• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.265-271
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• 1992

It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a single-input single-output feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a non-minimum phase process. The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order. The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response. The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.

• Journal of Welding and Joining
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• v.28 no.6
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• pp.76-83
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• 2010

The purpose of this study is to propose a method to decide near-optimal settings of the constant current fuzzy control parameters using a controlled random search. This method tries to find the near-optimal settings of the constant current fuzzy control parameters through experiments. It has an advantage of being able to carry out searches in the search domain which includes some irregular points. The method suggested in this study was used to determine the fuzzy control parameters by which the desired welding current were formed during inverter DC resistance spot welding. The output variable was the ITAE (integral of time multiplied by the absolute error). This output variable was determined according to the input variables, which are the GE, GDE, and GDU. This study described how to obtained near-optimal welding current condition over a wide search space conducting a relatively small number of experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.119-123
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• 2005

This paper describes a fuzzy-based system for analyzing the stress intensity factors (SIFs) of three-dimensional (3D) cracks. 3D finite element method(FEM) was used to obtain the SIF for subsurface cracks and surface cracks existing in inhomogeneous materials. A geometry model, i.e. a solid containing one or several 3D cracks is defined. Several distributions of local node density are chosen, and then automatically superposed on one another over the geometry model by using the fuzzy theory. Nodes are generated by the bucketing method, and ten-noded quadratic tetrahedral solid elements are generated by the Delaunay triangulation techniques. The singular elements such that the mid-point nodes near crack front are shifted at the quarter-points, and these are automatically placed along the 3D crack front. The complete FE model is generated, and a stress analysis is performed. The SIFs are calculated using the displacement extrapolation method. The results were compared with those surface cracks in homogeneous materials. Also, this system is applied to analyze cladding effect of surface cracks in inhomogeneous materials.

• Tunnel and Underground Space
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• v.14 no.4
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• pp.275-286
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• 2004

It is studied to select the reinforcement method of an abandoned tunnel which are intersected under the new roadway line. In the various decision makings, the reasonability for the reinforcement method of an abandoned tunnel was estimated using the pair-wise comparison and the fuzzy approximate reasoning to simplify the process of survey research. And there is reflected all the qualitative and quantitative characterizations by investigation items. In order to select the reinforcement method of an abandoned tunnel, 4 characteristic factors of construction, economical efficiency, safety and maintenance were used. Using the simple survey research and pair-wise comparison matrix, the weight of 4 factors was decided. The fuzzy approximate reasoning was used to calculate the quantitative value of each factor And then reflecting each weight to these results, the final reinforcement method of an abandoned tunnel could be determined.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.209-216
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• 2005

Performance assessment has Problems about possibilities of assessment fault by appraisal, fairness, reliability, and validity of grading, ambiguity of grading standard, difficulty about objectivity security etc. This study proposes fuzzy Performance assessment system to solve problem of the conventional performance assessment. This Paper presented an objective and reliable performance assessment method through fuzzy reasoning, design fuzzy membership function and define fuzzy rule analyzing factor that influence in each sacred ground of performance assessment to account principle subject. Also, performance assessment item divides by formation estimation and subject estimation and designed membership function in proposed performance assessment method. Performance assessment result that is worked through fuzzy Performance assessment system can pare down burden about appraisal's fault and provide fair and reliable assessment result through grading that have correct standard and consistency to students.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.705-707
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• 1997

An optimal tuning algorithm of scaling factors is presented in this paper to automatically improve the performance of fuzzy controller. Especially, fuzzy controller has determined an moderate Scaling factor through trial and error. The presented method estimates automatically the optimal values of I/O scaling factors, using modified steepest descent method and this optimal tuning is for nonlinear input/output scaling factors. Simulation results verify the validity of the presented method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.605-614
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• 2021

Water inrush from fault is one of the most severe hazards during tunnel excavation. However, the traditional evaluation methods are deficient in both quantitative evaluation and uncertainty handling. In this paper, a comprehensive methodology method combined intuitionistic fuzzy AHP with a Bayesian network for the risk assessment of water inrush from fault in the subsea tunnel was proposed. Through the intuitionistic fuzzy analytic hierarchy process to replace the traditional expert scoring method to determine the prior probability of the node in the Bayesian network. After the field data is normalized, it is classified according to the data range. Then, using obtained results into the Bayesian network, conduct a risk assessment with field data which have processed of water inrush disaster on the tunnel. Simultaneously, a sensitivity analysis technique was utilized to investigate each factor's contribution rate to determine the most critical factor affecting tunnel water inrush risk. Taking Qingdao Kiaochow Bay Tunnel as an example, by predictive analysis of fifteen fault zones, thirteen of them are consistent with the actual situation which shows that the IFAHP-Bayesian Network method is feasible and applicable. Through sensitivity analysis, it is shown that the Fissure development and Apparent resistivity are more critical comparing than other factor especially the Permeability coefficient and Fault dip. The method can provide planners and engineers with adequate decision-making support, which is vital to prevent and control tunnel water inrush.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.17-27
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• 1996

More fuzzy hardware are expected to be utilized in the future to construct fuzzy logic controllers (FLCs). It is hard to find an existing fuzzy hardware which is adopting advanced functions such as self-tuning algorithm in addition to the conventional inference calculation. That is mainly because conventional self-tuning algorithms designed to implement with some hardware circuits is required for fuzzy hardwares to have self-tuning capability. As a first step toward the feature, a novel self-tuning algorithm is proposed in this paper. Based on the search method, the main idea of the proposed algorithm is to detemine valid ranges of input variables of an FLC in order to maximize performance indices fo the control system. The performance indices are so ismple as to be realized by hardware circuit. in dadditon to the conventional scaling-factor adjustment, the algorithm adjusts offset values as well, which, in effect, modifies fuzzy rules of the FLC. To justify the performance of the proposed algorithm, a simulation study is executed.