• Journal of Nutrition and Health
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• v.55 no.3
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• pp.330-347
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• 2022

Folate, a water-soluble vitamin, acts as a coenzyme for one-carbon metabolism in nucleic acid synthesis and amino acid metabolism. Adequate folate nutritional status during the periconceptional period is known to prevent neural tube defects. In addition, insufficient folate intake is associated with various conditions, such as anemia, hyperhomocysteinemia, cardiovascular disease, cancer, cognitive impairment, and depression. This review discusses the rationale for the revision of the 2020 Korean dietary reference intakes for folate, and suggestions for future revisions. Based on the changes in the standard body weight in 2020, the adequate intake (AI) for infants (5-11 months) and the estimated average requirements (EARs) for 15-18 years of age were revised, but there were no changes in the recommended nutrient intakes (RNIs) and tolerable upper intake levels (ULs) for all age groups. Mean folate intake did not reach RNI in most age groups and was particularly low in women aged 15-29 years, according to the results of the 2016-2018 Korea National Health and Nutrition Examination Survey (KNHANES). The percentages of folate intake to RNI were lower than 60% in pregnant and lactating women, but serum folate concentrations were higher than those in other age groups, presumably due to the use of supplements. Therefore, total folate intake, from both food and supplements, should be evaluated. In addition, the database of folate in raw, cooked, and fortified foods should be further expanded to accurately assess the folate intake of Koreans. Determination of the concentrations of erythrocyte folate and plasma homocysteine as well as serum folate is recommended, and quality control of the analysis is critical.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.577-585
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• 2023

Lateral torsional buckling causessafety accidentssuch as collapse accidents during erection. Therefore, anaccurate safety designshould be conducted. Lateral torsional buckling canbe prevented by reinforcing the end orreducing the unbraced length. The method ofreducing the unbraced length by installing a crossframe has high material and installation costs and low maintenance performance.In addition, structuralsafety may be deteriorated due to cracks. The end reinforcement method using Concrete Filled Half Pipe Stiffeneris a method ofreinforcing the end of a plate girder using a stiffenerin the form of a semi-circular column. This method increasesthewarping strength ofthe girder and increasesthe lateral torsional buckling strength.In thisstudy, the effect ofincreasing the warping strengthof plate girders with concrete filled half pipe stiffeners was confirmed. To verify the effect, the results ofthe designequationand the finite element analysis were compared and verified through a experiment. As a result, the plate girderwithCFHPS increased thewarping strengthand confirmed that the lateral torsional buckling strength was increased.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.42-57
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• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.39-49
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• 2024

The construction of underground infrastructure is garnering growing increasing research attention owing to population concentration and infrastructure overcrowding in urban areas. An important associated task is establishing a monitoring system to evaluate stability during infrastructure construction and operation, which relies on developing techniques for ground investigation that can evaluate ground stability, verify design validity, predict risk, facilitate safe operation management, and reduce construction costs. The method proposed here uses satellite imaging in a cost-effective and accurate ground investigation technique that can be applied over a wide area during the construction and operation of infrastructure. In this study, analysis was performed using Synthetic Aperture Radar (SAR) data with the time-series radar interferometric technique to observe surface displacement during the construction of urban underground roads. As a result, it was confirmed that continuous surface displacement was occurring at some locations. In the future, comparing and analyzing on-site measurement data with the points of interest would aid in confirming whether displacement occurs due to tunnel excavation and assist in estimating the extent of excavation impact zones.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.205-212
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• 2023

Among cardiac diseases, arrhythmias can lead to serious complications such as stroke, heart attack, and heart failure if left untreated, so continuous and accurate ECG monitoring is crucial for clinical care. However, the accurate interpretation of electrocardiogram (ECG) data is entirely dependent on medical doctors, which requires additional time and cost. Therefore, this paper proposes an arrhythmia recognition module for the purpose of developing a medical platform through the analysis of abnormal pulse waveforms based on Lifelogs. The proposed method is to convert ECG data into image format instead of time series data, apply visual pattern recognition technology, and then detect arrhythmia using CNN model. In order to validate the arrhythmia classification of the CNN model by image type conversion of ECG data proposed in this paper, the MIT-BIH arrhythmia dataset was used, and the result showed an accuracy of 97%.

