• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.549-554
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• 2011

This paper presents the stabilization method for polynomial fuzzy large-scale system by using output feedback controller. Each sub system of the large-scale system is transformed into polynomial fuzzy model, and then output feedback controller is designed to stabilize the large-scale system. Stabilization condition is derived as sum-of-square (SOS) condition by applying the polynomial Lyapunov function. This condition can be easily solved by SOSTOOLS which is the third party of the MATLAB. From these solutions, output feedback controller gain can be obtained by SOS condition. Finally, a simulation example is presented to illustrate the effectiveness and the suitability of the proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.287-292
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• 2005

This paper presents a rational polynomial approximation method to estimate modal parameters of wind excited structures using incomplete noisy measurements of structural responses and partial measurements of wind velocities only. A stochastic model of the excitation wind force acting on the structure is estimated from partial measurements of wind velocities. Then the transfer functions of the structure are approximated as rational polynomial functions. From the poles and zeros of the estimated rational polynomial functions, the modal parameters, such as natural frequencies, damping ratios, and mode shapes are extracted. Since the frequency characteristics of wind forces acting on structures can be assumed as a smooth Gaussian process especially around the natural frequencies of the structures according to the central limit theorem (Brillinger, 1969; Yaglom, 1987), the estimated modal parameters are robust and reliable with respect to the assumed stochastic input models. To verify the proposed method, the modal parameters of a TV transmission tower excited by gust wind are estimated. Comparison study with the results of other researchers shows the efficacy of the suggested method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.5 s.108
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• pp.408-415
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• 2006

This paper suggests an improved system-level model of RF power amplifiers(PAs) including memory effects, and validates the suggested model by analyzing the power spectral density of the output signal with a predistortion linearizer. The original three-box(Wiener-Hammerstein) model uses input and output filters to capture RF frequency response of PAs. The adjacent spectral regrowth that occurs in three-box model can be perfectly removed by Hammerstein structure predistorter. However, the predistorter based on Hammerstein structure achieves limited performance in real PA applications due to other memory effects except RF frequency response. The spectrum of the output signal can be predicted accurately using the suggested model that changes a memoryless block in a three-box model with a memory polynomial. The proposed model accurately predicts the output spectrum density of PA with Hammerstein structure predistorter with less than 2 dB errors over ${\pm}30$ MHz adjacent channel ranges for IEEE 802.11 g WLAN signal.

• Applied Chemistry for Engineering
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• v.17 no.3
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• pp.317-320
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• 2006

The optimum mobile phase condition for analysis of the six purine derivatives (caffeine, guanine, hypoxanthine, purine, theobromine, and theophylline) were determined by a HCI program. Reversed-phase HPLC system was used with the binary mobile phase, water and methanol. Three retention models (Snyder, Langmuir, and Binary polynomial) were considered to predict the retention factors. The elution profiles were calculated by the plate theory based on the binary polynomial retention model. From the final calculated results, the binary polynomial retention model showed the best agreements between the calculated and experimental data. In the isocratic mode, the optimum mobile phase composition of water/methanol is 93/7(v/v). However, we used step-gradient mode to decrease the run-time ($1^{st}$ mobile phase : water/methanol = 93/7 (v/v), gradient time : 5 min, $2^{nd}$ mobile phase : water/methanol = 75/25 (v/v)). The experimental and simulated profiles of above the two conditions show a good agreement.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of Food Hygiene and Safety
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• v.26 no.2
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• pp.120-124
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• 2011

Predictive mathematical models were developed for predicting the kinetics of growth of Listeria monocytogenes in smoked salmon, which is the popular ready-to-eat foods in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At these storage temperature, the primary growth curve fit well ($r^2$=0.989~0.996) to a Gompertz equation to obtain specific growth rate (SGR) and lag time (LT). The Polynomial model for natural logarithm transformation of the SGR and LT as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). Results indicate L. monocytogenes growth was affected by temperature mainly, and SGR model equation is $365.3-31.94^*Temperature+0.6661^*Temperature^{\wedge^2}$ and LT model equation is $0.1162-0.01674^*Temperature+0.0009303^*Temperature{\wedge^2}$. As storage temperature decreased $30^{\circ}C$ to $4^{\circ}C$, SGR decreased and LT increased respectively. Polynomial model was identified as appropriate secondary model for SGR and LT on the basis of most statistical indices such as bias factor (1.01 by SGR, 1.55 by LT) and accuracy factor (1.03 by SGR, 1.58 by LT).

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.417-422
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• 2003

The KOMPSAT-2 MSC(Multi-Spectral Camera), with high spatial resolution, is currently under development and will be launched in the end of 2004. A sensor model relates a 3-D ground position to the corresponding 2-D image position and describes the imaging geometry that is necessary to reconstruct the physical imaging process. The Rational Function Model (RFM) has been considered as a generic sensor model. form. The RFM is technically applicable to all types of sensors such as frame, pushbroom, whiskbroom and SAR etc. With the increasing availability of the new generation imaging sensors, accurate and fast rectification of digital imagery using a generic sensor model becomes of great interest to the user community. This paper describes the procedure to generation of the RPC (Rational Polynomial Coefficients) for KOMPSAT-2 MSC.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.135-140
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• 2007

Due to the expensiveness of IKONOS Pro and Precision Products, it is attractive to use the low-cost IKONOS Geo Product with vendor-provided RPCs to produce highly accurate mapping products. The imaging geometry of IKONOS high-resolution imagery is described by RFs instead of rigorous sensor models. This paper presents four different models defined respectively in object space and image space to improve the accuracies of the RF-derived ground coordinates. The four models include the offset model, the scale & offset model, the affine model and the 2nd-order polynomial model. Different configurations of ground control points (GCPs) are carefully examined to evaluate the effect of the GCPs arrangement on the accuracy of ground coordinates. The experiment also evaluates the effect of different cartographic parameters such as the number location, and accuracy of GCPs on the accuracy of geopositioning.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.04a
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• pp.267-274
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• 2001

In this paper, a analytical model which can simulate the post-cracking behavior and tension stiffening effect in a reinforced concrete(RC) tension member is proposed. Unlike the classical approaches using the bond stress-slip relationship or the assumed bond stress distribution, the tension stiffening effect at post-cracking stage is quantified on the basis of polynomial strain distribution functions of steel and concrete, and its contribution is implemented into the reinforcing steel. The introduced model can be effectively used in constructing the stress-strain curve of concrete at post-cracking stage, and the loads carried by concrete and by reinforcing steel along the member axis can be directly evaluated on the basis of the introduced model. In advance, the prediction of cracking loads and elongations of reinforced steel using the introduced model shows good agreements with results from previous analytical studies and experimental data.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.1
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• pp.76-84
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• 2012

This paper presents the measurement system of arrow's point of impact using laser scan camera and describes the image calibration method. The calibration process of distorted image is primarily divided into explicit and implicit method. Explicit method focuses on direct optical property using physical camera and its parameter adjustment functionality, while implicit method relies on a calibration plate which assumed relations between image pixels and target positions. To find the relations of image and target position in implicit method, we proposed the performance criteria based polynomial theorem model that overcome some limitations of conventional image calibration model such as over-fitting problem. The proposed method can be verified with 2D position of arrow that were taken by SICK Ranger-D50 laser scan camera.