• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.33-44
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• 2014

Recently, blogs have attracted much attention as personal media. The power of blogs as a way to provide valuable resources on Internet is so tremendous because of the high speed of information dissemination and the huge influence of the circulated information on Internet users even when the information itself is not true. Especially, contents on blogs that attract a lot of public attention are sometimes reproduced or magnified in an inappropriate way. In this paper, a method to measure the trust level of contents posted on personal blogs is proposed to reduce the damage of wrong information circulated along with blog networks. Trust variables such as relationship data in SNS are used to measure the comparative trust level of blog contents. The structure of the prototype system is also designed to apply this framework to blogsphere.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1131-1140
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• 2017

This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.

• The Korean Journal of Franchise Management
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• v.8 no.1
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• pp.31-41
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• 2017

Purpose - As the domestic coffee market is rapidly growing and competition is intensifying, coffee shops need to establish a marketing strategy that grasps the needs and desires of consumers in order to secure a competitive advantage in terms of survival. From this point of view, this study suggests what choice attributes consumers consider when visiting coffee shops, and analyzes the effect of customer choice attributes on franchise and private coffee shops using fsQCA. Research design, data, and methodology - In the present study, we tried to understand the effect of the combination of choice attribute on satisfaction by the type of coffee shop based on the complex system theory, while studying the existing coffee shop choice attribute focuses on the causal relationship. FsQCA is a complementary analytical method between quantitative and qualitative research, and is a method for effectively analyzing the complex combination of causal variables. Result - The results of the study are as follows. First, cleanliness was found to be the most important factor in determining coffee quality, which is the most important factor affecting customer satisfaction. Second, customers who prefer franchise coffee shops seem to be most concerned about atmosphere, menu, cleanliness and price. On the other hand, customers who prefer private coffee shops consider image the most important. Conclusions - The implications of this study are as follows. Overall, coffee shops should manage cleanliness basically regardless of the type of store, but they should manage the choice attributes differently depending on the type of coffee shop. Franchise coffee shops will be able to increase the level of store satisfaction by systematically managing the store atmosphere, menu, cleanliness, and price according to the manual using the advantages of the franchise system. On the other hand, unlike the franchise coffee shops, private coffee shops can operate autonomous stores, so customers can use various marketing mixes to enhance their store image.

• 한국사회정책
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• v.19 no.3
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• pp.185-214
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• 2012

After the introduction of the three welfare regimes by Esping-Andersen, discussion on 'other' types of welfare regimes was facilitated and the scholarly focus on East Asian economic development gradually shifted to the East Asian welfare states discussion from the late 1990s. Literature on East Asian welfare states increased our understanding on the characteristics of not only the East Asian welfare state as a whole but also of each country in the region. However, compared the attention given to developing variables and empirical studies on the East Asian welfare state, less attention has been given to the concept of East Asian welfare states. Recognizing the limitation in developing comparable variables without a concept analysis of the East Asian welfare states, this study highlights the importance of conceptualization and concept analysis in comparative social policy studies. This paper first discusses on the concepts, conceptualization and on the use of set theory in comparative social policy research. Next, the study argues the validity of 'functional equivalence' in the East Asian welfare state studies and critically reviews the existing literature. Lastly, this paper suggests how the concept of functional equivalence can be successfully employed for the East Asian welfare states studies with a concept analysis and by applying a set theory including the fuzzy set theory.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.869-874
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• 2005

In the recent years there has been an increase of service competition in the activity of product selling, especially in the extension of warranty coverage and qualify. The variables in connection with the service competition are not crisp, and required the expertise of the production line. It thus becomes all the more necessary to use subtler tools as decision supports. These problems are typical not only of product companies but also of financial organizations, credit institutions, insurance, which need predictions of credibility for firms or persons in which they have any kind of interest. A suitable approach for minimizing the risk is to use a knowledge-based system. Most often expert systems are not standalone programs, but are embedded into a larger application. The aim of this paper is to discuss an approach for developing an embedded fuzzy expert system with respect to the product selling policy, especially to present the decision system of automobile selling activity around the extension of warranty coverage and quality. We use the MATLAB tools which integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Also, we present the API functions embedding into the existing application.

• Journal of Korea Port Economic Association
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• v.26 no.3
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• pp.221-239
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• 2010

This study aims to provide solutions concerned with to residential moving companies about their operational problems. In order to shed light on these problem, factor analysis and fuzzy AHP method are adopted. Selected factors are low quality of workers and equipments, weak condition of business, policy and form of a contract, and excessive competition. The results of survey show that excessive competition is the most urgent problem compared with other problems. In the twelve-measured variables, The problem of non-certificated firms, low service quality caused by excessive competition, and claims caused by lack of service instruction are chosen as the urgent matters. As a result of the analyses, the study would propose policy directions how to solve these problems. Firstly, the government should make the legal basis of operating the industry because there is too competitive in the field. Secondly, the government should regulate firms which have not a certificate because they are the cause of low service quality in the field. Thirdly, in order to improve the service quality, the study would suggest that the managers should instruct the workers of residential moving companies. Lastly, the paper would suggest that customers check the certificate of the firm allowed by the government authorities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.242-247
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• 2015

In this study, we propose the design of Radial Basis Function Neural Network(RBFNN) classifier in order to classify between precipitation and non-precipitation echo. The characteristics of meteorological radar data is analyzed for classifying precipitation and non-precipitation echo. Input variables is selected as DZ, SDZ, VGZ, SPN, DZ_FR, VR by performing pre-processing of UF data based on the characteristics analysis and these are composed of training and test data. Finally, QC data being used in Korea Meteorological Administration is applied to compare with the performance results of proposed classifier.