• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.536-555
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• 2021

Sea-surface wind is an important variable in ocean-atmosphere interactions, leading to the changes in ocean surface currents and circulation, mixed layers, and heat flux. With the development of satellite technology, sea-surface winds data retrieved from scatterometer observation data have been used for various purposes. In a complex marine environment such as the Korean Peninsula coast, scatterometer-observed sea-surface wind is an important factor for analyzing ocean and atmospheric phenomena. Therefore, the validation results of wind accuracy can be used for diverse applications. In this study, the sea-surface winds derived from ASCAT (Advanced SCATterometer) mounted on MetOp-A/B (METeorological Operational Satellite-A/B) were validated compared to in-situ wind measurements at 16 marine buoy stations around the Korean Peninsula from January to December 2020. The buoy winds measured at a height of 4-5 m from the sea surface were converted to 10-m neutral winds using the LKB (Liu-Katsaros-Businger) model. The matchup procedure produced 5,544 and 10,051 collocation points for MetOp-A and MetOp-B, respectively. The root mean square errors (RMSE) were 1.36 and 1.28 m s^-1, and bias errors amounted to 0.44 and 0.65 m s^-1 for MetOp-A and MetOp-B, respectively. The wind directions of both scatterometers exhibited negative biases of -8.03° and -6.97° and RMSE values of 32.46° and 36.06° for MetOp-A and MetOp-B, respectively. These errors were likely associated with the stratification and dynamics of the marine-atmospheric boundary layer. In the seas around the Korean Peninsula, the sea-surface winds of the ASCAT tended to be more overestimated than the in-situ wind speeds, particularly at weak wind speeds. In addition, the closer the distance from the coast, the more the amplification of error. The present results could contribute to the development of a prediction model as improved input data and the understanding of air-sea interaction and impact of typhoons in the coastal regions around the Korean Peninsula.

• 한국노년학
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• v.38 no.3
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• pp.645-666
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• 2018

The purpose of this study is to investigate the characteristics of the elderly with disability focused on comparison between aging with disability and disability with aging in Korea. Disability in older age has been related to several psychosocial characteristics, including social support, self-esteem, life satisfaction and depression. However, the exact role of these characteristics in the disablement process remains uncertain and this study focused on comparison between aging with disability and disability with aging in Korea. This study analyzed the 12th wave (2017) KWPS(Korean Welfare Panel Study)and Disability Study which included 692 elderly with disability aged 65 and over. The data were processed by SEM and multi-group SEM analysis. The findings were as follows; First, family support and the significant others support showed direct effects on the life satisfaction of the elderly with disability. Second, family support and the significant others support reduced the level of depression and enhanced self-esteem and finally impact on the life satisfaction of the elderly with disability. The formal support enhanced the depression and reduced self-esteem and eventually reduced the life satisfaction. Third, the disability with aging group showed more higher perception and more experience of formal support and formal service and more higher depression than the aging with disability group. Forth, the significant others support on life satisfaction only showed significance in disability with aging group and depression had significance in disability with aging group. Finally, aging with disability group showed positive effects on the formal support of life satisfaction but showed negative effects on the depression and self-esteem. Based on these findings, practical implications of future directions for research are discussed.

• Journal of Climate Change Research
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• v.2 no.4
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• pp.317-331
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• 2011

The CERES-Barley crop simulation model of DSSAT package was used to assess the impacts of climate change on potential yield of winter covered barley in Korea. 56 sites over the southern part of Korean peninsula were selected to compare the climate change impacts in various climatic conditions. The climatological normals (1971~2000) and the three future climatological normals (2011~2040, 2041~2070, and 2071~2100), based on A1B climate change scenarios of Korea, were used in this study, and the three future climatological normals were simulated under three environmental conditions, where only temperature change, only carbon dioxide change, and both of temperature and carbon dioxide change with future A1B climate change scenarios, respectively. Results: The CERES-Barley model was suitable for predicting climate change impacts on the potential yield of winter covered barley, because of the agreement between observed and simulated outcomes (e.g., the coefficient of determination of grain yield equals 0.84). (1) The only increased temperature effect with the climate change scenarios was mostly negative to the potential yield of winter covered barley and its magnitude ranges from -21% to +1% for the three future normals. (2) The effect of the only elevated carbon dioxide on the potential yield of winter covered barley was positive and its magnitude ranged from 12% to 43% for the three future normals. (3) For increased temperature and elevated carbon dioxide change cases, potential yields increased by 13%, 21%, 19% increase for the 2011~2040, 2041~2070, 2071~2100 normals, respectively.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.127-137
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• 2014

