• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.27-42
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• 2020

Startup accelerators have emerged as new investment entities that help early startups, which are not easy to survive continuously due to lack of funds, commercialization capabilities, and experiences. As their positive performance on early startups and the ecosystem has been proven, the number of early startups which want to receive their investment is also increasing. However, they are vaguely preparing to attract accelerators' investment because they do not have any information on what factors the accelerators consider important. In addition, researches on startup accelerators are also at an early level, so there are no remarkable prior studies on factors that decide on investment. Therefore, this study aims to help startups prepare for investment attraction by looking at what factors are important for accelerators to invest, and to provide meaningful implications to academia. In the preceding study, we derived five upper level categories, 26 lower level accelerators' investment determinants through the qualitative meta-synthesis method, secondary data analysis, observation on US accelerators and in-depth interviews. In this study, we want to derive important implications by deriving priorities of the accelerators' investment determinants. Therefore, we used AHP that are evaluated as the suitable methodology for deriving importance and priority. The analysis results show that accelerators value market-related factors most. This means that startups that are subject to investment by accelerators are early-stage startups, and many companies have not fully developed their products or services. Therefore, market-related factors that can be evaluated objectively seem to be more important than products (or services) that are still ambiguous. Next, it was found that the factors related to the internal workforce of startups are more important. Since accelerators want to develop their businesses together with start-ups and team members through mentoring, ease of collaboration with them is very important, which seems to be important. The overall priority analysis results of the 26 investment determinants show that 'customer needs' and 'founders and team members' understanding of customers and markets' (0.62) are important and high priority factors. The results also show that startup accelerators consider the customer-centered perspective very important. And among the factors related to startups, the most prominent factor was the founder's openness and execution ability. Therefore, it can be confirmed that accelerators consider the ease of collaboration with these startups very important.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.41-54
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• 2021

This study targets the urban park corresponding to the core areas (Hubs) of Green Infrastructure and estimates their value utilizing the Contingent Valuation Method (CVM) and determines the planning factors which affect them. The research aims to provide basic data for supporting the value improvement in the planning stage for urban parks representing green infrastructure. The primary purpose of this research is to derive variables that affect economic value and planning factors to improve the use-value of urban parks, one of the Hubs of the green infrastructure. In this study, Sejong Lake Park, located in Sejong City, is the target site. This study collected the responses of 105 people by conducting a survey on the intention to pay for the use-value and the planning factors that affect it, targeting visitors to Sejong Lake Park. The study conducts Contingent Valuation Method (CVM) on this survey responses. The results are as follows: first, as a result of analyzing the variables which affect willingness to pay for use-value, residence and age influence the willingness to pay significantly among socioeconomic characteristics. Next, the survey responses of Double-bounded dichotomous choices (DB-DC) CVM are converted into variables through statistic techniques. Furthermore, the variables are used for a Logit model to draw coefficients. The average willingness to pay per person for the use-value of Sejong Lake Park using the derived coefficients was approximately found to be 8,597 won. Therefore, as of 2019, Sejong Lake Park, with a total of 430,000 visitors, is estimated to have an annual economic value of 3.7 billion won. Third, the average Likert scale of the planning factor affecting the decision to pay for the economic value of Sejong Lake Park was the highest along the waterfront landscape, and the convenience facilities and waterfront landscape showed the highest willingness to pay, 10,000 won. In the range between 2,500 won and 5,000 won, the waterfront area ranks highest. Therefore, it can be said that visitors to Sejong Lake Park take account of the economic value of using the waterfront landscape the most. This study is meaningful as a thesis on use-value and the planning factors that affected value evaluation results of urban parks, and the analysis of the correlation between the planning factors of urban parks as hubs located in urban areas.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.4
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• pp.10-23
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• 2021

