• Journal of the Korean Society of Earth Science Education
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• v.14 no.3
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• pp.248-256
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• 2021

The purpose of this study is to investigate the perception of pre-service elementary teachers on the educational approach to metaverse. Questions about metaverse were asked to 71 pre-service elementary teachers who were taking the course 'Study of Textbooks in Elementary Science II'. The results of analyzing the contents of the questions are as follows. The results and conclusions were presented through numerical analysis and static analysis based on the responses to questions presented using the university's LMS system. First, the level of understanding of the metaverse of pre-service elementary teachers is very high. Pre-service elementary teachers, as the MZ generation, are already living in a very fast IT environment that can be the basis of the metaverse, so it would have been helpful to understand the metaverse. Second, the need for the metaverse of pre-service elementary teachers is very high. There was a tendency to think that the perception of pre-service elementary teachers is because the metaverse has many factors that can provide higher quality education beyond the current educational environment. Third, in the question of applicability exploration in the 'Earth and Space' domain of Pre-service elementary teachers, there have been few cases in which instructional design was planned based on instructional design principles. Based on these results, if the possibility of metaverse application is proposed in the 'Earth and Space' domain, educational contents using virtual space that can transcend time and space will be very necessary. Based on these results, suggestions are made as follows. First, educational content incorporating the metaverse technique based on instructional design should be developed and utilized. Second, financial support should be provided so that the metaverse can be implemented in the educational environment. Third, it is necessary to provide training opportunities for teachers (including Pre-service elementary teachers) to give lectures on metaverse.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.430-445
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• 2022

The purpose of this study was to explore the learning effects in elementary school students who participated in a geological field trip conducted under the theme 'minerals and rocks', focusing on novelty space. A total of 10 sixth-grade students participated in this program held at a public elementary school in Seoul as part of after-school club activities. Students observed mineral and rock samples in a classroom and outdoor learning environment. The authors collected activity papers (texts, drawing), researchers' participation notes, video and audio recordings containing the study participants' activities, and post-interview data To analyze the learning effects in the cognitive domain of students, the observation analysis framework for rock classification of Remmen and Frøyland (2020) and the rock description analysis framework of Oh (2020) were used. Additionally, to explore the learning effects of psychological and geographic areas, students' drawings, texts, discourses, and interview data were inductively analyzed. The results showed that the students demonstrated 'everyday' and 'transitional' observations in the classroom learning environment, while in the outdoor learning environment (school playground, community-based activities), they demonstrated 'transitional' and 'scientific' observations. Moreover, as the scientific observation stage progressed, more types of descriptive words for rocks were used. In terms of psychological and geographic aspects, students showed their selection of places to explore familiar outdoor learning environments, positive perceptions of outdoor learning, and aesthetic appreciation. Finally, this study not only discussed novelty space as a tool for analyzing students' learning effects but also suggested the need for an academic approach considering new learning environments, such as learning through virtual field trips.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.218-227
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• 2022

Purpose: In the case of cavity discovered by ground penetrating radar exploration, it is necessary to accurately predict the filling amount in the cavity in advance, fill the cavity sufficiently and exert strength to ensure stability and prevent ground subsidence. Method: The cavity waveform analysis method by GPR exploration and the method using the cavity shape imaging equipment were performed to measure the cavity shape with irregular size and shape of the actual cavity, and the amount of cavity filling of the injection material was calculated during rapid restoration. Result: The expected filling amount was presented by analyzing the correlation between the cavity size and the filling amount of injection material according to the cavity scale and soil depth through the method by GPR exploration and the cavity scale calculation using the cavity shaping equipment. Conclusion: The cavity scale measured by the cavity imaging equipment was found to be in the range of 20% to 40% of the cavity scale by GPR exploration. In addition, the filling amount of injection material compared to the cavity scale predicted by GPR exploration was in the range of about 60% to 140%, and the filling amount of the injection material compared to the cavity size by the cavity shaping equipment was confirmed to be about 260% to 320 Purpose: The purpose of this study is to examine the types of crimes taking place in the metaverse, and to establish a crime prevention strategy and find a legal deterrent against it. Method: In order to classify crime types in the metaverse, crime types were analyzed based on the results of previous studies and current incidents. Result: Most of the crimes taking place in the metaverse are done in games such as Roblox or Zeppetto. Most of the game users were teenagers. Looking at the types, there are many teens for sexual crimes, violent crimes, and defamation, but professional criminals are often included in copyright infringement, money laundering using virtual currency, and fraud. Conclusion: Since the types of crimes in the metaverse are diverse, various institutional supplementary mechanisms such as establishment of police crime prevention strategies, legal regulations, and law revisions will have to be prepared.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.1
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• pp.91-102
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• 2022

