• 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.

• Korean Journal of Microbiology
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• v.53 no.2
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• pp.83-96
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• 2017

A constructed sea stream in Yeongdo, Busan, Republic of Korea is mostly static due to the lifted stream bed and tidal characters, and receives domestic wastewater nearby, causing a consistent odor production and water quality degradation. Bioaugmentation of a microbial consortium was proposed as an effective and economical restoration technology to restore the polluted stream. The microbial consortium activated on site was augmented on a periodic basis (7~10 days) into the most polluted site (Site 2) which was chosen considering the pollution level and tidal movement. Physicochemical parameters of water qualities were monitored including pH, temperature, DO, ORP, SS, COD, T-N, and T-P. COD and microbial community analyses of the sediments were also performed. A significant reduction in SS, COD, T-N, and COD (sediment) at Site 2 occurred showing their removal rates 51%, 58% and 27% and 35%, respectively, in 13 months while T-P increased by 47%. In most of the test sites, population densities of sulfate reducing bacterial (SRB) groups (Desulfobacteraceae_uc_s, Desulfobacterales_uc_s, Desulfuromonadaceae_uc_s, Desulfuromonas_g1_uc, and Desulfobacter postgatei) and Anaerolinaeles was observed to generally decrease after the bioaugmentation while those of Gamma-proteobacteria (NOR5-6B_s and NOR5-6A_s), Bacteroidales_uc_s, and Flavobacteriales_uc_s appeared to generally increase. Aerobic microbial communities (Flavobacteriaceae_uc_s) were dominant in St. 4 that showed the highest level of DO and least level of COD. These microbial communities could be used as an indicator organism to monitor the restoration process. The alpha diversity indices (OTUs, Chao1, and Shannon) of microbial communities generally decreased after the augmentation. Fast uniFrac analysis of all the samples of different sites and dates showed that there was a similarity in the microbial community structures regardless of samples as the augmentation advanced in comparison with before- and early bioaugmentation event, indicating occurrence of changing of the indigenous microbial community structures. It was concluded that the bioaugmentation could improve the polluted water quality and simultaneously change the microbial community structures via their niche changes. This in situ remediation technology will contribute to an eco-friendly and economically cleaning up of polluted streams of brine water and freshwater.

• Journal of Climate Change Research
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• v.3 no.2
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• pp.117-128
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• 2012

The Government of South Korea has continued its effort to fixate virtuous circle of economic growth and climate change response to cope with international demands and pressure to commitment for greenhouse gas reduction effectively. Nationally, Korean Government has established "Enforcement of the Framework Act on Low carbon, Green Growth"(2010. 4. 13) to implement national mid-term GHG mitigation goal(30% reduction by 2020 compare to BAU), which established the foundation for phased GHG mitigation by setting up the sectoral and industrial goal, adopting GHG and Energy Target Management System. Also, follow-up measures are taken such as planning and control of mid-term and short-term mitigation target by detailed analysis of potential mitigation of sector and industry, building up the infrastructure for periodic and systematic analysis of target management. Likewise, it is required to establish more accurate, reliable and detailed sectoral GHG inventory for successfully establishment and implement the frame act. In comparison to the $CO_2$ emission, Especially fluorinated greenhouse gases (HFCs, PFCs, $SF_6$) are lacking research to build the greenhouse gas inventories to identify emissions sources and collection of the applicable collection activities data. In this study, with the refrigeration and air conditioning sector being used to fluorine refrigerant(HFCs) as the center, greenhouse gas emission estimation methodology for evaluating the feasibility of using this methodology look over and mobile air conditioning, fixed air conditioning, household refrigeration equipment, commercial refrigeration equipment for the greenhouse gas emissions were calculated. First look at in terms of methodology, refrigeration and air conditioning sector GHG emissions in developing country-specific emission factors and activity data of the industrial sector the construction of the DB is not enough, it's 2006 IPCC Guidelines Tier 2a (emission factor approach) rather than the Tier 2b (mass balance approach) deems appropriate, and each detail by process, sectoral activity data more accurate, if DB is built Tier 2a (emission factor approach) can be applied will also be judged. Refrigeration and air conditioning sector in 2009 due to the use of refrigerant greenhouse gas emissions ($CO_2eq.$) assessment results, portable air conditioner 1,974,646 ton to year, fixed-mount air conditioner 1,011,754 ton to year, household refrigeration unit 4,396 ton to year, commercial refrigeration equipment 1,263 ton to year was estimated to total 2,992,037 tons.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.4
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• pp.67-83
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• 2022

Biochar is a carbon material produced through the pyrolysis of agricultural biomass with limited oxygen condition. It has been suggested to enhance the carbon sequestration and mineralization of soil carbon. Objective of this study was to investigate soil potential carbon mineralization and carbon dioxide(CO₂) emissions in different soils cooperated with barely straw and livestock manure biochars in the closed chamber. The incubation was conducted during 49 days using a closed chamber. The treatments consisted of 2 different biochars that were originated from barley straw and livestock manure, and application amounts were 0, 5, 10 and 20 ton ha^-1 with different soils as upland, protected cultivation, converted and reclaimed. The results indicated that the TC increased significantly in all soils after biochar application. Mineralization of soil carbon was well fitted for Kinetic first-order exponential rate model equation (P<0.001). Potential mineralization rate ranged from 8.7 to 15.5% and 8.2 to 16.5% in the barely straw biochar and livestock manure biochar treatments, respectively. The highest CO₂ emission was 81.94 mg kg^-1 in the upland soil, and it was more emitted CO₂ for barely straw biochar application than its livestock biochar regardless of their application rates. Soil amendment of biochar is suitable for barely straw biochar regardless of application rates for mitigation of CO₂ emission in the cropland.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.533-544
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• 2023

