• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.2
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• pp.82-95
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• 2021

Current international negotiation and cooperation for sustainable development are focused on three main themes. The first theme is implementation of Sustainable Development Goals (SDGs). The second theme is development of measures for climate change. The third theme is sustainable management of biodiversity and ecosystem services. In South Korea, responses of government policies and academic studies have been predominantly to one of these three themes. There have not been many integrated efforts to develop countermeasures considering all three international themes. In addition, while "green growth" policies have been setting national agendas for Korea's sustainable development, they must be scrutinized such as why they have not dealt with some parts of these three themes and whether they have ignored one of these themes due to lack of integrated responses. This study finds critical issues in South Korea on how to harmoniously respond to the three themes of international efforts and improve green growth policies. First, to achieve SDGs, the domestic statistical system must be reorganized to track the achievement of "inclusiveness" and "green growth". Second, the climate change response policy should seek inclusion between countries and between social groups. Third, in the field of biodiversity and ecosystem services, it is necessary to establish Korea's identity in global geopolitics and enhance its own traditional ecological knowledge. Fourth, it is necessary to consider how to solve discrepancy between climate change response policies and biodiversity-ecosystem service management policies. Finally, proactive improvement of laws and institutions must occur to promote inclusive green growth.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.750-759
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• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.33 no.12
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• pp.13-18
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• 2017

Recently, the form of traditional families has been disintegrated due to low birthrate, aging, declining marriage, individualism, etc. In particular, the number of single households has increased due to the shift to a low-growth advanced economic structure, women's social participation, diversification of lifestyles, and so on. According to the National Statistical Office, the number of single households living alone by 2015 is estimated to be about 5,060,000 households, which is estimated to account for 34.3% of all households, which has greatly increased compared with about 660,000 (6.9%) in 1985. However, the housing market has not been able to respond to such social changes. Therefore, in this research, we presented a plan to visualize the public data of single household in Seoul city and prediction result of occupancy shape for the purpose of supporting decision making of single household consumers.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.963-971
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• 2021

This study aims to determine the effect of financial literacy and taxation on competitive advantage, in order to determine the effect of financial literacy and taxation on the performance of MSMEs, and to determine the effect of competitive advantage on the performance of MSMEs. This study uses primary data through a questionnaire. The population is Creative MSME actors in DIY. Creative MSMEs in Yogyakarta were chosen because Yogyakarta is an area that is rich in traditional culture and various characters of its inhabitants, thus encouraging the development of the potential of Creative MSMEs. This research uses primary data sources on Creative MSMEs in Yogyakarta. The samples were determined by the proportional simple random sampling technique; taking a sample of 20% of the total Creative MSMEs in each district/city. The samples that deserve to be respondents in this study were 210 samples of Creative MSMEs actors in DIY. The statistical technique for analyzing data is the AMOS Structural Equation Modeling (SEM). The results showed that financial literacy had an effect on the competitive advantage and performance of MSMEs, tax literacy had an effect on competitive advantage, competitive advantage had an effect on the performance of MSMEs. However, tax literacy has no effect on the performance of MSMEs.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• Journal of Korea Trade
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• v.25 no.2
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• pp.22-45
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• 2021

Purpose - This study aims to identify new attributes by analyzing reviews conducted by global automaker customers and to examine the influence of these attributes on satisfaction ratings in the U.S. automobile sales market. The present study used J.D. Power for customer responses, which is the largest online review site in the USA. Design/methodology - Automobile customer reviews are valid data available to analyze the brand personality of the automaker. This study collected 2,998 survey responses from automobile companies in the U.S. automobile sales market. Keyword analysis, topic modeling, and the multiple regression analysis were used to analyze the data. Findings - Using topic modeling, the author analyzed 2,998 responses of the U.S. automobile brands. As a result, Topic 1 (Competence), Topic 5 (Sincerity), and Topic 6 (Prestige) attributes had positive effects, and Topic 2 (Sophistication) had a negative effect on overall customer responses. Topic 4 (Conspicuousness) did not have any statistical effect on this research. Topic 1, Topic 5, and Topic 6 factors also show the importance of buying factors. This present study has contributed to identifying a new attribute, personality. These findings will help global automakers better understand the impacts of Topic 1, Topic 5, and Topic 6 on purchasing a car. Originality/value - Contrary to a traditional approach to brand analysis using questionnaire survey methods, this study analyzed customer reviews using text mining. This study is timely research since a big data analysis is employed in order to identify direct responses to customers in the future.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.730-738
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• 2022

