• 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.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.487-499
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• 2020

This study aims to planned Elementary Multicultural Education Program for Promoting Self Esteem. To this end, consider the application force of education and teaching system designed to reorganize the ADDIE Model study was carried out in accordance with the analysis, design, development, process evaluation. To this end, analyzing the between self-esteem and group theory related to them and the social identity theory and key concepts to consider in the context of multicultural education programs for elementary self-esteem enhancement identity, prejudice, cooperation and was composed of equality. Four key concepts are sub-elements of self-esteem individuals, homes, schools, set up a detailed learning objectives in accordance with the social and selecting the learning content and organization, which was composed of a total of 16 class periods. Considering the characteristics and interests of the target students, the teaching-learning method was designed to utilize various methods, including games, quizzes, and discussions, and each class was designed to be developed by organizing them into subjects-learning goals-activities(recognition-exploration-clearing). Since in the three-member expert review, modify the content on the basis of opinions and presented the final program. The program planned by this study, further research is required to verify the effectiveness and utilization in the educational field and expects effective multicultural education.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12A
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• pp.962-970
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• 2009

This paper proposes a self-organizing scheme which collects the information of neighboring cells, and then assigns a Physical Cell Identifier (PCI) for a newly deployed cell in LTE systems. The number of PCIs are limited, so the reuse of PCIs in different cells is unavoidable. In a dense urban environment where many small cells such as Pico/Femto cells are deployed under macro cells, it is expected that the number of PCIs becomes more limited. Therefore, the limited number of PCIs needs to be allocated very efficiently. We propose a PCI allocation scheme for a newly deployed cell that can autonomously select a PCI to increase PCI reuse efficiency by utilizing the levels of received signal strength from neighboring cells. To evaluate the performance of the proposed scheme, simulations have been developed and performed with two scenarios on the coverage types of newly deployed cells. When the proposed scheme is applied, the simulation results show that the number of PCIs required for the operation of the system can be saved so that PCIs can be efficiently reused.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.395-400
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• 2017

The paper discussed the air-to-fuel ratio control of automotive fuel-injection systems using the cerebellar model articulation controller(CMAC) neural network. Because of the internal combustion engines and fuel-injection's dynamics is extremely nonlinear, it leads to the discontinuous of the fuel-injection and the traditional method of control based on table look up has the question of control accuracy low. The advantages about CMAC neural network are distributed storage information, parallel processing information, self-organizing and self-educated function. The unique structure of CMAC neural network and the processing method lets it have extensive application. In addition, by analyzing the output characteristics of oxygen sensor, calculating the rate of fuel-injection to maintain the air-to-fuel ratio. The CMAC may easily compensate for time delay. Experimental results proved that the way is more good than traditional for petrol control and the CMAC fuel-injection controller can keep ideal mixing ratio (A/F) for engine at any working conditions. The performance of power and economy is evidently improved.

• Korean Journal of Health Education and Promotion
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• v.24 no.3
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• pp.99-118
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• 2007

There are little program and services that have been developed to address the health and health care needs of vulnerable women. The access for their timely and appropriate health care and health promotion services have been a increasing concern. The purpose of this study was to suggest health promotion program for vulnerable women with collaboration of women's NGOs. At the first part of this study, we reviewed a conceptual framework for identifying vulnerable population, and issues regarding health problems, unmet needs, policies and programs that have been developed to address their need. In second part, we focused on investigating the role, subjects and activities of women's NGOs and their capacity for health promotion program. The last part of this study proposed health promotion programme with integrating above two parts of study. In describing what type of health promotion program available in women's NCOs, eight major programs and services were summarized. 1. Direct health promotion program and collaborating program with other services. 2. Education and training for empowerment of vulnerable women 3. Organizing mutual support system such as self-help group 4. Community supports. Vulnerable women living at home may benefit from linkage to community services as much as women living in facility 5. Organizing collaboration system with program for economic support and job training, social rehabilitation 6. Trainer's training for practitioners in NGOs 7. Technical, informational support from professional groups 8. A national coordinating policies for vulnerable population should be established at the central level. National support for NGOs' health promotion program are needed hi solving unmet needs of vulnerable women.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.15-37
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• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1136-1142
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• 1999

A difficulty of the visual inspection for translated, magnified and rotated objects exists owing to the limitation of recognition rate. In this paper, we perform to define Integral Logarithm Transform(ILT), to consider its characteristic for implementation of Translation-, Magnification- and Rotation-invariant inspection system, and to compare with other methods in inspection error rate. By using magnification and rotation invariance properties of ILT, it makes easier than other methods to extract the rotation degree. The new method employs the ILT for the good/bad inspection of translated, magnified and rotated objects and experiment is performed to achieve translation, magnification and rotation invariance. In other methods both magnification and rotation invariance can't be available. As the result of he experiment, it is not better than the self-organizing map in the improvement of recognition rate, but it shows us the possibility to be used as a tool for the good/bad inspection system.

• Journal of Digital Contents Society
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• v.7 no.2
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• pp.103-108
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• 2006

Short-range wireless communication and networking technologies are becoming increasingly important in enabling useful mobile applications. for example, ZigBee technology is expected to provide low cost and low power connectivity for equipment that needs battery life as long as several months to several years. In addition, ZigBee can be implemented in mesh networks larger than is possible with Bluetooth. The main features of this ZigBee standard are network flexibility, low cost, very low power consumption, and low data rate in an adhoc self-organizing network among fixed, portable and moving devices. Home network/Home automation is one of the key market areas for Zigbee, with an example of a simple network This paper investigates the effect of short range wireless channel on the performance of Zigbee system and DSSS-BPSK signal transmission in AWGN, interference and Rician fading environments. And we investigate performance degradation due to interference and fading effects in short range wireless channel. In particular, the impacts of the fading and interference level on the bit error probability is shown in BER performance figures.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.115-132
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• 2004

Recently, as most companies recognize the importance of the customer relationship management, they strongly believe that they must know who their customers are. The job of a customer is very important information for us to understand the customer. However, since most customers are reluctant to reveal them-selves, they do not let us know their jobs, and even provide false information about their jobs. The target domain of our research is mobile telecommunication. In this research, we developed a system that identifies the customer's job by utilizing the Call Detail Record. Using artificial neural networks, we developed a two-step Job Identification System. In the first step, it identifies the four job classes, then in the second step, it subdivides these four job classes into seven jobs. The accuracy of identifying the seven jobs was $71.9\%$.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.2
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• pp.45-52
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• 2006

Mobile Ad-hoc Network(MANET) could be applied to various applications by virtue of its characteristics such as self-organizing and infrastructure-free network. This paper proposes a multimedia application service model for MANET, called multi-hop Relay(mRelay) system. It supports a multi-hop communication-based multimedia service interworked with Internet by using an unmanned moving robot with wireless multimedia communication function as a mRelay terminal. The modified AODV routing protocol was applied to the terminal and it was verified that composition and maintenance of mobile Ad-hoc network in mRelay system under the various indoor scenarios were successful and stable multi-hop multimedia services were possible. The mRelay system can be applicable to various application services of mobile Ad-hoc networks.