• Journal of Digital Convergence
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• v.17 no.11
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• pp.1-7
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• 2019

This study analyzed the effects of labor aging on labor productivity using panel statistics of 16 local governments from 1995 to 2017. The aging of the labor force, defined as the proportion of workers aged 60 or older in total employment, in the results of the panel regression analysis considering regional fixed effects and various adjustment variables, has a very consistent and significant negative effect on labor productivity. For every 1% increase in aging, labor productivity decreases by about 0.14 ~ 0.20%. In addition, the per capita capital stock and human capital considered as adjustment variables contributed to the increase of labor productivity, and the unemployment rate, which is a proxy variable of the economic fluctuation, has a significant negative effect on labor productivity as expected. The coefficient of the industrial structure, which represents the share of the service industry in the whole industry, was positive, but is not significant. The results of this study suggest that the design and construction of economic and educational policies that can maintain and expand human capital are necessary to curb the reduction in labor productivity expected by the aging workforce.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.4
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• pp.397-410
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• 2018

When considering the long-term safety assessment of spent-nuclear fuel management, americium is one of the most radio-toxic actinides. Although spectroscopic methods are widely used for the study of actinide chemistry, application of those methods to americium chemistry has been limited. Herein, we purified $^{241}Am$ to obtain a highly pure stock solution required for spectroscopic studies. Quantitative and qualitative analyses of purified $^{241}Am$ were carried out using liquid scintillation counting, and gamma and alpha radiation spectrometry. Highly sensitive absorption spectrometry coupled with a liquid waveguide capillary cell and time-resolved laser fluorescence spectroscopy were employed for the study of Am(III) hydrolysis and oxalate (Ox) complexation. $Am^{3+}$ ions under acidic conditions exhibit maximum absorbance at 503 nm, with a molar absorption coefficient of $424{\pm}8cm^{-1}{\cdot}M^{-1}$. $Am(OH)_3(s)$ colloidal particles formed under near neutral pH conditions were identified by monitoring the absorbance at around 506-507 nm. The formation of ${Am(Ox)_3}^{3-}$ was detected by red-shifts of the absorption and luminescence spectra of 4 and 5 nm, respectively. In addition, considerable enhancements of the luminescence intensities were observed. The luminescence lifetime of ${Am(Ox)_3}^{3-}$ increased from 23 to 56 ns, which indicates that approximately six water molecules are replaced by carboxylate ligands in the inner-sphere of the Am(III). These results suggest that ${Am(Ox)_3}^{3-}$ is formed through the bidentate coordination of the oxalate ligands.

• Journal of Industrial Convergence
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• v.19 no.5
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• pp.21-34
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• 2021

The purpose of this study is to find out what the investment efficiency of BW is from an investor's point of view and to suggest an efficient investment plan to investors. The research method is to investigate the coupon interest rate, maturity interest rate, issuance date, right exercise start and end date, maturity date, exercise price, etc. for BW issued from 2014 to July 2021. By connecting them, it was attempted to quantitatively understand the efficiency of investment in BW and the effect of new stock acquisitions. As a result of the study, the ratio of the number of days in excess of the exercise price was 41.3% of the available days for new stocks, so it was analyzed that the investment efficiency of bonds with warrants was not high. The return on the exercise start date was 24.8% on average and the return on the end date was 52.6% on average, showing a positive return on average, so it was derived in line with investor expectations. The number of stocks with negative returns on the exercise start date was 1.47 times higher than the number of stocks with positive returns, and the number of stocks with negative returns on the end date was 1.16 times higher than the number of positive stocks.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.1-12
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• 2019

It is important to measure the height of trees as an essential element for assessing the forest health in urban areas. Therefore, an automated method that can measure the height of individual tree as a three-dimensional forest information is needed in an extensive and dense forest. Since airborne LiDAR dataset is easy to analyze the tree height(z-coordinate) of forests, studies on individual tree height measurement could be performed as an assessment forest health. Especially in urban forests, that adversely affected by habitat fragmentation and isolation. So this study was analyzed to measure the height of individual trees for assessing the urban forests health, Furthermore to identify environmental factors that affect forest growth. The survey was conducted in the Mt. Bongseo located in Seobuk-gu. Cheonan-si(Middle Chungcheong Province). We segment the individual trees on coniferous by automatic method using the airborne LiDAR dataset of the two periods (year of 2016 and 2017) and to find out individual tree growth. Segmentation of individual trees was performed by using the watershed algorithm and the local maximum, and the tree growth was determined by the difference of the tree height according to the two periods. After we clarify the relationship between the environmental factors affecting the tree growth. The tree growth of Mt. Bongseo was about 20cm for a year, and it was analyzed to be lower than 23.9cm/year of the growth of the dominant species, Pinus rigida. This may have an adverse effect on the growth of isolated urban forests. It also determined different trees growth according to age, diameter and density class in the stock map, effective soil depth and drainage grade in the soil map. There was a statistically significant positive correlation between the distance to the road and the solar radiation as an environmental factor affecting the tree growth. Since there is less correlation, it is necessary to determine other influencing factors affecting tree growth in urban forests besides anthropogenic influences. This study is the first data for the analysis of segmentation and the growth of the individual tree, and it can be used as a scientific data of the urban forest health assessment and management.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.454-460
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• 2020

