• Journal of Environmental Impact Assessment
• /
• v.32 no.6
• /
• pp.503-515
• /
• 2023

The disconnection and fragmentation of ecological spaces that occur during the development process pose a significant threat to biodiversity. Urban center areas with high development pressure are particularly susceptible to low connectivity due to a scarcity of ecological space. This issue tends to be more pronounced in larger cities.To address this challenge, continuous efforts are needed to assess and improve the current state of ecological space connectivity at the level of individual projects and urban management. However, there is a lack of discussion regarding the analysis and improvement of ecological connectivity in metropolitan cities In line with this objective, this study evaluated the connectivity of ecological spaces in the city centers of Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan. The evaluation revealed that city centers exhibited lower connectivity of ecological spaces compared to their peripheries or the overall city. In addition, in the ecological network analysis that reflected regional characteristics, such as the species distribution model conducted on Daejeon, 510 optimal paths connecting forests of more than 1ha were derived. This study is significant as an example of deriving an ecological network based on regional characteristics, including quantitative figures necessary for establishing goals to improve urban ecological connectivity and biodiversity. It is anticipated that the results can be utilized to propose directions for enhancing ecological connectivity in environmental impact assessments or urban management and to establish an evaluation framework.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.11
• /
• pp.29-42
• /
• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Journal of the Korean Geotechnical Society
• /
• v.40 no.2
• /
• pp.95-103
• /
• 2024

The global increase in population and subsequent scarcity of terrestrial living spaces necessitates exploration of alternative habitats. Research into the development of underwater living areas provides promising avenues for the expansion of human living spaces and the use of marine environments. This study focuses on the failure envelope of suction caisson foundations subjected to combined loads in a marine setting, utilizing finite element analysis. The foundation is assumed to be embedded in clay characterized by a linear increase in undrained shear strength with depth, employing the von Mises constitutive model for the clay. The resulting failure envelope is represented as a tilted ellipse which expands as the undrained shear strength increases, maintaining a constant ratio between the major and minor axes. A comparative analysis of two suction caisson foundations with varying length-to-diameter ratios revealed that this ratio influences the dimensions of the failure envelope, with a tendency for the major-to-minor axis ratio to increase as the length-to-diameter ratio increases. These findings are critical for the design of suction caisson foundations in offshore environments.

• Journal of Industrial Convergence
• /
• v.22 no.1
• /
• pp.1-20
• /
• 2024

Regarding the discourse on the correlation between governmental financial support and firm performance, much emphasis has been placed on the role of individual corporate characteristics as well as spatial features. However, there is a notable scarcity of empirical research examining the integrated impact of corporate and cluster characteristics on managerial performance. This study addresses this gap by empirically analyzing the financial and non-financial outcomes resulting from specific allocations of governmental financial support. Additionally, it explores corporate and cluster characteristics predicted to moderate the influence between governmental financial support and firm performance. The analysis employs a two-level hierarchical linear model (HLM) at individual and group levels. The data, reorganized based on business registration numbers at the firm and cluster levels, ultimately utilized panel data from 83,395 firms and 641 clusters. The research findings indicate that governmental financial support demonstrates a positive effect (+) on both sales and patents for firms, suggesting its effectiveness in complementing market failures. Results from the hierarchical linear model analysis show that when combined with human capital capacity, absorptive capacity, and cluster network density, governmental financial support exhibits significant positive effects on sales. This study contributes theoretical and practical insights by analyzing the relationship between governmental financial support and firm performance using a two-level hierarchical linear model. It highlights the role of corporate characteristics such as human capital and absorptive capacity, along with cluster characteristics like cluster network density, in moderating the effects of governmental financial support on firm performance.

• Knowledge Management Research
• /
• v.25 no.2
• /
• pp.193-218
• /
• 2024

The current study investigated the factors influencing the buyer's repurchase intention for second-hand products. This study first identified perceived risk and purchase value as the two primary influencing variables. Additionally, some exogenous variables influencing these two variables were examined. Statistical analysis using Partial Least Squares (PLS) revealed that product uncertainty, seller uncertainty, and site trust had statistically significant relationships with perceived transaction risk. However, while economic benefit showed a significant impact on purchase value, product scarcity and resale value did not exhibit a significant relationship with purchase value. Perceived transaction risk was found to have an insignificant relationship with repurchase intention, but indirectly influenced repurchase intention through purchase value. Purchase value was identified as having a significant influence on repurchase intention. Therefore, it was concluded that purchase value is the most important factor influencing repurchase intention in the purchase of second-hand products, while transaction risk indirectly influences repurchase intention through purchase value. The study indicates that product uncertainty and economic benefit are the most significant exogenous factors influencing transaction risk and purchase value, respectively.

