• Journal of the Korea Organic Resources Recycling Association
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• v.18 no.3
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• pp.77-86
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• 2010

In this study, the model of the indirect wind suction waste sorting machine for characteristics of the screening of waste was studied using computational fluid dynamics and the drag coefficient for the model and the suction wind speed were obtained. The wind separator are developing by installing a cyclone air outlet to the suction blower impeller waste is selective in a way that does not pass the features and characteristics of the inlet pipe of the pressure loss and separation efficiency can have a significant impact on. Using Wind separator for selection of waste in the waste prior research on the aerodynamic properties are essential. For plastic cases, it is reasonable to take the drag coefficient between 0.8 and 1.0, and for cans, compression depending on whether the cans, the drag coefficient is in the range from 0.2 to 0.7. The separation efficiency of waste as change suction speed was the highest efficiency when the suction speed was 25~26 m/s. Shape of the inlet, depending on how the transfer pipe of the duct pressure loss occurs because the inlet velocity changes through the appropriate design standards to allow for continued research is needed.

• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.395-410
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• 2024

Text data is usually made up of a wide variety of unique words. Even in standard text data, it is common to find tens of thousands of different words. In text data analysis, usually, each unique word is treated as a variable. Thus, text data can be regarded as a dataset with a large number of variables. On the other hand, in text data classification, we often encounter class label imbalance problems. In the cases of substantial imbalances, the performance of conventional classification models can be severely degraded. To improve the classification performance of support vector machines (SVM) for imbalanced data, algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) can be used. The SMOTE algorithm synthetically generates new observations for the minority class based on the k-Nearest Neighbors (kNN) algorithm. However, in datasets with a large number of variables, such as text data, errors may accumulate. This can potentially impact the performance of the kNN algorithm. In this study, we propose a method for enhancing prediction performance for the minority class of imbalanced text data. Our approach involves employing variable selection to generate new synthetic observations in a reduced space, thereby improving the overall classification performance of SVM.

• 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.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.51-75
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• 2022

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation that is able to alter neuronal activity in particular brain regions. Many studies have researched how tDCS modulates neuronal activity and reorganizes neural networks. However it is difficult to conclude the effect of brain stimulation because the studies are heterogeneous with respect to the stimulation parameter as well as individual difference. It is not fully in agreement with the effects of brain stimulation. In particular few studies have researched the reason of variability of brain stimulation in response to time so far. The study investigated individual variability of brain stimulation based on circadian rhythm and chronotype. Participants were divided into two groups which are morning type and evening type. The experiment was conducted by Zoom meeting which is video meeting programs. Participants were sent experiment tool which are Muse(EEG device), tdcs device, cell phone and cell phone holder after manuals for experimental equipment were explained. Participants were required to make a phone in frount of a camera so that experimenter can monitor online EEG data. Two participants who was difficult to use experimental devices experimented in a laboratory setting where experimenter set up devices. For all participants the accuracy of 98% was achieved by SVM using leave one out cross validation in classification in the the effects of morning stimulation and the evening stimulation. For morning type, the accuracy of 92% and 96% was achieved in classification in the morning stimulation and the evening stimulation. For evening type, it was 94% accuracy in classification for the effect of brain stimulation in the morning and the evening. Feature importance was different both in classification in the morning stimulation and the evening stimulation for morning type and evening type. Results indicated that the effect of brain stimulation can be explained with brain state and trait. Our study results noted that the tDCS protocol for target state is manipulated by individual differences as well as target state.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.863-874
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• 2018

The purpose of this study is to evaluate the future climate change impact on stream aquatic ecology health of Han River watershed ($34,148km^2$) using SWAT (Soil and Water Assessment Tool) and random forest. The 8 years (2008~2015) spring (April to June) Aquatic ecology Health Indices (AHI) such as Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI) and Fish Assessment Index (FAI) scored (0~100) and graded (A~E) by NIER (National Institute of Environmental Research) were used. The 8 years NIER indices with the water quality (T-N, $NH_4$, $NO_3$, T-P, $PO_4$) showed that the deviation of AHI score is large when the concentration of water quality is low, and AHI score had negative correlation when the concentration is high. By using random forest, one of the Machine Learning techniques for classification analysis, the classification results for the 3 indices grade showed that all of precision, recall, and f1-score were above 0.81. The future SWAT hydrology and water quality results under HadGEM3-RA RCP 4.5 and 8.5 scenarios of Korea Meteorological Administration (KMA) showed that the future nitrogen-related water quality in watershed average increased up to 43.2% by the baseflow increase effect and the phosphorus-related water quality decreased up to 18.9% by the surface runoff decrease effect. The future FAI and BMI showed a little better Index grade while the future TDI showed a little worse index grade. We can infer that the future TDI is more sensitive to nitrogen-related water quality and the future FAI and BMI are responded to phosphorus-related water quality.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.3
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• pp.256-264
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• 2021