The Ministry of Agriculture, Food and Rural Affairs has been promoting policies to strengthen the functions of rural centers (culture, welfare, economy, education, etc.) and to ensure that services from the centers are delivered to and connected to hinterland villages. For this policy purpose, the rural center revitalization project and the basic living base creation project within the rural development projects are being promoted. However, in the process of carrying out the actual project, as the focus is on strengthening the functions of rural centers, service delivery and connection with hinterland villages are not being actively promoted. therefore, in this study, we analyze the projects previously carried out in Jeoksang-myeon, Muju-gun and the regional status, analyze the reasons why hinterland village services were not connected and activated, and propose a direction for the second phase of the basic living base creation project to be carried out in the future. As a result of analyzing the reasons for the failure of hinterland village services to be activated, problems such as disadvantages in accessing services due to dispersed residence in rural areas and limitations in topographical structure, and the lack of a service delivery system to develop demand in hinterland areas were found to be problems. Improvement measures were derived as follows. First, it is a stepping stone construction plan proposed to overcome topographical limitations. Establish a stepping base that will function as a service intermediate terminal to ensure efficient service delivery. Second, for a rational decision-making structure, we proposed a plan for deploying communication channels that could closely collect local opinions by operating various small-scale communities along with the efficient composition of a resident committee that includes residents of the central and hinterland villages and various classes. Third, it is a virtuous cycle of local manpower training plans that train local residents into professional instructors. We aim to complete a sustainable, resident-led service supply system by nurturing the most important service deliverers, that is, activists, in service delivery.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of the Korean Wood Science and Technology
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• v.2 no.2
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• pp.32-34
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• 1974

The important results which have been obtained in the investigation can be recapitulated as follows. 1. As demonstrated by the experimental results and analyses concerning their effects in the on-ground type mushroom house, the constructions in relation to the side wall and ceiling of the experimental house showed a sufficient heat insulation on effect to protect insides of the house from outside climatic conditions. 2. As the effect on the solar type experimental mushroom house which was constructed in a half basement has been shown by the experimental results and analyses, it has been proved to be effective for making use of solar heat. However there were found two problems to be improved for putting solar house to practical use in the farm mushroom growing: (1) the construction of the roof and ceiling should be the same as for the on ground type house, and (2) the solar heat generating system should be reconstructed properly. 3. Among several ventilation systems which have been studied in the experiments, the underground earthen pipe and ceiling ventilation, and vertical side wall and ceiling ventilation systems have been proved to be most effective for natural ventilation. 4. The experimental results have shown that ventilation systems such as the vertical side wall and underground ventilation systems are suitable to put to practical use as natural ventilation systems for farm mushroom house. These ventilation systems can remarkably improve the temperature of fresh air which is introduced into the house by heat transfers within the ventilation passages, so as to approach to the desired temperature of the house without any cooling or heating operation. For example, if it is assuming that X is the outside temperature and Y is the amount of temperature adjustment made by the influence of the ventilation system, the relationships that exist between X and Y can be expressed by the following regression lines. Underground iron pipe ventilation system. Y=0.9X-12.8 Underground earthen pipe ventilation system. Y=0.96X-15.11 Vertical side wall ventilation system. Y=0.94X-17.57 5. The experimental results have 8hown that the relationships existing between the admitted and expelled air and the $CO_2$ concentration can be described with experimental regression lines or an exponent equation as follows: 5.1 If it is assumed that X is an air speed cm/sec. and Y is an expelled air speed in cm/sec. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below: 5.2 If it IS assumed that X is an admitted volume of air in $m^3$/hr. and Y is an expelled volume of air in $m^3$/hr. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below. 5.3 If it is assumed that expelled air speed in emisec. and replacement air speed in cm/sec. at the bed surface in a natural ventilation system are shown as X and Y. respectively, since the Y is a function of the X. the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV (50%) ventilation system. Y=-0.54X+0.84 5.4 If it is assumed that the replacement air speed in cm/sec. at the bed surface is shown as X, and $CO_2$ concentration which is expressed by multiplying 1000 times the actual value of $CO_2$ % is shown as Y, in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV(50%) ventilation system. Y=114.53-6.42X 5.5 If it is assumed that the expelled volume of air is shown as X and the $CO_2$ concencration which is expressed by multiplying 1000 times the actual of $CO_2$% is shown as Y in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following exponent equation: GE(100%)-CV(50%) ventilation system. Y=$127.18{\times}1.0093^{-x}$ 5.6 The experimental results have shown that the ratios of the cross sectional area of the GE and CV vent to the total cubic capacity of the house, required for providing an adequate amount of air in a natural ventilation system, can be estimated as follows: GE(admitting vent of the underground ventilation) 0.3-0.5% (controllable) CV(expelling vent of the ceiling ventilation) 0.8-1.0% (controllable) 6. Among several heating devices which were studied in the experiments, the hot-water boilor which wasmodified to be fitted both as hot-water boiler and as a pressureless steam-water was found most suitable for farm mushroom growing.