• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.334-346
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• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Journal of Korean Dental Hygiene Science
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• v.6 no.1
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• pp.37-50
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• 2023

Background: This study investigated the structural relationships among justice, test anxiety, and school reliability s non-face-to-face tests of dental hygiene students. Methods: A survey was conducted with 267 dental hygiene students. The survey items included general characteristics, opinions on evaluation, the fairness of non-face-to-face tests (distributive, procedural, and interactional justice), school satisfaction, and school reliability. For statistical analysis, independent-sample t-tests, one-way ANOVA, and structural modeling analyses were performed. Results: Among factors that directly affected distributive justice and reliability towards non-face-to-face tests, the higher the interactional justice (β=0.401, p<0.001) and distributive justice (β=0.232, p=0.002) levels, the higher the school satisfaction. The higher the school satisfaction (β=0.606, p<0.001) and procedural justice (β=0.299, p<0.001) levels, the higher the perceived reliability of the school. Factors that indirectly affected school reliability included interactional justice (β=0.243, p=0.010) and distributive justice (β=0.141, p=0.010). Interactional justice (β=0.592, p=0.010) and distributive justice (β=0.208, p=0.010) were the factors affecting school satisfaction. Moreover, factors that influenced school reliability were distributive justice (β=0.56, p=0.010), interactional justice (β=0.332, p=0.010), procedural justice (β=0.229, p=0.010), and distributive justice (β=0.116, p=0.010). Conclusions: Students will trust and be satisfied with schools when schools and professors sufficiently provide information on face-to-face tests and ensure proper procedures to achieve reasonable grades as rewards for exerted time and effort. Furthermore, this study provides a reference base for developing a variety of content for fair, non-face-to-face tests, thereby allowing students to trust their schools.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.2
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• pp.210-223
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• 2023

This study developed elementary and middle school career education programs linked to science subjects and local natural resources, and explored learning effects and implications for developing and operating career programs. In order to achieve the research purpose, a 10-hour career education program using local natural and social resources was developed and applied to 25 elementary and middle school students in rural areas. As a result of the study, most of the elementary and middle school students who participated in this study were not well aware of the natural and social resource value of the area where they lived. Therefore, when developing and operating a regional-based career education program for elementary and middle school students in rural areas, it is necessary to operate a separate teaching/learning activity time so that students can fully know the natural and social information and resource values of the region. In addition, in order to enhance students' participation and interest in career education programs, it is necessary to organize the operation of the program in groups, not individuals, and to guide students in detail by dividing the program's performance process into several sub-steps. Finally, the core material of regional-linked career education-related programs focused more on their own content, that is, agricultural products grown by parents, and future job settings were higher in start-ups that directly operate companies such as travel agencies and manufacturing companies. Given the recent emphasis on career education in the curriculum, it is suggested that local students should pay more attention to finding materials with local resource value in the field of geoscience, which is closely related to natural resources, and developing and operating them as career education programs linked to local resources.

• Journal of agriculture & life science
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• v.50 no.2
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• pp.107-116
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• 2016

The study was aimed to analyze pedigree structure and inbreeding coefficients for performance tested Holstein cows in Korea. A total of 400,029 Holstein cows data which born between 2002 and 2012 were obtained from Dairy Cattle Improvement Center of National Agricultural Cooperative Federation(NACF). Their related pedigrees, as obtained from Korean Animal Improvement Association(KAIA), consisted of 509,740 animals. Pedigree depth of the cows were traced back to 3 generations earlier. The percentage of cows with fully identified ancestors in various provinces of Korea were 55.18%(Gyeonggi-do), 23.49%(Gangwon-do), 47.83%(Chungcheongnam-do), 53.62%(Chungcheongbuk-do), 56.38%(Gyeongsangbuk-do), 51.35% (Gyeongsangnam-do), 26.58%(Jeollanam-do), 49.41%(Jeollabuk-do), and 56.90%(Jeju-do), whereas, it was about 63.20% as a whole in Korea. The average inbreeding coefficients showed increment across the consecutive years of birth such as, 0.43(2002), 0.44(2003), 0.58(2004), 0.64(2005), 0.78(2006), 0.93(2007), 1.08(2008), 1.23(2009), 1.46(2010), 1.77(2011), and 2.03 (2012). However, this coefficient was 0.93 in overall Korean population. An average generation interval for sire to daughter genetic path was 8.15 years; which was about 4.20 years considering dam to daughter genetic path. The estimated effective population sizes (Ne) were 56.5, 51.3, and 32.2 animals born in 2004, 2009, and 2012, respectively. These results indicated that an increased rate of inbreeding has led to a significant reduction in the Ne over the decade.

