• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Journal of Korea Technology Innovation Society
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• v.9 no.4
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• pp.812-841
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• 2006

Previous studies have shown that the ex-ante financial ratios, mainly used by financial institutions for loan evaluation purpose, are related to the ex-post finn's performance of venture firm's. The main objective of this study is to examine whether non-financial variables such as 'technology', 'marketability', and 'other business indexes' have extra explanatory power in forecasting the ex-post firm's performance of small and medium size venture firm's in Korea. The implications and results of this study are expected to be useful in loan evaluation, investment decision and internal management decisions of venture firms. Among small and medium sized manufacturing firms funded in the year of 1999 through 2005, 416 firms are selected for our analysis. The relationship between evaluation indexes and firm's success/failure is investigated using binary logistic regression analysis and factor analysis with an aid of SPSS program. The summarized results are as follows. First, current evaluation model, used for loan evaluation purpose for small and medium size manufacturing firms show the same discriminatory power as previous prediction model. Second, among the tested additional variables, significant indices are 'technological capability of CEO', 'managerial capability of CEO', and 'business feasibility'. Third, while previous studies on evaluation structure had 3 factors, this study showed that valuation's structure has 6 factors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.691-702
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• 2019

Flood inundation map has various important roles in terms of municipal planning, timely dam operation, economic levee design, and building flood forecasting systems. Considering that the riparian areas adjacent to national rivers with high potential flood vulnerability conventionally imposed special cares to justify applications of recently available two- or three-dimensional flood inundation numerical models on top of digital elevation models of dense spatial resolution such as LiDAR irrespective of their high costs. On the contrary, local streams usually could not have benefits from recent technological advances, instead they inevitably have relied upon time-consuming manual drawings or have accepted DEMs with poor resolutions or inaccurate 1D numerical models for producing inundation maps due mainly to limited budgets and suitable techniques. In order to efficiently and cost-effectively provide a series of flood inundation maps dedicatedly for the local streams, this study proposed an OpenGIS-based flood mapping tool named Open Flood Mapper (OFM). The spatial accuracy of flood inundation map derived from the OFM was validated throughout comparison with an inundation trace map acquired after typhoon Nari in Hancheon basin located in Jeju Island. Also, a series of inundation maps from the OFM were comprehensively investigated to track the burst of flood in the extreme flood events.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.409-416
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• 2020

Artificial intelligence, which is in the spotlight as the core driving force of the 4th industrial revolution, is expanding its scope to various industrial fields such as smart factories and autonomous driving with the development of high-performance hardware, big data, data processing technology, learning methods and algorithms. In the field of defense, as the security environment has changed due to decreasing defense budget, reducing military service resources, and universalizing unmanned combat systems, advanced countries are also conducting technical and policy research to incorporate artificial intelligence into their work by including recognition systems, decision support, simplification of the work processes, and efficient resource utilization. For this reason, the importance of technology-driven planning and investigation is also increasing to discover and research potential defense future technologies. In this study, based on the research data that was collected to derive future defense technologies, we analyzed the characteristic evaluation indicators for future technologies in the field of artificial intelligence and conducted empirical studies. The study results confirmed that in the future technologies of the defense AI field, the applicability of the weapon system and the economic ripple effect will show a significant relationship with the prospect.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.108-126
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• 2013

Recently, it becomes essential for firms or nations to forecast the future and identify emerging technologies in order to improve R&D efficiency and gain a competitive advantage. Particularly, the mobile communications industry is characterized by rapid advance and wide application of its technology and thus identifying emerging technologies is more important in the industry than in others. Nevertheless, few attempts have been made to explore its emerging technologies. Therefore, this research aims to develop a methodology to identify the future and emerging technologies especially for the industry and applied it to list top ten emerging technologies for 2020 in the industry. For this purpose, firstly, we focused the key issues in the future targeting 2020 and identified user needs relating to them. Then, candidates of emerging technologies were defined from a set of technologies to meet the needs, for which technological and economic feasibility is assessed to determine their priorities. Finally, the top ten most important technologies were selected and verified. This research is distinct from the previous studies in that it takes a market-pull approach instead of a technology-push approach. The research results are expected to provide valuable information to support strategy- and policy-makings in the mobile communications industry.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.104-114
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• 2019