The main purpose of this study is to estimate tradial growth response and to predict the potential spatial distribution of major tree species(Pinus densiflora, Quercus mongolica, Quercus spp., Castanea crenata and Larix kaempferi) in South Korea, considering climate and topographic factors. To estimate radial growth response, $5^{th}$ National Forest Inventory data, Topographic Wetness Index (TWI) and climatic data such as temperature and precipitation were used. Also, to predict the potential spatial distribution of major tree species, RCP 8.5 Scenario was applied. By our analysis, it was found that the rising temperature would have negative impacts on radial growth of Pinus densiflora, Castanea crenata and Larix kaempferi, and positive impacts on that of Quercus mongolica, Quercus spp.. Incremental precipitation would have positive effects on radial growth of Pinus densiflora and Quercus mongolica. When radial growth response considered by RCP 8.5 scenario, it was found that the radial growth of Pinus densiflora, Castanea crenata and Larix kaempferi would be more vulnerable than that of Quercus mongolica and Quercus spp. to temperature. According to the climate change scenario, Quercus spp. including Quercus mongolica would be expected to have greater abundance than its present status in South Korea. The result of this study would be helpful for understanding the impact of climatic factors on tree growth and for predicting the distribution of major tree species by climate change in South Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.157-167
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• 2019

This study empirically analyzed the effects of the use of a single human resource development system in the enterprise on corporate performance using the Human Capital Enterprise Panel (HCCP) data. The results of the hierarchical regression analysis on the sales per log of job analysis use, The use of job analysis confirms that $R^2=.294$ and ${\beta}=.165$ can have a positive effect on sales per log, and Hypothesis 1 is supported. The participation in the work parallelism participation was negatively influenced by the sales per log in $R^2=.283$ and ${\beta}=-.129$, and Hypothesis 2 was rejected. This is attributed to the lack of data of 66, and it was judged that there were 45 new companies entering the company. In addition, we conducted a hierarchical regression analysis that confirms the moderating effect of the training and training obligation by using interaction variables of job analysis use and education and training obligation. It was confirmed that the use of job analysis could have a negative impact on the sales per log, and Hypothesis 3 was rejected. As the labor productivity increases, firms have supported the previous study that productivity effect is not significant because they do not want to invest in education and training. In addition, it was confirmed that the participation of the training system in the job training system could strengthen the positive sales (+). Therefore, Hypothesis 4 was supported. In order to reflect the effective aspects of job analysis, the voluntary activation of enterprises should be premised. In addition, if employing talented people with diverse backgrounds such as academic backgrounds, gender, religion, nationality, etc. and investing in human resources development through education and training focused on job analysis, recruitment of learning workers in parallel with work- It will be possible to contribute to the creation of performance.

• Korean Journal of Environment and Ecology
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• v.33 no.3
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• pp.354-365
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• 2019

This study measured and analyzed the weather characteristics and the air-ion characteristics of walking space by land use type in Chungju, Chungcheongbuk Province during the heat wave. We used the land registration map to classify the type of land use in walking areas in the studied into the production and green area, the residential area, and the commercial area. We then selected 44 measurement points in about 4.1 km. They included 12 walking space points in the green area, 14 in the residential area, and 18 in the commercial area. Moreover, we calculated the ion index by analyzing the impact of weather factors such as temperature, relative humidity, solar radiation, and net radiation in the walking space on the anion generation and cation generation by land use type during the heat wave. Comparison of air ion characteristics in walking space by type of land use during the heat wave showed that the average cation generation was in the order of commercial area ($700.73cations/cm^3$) > residential area ($600.76cations/cm^3$) > green area ($589.73cations/cm^3$). The average anion generation was in the order of green area ($663.95anions/cm^3$) > residential area ($628.48anions/cm^3$) > commercial area ($527.48anions/cm^3$). The average ion index was in the order of green area (1.13) > residential area (1.04) > commercial area (0.75). This study checked the weather characteristics, cation generation, and anion generation in walking space according to the land use type during the heat wave and checked the difference of ion indexes in the walking space according to the land use type. However, there were limitations in the lack of accurate comparison according to the land use due to the moving measurement and the insufficient quantitative comparison according to the change of road width. Therefore, we recommend further studies that consider the road characteristics.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.4
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• pp.510-520
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• 2021

