• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.61-74
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• 1995

These days, many kinds of graphical methods have been developed, and it is possible to get information directly from data. Especially, R-code (Cook and Weisberg, 1994) make it possible to draw various kinds of two and three dimensional plots, and to rotate the axis of the plots. But the maximum dimensional of the plot is three, so we can not draw plot of one response variable with more than three explanatory variables. Li(1991, 1992) has developed a method to reduce the dimension of the explanatory variables, so it is possible to draw lower dimensional plots to get information of the full explanatory variables. One of the dimension reduction method developed by Li is pHd. In this paper, we have tried to apply the pHd method for the model with multivariate response.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.7
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• pp.615-622
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• 2014

Copper pipes have been widely used as components of System Air-Conditioner due to high thermal conductivity. This system consists of 150 pipes, which are approximately 10m long in total. Dimensional changes occur during pipe processing such as expansion, reduction and bending. This processing induces changes in length of pipes and makes dimensional differences from original pipes. The summation of the differences of pipes components leads to make huge cumulative dimensional differences. The cumulative differences can cause serious problems such as crack, refrigerant leakage. However the differences have not been considered so far. To satisfy target quality of the system, it is essential to predict and calibrate the differences. In this paper, the changes in dimension were predicted using FEM and it was found that cumulative differences could cause indesirable stress during assembly process. As a result, dimensional differences or indesirable stress could be reduced using the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.87-95
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• 2006

In this paper, we propose a novel feature extraction method for face recognition, based on Discrete Cosine Transform (DCT), Energy Probability (EP), and Linear Discriminant Analysis (LDA). We define an energy probability as magnitude of effective information and it is used to create a frequency mask in OCT domain. The feature extraction method consists of three steps; i) the spatial domain of face images is transformed into the frequency domain called OCT domain; ii) energy property is applied on DCT domain that acquire from face image for the purpose of dimension reduction of data and optimization of valid information; iii) in order to obtain the most significant and invariant feature of face images, LDA is applied to the data extracted using frequency mask. In experiments, the recognition rate is 96.8% in ETRI database and 100% in ORL database. The proposed method has been shown improvements on the dimension reduction of feature space and the face recognition over the previously proposed methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.3
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• pp.275-280
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• 2008

A technique for reliability-based design optimization(RBDO) is developed based on the Bayesian approach, which can deal with the epistemic uncertainty arising due to the limited number of data. Until recently, the conventional REDO was implemented mostly by assuming the uncertainty as aleatory which means the statistical properties are completely known. In practice, however, this is not the case due to the insufficient data for estimating the statistical information, which makes the existing RBDO methods less useful. In this study, a Bayesian reliability is introduced to take account of the epistemic uncertainty, which is defined as the lower confidence bound of the probability distribution of the original reliability. In this case, the Bayesian reliability requires double loop of the conventional reliability analyses, which can be computationally expensive. Kriging based dimension reduction method(KDRM), which is a new efficient tool for the reliability analysis, is employed to this end. The proposed method is illustrated using a couple of numerical examples.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.1
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• pp.1-8
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• 2004

According to the development of the Internet and the pervasion of Data Base, it is not easy to search for necessary information from the huge amounts of data. In order to do efficient analysis of a large amounts of data, this paper proposes a method for pattern classification based on the effective strategy for dimension reduction for narrowing down the whole data to what users wants to search for. To analyze data effectively, Radial Basis Function Networks based on VC-dimension of Support Vector Machine, a model of statistical teaming, is proposed in this paper. The model of Radial Basis Function Networks currently used performed the preprocessing of Perceptron model whereas the model proposed in this paper, performing independent analysis on VD-dimension, classifies each datum putting precise labels on it. The comparison and estimation of various models by using Machine Learning Data shows that the model proposed in this paper proves to be more efficient than various sorts of algorithm previously used.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.83-85
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• 1995

In a permanent magnet motor, cogging torque arises from the intersection of the rotor magnets with the steel teeth on the stator. This paper describes design measures which can be taken to reduce the cogging torque. In this paper for the optimal shape design of brushless DC motor, evolution strategy is investigated to find the dimension of stator of BLDC motor that minimizes the cogging torque. The corresponding field analysis is performed by two-dimensional finite element method.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.23 no.3
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• pp.270-276
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• 2001

The treatment of mandibular subcondylar fractures is a matter of controversy. The majority of mandibular subcondylar fracture are treated by closed reduction, but the displaced or dislocated mandibular subcondylar fractures may be treated by open reduction. The characteristics of open reduction are the anatomical reduction, the functional restoration, the rapid function, the maintenance of vertical ramus dimension, the better appearance and the less resultant TMJ problem etc. When an open reduction is considered, the wire, miniplate, lag screw and Kirschner wire are available with internal fixation. Of these, Kirschner wire is a simple method relatively and correct positioning of the wire achieves rigid fixation. But many open reduction methods for mandibular subcondylar fractures require extraoral approach. The extraoral approach has some problems, the facial scar and the risk of facial nerve injury. On the other hand, the intraoral approach eliminates the potency of the facial scar and the facial nerve injury, but is difficult to access the operation site. Since the intraoral approach was first described by Silverman (1925), the intraoral approach to the mandibular condyle has been developed with modifications. The purpose of this article is to describe the intraoral technique with the Kirschner wire on mandibular subcondylar fractures. Conclusion : The intraoral reduction with Kirschner wire on mandubular subcondylar fractures avoids the facial scar and facial nerve injury and is simple method to the extraoral approach. And it has minimal morbidity and better esthetics.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.313-329
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• 2012

A challenging issue in design and implementation of an effective structural health monitoring (SHM) system is to determine where a number of sensors are properly installed. In this paper, research on the optimal sensor placement (OSP) is carried out on the Canton Tower (formerly named Guangzhou New Television Tower) of 610 m high. To avoid the intensive computationally-demanding problem caused by tens of thousands of degrees of freedom (DOFs) involved in the dynamic analysis, the three dimension finite element (FE) model of the Canton Tower is first simplified to a system with less DOFs. Considering that the sensors can be physically arranged only in the translational DOFs of the structure, but not in the rotational DOFs, a new method of taking the horizontal DOF as the master DOF and rotational DOF as the slave DOF, and reducing the slave DOF by model reduction is proposed. The reduced model is obtained by IIRS method and compared with the models reduced by Guyan, Kuhar, and IRS methods. Finally, the OSP of the Canton Tower is obtained by a kind of dual-structure coding based generalized genetic algorithm (GGA).