• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.10
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• pp.1555-1564
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• 2010

This study investigated consumer's recognition of food labeling in order to contribute to the development of food labels which are more informative to consumers. The questionnaires had been collected from 120 male and female customers living in Seoul with the age between 10's and 60's from November 2nd to November 7th 2009. For checking the food label at the time of purchase, 58.3% of the consumers checked the food label and the main reason for checking the food label was to confirm sell-by date (60.1%). Sixty percent of the consumers were satisfied with the current food labeling. Among those who are not satisfied, 30.6% complained about difficult terms to understand and 25.8% were dissatisfied with insufficient information. In every age group, most people were not satisfied with labeling on food ingredient and additives, followed by date of manufacture and sell-by date. 53.1% of consumers demanded to label date of manufacture and sell-by date together. For more clear information, consumers wanted use-by date (47.5%) rather than sell-by date (23.3%). 56.7% of consumers was dissatisfied with warning information such as allergic warning and the reasons for dissatisfaction were poor visibility (37.5%) and insufficient information (33.4%). Moreover most consumers (90.0%) showed little knowledge on irradiation. To improve of the food labeling standards into consumer-oriented standards, both amendment of the food labeling standards and consumer education will be necessary.

• Journal of Korean Philosophical Society
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• v.145
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• pp.271-296
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• 2018

In this article, I intend to reveal critically both the intrinsic crux and main problems of the politics of hostile exclusion based on the effect of labeling which was designed precisely as an impure political technique and has been operated for too long in Korean society by the conservative ruling class that centered on various negative ideological labels like 'pro-North Korean leftist forces.' Firstly, what is called the 'conservative ruling class' in Korean society is in itself an antinationalistic and antidemocratic pro-Japanese dictatorship group. Secondly, the conservative ruling class as a pro-Japanese dictatorship group has utilized politically the labeling effect which regards antigovernment Korean members as pro-North Korean or rebellious persons. This group's hostile politics, based on the ideological labelling effect, deprives antigovernment persons and groups of the qualification of Korean citizenship, in order to hold and retain their supreme power in Korean society. Thirdly, the conservative ruling class has attempted to stigmatize the citizens who participate in a movement for democracy as a pro-North Korean leftist force, but such a politically impure manner is typically completely unjustified groundless labeling. Fourthly, the attempt to define the conservative ruling class as a pro-Japanese dictatorship force is normatively justified and resonably appraised insofar as such a definition has been proved to be worthy of confidence. Finally, the trial to consider Roh's regime and pro-Roh (pro-Moon) groups as a kind of Yeongnam hegemonism by several critical intellectuals and current politicians from Honam region is not only merely a groundless and unconvincing labelling, but also the failed outcome of the attempt to systemize logically their emotional antipathy and repulsion toward Roh and pro-Roh (pro-Moon) groups.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Nutrition and Health
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• v.54 no.2
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• pp.199-210
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• 2021

Purpose: This study investigated the food consumption behaviors in Korean adults, according to the agrifood consumer competency index (ACCI). Methods: Data obtained from the 2019 Consumption Behaviors Survey for Food were analyzed. A total of 6,176 adults (2,783 males, 3,393 females) aged ≥ 19 years, were included in the study. Based on the score of agrifood consumer competency index, the subjects were classified into three groups. The dietary habits, eating-out and food-delivery/take-out behaviors, opinion of food labeling, and concerns for domestic products were compared among the 3 groups. Results: The ACCI scores of the male and female subjects were 63.6 and 64.8, respectively. Subjects of both genders in the highest tertile of the ACCI were more likely to have a higher education level and higher health concerns, as compared to subjects in the lowest tertile (p < 0.05). Male subjects having highest tertile of the ACCI reported significantly more exercise and alcohol consumption, as compared to subjects in the lowest tertile (p < 0.05). A higher score of the ACCI also portrayed a higher satisfaction in own diet and greater checking of the food label. Moreover, subjects with a higher score of the ACCI showed greater satisfaction and reliability in the food label, as well as increased concerns for domestic agrifoods, local foods, and eco-friendly foods. Subjects in the lowest tertile of the ACCI acquired their dietary information from acquaintances, whereas subjects in the highest tertile of the ACCI learnt the information from food labels themselves. Conclusion: These results are indicative of the food consumption and behaviors of Korean adults according to their ACCI scores, and provide basic data that will be useful for implementing an effective food policy.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.528-536
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• 2023

In Korea, from January 2023, the Act on Labeling and Advertising of Food was revised to reflect the use-by-date rather than the sell-by-date. Hence, the purpose of this study was to establish a system for calculating the safety factor and determining the recommended use-by-date for each food type, thereby providing a scientific basis for the recommended use-by-date labels. A safety factor calculation technique based on scientific principles was designed through literature review and simulation, and opinions were collected by conducting surveys and discussions including industry and academia, among others. The main considerations in this study were pH, Aw, sterilization, preservatives, packaging for storage improvement, storage temperature, and other external factors. A safety factor of 0.97 was exceptionally applied for frozen products and 1.0 for sterilized products. In addition, a between-sample error value of 0.08 was applied to factors related to product and experimental design. This study suggests that clearly providing a safe use-by-date will help reduce food waste and contribute to carbon neutrality.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.2
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• pp.147-155
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• 2024

