• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.55-73
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• 2007

Festivals are an indispensable feature of cultural tourism(Formica & Uysal, 1998). Cultural tourism festivals are increasingly being used as instruments promoting tourism and boosting the regional economy. So much research related to festivals is undertaken from a variety of perspectives. Plans to revisit a particular festival have been viewed as an important research topic both in academia and the tourism industry. Therefore festivals have frequently been leveled as cultural events. Cultural tourism festivals have become a crucial component in constituting the attractiveness of tourism destinations(Prentice, 2001). As a result, a considerable number of tourist studies have been carried out in diverse cultural tourism festivals(Backman et al., 1995; Crompton & Mckay, 1997; Park, 1998; Clawson & Knetch, 1996). Much of previous literature empirically shows the close linkage between tourist satisfaction and behavioral intention in festivals. The main objective of this study is to investigate the effects of evaluation attributes of cultural tourism festivals on satisfaction and behavioral intention. accomplish the research objective, to find out evaluation items of cultural tourism festivals through the literature study an empirical study. Using a varimax rotation with Kaiser normalization, the research obtained four factors in the 18 evaluation attributes of cultural tourism festivals. Some empirical studies have examined the relationship between behavioral intention and actual behavior. To understand between tourist satisfaction and behavioral intention, this study suggests five hypotheses and hypothesized model. In this study, the analysis is based on primary data collected from visitors who participated in '2006 Gwangju Kimchi Festival'. In total, 700 self-administered questionnaires were distributed and 561 usable questionnaires were obtained. Respondents were presented with the 18 satisfactions item on a scale from 1(strongly disagree) to 7(strongly agree). Dimensionality and stability of the scale were evaluated by a factor analysis with varimax rotation. Four factors emerged with eigenvalues greater than 1, which explained 66.40% of the total variance and Cronbach' alpha raging from 0.876 to 0.774. And four factors named: advertisement and guides, programs, food and souvenirs, and convenient facilities. To test and estimate the hypothesized model, a two-step approach with an initial measurement model and a subsequent structural model for Structural Equation Modeling was used. The AMOS 4.0 analysis package was used to conduct the analysis. In estimating the model, the maximum likelihood procedure was used.In this study Chi-square test is used, which is the most common model goodness-of-fit test. In addition, considering the literature about the Structural Equation Modeling, this study used, besides Chi-square test, more model fit indexes to determine the tangibility of the suggested model: goodness-of-fit index(GFI) and root mean square error of approximation(RMSEA) as absolute fit indexes; normed-fit index(NFI) and non-normed-fit index(NNFI) as incremental fit indexes. The results of T-test and ANOVAs revealed significant differences(0.05 level), therefore H1(Tourist Satisfaction level should be different from Demographic traits) are supported. According to the multiple Regressions analysis and AMOS, H2(Tourist Satisfaction positively influences on revisit intention), H3(Tourist Satisfaction positively influences on word of mouth), H4(Evaluation Attributes of cultural tourism festivals influences on Tourist Satisfaction), and H5(Tourist Satisfaction positively influences on Behavioral Intention) are also supported. As the conclusion of this study are as following: First, there were differences in satisfaction levels in accordance with the demographic information of visitors. Not all visitors had the same degree of satisfaction with their cultural tourism festival experience. Therefore it is necessary to understand the satisfaction of tourists if the experiences that are provided are to meet their expectations. So, in making festival plans, the organizer should consider the demographic variables in explaining and segmenting visitors to cultural tourism festival. Second, satisfaction with attributes of evaluation cultural tourism festivals had a significant direct impact on visitors' intention to revisit such festivals and the word of mouth publicity they shared. The results indicated that visitor satisfaction is a significant antecedent of their intention to revisit such festivals. Festival organizers should strive to forge long-term relationships with the visitors. In addition, it is also necessary to understand how the intention to revisit a festival changes over time and identify the critical satisfaction factors. Third, it is confirmed that behavioral intention was enhanced by satisfaction. The strong link between satisfaction and behavioral intentions of visitors areensured by high quality advertisement and guides, programs, food and souvenirs, and convenient facilities. Thus, examining revisit intention from a time viewpoint may be of a great significance for both practical and theoretical reasons. Additionally, festival organizers should give special attention to visitor satisfaction, as satisfied visitors are more likely to return sooner. The findings of this research have several practical implications for the festivals managers. The promotion of cultural festivals should be based on the understanding of tourist satisfaction for the long- term success of tourism. And this study can help managers carry out this task in a more informed and strategic manner by examining the effects of demographic traits on the level of tourist satisfaction and the behavioral intention. In other words, differentiated marketing strategies should be stressed and executed by relevant parties. The limitations of this study are as follows; the results of this study cannot be generalized to other cultural tourism festivals because we have not explored the many different kinds of festivals. A future study should be a comparative analysis of other festivals of different visitor segments. Also, further efforts should be directed toward developing more comprehensive temporal models that can explain behavioral intentions of tourists.

