• Tunnel and Underground Space
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• v.33 no.1
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• pp.57-69
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• 2023

In this study, we simulated induced seismicity in the Groningen natural gas reservoir using 2D hydro-mechanical coupled discrete element modelling (DEM). The code used is PFC2D (Particle Flow Code 2D), a commercial software developed by Itasca, and in order to apply to this study we further developed 1)initialization of inhomogeneous reservoir pressure distribution, 2)a non-linear pressure-time history boundary condition, 3)local stress field monitoring logic. We generated a 2D reservoir model with a size of 40 × 50 km² and a complex fault system, and simulated years of pressure depletion with a time range between 1960 and 2020. We simulated fault system failure induced by pressure depletion and reproduced the spatiotemporal distribution of induced seismicity and assessed its failure mechanism. Also, we estimated the ground subsidence distribution and confirmed its similarity to the field measurements in the Groningen region. Through this study, we confirm the feasibility of the presented 2D hydro-mechanical coupled DEM in simulating the deformation of a complex fault system by hydro-mechanical coupled processes.

• Journal of Life Science
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• v.33 no.2
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• pp.191-198
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• 2023

As a member of the focal adhesion complex of the plasma membrane, Src is a nonreceptor tyrosine kinase that controls cell adhesion and motility. However, how Src activity is regulated in the plasma membrane microdomain in response to components of the extracellular matrix (ECM) remains unclear. This study compared and investigated the activity of Src in response to three representative ECM proteins: collagen type 1, fibronectin, and laminin. Genetically encoded FRET-based Src biosensors for plasma membrane subdomains were used. FRET-based biosensors allow the real-time analysis of protein activity in living cells based on their high spatiotemporal resolution. The results showed that Src activity was maintained at a high level under all ECM conditions of the lipid raft, and there was no significant difference between the ECM conditions. In contrast, Src activity was maintained at a low level in the non-lipid raft membrane. In addition, the Src activity of lipid rafts remained significantly higher than that of non-lipid raft regions under the same ECM conditions. In conclusion, this study demonstrates that Src activity can be controlled differently by lipid rafts and non-lipid raft microdomains.

• Science of Emotion and Sensibility
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• v.26 no.2
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• pp.53-66
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• 2023

In recent times, more fashion brands have been seeking ways to use metaverse platforms, in which users can actively participate, as their new brand touch-points. This study aims to compare the brand experiences of the fashion brand Gentle Monster's offline store and its equivalent metaverse store. By changing the order of offline and metaverse visits, two groups participated in the field study that allowed them to experience directly the offline and metaverse stores. As a result of the analysis, the following findings were discovered: (1) In the overall experiential response, the frequency of sensory modules responding to new information was much higher than that of feeling experiences; (2) Experiential responses were more active in the offline store where the subjects could touch and use products directly rather than in the metaverse; (3) Among the four types of theme space, the experiential response was the most frequent in the product space; (4) The first group that visited the metaverse store before the offline store showed a more active experience than the second group that visited the offline store first. Finally, the results of this study show that metaverse brand stores in virtual space not only provide differentiated experiences beyond the spatiotemporal constraints of real space but can also be used as a strategic tool to make offline store experiences more meaningful and rich.

• Korean Journal of Environmental Biology
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• v.40 no.1
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• pp.25-53
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• 2022

In this study, trends in research methods and topics of seabird and marine fish migration were examined. Based on the framework of existing animal migration studies, future research directions were proposed in relation to the migration of seabirds and fish. In terms of research methodology, with the development of science and technology, tracking techniques using radio telemetry, acoustic telemetry, RFID (radio-frequency identification), satellite tracking, and geolocators are widely used to study seabird and fish migration. Research is also conducted indirectly through a population survey and the analysis of substances in the body. Research contents are largely classified into extrinsic factors that affect migration(such as environmental variables and interspecific competition), intrinsic factors such as hormones, anthropogenic activities including fishery and offshore wind farm, and the effect of global climate change. In future studies, physiological factors that influence or cause migration and dispersal should be identified concerning intrinsic factors. For the analysis of migration ability, it is necessary to study effects of changes in the magnetic field on the migration ability of seabirds and fish, interspecific differences in spatiotemporal migration ability, and factors that influence the migration success rate. Regarding extrinsic factors, research studies on effects of anthropogenic disturbances such as fishery and offshore wind farm and global climate change on the migration and dispersal patterns of marine animals are needed. Finally, integrated studies on the migration of seabirds and fish directly or indirectly affecting each other in various ecological aspects are required.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.41-52
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• 2022

