• Elastomers and Composites
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• 제51권4호
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• pp.308-316
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• 2016

The rubber materials are widely used in automobile industry due to their capability of a large amount of elastic deformation under a force. Current trend of design process requires prediction of functional properties of parts at early stage. The behavior of rubber material can be modeled using strain energy density function. In this study, five different strain energy density functions - Neo-Hookean model, Reduced Polynomial $2^{nd}$ model, Ogden $3^{rd}$ model, Arruda Boyce model and Van der Waals model - were used to estimate the behavior of carbon black added natural rubber under uniaxial load. Two kinds of tests - uniaxial tension test and biaxial tension test - were performed and used to correlate the coefficients of the strain energy density function. Numerical simulations were carried out using finite element analysis and compared with experimental results. Simulation revealed that Ogden $3^{rd}$ model predicted the behavior of carbon added natural rubber under uniaxial load regardless of experimental data selection for coefficient correlation. However, Reduced Polynomial $2^{nd}$, Ogden $3^{rd}$, and Van der Waals with uniaxial tension test and biaxial tension test data selected for coefficient correlation showed close estimation of behavior of biaxial tension test. Reduced Polynomial $2^{nd}$ model predicted the behavior of biaxial tension test most closely.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권6호
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• 전기학회논문지
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Animal Bioscience
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• 제37권5호
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• pp.817-825
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• 2024

Objective: The aim of this study was to identify suitable polynomial regression for modeling the average growth trajectory and to estimate the relative development of the rib eye area, scrotal circumference, and morphometric measurements of Guzerat young bulls. Methods: A total of 45 recently weaned males, aged 325.8±28.0 days and weighing 219.9±38.05 kg, were evaluated. The animals were kept on Brachiaria brizantha pastures, received multiple supplementations, and were managed under uniform conditions for 294 days, with evaluations conducted every 56 days. The average growth trajectory was adjusted using ordinary polynomials, Legendre polynomials, and quadratic B-splines. The coefficient of determination, mean absolute deviation, mean square error, the value of the restricted likelihood function, Akaike information criteria, and consistent Akaike information criteria were applied to assess the quality of the fits. For the study of allometric growth, the power model was applied. Results: Ordinary polynomial and Legendre polynomial models of the fifth order provided the best fits. B-splines yielded the best fits in comparing models with the same number of parameters. Based on the restricted likelihood function, Akaike's information criterion, and consistent Akaike's information criterion, the B-splines model with six intervals described the growth trajectory of evaluated animals more smoothly and consistently. In the study of allometric growth, the evaluated traits exhibited negative heterogeneity (b<1) relative to the animals' weight (p<0.01), indicating the precocity of Guzerat cattle for weight gain on pasture. Conclusion: Complementary studies of growth trajectory and allometry can help identify when an animal's weight changes and thus assist in decision-making regarding management practices, nutritional requirements, and genetic selection strategies to optimize growth and animal performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• International Journal of Control, Automation, and Systems
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• 제2권4호
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1491-1498
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• 2014

최적실험의 제일 큰 약점은 실험기준이 지나치게 모형과 그에 수반되는 가정에 의존한다는 점이다. 이는 종종 모형의 모수의 개수와 받힘점의 개수가 일치를 하는 경우로 이루어지는데 이는 가정된 모형이 참이 아닌 경우를 대비한 실험이 될 수 없다. 이런 경우 문헌에서는 가정된 다항회귀모형의 차수보다 큰 차수를 가진 다항회귀모형을 가정하고 최적실험을 제안하나 이는 D-효율에 근거한 관행적인 방법일 뿐이다. 본 연구에서는 O'Brien (1995)이 제안한 가정된 모형의 일반적인 이탈을 염두에 둔 추가받힘점 생성에 관하여 알아보고 단순회귀모형과 2차 회귀모형에 대한 실험들을 D-효율로 카타로그화 하여 실험자로 하여금 선택을 할 수 있게 하였다. O'Brien은 비선형모형에 대해 추가받힘점의 선택 방법을 제시하였지만 방법을 구현하는 데 있어 명확치 않은 기준이 있어 모수에 의존하는 비선형모형에 대한 최적실험보다는 다항회귀모형을 중심으로 심층적으로 사용방법을 알아보았다.

• 전기학회논문지
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• 제58권1호
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• pp.18-22
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• 2009

Short-term load forecasting (STLF) is an important task in power system planning and operation. Its accuracy affects the reliability and economic operation of power systems. STLF is to be classified into load forecasting for weekdays, weekends, and holidays. Due to the limited historical data available, it is more difficult to accurately forecast load for holidays than to forecast load for weekdays and weekends. It has been recognized that the forecasting errors for holidays are large compared with those for weekdays in Korea. This paper presents a polynomial regression with data mining technique to forecast load for holidays. In statistics, a polynomial is widely used in situations where the response is curvilinear, because even complex nonlinear relationships can be adequately modeled by polynomials over a reasonably small range of the dependent variables. In the paper, the coefficient of determination is proposed as a selection criterion for screening weekday data used in holiday load forecasting. A numerical example is presented to validate the effectiveness of the proposed holiday load forecasting method.

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.793-798
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• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

• International Journal of CAD/CAM
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• 제7권1호
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• pp.51-60
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• 2007

This paper presents an algorithm of optimal cutting tool selection for machining of the point-based surface that is defined by a set of surface points rather than parametric polynomial surface equations. As the ball-end and fillet-end mills are generally used for finish machining in a 3-axis computer numerical control machine, the algorithm is applicable for both cutters. The optimum tool would be as large as possible in terms of the cutter radius and/or corner radius which maximise (s) the material removal rate (i.e., minimise (s) the machining time), while still being able to machine the entire point-based surface without gouging any surface point. The gouging are two types: local and global. In this paper, the distance between the cutter bottom and surface points is used to check the local gouging whereas the shortest distance between the surface points and cutter axis is effectively used to check the global gouging. The selection procedure begins with a cutter from the tool library, which has the largest cutter radius and/or corner radius, and then adequacy of the point-density is checked to limit the accuracy of the cutter selection for the point-based surface within tolerance prior to the gouge checking. When the entire surface is gouge-free with a chosen cutting tool then the tool becomes the optimum cutting tool for a list of cutters available in the tool library. The effectiveness of the algorithm is demonstrated considering two examples.