• Journal of Radiation Protection and Research
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• v.47 no.2
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• pp.77-85
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• 2022

Background: After the Fukushima Daiichi Nuclear Power Station (FDNPS) accident, a model was developed to estimate the external exposure doses for residents who were expected to return to their homes after evacuation orders were lifted. However, the model's accuracy and uncertainties in parameters used to estimate external doses have not been evaluated. Materials and Methods: The model estimates effective doses based on the integrated ambient dose equivalent (H^*(10)) and life patterns, considering a dose reduction factor to estimate the indoor H^*(10) and a conversion factor from H^*(10) to the effective dose. Because personal dose equivalent (H_p(10)) has been reported to agree well with the effective dose after the FDNPS accident, this study validates the model's accuracy by comparing the estimated effective doses with H_p(10). The H_p(10) and life pattern data were collected for 36 adult participants who lived or worked near the FDNPS in 2019. Results and Discussion: The estimated effective doses correlated significantly with H_p(10); however, the estimated effective doses were lower than H_p(10) for indoor sites. A comparison with the measured indoor H^*(10) showed that the estimated indoor H^*(10) was not underestimated. However, the H_p(10) to H^*(10) ratio indoors, which corresponds to the practical conversion factor from H^*(10) to the effective dose, was significantly larger than the same ratio outdoors, meaning that the conversion factor of 0.6 is not appropriate for indoors due to the changes in irradiation geometry and gamma spectra. This could have led to a lower effective dose than H_p(10). Conclusion: The estimated effective doses correlated significantly with H_p(10), demonstrating the model's applicability for effective dose estimation. However, the lower value of the effective dose indoors could be because the conversion factor did not reflect the actual environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.6
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• pp.843-852
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• 2022

Despite continuous safety management, the death rate of construction workers is not decreasing every year. Accordingly, various studies are in progress to prevent construction site accidents. In this paper, we developed an AI-based priority inspection target selection model that preferentially selects sites are expected to cause construction accidents among construction sites with construction costs of less than 5 billion won (KRW). In particular, Random Forest (90.48 % of accident prediction AUC-ROC) showed the best performance among applied AI algorithms (Classification analysis). The main factors causing construction accidents were construction costs, total number of construction days and the number of construction performance evaluations. In this study an ROI (return of investment) of about 917.7 % can be predicted over 8 years as a result of better efficiency of manual inspections human resource and a preemptive response to construction accidents.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.51-58
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• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.

• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.433-438
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• 2022

A carbon cycle model based method is proposed in order to evaluate the effectiveness of various policies and projects to achieve carbon neutrality. The proposed model was validated by properly reproducing the increase in the concentration of carbon dioxide in the atmosphere and the rise of the global average temperature from the data of anthropogenic carbon emissions and deforestation since the industrial revolution. As a case study, a carbon cycle impact assessment was performed for deforestation, reforestation, and afforestation. It was verified that the increase of carbon dioxide in the atmosphere is attributed not only to fossil fuel usage, but also to deforestation, and that even if deforestation is immediately followed by reforestation, it takes very long to return to the initial concentration. The proposed method is expected to be eventually applicable to simulation of potential climate control in the future, contributing to safety verification of various climate engineering techniques.

• Journal of Technology Innovation
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• v.31 no.1
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• pp.175-213
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• 2023

As the importance of industry-university cooperation continues to increase in a knowledge-based society, the government is implementing various projects related to industry-university cooperation. However, despite the government's support, it has not achieved satisfactory results, and the need for empirical performance analysis to diffuse the results of industry-university cooperation is increasing. In this study, DEA was used to analyze the Industry-University cooperation performance of universities participating in LINC+ program. Efficiency analysis was performed using the CCR model and the BCC model, and the return to scale and causes of inefficiency were analyzed through the scale efficiency analysis. As a result of the analysis, it was found that there were differences in LINC+ performance depending on the region where the university is located and that each university had different goals for inefficiency improvement. The results of this study will contribute to improving the university's operational efficiency and strengthening competitiveness, and are expected to be utilized in the establishment of follow-up program plans for LINC+.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.39-48
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• 2023

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

• Smart Media Journal
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• v.11 no.1
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• pp.38-45
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• 2022

A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• Journal of Korea Trade
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• v.27 no.1
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• pp.1-18
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• 2023

Purpose - Using a sample of Korean multinational corporations, we examine whether the relationship between tax risk and the implied cost of capital discriminates between the environmental, social, and corporate governance (ESG) of highly rated firms. Design/methodology - Firms with high tax risks have an increased uncertainty of future cash flows. Therefore, as the volatility of future cash flow increases, information asymmetry and the required return increases. Highly rated ESG firms can reduce information asymmetry, thereby weakening the positive relationship between tax risk and cost of capital. We employ the standard deviation of the cash effective tax rate as proxy of tax risk. We utilize the ESG rating data of the Korea Corporate Governance Service (KCGS). We use a PEG model, MPEG model, and GM model to measure the implied cost of capital. Findings - We find a positive association between the implied cost of capital and tax risk. The positive relationship between tax risk and the implied cost of capital weakens in highly rated ESG firms. Highly rated ESG firms prefer a stable tax position to invest after-tax cash flows into sustainable management. Therefore, the negative effects of tax risk on cost of capital can be reduced. Originality/value - This study provides empirical evidence that ESG activities can mitigate the negative impact of tax risk on the cost of capital for Korean multinational corporations. In a business environment where ESG activities are more important, the empirical results that ESG activities can reduce the corporate risk of Korean FDI companies are expected to provide implications for the ESG activities of multinational corporations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.513-522
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• 2010

Underground construction for traffic networks, complexes, and storage facilities has risen as an effective land use plan for dealing with emerging problems such as overcrowded urban cities and traffic jams. This paper performed an economic feasibility analysis of the development of the integrated field management system which provides field workers and managers with 3D-based location tracking and clear communication during underground construction works. To conduct the analysis, processes and problems of field management for underground construction were analyzed and deduction in accidents and field management costs and productivity improvement were estimated as expected benefits. Based on computed benefits and costs, an economic analysis was conducted using Benefit/Cost ratio(B/C), Net Present Value(NPV), and Internal Rate of Return(IRR) and then sensitivity analysis was performed to cope with the uncertainty of assumed variables.