The goal of this study was to classify the area of Larix kaempferi forest available for timber production, considering the legal, physical, environmental, and technical constraints, and to optimize the forest resources and the potential of timber production in South Korea for 50 years. The results showed that the area of Larix kaempferi forest available for timber production is limited to 124,000 ha, 48% of the total area. Without further efforts in resource management, sustainable timber production of Larix kaempferi cannot be guaranteed even at the current level of timber supply. To enhance the potential of timber production for this species, two alternatives were considered: a 705-km annual increment of forest roads (alternative 1) and a 1,000-kmannual increment of forest roads (alternative 2) to enlarge forest areas available for timber production. The results showed that alternative 1 is feasiblefor sustainable timber production both at the current level and with a 5% increment of timber supply. However, alternative 1 is not sufficient in the case of a 10% increment of timber supply with a low growing stock, whereas alternative 2 is sufficient. Therefore, an increase in forests roads as well as regeneration efforts are required to improve the timber production potential of Larix kaempferi in South Korea.

• Asia-Pacific Journal of Business
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• v.12 no.1
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• pp.135-149
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• 2021

Purpose - I analyzes risk-to-performance evaluated in the market using data from sale and lease back. Specifically, I analyze from the perspective of financial institutions that purchase sale and lease back based on the cases of investment by ship investment companies and acquisition of ships. Design/methodology/approach - I use 49 sale and lease back data from 2017 to 2019 for empirical analysis. Findings - The main results of this paper are as follows. First, after sale and lease back of domestic ships, the average amount of sales by the leased shipping company is 25.1 billion won, the average amount of investment by the purchased financial institution is 14.6 billion won (60%) and the average length of the ship is nine years. In ship finance, sale and lease back is deemed to be appropriately used as a means of restructuring for a large amount of money. Second, the main risk factor for sale and lease back of domestic ships is credit risk and can be measured in VaR in practice. As a result of the empirical analysis, the average credit risk burden ratio is 9%. As a major risk factor, low creditworthiness of restructuring companies is the key. Third, as a result of measuring the profitability of financial institutions that purchase sale and lease back of domestic ships at a net current price, it has an average value of 300 million won, but the deviation by case is very large. Fourth, the risk adjusted performance of sale and lease back of domestic ships is 0.54 on average compared to the total risk capital, and 0.52 compared to the stock-risk capital, and as with profitability earlier, the deviation of each case is very large and misaligned. In order to boost the sale and lease back market for large and long-term assets, in order to overcome low profitability as a prerequisite for future participation of commercial purchased financial institutions, it is expected that purchase decisions based on expectations versus risk will be necessary. Research implications or Originality - The results of this paper are expected to broaden the understanding of sale and lease back and foster the ability to assess long-term risk and performance. Based on this, it is believed that rapid restructuring of companies through sale and lease back of large amounts of long-term assets will greatly increase the utility of the domestic financial market.

• Korean small business review
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• v.42 no.2
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• pp.1-22
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• 2020

This paper examines the effect of the adoption of international financial reporting standards(IFRS) on the financial reporting of SMEs. As IFRS is principle-based, management's discretion is needed to reflect the economic substance of transactions, and a sound internal accounting infrastructure is needed to support the judgment process. In the case of SMEs, the internal accounting infrastructure is not well established, which makes it difficult to apply principle-based accounting. The survey analysis of 132 small and medium-sized business accounting managers listed in the domestic stock market showed that the reliability of financial statements has increased due to the introduction of IFRS. In particular, SMEs perceived their financial statements as being more reliable after the adoption of IFRS than midsize companies. However, it was found that the costs and risks from the preparation of financial statements have increased significantly, and conflicts between auditors and supervisory authorities related to the application of the principles have increased. In particular, midsize companies felt the increase in conflict with auditors and supervisory authorities bigger than small companies. As for the practical difficulties in applying IFRS, both small and medium-sized companies have difficulty in interpreting the standards and lacked guidelines. In order to resolve these difficulties, it is necessary to enhance the function of Q&A by the Korea Accounting standard board(KASB) or Financial Supervisory Service(FSS). In conclusion, the reliability of the financial statements of SMEs has improved with the introduction of IFRS. However, we believe that policy and institutional support is needed in order to have better financial reporting for SMEs.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.209-235
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• 2022

