• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.6
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• pp.111-122
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• 2017

This study analyzes the current state of entrepreneurship education and start-up foundations by country in order to find ways to improve the domestic entrepreneurial environment and to promote the recognition of desirable entrepreneurship practices. It also investigates the relationship between entrepreneurship, entrepreneurial will, and the level of opportunity-based entrepreneurships, by using data from the 2016 Global Entrepreneurship Trend Report (GETR). First, the results show the urgent need for the expansion of entrepreneurship education in Korea. In the GETR category of 'experience of entrepreneurship education in elementary, middle and high schools', Korea was ranked very low (19th place), among the 20 countries. In the 'college' and 'lifelong entrepreneurship education' categories, it procured a mid-level ranking (15th). While entrepreneurship education for all ages is being promoted globally, entrepreneurship education for middle-aged individuals in Korea is relatively weak. This implies that the expansion of entrepreneurship education to lifelong education and education for employees and retirees is required. Second, the individual's entrepreneurial intention in Korea was 3.8 points, implying a mid-level ranking (15th), and it ranked the lowest in terms of opportunity-based entrepreneurship (20th). In comparison to China (4.55) and the United States (4.01), the entrepreneurial intention of Koreans was found to be low. The level of opportunity-driven entrepreneurship was also found to be very low, compared to China (4.35), Japan (4.04) and the United States (4.59). In general, the proportion of the level in opportunity-driven entrepreneurship, increases from the factor-driven and efficiency-driven, to the innovation-driven type. In Korea, the percentage of entrepreneurial ventures centered around involuntary entrepreneurship and small businesses is high. It is also interpreted that opportunity-based entrepreneurships are low in number because of this high proportion of involuntary start-up and small businesses. Last, the entrepreneurial intention in all types (factor-driven, efficiency-driven, and innovation-driven) was exceptionally high. It has been confirmed that exposure to all entrepreneurship education (elementary, junior high, university, and lifelong education) in innovation-driven countries, greatly increases entrepreneurial intention. In the case of Korea, which is an innovation-driven country, qualitative improvement based on quantitative expansion of entrepreneurship education is expected to be a major driving force for individuals' entrepreneurial intention to obtain a mid-level ranking (15th).

• 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.

• Knowledge Management Research
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• v.16 no.3
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• pp.23-40
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• 2015

Despite much interest and investment in the Internet of Things (IoT) which expand the Internet to a ubiquitous network including objects in the physical world, there is growing concerns of privacy protections. Because the risk of privacy invasion is higher in IoT environments than ever before, privacy need to be a key issue in the diffusion of IoT. Considering that the privacy concern is a critical barrier for user to adopt information technologies, it is important to investigate users' privacy concerns related to IoT applications. From the triad perspective (i.e., risk on technology, risk on service provider, and trust on legislation), this study aims to examine users' privacy concerns in the context of activity trackers.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.385-392
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• 2012

The definition of the product market in a supplier's point of view can cause various problems in the market activities of companies because specific situations are excluded and the consideration for discontinuity is lacking by identifying segmented markets with processes, raw materials, the similarity of product functions and so forth. Furthermore, as this definition is static and general, it is difficult to express and predict the dynamic market changes. Meanwhile, customer-oriented market segment can be obtained by grouping substitutable products and related customers in the situation pursuing specific benefits. This definition of the product market enables us to find threats and opportunities emerging in markets and promotes effective performance assessments and resource allocation. The purpose of this paper is suggesting a framework to select data mining techniques proper for the customer data characteristics to identify customer oriented product market.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.8-13
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• 2020

In this paper, we study smart healthcare devices that enable active health care by building health care system with acquaintances or family members rather than single health care. The company develops health care services for families regardless of age and gender through intuitive UI design as a target for young users who serve elderly parents. Automated collection of health information and real-time feedback are available, and data can be aggregated and analyzed through repeaters. It can also utilize structured databases in the form of big data. The services offered can be used to prevent diseases and reduce medical expenses through health care, while automatic management can maximize users' convenience and increase demand. By reducing the development period of products that are based on this technology, reducing the development period of products and strengthening competitiveness, the company has the advantage of inducing generation-to-generation communication in an era when it is becoming a nuclear family.

• Journal of KIISE
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• v.45 no.3
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• pp.242-250
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• 2018

For high-quality software development, it is necessary to detect and fix the defects inserted. If defect management activities are not properly performed, it will lead to the project delay and project failure due to rework. Therefore, organizations need to establish defect management process and institutionalize it. Process standard models handle defect management in the area of project monitoring and control. However, small organizations experience difficulties in implementing and applying defect management process in a real situation. In this paper, we propose a defect management process for small organization which is designed in accordance with the characteristics of a small projects such as few participants and short development period. The proposed defect management process will be based on a tool chain with open source software such as Redmine, Subversion, Maven, Jenkins that support a defect management process and SW Visualization in systematic way. We also proposed a way of constructing defect database and various methods of analyzing and controlling defect data based on it. In an effort to prove the effectiveness of the proposed process, we applied the process and tool chain to a small organization.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.111-119
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• 2010

With the rapid progress of wireless technologies today, mobile terminals with multiple access interfaces are emerging. In recent years, WLAN (Wireless Local Area Networks) has become the premier choice for many homes and enterprises. WiMAX (Worldwide Interoperability for Microwave Access) has also emerged as the wireless standard that aims to deliver data over long distances. Therefore, it is important to explore efficient integration methods for delivering multimedia data between heterogeneous wireless networks. In this paper, we developed the simulation models and environments for the mobile multihomed node that has both WiMAX and WLAN interfaces and can move around in both networks by using mobile IP. In order to verify the developed models, we designed and constructed several simulation scenarios, e.g. movement in WiMAX/WLAN, group mobility, MANET, and nested MIP under the various traffic environments such as oneway or bothway UDP packets, FTP traffic, and voice with SIP protocol. The simulation results show that the developed models are useful for mobility studies in various integrated wireless networks.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.75-82
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• 2017

Recently, as the roles and utilization fields of UAV become more diverse, demand for high - level mission has been increasing. To solve this issue, researches on the operation of multiple small UAVs and related systems have been actively carried out. The multiple small UAVs based application system has a problem that the task complexity of control personnel increases because the control personnel must continuously control and manage several small UAVs. Hence, it is necessary to develop a software platform that can perform efficient control in order to employ a multiple small UAVs based application system successfully. In this paper, we propose an effective ground control software platform for application systems using multiple small UAVs. We first analyze the requirements for the software platform, and design and implement software based on the analysis. Simulation using the X-plane flight simulator shows that multiple flight data are effectively displayed and that the image data transmitted from many small UAVs are simultaneously displayed in real time.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.54-61
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• 2019

Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver's behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.134-139
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• 2019

This paper describes the effect of multiple signal regeneration in quadrature digital radio frequency memory(DRFM). Single channel DRFM have good reproducibility after storing a single signal. However, when reproduced after storing multiple signals, the spurious signal is large. The quadrature DRFM consists of I and Q channels, which can greatly reduce the spurious signal. The amplitude of the spurious signal depends on the number of bits of data stored in the DRFM. In this paper, we have obtained the number of bits of signal regeneration according to the application of radio frequency memory by obtaining the size of the spurious signal according to the number of bits of the stored data of the DRFM for multiple signals. As a result of this study, 4 bits quadrature DRFM can achieve a spurious output of less than -20dB, which is used for 4 signals. Those are expected to greatly contribute to the signal analysis of electronic warfare equipment and the development of jamming device.