• Journal of agricultural medicine and community health
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• v.48 no.2
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• pp.132-143
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• 2023

Objectives: We aimed to describe the reporting patterns of 6 notifiable surveillance diseases in the Republic of Korea, including water- and foodborne infections, from 2012 to 2021. Methods: For the 12,296 cases that met the reporting criteria, we calculated the number of reported cases, including the number of cases confirmed by lab tests or suspected by a physician, the number of cases with delayed reporting and their average days of delay, and the median days required to report the confirmatory test results. Results: The overall number of reported cases consistently increased over the ten years, with a significant rise in the reported cases of typhoid fever, paratyphoid fever, and EHEC. Ninety-five percent of all reported cases were timely reported within one day of diagnosis. Vibrio vulnificus had the highest rate of delayed reporting (6.8% delayed over 1 day, 3.0% delayed over 3 days), while cholera had the lowest rate (1.9% delayed over 1 day, 0.1% delayed over 3 days). The average days of delayed reporting was 6.1 days: the highest for paratyphoid fever (10.8 days) and the lowest for cholera (2.7 days). For typhoid fever and paratyphoid fever, there has been an increase in the proportion of cases with negative test results. For vibrio vulnificus, there has been an increase in the proportion of cases with confirmed positive test results. As for EHEC, there has been a recent increase in cases with no confirmatory tests. Conclusions: Reported cases of water- and foodborne infectious diseases increased, indicating improved surveillance system completeness. However, for paratyphoid fever, improvements are needed in terms of timely notification by healthcare facilities and timely reporting of confirmatory test results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5A
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• pp.557-564
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• 2009

In recent years, there have been numerous explosion-related accidents due to military and terrorist activities. Such incidents caused not only damages to structures but also human casualties, especially in urban areas. To protect structures and save human lives against explosion accidents, better understanding of the explosion effect on structures is needed. In an explosion, the blast overpressure is applied to concrete structures as an impulsive load of extremely short duration with very high pressure and heat. Generally, concrete is known to have a relatively high blast resistance compared to other construction materials. However, information and test results related to the blast experiment of internal and external have been limited due to military and national security reasons. Therefore, in this paper, to evaluate blast effect on reinforced have concrete structure and its protective performance, blast tests are carried out with $1.0m{\times}1.0m{\times}150mm$ reinforce concrete slab structure at the Agency for Defence Development. The standoff blast distance is 1.5 m and the preliminary tests consists with TNT 9 lbs and TNT 35 lbs and the main tests used ANFO 35 lbs. It is the first ever blast experiment for nonmilitary purposes domestically. In this paper, based on the basic experiment procedure and measurement details for acquiring structural behavior data, the blast experimental measurement system and procedure are established details. The procedure of blast experiments are based on the established measurement system which consists of sensor, signal conditioner, DAQ system, software. It can be used as basic research references for related research areas, which include protective design and effective behavior measurements of structure under blast loading.

• Korean Journal of Breeding Science
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• v.51 no.4
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• pp.386-394
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• 2019

Rehmannia glutinosa, one of the major medicinal crops in Korea, can be classified into three types: fresh, dried and prepared Rehmannia root. In this study, the quality characteristics of prepared rehmannia root were evaluated using four different cultivars that are commonly used in the market. In making prepared rehmannia root, roots of Jihwang 1, Kokang, Togang, and Dagang were dried, soaked in rice wine, and steamed nine times. At each stage, physiochemical properties were analyzed, including yield, which is one of the most important industrial factors to consider. The yield was the highest in Togang at 23.61% and the lowest in Dagang at 21.16%. These yield values showed a highly negative correlation with the moisture content of roots. The fructose and glucose contents were increased during the 3^rd, 4^th and 5^th steaming but then decreased. The sucrose, raffinose, and stachyose content gradually decreased during the first three steaming and were not detected during the 4^th steaming. Additionally, the catalpol content was not detected after the 4^th steaming. On the contrary, the 5-hydroxymethylfurfural content was not detected in the raw root but increased during the steaming. Jihwang1 and Togang exceeded the 0.1% Korean Pharmacopoeia standard after the 5^th steaming, reaching it faster than did the other cultivars. Overall, Togang was the optimal cultivar considering the overall characteristics of its high yield and short steaming time. These results could provide useful information for the industrial use of prepared Rehmannia root based on the requirements and characteristics of each cultivar.