• Ocean Systems Engineering
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• v.5 no.3
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• pp.161-179
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• 2015

An emergency response system (ERS) for vessel oil spills is a complex and dynamic system comprising a number of subsystems and activities. Failures may occur during the emergency response operations, this has negative impacts on the effectiveness of the ERS. Of the classes of problems in analyzing failures, the lack of quantitative data is fundamental. In fact, most of the empirical data collected via questionnaire survey is subjective in nature and is inevitably associated with uncertainties caused by the human being's inability to provide complete judgement. In addition, incomplete information and/or vagueness of the meaning about the failures add difficulties in evaluating the effectiveness of the system. Therefore this paper proposes a framework to evaluate the ERS effectiveness by using the combination of fuzzy reasoning and evidential synthesis approaches. Based on analyzing the procedure of ERS for oil spills, the failures in the system could be identified, using Analytic Hierarchy Process(AHP)to determine the relative weight of identified failures. Fuzzy reasoning combined with evidential synthesis is applied to evaluate the effectiveness of ERS for oil spills under uncertainties last. The proposed method is capable of dealing with uncertainties in data including ignorance and vagueness which traditional methods cannot effectively handle. A case study is used to illustrate the application of the proposed method.

• Journal of Nutrition and Health
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• v.27 no.8
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• pp.856-863
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• 1994

To develop a optimized method for educing the errors associated with the estimation of portion size of foods, fuzzy-dta processing of portion size was performed. Cooked-rice was chosen as a food item. The experiment was conducted in two parts. First, to study the conceptions of respondents to bowl size(large, medium, small), 11 bowls of different size and shape were used and measured the actual weights of cooked-rice. Second, to study the conceptions of respondents to volume(1, 1/2, 1/3, 1/4), 16 different volumes of cooked-rice in bowls of same size and shape were used. Respondents for this study were 31 graduate students. After collecting the data of respondents to size and volume, fuzzy sets of size and volume were produced. The critical values were calculated by defuzzification(mean of maximum method, center of area method). The differences of the weights of cooked-rice in various bowl size and volume between the critical values and the calculated values by average portion size using in conventional methods were compared. The results hows large inter-subject variation in conception to bowl size, especially in large size. However, conception of respondents to volume is relatively accurate. Conception to bowl size seems to be influenced by bowl shape. Considering that the new fuzzy set was calculated by cartesian product(bowl size and volume), bowl shape should be considered in estimation of bowl size to make more accurate fuzzy set for cooked-rice portion size. The limitations of this study were discussed. If more accurate data for size and volume of many other food items are collected by the increased number of respondents, reducing the errors associated with the estimation of portion size of foods and rapid processing will be possible by constructing computer processing systems.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.91-100
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• 2016

Fuzzy matching is proposed to make propensities of two groups similar with their propensity scores and a way to select control variable to make propensity scores with a process that shows how to acquire propensity scores using logic regression analysis, is presented. With such scores, it was a method to obtain an experiment group and a control group that had similar propensity employing the Fuzzy Matching. In the study, it was proven that the two groups were the same but with a different distribution chart and standardization which made edge tolerance different and we realized that the number of chosen cases decreased when the edge tolerance score became smaller. So with the idea, we were able to determine that it is possible to merge groups using fuzzy matching without a precontrol and use them when data (big data) are used while to check the pros and cons of Fuzzy Matching were made possible.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.312-320
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• 2012

In this study, we introduce a new design methodology of a granular-oriented self-organizing polynomial neural networks (GoSOPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a methodological design strategy of GoSOPNNs as follows : (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context-based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets (so-called contexts) defined in the output space. (b) The proposed design procedure being applied at each layer of GoSOPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed GoSOPNN network, we describe a detailed characteristic of the proposed model using a well-known learning machine data(Automobile Miles Per Gallon Data, Boston Housing Data, Medical Image System Data).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.125-133
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• 2009