In the global market competition, countries and businesses are actively engaged in technology prediction activities to maximize their profits by attempting to enter and preempting the core technology of the future. In this paper, we propose a growth model based on patent application trends to predict the time to replace a product with a promising new technology to dominate the market. Although the Fisher-Pry model that Bhargava generalized to predict the emergence of promising alternative technologies was relatively satisfactory compared to the original Fisher-Pry model, it was difficult to predict the replacement rate behavior properly due to a parameter problem. The application of the Fisher-Pry Modification Model in the form of a quadratic equation through the patent trend analysis of the optical storage system for the purpose of verifying the time alternative to the light storage technology has resulted in satisfactory verification results. It is expected that small and medium-sized companies and individual researchers will apply this model and use it more easily to predict the time to replace the market for promising replacement technologies.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.223-252
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• 2008

In this study, we attempt to propose a choice-based diffusion model with switching cost, which can be used to forecast the dynamic substitution and competition among previous and new products at both individual-level and aggregate level, especially when market data for new products is insufficient. Additionally, we apply the proposed model to the empirical case of substitution and competition among Analog Cable TV that represents previous fixed charged broadcasting service and Digital Cable TV and Internet Protocol TV (IPTV) that are new ones, verify the validities of our proposed model, and finally derive related empirical implications. For empirical application, we obtained data from survey conducted as follows. Survey was administered by Dongseo Research to 1,000 adults aging from 20 to 60 living in Seoul, Korea, in May of 2007, under the title of 'Demand analysis of next generation fixed interactive broadcasting services'. Conjoint survey modified as follows, was used. First, as the traditional approach in conjoint analysis, we extracted 16 hypothetical alternative cards from the orthogonal design using important attributes and levels of next generation interactive broadcasting services which were determined by previous literature review and experts' comments. Again, we divided 16 conjoint cards into 4 groups, and thus composed 4 choice sets with 4 alternatives each. Therefore, each respondent faces 4 different hypothetical choice situations. In addition to this, we added two ways of modification. First, we asked the respondents to include the status-quo broadcasting services they subscribe to, as another alternative in each choice set. As a result, respondents choose the most preferred alternative among 5 alternatives consisting of 1 alternative with current subscription and 4 hypothetical alternatives in 4 choice sets. Modification of traditional conjoint survey in this way enabled us to estimate the factors related to switching cost or switching threshold in addition to the effects of attributes. Also, by using both revealed preference data(1 alternative with current subscription) and stated preference data (4 hypothetical alternatives), additional advantages in terms of the estimation properties and more conservative and realistic forecast, can be achieved. Second, we asked the respondents to choose the most preferred alternative while considering their expected adoption timing or switching timing. Respondents are asked to report their expected adoption or switching timing among 14 half-year points after the introduction of next generation broadcasting services. As a result, for each respondent, 14 observations with 5 alternatives for each period, are obtained, which results in panel-type data. Finally, this panel-type data consisting of $4{\ast}14{\ast}1000=56000$observations is used for estimation of the individual-level consumer adoption model. From the results obtained by empirical application, in case of forecasting the demand of new products without considering existence of previous product(s) and(or) switching cost factors, it is found that overestimated speed of diffusion at introductory stage or distorted predictions can be obtained, and as such, validities of our proposed model in which both existence of previous products and switching cost factors are properly considered, are verified. Also, it is found that proposed model can produce flexible patterns of market evolution depending on the degree of the effects of consumer preferences for the attributes of the alternatives on individual-level state transition, rather than following S-shaped curve assumed a priori. Empirically, it is found that in various scenarios with diverse combinations of prices, IPTV is more likely to take advantageous positions over Digital Cable TV in obtaining subscribers. Meanwhile, despite inferiorities in many technological attributes, Analog Cable TV, which is regarded as previous product in our analysis, is likely to be substituted by new services gradually rather than abruptly thanks to the advantage in low service charge and existence of high switching cost in fixed charged broadcasting service market.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.