• Journal of agricultural medicine and community health
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• v.47 no.1
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• pp.14-26
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• 2022

Objectives: Handwashing is one of the most effective methods to prevent the spread of infectious diseases. This study assessed the related factors and reasons for not practicing handwashing at school among adolescents. Methods: We analyzed data collected from 57,303 adolescents who participated in the 15th Korea Youth Risk Behavior Survey 2019. Results: The proportions of not washing hands "before meals at school" and "after using the toilet at school" were 15.9% and 4.4%, respectively. The adjusted odds ratio for not washing hands before meals at school was significantly higher in girls (Odds Ratio [OR]=1.52, 95% Confidence Intervals [CI]=1.42-1.63), metropolitan city (OR=1.32, 95% CI=1.11-1.56), city (OR=1.29, 95% CI=1.08-1.54), higher grade, higher academic performance, lower economic status, not handwashing at home (OR=14.36, 95% CI=13.37-15.42), and without annual personal hygiene education (OR=1.41, 95% CI=1.33-1.49). Reasons for not washing hands at school among adolescents who do not wash their hands before meals at school included 'it is bothersome (52.3%)', 'there is no soap or hand sanitizer (13.8%)', and 'I do not feel the need (9.5%)'. Conclusions: Improving handwashing before meals at school among adolescents requires raising awareness of the importance of handwashing before meals and establishing a suitable environment and handwashing-encouraging culture.

• Composites Research
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• v.35 no.4
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• pp.269-276
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• 2022

In this paper, a composite stiffened panel was fabricated using a three-dimensional weaving method that can reduce the risk of delamination, and mechanical properties such as buckling load and natural frequency were investigated. The preform of the stringer and skin of the stiffened panel were fabricated in one piece using T800 grade carbon fiber and then, resin (EP2400) was injected into the preform. The compression test and natural frequency measurement were performed for the stiffened panel, and the results were compared with the finite element analyses. In order to compare the performance of 3D weaving structures, the stiffened panels with the same configuration were fabricated using UD and 2D plain weave (fabric) prepregs. Compared to the tested buckling load of the 3D woven panel, the buckling loads of the stiffened panels of UD prepreg and 2D plain weave exhibited +20% and -3% differences, respectively. From this study, it was confirmed that the buckling load of the stiffened panel manufactured by 3D weaving method was lower than that of the UD prepreg panel, but showed a slightly higher value than that of the 2D plain weave panel.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.363-368
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• 2021

Ship engine rooms have improved automation systems owing to the advancement of technology. However, there are many variables at sea, such as wind, waves, vibration, and equipment aging, which cause loosening, cutting, and leakage, which are not measured by automated systems. There are cases in which only one engineer is available for patrolling. This entails many risk factors in the engine room, where rotating equipment is operating at high temperature and high pressure. When the engineer patrols, he uses his five senses, with particular high dependence on vision. We hereby present a preliminary study to implement an engine-room patrol robot that detects and informs the machine room while a robot patrols the engine room. Images of ship engine-room equipment were classified using a convolutional neural network (CNN). After constructing the image dataset of the ship engine room, the network was trained with a pre-trained CNN model. Classification performance of the trained model showed high reproducibility. Images were visualized with a class activation map. Although it cannot be generalized because the amount of data was limited, it is thought that if the data of each ship were learned through transfer learning, a model suitable for the characteristics of each ship could be constructed with little time and cost expenditure.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.1
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• pp.15-21
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• 2022

The purpose of our study was to investigate the migration level of lead (Pb), cadmium (Cd), and barium (Ba) from glassware into a food simulant and to evaluate the exposure of each element. The test articles were glassware, including tableware, pots, and other containers. Pb, Cd, and Ba were analysed by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The analytical performance of the method was validated in terms of its linearity, limit of detection (LOD), limit of quantification (LOQ), recovery, precision, and uncertainty. The monitoring was performed for 110 samples such as glass cups, containers, pots, and bottles. a food simulant. Migration test was conducted at 25? for 24 hours in a dark place using 4% acetic acid as a food simulant. Based on the data; exposure assessment was carried out to compare the estimated daily intake (EDI) to the human safety criteria. The risk levels of Pb and Ba determined in this study were approximately 1.9% and 0.3% of the provisional tolerable weekly intake (PTWI) and tolerable daily intake (TDI) value, respectively, thereby indicating a low exposure to the population.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Journal of Korea Entertainment Industry Association
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• v.15 no.7
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• pp.181-192
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• 2021

This study was conducted to investigate the effects of a participatory rehabilitation program on sit-rise and rise-to-walk test performances, and perception and motor skills in adults with medically vulnerable individuals and, adults with developmental disabilities in particular. Seventeen adults with developmental disabilities participated in a participatory rehabilitation program using resistance bands and exercise balls, for 60 minutes once weekly over 13 weeks. Their performances were measured before and immediately after the intervention, and 12 weeks after. The findings were as follows. In the sit-rise test, the number of times rising from sitting posture increased after the intervention versus before, but the difference was not statistically significant. In the rise-to-walk test, the performance showed statistically significant difference over time, and the post-hoc test showed a significant effect after the intervention versus before. There was no significant difference in perception and motor skills. In sum, the participatory rehabilitation program positively influenced dynamic balancing related to functional activities but had no significant effect on perception and motor skills, which is related to motor control and motor learning. It is suggested that to increase the participation in community activities, reduce fall risk, and improve dynamic balancing abilities in adults with developmental disabilities, participatory rehabilitation programs should be utilized to promote the physical wellbeing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.375-380
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• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.