• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.201-219
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• 2014

The Republic of Korea in the real economy to a knowledge economy, and a center of creativity and imagination in the creative economy is changing the paradigm. As the core of creating economic, creative industries with the technology and information play an important role in the industry individuals. In order to solve the problem of the polarization of the economy and high youth unemployment rate of Korea, to recognize the role of the creative industries, as objection part, dimensions pan-national and one creative companies in industries of Mobile Apps various policies that support has been promoted. Support these policies to be able to contribute to the establishment of the success of mobile apps one-person creating company, we performed this study targeting one-person company that creates mobile apps area, we conducted a demonstration study of success factors, and thus more effective and efficient in an attempt to seek out support measures. In this study, we derive a research 4 hypothesis about the success factors of one creative enterprise through literature discussion, a study was made on the basis of empirical data of one-person company that creates mobile apps. The results of the analysis, first, if the development rate of the mobile application technology is fast and a new competition associated product is appeared, it was possible to find a tendency to be higher at the performance quantitative companies. Second, if the founder is a founding for the benefit and rewarding work and come to terms with the risk, it was possible to discover tends to be higher achievement quantitative. Third, if one-person company select a target market with capture intensively, it was possible to find a tendency for higher qualitative results. Fourth, it could be found that the reliability of the contact frequency of the network related performance business environment these characteristics enterprise management strategy and act as a significant modulatory effect. Provision of information relating to management and entrepreneurship education to be one creative enterprise is required, these results suggest that there is a provision continuing need for the opportunity to be able to meet and network and reliable variety have. In this study, to take advantage to promote the elimination measures that can increase the likelihood of success of the company of institutions to support one company that creates knowledge-based, such as in the field of mobile application.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.36-44
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• 2010

In order to rehabilitate forest sites damaged by wildfire via natural or artificial restoration, it is important to determine right tree species, which can acclimate to biogeoclimatic environment at the sites. The objectives of this study were to develop site index equation of different tree species for estimating forest productivity and to provide information on species selection for post-wildfire restoration. Site index equation was developed based on environmental information from wildfire damaged areas in Gangneung, Goseong, Donghae, and Samcheok, where were located in east coastal areas of South Korea. Despite the small numbers (4~5) of environmental variables used for the development of the site index equations, statistical analysis (e.g. mean difference, standard deviation of difference, and standard error of difference) showed relatively low bias and variation, suggesting that those equations can provide relatively high capability of estimation and practical applicability with high effectiveness. The small numbers of the variables enabled the model to be applied in a wide range of usages including determination of appropriate tree species for post-wildfire restoration. The estimation of forest site productivity showed the possibility of large distribution in east coastal region as the best site for Korean ash (Fraxinus rhynchophylla) and original oak (Quercus variabilis) that can be used for firebreak in the region. These results imply that damages by forest fire can be reduced significantly by replacing existing pure coniferous forests in the area with ones dominated by broad-leaved deciduous stands, which can play an important role as fire break and/or prevent a transition from surface fire to crown fire.

• Journal of the Korean Society for Library and Information Science
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• v.12
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• pp.269-327
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• 1985

