• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Nutrition Research and Practice
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• v.13 no.4
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• pp.344-351
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• 2019

BACKGROUND/OBJECTIVES: Adequate dietary fatty acid intake is important for toddlers between 12-24 months of age, as this is a period of dietary transition in conjunction with rapid growth and development; however, actual fatty acid intake during this period seldom has been explored. This study was conducted to assess the intake status of n-3 and n-6 polyunsaturated fatty acids by toddlers during the 12-24-month period using 2010-2015 Korea National Health and Nutrition Examination Survey data. SUBJECTS/METHODS: Twenty-four-hour dietary recall data of 12-24-month-old toddlers (n = 544) was used to estimate the intakes of ${\alpha}$-linolenic acid (ALA; 18:3n-3), eicosapentaenoic acid (EPA; 20:5n-3), docosahexaenoic acid (DHA; 22:6n-3), linoleic acid (LA; 18:2n-6), and arachidonic acid (AA; 20:4n-6), as well as the major dietary sources of each. The results were compared with the expected intake for exclusively breastfed infants in the first 6 months of life and available dietary recommendations. RESULTS: Mean daily intakes of ALA, EPA, DHA, LA, and AA were 529.9, 22.4, 37.0, 3907.6, and 20.0 mg/day, respectively. Dietary intakes of these fatty acids fell below the expected intake for 0-5-month-old exclusively breastfed infants. In particular, DHA and AA intakes were 4 to 5 times lower. The dietary assessment indicated that the mean intake of essential fatty acids ALA and LA was below the European and the FAO/WHO dietary recommendations, particularly for DHA, which was approximately 30% and 14-16% lower, respectively. The key sources of the essential fatty acids, DHA, and AA were soy (28.2%), fish (97.3%), and animals (53.7%), respectively. CONCLUSIONS: Considering the prevailing view of DHA and AA requirements on early brain development, there remains considerable room for improvement in their intakes in the diets of Korean toddlers. Further studies are warranted to explore how increasing dietary intakes of DHA and AA could benefit brain development during infancy and early childhood.

• Korean Journal of Community Nutrition
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• v.28 no.4
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• pp.282-292
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• 2023

Objectives: Bone health in early adulthood, as individuals approach peak bone mass, plays a critical role in preventing osteoporosis later in life. This study aimed to investigate the associations between lifestyle and dietary factors, anthropometric measurements, and urinary bone resorption markers in young adults. Methods: A cross-sectional study was conducted with 100 healthy Korean adults (50 men and 50 women) in their 20s and early 30s. Bone mineral density (BMD), anthropometric measurements, dietary intake (24-hour recall), and urinary bone resorption indicators (deoxypyridinoline and N-terminal telopeptide of type I collagen) were analyzed. Variables were compared between the osteopenia and osteoporosis groups (OSTEO group: 30% men and 60% women) and the healthy control group. Results: Men in the OSTEO group were significantly taller than those in the control group (P < 0.05). Women in the OSTEO group had significantly lower body weight and body composition (muscle and body fat) than those in the normal group (P < 0.01). Men in the OSTEO group had a significantly higher intake of animal calcium (Ca) than those in the normal group (P < 0.05). Women in the OSTEO group had significantly higher dietary fiber, vitamin A, Ca, plant Ca, and potassium intake than did those in the normal group (P < 0.05). There were no significant differences in caffeinated beverage consumption, eating habits, or urinary bone resorption indicators between the OSTEO and control groups of either sex. Conclusions: In our study of young South Korean adults, we observed low bone density levels, with particularly low BMD in taller men and underweight women. We found a higher nutrient intake in the OSTEO group, indicating the possibility of reverse causality, a phenomenon often found in cross-sectional studies. Therefore, there is a need to further elucidate dietary factors related to osteoporosis in young adults through prospective cohort studies involving a larger population.

• Nutrition Research and Practice
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• v.17 no.2
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• pp.307-315
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• 2023