During the Japanese colonial period, the palaces of Joseon were damaged in many parts. Changgyeonggung Palace is the most demolished palace with the establishment of a zoo, botanical garden, and museum. During the Japanese colonial period, the palaces of Joseon were damaged in many parts. This study examined the construction process of Jangseogak(Yi Royal-Family Museum), located right next to the Jagyeongjeon site, which was considered the most important space in the Changgyeonggung residential area of royal family zone, through historical materials and field research. Built in 1911, Jangseogak is located at a location overlooking the entire Changgyeonggung Palace and overlooking the Gyeongseong Shrine of Namsan in the distance. Changes in the surrounding space during the construction of Jangseogak can be summarized as follows. First, in the early 1910s, the topography of the garden behind Jagyeongjeon and part of the Janggo were damaged to create the site of Jangseogak. The front yard was built in the front of Jangseogak, and a stone pillar was installed, and a staircase was installed to the south. In the process, the original stone system at the rear of Yanghwadang was destroyed, and it is presumed that Jeong Iljae and other buildings were demolished. Second, in the 1920s, many pavilions were demolished and the zoo and botanical gardens and museums were completed through leveling. After the Jangseogak was completed, the circulation of the Naejeon and surrounding areas was also changed. Cherry trees and peonies were planted in the flower garden around the front yard of Jangseogak and the stairs, and a Japanese-style garden was created between Yanghwadang and Jibbokheon. Third, in the 1930s, the circulation around Jangseogak was completed in its present form, and the museum, Jangseogak, Zoological and Botanical Gardens, and Changgyeonggung, which became a cherry tree garden, were transformed into a Japanese-style cultural park. After that, the surrounding space did not change much until it was demolished. The restoration of the present palace is a long-term, national project of the Cultural Heritage Administration. The results of this study will provide important data for the restoration plan of Changgyeonggung Palace in the future, and it is expected that it will provide additional information to related researchers in the future.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.2
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• pp.135-142
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• 2022

Purpose. This study aims to evaluate the combined effect of reduced thickness in different regions on the fracture resistance of monolithic zirconia crowns. Materials and methods. Seven nickel-chromium dies were generated from a 3D model of mandibular first molar using the digital scanner with the following geometries: 1.5 mm occlusal reduction, 1.0 mm deep chamfer. Based on the abutment model, Zirconia blocks (Luxen Zirconia) were selected to fabricate Sixty-three zirconia crowns with occlusal thicknesses of 0.3 mm, 0.5 mm, and 1.5 mm, and different axial thicknesses of 0.3 mm, 0.5 mm, and 1.0 mm. All crowns were cemented by resin cement. Next, the crowns were subjected to load-to-fracture test until fracture using an electronic universal testing machine. In addition, fracture patterns were observed with a scanning electron microscope (SEM). Two-way ANOVA and the Tuckey HSD test for post hoc analysis were used for statistical analysis (P < .05). Results. The mean values of fracture resistancerecorded was higher than the average biting force in the posterior region. The two-way ANOVA showed that the occlusal and axial thickness affected the fracture resistance significantly (P < .05). However, the effect of axial thickness on fracture resistance did not show a statistical difference when thicker than 0.5 mm. The observed failure modes were partial or complete fracture depending on the severity of crack propagation. Conclusion. Within the limitations of the present study, the CAD-CAM monolithic zirconia crown with extremely reduced thickness showed adequate fracture resistance to withstand occlusal load in molar regions. In addition, both occlusal and axial thickness affected the fracture resistance of the zirconia crown and showed different results as combined.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.41-63
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• 2022

The purpose of this study is to secure objective and precise data through ecosystem monitoring, to reveal ecological characteristics through comparison and analysis with past survey data, and to accumulate basic data for diagnosing the current situation and predicting changes in the ecosystem. The target site is the 'Quercus mongolica forest on the Buksa-myeon of Namsan', which was designated as an Ecological Landscape Conservation Area (ELCA) of Seoul in July 2006. The research contents are analysis of soil environment change (1986~2016), change of actual vegetation (1978~2016), and change of plant community structure (1994~2016). A total of 8 fixed surveys (400~1,200m²) were established in 1994 and 2000. Analysis items are importance value, species and population, and Shannon's species diversity. The soil environment of Namsan is acidic (pH 4.40 in 2016), which is expected to have a negative impact on tree growth and vegetation structure due to its low capacity for exchangeable cations. Quercus mongolica forest in Namsan is mainly distributed on the northern slopes. The actual vegetation area changed from 49.4% in 1978 → 80.7% in 1986 → 82.4% in 2000 → 88.3% in 2005 → 88.3% in 2009 → 70.3% in 2016. In 2016, the forest decreased by 18% compared to 2009. While there was increased growth of Quercus mongolica in the tree layer from 2009 to 2016, the overall decline in vegetation area was due to logging and fumigation management following the spread of oak wilt in 2012. As for the changes in the plant community structure, Quercus mongolica of the tree layer was damaged by oak wilt, and the potential vegetation that can form the next generation was ambiguous. In the subtree layer, the force of urbanization tree species such as Styrax japonicus, Sorbus alnifolia, and Acer palmatum. was maintained or increased. In the shrub layer, the number of trees and species increased significantly due to the open tree crown, and accordingly, the species diversity of Shannon for woody plants also increased. In Quercus mongolica forest of Namsan, various ecological changes are occurring due to the effects of urban environments such as air pollution and acid rain, the limitation of Quercus mongolica pure forest due to oak wilt, and the introduction of exotic species, thus, it is necessary to establish a management plan through continuous monitoring.

