• Environmental and Resource Economics Review
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• v.17 no.1
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• pp.147-164
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• 2008

When measuring the value of environmental attributes of housing, the conventional Hedonic Pricing Method assumes market equilibrium. Thus each attribute is believed to be implicitly valued based on the market price. The revealed preference is the basic logic in this approach. However, if the participants in the housing market are not perfectly informed or feel vagueness regarding the attributes of the housing, the conventional Hedonic Pricing Approach could not provide relevant value of the attribute in question. A Fuzzy Regression Method is suggested to handle with the lack of information or preference uncertainty problem m the Hedonic Pricing Approach. In this paper, our main concern IS given to the fuzziness effect on the airport noise in the metropolitan areas of South Korea.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.1-10
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• 2009

Wooded areas that are a part of school campuses are one type of urban forest. Most schools located in an urban environment make an excellent setting for a forest in terms of location and area. These kinds of wooded spaces also make the city greener and healthier. As a place where students spend a great deal of time, schools can also be a venue for environmental education. The creation of wooded areas in schools currently has focused on the end result only; by ignoring student needs and participation, these areas have not had a significant influence on student environmental education. Previous studies based on questionnaire survey are significant in that they have quantified subjective qualitative data via Likert Scale. There has been, however, a problem in quantifying the more ambiguous subjective data. Therefore, this paper has attempted to investigate those factors that have an influence on student satisfaction with the wooded areas of their school campus using Fuzzy Theory with elementary school students in Gyeongsangbuk-do. A change was observed in terms of the ranking of arithmetic mean values of 'school peculiarity' and 'emotion evolution' and center of gravity, which has adopted Fuzzy Theory, proving that Fuzzy Theory could rationally objectify qualitative data such as human thoughts. In terms of the influential factors on the satisfaction with school forests(regression coefficient), 'school uniqueness(0.159)' was the highest, followed by 'many trees(0.142),' 'importance of nature(0.136)' and 'emotion evolution(0.130).' This paper may therefore be useful as basic data for objective questionnaire surveys and the development of school forests.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.5
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• pp.3-10
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• 2018

The purpose of this study is to estimate the attractiveness value according to the preference level on architectural aesthetic. This research starts from the concept that aesthetic is phenomenon and from the viewpoint of 'attraction value' which affects goods. Interactive internet surveys were conducted for 500 citizens of Seoul metropolitan city who are potential users of the Nodle Islet Cultural Center. Based on the scenarios and questionnaires with fuzzy models, we have examined the evaluation of architectural aesthetic and monetary willing-to-payment, and estimated the economic value by preference level of architectural aesthetic through linear regression analysis. The main results of the study are as follows: First, the economic value of the Nodle Islet Cultural Center was estimated at ?15,683.43/person. Residents of Seoul metropolitan city were willing to accept the increase in the above-mentioned amount of the facility fares when their preferred works (average 86.81 points) were constructed. (P <0/05) Second, it is confirmed that the economic value increases dramatically as the preference level of architectural aesthetic increases. Third, it is presumed that the infinite valuation of architectural aesthetic and the problem of free riding coexist in the estimation of economic valuation of architectural aesthetic for public buildings. Fourth, by mixing the fuzzy logic with contingent valuation method, starting point bias and no response biases that happened in contingent valuation could be disappeared. bias elimination must be considered seriously because another bias could be happened in full process of the research. The results of this study will serve as a basis for spreading architectural aesthetic value-oriented research from the vague and obscure aesthetic-centered discussion on the existing architectural aesthetic. In addition, it will be an opportunity to draw institutional application and utilization strategy of architectural aesthetic through architectural aesthetic value research.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.801-809
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• 2008

Nowadays almost all universities are endeavoring to improve the quality of education offered. The quality of education that is provided should be measured beforehand. This study examined three methods, SERVQUAL, SERVPERF and Lee and Yun(2004)'s method of measuring service quality: SERVQUAL, SERVPERF are well known and the most commonly used measurement of the quality of education offered and the Lee and Yun(2004)'s method measures service quality using fuzzy numbers. As a result of this research, SERVQUAL was proven to be the most efficient method to measure the quality of education offered. Even though, more actual analysis related to this study should be followed, the research is meaningful in a way that it provided clues and reasons for the study.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.88-96
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• 2010

This paper presents a two-phase quantitative framework to aid the decision making process for effective selection of an efficient freight logistics hub from 8 alternatives in Thailand on the North-South economic corridor. Phase 1 employs both multiple regression and Pearson Feature selection to find the important criteria, as defined by logistics hub score, and to reduce number of criteria by eliminating the less important criteria. The result of Pearson Feature selection indicated that only 5 of 15 criteria affected the logistics hub score. Moreover, Genetic Algorithm (GA) was constructed from original 15 criteria data set to find the relationship between logistics criteria and freight logistics hub score. As a result, the statistical tools are provided the same 5 important criteria, affecting logistics hub score from GA, and data mining tool. Phase 2 performs the fuzzy stochastic AHP analysis with the five important criteria. This approach could help to gain insight into how the imprecision in judgment ratios may affect their alternatives toward the best solution and how the best alternative may be identified with certain confidence. The main objective of the paper is to find the best alternative for selecting freight logistics hub under proper criteria. The experimental results show that by using this approach, Chiang Mai province is the best place with the confidence interval 95%.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.301-308
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• 2024

Concrete carbonation is a continuous and slow process from the outside to the inside, in which its penetration slows down with the increased depth of carbonation. In this paper, the results of the evaluation of the measurement of concrete carbonation depth using a non-destructive ultrasonic testing method are presented. According to the results, the relative nonlinear parameter caused more sensitivity in carbonation changes compared to Rayleigh's fuzzy velocity. Thus, the acoustic nonlinear parameter is expected to be applied as a quantitative index to recognize carbonation effects. In this research, combo diagrams were developed based on the results of ultrasonic testing and the experiment to determine carbonation depth using a phenolphthalein solution, which could be considered as instructions in the projects involving non-destructive ultrasonic test methods. The minimum and maximum accuracy of this method were 89% and 97%, respectively, which is a reasonable range for operational projects. From the analysis performed, some useful expressions are found by applying the regression analysis for the nonlinearity index and the carbonation penetration depth values as a guideline.