BACKGROUND/OBJECTIVES: Previous studies have shown an association between breastfeeding and higher fruit and vegetable consumption and the level of dietary variety in children. However, few studies have reported this association on the feeding characteristics. Therefore, this study examined the association of the feeding characteristics with the consumption of fruit and vegetable and dietary variety in children. SUBJECTS/METHODS: This study recruited 802 participants from their parents with information on their feeding, and 24-h dietary recall. The associations of the feeding characteristics with fruit and vegetable consumption and dietary variety score (DVS) were analyzed using a multiple logistic regression model. RESULTS: Compared to the feeding type of exclusive breastfed children, exclusive formula-fed children had a significant association with a lower DVS (odds ratio [OR], 0.42, 95% confidence interval [CI], 0.23-0.77). Fruit and vegetable consumption was classified into 6 groups: non-salted vegetables (NSV), salted vegetables (SV), fruit (F), total vegetables (TV), non-salted vegetables + fruit (NSVF), and total vegetables + fruit (TVF). According to the mean level of fruit and vegetable consumption, compared to the duration of total breastfeeding for 6 month or less, a greater duration of breastfeeding for 12 mon had a significant association with a higher intake of NSVF and TVF (OR, 1.85, 95% CI, 1.20-2.85 and OR, 1.89, 95% CI, 1.22-2.92). On the other hand, the early introduction of formula feeding for 4 mon had a significant association with a lower intake of F and NSVF (OR, 0.59, 95% CI, 0.38-0.91 and OR, 0.63, 95% CI, 0.40-0.99). CONCLUSIONS: These results confirm that breastfeeding is associated with higher fruit and vegetable consumption and dietary variety, whereas formula feeding is associated with lower fruit and vegetable consumption and dietary variety. Therefore, the feeding characteristics in infants may affect fruit and vegetable consumption and dietary variety in children.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3712-3729
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• 2020

Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

• Journal of Korean Library and Information Science Society
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• v.39 no.4
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• pp.283-304
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• 2008

This is to develop Intelligent Retrieval System which can automatically present early query's category terms(association terms connected with knowledge structure of relevant terminology) through learning function and it changes searching form automatically and runs it with association terms. For the reason, this theoretical study of Intelligent Automatic Indexing System abstracts expert's index term through learning and clustering algorism about automatic classification, text mining(categorization), and document category representation. It also demonstrates a good capacity in the aspects of expense, time, recall ratio, and precision ratio.

• Korean Journal of Biological Psychiatry
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• v.20 no.3
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• pp.104-110
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• 2013

Objectives : The aim of this study was to examine the prospective impact of the apolipoprotein E (APOE) ${\varepsilon}4$ on cognitive performance in the community-dwelling elderly individuals with Alzheimer's disease (AD). Methods : The total number of subjects was 30 (12 men and 18 women) who were diagnosed with AD from a Korean project of "Early Detection of Dementia". People aged 65-85 years were included in the analysis. The eight neuropsychological domains from the Korean version of Consortium to Establish a Registry of Alzheimer's Disease (CERAD-K) were conducted to test subjects. They have been followed at 24-month intervals with the same assessments at each interval. Their cognitive performance at 2 year intervals was compared by the occurrence of the APOE ${\varepsilon}4$. Results : The impact of ${\varepsilon}4$ allele was significant in the Word List Memory Test (WLMT, F = 4.345, df = 1, p = 0.021) and Word List Recall Test (WLRT, F = 5.569, df = 1, p = 0.033). Conclusions : The APOE ${\varepsilon}4$ allele was significantly correlated especially with verbal episodic memory domain in community-dwelling elders diagnosed with AD.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.338-347
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• 2023

Objectives: Young children's feeding characteristics can play an important role in eating habits and health during later childhood. This study was conducted to examine the associations of feeding characteristics with dietary patterns and obesity in children. Methods: This study utilized data from the Korea National Health and Nutrition Examination Survey conducted between 2013 and 2017. In total, 802 toddlers were included, with information on their demographic characteristics, feeding practices and duration, and 24-hour recall obtained from their parents. Feeding characteristics were categorized into feeding type, duration of total breastfeeding, duration of total formula feeding, duration of exclusive breastfeeding, and age when starting formula feeding. Dietary patterns were identified based on factor loadings for the food groups for 3 major factors, with "vegetables & traditional," "fish & carbohydrates," and "sweet & fat" patterns. Overweight/obesity was defined as ≥85th percentile in body mass index based on the 2017 Korean National Growth charts for children and adolescents. Multiple regression analysis was conducted to examine associations between feeding characteristics and dietary patterns. The association between dietary patterns and obesity was analyzed using multivariable logistic regression analysis. Results: The early introduction of formula feeding was inversely associated with the "vegetables & traditional" pattern (β=-0.18; 95% confidence interval [CI], -0.34 to -0.02). A higher "vegetables & traditional" intake was associated with a lower risk of obesity (odds ratio, 0.48; 95% CI, 0.24 to 0.95). Conclusions: Feeding characteristics are associated with dietary patterns in later childhood, and dietary patterns were shown to have a potential protective association against obesity.