• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.991-997
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• 2011

Soil erosion and sediment has been known as one of pollutants causing water quality degradation in water bodies. With global warming issues worldwide, various soil erosion studies have been performed. Although on-site monitoring of sediment loss would be an ideal method to evaluate soil erosion condition, modeling approaches have been utilized to estimate soil erosion and to evaluate various best management practices on soil erosion reduction. Although the USLE has been used in soil erosion estimation for the last 40 years, the USLE model has limitations in estimating event-based soil erosion reflecting rainfall intensity and rainfall duration for long-term period. Thus, the calibrated model, capable of simulating soil erosion using hourly rainfall data, was utilized in this study to evaluate the effects of rainfall amount and rainfall intensity on soil erosion. It was found that USLE soil erosion value is $3.06ton\;ha^{-1}\;yr^{-1}$, while soil erosion values from 2006~2010 were $2.469ton\;ha^{-1}\;yr^{-1}$, $0.882ton\;ha^{-1}\;yr^{-1}$, $1.489ton\;ha^{-1}\;yr^{-1}$, $2.158ton\;ha^{-1}\;yr^{-1}$, $1.602ton\;ha^{-1}\;yr^{-1}$, respectively. Especially, soil erosion from single storm event for 2008-2010 would be responsible for 30% or more of annual soil loss. As shown in this study, hourly soil erosion estimation system would provide more detailed output from the study area. In addition, the effects of rainfall intensity on soil erosion could be evaluated with this system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Journal of radiological science and technology
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• v.44 no.5
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• pp.545-554
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• 2021

According to the changes of the medical environment of the times, it is necessary to discuss the issues of the doctor's medical guidance and to conduct continuous research so that alternatives can be prepared systematically. Furthermore, in order to enhance the professionalism of radiological technologists and to develop the medical technician system, the new Radiological Technologist Independent Act has been established, which contains the overall contents of the scope of work, professional qualifications, and specialized education of radiological technologists, and provides quality medical services to patients through professional procedures and treatment. In order to increase the level of medical care, the purpose, definition, mission, role, and scope of work specified in the Medical Act, Medical Service Technologists, etc. Act, the Enforcement Decree, and the Enforcement Rules were variously analyzed and new directions were presented. First, the definition of a medical technician should use a generic term so that the factors of conflict and prejudice could be resolved. Second, change the doctor's guide to doctor's prescription; and then legislate the authority to sign and write medical records after examination by radiological technologists, thereby prohibiting unlicensed technicians that seriously endanger patient safety. Third, an accurate definition of radiological technologists' roles should be established; not only selection and management of radiological technologists' work but also procedures and treatment for each radiology field should be specified to suit the current medical system. Fourth, a professional radiological technologists' qualification system and a specialized education system should be established in order to secure human resources that could provide patients trust in procedures and treatment based on professional knowledge and experience in the field of radiology. Fifth, the Education and Evaluation Institute should be operated in Korea education system to educate the professional knowledge and competency for students. In addition, it is necessary to in-depth analysis of foreign cases could be applied to the medical system and education system in Korea; it could strive to nurture systematic human resources.

• Economic and Environmental Geology
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• v.54 no.5
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• pp.561-572
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• 2021

Aggregate collection is taking place in many areas in Korea, resulting in large cut slopes or large amounts of cut rocks. If the development site for such aggregate collection is a stratum accompanied by sulfide minerals, Acid Rock Drainage (ARD) may occur, which may cause environmental pollution in the development site and surrounding areas. As a result of the study on forest aggregate samples, most of the samples were classified as acid-forming potential samples, and among them, some samples from Gwangju, Goyang, and Sokcho were classified as potential acid-generating samples. This can be expected to affect the quality of aggregates when a large amount of aggregate is used in the future. Therefore, it is judged that these forest aggregates need to be managed when they are used. By predicting the occurrence of ARD through the acid-generating ability test, it is expected that economic losses that may occur in the future can be reduced, and it is judged that the problem of surrounding environmental pollution can be further alleviated.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.377-382
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• 2021

Oriental melon (Cucumis melo L.) is generally cultivated on the ground by creeping culture. A farmer has a higher workload for training stems. This study was conducted to find out a new cultivation of oriental melon to reduce a workload and improve the quality of fruit. There were three treatments for training stem of oriental melon; upward stem growing, downward stem growing, control (creeping stem growing). The results of the plant growth and the net photosynthesis showed higher in upward stem growing. The root activity was higher in downward stem attract. The yield was not significant as 4,055kg/10a in upward stem attract and 3,983kg/10a in downward stem attract. According to the results of the ergonomic agricultural workload evaluation, in the case of the working posture, the working posture of creeping cultivation methods (squatting, bending) showed a higher risk level than the upward and downward cultivation methods. Therefore, it is recommended the upward stem attract of oriental melon is a new cultivation as well as an alternative method for creeping stem attract in terms of improving the plant growth and yield, and reducing the workload.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.343-352
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• 2021

A convergence study was conducted to develop a smartphone application for self-practice of core nursing skills and evaluate its usefulness for undergraduate nursing students. Mobile Application Rating Scale and seven essay questionnaire were used to for usability evaluation among 22 undergraduate nursing students. The score of the information domain was the highest with 4.19(SD 0.79). The subjective quality domain showed the lowest score of 3.08(SD 0.87). Participants' performance confidence score was 8.23(SD 1.60), and learning satisfaction score was 7.89(SD 0.87). Participants reported that the convenience and repetitive self-learning were the strengths of the app. In addition, design and technical supplementation, and lecturer-feedback would improve effectiveness of the current educational app. Findings of this convergent study would be helpful to promote the application of mobile apps for effective self-learning of core nursing skills in undergraduate nursing education. Future resesarch is needed to examine effectiveness study of mobile app on the performance of core nursing skills.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1255-1263
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• 2021

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

• Food Engineering Progress
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• v.15 no.4
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• pp.362-369
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• 2011

This study was conducted to investigate the physicochemical and sensory characteristics of the fish paste prepared with Astragalus membranaceus powder (0-2.0%). The pH of the samples ranged from 6.75 to 6.86 and the moisture content ranged from 77.19 to 80.18%. Increasing amount of Astragalus membranaceus powder in the fish paste tended to increase the redness and yellowness in Hunter color value. Textural property analysis indicated that the strength of the sample paste increased and its springiness decreased with an increasing amount of Astragalus membranaceus powder. The fish paste prepared with Astragalus membranaceus powder (2.0%), especially, showed the highest values in cohesiveness, brittleness, and gumminess. In addition, all the samples had good flexibility. In sensory evaluation, the addition of 1.0% Astragalus membranaceus powder had the best score in color, texture, taste, and overall preference. Therefore, this results suggest that 1.0% Astragalus membranaceus powder can be applied to produce fish paste with high functionality and sensory preference.