• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.155-178
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• 2021

In the era of the 4th industrial revolution, public libraries need a strategy for promoting intelligent library services in order to actively respond to changes in the external environment such as artificial intelligence. Therefore, in this study, based on the concept of artificial intelligence and analysis of domestic and foreign artificial intelligence related trends, policies, and cases, we proposed the future direction of introduction and development of artificial intelligence services in the library. Currently, the library operates a reference information service that automatically provides answers through the introduction of artificial intelligence technologies such as deep learning and natural language processing, and develops a big data-based AI book recommendation and automatic book inspection system to increase business utilization and provide customized services for users. Has been provided. In the field of companies and industries, regardless of domestic and overseas, we are developing and servicing technologies based on autonomous driving using artificial intelligence, personal customization, etc., and providing optimal results by self-learning information using deep learning. It is developed in the form of an equation. Accordingly, in the future, libraries will utilize artificial intelligence to recommend personalized books based on the user's usage records, recommend reading and culture programs, and introduce real-time delivery services through transport methods such as autonomous drones and cars in the case of book delivery service. Service development should be promoted.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.

• Journal of the Society of Disaster Information
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• v.14 no.2
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• pp.130-140
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• 2018

Purpose: In this paper, we propose a design technique for the safety characterization grounding in the construction of the photovoltaic power generation complex which can be useful and useful as an alternative power energy source in our society. In other words, we will introduce the application of safety grounding for each application, which can improve and optimize the reliability of the internal grid from the cell module to the electric room in the photovoltaic power generation complex. Method: We analyze the earth resistivity of the soil in the solar power plant and use the computer program (CDEGS) to analyze the contact voltage and stratospheric voltage causing the electric shock, and propose the calculation and calculation method of the safety ground. In addition, we will discuss the importance of semi-permanent ground electrode selection in consideration of soil environment. Results: We could obtain the maximum and minimum value of ground resistivity for each of the three areas of the data measured by the Wenner 4 - electrode method. The measured data was substituted into the basic equation and calculated with a MATLAB computer program. That is, it can be determined that the thickness of the minimum resistance value is the most favorable soil environment for installing the ground electrode. Conclusion: Through this study, we propose a grounding system design method that can suppress the potential rise on the ground surface in the inner grid of solar power plant according to each case. However, the development of smart devices capable of accumulating big data and a monitoring system capable of real-time monitoring of seismic changes in earth resistances and grounding systems should be further studied.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.49-58
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• 2019

There have been studies on the satisfaction, preference, and post occupancy evaluation of urban parks in order to reflect users' preferences and activities, suggesting directions for future park planning and management. Despite using questionnaires that are proven to be affective to get users' opinions directly, there haven been limitations in understanding the latest changes in park use through questionnaires. This study seeks to address the possibility of utilizing the thirdgeneration SNS data, Instagram and Google, to compare behavior patterns and trends in park activities. Instagram keywords and photos representing user's feelings with a specific park name were collected. We also examined reviews, peak time, and popular time zones regarding selected parks through Google. This study tries to analyze users' behaviors, emerging activities, and satisfaction using SNS data. The findings are as follows. People using park near residential areas tend to enjoy programs being operated in indoor facilities and to like to use picnic places. In an adjacent park of commercial areas, eating in the park and extended areas beyond the park boundaries is found to be one of the popular park activities. Programs using open spaces and indoor facilities were active as well. Han River Park as a detached park type offers a popular venue for excercises and scenery appreciation. We also identified companionship characteristics of different park types from texts and photos, and extracted keywords of feelings and reviews about parks posted in $3^{rd}$ generation SNS. SNS data can provide basis to grasp behavioral patterns and satisfaction factors, and changes of park activities in real time. SNS data also can be used to set future directions in park planning and management in accordance with new technologies and policies.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.432-448
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• 2018

Education will be undergoing major changes with the 4th industrial revolution. As education contents will be important in future smart home service, in-depth interviews were conducted against experts and analyzed by the grounded theory approach. Eleven categories emerged through the analysis. In order for educational content to be utilized in smart home services, value creation (central phenomenon) seems to be most critical with preceding overcome of hamlet syndrome. Diversity of content and connectivity (context) should be ensured, and studies that could enhance user experience (intermediary situations) should be conducted and reflected in the content curation and realtime response (interaction strategy). As a result, it can be inferred that the education content service can be expanded in smart home services while satisfying self-development desire of individuals through these processes. Additional selective coding revealed four immediate need area: self-development, home-improvement, health care, and mindful healing.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.1
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• pp.15-25
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• 2013

The RIMGIS(River Information Management GIS) has been developed since 2000 for public service and practical applications of related works after the standardization of national river data such as the river facility register report, river survey map, attached map, and etc. The RIMGIS has been improved in order to respond proactively to change in the information environment. Recently, Smart River-based river information services and related data have become so large as to be overwhelming, making necessary improvements in managing big data. In this study a plan was suggested both to respond to these changes in the information environment and to provide a future Smart River-based river information service by understanding the current state of RIMGIS, improving RIMGIS itself, redesigning the database, developing distribution, and integrating river information systems. Therefore, primary and foreign key, which can distinguish attribute information and entity linkages, were redefined to increase the usability of RIMGIS. Database construction of attribute information and entity relationship diagram have been newly redefined to redesign linkages among tables from the perspective of a river standard database. In addition, this study was undertaken to expand the current supplier-oriented operating system to a demand-oriented operating system by establishing an efficient management of river-related information and a utilization system capable of adapting to the changes of a river management paradigm.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.6
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• pp.27-36
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• 2021

At a time when the equitable use of urban parks is gradually emerging as a social issue, this study was initiated to expand the influence of urban parks by improving the quality of park services, thereby resolving areas not covered by urban park services. This study targeted the Hangang Park in Seoul, where the qualitative service of parks shows the greatest difference. The influence relationship between the qualitative services of the park and the user's sphere of influence, which indicates the distribution of park users, was proposed to assess the influence of improvements in the quality of service. As a research method, the top three districts and the bottom three districts were selected through the Han River Park user satisfaction survey conducted from 2017 to 2019, and a qualitative service evaluation was carried out. It was derived using the data acquired in September. Afterward, by performing a spatial autocorrelation analysis on the user's sphere of influence, additional verification of the user's sphere of influence was performed numerically and visually. As a result of the study, the user influence in the top three districts, with high-quality service, was stronger and wider than that of the lower three districts. It was confirmed that the quality of service of the park affects the user influence. This shows that to realize park equity, it is necessary to improve the quality of services through continuous management and improvement of individual parks and the creation of new parks. This study has significance in that it recognizes the limitations of research on park services from a supplier's point of view and evaluates the qualitative services of parks from the perspective of actual park users. We propose an alternative to deal with the lower the park deprivation index.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.261-269
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• 2017

The purpose of this paper is to provide a search system using a method of accessing the web in real time without using a database server in order to guarantee the up-to-date information in a single network, rather than using a plurality of bots connected by a wide area network Design. The method of the research is to design and analyze the system which can search the person and keyword quickly and accurately in crepe system. In the crepe server, when the user registers information, the body tag matching conversion process stores all the information as it is, since various styles are applied to each user, such as a font, a font size, and a color. The crepe server does not cause a problem of body tag matching. However, when executing the crepe retrieval system, the style and characteristics of users can not be formalized. This problem can be solved by using the html_img_parser function and the Go language html parser package. By applying queues and multiple threads to a general-purpose web crawler, rather than a web crawler design that targets a specific site, it is possible to utilize a multiplier that quickly and efficiently searches and collects various web sites in various applications.