• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Archives of design research
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• v.17 no.1
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• pp.89-98
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• 2004

A systemic approach to behavior on the basis of human psychology is needed for behavior-centered space design. Also, the recognition that human and environment, in all, have complementarity is needed- human and space shall be understood as a general phenomenon, supposing interaction. Design of behavior-oriented space means configuration and coordination of physical subjects as well as understanding, analysis and reflection of psychological and behavioral phenomena. It is analysis of a private individual as well as understanding of interaction between human groups, as well. In respect of space recognition, analysis not on material movement but on energy circulation and variable is important. It means that the understanding of user's behavior and psychology does not orient reasonable purpose just for convenience. That is, such understanding intends to understand behavioral patterns and psychological phenomena between space and human beyond the decomposition of structure of human and space into physical elements and the design based on standardized data. Thereby, more human-oriented space design might be implemented by the understanding of behavioral essence. Also, a user-centered design process from another viewpoint might be created, and the general amenity among man, space and environment - better environmental quality - might be produced. For this, the consciousness of human activity that is, activity system shall be ahead of it, and the approaches for design shall be implemented into a process not in predictive ideas but in semi-scientific system. On the basis of the above view, this study was attempted to investigate the orientation of design to recognize space as another life, and explore a process where it is drawn into a design language on the basis of human behavior. If the essence of space behavior and the activity system are analyzed through user observation and it is reflected upon a space design program and then developed into a formative language, a new design process on human and environment might be produced. In conclusion, the reflection of user's behavior and psychology into design, contrary to existing public space design based on physical data, can orient quality improvement of human life and ultimately be helpful to the proposition, 'humanization of space'.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.348-351
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• 2003

산소 과전압이 낮은 $MnO_2$를 촉매로 사용하여 반도체 산화물계의 산소선택성 전극을 제조하고 산화물 coating층의 미세구조와 전기화학적 특성을 분석하였다. Ti 기판에 열분해 법을 이용하여 $MnO_2$ 피막을 형성하였고, 또한 PVDF : $MnO_2$의 함량비를 1 : 1에서 1 : 40까지 정량적으로 변화시키고 DMF의 함량을 각각의 고정된 PVDF : $MnO_2$의 함량비에서 변화시켜 Pb전극에 1.5 mm/sec의 속도로 5회 dipping하여 $MnO_2$ 피막층을 형성 하였다. $450^{\circ}C$에서 1시간 열분해하여 약 $1\;{\mu}m$의 $MnO_2$ 피막층이 형성되었으나 Ti 기판과의 접착력이 약하여 피막자체에 대한 전기화학적 특성을 관찰할 수 없었다. PVDF : DMF = 4 : 96인 경우 pb 전극의 피막층이 얇기 때문에 박리현상이 일어났으며 이는 산화물 용제의 낮은 점도 때문인 것으로 판단된다. 또한 PVDF : DMF = 10 : 90의 경우는 5회 dipping 하여 약 $150\;{\mu}m$의 피막층을 형성하였다. PVDF : $MnO_2$의 함량비가 1:1에서 1:6 까지는 DMF의 함량에 무관하게 전극 특성이 나타나지 않았지만 $MnO_2$의 양이 상대적으로 증가하면 cycle이 증가하더라도 거의 일정한 전류 값을 갖고 $MnO_2$와 PVDF의 비가 20:1 이상의 조성에서는 균일한 CV 특성을 나타냈다. 이는 $MnO_2$가 효과적으로 촉매 작용을 한 것으로 판단되며 anodic polarization에 의한 산소 발생 과전압도 약 1.4V 정도로 감소되었다.동등한 MSIL 코드를 생성하도록 시스템을 컴파일러 기법을 이용하여 모듈별로 구성하였다.적용하였다.n rate compared with conventional face recognition algorithms. 아니라 실내에서도 발생하고 있었다. 정량한 8개 화합물 각각과 총 휘발성 유기화합물의 스피어만 상관계수는 벤젠을 제외하고는 모두 유의하였다. 이중 톨루엔과 크실렌은 총 휘발성 유기화합물과 좋은 상관성 (톨루엔 0.76, 크실렌, 0.87)을 나타내었다. 이 연구는 톨루엔과 크실렌이 총 휘발성 유기화합물의 좋은 지표를 사용될 있고, 톨루엔, 에틸벤젠, 크실렌 등 많은 휘발성 유기화합물의 발생원은 실외뿐 아니라 실내에도 있음을 나타내고 있다.>10)의 $[^{18}F]F_2$를 얻었다. 결론: $^{18}O(p,n)^{18}F$ 핵반응을 이용하여 친전자성 방사성동위원소 $[^{18}F]F_2$를 생산하였다. 표적 챔버는 알루미늄으로 제작하였으며 본 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임 보정 후 영상을 이용하여 비교한 경우, 결합능 변화가 선조체 영역에서 국한되어 나타나며 그 유의성이 움직임 보정 전에 비하여 낮음을 알 수 있었다. 결론: 뇌활성화 과제 수행시에 동반되는 피험자의 머리 움직임에 의하여 도파민 유리가 과대평가되었으며 이는 이 연구에서 제안한 영상정합을 이용한 움직임 보정기법에 의해서 개선되었다. 답이 없는 문제, 문제 만들기, 일반화가 가능한 문제 등으로 보고, 수학적 창의성 중 특히 확산적 사고에 초점을 맞추어 개방형 문제가 확산적 사고의 요소인 유창성, 독창성, 유연성 등에 각각 어떤 영향을 미치는지 20주의 프로그램을 개발, 진행하여 그 효과를 검증하고자

