• The Journal of Engineering Geology
• /
• v.30 no.4
• /
• pp.617-634
• /
• 2020

The purpose of this study is to investigate the relationship of between earthquakes and fluctuation of water level in a groundwater well of the active-fault zone and 124 national groundwater monitoring wells in Gyeongju area. The spatial and temporal relationships between the fluctuation of water level and the earthquake were analyzed by the calculation of earthquake effectiveness (ε) and q-factor which are the function of earthquake magnitude and distance from epicenter. Two earthquake events of E1 (April 22, 2019, M 3.8) and E2 (June 11, 2019, M 2.5) show a close relationship with a post-seismic 83 cm decrease and a pre-seismic 76 cm increase in water level at the active fault zone of Dangu-ri, respectively. The spatial analysis of water level fluctuation data in National Groundwater Monitoring Networks caused by earthquake events shows a more distinct response in deep groundwater around fault zones than other area, and a greater change in deep groundwater than shallow groundwater. It's inferred that the decrease and increase in groundwater level are affected by the expansion of fractures and compression of rock mass due to seismic stress, respectively. The effective ranges of ε-value and q-factor of the monitoring well in Dangu-ri were calculated as 2.70E-10~5.60E-10 and 14.4~18.0, respectively.

• Journal of Technology Innovation
• /
• v.30 no.2
• /
• pp.29-58
• /
• 2022

Startups lack resources and manpower to build internal capabilities to strengthen market competitiveness; external cooperation such as joint research and networking plays is important. In this study, we analyzed the effect of startups' industry-university cooperation on organizational learning capability and innovation performance. Empirical results demonstrate the mechanism by which government R&D investment strengthens organizational learning capability and creates innovative results by promoting cooperation between startups and universities. First, industry-university cooperation strengthened organizational learning capability. An empirical analysis shows that startups increase internal capabilities through external cooperation. Second, startups' organizational learning capability had a significant effect on innovation performance. We analyze how organizations with high learning capabilities positively develop corporate innovation performance by having a culture of discovery and sharing new ideas. Finally, industry-university cooperation had different effects on organizational learning capability and innovation performance according to the previous experiences of startup founders. In particular, small- and medium-sized (startup) businesses and individual-based experience groups positively affected the creation of organizational learning capabilities and innovation performance through industry-university cooperation. Small- and medium-sized businesses and individual founders have a relatively small cooperative network with the outside world compared to founders of large companies, universities, and research institutes; therefore, they strengthen organizational learning capabilities through cooperation with universities. This study demonstrates that government should create policy inducements for cooperation with universities to maximize the R&D performance of startups. Criticism exists that lending support to startups and universities will hinder innovation performance; nevertheless, government investment plays a role in expanding intangible resources such as accumulating technologies, fostering high-quality human resources, and strengthening innovation networks. Therefore, the government should appropriately utilize the its authority to strengthen investment strategies for startup growth.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1723-1735
• /
• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean small business review
• /
• v.43 no.4
• /
• pp.59-93
• /
• 2021

This study surveyed 413 small business owners who experienced closure to see how the loss perception experienced by small business owners affects their comeback through fear of failure. The analysis results are as follows. First, the larger the received loss of financial capital, market capital, and social capital, the greater the fear of failure. Second, the greater the fear of failure, the less willingness to re-start-up, but it did not affect the willingness to work. Third, perceived loss of financial capital, market capital, and social capital grew fear of failure, which negatively affected the willingness to re-start. However, as for the willingness to work, only the perception of loss to market competitiveness strengthened the willingness to work through fear of failure. This suggests that if you think you are out of business due to market competitiveness, you are more likely to choose to get a job than to start a business. Fourth, those with higher entrepreneurial self-efficiency had less effect of perceived loss on fair of failure than those with lower entrepreneurial loss. In other words, it can be seen that a person with high entrepreneurial self-efficiency is likely to start-up. It is noteworthy that despite the tendency to fail due to market competition and lack of understanding of risks, small business operators were most aware of the loss of social capital. This is presumed to have had the greatest impact on fear of failure because small business owners try to receive funding or business revitalization support through social networks such as acquaintances and relatives. Based on the above results, this study requires sufficient market research to secure a competitive advantage when preparing for start-ups through policy practice suggestions, and suggests ways to reduce financial loss through the establishment of sophisticated business plans.

• The Mathematical Education
• /
• v.62 no.3
• /
• pp.435-455
• /
• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Korean Journal of Ecology and Environment
• /
• v.55 no.1
• /
• pp.10-18
• /
• 2022

This study analyzed the relationship between the community structure of benthic macroinvertebrates and habitat changes in open estuaries among the sites included in the national estuary monitoring program. The estuary survey was conducted under the "Guidelines for Investigation and Evaluation of Biometric Networks" and classified by sea area, 80 places in the East Sea, 102 places in the South Sea, and 19 places in the West Sea were investigated. In a total of 201 open estuaries, benthic macroinvertebrates were identified with 4 phyla, 9 classes, 41 orders, 139 families, 269 species and 196 species in the East Sea, 182 species in the South Sea, and 90 species in the West Sea. The highest population densities were Insecta in the East Sea, the Malacostraca in the South Sea, and the Annelida in the West Sea. Through SIMPER analysis, species contributing to the similarity of benthic macroinvertebrates communities in each sea area were identified. Some species greatly influenced the similarity of clusters. The benthic community in the East Sea was affected by the salinity, so the contribution rate of freshwater species was high. On the other hand, the benthic communities of the South and West Seas showed species compositions are influenced by the substrate composition. As results, the benthic macroinvertebrate community in Korean estuaries was impacted by salinity and substrate simultaneously, and the close relationship with geographical distance was not observed. The result of this study is expected to be used to respond to environmental changes by identifying and predicting changes in the diversity and distribution of benthic macroinvertebrates in Korea estuaries.

