• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.309-316
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• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.769-777
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• 2021

Purpose: Earthquake disasters are frequently occur unpredictable situations due to various variables and unexpected situations. As a result, the work process itself is not uniform, making it difficult for public officials in the disaster safety department to familiarize themselves with the earthquake field manual. This paper is specifically and accurately grasp the current work situation conducted by the Disaster and Safety Countermeasures Headquarters of the Gyeongsangbuk-do Office and present a plan for designing serious game scenarios necessary for field manual learning. Method: In this study, scenarios were designed based on the GBS(Goal Based Scenario) model, and in the process of assigning missions and roles based on the Gyeongsangbuk-do earthquake field manual, public officials related to earthquakes were able to acquire knowledge and skills to solve practical tasks. Result: Scenario data of the proposed technique was implemented as a systematic procedure by processing various earthquake-related information into logical data and simplifying and abstracting it for game expression for earthquake situation training. Conclusion: In the event of an earthquake due to learning through serious games, related public officials of Gyeongsangbuk-do provincial are expected to be able to respond quickly to various earthquake disasters.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.367-372
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• 2022

With the recent advent of the New Normal era, realistic technologies and non-contact technologies are receiving social attention. However, the hair styling field focuses on the direction of the hair itself, individual movements, and modeling, focusing on hair simulation. In order to create an improved practice environment and demand of the times, this study proposed a non-contact hair styling VR system. In the theoretical review, we studied the existing cases of hair cut research. Existing haircut-related research tend to be mainly focused on force-based feedback. Research on the interactive haircut work in the virtual environment as addressed in this paper has not been done yet. VR controllers capable of finger tracking the movements necessary for beauty enable selection, cutting, and rotation of beauty tools, and built a non-contact collaboration environment. As a result, we conducted two experiments for interactive hair cutting in VR. First, it is a haircut operation for synchronization using finger tracking and holding hook animation. We made position correction for accurate motion. Second, it is a real-time interactive cutting operation in a multi-user virtual collaboration environment. This made it possible for instructors and learners to communicate with each other through VR HMD built-in microphones and Photon Voice in non-contact situations.

• Journal of Science Education
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• v.45 no.3
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• pp.349-363
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• 2021

The purpose of this study is to develop an instrument for school STEAM education diagnostic assessment. Literature reviews, the developmental study of a prototype instrument, experts' advices, and pilot study were administrated. The school STEAM education diagnostic assessment was consist of five areas: 'STEM education action and sustainability plan,' 'STEAM curriculum and instruction,' 'STEAM professional development,' 'process-based evaluation,' and 'community and partnerships.' Each area had one to five sub-areas. A total of 14 diagnosis items were developed, including items that can diagnose the school's STEAM environment base and STEAM education execution level at the school unit and member level for each area. The validation of the diagnostic assessment was conducted through the content validity of the expert group and the validity of a survey targeting school teachers. For applying the instrument for STEAM Education School Assessment to schools, a total of 267 elementary, middle, and high schools participated. As a result, the average of the five areas was 1.46 to 2.18. This instrument comprehensively diagnoses and evaluates the implementation and effectiveness of STEAM education in schools, and is expected to be used as basic data and core data for implementing STEAM education.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• Communications of Mathematical Education
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• v.36 no.1
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• pp.107-138
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• 2022

This study is a study that developed class materials that can apply Process-Focused Assessment to classes by paying attention to feedback using teacher learning community programs centered on teachers belonging to the same school in the field. In particular, this study was conducted with the aim of developing class materials applicable to actual classes. At this time, We thought about how to provide appropriate feedback when applying course-based evaluation in school field classes. It was conducted according to the procedure of data development research by Lee & Ahn(2021). As for the procedure of data development itself, an evaluation plan was established by establishing a strategy to reconstruct achievement standards and confirm understanding based on curriculum analysis. Next, an evaluation task, a scoring standard table, and a preliminary feedback preparation table were developed. In addition, based on these development materials, a learning guidance plan that can predict scenes when applying actual classes was developed as a result. This study has value as a practical study that can contribute to providing a link between theory and field schools. It is also meaningful in that it considered how the teacher would grasp when to provide feedback in performing rocess-Focused Assessment. Likewise, in providing feedback by teachers, it is meaningful in that it reflects in the data development how to prepare in advance and take classes according to the characteristics of the subject. Finally, it seems that the possibility of field application can be improved in that the results of the 4th class developed in this study are presented in a form applicable to the class directly in the field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4139-4147
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• 2014

The aim of this study was to review the characteristics of teacher education curriculum and reform tendency between China and Korea. This study used literature analysis of various studies, catalogs, documents of education universities in China and Korea. The results were as follows. Some common features in the teacher education curriculum were observed, such as the purposes of education, constituent area of the curriculum, and subjects, such as pedagogy and practice teaching. Other differences included that China requires more credits for graduation than Korea, but the elective subjects are assigned fewer credits. In both countries, it is necessary to increase the relevant subjects (pedagogy, practice teaching) for the specialty of a preliminary teacher and establish a permanent system for the curriculum needs of students. In terms of reform tendency, both countries should change the training concept and teacher education philosophy, mainly on enhancing quality-oriented education, emphasizing the students' sustainable self development ability, as well as attaching importance to concept of lifelong education. These results are expected to be helpful in improving the teacher education curriculum in China and Korea.

• Analytical Science and Technology
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• v.28 no.3
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• pp.187-195
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• 2015

The heat of sublimation (HOS) is an essential parameter used to resolve environmental problems in the transfer of organic contaminants to the atmosphere and to assess the risk of toxic chemicals. The experimental measurement of the heat of sublimation is time-consuming, expensive, and complicated. In this study, quantitative structural property relationships (QSPR) were used to develop a simple and predictive model for measuring the heat of sublimation of organic compounds. The population-based forward selection method was applied to select an informative subset of descriptors of learning algorithms, such as by using multiple linear regression (MLR) and the support vector machine (SVM) method. Each individual model and consensus model was evaluated by internal validation using the bootstrap method and y-randomization. The predictions of the performance of the external test set were improved by considering their applicability to the domain. Based on the results of the MLR model, we showed that the heat of sublimation was related to dispersion, H-bond, electrostatic forces, and the dipole-dipole interaction between inter-molecules.

• 한국정보교육학회:학술대회논문집
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• 2004.08a
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• pp.28-37
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• 2004

The purpose of this paper is Inflect ICT in each subject class of common school by effort about education Information that was regularized with enforcement of the seventh training courses and arrived in visual point that realize education information concretely. But, such realistic that specific discussion and research about actual ICT introduction direction or practical use way consisted enough up to now despite Is urgent example difficult. Decide target connected with Information Technology by classified grade-class and plan that teach learners systematically may have to flow fair discussion and trial run success or failure of education and point that is connected directly problem that establish date or the level and target that it is very as itself value how. Therefore, this research wished to premise computer-aided education target in primary school by analyze education target of each subject that come out in primary grade subject and abroad training courses.