• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.197-215
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• 2021

This paper analyzes the machine learning predictions of the economic effects of Busan's strategic industries on the employment and income using the Ridge Regression and Lasso Regression models with regulation terms. According to the Ridge estimation and Lasso estimation models of employment, the intelligence information service industry such as the service platform, contents, and smart finance industries and the global tourism industry such as MICE and specialized tourism are predicted to influence on the employment in order. However, the Ridge and Lasso regression model show that the future transportation machine industry does not significantly increase the employment and income since it is the primitive investment industry. The Ridge estimation models of the income show that the intelligence information service industry and global tourism industry are also predicted to influence on the income in order. According to the Lasso estimation models of income, four strategic industries such as the life care, smart maritime, the intelligence machine, and clean tech industry do not influence the income. Furthermore, the future transportation machine industry may influence the income negatively since it is the primitive investment industry. Thus, we have to select the appropriate economic objectives and priorities of industrial policies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.97-105
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• 2022

This study deals with the credibility of citizens when investing in uncertain project companies, as well as the Social Performance Compensation Project (SIB) and the IT sharing economy. This allows the convergence of the three sectors to address investment inequalities in economic effectiveness and social security. Activates the model of the overall Social Impact Bond (SIB) process that successfully activates the exchange of information. The empirical presentation of the operations and techniques for social IT service finance examines how the innovation ecosystem can be created with social performance and reward projects. The analysis shows that small sharing institutions or citizens can participate directly to create the ability to connect with private investors, identify the possibility of recognizing non-shared barriers to participation, and show the great impact of citizen trust in IT sharing projects in uncertain areas. As a result, for the sake of social sharing and IT cooperation promoted by the City of Seoul, before the project has the ability to design directly, it will be responsible for reliability and safety in the planning of the project. Therefore, non-shared citizens can also participate in the platform that has been effectively constructed and created.

• Management & Information Systems Review
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• v.39 no.2
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• pp.39-60
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• 2020

Block chain technology is one of the core elements for realizing the 4^th industrial revolution, and many efforts have been made by government and companies to provide services based on block chain technology. In this study we analyzed the benefits of block chain technology for EVMS and designed EVMS block chain platform with increased data security and work efficiency for project management data, which are important assets in monitoring progress, foreseeing future events, and managing post-completion. We did the case studies on the benefits of block chain technology and then conducted the survey study on security, reliability, and efficiency of block chain technology, targeting 18 block chain experts and project developers. And then, we interviewed EVMS system operator on the compatibility between block chain technology and EVM Systems. The result of the case studies showed that block chain technology can be applied to financial, logistic, medical, and public services to simplify the insurance claim process and to improve reliability by distributing transaction data storage and applying security·encryption features. Also, our research on the characteristics and necessity of block chain technology in EVMS revealed the improvability of security, reliability, and efficiency of management and distribution of EVMS data. Finally, we designed a network model, a block structure, and a consensus algorithm model and combined them to construct a conceptual block chain model for EVM system. This study has the following contribution. First, we reviewed that the block chain technology is suitable for application in the defense sector and proposed a conceptual model. Second, the effect that can be obtained by applying block chain technology to EVMS was derived, and the possibility of improving the existing business process was derived.

• Korea Trade Review
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• v.48 no.4
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• pp.303-320
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• 2023

Recently, import and export companies have been using T/T remittance and Surrender B/L more frequently than L/C when selecting the process and method of trade payment settlement. The new trade and logistics environment is thriving in the era of the Fourth Industrial Revolution (4IR). Document-based trade transactions are undergoing a digitalization as bills of lading or smart contracts are being developed. The purpose of this study is to verify whether exporters choose export payment types based on negotiating factors. In addition, we would like to discuss the application of the characteristics of the new trade and logistics environment. Data for analysis was collected through surveys. The collection method consisted of direct visits to the company, e-mail, fax, and online surveys. The survey distribution period is from February 1, 2023, to April 30, 2023. The questionnaire was distributed in 2,000 copies, and 447 copies were collected. The final 336 copies were used for analysis, excluding 111 copies that were deemed inappropriate for the purpose of this study. The results of the study are shown below. First, among the negotiating factors, the product differentiation of exporters did not significantly affect the selection of export payment types. Second, among the negotiating factors, the greater the purchasing advantage recognized by exporters, the higher the possibility of using the post-transfer method. In addition to analyzing the results, this study suggests that exporters should consider adopting new payment methods, such as blockchain technology-based bills of lading and trade finance platforms, to adapt to the characteristics of the evolving trade and logistics environment. Therefore, exporters should continue to show interest in initiatives aimed at digitizing trade documents as a response to the challenges posed by bills of lading. In future studies, it is necessary to address the lack of social awareness in Korea by conducting advanced research abroad.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.