• Journal of Preventive Medicine and Public Health
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• v.30 no.2 s.57
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• pp.308-326
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• 1997

Many studies have led to the notion that essential hypertension in adults is the result of a process that starts early in life: investigation of blood pressure(BP) in children and adolescents can therefore contribute to knowledge of the etiology of the condition. A unique longitudinal study on BP in Korea, known as Kangwha Children's Blood Pressure(KCBP) Study was initiated in 1986 to investigate changes in BP in children. This study is a part of the KCBP study. The purposes of this study are to show changes in BP and to determine factors affecting to BP level and change in Korean adolescents during age period 12 to 16 years. A total of 710 students(335 males, 375 females) who were in the first grade at junior high school(12 years old) in 1992 in Kangwha County, Korea have been followed to measure BP and related factors(anthropometric, serologic and dietary factors) annually up to 1996. A total of 562 students(242 males, 320 females) completed all five annual examinations. The main results are as follows: 1. For males, mean systolic and diastolic BP at age 12 and 16 years old were 108.7 mmHg and 118.1 mmHg(systolic), and 69.5 mmHg and 73.4 mmHg(diastolic), respectively. BP level was the highest when students were at 15 years old. For females, mean systolic and diastolic BP at age 12 and 16 years were 114.4 mmHg and 113.5 mmHg(systolic) and 75.2 mmHg and 72.1 mmHg(diastolic), respectively. BP level reached the highest point when they were 13-14 years old. 2. Anthropometric variables(height, weight and body mass index, etc) increased constantly during the study period for males. However, the rate of increase was decreased for females after age 15 years. Serum total cholesterol decreased and triglyceride increased according to age for males, but they did not show any significant trend fer females. Total fat intake increased at age 16 years compared with that at age 14 years. Compositions of carbohydrate, protein and fat among total energy intake were 66.2:12.0:19.4, 64.1:12.1:21.8 at age 14 and 16 years, respectively. 3. Most of anthropometric measures, especially, height, body mass index(BMI) and triceps skinfold thickness showed a significant correlation with BP level in both sexes. When BMI was adjusted, serum total cholesterol showed a significant negative correlation with systolic BP at age 12 years in males, but at age 14 years the direction of correlation changed to positive. In females serum total cholesterol was negatively correlated with diastolic BP at age 15 and 16 years. Triglyceride and creatinine showed positive correlation with systolic and diastolic BP in males, but they did not show any correlation in females. There was no consistent findings between nutrient intake and BP level. However, protein intake correlated positively with diastolic BP level in males. 4. Blood pressure change was positively associated with changes in BMI and serum total cholesterol in both sexes. Change in creatinine was associated with BP change positively in males and negatively in females. Students whose sodium intake was high showed higher systolic and diastolic BP in males, and students whose total fat intake was high maintained lower level of BP in females. The major determinants on BP change was BMI in both sexes.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.141-153
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• 1986

This study intends to document the existing cyclical fluctuations of garic and onion price at farm gate level during the period of 1966-1986 in Korea. The existing patterns of such cyclical fluctuations were estimated systematically by removing the seasonal fluctuation and irregular movement as well as secular trend from the original price through the moving average method. It was found that the cyclical fluctuations of garic and onion prices repeated six and seven times respectively during the same period, also the amplitude coefficient of cyclical fluctuations showed speed up in recent years. It was noticed that the cyclical fluctuations of price in onion was higher than that of in garic.