Sinsu Island in Korea National Marine Park is suitable for geological learning and geotourism as various geological structures, geological activities, and fossils can be observed. In this study, eight geosites were developed by analyzing 2015 revision science curriculum. The 3D panoramic geological virtual field trips were developed according to the three-step outdoor learning model. The 3D panoramic geological virtual field trips, which consist of 8 classes, are composed of cooperative learning by group, enabling autonomous inquiry activities. It is designed to realize convergence education that can learn not only geology but also creativity and humanity through nodular Limestones, exfoliation, salt weathering, perforated shell holes, sedimentary structures and environments, dinosaurs habitats, and volcanic activity. Five experts revised and supplemented the Delphi analysis method to verify the validity of the development purpose and direction. The satisfaction with the geological field for Sinsu Island course was 4.52, indicating that the overall satisfaction with the field course was high. By reflecting on the opinions of each student and reinforcing safety education, we completed the 3D panoramic geological virtual field trips for Sinsu Island. The 3D panoramic geological virtual field trips in Sinsu Island in Korea National Marine Park will be a good example of geology learning tourism where you can make memories and enjoy while studying geology.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.63-71
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• 2021

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

• Smart Media Journal
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• v.11 no.5
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• pp.75-81
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• 2022

In order to respond to carbon neutrality and climate change in agriculture, agricultural machinery, which has been developed centered on internal combustion engines, needs to be converted to an electric-based technology that does not emit greenhouse gases. In this study, simulations for electric UTV suspension design were performed to reduce vibration and shock of electric UTV for agricultural use and to improve driving stability and control performance of the vehicle. The simulation was performed by dividing the tolerance load of the vehicle body and the loaded load state. The range of motion of the suspension spring of UTV is within 30% of the range of motion under condition B under tolerance, the displacement of the UTV suspension with full load is reduced from 264mm to 121mm, and the damping speed is 260mm/s to 300mm/s that it can be seen that the range of motion is within 60%. Suspension design of electric UTV for multi-purpose agricultural work is a very important factor for maintaining agricultural work ability in towing work such as tillage as well as driving and terrain adaptation. The results of this study can be usefully used to determine the spring parameters with the appropriate damping range so that the electric UTV can be used for various agricultural tasks.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.733-741
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• 2022

Among the various evidence found in maritime crimes, fingerprints and DNA are very important in that they can identify a suspect. In this study, 5 types of non-porous surfaces (plastic, stainless, glass, ceramic, FRP), which are often found as evidence in the actual marine environment, were selected, and latent and blood fingerprints were passed down and immersed at the Donghae Maritime Police Station's exclusive pier for about 7 days. After that, DNA extraction, quantification, and STR profile were analyzed after fingerprint developing CA fumming method and 4 powder methods (Swedish black powder, Concentrated black powder, Supranano red powder, Dazzle orange powder). Among the fingerprint developing methods, when Supranano red powder was applied, a relatively high amount of DNA was found. As a result of STR profile analysis, an average of 16.8 to 9 loci were secured, and all 20 were confirmed in glass and ceramic materials. As a result of the study, it was possible to secure the STR profile by extracting and quantifying DNA after applying the fingerprint developing method to virtual evidence immersed for about 7 days, and further research is needed to secure the STR profile by analyzing DNA after applying various fingerprint developing methods such as VMD and SPR.