완화 시간을 가열 11분후부터 감소하였다. 이들 파라미터들은 또한 요소(尿素)의 첨가 농도에 따라 상이하게 변화되었다.

• International Journal of Computer Science & Network Security
• /
• 제21권7호
• /
• pp.182-190
• /
• 2021

Deep learning is an advanced technology for large-scale data analysis, with numerous promising cases like image processing, object detection and significantly more. It becomes customarily to use transfer learning and fine-tune a pre-trained CNN model for most image recognition tasks. Having people taking photos and tag themselves provides a valuable resource of in-data. However, these tags and labels might be noisy as people who annotate these images might not be experts. This paper aims to explore the impact of noisy labels on fine-tuning pre-trained CNN models. Such effect is measured on a food recognition task using Food101 as a benchmark. Four pre-trained CNN models are included in this study: InceptionV3, VGG19, MobileNetV2 and DenseNet121. Symmetric label noise will be added with different ratios. In all cases, models based on DenseNet121 outperformed the other models. When noisy labels were introduced to the data, the performance of all models degraded almost linearly with the amount of added noise.

• Nutrition Research and Practice
• /
• 제13권6호
• /
• pp.521-528
• /
• 2019

BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. MATERIALS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of $150{\times}150$ and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.