• Journal of Korea Entertainment Industry Association
• /
• v.13 no.7
• /
• pp.307-319
• /
• 2019

This study aims to provide the basic materials for the development of leisure and tourism contents in connection with Osaek cableway for the revitalization of Osaek District. For such a purpose, the following policy directions were presented through the analysis of the present situation and conditions of Osaek District, the direction of development of leisure and tourism contents of Osaek District, etc. The first is increasing the participation of local residents and reinforcing their capabilities. The suggested promotion plans are ① establishing organizational system and strengthening support, ② reinforcing the capabilities of local residents and ③ constructing networks with external human resources. The second is setting the guidelines for contents development. It was proposed to prepare contents for leisure experience using the natural environment of Osaek District in response to the trend of increase of people who enjoy "contents using culture and arts" and leisure. The third is typological approach to contents. It was proposed to develop cultural contents with the theme of Osaek such as "Osaek Light Festival", "Osaek Concert", "Osaek Photo Exhibition" and "Osaek Good Men and Women Contest" for the promotion of the brand of the place name of Osaek and the creation of the "Picture Book Village" for the compilation of the history and culture of Osaek District with pictures. The fourth is securing marketing channels. For this, it was proposed to produce the website of Yangyang County or a website tentatively named as "Osaek-ri with Beautiful Osaek" and introduce an integrated travel product (transportation + lodging + foods + experience (hot spring, mineral water therapy, leisure experience, etc.) + purchasing local specialty products, etc.) composed of the leisure and tourism contents, transportation, lodging, foods, etc. of Osaek District through travel agencies. The final policy direction presented was phased implementation of the development and operation of the contents. Proposed policies include support of a consulting project to upgrade the organization of local residents; implementation of "Tourism Dure (Cooperative)" project for the solution of the problem of tourism in Osaek District by the residents themselves together using the space of culture and arts made by remodeling idle public and private facilities after benchmarking exemplary places; system improvement for the introduction of leisure and tourism contents appropriate for local conditions; and the establishment of a master plan for the introduction of various leisure and tourism contents in Osaek District.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.17 no.5
• /
• pp.67-80
• /
• 2022

This study was conducted to derive policy measures such as fostering and supporting by examining the actual conditions of domestic startups. The subject of this study was the start-ups located in Seongnam-si, where Pangyo Techno Valley, which is the highest-level innovation cluster in Korea and is evaluated as a start-up mecca. Startups were defined as startups under 7 years old based on new technologies such as IT, BT, and CT, and the subjects of the study were selected. This can be seen as a step forward from previous research in that it embodies the concept of a startup that was previously abstract in a quantitatively measurable way. As a result of the analysis, about 94% of startups are distributed in the so-called "Death Valley" growth stage, and startups above scale-up, which means full-scale growth beyond BEP, account for about 6%. appeared to be occupied. He cited the problem of start-up funds as the biggest difficulty in the early stages of startups, and cited the loan evaluation method that prioritizes sales or collateral in raising funds as the biggest problem. In addition, start-ups rated the access to private investment capital such as VC, AC, and angel investors at a low level compared to policy funds, which are public funds. Most startups showed a lot of interest in overseas expansion, and they chose matching overseas investors such as overseas VCs as the biggest support for overseas expansion. The overall competitiveness in the overseas market was 49.6 points, which is less than 50 points out of 100, indicating that the overall competitiveness was somewhat inferior. It was analyzed that public support and investment in overseas sales channels (sales channels, distribution networks, etc.) should be prioritized along with enhancement of technological competitiveness in order for domestic startups to increase their competitiveness in overseas markets as well as in the domestic market.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.6
• /
• pp.173-189
• /
• 2023

As supreme technologies continue to be developed, industries such as artificial intelligence, biotechnology, robots, aerospace, electric vehicles, and solar energy are created, and the macro business environment is rapidly changing. Due to these large-scale changes and increased complexity, it is necessary to pay attention to the effect of social capital, which can create new value by utilizing capital increasing the importance of relationships rather than technology or asset ownership itself at the level of start-up strategy. Social capital is a concept first proposed by Hanifan in 1916, and refers to the overall sum of capabilities or resources that are latent or available for use in mutual, continuous, organic relationships or accumulated human relationship networks between individuals or social members. In addition, the diversity of start-up teams with diverse backgrounds, characteristics, and capabilities, rather than one exceptional founder, has been emphasized. Founding team diversity refers to the diversity of in-depth factors such as demographic factors, beliefs, and values of the founding team. In addition, changes in the macro environment are emphasizing the importance of technology start-ups and laboratory start-ups that lead industrial innovation and create the nation's core growth engines. This study focused on the I-Corps' program. I-Corps, which means innovation corps, is a laboratory startup program launched by the National Research Foundation (NSF) in 2011 to encourage entrepreneurship and commercialization of research results. It focuses on forming a startup team involving professors, researchers and market discovery activities. Taking these characteristics into account, this study empirically verified the impact of social capital from a network perspective and founding team diversity on I-Corps start-up performance. As a result of the analysis, the educational diversity of the founding team had a negative (-) effect on the financial performance of the founding team. On the other side, the gender diversity and the cognitive dimension of social capital had a positive (+) effect on the financial performance of the founding team. This study is expected to provide more useful theoretical and practical implications regarding the diversity, social capital, and performance interpretation of the I-Corps Lab startup team.

• Journal of Wetlands Research
• /
• v.26 no.2
• /
• pp.147-159
• /
• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.