Starting from 2010, blockchain technology, along with the development of artificial intelligence, has been in the spotlight as the latest technology to lead the 4th industrial revolution. Furthermore, previous research regarding blockchain's technological applications has been ongoing ever since. However, few studies have been examined the standards for classifying the blockchain economic ecosystem from a capital market perspective. Our study is classified into a collection of interviews of software developers, entrepreneurs, market participants and experts who use blockchain technology to utilize the blockchain economic ecosystem from a capital market perspective for investing in stocks, and case study methodologies of blockchain economic ecosystem according to application fields of blockchain technology. Additionally, as a way that can be used in connection with equity investment in the capital market, the blockchain economic ecosystem classification methodology was established to form an investment universe consisting of global blue-chip stocks. It also helped construct an intelligent portfolio through quantitative and qualitative analysis that are based on quant and artificial intelligence strategies and evaluate its performances. Lastly, it presented a successful investment strategy according to the growth of blockchain economic ecosystem. This study not only classifies and analyzes blockchain standardization as a blockchain economic ecosystem from a capital market, rather than a technical, point of view, but also constructs a portfolio that targets global blue-chip stocks while also developing strategies to achieve superior performances. This study provides insights that are fused with global equity investment from the perspectives of investment theory and the economy. Therefore, it has practical implications that can contribute to the development of capital markets.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.143-159
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• 2017

Due to the rapid development of IT in recent years, not only personal information but also the key technologies and information leakage that companies have are becoming important issues. For the enterprise, the core technology that the company possesses is a very important part for the survival of the enterprise and for the continuous competitive advantage. Recently, there have been many cases of technical infringement. Technology leaks not only cause tremendous financial losses such as falling stock prices for companies, but they also have a negative impact on corporate reputation and delays in corporate development. In the case of SMEs, where core technology is an important part of the enterprise, compared to large corporations, the preparation for technological leakage can be seen as an indispensable factor in the existence of the enterprise. As the necessity and importance of Information Security Management (ISM) is emerging, it is necessary to check and prepare for the threat of technology infringement early in the enterprise. Nevertheless, previous studies have shown that the majority of policy alternatives are represented by about 90%. As a research method, literature analysis accounted for 76% and empirical and statistical analysis accounted for a relatively low rate of 16%. For this reason, it is necessary to study the management model and prediction model to prevent leakage of technology to meet the characteristics of SMEs. In this study, before analyzing the empirical analysis, we divided the technical characteristics from the technology value perspective and the organizational factor from the technology control point based on many previous researches related to the factors affecting the technology leakage. A total of 12 related variables were selected for the two factors, and the analysis was performed with these variables. In this study, we use three - year data of "Small and Medium Enterprise Technical Statistics Survey" conducted by the Small and Medium Business Administration. Analysis data includes 30 industries based on KSIC-based 2-digit classification, and the number of companies affected by technology leakage is 415 over 3 years. Through this data, we conducted a randomized sampling in the same industry based on the KSIC in the same year, and compared with the companies (n = 415) and the unaffected firms (n = 415) 1:1 Corresponding samples were prepared and analyzed. In this research, we will conduct an empirical analysis to search for factors influencing technology leakage, and propose an early warning system through data mining. Specifically, in this study, based on the questionnaire survey of SMEs conducted by the Small and Medium Business Administration (SME), we classified the factors that affect the technology leakage of SMEs into two factors(Technology Characteristics, Organization Characteristics). And we propose a model that informs the possibility of technical infringement by using Support Vector Machine(SVM) which is one of the various techniques of data mining based on the proven factors through statistical analysis. Unlike previous studies, this study focused on the cases of various industries in many years, and it can be pointed out that the artificial intelligence model was developed through this study. In addition, since the factors are derived empirically according to the actual leakage of SME technology leakage, it will be possible to suggest to policy makers which companies should be managed from the viewpoint of technology protection. Finally, it is expected that the early warning model on the possibility of technology leakage proposed in this study will provide an opportunity to prevent technology Leakage from the viewpoint of enterprise and government in advance.