• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.637-640
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• 2011

The hybrid neural networks have characteristics such as fast learning times, generality, and simplicity, and are mainly used to classify learning data and to model non-linear systems. The middle layer of a hybrid neural network clusters the learning vectors by grouping homogenous vectors in the same cluster. In the clustering procedure, the homogeneity between learning vectors is represented as the distance between the vectors. Therefore, if the distances between a learning vector and all vectors in a cluster are smaller than a given constant radius, the learning vector is added to the cluster. However, the usage of a constant radius in clustering is the primary source of errors and therefore decreases the recognition success rate. To improve the recognition success rate, we proposed the enhanced hybrid network that organizes the middle layer effectively by using the enhanced ART1 network adjusting the vigilance parameter dynamically according to the similarity between patterns. The results of experiments on a large number of calling card images showed that the proposed algorithm greatly improves the character extraction and recognition compared with conventional recognition algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.287-290
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• 2000

In this paper we have presented approach to automatic the direction feature vectors detection, which detects the ridge line directly in gray scale images. In spite of a greater conceptual complexity, we have shown that our technique has less computational complexity than the complexity of the techniques which require binarization and thinning. Afterwards a various direction feature vectors is changed four direction feature vectors. In this paper used matching method is four direction feature vectors based matching. This four direction feature vectors consist feature patterns in fingerprint images. This feature patterns were used for identification of individuals inputed multilayer Neural Networks(NN) which has capability of excellent pattern identification.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.9 s.174
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• pp.202-211
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• 2005

This paper introduces and illustrates the results of a new method fer offsetting triangular mesh by moving all vertices along the multiple normal vectors of a vertex. The multiple normal vectors of a vertex are set the same as the normal vectors of the faces surrounding the vertex, while the two vectors with the smallest difference are joined repeatedly until the difference is smaller than allowance. Offsetting with the multiple normal vectors of a vertex does not create a gap or overlap at the smooth edges, thereby making the mesh size uniform and the computation time short. In addition, this offsetting method is accurate at the sharp edges because the vertices are moved to the normal directions of faces and joined by the blend surface. The method is also useful for rapid prototyping and tool path generation if the triangular mesh is tessellated part of the solid models with curved surfaces and sharp edges. The suggested method and previous methods are implemented on a PC using C++ and illustrated using an OpenGL library.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.2
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• pp.201-210
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• 2000

The large deflection of plate is analyzed by co-rotational formulations using small local displacements and rotating unit vectors on the nodal points. The rotational degrees of the freedom are represent ed by the unit vectors1 In the nodal points, and the equilibrium equations are formulated by using small deflection theories of the plates by assuming that the directions of the unit vectors of the nodal points are known apriori. The translational degrees of freedom are independently solved from the rotational degrees of freedom in the equilibrium equations, and the correct directions of the unit vectors are computed by the iterative scheme by imposing the moment equilibrium constraint. The equilibrium equations and the associated solution procedure are explained, and the verification problems are solved.

• MALSORI
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• no.67
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• pp.167-180
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• 2008

This paper describes a novel spectral envelope conversion method based on Gaussian mixture model (GMM). The core of this paper is rearranging source feature vectors in input space to the transformed feature vectors in feature space for the better modeling of GMM of source and target features. The quality of statistical modeling is dependent on the distribution and the dimension of data. The proposed method transforms both of the distribution and dimension of data and gives us the chance to model the same data with different configuration. Because the converted feature vectors should be on the input space, only source feature vectors are rearranged in the feature space and target feature vectors remain unchanged for the joint pdf of source and target features using KPCA. The experimental result shows that the proposed method outperforms the conventional GMM-based conversion method in various training environment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.130-139
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• 1994

Video compression algorithms are based on removing spatial and/or temproal redundancy inherent in image sequences by predictive(DPCM) encoding, transform encoding, or a combination of predictive and transform encoding. In this paper, each 8$\times$8 DCT coefficient of DFD(displaced frame difference) is adaptively quantized by one of the four quantizers depending on total distortion level, which is determined by characteristics of HVS(human visual system) and buffer status. Therefore, the number of possible quantizer selection vectors(patterns) is 4$^{64}$. If this vectors are coded, toomany bits are required. Thus, the quantizer selection vectors are limited to 2048 for Y and 512 for each U, V by the proposed method using SWAD(sum of weighted absolute difference) for discriminating vectors. The computer simulation results, using the codebook vectors which are made by the proposed method, show that the subjective and objective image quality (PSNR) are goor with the limited bit allocation. (17Mbps)

• Communications of the Korean Mathematical Society
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• v.15 no.4
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• pp.617-626
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• 2000

We study the vectors related tro states on the Cuntz algebra Ο(sub)n and prove hat, for tow states $\omega$ and $\rho$ on Ο(sub)n with $\omega$│UHF(sub)n = $\rho$│UHF(sub)n, if ($\omega$(s$_1$), …, $\omega$(s(sub)n)) and ($\rho$(s$_1$),…, $\rho$(s(sub)n)) are unit vectors, then they and linearly dependent. We also study the linear functional on Ο(sub)n associated to a sequence of unit vectors in C(sup)n which is the generalization of the Cuntz state. We show that if the linear functional associated to a sequence of unit vectors with a certain condition is a state, then it is just the Cuntz state.

• Proceedings of the Microbiological Society of Korea Conference
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• 2002.10a
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• pp.109-115
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• 2002

Gene therapy is to treat and cure diseases by an introduction of therapeutic genes in defective cells or tissues of human body. Gene delivery system, gene expression system, and therapeutic gene are three core elements for gene therapy. The efficient delivery of therapeutic genes and appropriate gene expression are the crucial issues for therapeutic outcome of gene delivery. Because it can be used in common for the treatment and cure of various diseases, gene delivery system is the most important core element for a successful gene therapy. Viruses are naturally evolved to transfer their genomes into host cells efficiently. This ability has made vectorologists exploit viruses as attractive vehicles for the delivery of therapeutic genes. Viral vectors based on adenovirus (Ad) and adeno-associated virus (AAV) have been often used for gene delivery in laboratory. Ad and AAV vectors derived from human DNA viruses differ greatly in their life cycle, expression level and duration of transgenes, immunogenicity, and vector preparation. Both vectors can be used as effective tools for gene therapy and more recently in functional genomics. Here, the characteristics of Ad and AAV vectors are discussed.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.267-271
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• 2002

The Hotelling transform is based on statistical properties of an image. The principal uses of this transform are in data compression. The basic concept of the Hotelling transform is that the choice of basis vectors pointing the direction of maximum variance of the data. This property can be used for rotation normalization. Many objects of interest in pattern recognition applications can be easily standardized by performing a rotation normalization that aligns the coordinate axes with the axes of maximum variance of the pixels in the object. However, this transform can not be used to rotation normalization of color images directly. In this paper, we propose a new method for rotation normalization of color images based on the Hotelling transform. The Hotelling transform is performed to calculate basis vectors of each channel. Then the summation of vectors of all channels are processed. Rotation normalization is performed using the result of summation of vectors. Experimental results showed the proposed method can be used for rotation normalization of color images effectively.