• Journal of Soil and Groundwater Environment
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• v.23 no.4
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• pp.26-41
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• 2018

The purpose of this study is to improve the Original DRASTIC Model (ODM) for the assessment of groundwater contamination vulnerability on the GIS platform. Miryang City of urban and rural features was selected for the study area to accomplish the research purpose. Advanced DRASTIC Model (ADM) was developed adding two more DRASTIC factors of lineament density and landuse to ODM. The fuzzy logic was also applied to ODM and ADM to improve their ability in evaluating the groundwater contamination vulnerability. Although the vulnerability map of ADM was a little simpler than that of ODM, it increased the area of the low vulnerability sector. The groundwater vulnerability maps of ODM and ADM using DRASTIC Indices represented the more detailed descriptions than those from the overlap of thematic maps, and their qualities were improved by the application of fuzzy technique. The vulnerability maps of ODM, ADM and FDM was evaluated by NO3-N concentrations in the study area. It was proved that ADM including lineament density and landuse factors produced a more reliable groundwater vulnerability map, and fuzzy ADM (FDM) made the best detailed groundwater vulnerability map with the significant statistical results.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.4 s.111
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• pp.97-107
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• 2005

This study was carried out to construct accurate and scientific system of assessment indicators in selection of National Trust conservation areas, which was new concept of domestic environment movement and offer the raw data of new analytic method by introducing the fuzzy theory and weight for overcoming the uncertainty of ranking decision. To transform the Likert's scale granted to assessment indicators into the type of triangular fuzzy number(a, b, c), there was conversion to each minimum(a), median(b), and maximum(c) in applying membership function, and in using the center of gravity and eigenvalue, there was to decide the ranking. The rankings of converted values applied a mean importance and weight were confirmed that they were generally changed. Therefore, the ranking decision was better to accomplish objective and rational ranking decision by applying weight that was calculated in grouping of indicator than to judge the singular concept and to be useful in assessment of diverse National Trust site. In the future, because AHP, which was general method of calculating weight, was lacked, there was to understand the critical point to fix a pertinent weight, and to carry out the study applying engineering concept like fuzzy integral using $\lambda-measure$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3183-3192
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• 2009

The purpose of this paper is to introduce Naming application model for sensor networks. Currently, sensor networks comprised of sensor nodes have provided an application range which could not function before. However, unlike general network, current sensor networks are designed to cooperate with major wireless-capable sensor devices with limited resources. Thus, exporting/importing between individual sensor and current sensor networks is very inefficient and unstable. Attribute, schema and DIT(Directory Information Tree) must be designed for sensor network using SN LDAP application model in order to maintain transparency and provide constant service in a situation of data defect. With the system explained as above, Naming application model is made to manage SN Fuzzy Query. It shall be more efficient and stable structure as long as Naming application using a virtual equation in a certain environment with information collected from sensor node is provided. In this paper, I would like to introduce SN Fuzzy LDAP model for sensor network by quick Naming method. Also, naming application which is possible for fuzzy query in a certain environment based on the system will be proved.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.57-73
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• 2006

This paper describes a methodology for automating the material handling equipment (MHE) evaluation and selection processes by combining knowledge-based rules and fuzzy multi-criteria decision making approach. The methodology is proposed to solve the MHE selection problems under fuzzy environment. At the primary stage, the most appropriate MHE type among the alternatives for each material flow link is searched. Knowledge-based rules are employed to retrieve the alternatives for each material flow link. To consider and compare the alternatives, multiple design factors are considered. These factors include both quantitative and qualitative measures. The qualitative measures are converted to numerical measures using fuzzy logic. The concept of fuzzy logic is applied to evaluation matrices used for the selection of the most suitable MHE through a fuzzy linguistic approach. Thus, this paper demonstrates the potential applicability of fuzzy theory in the MHE applications and provides a systemic guidance in the decision-making process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3636-3643
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• 2011

To prevent the electrical accidents in the laboratory, we identify problems for periodic inspections of the electric field and develop a fuzzy inference system that can be practically applied to check items. Focusing on electrical safety in the lab environment, we draw check items that can be applied in common and develop a standard checklist that is consistent with the laboratory electrical safety and the periodic inspections. Using the standard checklist we select the items that may contain a linguistic ambiguity and define the membership functions for these items. We also have a safety rating defined by the membership function. Using these fuzzy variables we form the fuzzy rules in the form of 'If-Then' and develop a fuzzy inference system through the fuzzy engine. From this, electrical accidents could be prevented in advance continuously by managing the intelligent and efficient inspection and electrical safety to prevent the electrical accidents in the laboratory.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.59-65
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• 2020