This study was undertaken to meet more fully the demands for improved training of library personnel, occasioned by the rapidly changing roles and functions of libraries as they try to adapt to the vast social, economic and technological changes currently in progress in the Korean society. The specific purpose of this research is to develop a standard curriculum at the batchelor's level that will properly equip the professional personnel in Korean libraries for the changes confronting them. This study started with the premise that to establish a sound base for curriculum development, it was necessary first to determine what concepts, knowledge, and techniques are required for professional library personnel to perform it at an optimal level of efficiency. Explicitly, it was felt that for the development of useful curricula and courses at the batchelor's level, a prime source of knowledge should be functional behaviours that are necessary in the job situation. To determine specifically what these terminal performance behaviours should be so that learning experience provided could be rooted in reality, the decision was reached to use a systems approach to curriculum development, which is an attempt to break the mold of traditional concepts and to approach interaction from an open, innovative, and product-oriented perspective. This study was designed to: (1) identify what knowledge and techniques are required for professional library personnel to perform the job activities in which they are actually engaged, (2) to evaluate the educational needs of the knowledge and techniques that the professional librarian respondents indicate, and (3) to categorise the knowledge and techniques into teaching subjects to present the teaching subjects by their educational importance. The main data-gathering instrument for the study, a questionnaire containing 254 items, was sent to a randomly selected sample of library school graduates working in libraries and related institutions in Korea. Eighty-three librarians completed and returned the questionnaire. After analysing the returned questionnaire, the following conclusions have been reached: (A) To develop a rational curriculum rooted in the real situation of the Korean libraries, compulsory subjects should be properly chosen from those which were ranked highest in importance by the respondents. Characters and educational policies of, and other teaching subjects offered by, the individual educational institution to which a given library school belongs should also be taken into account in determining compulsory subjects. (B) It is traditionally assumed that education in librarianship should be more concerned with theoretical foundations on which any solution can be developed than with professional needs with particulars and techniques as they are used in existing library environments. However, the respondents gave the former a surprisingly lower rating. The traditional assumption must be reviewed. (C) It is universally accepted in developing library school curricula that compulsory subjects are concerned with the area of knowledge students generally need to learn and optional subjects are concerned with the area to be needed to only those who need it. Now that there is no such clear demarcation line provided in librarianship, it may be a realistic approach to designate subjects in the area rated high by the respondents as compulsory and to designate those in the area rated low as optional. (D) Optional subjects that were ranked considerably higher in importance by the respondents should be given more credits than others, and those ranked lower might be given less credits or offered infrequently or combined. (E) A standard list of compulsory and optional subjects with weekly teaching hours for a Korean library school is presented in the fourth chapter of this report.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Korean Journal of Heritage: History & Science
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• v.54 no.4
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• pp.60-77
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• 2021

From 2007 to 2011, four Goryeo Dynasty vessels, namely the Taean treasure ship, Taean Mado Shipwreck No.1, No.2, No.3 were discovered in Taean area, Chungcheongnam-do province. From the shipwrecks, 175 pieces of mokkan (wooden tablet) were excavated. These mokkans are the only case of Goryeo Dynasty and represent the unique usage of mokkan as cargo tags, after the paper replaced the wooden tablets as writing materials. The Taean mokkans provide details, such as the year, recipient, port of origin, types of the cargo, quantity and unit, the name of the responsible person for shipment. Thus, they enable us to speculate about the characteristics of the cargo. Furthermore, through studying the writing style, form, material and manufacturing method, researchers can extract which form and characteristics were favored at that time. The Taean mokkans have no preset style for writing. Therefore, they can be written selectively and freely. And since the mokkan were attached to cargos, mokkans with furrows on upper side were favored, and efficiency and simplification of the manufacturing process were priorities in making mokkans. The Taean mokkans can be compared to those from the Shinan ship and the Quanzhou ship because those are of the same era and use. On the writing styles and information, Chinese mokkans are focused on the cargo owners, while The Taean mokkan includes more detailed information, such as the recipients. In forms, Chinese mokkans have maximum thickness of 1.0 centimeter and have pointed edges in lower parts, while mokkans from Taean do not have fixed thickness or edges. Furthermore, Chinese mokkans and Korean mokkans have different styles from manufacturing methods and material selections. These differences between Chinese and Goryeo mokkan are related to the differences between littoral-transport Goryeo ships and ocean-transport Chinese ships, such as shipping distances, types of cargo, shipping systems, packing methods, and transport operators. At the moment, because there are only small amount of data and materials of Chinese mokkan, comparative studies regarding Goryeo and Chinese mokkan can only be fragmentary. However, this article can be a base from which to expand the scope of Goryeo mokkan studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.10-20
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• 2019

Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.