• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.115-125
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• 2022

In today's era when the concept of a lifelong job has disappeared, starting a business is an essential consideration for university students not only as an alternative factor in finding employment, but also from the perspective of the entire life. Today, most universities in Korea are operating entrepreneurship education programs, such as entrepreneurship classes as a curriculum, and start-up clubs as a non-curricular program to foster entrepreneurship among university students. In previous studies, entrepreneurship is a factor influencing the entrepreneurship intention. The purpose of this study is to empirically examine the effects of university students' entrepreneurship and the entrepreneurship intention through a entrepreneurship education program (participation in a start-up club, taking an entrepreneurship course) on entrepreneurship behavior. There are some empirical studies on whether entrepreneurship education programs such as participation in startup contests affect the entrepreneurship intention of university students, but not much compared to their importance. It is difficult to find an empirical study examining the effects of entrepreneurship and start-up education programs on entrepreneurship intention and entrepreneurship behavior in domestic and foreign studies. Therefore, in this study, one domestic university that operates a start-up club and a entrepreneurship course was selected and the online questionnaire was distributed to all current students, and the collected 127 questionnaires were used for empirical analysis As a result of the study, first, it was confirmed that initiative and risk-taking, which are sub-factors of entrepreneurship of university students, had a significant positive effect on entrepreneurial intention, respectively, and that innovation did not have a significant positive effect. Second, it was confirmed that university students' participation in a start-up club, a sub-factor of the start-up education program, had a significant positive effect on their entrepreneurial intention, and that taking a start-up course did not have a significant positive effect. Third, it was confirmed that the entrepreneurial intention of university students had a significant positive effect on entrepreneurship behavior. Fourth, it was confirmed that the entrepreneurial intention had a mediating effect between each of the factors of risk-taking, and participation in a start-up club and entrepreneurial behavior. This study suggests that university students can increase their risk-taking, increase their entrepreneurial intention by participating in a startup club, and reach a entrepreneurial behavior through this as a medium.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of the Korean Wood Science and Technology
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• v.2 no.2
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• pp.5-7
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• 1974

1. This study indicates that above the fiber saturation point the drying rate can be increased with increasing the velocity of the air circutation, i.e., the drying rate of sample boards is proportional to the air velocity, but below the fiber saturation point, the effect of the velocity of air circulation is very low as shown in Figs. 1 and 2. 2. Under the controlled temperature and humidity in the kiln, the more the sample boards have moisture, the higher drying rate of it can be obtained. In other words, this means that even though in the case of drying various moisture content of wood, at the final drying stage, approximately the same percentage of moisture content of wood can be secured by employing the higher velocity of air circulation. 3. This study shows that the rate of drying in kiln changes distinctly at the fiber saturation point, i, e., above the fiber saturation point, the drying curve shows concave aginst the X axsis, but below the fiber saturation point, in the range from 30 percent of moisture content to 20 percent of moisture content, the curve shows convex as shown in Fig. 3. As the drying progresses, however, the drying curve shows concave again below 20 percent of moisture content. This means that inflection point of drying curve may be located clearly at the fiber saturation point, i.e., 30 percent of moisture content. As mentioned above, the 30 percent of moisture content of wood at which the inflectional point appears can be recognized as a critical point, i. e., the fiber saturation point at which all free water was removed from wood. The existence of inflectional point indicates that the evaporation of hygroscopic water in a cell wall is more difficult than the evaporation of free water in a cell cavity and the minor space of cell wall. The convex curve in the range of moisture content from 30 percent to 20 percent means that the evaporation of capillary condensed water has a tendency of the same rates of drying approximately, but as approaching to the 20 percent of moisture, the transfusion of moisture from wood becomes difficult because of having less moisture in cell wall. Below 20 percent of moisture content, the drying curve shows concave again, which means that it is difficult to remove the moisture located nearer to the surface of cellulose molecules and the surface bound water. These relations were revealed in Fig. 4. In comparison AC curve which does not have the two inflection points with BD curve which has two inflection points, i.e., Band D, they are mentioned already, by existence of the inflection points, the curve BD shows that the change of drying rate in the interval from 20 percent of moisture content to 30 percent of moisture content is not greater than in the case of the curve AC in the same interval. At the inflection point of 30 percent of moisture content, it can be noticed that the changing of the drying rate is very conspicuous. This phenomenon also can be recognized, as it is noticed by the Fig. 3, the drying rate from green to 30 percent of moisture content is very great. But the inclination of the curve is very slow from 30 percent of moisture content to 20 percent of moisture content, i.e., the inclination of the curve becomes almost horizontal lines. Acknowledgments Gratitude is expressed to Fred E. Dickinson, Professor of 'Wood Technology, School of Natural Resources, University of Michigan, USA for his suggestion to carry out this study.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Asia Marketing Journal
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• v.12 no.2
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• pp.27-52
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• 2010

