• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.693-703
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• 2010

Diseases of human or economical traits of cattles are occured by interaction of genes. We introduce expanded multifactor dimensionality reduction(E-MDR), dummy multifactor dimensionality reduction(D-MDR) and SNPHarvester which are developed to find interaction of genes. We will select interaction of outstanding gene combinations and select final best genotype groups.

• Journal of the Ergonomics Society of Korea
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• v.23 no.2
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• pp.25-36
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• 2004

The effects of task characteristics on the relative efficiency of visual display dimension were studied using a simulated tele-robotic task. Through a conventional method of task analysis. the tele-robotic task was divided into two categories: the task element requiring focused attention (FA task) and the task element requiring global attention (CA task). Time-ta-completion data were collected for a total of 120 trials involving 10 participants. For the CA task. there was no significant difference between the multiple two-dimensional (20) display and the three-dimensional (3D) monocular display. For the FA task. however. the multiple 20 display was superior to the 3D monocular display. The results suggest that the characteristics of a given task have a considerable effect on the choice of display dimensionality and the multiple 3D display is better for human operators to effectively judge depth if the task requires frequent use of focused attention.

• Journal of Information Processing Systems
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• v.4 no.2
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• pp.77-86
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• 2008

This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.1-7
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• 2020

Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.

• ETRI Journal
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• v.35 no.6
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.842-847
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• 2005

This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction virtual hand. From experimental results, we show that all processes, including virtual hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.5
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• pp.746-754
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• 1987

A method for measuring three-dimensional turbulent flows by the hot-wire anemometer is introduced. Mojolla's method using the X-type probe is adopted and modified for the slantwire probe without the linearizer. The probe is aligned with specified angles to the given uniform flow and the shear layer to verify the measuring errors due to the three-dimensionality and the turbulence level. Errors in the measurements of mean velocities and Reynolds stresses increase with the degree of three dimensionality in the flow. The incoming flow angle of 20 degree seems to be the limit of reasonable flow measurements. But there still appear large data scatterings in Reynolds shear stresses.

• Journal of the Korean Society of Visualization
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• v.8 no.1
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• pp.13-18
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• 2010

An experimental investigation of the flow around a vertical fence was carried out using a PIV velocity field measurement technique. The vertical fence was embedded in a turbulent boundary layer. The instantaneous velocity fields measured at cross-sectional planes reveal complex longitudinal vortices that vary in size and strength, developing from the upstream location. In the instantaneous vorticity and velocity field data, the shear flow separated from the fence top is highly turbulent and shows unsteady flow characteristics. The topography of the ensemble averaged velocity fields, especially the separation bubble formed behind the fence, shows that the spatial distributions of streamwise velocity (U) and vertical (V) are symmetric, the spanwise velocity (W) is skew-symmetric with respect to the central xy-plane(z=0).

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.250-254
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• 2010

As the number of input and fuzzy set of a fuzzy system increase, the size of the rule base increases exponentially and becomes unmanageable (curse of dimensionality). In this paper, tree architectures of fuzzy controller (TAFC) is proposed to overcome the curse of dimensionality problem occurring in the design of fuzzy controller. TAFC is constructed with the aid of AND and OR fuzzy neurons. TAFC can guarantee reduced size of rule base with reasonable performance. For the development of TAFC, genetic algorithm constructs the binary tree structure by optimally selecting the nodes and leaves, and then random signal-based learning further refines the binary connections (two-step optimization). An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation.