• The Journal of Bigdata
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• v.5 no.2
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• pp.121-133
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• 2020

This study was carried out to set the direction of the new industry policy of Busan city by analyzing the changing trend of artificial intelligence technology that has recently developed rapidly and predicting the direction of future development. The company wanted to draw up support measures to utilize artificial intelligence technology, which has been rapidly emerging in the market, in the region's specialized industry. Artificial intelligence is a key keyword in the fourth industrial revolution and artificial intelligence-based data utilization technology can be used in various fields from manufacturing processes to services, and is entering an era of super-fusion in which barriers between technologies and industries will be broken down. In this study, the direction of promotion for fostering Busan as an artificial intelligence city was derived based on the comparison and analysis of artificial intelligence-related ecosystems among major local governments. In this study, we wanted to present a plan to create an artificial intelligence industrial ecosystem that can be called a key policy to foster Busan as an 'AI City'. Busan's plan to foster the AI industry ecosystem is aimed at establishing a policy direction to ultimately nurture the artificial intelligence industry as Busan's future food source.

• Informatization Policy
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• v.24 no.3
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• pp.27-41
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• 2017

With the growing interest in the 4th industrial revolution and big data, various policies are being developed for facilitating the use of public open big data, which are leading to a wide range of added values created from use of such data. Despite the expanded requirements for public data disclosure and the legal system improvement, however, the use of public open big data is still limited. According to the literature review, there are studies on policy proposals for the government guiding directions for public open big data, but there is a lack of studies that handle the issue from the users' viewpoint. Therefore, this study aims to analyze the public open data ecosystem in Korea and to analyze public open big data through interviews with the providers (the government and public institutions) and users (private sector companies and citizens). This way, the study finds inhibition factors and facilitation factors, draws out issues and suggests solutions through a causal relationship analysis between each factor. Being a research on finding measures for facilitating both public big data release and use, this study has theoretical implications. In the meanwhile, the derived issues and alternatives provide practical implications also for stakeholders who are planning to facilitate release and use of public open big data.

• Journal of Technology Innovation
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• v.28 no.4
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• pp.97-126
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• 2020

This study evaluated its potentials by quantitatively analyzing the national economic impact of the smart home-related industry, which is attracting attention as a core industry of the 4th industrial revolution. For the analysis, the smart home-related industries were classified into manufacturing and service industries through a literature review of the previous studies. Using the 2018 input-output table, this paper analyzed linkage effects between industries as well as spillover effects in the production, value-added, employment and job. As a result, the smart home manufacturing and service sectors showed a higher spillover effect in value-added than other industries in each industrial field. In the smart home industry, the spillover effects of manufacturing sector to service sector are larger than those of service sector to manufacturing sector. Moreover, it was confirmed that smart home industry was highly related to not only the technology-intensive industry, but also the service sector for smart cities, smart cars, Fin-tech, and etc. On the other hand, the smart home manufacturing sector is a final demanding industry with relatively higher backward linkage effect than forward linkage effect. In the smart home service sector, the forward linkage effect was relatively high compared with the backward linkage effect, indicating that it was an industry with a high supply function to other industries.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.681-689
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• 2019

The 46th Davos Forum of the World Economic Forum (WEF) predicts the continued growth of the 4th industry in the future. Currently, the 4th industry is attracting attention in various academic and practical fields. As a core technology of the 4th industry, Big Data is regarded as a major resource to lead the 4th industrial revolution along with artificial intelligence. As the growing interest in Big Data, researches on it are actively being done. However, literature studies on existing Big Data are focused on qualitative research, and quantitative research is insufficient. Therefore, this study aims to analyze the big data research flow in MIS field and to make academic thirst for quantification. This study has collected 145 abstracts of big data papers published in major journals in MIS field and confirmed that a majority of papers are published in Decision Support Systems Journal. Text mining and text network analysis were performed only for DSS journals to eliminate bias. As a result of the analysis, it was found out that researches on combining big data in the management field between 2012 and 2014, and researches on system development and analysis method for using big data from 2015 to 2017 were conducted.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.115-133
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• 2020

With the rapid changes in the industry paradigm including the 4th industrial revolution, the survival and sustainable growth of SMEs and venture capital companies are facing a more difficult environment. The organizational capabilities help these companies to overcome the difficulties, such as absorption capacity, innovation capacity, adaptation capacity. It require many interconnected functions and capabilities to increase company performance. This study is based on the research about market adaptation capacity (agility, flexibility)'s mediating effect between organizational capacity(absorption capacity, innovation capacity, adaptation capacity) and corporate performance(financial, non-financial performance). According to the results of empirical analysis, First, Absorption capacity have a significant effect on agility. Second, Innovation capacity have a significant effect on flexibility. Third, Innovation capability have a significant effect on corporate performance. Fourth, Flexibility have a significant effect on corporate performance. Fifth, The mediating effect of flexibility between organizational capability and corporate performance was verified. Finally this paper also propose some suggestions on how to increase corporate performance for SMEs.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.447-454
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• 2022

In the era of the 4th industrial revolution, we are living in a flood of information. It is very difficult and complicated to find the information people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Among these recommendation systems, many studies have been conducted on each recommendation system for movies, music, food, and clothes. To date, most personalized recommendation systems have recommended clothes, books, or movies by checking individual tendencies such as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checking age, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and food at once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data as user's basic emotion according to Paul Eckman's theory. The basic emotions obtained in this way were converted into colors by applying Hayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing is recommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according to the contents of comfort food according to emotions.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.147-155
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• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.572-586
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• 2021

With the advent of the 4th Industrial Revolution, the modern economy is transforming into a service-oriented economic society, and there is a growing need for companies to reorganize organizational culture, values, and human resources to suit the changing environment. Especially, even when times change, human capital is the most important factor in maintaining sustainable development and Corporate value, since all work is done by people. Therefore, in this study, in order to recognize the importance of human resources and verify the appropriate human resources for the changing service economy era, we reviewed the literature and that drew the human resources suitable for the recent service economy era Based on this, an empirical study was conducted on whether the suggested human resources factors were useful. To this end, a feasibility analysis, reliability analysis, correlation analysis, confirmatory factor analysis, and reference validity were conducted by dividing into a preliminary survey and a main survey. As a result, it was finally drawn with 30 items, 7 factors. The results of this study are expected to be used as basic data in various fields of research on human resources in the service economy era, and in-depth empirical studies are needed in the future.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.