• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.59-69
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• 2021

Historical records of earthquakes are generally used as a basis to extrapolate the instrumental earthquake catalog in time and space during the probabilistic seismic hazard analysis (PSHA). However, the historical catalogs' input parameters determined through historical descriptions rather than any quantitative measurements are accompanied by considerable uncertainty in PSHA. Therefore, quantitative assessment to verify the historical earthquake parameters is essential for refining the reliability of PSHA. This study presents an approach and its application to constrain reliable ranges of the magnitude and corresponding epicenter of historical earthquakes. First, ranges rather than specific values of ground motion intensities are estimated at multiple locations with distances between each other for selected historical earthquakes by reviewing observed co-seismic natural phenomena, structural damage levels, or felt areas described in their historical records. Based on specific objective criteria, this study selects only one earthquake (July 24, 1643), which is potentially one of the largest historical earthquakes. Then, ground motion simulations are performed for sufficiently broadly distributed epicenters, with a regular grid to prevent one from relying on strong assumptions. Calculated peak ground accelerations and velocities in areas with the historical descriptions on corresponding earthquakes are converted to intensities with an empirical ground motion-intensity conversion equation to compare them with historical descriptions. For the ground motion simulation, ground motion prediction equations and a frequency-wavenumber method are used to consider the effects of possible source mechanisms and stress drop. From these quantitative calculations, reliable ranges of epicenters and magnitudes and the trade-off between them are inferred for the earthquake that can conservatively match the upper and lower boundaries of intensity values from historical descriptions.

• Earthquakes and Structures
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• v.21 no.5
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• pp.531-549
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• 2021

Analytical models were developed and seismic behaviors were analyzed for a three-story stone pagoda at the Cheollyongsa temple site, which was damaged by the Gyeongju earthquake of 2016. Both finite and discrete element modeling were used and the analysis results were compared to the actual earthquake damage. Vulnerable parts of stone pagoda structure were identified and their seismic behaviors via sliding, rocking, and risk analyses were verified. In finite and discrete element analyses, the 3F main body stone was displaced uniaxially by 60 and 80 mm, respectively, similar to the actual displacement of 90 mm resulting from the earthquake. Considering various input conditions such as uniaxial excitation and soil-structure interaction, as well as seismic components and the distance from the epicenter, both models yielded reasonable and applicable results. The Gyeongju earthquake exhibited extreme short-period characteristics; thus, short-period structures such as stone pagodas were seriously damaged. In addition, we found that sliding occurred in the upper parts because the vertical load was low, but rocking predominated in the lower parts because most structural members were slender. The third-floor main body and roof stones were particularly vulnerable because some damage occurred when the sliding and rocking limits were exceeded. Risk analysis revealed that the probability of collapse was minimal at 0.1 g, but exceeded 80% at above 0.3 g. The collapse risks at an earthquake peak ground acceleration of 0.154 g at the immediate occupancy, life safety, and collapse prevention levels were 90%, 52%, and 6% respectively. When the actual damage was compared with the risk analysis, the stone pagoda retained earthquake-resistant performance at the life safety level.

• Journal of Fashion Business
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• v.27 no.1
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• pp.61-76
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• 2023

The purpose of this study was to derive exotic products handled by Bon Marche department store, examine characteristics, and reveal the meaning of exotic taste in the 19th century. The research method is literature research centered on original materials of the time, such as old literature related to department stores, and films, newspapers, magazines, and periodicals as well as books and papers. Commercial catalogs and Agenda-Buvard published by Bon Marché were used as visual materials. Results are as follows. Exotic tastes of products of the Bon Marche department store in the late 19th century showed an oriental style centered on Persia, India, China, and Japan. Products from various regions such as Turkey, Spain, Algeria, Tunisia, England, Italy, and Russia were also accepted. These exotic tastes had the following contributions and meanings. First, they expanded items and product development and activated domestic industry through production of imitations and replicas. Second, before the advent of department stores, foreign items that came to Europe were exclusive properties of a few privileged classes. However, in the late 19th century, as the number of consumers who wanted to have a new status higher than themselves increased, exotic products of the Bon Marche department store became the object of desire for material pursuits toward the upper class rather than sentimental fantasies. Third, exotic tastes gradually spread to the public as production of products imitating foreign products became possible industrially with an easy access to them. From this, it can be seen that department stores were mediators of trends and the epicenter of popularizing exotic tastes.

• Fisheries and Aquatic Sciences
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• v.25 no.11
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• pp.549-558
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• 2022

The Indonesian Archipelago has a very complex geological history, along with equatorial warm sea temperature, resulting in diverse types of coral reefs and high diversity of coral reef fish. Many livelihoods of the coastal community are dependent on coral reef fisheries. The present study aimed to determine which region and location in the Indonesian Archipelago has the most diverse and abundant coral reef fish. The archipelago was divided into four regions: the Indian Ocean, Sunda Shelf, Wallacea, and the Pacific Ocean. Data were obtained from a national coral reef monitoring program of the Indonesian Research Center for Oceanography (RCO)-the National Board for Research and Innovation (BRIN). The reef fish data were collected using the underwater visual census method, from 321 belt transects on 24 locations (districts) across the archipelago. The results show that coral reef fish diversity of the Pacific region was the highest across the archipelago for all three trophic levels, i.e., corallivore, herbivore, and carnivore fish. The Pacific Ocean region also had the highest fish abundance for the three trophic levels. Comparison among locations revealed that the best ten locations in reef fish diversity and abundance were Sabang, Mentawai, Makassar, Selayar, Buton, Luwuk, Ternate, Raja Ampat, Biak, and Wakatobi. Wakatobi reefs showed their supremacy in carnivore fish diversity and abundance, while Biak reefs were the best in herbivore fish. The abundance of corallivore fish was also considerably high in Sabang reefs, but it is still lower than in Raja Ampat, Biak, and Wakatobi reefs. These results provide empirical evidence that the coral reefs of Wakatobi and Biak are the hottest hotspots of coral reef fish diversity and abundance in the Indonesian Archipelago.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.307-316
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• 2024

