• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.199-199
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• 2000

Most of linear time-varying(LTV) systems except special cases have no general solution for the dynamic equations. Thus, it is difficult to design time-varying controllers in analytic ways, and other control design approaches such as robust control have been applied to control design for uncertain LTI systems which are the approximation of LTV systems have been generally used instead. A robust control method such as quantitative feedback theory(QFT) has an advantage of guaranteeing the stability and the performance specification against plant parameter uncertainties in frozen time sense. However, if these methods are applied to the approximated linear time-invariant(LTI) plants which have large uncertainty, the designed control will be constructed in complicated forms and usually not suitable for fast dynamic performance. In this paper, as a method to enhance the fast dynamic performance, the approximated uncertainty of time-varying parameters are reduced by the proposed QFT parameter-scheduling control design based on radial basis function (RBF) networks for LTV systems with bounded time-varying parameters.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.439-442
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• 1997

In our research, we will extract diagnostic parameters by LPC method and wavelet transform. Then, we will design artificial neural network which is based on RBF that can express input features in terms of fuzzy. Because PVC(Premature Ventricular Contraction) has possibility to cause heart attack, the detection of PVC is a very significant problem. To deal with this problem, LPC method which gives different coefficients or different morphologies and wavelet transform which has superior localization nature of time-frequency, are used to extract effective parameters or classification of normal and PVC. Because RBF network can allocate an input feature to the membership degree of each category, total system will be more flexible.

• Journal of The Korean Astronomical Society
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• v.45 no.1
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• pp.1-6
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• 2012

We use a Probabilistic Neural Network (PNN) technique to derive the orbital parameters of spectroscopic binary stars. Using measured radial velocity data of five double-lined spectroscopic binary systems (i.e., EQ Tau, V376 And, V776 Cas, V2377 Oph and EE Cet), we find the corresponding orbital and spectroscopic elements. Our numerical results are in good agreement with those obtained by other groups via more traditional methods.

• The Journal of Korean Medicine
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• v.38 no.4
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• pp.41-54
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• 2017

Objectives: The purpose of this study is to investigate the effects of Sa-am acupuncture with Pericardium jung-gyuck (SA-acu-PC) and Triple Energizer jung-gyuck (SA-acu-TE) on the radial pulse in healthy human subjects. Methods: Sixty healthy subjects participated in this study and divided into three groups randomly: Control group(C), Pericardium jung-gyuck group(PC), Triple Energizer jung-gyuck group(TE). Radial pulse was measured by 3 dimensional pulse imaging system (DMP-3000) before, right after, 30 minutes after, and 60 minutes after acupuncture. Results: 1. The changes observed in periodic parameters are considered as the common effects of both SA-acu-TE and SA-acu-PC. SA-acu-TE was more effective than SA-acu-PC in these effects. 2. The effects of SA-acu-PC appeared in the pulse amplitude, pulse area, and pulse power volume mainly at the left chon position, and 3. The effects of SA-acu-TE appeared in the radial augmentation index and main peak angle mainly at the right gwan position. Conclusions: The effects of SA-acu-PC and SA-acu-TE can be observed in the radial pulse. The increases in pulse amplitude, pulse area, and pulse power volume at the left chon may imply the effects of SA-acu-PC. And the decrease in the pulse power volume, the increases in radial augmentation index and main peak angle may imply the effects of SA-acu-TE.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.554-560
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• 1998

Acoustic performance of the absorptive mufflers used in air-conditioning and ventilating ducts, gas turbines, industrial fans influenced by the design parameters such as the length of mufflers, lining thickness, properties of the acoustic lining, and so forth. So we predicted the acoustic performance according to the design parameters. For the analysis, we assumed the radial propagation constant is locally reacted to the acoustic lining, and analyzed the acoustic performance based on the plane wave theory by using four-pole parameters.

• Transactions of Materials Processing
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• v.9 no.6
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• pp.561-566
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• 2000

Recently, the hydroforming technology has been recognized as a general technique in manufacturing industry, especially in automotive industry. Hydroforming is applied to increase strength, and to decrease weight, cost and parts. Hydroforming is based on the inflation of, for Instance, a tube, coupled with axial or radial compression and by subsequent expansion and sizing against the die wall. Expansion, axial feeding, calibration are important parameters in this process. In this paper, the effects of various parameters such as internal pressure, axial feeding and friction on hydroforming of automotive bumper rail have been considered.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.9
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• pp.898-906
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• 2008

A tested turbine consists of two stages, and an axial-type and a radial-type turbine are applied to the first and second stage, respectively. The mean diameter of the axial-type turbine rotor is 70 mm, and the outer diameter of the radial-type turbine is 68mm at the inlet. In this experiment, an axial-type turbine, two different radial-type turbines, and three different nozzle flow angles are applied to find the optimal design parameters. To compare the turbine performance, the net specific output torque is evaluated. The test results show that the nozzle flow angle on the first stage is a more important parameter than other design parameters for partially admitted small turbines to obtain high operating torque. For a 3.4% partial admission rate, the net specific output torque is increased by 13% with the addition of a radial-type rotor to the second stage when the turbine operates at $75^{\circ}$ nozzle flow angle.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.2
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• pp.154-161
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• 2005

In the machine tool industry, direct drive linear motor technology is an interesting means to achieve high acceleration, and to increase reliability. This paper analyzes and compares the characteristics of a tubular linear motor with Halbach and radial magnet array, respectively. First, the governing equations are established analytically in terms of the magnetic vector potential and two dimensional cylindrical coordinate systems. Then, we derive magnetic field solutions due to the PMs and the currents. Motor thrust, flux linkage and back emf are also derived. The results are shown to be in good conformity with those obtained from the commonly used finite element method. Finally, control parameters are obtained from analytical solutions.