Although domestic public libraries achieved quantitative growth based on the 1st and 2nd comprehensive library development plans, there were some qualitative shortcomings, and various studies have been conducted to improve them. Most of the preceding studies have limitations in that they are limited to social and economic factors and statistical analysis. Therefore, in this study, by applying the spatiotemporal concept to quantitatively calculate the decrease in public library loan demand due to rainfall and heatwave, by clustering areas with high demand for book loan due to weather changes and areas where it is not, factors inside and outside public libraries and After the combination, changes in public library loan demand according to weather changes were analyzed. As a result of the analysis, there was a difference in the decrease due to the weather for each public library, and it was found that there were some differences depending on the characteristics and spatial location of the public library. Also, when the temperature was over 35℃, the decrease in book loan demand increased significantly. As internal factors, the number of seats, the number of books, and area were derived. As external factors, the public library access ramp, cafe, reading room, floating population in their teens, and floating population of women in their 30s/40s were analyzed as important variables. The results of this analysis are judged to contribute to the establishment of policies to promote the use of public libraries in consideration of the weather in a specific season, and also suggested limitations of the study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4B
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• pp.389-398
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• 2006

The present study compositely applied the self-organizing map (SOM), which is a kind of artificial neural networks (ANNs), and the back propagation algorithm (BPA) for the rainfall-runoff prediction model taking account of the irregular variation of the spatiotemporal distribution of rainfall. To solve the problems from the previous studies on ANNs, such as the overestimation of low flow during the dry season, the underestimation of runoff during the flood season and the persistence phenomenon, in which the predicted values continuously represent the preceding runoffs, we introduced SOM theory for the preprocessing in the prediction model. The theory is known that it has the pattern classification ability. The method proposed in the present research initially includes the classification of the rainfall-runoff relationship using SOM and the construction of the respective models according to the classification by SOM. The individually constructed models used the data corresponding to the respectively classified patterns for the runoff prediction. Consequently, the method proposed in the present study resulted in the better prediction ability of runoff than that of the past research using the usual application of ANNs and, in addition, there were no such problems of the under/over-estimation of runoff and the persistence.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.446-454
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• 2023

The impact of climate change on agriculture is substantial, especially as global warming is projected to lead to varying temperature and humidity patterns in the future. These changes pose a higher risk for both crops and livestock, exposing them to environmental stressors under altered climatic conditions. Specifically, as temperatures are expected to rise, the risk of heat stress is assessable through the Temperature-Humidity Index (THI), derived from temperature and relative humidity data. This study involved the comparison of THI collected from 10 Korea Meteorological Administration ASOS stations spanning a 60-year period from 1961 to 2020. Moreover, high-resolution temperature and humidity distribution data from 1981 to 2020 were employed to generate high-resolution TH I distributions, analyzing temporal changes. Additionally, the number of days characterized by heat stress, derived from TH I, was compared over different time periods. Generally, TH I showed an upward trend over the past, albeit with varying rates across different locations. As TH I increased, the frequency of heat stress days also rose, indicating potential future cost increases in the livestock industry due to heat-related challenges. The findings emphasize the feasibility of evaluating heat stress risk in livestock using THI and underscore the need for research analyzing THI under future climate change scenarios.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.415-426
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• 2023

In this study, the horizontal and vertical structure of wind speed and wind direction were analyzed at the origin of migratory insect pests in China. Wind rose analysis was carried out using the Land-Atmosphere Modeling Package (LAMP) - WRF data, which has the spatiotemporal resolution of about 20 km and 1 hour intervals. Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) was employed for backward trajectory analysis between South Korea and Southeastern China with Global Data Assimilation System (GDAS). The research interest date is July 16, when rice planthopper and leafhopper were observed at the same time. In order to examine where a jet stream occurs in the vertical in source regions and South Korea during the period (July 8 to July 17 in 2021), three-dimensional wind information was extracted and analyzed using the east-west, north-south, and vertical component wind data of the LAM P. The vertical distribution of wind showed that the wind changed in favor of the inflow of migratory insect pests during the period. As a result of analyzing the wind rose, about 30% or more of the wind at a point close to South Korea was classified into the low-level jet stream. In addition, majority of the wind directions for the low-level jet streams (rather than high-level jet streams) at the five origin sites were heading toward South Korea and even Japan, and this was supported by the HYSPLIT-based backward trajectory analysis.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.367-375
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• 2023

This study developed a new distance metric for vessel trajectories, applicable to marine traffic control services in the Korean coastal waters. The proposed metric is designed through the weighted summation of the traditional Hausdorff distance, which measures the similarity between spatiotemporal data and incorporates the differences in the average Speed Over Ground (SOG) and the variance in Course Over Ground (COG) between two trajectories. To validate the effectiveness of this new metric, a comparative analysis was conducted using the actual Automatic Identification System (AIS) trajectory data, in conjunction with an agglomerative clustering algorithm. Data visualizations were used to confirm that the results of trajectory clustering, with the new metric, reflect geographical distances and the distribution of vessel behavioral characteristics more accurately, than conventional metrics such as the Hausdorff distance and Dynamic Time Warping distance. Quantitatively, based on the Davies-Bouldin index, the clustering results were found to be superior or comparable and demonstrated exceptional efficiency in computational distance calculation.