• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.

• Journal of The Korean Association For Science Education
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• v.41 no.2
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• pp.155-169
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• 2021

This study aims at exploring the features of science teaching orientation (STO) and its relationships with other PCK (pedagogical content knowledge) components. To do this, based on the definition of STO by Friedrichsen, Driel, & Abell(2011) and PCK model by Magnusson, Krajcik, & Borko(1999), we observed one experienced elementary teacher's science lessons for 21 lesson hours (10 hours of 'Motion of Objects' and 11 hours of 'Light and Lens') and carried out qualitative analyses of the data obtained from lessons observation, teacher interviews, and CoRe (content representation) responses. We analyzed the teacher's three aspects of STO (i.e. beliefs about the goals and purpose of science teaching, beliefs about the nature of science, and beliefs about science teaching and learning) which can converge into an overall STO of 'inquiry'. And these aspects of STO appear to interact differently with four PCK components (i.e. curriculum knowledge, learner knowledge, instructional knowledge, and assessment knowledge) depending on the topic of the lesson. It is hoped that this in-depth understanding of the features of STO and its relationship with other PCK components would provide useful information on how to monitor and improve STO and PCK of elementary teachers.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.457-468
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• 2022

In aged society, it is important to prevent older people from being disability needing long-term care. The purpose of this study is to develop a prediction model to discover high-risk groups who are likely to be beneficiaries of Long-Term Care Insurance. This study is a retrospective study using database of National Health Insurance Service (NHIS) collected in the past of the study subjects. The study subjects are 7,724,101, the population over 65 years of age registered for medical insurance. To develop the prediction model, we used logistic regression, decision tree, random forest, and multi-layer perceptron neural network. Finally, random forest was selected as the prediction model based on the performances of models obtained through internal and external validation. Random forest could predict about 90% of the older people in need of long-term care using DB without any information from the assessment of eligibility for long-term care. The findings might be useful in evidencebased health management for prevention services and can contribute to preemptively discovering those who need preventive services in older people.

• Journal of Dental Anesthesia and Pain Medicine
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• v.23 no.2
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• pp.101-110
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• 2023

Background: Dentists make various efforts to reduce patients' anxiety and fear associated with dental treatment. Dental sedation is an advanced method that dentists can perform to reduce patients' anxiety and fear and provide effective dental treatment. However, dental sedation is different from general dental treatment and requires separate learning, and if done incorrectly, can lead to serious complications. Therefore, sedation is performed by a limited number of dentists who have received specific training. This study aimed to investigate the proportion of dentists who practice sedation and the main sedatives they use in the context of the Republic of Korea. Methods: We used the customized health information data provided by the Korean National Health Insurance. We investigated the number of dental hospitals or clinics that claimed insurance for eight main sedatives commonly used in dental sedation from January, 2007 to September, 2019 at the Health Insurance Review and Assessment Service. We also identified the changes in the number of dental medical institutions by region and year and analyzed the number and proportion of dental medical institutions prescribing each sedative. Results: In 2007, 302 dental hospitals prescribed sedatives, and the number increased to 613 in 2019. In 2007, approximately 2.18% of the total 13,796 dental institutions prescribed sedatives, increasing to 3.31% in 2019. In 2007, 168 institutions (55.6%) prescribed N₂O alone, and in 2019, 510 institutions (83.1%) made claims for it. In 2007, 76 (25.1%) hospitals made claims for chloral hydrate, but the number gradually decreased, with only 29 hospitals (4.7%) prescribing it in 2019. Hospitals that prescribed a combination of N₂O, chloral hydrate, and hydroxyzine increased from 27 (8.9%) in 2007 to 51 (9%) in 2017 but decreased to 38 (6.1%) in 2019. The use of a combination of N₂O and midazolam increased from 20 hospitals (6.6%) in 2007 to 51 hospitals (8.3%) in 2019. Conclusion: While there is a critical limitation to the investigation of dental hospitals performing sedation using insurance claims data, namely exclusion of dental clinics providing non-insured treatments, we found that in 2019, approximately 3.31% of the dental clinics were practicing sedation and that N₂O was the most commonly prescribed sedative.

• Economic and Environmental Geology
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• v.57 no.1
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.

