• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.1
• /
• pp.91-96
• /
• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.6
• /
• pp.586-591
• /
• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.7
• /
• pp.878-882
• /
• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.2
• /
• pp.377-386
• /
• 2021

The A60 class deck penetration piece is a fire-resistant system installed on a horizontal compartment to prevent flame spreading and protect lives in fire accidents in ships and offshore plants. This study deals with approximate optimization using discrete variables for the fire resistance design of an A60 class deck penetration piece using different surrogate models and a genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class deck penetration piece. For the approximate optimization of the piece, the length, diameter, material type, and insulation density were applied to discrete design variables, and temperature, productivity, and cost constraints were considered. The approximate optimum design problem based on the surrogate models was formulated such that the discrete design variables were determined by minimizing the weight of the piece subjected to the constraints. The surrogate models used in the approximate optimization were the response surface model, Kriging model, and radial basis function-based neural network. The approximate optimization results were compared with the actual analysis results in terms of approximate accuracy. The radial basis function-based neural network showed the most accurate optimum design results for the fire resistance design of the A60 class deck penetration piece.

• Journal of KIISE:Software and Applications
• /
• v.31 no.5
• /
• pp.563-569
• /
• 2004

A set of Zernike moments has been successfully used for object recognition or content-based image retrieval systems. Real time applications using Zernike moments, however, have been limited due to its complicated definition. Conventional methods to compute Zernike moments fast have focused mainly on the radial components of the moments. In this paper, utilizing symmetric/anti-symmetric properties of Zernike basis functions, we propose a fast and efficient method for Zernike moments. By reducing the number of operations to one quarter of the conventional methods in the proposed method, the computation time to generate Zernike basis functions was reduced to about 20% compared with conventional methods. In addition, the amount of memory required for efficient computation of the moments is also reduced to a quarter. We also showed that the algorithm can be extended to compute the similar classes of rotational moments, such as pseudo-Zernike moments, and ART descriptors in same manner.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.3
• /
• pp.242-247
• /
• 2015

In this study, we propose the design of Radial Basis Function Neural Network(RBFNN) classifier in order to classify between precipitation and non-precipitation echo. The characteristics of meteorological radar data is analyzed for classifying precipitation and non-precipitation echo. Input variables is selected as DZ, SDZ, VGZ, SPN, DZ_FR, VR by performing pre-processing of UF data based on the characteristics analysis and these are composed of training and test data. Finally, QC data being used in Korea Meteorological Administration is applied to compare with the performance results of proposed classifier.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.9 no.5
• /
• pp.467-477
• /
• 2004

A new scheme to estimate the moment of inertia in the servo motor drive system in very low speed is proposed in this paper. The typical speed estimation scheme in most servo system for low speed operation is sensitive to the variation of machine parameters, especially the moment of inertia. To estimate the motor inertia value, the observer using the Radial Basis Function Networks(RBFN) is applied. The effectiveness of the proposed inertia estimation method is verified by experiments. It is concluded that the speed control performance in the low speed region is improved with the proposed disturbance observer using RBFN.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.37 no.5
• /
• pp.433-441
• /
• 2009

Generally, the events in nature have multi-disciplinary characteristics. To solve this problems, these days loosely coupled methods are widely applied because of advantage of solvers which are already developed and well proved. Those solvers use different mesh system, so transformation and mapping of data are vital in the field of fluid-structure interaction(FSI). In this paper, the interpolation of deformation which is used globally and compactly supported radial basis functions(RBF), and mapping of force which use principle of virtual work are examined for computing time and accuracy to compare ability with simple 3-D problem. As the results, interpolation scheme of compactly supported radial basis functions are useful to interpolation and mapping for large-scale airplane in FSI with a k-dimensional tree(kd-tree) which is a space-partitioning data structure for organizing points in a k-dimensional space.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.293-298
• /
• 2017

The purpose of this study was to analyze the risk factors of depression in elementary school students in Korea. The subjects of the study were 23,291 elementary school students (12,016 male, 11,275 female) aged 9 to 12 years. Dependent variable was defined as experience of depression. Explanatory variables were included as sex, residential areas, social discrimination experience, experience of school violence for the past year, experience of Korean language education, experience of using multicultural family support center, reading to Korean, speaking to Korean, and writing to Korean, listening to Korean. In the RBF neural network analysis, experience of Korean education, experience of school violence, experience of Korean social discrimination, level of Korean reading were significantly associated with depression in elementary school students. In order to prevent depression in multicultural children, priority attention and counseling are needed for the group whose level of Korean reading is low.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.39 no.9
• /
• pp.823-831
• /
• 2011

According to the development of loading equipments, it is usual to change or replace the existing stores. It has been known that pylon-mounted under stores strongly affect aircraft dynamics characteristics due to the change of aerodynamics. To predict the aerodynamics and aero-elasticity is essentially requested with considering the configuration and shape of external stores during the development of aircraft and/or external stores. In this paper, computational fluid dynamics and computational structure dynamics interaction methodology are applied for prediction of aerodynamic characteristics for F-5 aircraft's horizontal tail with various shape of external stores. FLUENT and ABAQUS were used to calculate fluid and structural dynamics. Code-bridge was made base on the globally supported radial basis function to execute interpolation and mapping. As a result, even though the aeroelasticity of the horizontal tail slightly changes according to the shape of external store, the flutter was not occurred at the considered flight conditions in this study.