• Journal of the Korean earth science society
• /
• v.45 no.1
• /
• pp.72-84
• /
• 2024

Recently, the vulnerability of water resources has been increasing owing to climate change, highlighting the importance of groundwater. In particular, the Nakdong River Basin, located in the southern part of Korea, experiences frequent water scarcity phenomena, such as droughts. This study analyzed the hydrogeological characteristics of the study area by examining groundwater and stream water in the Gwangrye Stream, downstream of the Nakdong River Basin, in August and October 2023. Therefore, we collected samples from 54 groundwater wells and five streams for water quality analysis. The results of the field tests indicated an increasing trend in electrical conductivity from upstream to downstream in the study area. Laboratory analyses confirmed that the concentration of Na increased from upstream to downstream more than that of Ca. In conclusion, both stream water and groundwater were influenced by anthropogenic contamination. These changes were closely related to land use in the study area. The upstream areas are primarily composed of forests, whereas the downstream areas are composed of industrial complexes, wastewater treatment facilities, and agricultural areas, which are likely to affect both stream water and groundwater.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.3
• /
• pp.91-101
• /
• 2024

The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users often overprovision container applications to avoid container restarts caused by resource shortages. However, overprovisioning results in decreased CPU and memory resource utilization. To address this issue, oversubscription of container resources is commonly employed, although excessive oversubscription of memory resources can lead to a cascade of container restarts due to node memory scarcity. Container restarts can reset operations and impose substantial overhead on containers with high memory volatility that include numerous stateful applications. This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, an effect similar to a paused state is achieved. The suspension of the identified containers is released once it is determined that the corresponding node's memory usage has been reduced. The average number of container restarts was reduced by an average of 40% and a maximum of 58% when executing a high memory volatile workflow in a Kubernetes environment with the proposed method compared to its absence. Furthermore, the total execution time of a container workflow is decreased by an average of 7% and a maximum of 13% due to the reduced frequency of container restarts.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.4
• /
• pp.55-61
• /
• 2024

Recently, the number of aging concrete structures is steadily increasing. This is because many of these structures are reaching their expected lifespan. Such structures require accurate inspections and persistent maintenance. Otherwise, their original functions and performance may degrade, potentially leading to safety accidents. Therefore, research on objective inspection technologies using deep learning and computer vision is actively being conducted. High-resolution images can accurately observe not only micro cracks but also spalling and exposed rebar, and deep learning enables automated detection. High detection performance in deep learning is only guaranteed with diverse and numerous training datasets. However, surface damage to concrete is not commonly captured in images, resulting in a lack of training data. To overcome this limitation, this study proposed a method for generating concrete surface damage images, including cracks, spalling, and exposed rebar, using stable diffusion. This method synthesizes new damage images by paired text and image data. For this purpose, a training dataset of 678 images was secured, and fine-tuning was performed through low-rank adaptation. The quality of the generated images was compared according to three base models of stable diffusion. As a result, a method to synthesize the most diverse and high-quality concrete damage images was developed. This research is expected to address the issue of data scarcity and contribute to improving the accuracy of deep learning-based damage detection algorithms in the future.

• Journal of Korea Water Resources Association
• /
• v.57 no.9
• /
• pp.599-613
• /
• 2024

Unlike other disasters, the water shortage problem caused by drought is characterized by the long-lasting ripple effect of the social and economic sectors in all regions of Korea, and the types and purposes of water mainly used are different depending on the type of region, so the factors and scale of water shortage damage are different. In this study, a methodology to evaluate the risk of water shortage based on socioeconomic characteristics was developed and applied to Gwangju and Jeollanam-do to analyze the results. To this end, 20 impact indicators for risk, exposure, and vulnerability items were selected according to the climate risk concept of IPCC AR6. The results of the water shortage risk evaluation reflecting socioeconomic characteristics were different from the risk results considering only the existing meteorological and hydrological factors. The areas with the greatest risk of water shortage were calculated as Yeonggwang-eup in Yeonggwang-gun, Yeonsan-dong and Haean-dong 4-ga in Mokpo-si, Jeokryang-dong in Yeosu-si and Geumsan-myeon in Goheung-si. Through the evaluation results, risk factors and countermeasures for water shortage were derived in consideration of detailed characteristics of the region, which can be used as data contributing to the establishment of measures to reduce drought damage tailored to the region in the future.