• Journal of Distribution Research
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• v.15 no.3
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• pp.1-34
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• 2010

Introduction The purpose of this research is to develop overall model which involves the effect of ongoing support services by franchisor on franchisee's relationship quality(trust, satisfaction, and commitment) and business performance(financial and non-financial performance), and to investigate the relationships among trust, satisfaction, commitment, financial and non-financial performance. This study also suggests franchise business or franchise system should be based on long-term orientation between franchisor and franchisee rather than short-term orientation, or transactional relationship, and proposes the most effective way of providing on-going support services by franchisor with franchisee thru symbiotic relationship among franchisor and franchisee Research Model and Hypothesis The research model as Figure 1 shows the variables on-going support services which affect the relationship quality between franchisor and franchisee such as trust, satisfaction, and commitment, and also analyze the effects of relationship quality on business performance including financial and non-financial performance We established 12 hypotheses to test as follows; Relationship between on-going support services and trust H1: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's trust. Relationship between on-going support services and satisfaction H2: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's satisfaction. Relationship between on-going support services and commitment H3: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's commitment. Relationship among relationship quality: trust, satisfaction, and commitment H4: Franchisee's trust has positive effect on franchisee's satisfaction. H5: Franchisee's trust has positive effect on franchisee's commitment. H6: Franchisee's satisfaction has positive effect on franchisee's commitment. Relationship between relationship quality and business performance H7: Franchisee's trust has positive effect on franchisee's financial performance. H8: Franchisee's trust has positive effect on franchisee's non-financial performance. H9: Franchisee's satisfaction has positive effect on franchisee's financial performance. H10: Franchisee's satisfaction has positive effect on franchisee's non-financial performance. H11: Franchisee's commitment has positive effect on franchisee's financial performance. H12: Franchisee's commitment has positive effect on franchisee's non-financial performance. Method The on-going support services were defined as an organized system of continuous supporting services by franchisor for the purpose of satisfying the expectation of franchisee based on long-term orientation and classified into six constructs such as product category & price, logistics service, promotion, providing information & problem solving capability, supervisor's support, and education & training support. The six constructs were measured agreement using a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree)as follows. The product category & price was measured by four items: menu variety, price of food material provided by franchisor, and support for developing new menu. The logistics service was measured by six items: distribution system of franchisor, return policy for provided food materials, timeliness, inventory control level of franchisor, accuracy of order, and flexibility of emergency order. The promotion was measured by five items: differentiated promotion activities, brand image of franchisor, promotion effect such as customer increase, long-term plan of promotion, and micro-marketing concept in promotion. The providing information & problem solving capability was measured by information providing of new products, information of competitors, information of cost reduction, and efforts for solving problems in franchisee's operations. The supervisor's support was measured by supervisor operations, frequency of visiting franchisee, support by data analysis, processing the suggestions by franchisee, diagnosis and solutions for the franchisee's operations, and support for increasing sales in franchisee. Finally, the of education & training support was measured by recipe training by specialist, service training for store people, systemized training program, and tax & human resources support services. Analysis and results The data were analyzed using Amos. Figure 2 and Table 1 present the result of the structural equation model. Implications The results of this research are as follows: Firstly, the factors of product category, information providing and problem solving capacity influence only franchisee's satisfaction and commitment. Secondly, logistic services and supervising factors influence only trust and satisfaction. Thirdly, continuing education and training factors influence only franchisee's trust and commitment. Fourthly, sales promotion factor influences all the relationship quality representing trust, satisfaction, and commitment. Fifthly, regarding relationship among relationship quality, trust positively influences satisfaction, however, does not directly influence commitment, but satisfaction positively affects commitment. Therefore, satisfaction plays a mediating role between trust and commitment. Sixthly, trust positively influence only financial performance, and satisfaction and commitment influence positively both financial and non-financial performance.

• (The) Research of the performance art and culture
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• no.32
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• pp.701-738
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• 2016

