• Smart Media Journal
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• v.10 no.4
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• pp.102-110
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• 2021

Currently, the advanced construction technology endorsement system is being operated to promote the development of domestic construction technology. We tried to examine the implicit meanings inherent in advanced construction technologies by analyzing the relationship between emerging vocabularies with high importance in relation to the advanced construction technologies endorsed through this system. For this purpose, 918 cases of advanced construction technology information were collected. Based on the endorsed year and summary of the advanced construction technologies, the importance of the emerging vocabularies was measured for each advanced construction technology. And, based on the LDA model, the degree of influence between related vocabularies was evaluated for each of the four topic areas. Topics according to the technical application fields were analyzed. From 1990 to 2021, the trend of changes in highly influential vocabularies by each topic was inferred. In the future, changes in the degree of influence of the topics of environment, machinery, facilities, and maintenance and reinforcement of structures and related technology fields were predicted.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.27-33
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• 2021

With increasing interest in key areas of the 4th industrial revolution such as artificial intelligence, deep learning and big data, scientific approaches have developed in order to overcome the limitations of traditional decision-making methodologies. These scientific techniques are mainly used to predict the direction of financial products. In this study, the factors of apartment prices, which are of high social interest, were analyzed through SOM. For this analysis, we extracted the real prices of the apartments and selected a total of 16 input variables that would affect these prices. The data period was set from 1986 to 2021. As a result of examining the characteristics of the variables during the rising and faltering periods of the apartment prices, it was found that the statistical tendencies of the input variables of the rising and the faltering periods were clearly distinguishable. I hope this study will help us analyze the status of the real estate market and study future predictions through image learning.

• Smart Media Journal
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• v.11 no.8
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• pp.9-20
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• 2022

With the rise of the metaverse industry worldwide, relevant national strategies and nurturing systems have been prepared in Korea. As the complexity of policies increases, the importance of establishing data-based policymkaing is growing, and studies diagnosing national R&D trends in the metaverse field are still lacking. Therefore, this paper collected NTIS national R&D information for 9,651 R&D projects promoted from 2002 to 2020. And this study looked at the current status and identified major topics based on the topic modeling, and considered time-series changes in the topics. Eleven major topics of R&D tasks in the metaverse field were derived, hot topics were service/content/platform development and medical/surgical fields of application fields, and cold topics were urban/environment/spatial information fields. Strategic R&D Management, metaverse-related laws, and institutional studies were proposed as policy directions.

• Safety and Health at Work
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• v.13 no.3
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• pp.326-335
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• 2022

Background: One of the important actions for enhancing human reliability in any industry is assessing human error probability (HEP). The HEART technique is a robust tool for calculating HEP in various industries. The traditional HEART has some weaknesses due to expert judgment. For these reasons, a hybrid model is presented in this study to integrate HEART with Best-Worst Method. Materials Method: In this study, the blasting process in an iron ore mine was investigated as a case study. The proposed HEART-BWM was used to increase the sensitivity of APOA calculation. Then the HEP was calculated using conventional HEART formula. A consistency ratio was calculated using BWM. Finally, for verification of the HEART-BWM, HEP calculation was done by traditional HEART and HEART-BWM. Results: In the view of determined HEPs, the results showed that the mean of HEP in the blasting of the iron ore process was 2.57E-01. Checking the full blast of all the holes after the blasting sub-task was the most dangerous task due to the highest HEP value, and it was found 9.646E-01. On the other side, obtaining a permit to receive and transport materials was the most reliable task, and the HEP was 8.54E-04. Conclusion: The results showed a good consistency for the proposed technique. Comparing the two techniques confirmed that the BWM makes the traditional HEART faster and more reliable by performing the basic comparisons.

• Fashion & Textile Research Journal
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• v.24 no.5
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• pp.593-604
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• 2022

The purpose of this study is to analyze the flow of change in perception of metaverse before and after COVID-19 through big data analysis. This research method used Textom to collect all data, including metaverse for two years before COVID-19 (2018.1.1~2019.11.30) and after COVID-19 outbreak (2020.1.11~2021.12.31), and the collection channels were selected by Naver and Google. The collected data were text mining, and word frequency, TF-IDF, word cloud, network analysis, and emotional analysis were conducted. As a result of the analysis, first, hotels, weddings, and glades were commonly extracted as social issues related to metaverse before and after COVID-19, and keywords such as robots and launches were derived, so the frequency of keywords related to hotels and weddings was high. Second, the association of the pre-COVID-19 metaverse keywords was platform-oriented, content-oriented, economic-oriented, and online promotion-oriented, and post-COVID-19 clusters were event-oriented, ontact sales-oriented, stock-oriented, and new businesses. Third, positive keywords such as likes, interest, and joy before COVID-19 were high, and positive keywords such as likes, joy, and interest after COVID-19. In conclusion, through this study, it was found that metaverse has firmly established itself as a new platform business model that can be used in various fields such as tourism, travel, festivals, and education using smart technology and metaverse.

