• Journal of Korean Association for Spatial Structures
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• v.18 no.1
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• pp.45-53
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• 2018

Following the earthquake that shook the city of Gyeongju, Korea, in 2016, it became apparent that research on the safety of cultural heritages against the seismic hazards is necessary in Korea. Predictions of how historically significant stone pagodas would behave the earthquakes anticipated in near future, which are the subject of this study, is also required. In this study, the dynamic characteristics of 15 cultural heritage designated stone pagodas of Korea were investigated, including natural frequency and damping ratio, and the stiffness of the stone material and its contact area were determined using eigenvalue analysis by assuming the stone pagodas to be multi-degree-of-freedom structures. The results of this study enable the structural modeling of stone pagodas using a finite element analysis program and the method is expected to be useful in assessing the structural safety of stone pagodas against vertical loads as well as lateral forces, including earthquakes. Also, by identifying the dynamic characteristics of the structures, the results of this study can be utilized as a nondestructive testing method to determine the rigidity of cultural heritage structures and to identify inherent problems. The natural frequencies of the Korean stone pagodas were measured to be within 3.5~8.3Hz, excluding cases with distinct natural frequency results, and it was determined that the natural frequencies of the stone pagodas are influenced by various parameters including the height and joint stiffness of the structures.

• Geophysics and Geophysical Exploration
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• v.23 no.4
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• pp.230-242
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• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.

• Journal of Technology Innovation
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• v.26 no.1
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• pp.31-57
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• 2018

The general public's interest level towards safer life is increasing due to not only ever-changing faces of disasters and increased frequency of climate-change related disasters but also enhanced standard of living. Demand for disaster-safety industry is also increasing. Several policies for disaster-safety industry have been introduced. The policies, however, did not fully reflect the level of people's interest. This study is to investigate possible ways to reflect general public's interests towards disaster-safety industry using social media analysis, so that disaster-safety industry can be properly promoted. To examine the level of general public's interest, social media data during the last three years were compiled and analyzed. It was found that the interest level was highest towards, firstly, information on just-happened real disasters, secondly, necessary knowledge in real life which could be applied immediately if disasters strike. It was also confirmed that social media was useful in analyzing people's interest level quickly, because social data have been found to be sharply increased during the 2016 Gyeongju Earthquake in Korea. This study suggests applicable plans for disaster-related industry promotion based on social media data using general public's interest level.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.