• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.33-34
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• 2005

In this study, in odor to develop low temperature sintering multilayer piezoelectric actuator and ultrasonic vibrator, PMN-PNN-PZT ceramics were fabricated using $Li_2CO_3$ and $Na_2CO_3$ as sintering aids and their piezoelectric and dielectric characteristics were investigated according to the addition of dopant CuO and $Fe_2O_3$, respectively. The CuO added PMN-PNN-PZT ceramics improved mechanical quality factor Qm due to the acceptor doping effect. And also, $Fe_2O_3$ reacted as softner in this composition system in addition to the increase of grain size and sinterability. Taking into consideration electromechanical coupling factor kp of 0.62, dielectric constant $\varepsilon_r$, of 1275, Piezoelectric $d_{33}$ constant of 377[pC/N] and mechanical quality factor Qm of 975, it was concluded that the ceramics with the $Fe_2O_3$, added composition sintered at 900[$^{\circ}C$] were best for the multilayer piezoelectric actuator and ultrasonic vibrator application.

• Proceedings of the KIEE Conference
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• 1997.07d
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• pp.1330-1332
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• 1997

In this paper, PNN-PZN-PZT ceramics were fabricated with various mole ratio of the PZT[$Pb(Zr_{1/2}Tid_{1/2})O_3$]. PNN [$Pb(Ni_{1/3}Nb_{2/3})O_3$] and PZN[$Pb(Ni_{1/3}Nb_{2/3})O_3$ powders prepared by double calcination and PZT powders prepared by molten-salt synthesis method. The formation rate of perovskite phase in PNN-PZN-PZT ceramics could be obtained about 92% at PZT 0.3 mole ratio. The relative permittivity of specimen with PZT 0.3 mole ratio was shown 5,320 and appeared the relaxor ferroelectric feature. The maximum piezoelectric coefficient $d_{31}$ to be used for evaluation the displacement of piezoceramics in PNN-PZN-PZT ceramics was $324{\times}10^{-12}$(C/V) at the vicinity of morphotropic phase boundary and was larger than that of solid PZT ceramics($120{\times}10^{-12}C/V$).

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.195-200
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• 1998

Since the neural network was introduced, significant progress has been made on data handling and learning algorithms. Currently, the most popular learning algorithm in neural network training is feed forward error back-propagation (FFEBP) algorithm. Aside from the success of the FFEBP algorithm, polynomial neural networks (PNN) learning has been proposed as a new learning method. The PNN learning is a self-organizing process designed to determine an appropriate set of Ivakhnenko polynomials that allow the activation of many neurons to achieve a desired state of activation that mimics a given set of sampled patterns. These neurons are interconnected in such a way that the knowledge is stored in Ivakhnenko coefficients. In this paper, the PNN model has been developed using the plasma enhanced chemical vapor deposition (PECVD) experimental data. To characterize the PECVD process using PNN, SiO$_2$films deposited under varying conditions were analyzed using fractional factorial experimental design with three center points. Parameters varied in these experiments included substrate temperature, pressure, RF power, silane flow rate and nitrous oxide flow rate. Approximately five microns of SiO$_2$were deposited on (100) silicon wafers in a Plasma-Therm 700 series PECVD system at 13.56 MHz.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.254-259
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• 1998

A new design methology is proposed to identify the structure and parameters of fuzzy model using PNN and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and cubic besides the biquadratic polynomial used in the GMDH. The FPNN(Fuzzy Polynomial Neural Networks) algorithm uses PNN(Polynomial Neural networks) structure and a fuzzy inference method. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here a regression polynomial inference is based on consequence of fuzzy rules with a polynomial equations such as linear, quadratic and cubic equation. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture. In this paper, we will consider a model that combines the advantage of both FPNN and PNN. Also we use the training and testing data set to obtain a balance between the approximation and generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• 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 Korea institute for structural maintenance and inspection
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• v.6 no.4
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• pp.169-179
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• 2002

This paper presents an efficient algorithm for the estimation of damage location and severity in structure using Probabilistic Neural Network (PNN). Artificial neural network has been being used for damage assessment by many researchers, but there are still some barriers that must be overcome to improve its accuracy and efficiency. The major problems with the conventional neural network are the necessity of many training data for neural network learning and ambiguity in the relation of neural network architecture with convergence of solution. In this paper, PNN is used as a pattern classifier to overcome those problems in the conventional neural network. The basic idea of damage assessment algorithm proposed in this paper is that modal characteristics from a damaged structure are compared with the training patterns which represent the damage in specific element to determine how close it is to training patterns in terms of the probability from PNN. The training pattern that gives a maximum probability implies that the element used in producing the training pattern is considered as a damaged one. The proposed damage assessment algorithm using PNN is applied to a 2-span continuous beam model structure to verify the algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.11
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• pp.688-692
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• 2017

$Pb(Zr,\;Ti)O_3$ (PZT) is a piezoelectric material applied in a typical actuator and has been actively studied. However, in order to overcome the limitations of PZT, piezoelectric ceramics comprising mixed solid solutions of PZT with various relaxer electric materials have been studied. The $Pb(Zn_{1/3}Nb_{2/3})-Pb(Ni_{1/3}Nb_{2/3})-Pb(Zr,\;Ti)O_3$ (PZN-PNN-PZT) piezoelectric ceramic, known to have high piezoelectric constant and electromechanical coupling coefficient, was studied herein. The piezoelectric characteristics with various Zr contents (Zr/Ti ratios), PZN molar ratios, and sintering temperatures were compared. The piezoelectric properties of $d_{33}=580pC/N$ and $k_P=0.68$ were obtained with the $0.1PZN-0.2PNN-0.7PbZr_{0.46}Ti_{0.54}O_3$ composition sintered at $1,290^{\circ}C$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.799-811
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• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.