• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.63-79
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• 2024

The startup ecosystem is experiencing a paradigm shift in founding due to the acceleration of digital transformation, online platform companies have grown significantly into unicorns, but the lack of differentiated approaches and strategic support for deep tech startups has led to the inactivity of the startup ecosystem. is lacking. Therefore, in this study, we proposed ways to develop domestic startup development policies, focusing on the US system, which is an advanced example overseas. Focusing on the definition and characteristics of deep tech startups, current investment status, success stories, support policies, etc., we comprehensively analyzed domestic and international literature and derived suggestions. In particular, he proposed specific ways to improve support policies for domestic deep tech startups and presented milestones for their development. Currently, the United States is significantly strengthening the role of the government in supporting deep tech startups. The US government provides direct financial support to deep tech startups, including detergent support and infrastructure support. It has also established policies to foster deep tech startups, established related institutions, and systematized support. It is worth noting that US universities play a core role in nurturing deep tech startups. Leading universities in the United States operate deep tech startup discovery and development programs, providing research and development infrastructure and technology. It also works with companies to provide co-investment and commercialization support for deep tech startups. As a result, the growth of domestic deep tech startups requires the cooperation of diverse entities such as the government, universities, companies, and private investors. The government should strengthen policy support, and universities and businesses should work together to support R&D and commercialization capabilities. Furthermore, private investors must stimulate investment in deep tech startups. Through such efforts, deep tech startups are expected to grow and Korea's innovation ecosystem will be revitalized.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Korean Journal of Heritage: History & Science
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• v.47 no.1
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• pp.82-101
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• 2014

The purpose of this study was to survey and analyze the origin story and the legends associated with Yongyudam(龍遊潭, Dragon Creek), its scenic and spatial description in Climbing Writings(遊山記: Yusangi Notes), its geographical and geological features, its surrounding remains and letters chiseled on the rocks through the field study and the study on literatures associated with it so to identify its significance and value and then to ensure justification on preservation of Yongyudam scenic site. Conclusions of this study are as follow. As the traditional scenic place 'Geumdae-Jiri(金臺智異)' representing Hamyang-gun(咸陽郡) depicts Mount Cheonwangbong and 'Yongyudong Village(龍遊洞)', ancient maps and literatures have positioned Yongyudam as the center of Eomcheon-river Creek as well as the representing scenic site of Yongyudong Village. Core images in the spatial awareness of Yongyudam described in our ancestors' Climbing Writings Notes on Jirisan Mount are 'geographical and scenic peculiarity associated with swimming dragons', 'potholes in various shapes and sizes scattered on rocks', 'loud sound generated by swirling from shoals' and 'the scenic metaphor from the dragon legend', which have led scenic features of Yongyudam scenic site. In addition, significant scenic metaphors from legends such as 'Nine Dragons and Ascetic Majeog' and 'Kasaya Fish' as well as 'the Holy Place of Dragon God', the rain calling magic god have descended not only as the very nature of Yongyudam scenic site but also the catalyst deepening its mystic and place nature. On the other hand, Jangguso Place(杖?所, Place of Scholars) in the vicinity of Yongyudam was the place of resting and amusement for scholars from Yeongnam Province, to name a few, Kim Il-son, Cho Sik, Jung Yeo-chang and Kang Dae-su, where they experienced and recognized Jirisan Mount as the scenic living place. Letters Carved on the rocks at Jangguso Place are memorial tributes and monumental signs. Around Yongyudam, there are 3 stairs, letters chiseled on the rocks and the water rock artificially built to collect clean water, which are traditional scenic remains detectable of territoriality as the ritual place. In addition, The letters on the rock at Yongyudong-mun(龍遊洞門) discovered for the first time by this study are the sign promoting Yongyudam as the place of splendid landscape. The laconism, 'It is the Greatest Water in Jirisan Mount(方丈第一山水)' on a rock expresses the pride of Yongyudam as the representing scenic place of Mount Jirisan. Other than those, standing rocks such as Simjindae Rock, Yeong-gwidae Rock and Ganghwadae Rock show the sign that they are used as amusement and gathering places for ancestor scholars, which add significance to Yongyudam. By this study, it was possible to verify that Yongyudam in Mount Jirisan is not simply 'the scenic place in the tangible reality' but also has seamlessly inherited as the traditional scenic attraction spiritualized by overlapped historical and cultural values. Yongyudam, as the combined heritage by itself, shows that it is the product of the place nature as well as unique ensemble of cultural scenic attraction inherited through long history based on natural scenery. It is certain that not only the place value but also geographical, geological, historical and cultural values of Yongyudam are the essence of traditional scenic attraction, which should not be disparaged or damaged by whatever political or economic interests and logics.