• Korean Journal of Environment and Ecology
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• v.36 no.2
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• pp.165-176
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• 2022

This study surveyed the characteristics of fish communities in Hwapocheon Wetland, Korea, from May to September 2020. The survey collected 735 objects in 21 species belonging to 7 families from 8 survey stations. The dominant and subdominant species were Hemiculter eigenmanni(23.8%) and Micropterus salmoides(10.3%), respectively. The next most abundant species were Zacco platypus(9.5%), Carassius auratus(9.4%), Pseudorasbora parva(9.0%), Squalidus chankaensis tsuchigae(6.7%), Acheilognathus macropterus(5.4%), Lepomis macrochirus(5.2%), Pseudogobio esocinus(4.1%), Opsariichthys uncirostris amurensis(3.7%), and Carassius cuvieri(3.3%). Among the fish species collected, one species, Culter brevicauda, was class II endangered wildlife designated by the Ministry of Environment, and one species,S. c. tsuchigae(4.8%), was endemic to Korea.Additionally, three exotic species (M. salmoides, L. macrochirus, and C. cuvieri) and one landlocked species (Rhinogobius brunneus) were collected. Compared to previous studies, the proportion of fish living in the running water area tended to decrease, the proportion of fish living in the water purification area tended to increase, and ecosystem-disturbing species (M. salmoides and L. macrochirus) tended to increase gradually. Results of fish community analysis showed that the mainstream stations (St. 1, 3, 4, 5, 6, and 8) had low dominance, but high diversity and richness, and other stations (St. 2 and 7) had high dominance but low diversity and richness. The river health (index of biological integrity) evaluated using fish was assessed as bad (6 stations), normal (1 station), and very bad (1 station). The water quality grade was assessed as slightly bad due to the chemical oxygen demand (COD), total organic content (TOC), suspended solid (SS), and total coliforms (TC). The annual water quality showed a gradually increasing trend of biological oxygen demand (BOD), COD, SS, and chlorophyll-a. The stable life of fish and the improvement of river health in Hwapocheon Wetland require water quality improvement and the systematic management of ecosystem-disturbing species (M. salmoidesand L. macrochirus).

• Journal of Digital Convergence
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• v.19 no.11
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• pp.575-583
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• 2021

This study aims to analyze Taekwondo trends according to news articles by year by applying topic modeling. In order to examine the Taekwondo trend through media reports, articles including news articles and Taekwondo specialized media articles were collected through Big Kinds of the Korea Press Foundation. The search period was divided into three sections: before 2000, 2001~2010, and 2011~2020. A total of 12,124 items were selected as research data. For topic analysis, pre-processing was performed, and topic analysis was performed using the LDA algorithm. In this case, python 3 was applied for all analysis. First, as a result of analyzing the topics of media articles by year, 'World' was the most common keyword before 2000. 'South and North Korea' was next common and 'Olympic' was the third commonest topic. From 2001 to 2010, 'World' was the most common topic, followed by 'Association' and 'World Taekwondo'. From 2011 to 2020, 'World', 'Demonstration', and 'Kukkiwon' was the most common topic in that order. Second, as a result of analyzing news articles before 2000 by topic modeling, topics were divided into two categories. Specifically, Topic 1 was selected as 'South-North Korea sports exchange' and Topic 2 was selected as 'Adoption of Olympic demonstration events'. Third, as a result of analyzing news articles from 2001 to 2010 by topic modeling, three topics were selected. Topic 1 was selected as 'Taekwondo Demonstration Performance and Corruption', Topic 2 was selected as 'Muju Taekwondo Park Creation', and Topic 3 was selected as 'World Taekwondo Festival'. Fourth, as a result of analyzing news articles from 2011 to 2020 by topic modeling, three topics were selected. Topic 1 was selected as 'Successful Hosting of the 2018 Pyeongchang Winter Olympics', Topic 2 was selected as 'North-South Korea Taekwondo Joint Demonstration Performance', and Topic 3 was selected as '2017 Muju World Taekwondo Championships'.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.