The Internet of Things (IoT) era, in which all items used in daily life are equipped with a network connection function, and they are closely linked to increase the convenience of life and work, has opened wide. Robots also need to develop according to the IoT environment. A use of new type of evolved fuzzy machine (EFM) for generating legged robot trajectory in IoT enviornmentms is discussed in this paper. Fuzzy system has been widely used for describing nonlinear systems. In fuzzy system, determination of antecedent and consequent structures of fuzzy model has been one of the most important problems. EFM is described which carries out evolving antecedent and consequent structure of fuzzy system for legged robot. To generate the robot trajectory, parameters of each structure in the fuzzy system are tuned automatically by the EFM. The results demonstrate the performance of the proposed approach for the legged robot.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5229-5252
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• 2016

With proliferation of diverse network access technologies, users demands are also increasing and service providers are offering a Quality of Service (QoS) to satisfy their customers. In roaming, a mobile node (MN) traverses number of available networks in the heterogeneous wireless networks environment and a single operator is not capable to fulfill the demands of user. It is crucial task for MN for selecting a best network from the list of networks at any time anywhere. A MN undergoes a network selection situation frequently when it is becoming away from the home network. Multiple Attribute Group Decision (MAGD) method will be one of the best ways for selecting target network in heterogeneous wireless networks (4G). MAGD network selection process is predominantly dependent on two steps, i.e., attribute weight, decision maker's (DM's) weight and aggregation of opinion of DMs. This paper proposes Multi-Attribute Intuitionistic Fuzzy Group Decision Method (MAIFGDM) using TOPSIS for the selection of the suitable candidate network. It is scalable and is able to handle any number of networks with large set of attributes. This is a method of lower complexity and is useful for real time applications. It gives more accurate result because it uses Intuitionistic Fuzzy Sets (IFS) with an additional parameter intuitionistic fuzzy index or hesitant degree. MAIFGDM is simulated in MATLAB for its evaluation. A comparative study of MAIFDGM is also made with TOPSIS and Fuzzy-TOPSIS in respect to decision delay. It is observed that MAIFDGM have low values of decision time in comparison to TOPSIS and Fuzzy-TOPSIS methods.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.81-88
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• 2008

In the game balancing. it is so difficult to choose suitable arms among various actions, or arms and to accurately calculate to which level we adjust the balance. The fuzzy method can be properly used in a particular environment which cannot be correctly processed in mathematics or in lessening the time-consuming problems during the accurate number crunching. Because a variety of actions, relations with opponents. previous battle experiences etc. are not easy to be reflected in every occasion, the fuzzy method could be useful in these cases. When the balancing is needed. the data which have been played to that Point are processed by the fuzzy function and calculated to adapt intensity to each action. The ability of characters is regulated in this process. To demonstrate the efficiency of this method. I would like to make clear the excellence of fuzzy method through the following five experiments; a case with invariable ability adjustment, a case adjusted by a randomly chosen action, a case with the strongest weapon selection. a case with the weakest weapon selection and a case with the fuzzy method application.

• Computers and Concrete
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• v.5 no.6
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• pp.573-597
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• 2008

Among artificial intelligence-based computational techniques, adaptive neuro-fuzzy inference systems (ANFIS) are particularly suitable for modelling complex systems with known input-output data sets. Such systems can be efficient in modelling non-linear, complex and ambiguous behaviour of cement-based materials undergoing single, dual or multiple damage factors of different forms (chemical, physical and structural). Due to the well-known complexity of sulfate attack on cement-based materials, the current work investigates the use of ANFIS to model the behaviour of a wide range of self-consolidating concrete (SCC) mixture designs under various high-concentration sodium sulfate exposure regimes including full immersion, wetting-drying, partial immersion, freezing-thawing, and cyclic cold-hot conditions with or without sustained flexural loading. Three ANFIS models have been developed to predict the expansion, reduction in elastic dynamic modulus, and starting time of failure of the tested SCC specimens under the various high-concentration sodium sulfate exposure regimes. A fuzzy inference system was also developed to predict the level of aggression of environmental conditions associated with very severe sodium sulfate attack based on temperature, relative humidity and degree of wetting-drying. The results show that predictions of the ANFIS and fuzzy inference systems were rational and accurate, with errors not exceeding 5%. Sensitivity analyses showed that the trends of results given by the models had good agreement with actual experimental results and with thermal, mineralogical and micro-analytical studies.