To understand the status of organic matter and heavy metal pollution in surface sediment of a fish farming area, we have measured the concentrations of total organic carbon (TOC), total nitrogen (TN), and heavy metals (As, Cd, Cr, Cu, Fe, Hg, Mn, Pb, and Zn) in surface sediments of a fish farming area near Tongyoung-Geoje coast. The mean concentrations of TOC and TN were 22.7 mg/g and 3.4 mg/g, respectively, and were much higher than those in surface sediments of a semi-enclosed bay in the southern coast of Korea. The mean concentrations of As, Cd, Cr, Cu, Fe, Hg, Mn, Pb, and Zn were 10.5 mg/kg, 0.37 mg/kg, 82.9 mg/kg, 127 mg/kg, 4.19%, 0.041 mg/kg, 596 mg/kg, 39.5 mg/kg, and 175 mg/kg, respectively, and the mean concentrations of Cd and Cu were three times higher than those in surface sediments of shellfish farming area in the southeastern coast of Korea. In addition, the concentrations of TOC and corrected Cu exceeded the values of sediment quality guidelines applied in Korea, and pollution load index (PLI) and ecological risk index (ERI) showed that the metal concentrations in the sediments of some fish farming area have a strongly negative ecological impact on benthic organisms, although most metal concentrations did not exceed the sediment quality guidelines. Based on overall assessment results, the surface sediments of fish farming areas in the study region are polluted with organic matter and some heavy metals. Thus, a comprehensive management plan is necessary to improve the sedimentary environments, identify primary contamination sources, and reduce the input of pollution load for organic matter and heavy metals in the sediments of fish farming areas.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.2
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• pp.41-63
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• 2022

The purpose of this study is to secure objective and precise data through ecosystem monitoring, to reveal ecological characteristics through comparison and analysis with past survey data, and to accumulate basic data for diagnosing the current situation and predicting changes in the ecosystem. The target site is the 'Quercus mongolica forest on the Buksa-myeon of Namsan', which was designated as an Ecological Landscape Conservation Area (ELCA) of Seoul in July 2006. The research contents are analysis of soil environment change (1986~2016), change of actual vegetation (1978~2016), and change of plant community structure (1994~2016). A total of 8 fixed surveys (400~1,200m²) were established in 1994 and 2000. Analysis items are importance value, species and population, and Shannon's species diversity. The soil environment of Namsan is acidic (pH 4.40 in 2016), which is expected to have a negative impact on tree growth and vegetation structure due to its low capacity for exchangeable cations. Quercus mongolica forest in Namsan is mainly distributed on the northern slopes. The actual vegetation area changed from 49.4% in 1978 → 80.7% in 1986 → 82.4% in 2000 → 88.3% in 2005 → 88.3% in 2009 → 70.3% in 2016. In 2016, the forest decreased by 18% compared to 2009. While there was increased growth of Quercus mongolica in the tree layer from 2009 to 2016, the overall decline in vegetation area was due to logging and fumigation management following the spread of oak wilt in 2012. As for the changes in the plant community structure, Quercus mongolica of the tree layer was damaged by oak wilt, and the potential vegetation that can form the next generation was ambiguous. In the subtree layer, the force of urbanization tree species such as Styrax japonicus, Sorbus alnifolia, and Acer palmatum. was maintained or increased. In the shrub layer, the number of trees and species increased significantly due to the open tree crown, and accordingly, the species diversity of Shannon for woody plants also increased. In Quercus mongolica forest of Namsan, various ecological changes are occurring due to the effects of urban environments such as air pollution and acid rain, the limitation of Quercus mongolica pure forest due to oak wilt, and the introduction of exotic species, thus, it is necessary to establish a management plan through continuous monitoring.