Gallium-68-prostate-specific membrane antigen-11 (⁶⁸Ga-PSMA-11) is a positron emission tomography radiopharmaceutical that labels a Glu-urea-Lys-based ligand with ⁶⁸Ga, binding specifically to the PSMA. It is used widely for imaging recurrent prostate cancer and metastases. On the other hand, the preparation and quality control testing of ⁶⁸Ga-PSMA-11 in medical institutions takes over 60 minutes, limiting the daily capacity of ⁶⁸Ge/⁶⁸Ga generators. While the generator provides 1,110 MBq (30 mCi) nominally, its activity decreases over time, and the labeling yield declines irregularly. Consequently, additional preparations are needed, increasing radiation exposure for medical technicians, prolonging patient wait times, and necessitating production schedule adjustments. This study aimed to reduce the ⁶⁸Ga-PSMA-11 preparation time and optimize the automated synthesis system. By shortening the reaction time between ⁶⁸Ga and the PSMA-11 precursor and adjusting the number of purification steps, a faster and more cost-effective method was tested while maintaining quality. The final synthesis time was reduced from 30 to 20 minutes, meeting the standards for the HEPES content, residual solvent EtOH content, and radiochemical purity. This optimized procedure minimizes radiation exposure for medical technicians, reduces patient wait times, and maintains consistent production schedules, making it suitable for clinical application.

• Journal of Nutrition and Health
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• v.57 no.3
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• pp.292-306
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• 2024

Purpose: This study investigated fruit and vegetable intake and associated socio-demographic and dietary factors, and compared the nutritional intake according to the fruit and vegetable intake level among Korean adolescents. Methods: This study was conducted on 1,676 adolescents who participated in the 2016-2019 Korea National Health and Nutrition Examination Survey. The subjects were classified into four groups based on the fruit and vegetable intake recommendations in 2020 Dietary Reference Intakes for Koreans: Application (KDRIs Application): sufficient fruit intake (SF) group, sufficient vegetables intake (SV) group, sufficient fruit and vegetables intake (SFV) group, and not sufficient fruit and vegetable intake (NS) group The nutrient intake per day in each group was compared.. Logistic regression analysis was performed to examine the factors influencing fruit and vegetables intake. Results: In the sample of adolescents surveyed, only 1.40% met the recommended daily intake of fruits and vegetables, while 79.54% fell below the established threshold for adequate consumption. Female adolescents, those with fathers holding university degrees or above, and those who ate breakfast at least three times a week were likelier to have adequate fruit intake. Male adolescents and those from higher-income households were likelier to consume vegetables. Females, those who ate out daily, those from lower-income households, and those who understood food labels were likelier to have adequate fruit and vegetable intake. The daily nutrient intake and intake-to-requirement ratio significantly differed according to the fruit and vegetable intake groups. The NS and SF group had lower ratios for calcium and iron, while the NS group had the lowest vitamin A and C intake. By contrast, the SFV group met almost all daily nutrient requirements, except for calcium and vitamin A. Conclusion: This study highlights the need for nutrition education programs to encourage adolescents to consume adequate amounts of fruits and vegetables.

• Journal of Nutrition and Health
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• v.57 no.3
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• pp.336-348
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• 2024

Purpose: The use of food delivery services is increasing continuously in Korea, which may lead to nutritional problems and obesity. Despite this, the research on the association between delivered food consumption and obesity has been insufficient. This study examined the relationship between delivered food consumption and dietary behaviors and obesity among young adults in Jeju. Methods: An online survey was conducted from March 15 to April 5, 2023; 312 participants aged 19-39 years were included in the final analysis. The frequency, types, and time of delivered food consumption were measured using a questionnaire. The dietary behaviors included the following: eating out, breakfast consumption, recognition of nutrition labels, and eating salty foods, vegetables, and fruit. Obesity was defined using the body mass index based on self-reported body weight and height. Results: Approximately 59.3% of the participants ordered delivery foods more than one time/week. The frequency of delivered food consumption was higher in people who consumed breakfast < 5 times/week than those who consumed ≥ 5 times/week (p = 0.0088). People who usually eat salty foods tended to consume delivered food more frequently than those who did not (p = 0.0377). On the other hand, people who consumed fruits ≥ 1 time/day had a higher frequency of delivered food consumption than those who consumed fruits < 1 time/day (p = 0.0110). After adjusting for the confounding variables, the group who consumed delivered foods more than three times/week had an increased odds ratio (OR) of obesity compared to those who consumed less one time/week (OR, 2.38; 95% confidence intervals, 1.12-5.06). Conclusion: Young adults in Jeju who frequently consume delivered foods tended to have poor dietary habits including skipping breakfast and eating salty, and they had an increased odds of obesity. The overall dietary patterns can be improved by providing nutrition education and developing policies to promote or support healthy food choices when ordering delivered foods or eating out.