Rapeseed (Brassica napus L.) is generally sown in late autumn and harvested in early summer in Korea, however, spring cultivation has also been attempted in some areas because frequent climate changes induce reducing productivity. Therefore, there is a need for a transplanting technology that is relatively easy to control of cropping season according to changes in cultivation conditions. In this study, to find out the optimal characteristics of seedlings for machine transplanting of spring cultivation, seedling morphological characteristics were investigated according to the seedling age of three varieties for 2020 and 2021. The hypocotyl length was less than 2 cm in both years and the 40-day-old seedling was the shortest among all seedling ages. The number and size of leaf were increased with longer seedling age in both years. To evaluate seedling quality, total seedling length, seedling weight, and impact resistance were measured before transplanting. Total seedling length was the longest in 40-day-old seedlings and the shortest in 25-day-old seedlings in both years. In the case of seedling weight, no significant differences were observed depending on the seedling age and the impact resistance increased with increasing seedling age. Finally, 'Jungmo7001', 'Naehan', and 'Tamla' showed a high transplanting rate in seedlings grown for more than 30 days, 35 days, and 40 days, respectively, in the field using a general transplanter. These results suggest that the proper seedling age for transplanting is limited depending on the rapeseed varieties. The suitable seedling cultivation method can be selected for different cultivation environments.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.509-517
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• 2017

This study was conducted to assess effect of thiacloprid fogging on non-target insect communities in Korean white pine forests. In addition, we assessed effect of thiacloprid fogging on mortality of Monochamus saltuarius as a vector of pine wood nematodes, and Apis mellifera as a non-target species. We compared abundance, species richness, and compostion of insects, and mortality of two insects among four treatment groups (control and thiacloprid-fogged groups with 3 different doses) located in the Kangwon National University Forest. For sampling of insects, 6 pitfall and 2 multi-funnel traps and 3 waterproof cloth sheets were placed in each study plot. In addition, M. saltuarius and A. mellifera were put into each meshed cage which installed at 7 m and 15 m heights in center of each study plot. Thiacloprid was fogged only once (middle May) in each plot using a fogging machine. Overall, thiacloprid fogging was appeared to be low toxicity to the abundance, species richness, and compostion of insects and mortality of A. mellifera, while it seems effectively impact on the mortality of M. saltuarius. However, thiacloprid fogging seems more influenced by microclimates in forests because the mortality of M. saltuarius in mesh cages was different according to heights and spatial locations. To control the population density and dispersal of M. saltuarius using by fogging techniques, therefore, it may be necessary to minimize the uncertainty about the effectiveness of thiacloprid fogging by improving the fogging techniques.

• Journal of Biosystems Engineering
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• v.5 no.1
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• pp.1-14
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• 1980

Threshing operation may be one of the most important processes in the paddy post-production system as far as the grain loss and labor requirement are concerned . head-feeding type threshers commercially available now in Korea originally were developed for threshing dry paddy in the range of 15 to 17 % in wet basis. However, threshing wet-paddy with the grain moisture content above 20 % has been strongly recommended, especially for new high-yielding Indica -type varieties ; (1) to reduce high grain loss incurred due to the handling operations, and (2) to prevent the quantitative and qualitative loss of milled -rice when unthreshed grains are rewetted due to the rainfall. The objective of this study were to investigate the adaptability of both a head-feeding type thresher and a throw-in type thresher to wet-paddy , and to find out the possiblilities of improving the components of these threshers threshing. Four varieties, Suweon 264 and Milyang 24 as Tongil sister line varieties, minehikari and Jinhueng as Japonica-type varieties, were used at the different levels of the moisture content of grains. Both the feed rate and the cylinder speed were varied for each material and each machine. The thresher output quality , composition of tailing return, and separating loss were analyzed from the sampels taken at each treatment. A separate experiment for measurement opf the power requirement of the head-feeding type thresher was also performed. The results are summarized as follows : 1. There was a difference in the thresher output quality between rice varieties. In case of wet-paddy threshing at 550 rpm , grains with branchlet and torn heads for the Suweon 264 were 12 % and 7 % of the total output in weight, respectively, and for the Minehikari 4.5 % and 2 % respectively. In case of dry paddy threshing , those for the Suweon 264 were 8 % and 5% , and for the Minehikari 4% and 1% respectively. However, those for the Milyang 23 , which is highly susceptable to shattering, were much lower with 1 % and 0.5% respectively, regardless of the moisture content of the paddy. Therefore, it is desirable to breed rice varieties of the same physical properties as well as to improve a thresher adaptable to all the varieties. Torn heads, which increased with the moisture content of rall the varieties except the Milyang 23 , decreased as the cylinder speed increased, but grains with branchlet didnt decrease. The damaged kernels increased with the cylinder speed. 3. The thresher output quality was not affected much by the feed rate. But grains with branchlet and torn heads increased slightly with the feed rate for the head-feeding type thresher since higher resistance lowered at the cylinder speed. 4. In order to reduce grains with branchlet and torn heads in wet-paddy threshing , it is desirable to improve the head-feeding type thresher by developing a new type of cylinder which to not give excess impact on kernels or a concave which has differenct sizes of holes at different locations along the cylinder. 5. For the head-feeding type thresher, there was a difference in separating loss between the varieties. At the cylinder speed of 600 rpm the separating losses for the Minehikari and the Suweon 264 were 1.2% and 0.6% respectively. The separating loss of the head-feeding type thresher was not affected by the moisture content of paddy while that of the Mini-aged thresher increased with the moisture content. 6. From the analysis of the tailings return , to appeared that the tailings return mechanism didn't function properly because lots of single grains and rubbishes were unnecessarily returned. 7. Adding a vibrating sieve to the head-feeding type thresher could increase the efficiency of separation. Consequently , the tailing return mechanism would function properly since unnecessary return could be educed greatly. 8. The power required for the head-feeding type thresher was not affected by the moisture content of paddy, but the average power increased linearly with the feed rate. The power also increased with the cylinder speed.