• Journal of the Korean Geosynthetics Society
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• v.12 no.4
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• pp.67-76
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• 2013

Landfill require special care due to the dangers of nearby surface water and underground water pollution caused by leakage of leachate. The leachate does not leak due to the installation of the geomembrane but sharp wastes or landfill equipment can damage the geomembrane and therefore a means of protecting the geomembrane is required. In Korea, in accordance with the waste control act being modified in 1999, protecting the geosynthetics liner on top of the slope of landfill and installing a drainage layer to fluently drain leachate became mandatory, and technologies are being researched to both protect the geomembrane and quickly drain leachate simultaneously. Therefore, this research has its purpose in studying the drainage functions of leachate and protection functions of the geomembrane in order to examine the application possibilities of Geo-Multicell-Composite (GMC) as a Leachate Collection Removal and Protection System (LCRPs) at the slope on top of the geomembrane of landfill by observing methods of inserting filler with high-quality water permeability at the drainage net. GMC's horizontal permeability coefficient is $8.0{\times}10^{-4}m^2/s$ to legal standards satisfeid. Also crash gravel used as filler respected by vertical permeability is 5.0 cm/s, embroidering puncture strength 140.2 kgf. A result of storm drain using artificial rain in GMC model facility, maxinum flow rate of 1,120 L/hr even spray without surface runoff was about 92~97% penetration. Further study, instead of crash gravel used as a filler, such as using recycled aggregate utilization increases and the resulting construction cost is expected to savings.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.25 no.4
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• pp.282-290
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• 1992

Based on surplus production models using fishery data for the last 20 years, a stock assessment was conducted for the small yellow croaker in Korean waters. The maximum sustainable yields (MSY) from the Schaefer and Fox models were estimated to be 37,000 metric tons (mt) and 33,450 mt. Zhang's model using time-series biomass with instantaneous coefficients of fishing mortality (F) and using time-series biomass and catch yielded MSY estimates of 45,328 mt and 40,160 mt, respectively. A yield-per-recruit analysis showed that the current yield per recruit of about 20g with F= 1.11 $yr^{-l}$, where the age at first capture $(t_c)$ is 0.604, was much lower than the maximum possible yield per recruit of 43g. Fixing $t_c$ at the current level and reducing fishing intensity (F) from 1.11 $yr^{-l}$ to 0.4 $yr^{-l}$ yielded only a small increase in predicted yield per recruit, from 20 to 25g. However, estimated yield per recruit increased to 43g by increasing $(t_c)$ from the current age (0.604) to age three with F fixed at the current level. This age at first capture corresponded to the optimal length which was obtained from the $F_{0.1}$ method. According to the analysis of stock recovery strategies employing the Zhang model, the optimum equilibrium biomass $(B^*_{MSY})$ which produces the maximum yield could be achieved after approximately five years at the lower fishing intensity (F=0.5).

• Korean journal of food and cookery science
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• v.4 no.1
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• pp.75-85
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• 1988

This survey was conducted to investigate about purchasing, use and management of cooking oil, and the intake amounts of each food and fats & oils from each food on 296 housewives in a big city, medium and small city, farming village, fishing village, and mountain village from June 5 to July 20 in 1987. The percentage and $x^2$-test were used in data analysis and the arithmetic mean of the gross intake divided by the total subjects gave the intake amounts per capita per day. The results of this study are as following; 1. Purchasing and keeping of cooking oil. The reading ratio of label in purchasing cooking oil was high among housewives of cities and highly educated housewives. There were dissatisfactions about quality (46.7%), price (33.7%), and packing (19.5%) after purchasing cooking oil. Cooking oil was being kept mainly in glasses (64.7%) or in synthetic plastics (31.5%), and also in dark & cool places. 2. Use and refining of cooking oil. More housewives (70.6%) kept cooking oil used once in a different container after filtering. The housewives re-using used oil after adding new oil to it were only 30.0%. There were a lot of housewives frying foods twice or three times in the same oil. 3. Intake of fats & oils and foods containing fats & oils. Average intake amounts of food containing fats & oils per person per day was 6.85g in fats & oils, 42.96g in meat & its products, 95.13g in fishes & shellfishes, 22.89g in eggs, 60.69g in legumes & its products, 61.00g in milk & milk products, 4.22g in seeds & nuts, and 9.36g in instant noodles. Average intake amounts of fats & oils per person per day taken from these foods was 6.4g from fats & oils, 2.3g from meat & its products, 4.3g from fishes & shellfishes, 2.7g from eggs, 3.0g from legumes & its products, 2.5g from milk & milk products, 1.8g from seeds & nuts, and 1.7g from instant noodles.