• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.157-167
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• 2021

This research strives to find the effective Local Currency Model that considered regional characteristics of Innovation city. Currently, the issuance of the domestic local currency has been increased due to COVID-19 Pandemic, yet most regions operate it in the same way without consideration of the characteristics and environment for each region, which leads to a lack of motivation for local communities to continue to participate. The regional characteristics of innovative cities were analyzed in order of policy characteristics of innovation cities, population growth issues, settlement and consumption environment. The new local currency model was proposed for innovation cities based on a private blockchain system with the participation of public institutions to solve and supplement regional issues.

• Journal of KIBIM
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• v.14 no.1
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• pp.30-38
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• 2024

This study aims to propose a smart city governance logic model that can accommodate more diverse information service systems by mixing hub-and-spoke and blockchain technologies as a data management model. Specifically, the research focuses on deriving the logic of an operating system that can work across smart city planning based on the two data governance technologies. The first step of the logic is the generation and collection of information, which is first divided into information that requires information protection and information that can be shared with the public, and the information that requires privacy is blockchainized, and the shared information is integrated and aggregated in a data hub. The next step is the processing and use of the information, which can actively use the blockchain technology, but for the information that can be shared other than the protected information, the governance logic is built in parallel with the hub-and-spoke type. Next is the logic of the distribution stage, where the key is to establish a service contact point between service providers and beneficiaries. Also, This study proposes the establishment of a one-to-one data exchange relationship between information providers, information consumers, and information processors. Finally, in order to expand and promote citizen participation opportunities through a reasonable compensation system in the operation of smart cities, we developed virtual currency as a local currency and designed an open operation logic of local virtual currency that can operate in the compensation dimension of information.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.11-17
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• 2022

Currently, the income and funds of the local economy are flowing out to the metropolitan area, and talented people, the driving force for regional development, also gather in the metropolitan area, and the local economy is facing a serious crisis. Local currency is issued by local governments and is a currency with auxiliary and complementary functions that can be used only within the area concerned. In order to revitalize the local economy, as local governments have focused their attention on the introduction of local currency, studies on the issuance and use of local currency are continuously being conducted. In this study, by using big data from data materials such as portals and SNS, the consumer image of local currency issued in local governments was identified through big data analysis, and based on the research results, the issuance and operation of local currency was conducted. The purpose is to present implications for The results of this study are as follows. First, by inducing local consumption through the policy issuance of local currency, it is showing the effect of increasing the economic income of the region. Second, local governments are exerting efforts to revitalize the economy and establish a virtuous cycle system for the local economy by issuing and distributing local currency. Third, the introduction of blockchain technology shows the stable operation of local currency. With academic significance, it was possible to grasp the changed appearance and effect of local currency through big data analysis and the policy direction of local currency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.503-514
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• 2019

This paper proposes a blockchain-based urban service-operation model that can enhance usability by integrating several data-driven services operated in a city. In the proposed model, in order to encourage the participation of service users, the providers of data and values that can be consumed and utilized by each service acquire incentives, and consumers can use various services by paying the incentives. In this way, the proposed service model provides a structure in which various services can be interworked within the incentive system. The characteristics of blockchain technology can also guarantee service operation and management transparency. In addition, in this paper, by establishing and operating a prototype, the efficiency and operability of the proposed model are verified. As a result, three implemented data-driven urban management services are organically inter-compatible based on the concept of the proposed integrated incentive system. In the future, the proposed service model can be applied as an elemental technology of urban operational and management architectures based on citizen participation using local currency, and by cooperating with local economic revitalization projects of interest to many local governments. It is expected that the expansion of the blockchain technology area will also be possible through convergence with smart city services.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.55-62
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• 2018

Many domestic and international countries recently apply the blockchain technology to its related application fields. Not the enterprise-centered economy, the concept of the sharing economy which is controlled by individuals has been expanded. However, the development of the necessary system to realize the sharing economy in the agricultural sector has still been insufficient. Because the agricultural machinery rental business in the recent agricultural policies is managed by the government and the local government keeps and provides limited quantities and resources, its operation system has several problems. In case of high-priced agricultural machines, the machines can not be supplied adequately at the right time due to the limited quantities during the busy farming season. This paper proposes the sharing system that individual owners rent their agricultural machinery to growers using the blockchain technology. Thus, the proposed system provides the distributed method to solve agricultural machines of a lack of resources and also gives the secure service to all the growers.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.