• Korean Journal of Agricultural Science
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• v.40 no.4
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• pp.417-423
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• 2013

Recently, there is the huge interest to promote the export of Korean agro-food to Chinese market. However, small number of papers analyze the Chinese consumer to find the strategy for launching Korean agro-food in Chinese market. The purpose of this paper is to analyze Chinese consumer for Korean agro-food in Chine. Survey analysis was conducted in 4 big city (Beijing, Shanghai, Guangzhou, and Tsingtao) for this research. The results of studies present a few findings: First, many Chinese consumers prefer Korean agro-food. Second, among big cities, Beijing shows the highest level of preference for Korean agro-food, Third, Chinese consumers can pay higher price for Korean agro-food, Fourth, Chinese consumer usually buy small amount of agro-food. Fifth, the image of Korea is also important to promote the exportation of Korean agro-food to Chinese market.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.143-153
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• 2021

Recently, by applying the 4th industrial revolution technologies such as A.I., IoT, and big data to general residential infrastructure, various application services of residential-based are provided to residents through linkage and utilization between technologies. Accordingly, smart housing system is increasing as advanced living spaces that can improve the quality of life and convenience of residents. Such a smart housing is expected to be an item that can create new demands and markets in the construction industry since it provides a new paradigm that combines construction technology and IT by combining IT technology with existing construction industry. Based on this, it is expected that it will be possible to gradually develop large-scale markets such as smart buildings and smart cities. In this paper, therefore, we propose an artificial intelligence multiplex smart housing system as an intelligent platform that can autonomously manage and control the size of places and spaces, used for various purposes based on smart housing technology by using artificial intelligence systems.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.119-126
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• 2024

This paper reviewed and provides clarifications as to the meaning and concept of Smart Cities with particular reference to the Smart City Components. The paper also discusses Internet of Things and the Big Data in relation to the role they played in the development and evolution of smart cities. The paper further provides discussions on the 5G Wireless Networks and Industry 4.0 buttressing their significance in the smart cities concept. The paper as the name implies; discusses on the readiness and adaptability of this trending concept 'Smart City' in the African global space.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.573-580
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• 2013

With the growth of the smart-devices and information & communication technology, information society has developed and information can be produced, spread and consumed at much faster pace easily. Hence, individuals can utilize wireless communication and smart-devices to create, share and consume information at anytime and anywhere. The growth of technology has allowed the large-scale transfer and sharing of image, sound and video data; it changed the users' data consumption pattern that was mainly consisted of the text. Therefore, the amount of data that an individual consumes increased significantly. The importance of finding and analyzing practical and necessary data among huge amount of data has arisen. In this study, the current status of Big Data is researched and analyzed and the method to utilize Big Data in the electronic trading field is suggested.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.683-690
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• 2016

In modern society, disasters frequently occur, and the effect is getting more massive. Also, unpredictable future increases anxiety about social security. Accordingly, in order to prevent national-scale emergency from happening, it is highly required governments' role as ICT power nation and transition to disaster management system using big data applied service. Thus, e-gov necessarily acquires disaster response system in order to predict and manage disasters. Disasters are linked with some attributes of modern society in diversity, complexity and unpredictability, so various approach and remedies of them will appease the nation's anxiety upon them. For this reason, this manuscript suggests epidemics preactive warning algorithm model as a mean of reduce national anxiety on disaster using big data for social security. Also, by recognizing the importance of e-gov and analyzing problems in weak disaster management system, it suggests political implication for disaster response.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.955-969
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• 2019

Analyzing on-line consumer responses is directly related to the management performance of food companies. Therefore, this study collected and analyzed data from an on-line portal site created by consumers about food companies with issues and examined the relationships between the data and the management performance. Through this process, we identified consumers' awareness of these companies obtained from big data analysis and analyzed the relationship between the results and the sales and stock prices of the companies through a time-series graph and correlation analysis. The results of this study were as follows. First, the result of the text mining analysis suggests that consumers respond more sensitively to negative issues than to positive issues. Second, the emotional analysis showed that companies' ethics issues (Enterprise 3 and 4) have a higher level of emotional continuity than that of food safety issues. It can be interpreted that the problem of ethical management has great influence on consumers' purchasing behavior. Finally, In the case of all negative food issues, the number of word frequency and emotional scores showed opposite trends. As a result of the correlation analysis, there was a correlation between word frequency and stock price in the case of all negative food issues and also between emotional scores and stock price. Recently, studies using big data analytics have been conducted in various fields. Therefore, based on this research, it is expected that studies using big data analytics will be done in the agricultural field.

• The Journal of the Convergence on Culture Technology
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• v.2 no.4
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• pp.71-76
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• 2016

In this study, de-identification policies of the US, the UK, Japan, China and Korea are compared to suggest a future direction of de-identification regulations and a method for vitalizing the big data industry. Efficiently using the de-identification technology and the standard of adequacy evaluation contributes to using personal information for the industry to develop services and technology while not violating the right of private lives and avoiding the restrictions specified in the Personal Information Protection Act. As a counteraction, the re-identification issue may occur, for re-identifying each person as a de-identified data collection. From the perspective of business, it is necessary to mitigate schemes for discarding some regulations and using big data, and also necessary to strengthen security and refine regulations from the perspective of information security.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.295-305
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• 2016

The purpose of this study is to analyze the internal reputation factors that affect the job satisfaction by big data analysis in the social media for corporate reputation and verify the difference between large corporations and small-medium corporations for each factor of internal reputation. The result showed 'Salaries and Benefits' is a major factor that affects the job satisfaction for all research corporations, 'Senior Management' is a major factor for large corporations, and 'Salaries and Benefits' is a major factor for small-medium corporations. As for the difference factors of large corporations and small-medium corporations are 'Job Satisfaction', 'Salaries and Benefits', and 'Work-life Balance'. Unstructured data analysis shows some interesting features to be studied further.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.