It is not new that today's business organizations are expected to exhibit ethical and moral management and to carry out social responsibility as a good corporate citizen. Since South Korea emerged as a newly industrialized country during the 1980s, Korean corporations have become active in carrying out their social responsibility as a good corporate citizen to society. In spite of the short history of corporate social responsibility, Korean companies have actively participated in corporate philanthropy. Corporations' significant donations to various social causes, no-lay-off policies, corporate volunteerism and green marketing are evidences of their commitment to corporate citizenship. Corporate social responsibility is now an essential management practice whereby corporation can strengthen its sustainable value creation processes by enhancing the trust assets underlying the relationships between the business and the stakeholders. Much of the conceptual work in the area of corporate social responsibility(CSR) has originated from researches conducted in the management field. Carroll(1979) proposed that corporations have four types of social responsibilities: economic, legal, ethical and philanthropic responsibility. Most past research has investigated CSR and its impact on consumers' attitudes toward the corporations and corporate performances. Although there exists a large body of literature on how consumers perceive and respond to CSR, the majority of past studies were conducted in the United States. The stability and applicability of past findings need to be tested across different national/cultural settings, especially since corporate social responsibility is a reflection of implicit conformation with the expectations and criticism that society may have toward a corporation(Matten and Moon, 2004). In this study, we explored whether people in Korea perceive CSR of Korean corporations in the same four dimensions as done in the United States and what were the measurement items tapping each of these four dimensions. In order to investigate the dimensions of CSR and the measurement items for CSR perceived by Korean people, nine focus group interviews were conducted with several stakeholder groups(two with undergraduate students, two with graduate students, three with general consumers, and two with NGO groups). Scripts from the interviews revealed that the Korean stakeholders perceived four types of CSR which are the same as those proposed by Carroll(1979). However we found CSR issues unique to Korean corporations. For example for the economic responsibility, Korean people mentioned that the corporation needed to contribute to the economic development of the country by generating corporate profits. For the legal responsibility, Koreans included the "corporation need to follow the consumer protection law." For the ethical responsibility, they considered that the corporation needed to not promote false advertisement. In addition, Koreans thought that an ethical company should do transparent management. For the philanthropic responsibility, people in Korea thought that a corporation needed to return parts of its profits to the society for the betterment of society. The 28 items were developed based on the results of the nine focus group interviews, while considering the scale developed by Maignan and Ferrell(2001). Following the procedure proposed by Churchill(1979), we started by developing an item poll consisting of 28 items and purified the initial pool of items through exploratory, confirmatory factor analyses. 176 samples were sued for this analysis. Confirmatory factor analysis was performed on the 28 items in order to verify the underlying four factor structure. Study 1 provided new measurement items for tapping the Korean CSR dimensions, which can be useful for the future studies exploring the effects of CSR on Korean consumers' attitudes toward the corporations and corporate performances. And we found the CSR scale(17 items) has good reliability, discriminant validity and nomological validity. Economic Responsibility: "XYZ company continuously improves the quality of our products", "XYZ company has a procedure in place to respond to customer complaint", "XYZ company contributes to the economic development of our country by generating profits", "XYZ company is eager to hire people". Legal Responsibility: "XYZ company's products meet legal standards", "XYZ company seeks to comply with all laws regulating hiring and employee benefits", "XYZ company honors contractual obligations to its suppliers", "XYZ company's managers try to comply with the law related to the business operation". Ethical Responsibility: "XYZ company has a comprehensive code of conduct", "XYZ company does not promote a false or misleading advertisement", "XYZ company seems to conduct a transparent business", "XYZ company does a fair business with its suppliers or sub-contractors". Philanthropic Responsibility: "XYZ company encourages partnerships with local businesses and schools", "XYZ company supports sports and cultural activities", "XYZ company gives adequate contributions to charities considering its business size", "XYZ company encourages employees to support our community". Study 2 was condusted for comprehensive validity. 655 samples were used for this anlysis. Collected samples were tested by factor analysis and Crnbach's Alpha coefficiednts and were found to be satisfactory in terms of validity and reliability. Furthermore, fitness of the measurement model was tested by using conformatory factor analysis. χ²=880.73(df=160), GFI=0.891, AGFI=0.854, NFI=0.908, NNFI=0.913, RMR=0.059, RMESA=0.070. We hope that CSR scale could greatly facilitate research on Corporate social resposibility, it is by no means the final answer.

