• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.193-198
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• 2004

This paper describes an automatic finite element (FE) mesh generation for three-dimensional structures consisting of tree-form surfaces. This mesh generation process consists of three subprocesses: (a) definition of geometric model, (b) generation of nodes, and (c) generation of elements. One of commercial solid modelers is employed for three-dimensional solid structures. Node is generated if its distance from existing node points is similar to the node spacing function at the point. The node spacing function is well controlled by the fuzzy knowledge processing. The Voronoi diagram method is introduced as a basic tool for element generation. Automatic generation of FE meshes for three-dimensional solid structures holds great benefits for analyses. Practical performances of the present system are demonstrated through several mesh generations for three-dimensional complex geometry.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.329-338
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• 2012

This paper presents an effective design method for a gas identification system. The design method adopted the sequential combination between the hybrid genetic algorithms and the TSK fuzzy logic system. First, the sensor grouping method by hybrid genetic algorithms led the effective dimensional reduction as well as effective pattern analysis from a large volume of pattern dimensions. Second, the fuzzy identification sub-models allowed handling the uncertainty of the sensor data extensively. By these advantages, the proposed identification model demonstrated high accuracy rates for identifying the five different types of gases; it was confirmed throughout the experimental trials.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• Proceedings of KOSOMES biannual meeting
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• 2004.11a
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• pp.83-88
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• 2004

This paper suggests on evaluation of risk level for damage of marine accidents in SRRs. This paper intoduces a concept of fuzzy logic with the plenty of related literature riview, fuzzy measure t-seminormed fuzzy integral and in the Korean. SRRs of RCC and RSC. The methodology of this paper is max$\cdot$min composition of fuzzy extensive principle, defuzzifiation is centroid of gravity methods. And final evaluation value using t-seminormed fuzzy integral. At the result, the evaluation of risk level is especially over Serious for marine accident of Mokpo, Tongyoung, Busan SRRs. This paper recommends tint many Rescue Vessels and Equipments need to the reduction of risk level about those.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.347-353
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• 2019

Recent recommendation systems are developing toward the utilization of high-dimensional data. However, high-dimensional data can increase algorithm complexity by expanding dimensions and be lower the accuracy of recommended items. In addition, it can cause the problem of data sparsity and make it difficult to provide users with proper recommended items. This study proposed an algorithm that classify users' subjective data with objective criteria with fuzzy-AHP and make use of rules with repetitive patterns through fuzzy association rules. Trying to check how problems with high-dimensional data would be mitigated by the algorithm, we performed 5-fold cross validation according to the changing number of users. The results show that the algorithm-applied system recorded accuracy that was 12.5% higher than that of the fuzzy-AHP-applied system and mitigated the problem of data sparsity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.362-365
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• 2007

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.802-807
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• 2010

The objective of this study was to estimate potential of domestic long-term wood supply by using fuzzy linear programming (FLP). In order to construct a numerical formula model, maximization of total timber production was used for the objective function. Size limit of harvesting and sustained yield were used as the constraints. The results of comparison between LP and FLP were shown that LP is more suitable than FLP in terms of the amount of timber production and final forest stock. However, as long-term sustained yield was limitedly achieved by using LP, FLP was more desirable for prediction of potential wood supply. According to the results of this study, the potential of annual domestic wood supply was estimated about 10.5 million cubic meters. Gyeong buk, Jeon nam, Gangwon and Gyeong nam province were highly ranked in order of provincial potential of wood supply.