• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.111-122
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• 2010

In this study, a length adjustment algorithm for cyclic signals in manufacturing process using Time Invariant Feature point Extraction and Matching(TIFEM) is proposed. In order to precisely compensate the length of cyclic signals which have irregular length in the middle of signal as well as in the full length more feature points are needed. The extracted feature must involve information about the pattern of signal and should have invariant properties on time and scale. The proposed TIFEM algorithm extracts features having the intrinsic properties of the signal characteristics at first. By using those extracted features, feature vector is constructed for each time point. Among those extracted features, the only effective features are filtered and are chosen such as basis for the length adjustment. And then the partial length adjustment is performed by matching feature points. To verify the performance of the proposed algorithm, the experiments were performed with the experimental data mimicking the three kinds of signals generated from the actual semiconductor process.

• Journal of Photoscience
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• v.6 no.1
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• pp.29-36
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• 1999

Chlorophasts are a central structural feature of stomatal guard cells. Guard cell chloroplasts have both photosystems I and II (PS I and II), carry out O2 evoluation , cyclic and noncyclic photophosporylation, and possess the Calvin-Benson cycle enzymes involved in CO2 fixation. These imply that guard cell chloroplasts have a normal photosynthetic carbon reduction pathway just like their mesophyll counterparts, indicating similar fuctional organization of thylakoid membranes in both types of mesophyll and guard cell chloroplasts. It has been, however, found that guard cell chloroplasts have distinctive and comparative properties in their photosynthetic performance. In this article, I review the intrinsic features on the light reaction of and carbon reduction by guard cell chloroplasts.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.156-161
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• 2012

Random access protocol takes advantage of constructing a decentralized wireless network due to its intrinsic spectrum sensing capability. Such technical feature goes well with cognitive radio networks consisting of heterogeneous wireless systems, in which a centralized control between heterogeneous wireless systems is hard to be implemented. Motivated by the decentralized feature of the random access, we adopt the random access protocol in cognitive radio networks, and evaluate the performance of a CSMA-based cognitive radio network.

• Korean Journal of Computational Design and Engineering
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• v.12 no.5
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• pp.354-365
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• 2007

We present novel methods to generate a sequence of shapes that represents the pattern of morphological development or transformation of Bezier curves. The presented methods utilize the intrinsic geometric structures of a Bezier curve that are derived from rib and fan decomposition (RFD). Morphological development based on RFD shows a characteristic pattern of structural growth of a Bezier curve, which is the direct consequence of development path defined by fans. Morphological transformation based RFD utilizes development patterns of source and target curves to mimic the theory of evolutionary developmental biology: although the source and target curves are quite different in shapes, we can easily find similarities in their younger shapes, which makes it easier to set up feature correspondences for blending them. We also show that further controls on base transformation for intensity of feature blending, and extrapolation can compensate the immaturity of blended curves. We demonstrate the experimental results where transformation patterns are smoother and have unique geometric style that cannot be generated using conventional methods based on multi-linear blending.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.151-157
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• 2012

Recently, owing to the technology advances in computer science and image processing, age of face classification have become prevalent topics. It is difficult to estimate age of facial shape with statistical figures because facial shape of the person should change due to not only biological gene but also personal habits. In this paper, we proposed a robust age of face classification method by using Gabor feature and fuzzy support vector machine(SVM). Gabor wavelet function is used for extracting facial feature vector and in order to solve the intrinsic age ambiguity problem, a fuzzy support vector machine(FSVM) is introduced. By utilizing the FSVM age membership functions is defined. Some experiments have conducted to testify the proposed approach and experimental results showed that the proposed method can achieve better age of face classification precision.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.892-898
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• 2008

3D rapid prototyping is the manufacturing technology to fabricate a prototype with the data stored in a computer, which differs from conventional casting technology in terms of an additive process. Various 3D rapid prototyping techniques such as stereolithograpy. fused deposition modeling. selective laser sintering, laminated object manufacturing have been developed but among them, 3D inkjet printing has a unique feature that materials could be jetted to directly form the body of a prototype, which could be a finished product functionally and structurally. However, this needs ink with a high solid content, which tends to increase the dynamic viscosity of ink. The increase of ink viscositytends to restrict the jettable range of ink and hence the jetting conditions should be optimized. The intrinsic speed of sound in a hot melt ink with ceramic nanoparticles dispersed is one of key components to determine the jettable range of ink. In this paper, the way to measure the intrinsic speed of sound in a hot melt ceramic ink is proposed and its influence on the jetting condition is discussed.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1865-1873
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• 2017

The feature-based visual SLAM requires 3D positions for the extracted feature points to perform 3D-2D motion estimation. LiDAR can provide reliable and accurate 3D position information with low computational burden, while stereo camera has the problem of the impossibility of stereo matching in simple texture image region, the inaccuracy in depth value due to error contained in intrinsic and extrinsic camera parameter, and the limited number of depth value restricted by permissible stereo disparity. However, the sparsity of LiDAR data may increase the inaccuracy of motion estimation and can even lead to the result of motion estimation failure. Therefore, in this paper, we propose three interpolation methods which can be applied to interpolate sparse LiDAR data. Simulation results obtained by applying these three methods to a visual odometry algorithm demonstrates that the selective bilinear interpolation shows better performance in the view point of computation speed and accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• Carbon letters
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• v.27
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• pp.35-41
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• 2018

Surfactant-wrapped separation methods of metallic and semiconducting single-walled carbon nanotubes (SWCNTs) can result in large changes in intrinsic physical and chemical properties due to electronic interactions between a nanotube and a surfactant. Our approach to synthesize SWCNTs with an electronic feature relied on utilizing carbon nanorings, [n] cycloparaphenylenes ([n]CPPs), which are the fundamental unit of armchair type SWCNTs (a-SWCNTs) that possess a metallic feature without any surfactants. To obtain long tubular structures from [n]CPPs, the host-guest complexes formed with well-aligned [n]CPP hosts and various fullerene guests on a silicon substrate were pyrolyzed under an ethanol gas flow at a high temperature with focused-ultraviolet laser irradiation. The pyrolyzed [n]CPPs were observed to transform from nanorings to tubular structures with 1.5-1.7 nm diameters corresponding to the employed diameter of [n]CPPs. Our approach suggests that [n]CPPs are useful for structure-controlled synthesis of SWCNTs.