• Radiation Oncology Journal
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• v.18 no.2
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• pp.157-166
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• 2000

Purpose : This investigation was peformed in order to improve the health care of radiation workers, to predict a risk, to minimize the radiation exposure hazard to them and for them to realize radiation exposure danger when they work in radiation area in hospital. Methods and Materials : The documentations checked regularly for personal radiation exposure in four university hospitals in Pusan city in Korea between January 1, 1993 and December 31, 1997 were analyzed. There were 458 persons in this documented but 111 persons who worked less then one year were excluded and only 347 persons were included in this study. Results : The average of yearly radiation exposure of 347 persons was 1.52$\pm$1.35 mSv. Though it was less than 50mSv, the limitaion of radiation in law but 125 (36%) people received higher radiation exposure than non-radiation workers. Radiation workers under 30 year old have received radiation exposure of mean 1.87$\pm$1.01 mSv/year, mean 1.22$\pm$0.69 mSv between 31 and 40 year old and mean 0.97$\pm$0.43 mSv/year over 41year old (p<0.001). Men received mean 1.67$\pm$1.54 mSv/year were higher than women who received mean 1.13$\pm$0.61 mSv/year (p<0.01). Radiation exposure in the department of nuclear modicine department in spite of low energy sources is higher than other departments that use radiations in hospital (p<0.05). And the workers who received mean 3.59$\pm$1.81 msv/year in parts of management of radiation sources and injection of sources to patient receive high radiation exposure in nuclear medicine department (p<0.01). In department of diagnostic radiology high radiation exposure is in barium enema rooms where workers received mean 3.74$\pm$1.74 mSv/year and other parts where they all use fluoroscopy such as angiography room of mean 1.17$\pm$0.35 mSv/year and upper gastrointestinal room of mean 1.74$\pm$1.34 mSv/year represented higher radiation exposure than average radiation exposure in diagnostic radiology (p<0.01). Doctors and radiation technologists received higher radiation exposure of each mean 1.75$\pm$1.17 mSv/year and mean 1.50$\pm$1.39 mSv/year than other people who work in radiation area in hospital (p<0.05). Especially young doctors and technologists have the high opportunity to receive higher radiation exposure. Conclusions : The training and education of radiation workers for radiation exposure risks are important and it is necessary to rotate worker in short period in high risk area. The hospital management has to concern health of radiation workers more and to put an effort to reduce radiation exposure as low as possible in radiation areas in hospital.

• Korean Journal of Heritage: History & Science
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• v.44 no.1
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• pp.72-105
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• 2011

This study explores the value of the Dicentra spectabilis community as a nature reserve in provincial forests at San 1-2, Daea-ri, Dongsang-myeon, Wanju-gun, Jellabuk-do, also known as Gamakgol, while defining the appropriateness of its living environment and eventually providing basic information to protect this area. For these reasons, we investigated 'morphological and biological features of Dicentra spectabilis' and the 'present situation and problems of designing a herbaceous nature reserve in Korea.' Furthermore, we researched and analyzed the solar, soil and vegetation condition here through a field study in order to comprehend its nature reserve value. The result is as follows. According to the analytic result for information on the domestic wild Dicentra spectabilis community, it is evenly spread throughout mountainous areas, and there is one particularly outstanding in size in Wanju Gamakgol. Upon the findings from literature and the field study about its dispersion, Gamakgol has been discovered as an ideal district for Dicentra spectabilis since it meets all the conditions this plant requires to grow vigorously, such as a quasi-high altitude and rich precipitation during its period of active growth duration in May. Dicentra spectabilis grows in rocky soil ranging from 300~375m above sea level, 344.5m on average, towards the north, northwest and dominantly in the northeast. The mean inclination degree is $19.5^{\circ}$. Also, upon findings from analyzing solar conditions, the average light intensity during its growth duration, from Apr. to Aug., is 30,810lux on average and it tends to increase, as it gets closer to the end. This plant requires around 14,000~18,000lux while growing, but once bloomed, fruits develop regardless of the degree of brightness. The soil pH has shown a slight difference between the topsoil, at 5.2~6.1, and subsoil, at 5.2~6.2. Its mean pH is 5.54 for topsoil and 5.58 for subsoil. These results are very typical for Dicentra spectabilis to grow in, and other comparative areas also present similar conditions. Given the facts, the character of the soil in Gamakgol has been evaluated to have high stability. Analysis of its vegetation environment shows a wide variation of taxa numbering from 13 to 52 depending on area. The total number of taxa is 126 and they are a homogenous group while showing a variety of species as well. The Dicentra spectabilis community in the Daea-ri Arboretum is an herbaceous community consisting of dominantly Dicentra spectabilis, Cardamine leucantha, Boehmeria tricuspi and Impatiens textori while having many differential species such as Impatiens textori, Pueraria thunbergiana, Rubus crataegifolius vs Staphylea bumalda, Securinega suffruticosa, and Actinidia polygama. It suggests that it is a typical subcolony divided by topographic features and soil humidity. Considering the above results on a comprehensive level, this area is an excellent habitat for wild Dicentra spectabilis providing beautiful viewing enjoyment. Additionally, it is the largest wild colony of Dicentra spectabilis in Korea whose climate, topography, soil conditions and vegetation environment can secure sustainability as a wild habitat of Dicentra spectabilis. Therefore, We have determined that the Gamakgol community should be re-examined as natural asset owing to its established habitat conditions and sustainability.