Based on visual Thinking theory, VR(virtual reality) game changes the traditional form of memory and maps the content into game elements to realize the immersive spatial memory mode. This paper analyzes the influencing factors of game design and system function construction. This paper proposes a hypothesis: with the help of visual thinking theory, VR game is helpful to improve learners' visual memory, and carries out research. The experiment sets different levels of game through empirical research and case analysis of memory flip game. For example, when judging two random cards. If the pictures are the same, it will be judged as the correct combination; if they are different, the two cards will be restored to the original state. The results are analyzed by descriptive statistical analysis and AMOS data analysis. The results show that game content using the concept of "Memory Palace", which can improve the accuracy of memory. We conclude that the use of spatial localization characteristics in flip games combining visual thinking can improve users' memory by helping users memorize and organize information in a Virtual environment, which means VR games have strong feasibility and effectiveness in improving memory.

• Advances in aircraft and spacecraft science
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• v.9 no.5
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• pp.367-375
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• 2022

Modeling the properties of complex alloys such as nickel superalloys is an extremely challenging scientific and engineering task. The model should take into account a large number of uncorrelated factors, for many of which information may be missing or vague. The individual contribution of one or another chemical element out of a dozen possible ligants cannot be determined by traditional methods. Moreover, there are no general analytical models describing the influence of elements on the characteristics of alloys. Artificial neural networks are one of the few statistical modeling tools that can account for many implicit correlations and establish correspondences that cannot be identified by other more familiar mathematical methods. However, such networks require careful tuning to achieve high performance, which is time-consuming. Data preprocessing can make model training much easier and faster. This article focuses on combining physics-based deep network configuration and input data engineering to simulate the solvus temperature of nickel superalloys. The used deep artificial neural network shows good simulation results. Thus, this method of numerical simulation can be easily applied to such problems.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.107-115
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• 2022

Recently, big data applications need another database different from the Relation database. NoSQL databases are used to save and handle massive amounts of data. NoSQL databases have many advantages over traditional databases like flexibility, efficiently processing data, scalability, and dynamic schemas. Most of the current applications are based on the web, and the size of data is in increasing. NoSQL databases are expected to be used on a more and large scale in the future. However, NoSQL suffers from many security issues, and one of them is access control. Many recent applications need Fine-Grained Access control (FGAC). The integration of the NoSQL databases with FGAC will increase their usability in various fields. It will offer customized data protection levels and enhance security in NoSQL databases. There are different NoSQL database models, and a document-based database is one type of them. In this research, we choose the CouchDB NoSQL document database and develop an access control mechanism that works at a fain-grained level. The proposed mechanism uses role-based access control of CouchDB and restricts read access to work at the document level. The experiment shows that our mechanism effectively works at the document level in CouchDB with good execution time.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.705-708
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• 2010

An eigen color co-occurrence approach is proposed that exploits the correlation between color channels to identify the degree of image similarity. This method is based on traditional co-occurrence matrix method and histogram equalization. On the purpose of feature extraction, eigen color co-occurrence matrices are computed for extracting the statistical relationships embedded in color images by applying Principal Component Analysis (PCA) on a set of color co-occurrence matrices, which are computed on the histogram equalized images. That eigen space is created with a set of orthogonal axes to gain the essential structures of color co-occurrence matrices, which is used to identify the degree of similarity to classify an input image to be tested for various purposes. In this paper RGB, Gaussian color space are compared with grayscale image in terms of PCA eigen features embedded in histogram equalized co-occurrence features. The experimental results are presented.