Security of biometric data is particularly important as the compromise of the data will be permanent. To protect the biometric data, we need to store it in a non.invertible transformed version. Thus, even if the transformed version is compromised, its valid biometric data are securely remained. Fuzzy vault mechanism was proposed to provide cryptographic secure protection of critical data(e.g., encryption key) with the fingerprint data in a way that only the authorized user can access the critical data by providing the valid fingerprint. However, all the previous results cannot operate on the fingerprint image with a few minutiae, because they use fixed degree of the polynomial without considering the number of fingerprint minutiae. To solve this problem, we use adaptive degree of polynomial considering the number of minutiae. Also, we apply multiple polynomials to operate the fingerprint with a few minutiae. Based on the experimental results, we confirm that the proposed approach can enhance the security level and verification accuracy.

• Journal of IKEEE
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• v.5 no.2 s.9
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• pp.190-200
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• 2001

This paper is addressed to welding defect feature vector selection and implementation method of welding defect classifier using fuzzy techniques. We compare IAV, zero-crossing number as time domain analysis, power spectrum coefficient as frequency domain, histogram as both domain for welding defect feature selection. We choose histogram as feature vector by graph analysis and find out that maximum frequent occurrence number and section of corresponding signal scale in relative histogram show obvious difference between normal welding and voiding with penetration depth defect. We implement a fuzzy welding defect classifier using these feature vector, test it to verify its effectiveness for 695 welding data frame which consist of 4000 sampled data. As result of test, correct classification rate is 92.96%. Lab experimental results show a effectiveness of fuzzy welding defect classifier using relative histogram for practical Laser welding monitoring system in industry.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.241-245
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• 2001

The existing fuzzy traffic controllers construct the rule-base based on the intuitive knowledge and experience or the standard rule-base, but the rule-base constructed by the above methods has difficulty in representing exactly and detailedly the control knowledge of the export and the operator. Therefore, in this paper, we propose a method that can improve the performance of the fuzzy traffic control by designing the fuzzy traffic controller which represents the control knowledge more exactly. The proposed method so modifies the position and shape of the fuzzy membership function based on the input-output data clustering that the fuzzy traffic controller can represent the control knowledge more exactly. Our method use the rough control knowledge based on intuitive knowledge and experience as the evaluation function for clustering the input-output data. The fuzzy traffic controller designed by the our method could represent the control knowledge of the expert and the operator more exactly, and it outperformed the existing controller in terms of the number of passed vehicles and the wasted green-time.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.139-144
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• 2008

Generally, real system that is the object of decision-making is very variable and sometimes it lies situations with uncertainty. To solve these problem, it has used statistical methods as significance level, certainty factor, sensitivity analysis and so on. In this paper, we propose a method for fuzzy decision-making based on MFAC(Multiple Fuzzy Alpha Cut) to improve the definiteness of classification results with similarity evaluation. In the proposed method, MFAC is used for extracting multiple a ${\alpha}$-level with proximity degree at proximity relation between relative Hamming distance and max-min method and for minimizing the number of data which are associated with the partition intervals extracted by MFAC. To determine final alternative of decision-making, we compute the weighted value between extracted data by MFAC From the experimental results, we can see the fact that the proposed method is simpler and more definite than classification performance of the conventional methods and determines an alternative efficiently for decision-maker by testing significance of sample data through statistical method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3A
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• pp.172-178
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• 2012

This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capabilities of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.369-374
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• 2004

A cluster merging algorithm that merges convex clusters resulted by the Fuzzy Convex Clustering(FCC) method into non-convex clusters was proposed. This was achieved by proposing a fast and reliable distance measure between two convex clusters using Support Vector Machines(SVM) to improve accuracy and speed over other existing conventional methods. In doing so, it was possible to reduce cluster number without losing its representation of the data. In this paper, results for several data sets are given to show the validity of our distance measure and algorithm.