• Korean Journal of Community Nutrition
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• v.26 no.6
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• pp.482-494
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• 2021

Objectives: This study evaluated dietary behavior and nutritional status according to the metabolic syndrome status in Korean menopausal women. Methods: The subjects were 1,392 menopausal women aged 50 to 64 who took part in the Korea National Health and Nutrition Examination Survey of 2016 and 2017. Subjects were classified into normal (NOR) group, pre-metabolic syndrome (Pre-MetS) group, and metabolic syndrome (MetS) groups according to the number of metabolic syndrome risk factors present. Results: The overall prevalence of metabolic syndrome was 33.7%. Using the NOR group as a reference, the odds of belonging to the MetS group in Model 1 adjusted for age were higher at 53% (OR = 1.53, 95% CI:1.011-2.307) for 'not used' subjects compared to 'used' subjects of the nutrition labeling system. Using the NOR group as a reference, every 1g increase in the intake of monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) decreased the odds of belonging to the MetS group in Model 1 adjusted for age by 3% (MUFA, OR = 0.97, 95% CI:0.946-0.991; PUFA, OR = 0.97, 95% CI:0.942-0.993). Conclusions: These results suggest that to reduce the number of risk factors of metabolic syndrome in menopausal women, nutritional education should emphasize the adequate intake of riboflavin, unsaturated fatty acids, protein, and calcium, and also encourage the recognition and use of nutritional labeling. Results of this study are expected to be utilized as basic data for the health management of menopausal women.

• Korean Journal of Community Nutrition
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• v.28 no.4
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• pp.269-281
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• 2023

Objectives: This study aimed to examine the frequency of convenience food consumption at convenience stores (CVS) and the CVS usage patterns of middle and high school students as well as to understand students' attitude toward sodium and sugar reduction. Methods: We used an online questionnaire for data collection. The questionnaire comprised five distinct categories: general characteristics, CVS usage, frequency of consumption according to convenience food menus at CVS, attitude toward sodium and sugar reduction, and adherence to dietary guidelines. Results: A total of 75 students from Seoul (14 middle school students and 61 high school students) participated in the study. Most respondents visit CVS 3-5 times a week. CVS are predominantly used during weekdays, mostly during lunch, and dinner. The students mostly checked the caloric content and expiration date as food labeling information. The participants were aware of the need to reduce their sugar and sodium intake. Among frequent CVS convenience food consumers, there was an increased consideration of the need to reduce their sugar and sodium consumption, despite their actual selection of foods with high sugar and sodium content. Additionally, they did not check the sugar and sodium levels indicated in food labeling. Further, the dietary action guide from the Ministry of Health and Welfare were poorly followed by most students. Conclusions: There is a need for nutrition education specifically addressing the sugar and sodium content of the convenience foods predominantly consumed by students. Additionally, educating students with frequent convenience food consumption to actively check the sugar and sodium information on food labels could help promote healthier food choices.

• Nutrition Research and Practice
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• v.18 no.5
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• pp.687-700
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• 2024

BACKGROUND/OBJECTIVES: This study investigated the relationship between adolescent food-related lifestyles and food-related media use and eating behavior in Korea. SUBJECTS/METHODS: Participants were 392 Korean adolescents, ranging in age from 12 to 18, recruited via convenience sampling. They completed a self-report questionnaire survey consisting of questions about food-related lifestyle, food-related media use, food consumption behavior, food literacy, and nutrition quotient. Data analysis was conducted using SPSS 29.0. (IBM Co., Armonk, NY, USA). RESULTS: The factor analysis of food-related lifestyles identified four factors. Based on the cluster analysis results, participants were classified into three clusters reflecting different levels of interest: high interest in food, moderate interest in food, and low interest in food. The analysis revealed significant differences between groups in food-related liestyle factors (P < 0.05). Notably, the high-interest group demonstrated proactive engagement with food-related content, a willingness to explore diverse culinary experiences, and a conscientious consideration of nutritional labeling during food purchases. In contrast, the low-interest group reported tendencies toward overeating or succumbing to stimulating food consumption post-exposure to food-related content, coupled with a disregard for nutritional labeling when making food choices. A stronger inclination toward a food-related lifestyle was positively correlated with higher levels of food literacy and nutrition quotient. CONCLUSION: This study proposes that the implementation of a nutrition education program using media could effectively promote a healthy diet among adolescents with a high level of interest in their dietary habits. For adolescents with low interest in their dietary habits, it suggests that introducing an education program with a primary focus on enhancing food literacy could be beneficial in fostering a healthy diet. Our research findings provide insight for the development of tailored nutritional education programs and establishment of effective nutrition policies.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• MALSORI
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• no.58
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• pp.67-81
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• 2006

For a large corpus of time-aligned data, HMM based approaches are most widely used for automatic segmentation, providing a consistent and accurate phone labeling scheme. There are two methods for training in HMM. Flat starting method has a property that human interference is minimized but it has low accuracy. Bootstrap method has a high accuracy, but it has a defect that manual segmentation is required In this paper, a new algorithm is proposed to minimize manual work and to improve the performance of automatic segmentation. At first phase, voiced, unvoiced and silence classification is performed for each speech data frame. At second phase, the phoneme sequence is aligned dynamically to the voiced/unvoiced/silence sequence according to the acoustic phonetic rules. Finally, using these segmented speech data as a bootstrap, phoneme model parameters based on HMM are trained. For the performance test, hand labeled ETRI speech DB was used. The experiment results showed that our algorithm achieved 10% improvement of segmentation accuracy within 20 ms tolerable error range. Especially for the unvoiced consonants, it showed 30% improvement.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.185-190
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• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.

• Korean Journal of Clinical Pharmacy
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• v.27 no.4
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• Environmental Analysis Health and Toxicology
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• v.22 no.1 s.56
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• pp.91-101
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• 2007

The new chemicals are developed and circulated without the verified toxicity data. So, the accidents and occupational diseases, such as explosion, fire, suffocation about deadly poisons etc. are frequently to workers. Classifications of chemicals suited with guideline and an offer of correct chemical information data are the molt important thing for the establishment of suitable chemical management system. The GHS (Globally Harmonized System of classification and labeling of chemicals) is based with the chemical classifications and unification plan. The warning symbol and phrases are established for improvements of chemical information data system. According to these unified and improved systematic form of data, and the chemical information data, the workplaces will be presented many chemical safety and risk data correctly. In this paper, we will present constructions and accomplishment contents-based chemical management of workplace through development of chemical information data and the nice using for new chemical investigation and risk assessment of chemicals in workplaces.