• Journal of KIISE:Databases
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• v.31 no.1
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• pp.13-22
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• 2004

With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. Although there have been many efforts, the performance of existing multidimensional indexing methods is not satisfactory in high dimensions. Thus the dimensionality reduction and the approximate solution methods were tried to deal with the so-called dimensionality curse. But these methods are inevitably accompanied by the loss of precision of query results. Therefore, recently, the vector approximation-based methods such as the VA- file and the LPC-file were developed to preserve the precision of query results. However, the performance of the vector approximation-based methods depend largely on the size of the approximation file and they lose the advantages of the multidimensional indexing methods that prune much search space. In this paper, we propose a new index structure called the GC-tree for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for clustered high-dimensional images. It adaptively partitions the data space based on a density function and dynamically constructs an index structure. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional images.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.72-85
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• 1998

In this paper, we propose a new method that sequentially estimates TDOA(Time Delay Of Arrival) and FDOA(Frequency Delay Of Arrival) for extracting the information about the bearing and relative velocity of a target in passive radar or sonar arrays. The objective is to efficiently estimate the TDOA and FDOA between two sensor signal measurements, corrupted by correlated Gaussian noise sources in an unknown way. The proposed method utilizes the one dimensional slice function of the third order cumulants between the two sensor measurements, by which the effect of correlated Gaussian measurement noises can be significantly suppressed for the estimation of TDOA. Because the proposed sequential algoritjhm uses the one dimensional complex ambiguity function based on the TDOA estimate from the first step, the amount of computations needed for accurate estimationof FDOA can be dramatically reduced, especially for the cases where high frequency resolution is required. It is demonstrated that the proposed algorithm outperforms existing TDOA/FDOA estimation algorithms based on the ML(maximum likelihood) criterionandthe complex ambiguity function of the third order cumulant as well, in the MSE(mean squared error) sense and computational burden. Various numerical resutls on the detection probability, MSE and the floatingpoint computational burden are presented via Monte-Carlo simulations for different types of noises, different lengths of data, and different signal-to-noise ratios.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.755-765
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• 2015

This paper is concerned with Interval Type-2 Radial Basis Function Neural Network classifier realized with the aid of Census Transform(CT) and (2D)2LDA methods. CT is considered to improve performance of face recognition in a variety of illumination variations. (2D)2LDA is applied to transform high dimensional image into low-dimensional image which is used as input data to the proposed pattern classifier. Receptive fields in hidden layer are formed as interval type-2 membership function. We use the coefficients of linear polynomial function as the connection weights of the proposed networks, and the coefficients and their ensuing spreads are learned through Conjugate Gradient Method(CGM). Moreover, the parameters such as fuzzification coefficient and the number of input variables are optimized by Artificial Bee Colony(ABC). In order to evaluate the performance of the proposed classifier, Yale B dataset which consists of images obtained under diverse state of illumination environment is applied. We show that the results of the proposed model have much more superb performance and robust characteristic than those reported in the previous studies.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.5
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• pp.534-544
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• 1997

The permittivity distribution of a two-dimensional high-contrast object with large size, which leads to the global minimum of cost function, is reconstructed by iteratively using the hybrid algorithm of Levenberg-magquardt algorithm(LMA) plus Genetic Algorithm(GA). The scattered fields calculated in a cost function are expanded in angular spectral modes, of which only effective propagating modes are used. The definition of cost function based on the effective propagating modes enables us to formulate the minimum number of incident waves for the reconstruction of object. It is numerically shown that LMA has an advantage of fast convergence but can't reconstruct a high-contrast object with large size and GA can reconstruct a high-contrast object with large size but has an disadvantage of slow convergence, whereas an inverse scattering technique using the hybrid algorithm adopts only advantages of LMA and GA.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.25-36
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• 1996

Conventional radial-basis-function networks and fuzzy systems have serious problems in dealing with the non1inea:r approximations on high-dimensional spaces due to the explosive increase of the number of hidden nodes or fuzzy IF-THEN rules. In order to avoid such problems, this paper proposes a tree-structured intelligent system in which semi-local basis functions form its basic elements, and develops a training algorithm for the proposed system based on the modified genetic algorithm and LMS rule. Theoretical analysis is performed on the approximation capability of the proposed system, together with experimental studies which demonstrate the effectiveness of the developed methodology.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.1
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• pp.189-196
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• 2004

A modified generalized particle algorithm, MGPA, was suggested to improve the calculation efficiency of standard SPH Method in numerical analysis of high speed impact behavior. MGPA had a new weight function to reduce computation time. The efficiency of this method was proven through calculation for the sample problems of one dimensional rod impact problem and two dimensional plate impact problem. The MGPA method reduced the calculation error and stress oscillation near the boundaries. The validity of this approach was shown by the comparison with ABAQUS results in two dimensional plate impact problem.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.4
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• pp.50-56
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• 2011

Injection molding process is one of the popular manufacturing methods to produce plastic parts with high efficiency and low cost. Ball seat for automobile suspension is made by an injection molding process as a part to support pivot function of ball joint consisted of ball stud and housing. It is necessary for a ball seat to have a dimensional stability in the three dimensional inner area to be contacted with ball stud. In this paper, the dimensional stability of inner surface is indirectly analyzed by checking the difference of inner diameter around the circumferential direction and the thickness variation at the top part of ball seat. Measurement was performed by using the coordinate measuring machine and the fixture to hold ball seat. Optimization of injection molding processes such as injection time, cooling time and temperatures of cylinder barrel was derived to reduce the difference of inner diameter and the thickness variation at the top part of ball seat based on the Taguchi method.

• Journal of Powder Materials
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• v.3 no.2
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• pp.104-111
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• 1996

In order to obtain homogeneous and high quality products in powder compaction forging process, it is very important to control stress, strain, density and density distributions. Therefore, it is necessary to understand quantitatively the elasto-plastic deformation and densification behaviors of porous metals and metal powders. In this study, elasto-plastic finite element method using Lee-Kim's pressure dependent porous material yield function has been used for the analysis of three dimensional indenting process. The analysis predicts deformed geometry, stress, strain and density distribution and load. The calculated load is in good agreement with experimental one. The calculated results do not show axisymmetric distributions because of the edge effect. The core part which is in contact with the indentor and the outer diagonal edge part are in compressive stress states and the middle part is in tensile stress state. As a results, it can be concluded that three dimensional analysis is more realistic than axisymmetric assumption approach.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.7
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• pp.1005-1013
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• 2002

Spin-up is a transient flow phenomenon occurring in a container when it starts to rotate from rest or its rotational speed increases from a low to high value. However, most studies on this subject have been for two-dimensional approximation. In this study, spin-up flows in a shallow rectangular container are analysed by using three-dimensional computation. We compared our results with those obtained by others using basically two-dimensional computation. Effect of two parameters, Reynolds number and liquid depth on the flow evolution is studied. We found that 2-D result is not accurate enough, and the vertical velocity distribution should be assumed of a fourth-order polynomial function for a better comparison.

• Transactions of The Korea Fluid Power Systems Society
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• v.1 no.1
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• pp.1-9
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• 2004

This paper addresses an approximation and tracking control of recurrent neural networks(RNN) using two-dimensional iterative learning algorithm for an electro-hydraulic servo system. And two dimensional learning rule is driven in the discrete system which consists of nonlinear output function and linear input. In order to control the trajectory of position, two RNN's with the same network architecture were used. Simulation results show that two RNN's using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RNN was very effective to control trajectory tracking of electro-hydraulic servo system.