• 대한공업교육학회지
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• v.39 no.2
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• pp.101-121
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• 2014

The study aims to develop the NCS based vocational curriculum model for the practical and creative human resources. For effectiveness of the study, the study consists of literature studies of both domestic and international, contents analysis, case study, expert(9samples) consultation and review, and in-depth-interview of the three advisory members. The validity of the developed model is analyzed through mean, standard deviation and contents validity ratio(CVR). The main results of the model development in our study are as follow. First, our NCS based vocational curriculum model for the practical and creative human resources is developed with the analyses of NCS development manuals, training standard utilization and training curriculum organization manuals, NCS learning module development manual and case studies, NCS research report, NCS based curriculum pilot development resources directed toward the high schools and vocational school as well as the domestic and international literature study on career training model like NCS. Second, based on the findings of our analysis in combination with the findings from the consultations with the expert and advisory committee, total 19 sub-factors of each step and domain are extracted. The sub-factors of domain in step 1 are the competency unit, definition of competency unit, competency unit element, performance criteria, range of variable, guide of assessment, key competency; in step 2, they are subject title, subject objectives, chapter title, chapter objectives, pedagogical methods, assessment methods and basic job competence; and in step 2, they are NCS based subject matrix table, NCS based subject profile, NCS based job training curriculum table, NCS based subjects organization flowchart, NCS based job training operation plan. Third, the final model including step 3 NCS based subject profile are developed in association with the linked organizational sub-factors of step 1 and step 2. Forth, the validity tests for the final model by the step and domain yield the mean 4.67, CVR value 1.00, indicating the superior validity. Also, the means of each sub-factors are all over 4.33 with the CVR value 1.00, indicating the high validity as well. The means of the associated organizations within the model are also over 4.33 with the CVR value of 1.00. Standard deviations are all .50 or lower which are small. Fifth, based on the validity test results and the in-depth-interview of the expert and advisory committee, the model is adjusted complemented to establish final model of the NCS based vocational curriculum for the practical and creative human resources.

• Journal of The Korean Association For Science Education
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• v.33 no.7
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• pp.1367-1384
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• 2013

The purpose of this study was to develop a Science Academic Emotion Scale for Elementary Students. To make a scale, authors extract a core of 14 emotions related to science learning situations from Kim & Kim (2013) and literature review. Items on the scale consisted of 14 emotions and science learning situations. The first preliminary scale had 174 items on it. The number of 174 items was reduced and elaborated on by three science educators. Authors verified the scale using exploratory factor analysis, confirmatory factor analysis, inter-item consistency and concurrent validity. The second preliminary scale consisted of 141 items. The preliminary scale was reduced to seven factors and 56 items by applying exploratory factor analysis twice. The seven factors include: enjoyment contentment interest, boredom, shame, discontent, anger, anxiety, and laziness. The 56 items were elaborated on by five science educators. The scale with 56 items was fixed with seven factors and 35 items to get the final scale by applying confirmatory factor analysis twice. Except for Chi-square and GFI (Goodness of Fit Index), other various goodness of fit characteristics of the seven factors and 35 items model showed good estimated figures. The Cronbach of the scale was 0.85. The Cronbach of seven factors are 0.95 in enjoyment contentment interest, 0.81 in boredom, 0.87 in shame, 0.82 in discontent, 0.87 in anger, 0.77 in anxiety, 0.81 in laziness. The correlation coefficient was 0.59 in enjoyment contentment interest, 0.54 in anxiety, 0.42 in shame, and 0.28 in boredom, which were estimated using the Science Academic Emotion Scale and National Assessment System of Science-Related Affective Domain (Kim et al., 1998). Based on the results, authors judged that the Science Academic Emotion Scale for Elementary Students achieved an acceptable validity and reliability.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of The Korean Association For Science Education
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• v.20 no.1
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• pp.20-28
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• 2000

We examined how science teachers in academic high schools perceived the 6th science curriculum and how they practiced under the curriculum. A nationwide survey was administered to obtain the responses from 402 teachers of 135 high schools. Most thought that the main themes of curriculum revision were well-embedded in the 'objectives', and that the 'content and content structure' were proper. However, they thought that the 'objectives' were not stated explicitly enough to develop teaching materials and to improve actual teaching and evaluation, and that some statements in the sections of 'method' and 'evaluation' were not proper if considered actual teachers' ability to teach inquiry and educational facilities. Many teachers also felt that the information about the curriculum was not sufficiently included at in-service teacher training programs, and that students' knowledge, attitude, and problem solving ability were not enhanced. Only few teachers were found to apply the STS approaches, reconstruct lessons, vary the structure of learning group, and develop evaluation tools with their colleagues. The lack of the practices was explained by entrance-examination-centered instruction and assessment, poor educational facilities, and lack of innovative teaching materials.