• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.66-67
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• 2005

In this study, in order to develop multilayer piezoelectric actuator, PMN-PNN-PZT ceramics were fabricated using $Li_2CO_3-Na_2CO_3$ as sintering aids and their piezoelectric and dielectric characteristics were investigated with the function of PNN substitution. With increasing PNN substitution, dielectric constant(${\varepsilon}r$), electromechanical coupling factor(kp), and piezoelectric d constant($d_{33}$) were increased at 10~12[mol%] PNN substitution and then decreased at all sintering temperature. With increasing PNN substitution, phase changed from tetragonal to rhombohedral at [10~12mol%] PNN substitution. At the 12[mol%] PNN substituted PMN-PZT composition ceramic sintered at 950[$^{\circ}C$], density, ${\varepsilon}r$, kp, $d_{33}$ and Qm showed the optimum value of 7.79[$g/cm^3$], 1160, 0.599, 419[pC/N) and 894, respectively for multilayer piezoelectric actuator application.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.2
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• pp.90-94
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• 2016

In this work, [$Pb(Mg_{1/2}W_{1/2})_{0.03}(Ni_{1/3}Nb_{2/3})_x(Zr_{0.5}Ti_{0.5})_{0.97-x}O_3-BiFeO_3$] (x=0.02 to 0.12) composition ceramics were fabricated by the conventional soild state reaction method and their microstructure and piezoelectric properties were investigated according to PNN substitution. The addition of small amount of $BiFeO_3$, $Li_2CO_3$, and $CaCO_3$ were used in order to decrease the sintering temperature of the ceramics. The XRD (x-ray diffraction patterns) of all ceramics exhibited a perovskite structure. The sinterability of PMW-PNN-PZT-BF ceramics was remarkably improved using liquid phase sintering of $CaCO_3$, $Li_2CO_3$. However, it was identified from of the X-ray diffraction patterns that the secondary phase formed in grain boundaries decreased the piezoelectric properties. According to the substitution of PNN, the crystal structure of ceramics is transformed gradually from a tetragonal to rhombohedral phase. The x=0.10 mol PNN-substituted PMW-PNN-PZT-BF ceramics sintered at $920^{\circ}C$ showed the optimum values of piezoelectric constant($d_{33}$), piezoelectric figure of merit($d_{33{\cdot}}g_{33}$), planar piezoelectric coupling coefficient($k_p$) and density : $d_{33}=566$ [pC/N], $g_{33}=29.28[10^{-3}mV/N]$, $d_{33{\cdot}}g_{33}=16.57[pm^2/N]$, $k_p=0.61$, density=7.82 [$g/cm^3$], suitable for duplex ultrasonic sensor application.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1337-1338
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• 2007

In this study, in order to develop low temperature sintering ceramics for multilayer piezoelectric actuator, 0.07Pb$(Mn_{1/3}Nb_{2/3})O_{3}-xPb(Ni_{1/3}Nb_{2/3})O_{3}-(0.93-x)Pb(Zr,Ti)O_{3}$ ceramics system were fabricated using $Li_{2}CO_{3}-Bi_{2}O_{3}$-CuO sintering aids and the specimens were sintered at $930^{\circ}C$. Thereafter their piezoelectric and dielectric characteristics were investigated with the amount of PNN substitution. At 9[mol%] PNN substitution, electromechanical coupling factor (kp), mechanical quality factor (Qm) and piezoelectric constant ($d_{33}$) showed the optimum value of 0.60, 1323 and 387pC/N, respectively.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.9
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• pp.766-770
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• 2007

In this study, in order to develop low temperature sintering ceramics for multilayer piezoelectric actuator, $0.07Pb(Mn_{1/3}Nb_{2/3})O_3-xPb(Ni_{1/3}Nb_{2/3})O_3-(0.93-x)Pb(Zr,Ti)O_3$ ceramics system were fabricated using $Li_2CO_3-Bi_2O_3-CuO$ sintering aids and the specimens were sintered at $930^{\circ}C$. Thereafter, their piezoelectric and dielectric characteristics were investigated according to the amount of PNN substitution. At 9 mol% PNN substitution, density, electromechanical coupling factor ($k_p$), dielectric constant, mechanical quality factor ($Q_m$) and piezoelectric constant ($d_{33}$) showed the optimum value of $7.86g/cm^3$, 0.60, 1640, 1323 and 387 pC/N, respectively. It is considered that these values are suitable for piezoelectric divece application such ad multilayer piezoelectric actuator and ultrasonic vibrator with pure Ag internal electrode.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.483-492
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• 2004

This paper presents an efficient algorithm for the estimation of damage location and severity in bridge structures using Probabilistic Neural Network (PNN). Generally, the Back Propagation Neural Network (BPNN)-based damage detection methods need a lot of training patterns for neural network learning process and the optimum architecture of a BPNN is selected by trial and error. In this paper, the PNN instead of the conventional BPNN is used as a pattern classifier. The modal properties of damaged structure are somewhat different from those of undamaged one. The basic idea of proposed algorithm is that the PNN classifies a test pattern which consists of the modal characteristics from damaged structure, how close it is to each training pattern which is composed of the modal characteristics from various structural damage cases. In this algorithm, two PNNs are sequentially used. The first PNN estimates the damage location using mode shape and the results of the first PNN are put into the second PNN for the damage severity estimation using natural frequency. The proposed damage assessment algorithm using the PNN is applied to a cable-stayed bridge to verify its applicability.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.189-200
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• 2004

In this paper, we introduce a new soft computing technique that dwells on the ideas of combining fuzzy rules in a fuzzy system with polynomial neural networks (PNN). The PNN is a flexible neural architecture whose structure is developed through the modeling process. Unfortunately, the PNN has a fatal drawback in that it cannot be constructed for nonlinear systems with only a small amount of input variables. To overcome this limitation in the conventional PNN, we employed one of three principal soft computing components such as a fuzzy system. As such, a space of input variables is partitioned into several subspaces by the fuzzy system and these subspaces are utilized as new input variables to the PNN architecture. The proposed soft computing technique is achieved by merging the fuzzy system and the PNN into one unified framework. As a result, we can find a workable synergistic environment and the main characteristics of the two modeling techniques are harmonized. Thus, the proposed method alleviates the problems of PNN while providing superb performance. Identification results of the three-input nonlinear static function and nonlinear system with two inputs will be demonstrated to demonstrate the performance of the proposed approach.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1075-1084
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• 2005

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1026-1035
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• 1997

In this paper, we studied for phoneme recognition using GPFN and PNN as a kind of RBFN. The structure of RBFN is similar to a feedforward networks but different from choosing of activation function, reference vector and learnign algorithm in a hidden layer. Expecially sigmoid function in PNN is replaced by one category included exponential function. And total calculation performance is high, because PNN performs pattern classification with out learning. In phonemerecognition experiment with 5 vowel and 12 consant, recognition rates of GPFN and PNN as a kind of RBFN reflected statistic characteristic of speech are higher than ones of MLP in case of using test data and quantizied data by VQ and LVQ.