• Journal of Internet of Things and Convergence
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• v.5 no.1
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• pp.7-11
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• 2019

This study suggests a methodology to track crime suspects or anomalies through CCTV in order to expand the scope of CCTV use as the number of CCTV installations continues to increase nationwide in recent years. For the abnormal behavior classification, we use the existing studies to find out suspected criminals or abnormal actors, use CNN to track objects, and connect the surrounding CCTVs to each other to predict the movement path of objectified objects CCTVs in the vicinity of the path were used to share objects' sample data to track objects and to track objects. Through this research, we will keep track of criminals who can not be traced, contribute to the national security, and continue to study them so that more diverse technologies can be applied to CCTV.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.39-46
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• 2021

This study aims to empirically analyze the effect of CRM on the management performance of senior club market type senior job business. The purpose of this study was to provide basic data for the economic income creation of senior club market type senior job project. The results of this study are as follows: First, as a result of verifying the difference in management performance according to sociodemographic characteristics, there was a difference in age, academic background, and monthly average income. Second, the contact service and communication of senior club market type senior job business had a positive effect on the management performance. Based on the results of this study, the following suggestions are made. First, the database(DB) should be constructed reflecting the personal characteristics of consumers and the big data and artificial intelligence analysis should be utilized. Second, education using Internet environment such as YouTube and ZOOM should be strengthened and communication management should be strengthened based on information collected through customer monitoring.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.59-64
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• 2020

Recently, many difficulties have been faced in all parts of the world due to the impact of COVID-19. Personally, household income is decreasing sharply as many jobs disappear, and economically, many SMEs are increasingly going bankrupt. It is known that this phenomenon is highly likely to continue for the time being. In such a situation, the smart factory support project provides opportunities for difficult SMEs to improve productivity and change the corporate structure. In this study, the current status of smart factory promotion was examined, and problems occurring in the process of promoting smart factory support projects were identified. The improvement plans were derived so that more efficient projects could be promoted in the future.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.1-7
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• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.49-54
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• 2022

Rapid changes in modern society after the COVID-19 have highlighted artificial intelligence talent as a major influencing factor in determining national competitiveness. Accordingly, the Ministry of Education planned a large-scale SW·AI camp education project to develop the digital capabilities of 4th to 6th grade elementary school students and middle and high school students who are in a vacuum in artificial intelligence education. Therefore, this study aims to develop a camp-type SW·AI education program for students in grades 4-6 of elementary school so that students in grades 4-6 of elementary school can acquire basic knowledge in artificial intelligence. For this, the meaning of SW·AI education in elementary school is defined and SW·AI contents to be dealt with in elementary school are: understanding of SW AI, 'principle and application of SW AI', and 'social impact of SW AI' was set. In addition, an attempt was made to link the set elements of elementary school SW AI education and learning with related subjects and units of textbooks currently used in elementary schools. As for the program used for education, entry, a software coding learning tool based on block coding, is designed to strengthen software programming basic competency, and all programs are designed to be operated centered on experience and experience-oriented participants in consideration of the developmental characteristics of elementary school students. In order for SW·AI education to be organized and operated as a member of the regular curriculum, it is suggested that research based on the analysis of regular curriculum contents and in-depth analysis of SW·AI education contents is necessary.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.125-130
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• 2021

Implementation of livestock smart-farm can be done more effectively with IoT technology developing. An build of useful stock management system can be possibile if push messages of these judgement are notified on smart-phone after cattle's illness and estrus are judged using IoT technology. These judgement method of cattle's illness and estrus can be done with gathering living stock data using temperature sensor and 3 axis acceleration sensor and sending these data using IoT and internet network into server, and studying AI machine learning using these data. In this paper, to build this cattle management system based on IoT, effective system of the whole architecture is showed. Also an effective analysis and design method to develop this system software will be presented by showing user requirement analysis using object-oriented method, flowchart and screen design.

• The Korean Journal of Archival Studies
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• no.54
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• pp.211-248
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• 2017

This study examined ways to improve records management by using the new technology of the Fourth Industrial Revolution. To do this, we selected four technologies that have a huge impact on the production and management of records such as cloud, big data, artificial intelligence, and the Internet of Things. We tested these technologies and summarized their concepts, characteristics, and applications. The characteristics of the changed production records were analyzed by each technology. Because of new technology, the production of records has rapidly increased and the types of records have become diverse. With this, there is also a need for solutions to explain the quality of data and the context of production. To effectively introduce each technology into records management, a roadmap should be designed by classifying which technology should be applied immediately and which should be applied later depending on the maturity of the technology. To cope with changes in the characteristics of production records, a flexible data structure must be produced in a standardized format. Public authorities should also be able to procure Software as a Service (SaaS) products and use digital technology to improve the quality of public services.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.37-43
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• 2022

The fundamental basis of AI technology is learningable data. Recently, the types and amounts of data collected and produced by the government or private companies are increasing exponentially, however, verified data that can be used for actual machine learning has not yet led to it. This study discusses the conditions that data actually can be used for machine learning should meet, and identifies factors that degrade data quality through case studies. To this end, two representative cases of developing a prediction model using public big data was selected, and data for actual problem solving was collected from the public data portal. Through this, there is a difference from the results of applying valid data screening criteria and post-processing. The ultimate purpose of this study is to argue the importance of data quality management that must be most fundamentally preceded before the development of machine learning technology, which is the core of artificial intelligence, and accumulating valid data.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Smart Media Journal
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• v.12 no.2
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• pp.42-55
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• 2023

The combination of blockchain (BC), artificial Intelligence (AI), quantum computing (QC), and the Internet of Things (IoT) can potentially transform various industries and domains, including healthcare, logistics, and finance. In this paper, we look at the trends and developments in integrating these emerging technologies and the potential benefits and challenges that come with them. We present a conceptual framework for integrating BC, AI, QC, and IoT and discuss the framework's key characteristics and challenges. We also look at the most recent cutting-edge research and developments in integrating these technologies, as well as the key challenges and opportunities that come with them. Our analysis highlights the potential benefits of integrating the technologies and looks to increased security, privacy, and efficiency to provide insights into the future of these technologies.