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Journal of the Korean Institute of Gas
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• v.8 no.2 s.23
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• pp.15-27
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• 2004

The purpose of this paper is to improve the performance of Polymer electrolyte fuel cell(PEMFC) by studying the channel dimension of bipolar plates using commercial CFD program 'Fluent'. Simulations are done ranging from 0.5 to 3.0mm for different size in order to find the channel size which shoves the highst hydrogen consumption. The results showed that the smaller channel width, land width, channel depth, the higher hydrogen consumption in anode. When channel width is increased, the pressure drop in channel is decreased because total channel length Is decreased, and when land width is increased, the net hydrogen consumption is decreased because hydrogen is diffused under the land width. It is also found that the influence of hydrogen consumption is larger at different channel width than it at different land width. The change of hydrogen consumption with different channel depth isn't as large as it with different channel width, but channel depth has to be small as can as it does because it has influence on the volume of bipolar plates. however the hydrogen utilization among the channel sizes more than 1.0mm which can be machined in reality is the most at channel width 1.0, land width 1.0, channel depth 0.5mm and considered as optimum channel size. The fuel cell combined with 2cm${\times}$2cm diagonal or serpentine type flow field and MEA(Membrane Electrode Assembly) is tested using 100W PEMFC test station to confirm that the channel size studied in simulation. The results showed that diagonal and serpentine flow field have similarly high OCV and current density of diagonal (low field is higher($2-40mA/m^2$) than that of serpentine flow field under 0.6 voltage, but the current density of serpentine type has higher performance($5-10mA/m^2$) than that of diagonal flow field under 0.7-0.8 voltage.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.115-133
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• 2021