• Korean Journal of Heritage: History & Science
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• v.55 no.3
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• pp.74-100
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• 2022

The purpose of this study was to investigate the characteristics of Chinese seowons and to obtain data based on the characteristics of waterscapes unique to Korean seowons. The conclusion of this study from the results of investigation and analysis of the location, water system, and design characteristics of 10 representative traditional seowons in China including Yuelu Seowon(嶽麓書院) conducted based on literature research and field observation is as follows. The water system of Chinese seowons is dualized into an inner and an outer water system, and in general, two and a maximum of three water bodies are superimposed on the outside. The locations of seowons are classified into five types: Four double-sided round water type sites, three converted face water type sites, one three-sided round water type site, a four-sided round water type, and a waterproofing type(依山傍水型). Therefore, compared to the typical Korean seowon facing water in the front and a mountain in the back(背山面水型), the Chinese seowons showed a highly hydrophilic property. The water shapes of the external water system were meandering(46.0%), mooring(36.0%), and broad and irregular(9.0%). In addition, water conception(水態) were streams(31.8%), rivers(27.3%), springs(13.6%), falls(9.1%), lakes(4.5%) and ponds(4.5%), in that order. As for waterscapes of the water system inside the seowon, there were seven in Akrok Seowon and four in Mansong Seowon, indicating a comparatively higher number of waterscapes. Since the 27 detailed waterscapes in 10 seowons that were the subject of the study were classified into six types including ponds and half-moon ponds, they appeared to be more diverse than the Korean seowon. It is noteworthy that in the interior waterscape of the traditional Chinese seowon, the ritualistic order, where at least one half-moon pond or square pond(方池) was arranged, is well displayed. In particular, the half-moon pond(伴池), which is difficult to find in Korean seowon, was found to be a representative waterscape element, accounting for 42.8%. If the square pond of Nanxi Seowon based on Zhu Xi's poem 「Gwanseoyugam(觀書有感)」 is also treated as a square-shaped half-moon pond, the proportion of half-moon ponds in the waterscape will be as high as 50%. The pond shapes consisted of 28% square, 24% each for free curve and round shape, 20% for semi-moon shape, and 3.8% for mountain stream type. This seems to differ greatly from the square-shaped Korean seowon. On the other hand, there were a total of 10 types of structures related to the waterscape inside the Chinese seowon: 11(26.8%) pavilion and bridge sites, five gate room sites(牌坊: 16.5%), four gate and tower sites(樓, 1.4%), two Jae sites(齋, 6.2%), and one site each(3.1%) of Heon(軒), Sa(祠), Dae(臺), and Gak(閣). In particular, the pavilions inside seowon were classified into three types: landscape pavilion(景觀亭 27.2%), tombstone pavilion(碑亭, 18.2%), and banquet pavilion(宴集亭, 54.5%). In general, it was confirmed that the half-moon pond with a pedestal bridge, and the pavilion were the major components with a high degree of connection that dominate the waterscape inside the Chinese seowon.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.