• Journal of the Korean Geotechnical Society
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• v.38 no.9
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• pp.19-33
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• 2022

Planarity geotechnical exploration missions were actively performed during the 1970s and there was a period of decline from the 1 990s to the 2000s because of budget. However, exploring space resources is essential to prepare for the depletion of Earth's resources in the future and explore resources abundant in space but scarce on Earth, such as rare earth and helium-3. Additionally, the development of space technology has become the driving force of future industry development. The competition among developed countries for exoplanet exploration has recently accelerated for the exploration and utilization of space resources. For these missions and resource exploration/mining, geotechnical exploration is required. There have been several missions to explore exoplanet ground, including the Moon, Mars, and asteroids. There are Apollo, LUNA, and Chang'E missions for exploration of the Moon. The Mars missions included Viking, Spirit/Opportunity, Phoenix, and Perseverance missions, and the asteroid missions included the Hayabusa missions. In this study, space planetary mineral resource exploration technologies are explained, and the future technological tasks of Korea are described.

• Genomics & Informatics
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• v.20 no.3
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• pp.35.1-35.9
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• 2022

Microsatellites or simple sequence repeats are motifs of 1 to 6 nucleotides in length present in both coding and non-coding regions of DNA. These are found widely distributed in the whole genome of prokaryotes, eukaryotes, bacteria, and viruses and are used as molecular markers in studying DNA variations, gene regulation, genetic diversity and evolutionary studies, etc. However, in vitro microsatellite identification proves to be time-consuming and expensive. Therefore, the present research has been focused on using an in-house built java pipeline to identify, analyse, design primers and find related statistics of perfect and compound microsatellites in the seven complete genome sequences of coronavirus, including the genome of coronavirus disease 2019, where the host is Homo sapiens. Based on search criteria among seven genomic sequences, it was revealed that the total number of perfect simple sequence repeats (SSRs) found to be in the range of 76 to 118 and compound SSRs from 01 to10, thus reflecting the low conversion of perfect simple sequence to compound repeats. Furthermore, the incidence of SSRs was insignificant but positively correlated with genome size (R² = 0.45, p > 0.05), with simple sequence repeats relative abundance (R² = 0.18, p > 0.05) and relative density (R² = 0.23, p > 0.05). Dinucleotide repeats were the most abundant in the coding region of the genome, followed by tri, mono, and tetra. This comparative study would help us understand the evolutionary relationship, genetic diversity, and hypervariability in minimal time and cost.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.285-295
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• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• Geomechanics and Engineering
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• v.29 no.2
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• pp.113-131
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• 2022

A rockburst is a common disaster in deep-tunnel excavation engineering, especially for high-geostress areas. An anomalously low friction effect is one of the most important inducements of rockbursts. To elucidate the correlation between an anomalously low friction effect and a rockburst, we establish a two-dimensional prediction model that considers the discontinuous structure of a rock mass. The degree of freedom of the rotation angle is introduced, thus the motion equations of the blocks under the influence of a transient disturbing force are acquired according to the interactions of the blocks. Based on the two-dimensional discontinuous block model of deep rock mass, a rockburst prediction model is established, and the initiation process of ultra-low friction rockburst is analyzed. In addition, the intensity of a rockburst, including the location, depth, area, and velocity of ejection fragments, can be determined quantitatively using the proposed prediction model. Then, through a specific example, the effects of geomechanical parameters such as the different principal stress ratios, the material properties, a dip of principal stress on the occurrence form and range of rockburst are analyzed. The results indicate that under dynamic disturbance, stress variation on the structural surface in a deep rock mass may directly give rise to a rockburst. The formation of rockburst is characterized by three stages: the appearance of cracks that result from the tension or compression failure of the deformation block, the transformation of strain energy of rock blocks to kinetic energy, and the ejection of some of the free blocks from the surrounding rock mass. Finally, the two-dimensional rockburst prediction model is applied to the construction drainage tunnel project of Jinping II hydropower station. Through the comparison with the field measured rockburst data and UDEC simulation results, it shows that the model in this paper is in good agreement with the actual working conditions, which verifies the accuracy of the model in this paper.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.