• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1189-1198
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• 2015

For the effects of the global financial crisis cause hedge funds to have a strong influence on financial markets, it is needed to study new approach method to predict hedge fund survival. This paper proposes to organize fuzzy neural network using hedge fund data as input to predict hedge fund survival. The variables of hedge fund data are ambiguous to analyze and have internal uncertainty and these characteristics make it challenging to predict their survival from the past records. The object of this study is to evaluate the predictability of fuzzy neural network which uses grades of membership to predict survival. The results of this study show that proposed system is effective to predict the hedge funds survival and can be a desirable solution which helps investors to support decision-making.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9B
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• pp.745-750
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• 2008

In a cognitive radio based system, quality of service (QoS) for the secondary user must be maintained as much as possible even while that of the primary user is protected all he time. In particular, switching wireless links for the secondary user during the transmission of multimedia data causes delay and information loss, and QoS degradations occur inevitably. The efficient resource management scheme is necessary to support the seamless multimedia service to the secondary user. This paper proposes a novel frequency selection method based on Multi-Criteria Decision Making (MCDM), in which uncertain parameters such as received signal strength, cell load, data rate, and available bandwidth are considered during the decision process for the frequency selection with the fuzzy set theory. Through simulation, we show that our proposed frequency selection method provides a better performance than the conventional methods which consider the received signal strength only.

• Journal of Navigation and Port Research
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• v.41 no.6
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• pp.401-408
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• 2017

The Street Turn system in South Korea has been developing continuously with the advancement of information technology; however, this has not lead to an increase of the Street Turn transportation volume. As a result, this study presents the decision-making factors for using the system from the standpoint of users of the existing Street Turn transportation system. The people who have used domestic Street Turn transportation services or who are working in a shipping company were analyzed using Fuzzy-AHP. A total of five major factors and 17 detailed factors were derived from this analysis. As a result, timeliness was selected as the most important major factor, and in particular, the information provision time (0.207) was selected as the most important factor, followed by platform use process (0.079), and number of participating shippers (0.074).

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.635-643
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• 1997

The purpose of this paper is to propose an interactive method, using fuzzy Choquet's integral, which is designed to find out the mostly preferred solutions for deterministic MADM problems with many attributes and alternatives. The basic idea of the paper is essentially the same as that of the one we have published before[1]; subgrouping of attributes and eliminating of inefficient solutions. But the difference between these two methods lies in the fact that the present method evaluates and eliminates alternatives using fuzzy Choquet's integral on the basis of decision-maker's judgements about the relative importance of subgroups of attributes, rather than using mathematical programming on the basis of pair-wise comparisons of alternatives. If such information is obtainable from the decision-maker, the method can be proved to be much easier to understand and more efficient to compute.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.10
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• pp.98-105
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• 1997

In this work, we proesent a bidirectional approximate reasoning method and fuzzy inference network for interval valued decision making systems. For this, we propose a new type of similarity measure between two fuzzy vectors based on the Ordered Weighted Averaging (OWA) operator. Since the proposed similarity measure has a structure to give the extreme values by choosing a suitable weighting vector of the OWA operator, it can render an interval valued similarity value. From this property, we derive a bidirectional approximate reasoning method based on the similarity measure and show its fuzzy inference network implementation for the decision making systems requiring the interval valued decisions.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.310-315
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• 2014

The domestic maritime environment shows higher frequency of maritime accidents amidst greater traffic volume arising from increasing international seaborne trade and maritime leisure activities. To reduce such maritime accidents, there exist various kinds of safety navigation devices in the ship bridge aimed to mitigate burdens of navigators and support their accurate decision making. Amongst is the AIS considered very important, which is an automatic tracking system to assist understanding of the circumstances in the vicinity by receiving information of other ships and also sending its own; where the information contains errors initially, however, such wrong information is periodically transmitted, accordingly giving rise to hindrance sometimes in decision making by shore operators or ship navigators at sea. This study is to propose the error data and field management algorithm using fuzzy theory toward improving reliability and accuracy in ship related information received from AIS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.8-17
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• 2017

Infrastructures such as bridges and tunnels are crucial elements of national economic growth, and sudden collapses may lead to great catastrophes with significant social and economic losses, as well as a loss of lives. Hence, an efficient maintenance technique must be applied to guarantee safety, secure budgets to maintain a certain level of service, and prevent maintenance expenditures from being concentrated in a specific time period. Developed countries have experienced rapid increases in maintenance budgets, and maintenance costs now account for about 40% of the total maintenance budget. The level of service in asset management systems is an essential element for setting management goals and making priority decisions. Therefore, this study uses fuzzy theory to develop a new way to assess the level of service.The assessment model was applied to an actual bridge to evaluate the level of service for users.

• Journal of Korea Port Economic Association
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• v.26 no.2
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• pp.1-18
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• 2010

With the maturity and fierce competition of domestic logistics market, Korea government is actively working on the overseas investments in global logistics market after establishing the basic plan since 2006. In particular, India is selected one of promising countries for logistics investments since it has more 1.1 billion people and is regarded as post-China. In fact, a number of global logistics enterprises have started their businesses in the logistics market of India so that the competition already started. In this regard, it is highly necessary to find out feasible investment projects and then detemin the priority of the alternatives for successful investments. Therefore this study proposes a fuzzy-based AHP model by which the overseas investment problem was systematically structured and then evaluated. The model was established by exploiting a fuzzy theory and AHP for capturing the inexactness and vagueness of information. The results show that the investment of port operations is the number one priority in the India's logistics market and ODCY operations, road transportations, forwarding operations, inland depot operations in order. Finally the proposed model will help Korea's policy makers to have a better reliable investment strategy.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.736-741
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• 2006

The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.