• Journal of Biomedical Engineering Research
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• v.27 no.6
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• pp.370-375
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• 2006

The purpose of this work is to predict the systolic blood pressure (BP) during exercise from pulse transit time (PTT) for warning of possible danger. PTT was calculated as the time between R-peak of ECG and the peak of differential photoplethysmograph (PPG). For the PTT-BP model, we used regress equations from previous studies and 3 kinds of new models combining linear and nonlinear regress equation. The model parameters were estimated with the data measured under low to middle intensity exercise, and then was tested with the data measured under high intensity exercise. Predicted BP values after high intensity exercise were compared with those measured by cuff-type sphygmomanometer. The results showed that the error between measured and predicted values were acceptable for the monitoring BP. We tested PTT-BP models 1 month after the identification without further calibration. Models could predict the BP and the errors between measured and predicted BP were about 5mmHg. The suggested system is expected to be helpful in recognizing any danger during exercise.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.18-22
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• 2012

This paper presents a method and the experimental measurement system for the determination of Jiles-Atherton model parameters of the 30ZH120 electrical steel sheet. The paper utilizes Epstein Square devices to proceed with the experiment and measurement on a group of hysteresis loops of some certain transformers which use the 30ZH120 electrical steel sheet under two different lap ways. The approach relies on the simulated annealing optimization method in order to minimize the error between the measured and modeled hysteresis curves and yield the best five Jiles-Atherton model parameters. A convenient program, based on the Simulink platform, that can identify the J-A model parameters automatically from the experimental saturated hysteresis loop which is used to model the nonlinear characteristics of the electrical steel sheet, is developed. Research shows that the simulated annealing optimization method gets satisfactory results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1714-1725
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• 1993

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terms, parameterized by the scalar factors a1, a2, a3 and Presents its structure. The analysis of convergence leads to eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexities of MLMS algorithms are compared with those of the conventional LMS, sign, LFG, and QFG algorithms. The properties of convergence in the mean square are analyzed and the expressions of the mean square recursion and the excess mean square error are derived. The necessary condition for the CFG algorithm to be stable is attained. In the computer simulation applied to the system identification the CFG algorithm has the more computation complexities but the faster convergence speed than LMS, LFG and QFG algorithms.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.259-266
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• 2015

This study presents new scheme for various walking pattern of biped robot under the limitted enviroments. We show that the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multilayer backpropagation neural network identification is simulated to obtain a learning control solution of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The main advantage of our scheme is that we do not require any knowledge about the system dynamic and nonlinear characteristic, and can therefore treat the robot as a black box. It is also shown that the neural network is a powerful control theory for various trajectory tracking control of biped robot with same learning-vase. That is, we do net change the control parameter for various trajectory tracking control. Simulation and experimental result show that the neural network is practically feasible and realizable for iterative learning control of biped robot.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, 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 abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.43-50
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• 2001

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2856-2858
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• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.5
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• pp.401-414
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• 2018

Planing hull type ships are often equipped with interceptor or trim tab to improve the excessive trim angle which leads to poor resistance and sea keeping performances. The purpose of this study is to design a controller to control the attitude of the ship by controllable stern interceptor and validate the effectiveness of the attitude control by the towing tank test. Embedded controller, servo motor and controllable stern interceptor system were equipped with planing hull type model ship. Prior to designing the control algorithm, a model test was performed to identify the system dynamic model of the planing hull type ship including the stern interceptor. The matrix components of model were optimized by Genetic Algorithm. Using the identified model, PID controller which is a classical controller and sliding mode controller which is a nonlinear robust controller were designed. Gain tuning of the controllers and running simulation was conducted before the towing tank test. Inserting the designed control algorithm into the embedded controller of the model ship, the effectiveness of the active control of the stern interceptor was validated by towing tank test. In still water test with small disturbance, the sliding mode controller showed better performance of canceling the disturbance and the steady-state control performance than the PID controller.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.3
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• pp.275-281
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• 2015

In this study, vertical motion of a Tuned Liquid Column Damper(TLCD) is measured by a variable voltage measurement system in the electric field and design parameters of the TLCD are determined. First, nonlinear damping term of the TLCD is replaced as the equivalent viscous damping term. The natural frequency and damping ratio of dynamic characteristics of the TLCD are verified. In addition, a novel liquid level measurement system is developed for measuring vertical motion of the TLCD. For the experimental achievement, experimental characterizations of natural frequency and damping ratio of the TLCD are undertaken utilizing the developed variable voltage sensing. Also, shake table testing is performed to determine the dynamic characteristics of the TLCD. As a result, the feasibility of the proposed liquid level measurement system is verified by comparison with the capacitive type wavemeter.