• Journal of Korea Port Economic Association
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• v.38 no.1
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• pp.31-47
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• 2022

While the ship finance industry has long been struggling with diminishing involvements from the private sector, government-run banks have consistently increased their presence in maritime finance. To address such concerns, this research conceptually explores the creation of blockchain technology-driven security token offering (STO) platforms. To suggest a sound platform model, this piece first examines key design principles. Based on the integral perspective on the digital platform, this paper exhibits three core design principles to create a virtuous platform ecosystem, then sets out STO platform design guidelines. This paper further explores an STO platform model by considering conventional ship finance systems and practices in Korea. The STO platform has three main effects; 1) the wider availability of STOs can enlarge both the scope and size of ship finance users, 2) the activation of security token transactions leads to an increase in participation, and 3) possibilities to create complementary innovative financial services can further encourage the participation of private investors. The STO ecosystem may contribute to the shipping, shipbuilding, and ship finance industries by enhancing its attractiveness to the general public and by creating positive externalities for Busan as a maritime finance center.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Korea Journal of Hospital Management
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• v.27 no.2
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• pp.16-33
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• 2022

Purposes: Prompt evaluation of routes and contact tracing are very important for epidemiological investigations of coronavirus disease 2019 (COVID-19). To ensure better adoption of contact tracing apps, it is necessary to understand users' expectations, preferences, and concerns. This study aimed to identify main reasons why people use the apps, appropriate services, and basis for voluntary app services that can improve app participation rates and data sharing. Methodology/Approach: This study conducted an online survey from November 11 to December 6, 2020, and received a total of 1,048 survey responses. This study analyzed the questionnaire survey findings of 883 respondents in areas with many confirmed cases of COVID-19. This study used a multiple regression analysis. Findings: Respondents who had experience of using related apps showed a high intention to use contact-tracing apps. Participants wished for the contact tracking apps to be provided by the government or public health centers (74%) and preferred free apps (93.88%). The factors affecting the participants' intention to use these apps were their preventive value, performance expectancy, perceived risk, facilitative ability, and effort expectancy. The results highlighted the need to ensure voluntary participation to address participants' concerns regarding privacy protection and personal information exposure. Practical Implications: The results can be used to accurately identify user needs and appropriate services and thereby improve the development of contact tracking apps. The findings provide the basis for voluntary app that can enhance app participation rates and data sharing. The results will also serve as the basis for developing trusted apps that can facilitate epidemiological investigations.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Korean Association of Arts Management
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• no.53
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• pp.153-177
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• 2020

Arts organizations commonly face a range of operational challenges, from a lack of skilled workers to limited financial resources and thus are dependent on subsidies from the government. Yet, to fully realize their mission arts organizations must both develop strategies to effectively utilize government support and seek a way forward that does not depend on public subsidies. Business diversification, a strategy from corporate management, entails the expansion of products and services, and entry into new industries, enabling companies to disperse risks and increase profits. We propose that business diversification can be effectively applied to arts organization to address the myriad operational difficulties they face. To understand how an arts organization might deploy business diversification we conducted a case study of an organization that is actively pursuing the strategy: Sanwoollim Theater. We interviewed staff members of Sanwoollim including the executive director, as well as selected audiences, to understand how the business diversification model was being applied at Sanwoollim. Our findings indicate that, in a complex arts and cultural space, business diversification is a fresh and flexible new strategy that can enable private cultural arts organizations to thrive sustainably. It is also evident that government support in the initial stages of the process encourages diversification and that successful private arts organizations will leverage government subsidies into a sustainable business plan.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.141-166
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• 2023

This paper investigates synthetic data generation methods and their evaluation measures. There have been increasing demands for releasing various types of data to the public for different purposes. At the same time, there are also unavoidable concerns about leaking critical or sensitive information. Many synthetic data generation methods have been proposed over the years in order to address these concerns and implemented in some countries, including Korea. The current study aims to introduce and compare three representative synthetic data generation approaches: Sequential regression, nonparametric Bayesian multiple imputations, and deep generative models. Several evaluation metrics that measure the utility and disclosure risk of synthetic data are also reviewed. We provide empirical comparisons of the three synthetic data generation approaches with respect to various evaluation measures. The findings of this work will help practitioners to have a better understanding of the advantages and disadvantages of those synthetic data methods.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.401-418
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• 2023

Recently, the socio-economic interest in Large Language Models (LLMs) has been growing due to the emergence of ChatGPT. As a type of generative AI, LLMs have reached the level of script creation. In this regard, it is important to address the issue of discrimination (sexism, racism, religious discrimination, ageism, etc.) in the performing arts in general or in specific performing arts works or organizations in a large language model that will be widely used by the general public and professionals. However, there has not yet been a full-scale investigation and discussion on the issue of discrimination in the performing arts in large-scale language models. Therefore, the purpose of this study is to textually analyze the perceptions of discrimination issues in the performing arts from LMMs and to derive implications for the performing arts field and the development of LMMs. First, BBQ (Bias Benchmark for QA) questions and measures for nine discrimination issues were used to measure the sensitivity to discrimination of the giant language models, and the answers derived from the representative giant language models were verified by performing arts experts to see if there were any parts of the giant language models' misperceptions, and then the giant language models' perceptions of the ethics of discriminatory views in the performing arts field were analyzed through the content analysis method. As a result of the analysis, implications for the performing arts field and points to be noted in the development of large-scale linguistic models were derived and discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.133-142
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• 2023

The office environment is changing according to work types, Information Technology (IT) advancements, and the Coronavirus disease (COVID)-19 situation. In order for office space users to perform their tasks comfortably and efficiently, it is necessary to secure individual privacy as well as easy communication among members. In Korea, the demand for improving the acoustic performance of office spaces is also increasing, but the related performance criteria and guidelines have not been established. In this study, standardization of office space acoustic performance measurement and evaluation methods and European countries' acoustic performance criteria were compared and reviewed. It is proposed to comprehensively review international standardization trends and acoustic performance standards in each country and to establish and utilize criteria for evaluating the acoustic performance and satisfaction of office spaces in Korea through our survey. Considering the international standardization direction and compatibility with communication and Public Address (PA) systems, it is appropriate to establish criteria using the speech transmission index or Speech Transmission Index (STI) application index. This criterion will be highly utilizable and compatible. In addition, since the office furniture industry is interested in improving the acoustic performance of office space, it is necessary to establish a labelling system for speech level reduction of office furniture.

• Journal of Technology Innovation
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• v.30 no.1
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• pp.57-104
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• 2022

The government has supported the innovation of private firms by intervening the market for various purposes, such as preventing market failure, alleviating information asymmetry, and allocating resources efficiently. Although the government's R&D budget increased rapidly in the 2000s, it is not clear whether the government intervention has made desirable impact on the market. To address this, the current study attempts to explore this issue by doing a systematic literature review on foreign and domestic papers in an integrated way. In total, 168 studies are analyzed using contents analysis approach and various lens, such as policy additionality, policy tools, firm size, unit of analysis, data and method, are adopted for analysis. Overlapping policy target, time lag between government intervention and policy effects, non-linearity of financial supports, interference between different polices, and out-dated R&D tax incentive system are reported as factors hampering the effect of the government intervention. Many policy prescriptions, such as program evaluation indices reflecting behavioral additionality, an introduction of policy mix and evidence-based policy using machine learning, are suggested to improve these hurdles.