The Geomungo(거문고) is a instrument of Gogureoy(高句麗). The instrument had covered a lot of Korea, so it have become a important musical instrument in Korea. Hayasi Genjo(林謙三), Japanese scholar, had maintained his opinion that the Geomungo of Gogureoy is the Wagonghu(臥??), and the Geomungo was formed later, the record of Kimbusik, wrighter of the History of Three Kingdom(三國史記), was incredible. Lee-Hyegu refuted his hypothesis because the introduction on the Wagonghu of Japan have been inaccurate. Since then, many scholars of Korea have studed on the Geomungo of Gogureoy. But their study of the Geomungo was inclined to the topic, relation of the Geomungo and the Wagonghu, or the Wagonghu, the origin of the Geomungo. And They have thought that the record of Kimbusik's was truth. Kimbusik had recorded that Wangsanak(王山岳) had made the Geomungo from the Chilheoyn-Geum(七絃琴, Seven stringed Zither. 古琴). But the Geomungo was different from Geum(琴), but similar to Wagonghu. Many ancient tomb have been unearthed in the old land of Gogureoy, and the were many tomb painting of Gogureoy Geomungo. They were many different style, the form, the size, the number of strings and the position of the musician. So I think that many various type of the Geomungo had been exsited in Gogureoy they had become a prestyle of the Geomungo. The Geomungo was originated from the Wagonghu, its form was similar to the Geomungo. The many scholars considered that it is truth, the Wagonghu was handed down from China, and was spreded to Japan. But there were the Wagonghu in the early Joseon(古朝鮮), The song of the early Joseon, Gongmudohaga(公無渡河歌). The song was accompanied by the Wagonghu. We can read off, at the Song, the Wagonghu had exsisted in the early Joseon. So I think cautiously on that point, the Wagonghu of the Early Joseon was old than that of China, and thd Geomungo of Gogureoy was originated from the Wagonghu of the Early Joseon.

• Korean Journal of Breeding Science
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• v.41 no.1
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• pp.16-24
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• 2009

Although leaf characters are important in soybean [Glycin max (L.) Merr.] breeding and development of cultural methods, very little information has been reported. The objectives of this study were to evaluate and analyze the relationships among leaf characters and suggest possible classification criteria for cultivating and wild (Glycin soja Sieb. & Zucc.) soybeans. Total of 94 cultivating and 91 wild soybean accessions from the Soybean Germplasm Laboratory of Chungbuk National University were used for this study. Central leaflet of the second leaf from the top of the plant was selected to measure leaf characters. Average leaf length, leaf width, leaf area, leaf shape index (LSI) of cultivating and wild soybeans were 12.3$\pm$1.25 cm and 6.6$\pm$1.35 cm, 6.8$\pm$1.241 cm and 2.9$\pm$0.92 cm, 55.6$\pm$15.75 $cm^2$ and 14.3$\pm$7.83 $cm^2$, and 1.9$\pm$0.38 and 2.4$\pm$0.53, respectively. Based on LSI, three categories of leaf shape, i.e., oval, ovate and lanceolate, were defined as LIS$\leq$2.0, LSI 2.1~3.0 and 3.1$\leq$LSI, respectively. Percentage of oval, ovate and lanceolate leaf types among cultivating and wild soybean accessions were 78.7%, 17.0% and 4.3 %, and 40%, 15.4% and 4.4%, respectively. Based on leaf length, three categories for cultivating, i.e. short leaf ($\leq$11.0 cm), intermediate (11.1~13.0 cm), and long (13.1 cm$\leq$), and four categories, i.e. short ($\leq$5.0 cm), intermediate (5.1~7.0 cm), long (7.0~9.0 cm), and very long (9.1 cm$\leq$) for wild soybeans were defined. Short, intermediate and long leaf types were about 1/3, 1/2 and 1/6, respectively, in cultivating soybeans, and 15.4%, 40.7% and 39.5%, plus 4.4% of very long leaf type in wild soybean. Cultivating and wild soybeans had leaf thickness, leaf area ratio (LAR), angle and petiol length of 0.25$\pm$0.054 mm and 0.14$\pm$0.032 mm, 40.1$\pm$8.22 and 53.7$\pm$12.02, $37.6{\pm}5.89^{\circ}$ and $54.6{\pm}10.77^{\circ}$, and 23.9$\pm$5.89 cm and 5.9$\pm$2.33 cm, respectively. There were highly significant positive correlations between leaf length and leaf width, and negative correlation between LSI and leaf width both in cultivating and wild soybeans. Although leaf area showed significant correlations with leaf length, leaf width and LIS in cultivating soybeans, wild soybeans showed no significant relationships among these characters. In general, soybeans with oval, ovate and lanceolate leaves were significantly different in leaf width and thickness. Cultivating soybean with oval leaf had greater leaf area, while wild soybeans with oval or ovate leaf had longer petiol than with lanceolate leaf.