• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.852-854
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• 2000

The paper is described about the statatic and dynamic characteristics analysis of Transverse Swithed Reluctance Motor(TSRM). To investigate the nonlinearity of magnetic circuit, parameters of the modeling are computed by the finite element method as functions of input current and angular displacement. The current, torque, back EMF and output power wave of TSRM are simulated from the motion equation by MATLAB/Simulink.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2314-2316
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• 2000

In the construction of successful fuzzy models and/or controllers for nonlinear systems, identification of a good fuzzy Neural inference system is an important yet difficult problem, which is traditionally accomplished by trial and error process. In this paper, we propose a systematic identification procedure for complex multi-input single- output nonlinear systems with DNA coding method.DNA coding method is optimization algorithm based on biological DNA as are conventional genetic algothms (GAs). We also propose a new coding method for applying the DNA coding method to the identification of fuzzy Neural models. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1952-1954
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• 2001

In this paper, we propose the neuro-fuzzy modeling method using VmGA (Virus messy Genetic Algorithm) for the complex nonlinear system. VmGA has more effective and adaptive structure than sGA. in this paper, we suggest a new coding method for applying the model's input and output data to the optimal number of rules in fuzzy models and the structure and parameter identification of membership functions simultaneously. The proposed method realizes the optimal fuzzy inference system using the learning ability of neural network. For fine-tune of parameters identified by VmGA, back- propagation algorithm is used for optimizing the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through comparing with ANFIS.

• Structural Engineering and Mechanics
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• v.60 no.6
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• pp.1093-1117
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• 2016

Global sensitivity analysis (GSA) has been widely used to investigate the sensitivity of the model output with respect to its input parameters. In this paper a new single-solution search optimization algorithm is developed based on the GSA, and applied to the size optimization of truss structures. In this method the search space of the optimization is determined using the sensitivity indicator of variables. Unlike the common meta-heuristic algorithms, where all the variables are simultaneously changed in the optimization process, in this approach the sensitive variables of solution are iteratively changed more rapidly than the less sensitive ones in the search space. Comparisons of the present results with those of some previous population-based meta-heuristic algorithms demonstrate its capability, especially for decreasing the number of fitness functions evaluations, in solving the presented benchmark problems.

• ETRI Journal
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• v.41 no.3
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• pp.298-307
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• 2019

This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

• Elastomers and Composites
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• v.57 no.4
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• pp.147-156
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• 2022

In this study, we chose a flat die to optimize a general die geometry. The optimization was aimed at obtaining a uniform velocity distribution across the exit of the die. For the optimization, the input and output design parameters were randomly computed, and response surfaces were generated to obtain statistical data for the minimum and maximum sensitivities computed during optimization. Subsequently, object functions with constraints were numerically computed to obtain the minimum errors in the velocity difference (i.e., variable "Outp" in this study). Finally, we obtained the candidate optimized dataset. Note that the current numerical computations were simultaneously conducted for an entire extruder, i.e., screw plus die. The numerical outlet velocity distributions in the modified die geometry tended to be much more uniform than the conventional distributions in the current optimization processes for this specific flat die.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.36-39
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• 2009

We developed an optimal design interface based on VBA(Visual Basic Application) that takes advantage of API(Application Program Interface) function of commonly used EM analysis software. The developed interface is adopted for an optimal design of a septum in a waveguide tee junction using PSO(Particle Swarm Optimization) algorithm. The objective function of the optimal design is defined by $S_{11}$-parameter of the waveguide tee junction Design variables are established as position of the septum, that are changed to satisfy the design goal Using the developed design interface and PSO algorithm, the objective function converged to the smallest value, showing the validity of the proposed method. The design interface was developed using Microsoft Excel software, enabling easy control of design parameters for user. Also, various analysis parameters can be set in the Excel interface, including waveguide input mode and frequency. After completion of the design, field solutions at user-specified positrons can be extracted to the output files in complex number form.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.465-471
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• 2003

The purpose of this research was to evaluate chronic stress using physiological parameters. Wistar rats were exposed to the sound stress for 14 days. Biosignals were acquired hourly. To develop a fuzzy inference system which can integrate physiological parameters. the parameters of the system were adjusted by the adaptive neuro-fuzzy inference system. Of the training dataset, input dataset was the physiological parameters from the biosignals and output dataset was the target values from the cortisol production. Physiological parameters were integrated using the fuzzy inference system. then 24-hour results were analyzed by the Cosinor method. Chronic stress was evaluated from the degree of circadian rhythm disturbance. Suppose that the degree of stress for initial rest period is 1. Then. the degree of stress after 14-day sound stress increased to 1.37, and increased to 1.47 after the 7-day recovery period. That is, the rat was exposed to 37%-increased amount of stress by the 14-day sound and did not recover after the 7-day recovery period.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.217-220
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• 2001

The fuzzy equalization method does not require the usual learning step for generating fuzzy rules. However it is heavily depend on the given input-output data set. So, we adapt an hierarchical scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership functions obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to the Rice taste data and got better results than previous ones.