On January 1, 2024, an earthquake with a moment magnitude of 7.5 occurred on the Noto Peninsula in Japan. The earthquake caused significant surface displacement on the Noto Peninsula. The surface displacement is measured by global navigation satellite system (GNSS) base stations, but there are limitations in obtaining information in areas where base stations do not exist. Therefore, in this study, we aim to determine the horizontal land surface displacement across the Noto Peninsula using offset tracking, which can detect rapidly occurring displacement. As a result of analyzing the Noto Peninsula using the offset tracking technique, it was found that more horizontal surface displacement occurred in the northwest region than in the northeast region of the Noto Peninsula, where the epicenter was located, and the surface displacement value reached a maximum of 2.9 m. The results of this study can be used to calculate surface displacement values in areas where surface displacement data are not available through ground GNSS base stations.

• The Journal of Engineering Geology
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• v.28 no.2
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• pp.313-324
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• 2018

In order to study the earthquake precursor in the Korean peninsula, long-term variations of chemical composition, radon-222, and water level were measured at depths (-60 m, -100 m) in the groundwater monitoring wells of the Daejeon and the Cheongwon area. The pH and electrical conductivity of groundwater in the monitoring wells showed some relationship with the Pohang earthquake. The ${HCO_3}^-$ and $Cl^-$ concentration of groundwater in the Daejeon and $Mg^{2+}$, $Cl^-$ and ${NO_3}^-$ in the Cheongwon showed some relation with the Pohang earthquake. However, it is not distinct to find the relationship between their variation and earthquake. The radon-222 concentration in Daejeon was observed a significant increase from a minimum of 162 Bq/L prior to the earthquake to 573 Bq/L right after the earthquake, that indicating a strong correlation with earthquakes. In the case of groundwater levels, it can not find some correlation between earthquakes and continuous decreasing trend in the monitoring wells of Daejeon and Cheongwon area. However, water level of a national groundwater observation well within 10 kilometers of Pohang epicenter was recorded as an abrupt drop right before the earthquake. Conclusively, although the location of monitoring wells is more than 180 kilometers apart from the epicenter of the Pohang earthquake, the radon gas in groundwater can be considered as a reliable candidate among earthquake precursors. The pH, electrical conductivity, ${HCO_3}^-$ and $Cl^-$ among hydrochemicals showed some correlation with earthquake should be monitored during a longer term to recognize distinctly as a precursor of earthquake.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.3
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• pp.175-184
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• 2018

In this paper, comparative analysis of the 9.12 Gyeongju and 11.15 Pohang earthquakes was conducted in order to provide probable explanations and reasons for the damage observed in the 11.15 Pohang earthquake from both earthquake and structural engineering perspectives. The damage potentials like Arias intensity, effective peak ground acceleration, etc observed in the 11.15 Pohang earthquake were generally weaker than those of the 9.12 Gyeongju earthquake. However, in contrast to the high-frequency dominant nature of the 9.12 Gyeongju earthquake records, the spectral power of PHA2 record observed in the soft soil site was highly concentrated around 2Hz. The base shear around 2 Hz frequency was as high as 40% building weight. This frequency band is very close to the fundamental frequency of the piloti-type buildings severely damaged in the northern part of Pohang. Unfortunately, in addition to inherent vertical irregularity, most of the damaged piloti-type buildings had plan irregularity as well and were non-seismic. All these contributed to the fatal damage. Inelastic dynamic analysis indicated that PHA2 record demands system ductility capacity of 3.5 for a structure with a fundamental period of 0.5 sec and yield base shear strength of 10% building weight. The system ductility level of 3.5 seems very difficult to be achievable in non-seismic brittle piloti-type buildings. The soil profile of the PHA2 site was inversely estimated based on deconvolution technique and trial-error procedure with utilizing available records measured at several rock sites during the 11.15 Pohang earthquake. The soil profile estimated was very typical of soil class D, implying significant soil amplification in the 11.15 Pohang earthquake. The 11.15 Pohang earthquake gave us the expensive lesson that near-collapse damage to irregular and brittle buildings is highly possible when soil is soft and epicenter is close, although the earthquake magnitude is just minor to moderate (M 5+).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.3
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• pp.439-448
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• 2018

Conventional Earthquake Early Warning System (EEWS) detects the propagated P-wave from epicenter which should be achieved within 5 seconds to provide seconds to minutes of warning, allowing people to prepare for protective actions. EEWS in Korea is currently capable of providing a warning within 50 seconds after the primary P-wave detection, however, it is well-known that earthquake warning systems operating around Korean National Parks (KNP) have limited capability to fully monitor earthquake events. This study, therefore, presents a strategy to quantify the potential vulnerability to earthquake hazards by superimposing the distribution of Korea Integrated Seismic System (KISS) and the discretized map of KNP. Total 22 national parks are evaluated, and the results suggest that the improvement of the on-site systems should be necessitated for Gyoengju, Gyeryongsan, Songnisan, Gayasan, and Deogyusan national parks, whereas enhancement of regional systems is required for Bukhansan national park.