This study examines the concept and related technologies of the 4th industrial revolution that has been mixed so far and examines the socio-economic changes and influences resulting from it, and the cases of responding to the 4th industrial revolution in major countries. Based on this, by deriving SWOT factors and calculating the importance of each factor for Korean venture companies to prepare for the forth industrial revolution, it was intended to help the government and policymakers in suggesting directions for establishing related policies. Furthermore, the purpose of this study was to suggest a direction for securing global competitiveness to Korean venture entrepreneurs and to help with basic and systematic analysis for further academic in-depth research. For this study, a total of 21 items derived through extensive literature research and data research to understand what are the necessary competency factors for internal and external environmental changes in order for Korean venture companies to have global competitiveness in the era of the 4th Industrial Revolution. After reviewing SWOT factors by three expert groups and confirming them through Delphi survey, the importance of each item was analyzed by using AHP, a systematic decision-making technique. As a result of the analysis, it was shown that Strength(48%), Opportunity(25%), Threat(16%), Weakness(11%) were considered important in order. In terms of sub-items, 'quick and flexible commercialization capability', 'platform/big data/non-face-to-face service activation', and 'ICT infrastructure and it's utilization' were shown to be of the comparatively high importance. On the other hand, in the lower three items, 'macro-economic stability and social infrastructure', 'difficulty in entering overseas markets due to global protectionism', and 'absolutely inferior in foreign investment' were found to have low priority. As a result of the correlation verification by item to see differences in opinions by industry, academia, and policy expert groups, there was no significant difference of opinion, as industry and academic experts showed a high correlation and industry experts and policy experts showed a moderate correlation. The correlation between the academic and policy experts was not statistically significant (p<0.01), so it was analyzed that there was a difference of opinion on importance. This was due to the fact that policy experts highly valued 'quick and flexible commercialization', which are strengths, and 'excellent educational system and high-quality manpower' and 'creation of new markets' which are opportunity items, while academic experts placed great importance on 'support part of government policy', which are strengths. The implication of this study is that in order for Korean venture companies to secure competitiveness in the field of the 4th industrial revolution, it is necessary to have a policy that preferentially supports the relevant items of strengths and opportunity factors. The difference in the details of strength factors and opportunity factors, which shows a high level of variability, suggests that it is necessary to actively review it and reflect it in the policy.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.401-409
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• 2021

This study aimed to estimate the photosynthetic capacity of tomato plants grown in a semi-closed greenhouse using temperature response models of plant photosynthesis by calculating the ribulose 1,5-bisphosphate carboxylase/oxygenase maximum carboxylation rate (V_cmax), maximum electron transport rate (J_max), thermal breakdown (high-temperature inhibition), and leaf respiration to predict the optimal conditions of the CO₂-controlled greenhouse, for maximizing the photosynthetic rate. Gas exchange measurements for the A-C_i curve response to CO₂ level with different light intensities {PAR (Photosynthetically Active Radiation) 200µmol·m^-2·s^-1 to 1500µmol·m^-2·s^-1} and leaf temperatures (20℃ to 35℃) were conducted with a portable infrared gas analyzer system. Arrhenius function, net CO₂ assimilation (A_n), thermal breakdown, and daylight leaf respiration (R_d) were also calculated using the modeling equation. Estimated J_max, A_n, Arrhenius function value, and thermal breakdown decreased in response to increased leaf temperature (> 30℃), and the optimum leaf temperature for the estimated J_max was 30℃. The CO₂ saturation point of the fifth leaf from the apical region was reached at 600ppm for 200 and 400µmol·m^-2·s^-1 of PAR, at 800ppm for 600 and 800µmol·m^-2·s^-1 of PAR, at 1000ppm for 1000µmol of PAR, and at 1500ppm for 1200 and 1500µmol·m^-2·s^-1 of PAR levels. The results suggest that the optimal conditions of CO₂ concentration can be determined, using the photosynthetic model equation, to improve the photosynthetic rates of fruit vegetables grown in greenhouses.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.903-916
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• 2021

An area's topography refers to the shape of the earth's surface, described by its elevation, slope, and aspect, among other features. The topographical conditions determine energy flowsthat move water and energy from higher to lower elevations, such as how much solar energy will be received and how much wind or rain will affect it. Another common factor, the topographic wetness index (TWI), is a calculation in digital elevation models of the tendency to accumulate water per slope and unit area, and is one of the most widely referenced hydrologic topographic factors, which helps explain the location of forest vegetation. Analyses of topographical factors can be calculated using a geographic information system (GIS) program based on digital elevation model (DEM) data. Recently, a large number of free open source software (FOSS) GIS programs are available and developed for researchers, industries, and governments. FOSS GIS programs provide opportunitiesfor flexible algorithms customized forspecific user needs. The majority of biodiversity in island areas exists at about 20% higher elevations than in land ecosystems, playing an important role in ecological processes and therefore of high ecological value. However, island areas are vulnerable to disturbances and damage, such as through climate change, environmental pollution, development, and human intervention, and lacks systematic investigation due to geographical limitations (e.g. remoteness; difficulty to access). More than 4,000 of Korea's islands are within a few hours of its coast, and 88% are uninhabited, with 52% of them forested. The forest ecosystems of islands have fewer encounters with human interaction than on land, and therefore most of the topographical conditions are formed naturally and affected more directly by weather conditions or the environment. Therefore, the analysis of forest topography in island areas can be done more precisely than on its land counterparts, and therefore has become a major focus of attention in Korea. This study is focused on calculating the performance of different topographical factors using FOSS GIS programs. The test area is the island forests in Korea's south and the DEM of the target area was processed with GRASS GIS and SAGA GIS. The final slopes and TWI maps were produced as comparisons of the differences between topographic factor calculations of each respective FOSS GIS program. Finally, the merits of each FOSS GIS program used to calculate the topographic factors is discussed.

• Journal of Life Science
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• v.29 no.4
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• pp.410-420
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• 2019

Citrus unshiu peel extracts possess a variety of beneficial effects, and studies on their anticancer activity have been reported. However, the exact mechanisms underlying this activity remain unclear. In the current study, the apoptotic effect of ethanol extract of C. unshiu peel (EECU) on human breast adenocarcinoma MDA-MB-231 cells and related mechanisms were investigated. The results showed that the survival rate of MDA-MB-231 cells treated with EECU was significantly inhibited in a concentration-dependent manner, which was associated with the induction of apoptosis. EECU-induced apoptosis was associated with the activation of caspase-8 and caspase-9, which initiate extrinsic and intrinsic apoptosis pathways, respectively, and caspase-3, a representative effect caspase. EECU suppressed the expression of the inhibitor of apoptosis family of proteins, leading to an increased Bax/Bcl-2 ratio and proteolytic degradation of poly (ADP-ribose) polymerase. EECU also enhanced the loss of the mitochondrial membrane potential and cytochrome c release from the mitochondria to the cytosol, along with truncation of Bid. In addition, EECU activated AMP-activated protein kinase (AMPK), and compound C, an AMPK inhibitor, significantly weakened EECU-induced apoptosis and cell viability reduction. Furthermore, EECU promoted the generation of reactive oxygen species (ROS), which acted as upstream signals for AMPK activation as pretreatment of cells, with the antioxidant N-acetyl cysteine reversing both EECU-induced AMPK activation and apoptosis. Collectively, these findings suggest that EECU inhibits MDA-MB-231 adenocarcinoma cell proliferation by activating intrinsic and extrinsic apoptotic pathways, which was mediated through ROS/AMPK-dependent pathways.

• Journal of Environmental Impact Assessment
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• v.28 no.2
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• pp.101-112
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• 2019

Over the last few years, air pollution ($PM_{10}$, $PM_{2.5}$) in the Seoul metropolitan area (SMA) has emerged as one of the most concerned and threatening environmental issues among the residents. It brings about various harmful effects on human health, as well as ecosystem and industrial activities. Governments and individuals pay various costs to mitigate the level of air pollutants. This study aims to empirically find the willingness to pays (WTP) among the parents from different socio-cultural groups - international and domestic groups to mitigate air pollution ($PM_{10}$, $PM_{2.5}$) in their residential area. Contingent Valuation Methods (CVM) is used with employing single-bounded dichotomous choice technique to elicit the respondent's WTP. Using tobit (censored regression) and probit models, the monthly mean WTP of the pooled sample for green electricity which contributes to improve air quality in the region was estimated as 3,993 KRW (3.58 USD). However, the mean WTP between the international group and domestic group through a sub-sample analysis shows broad distinction as 3,325KRW (2.98 USD) and 4,449 KRW (3.98 USD) respectively. This is because that socio-cultural characteristics of each group such as socio-economic status, personal experience, trust in institutions and worldview are differently associated with the WTP. Based on the results, the society needs to raise awareness of lay people to find a strong linkage between the current PM issue and green electricity. Also, it needs to improve trust in the government's pollution abatement policy to mobilize more assertive participation of the people from different socio-cultural background.