• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.35-44
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• 2014

This paper proposes a 3D pointing interface that is designed for the efficient application of a hand mouse. The proposed method uses depth images to secure high-quality results even in response to changes in lighting and environmental conditions and uses the normal vector of the palm of the hand to perform 3D pointing. First, the hand region is detected and tracked using the existing conventional method; based on the information thus obtained, the region of the palm is predicted and the region of interest is obtained. Once the region of interest has been identified, this region is approximated by the plane equation and the normal vector is extracted. Next, to ensure stable control, interpolation is performed using the extracted normal vector and the intersection point is detected. For stability and efficiency, the dynamic weight using the sigmoid function is applied to the above detected intersection point, and finally, this is converted into the 2D coordinate system. This paper explains the methods of detecting the region of interest and the direction vector and proposes a method of interpolating and applying the dynamic weight in order to stabilize control. Lastly, qualitative and quantitative analyses are performed on the proposed 3D pointing method to verify its ability to deliver stable control.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.229-250
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• 2022

Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.117-126
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• 2010

In the case of failure images with controlling lighting like backlighting and excessive frontlinghting, the compensation scheme for a specific area in an image is required. The interested region is first selected by user in our method to compensate the first frame. Then we define the matching function of brightness and energy function is proposed with weight of matching function and the relationship among the neighbors. Finally, the energy is minimized by the graph-cut algorithm to compensate the brightness of the first frame. Other frames are straightforwardly compensated using the results of the first frame. The brightness variations of the previous frame is transmitted to the next frame via motion vectors. The reliability of the brightness variation is calculated based on the motion vector reliability. Video compensation result is achieved by the process of the image case. Simulation show that the proposed algorithm provides more natural results than the conventional algorithms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1295-1302
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• 2013

The structural deformed shape is important information to structural analysis. If the sufficient measuring points are secured at the structural monitoring system, reasonable and accurate structural deformation shapes can be obtained and structural analysis is possible using this deformation. However, the accurate estimation of the global structural shapes might be difficult if sufficient measuring points are not secure under cost limitations. In this study, SFSM-LS algorithm, the economic and effective estimation method for the structural deformation shapes with limited displacement measuring points is developed and suggested. In the suggested method, the global structural deformation shape is determined by the superposition of the pre-investigated structural deformed shapes obtained by preliminary FE analyses, with their optimum weight factors which lead minimization of the estimate errors. 2-span continuous bridge model is used to verify developed algorithm and parametric studies are performed. By the parametric studies, the characteristics of the estimation results obtained by the suggested method were investigated considering essential parameters such as pre-investigated structural shapes, locations and numbers of displacement measuring points. By quantitative comparison of estimation results with the conventional methods such as polynomial, Lagrange and spline interpolation, the applicability and accuracy of the suggested method was validated.

• Journal of Korea Water Resources Association
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• v.45 no.4
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• pp.409-418
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• 2012

A modified standardized precipitation index was developed by considering the length of dry period and surface run-off effect. The official reports and newspapers on drought from 1973 to 2009 were quantified to evaluate drought indices. The developed index was evaluated using the receiver operating characteristic analysis. In order to suggest improved drought index, we cut the precipitation amount that may do not contribute the mitigation of drought and weight dry period by considering cumulative distribution, decile distribution of dry periods. Drought detection capability of the suggested index has improved by weighting of dry period effects and considering precipitation amounts contributing drought mitigation.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.410-415
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• 2005

Recently, most of the speaker verification systems are based on the pattern recognition approach method. And performance of the pattern-classifier depends on how to classify a variety of speakers' feature parameters. In order to classify feature parameters efficiently and effectively, it is of great importance to enlarge variations between speakers and effectively measure distances between feature parameters. Therefore, this paper would suggest the positively mixed model scheme that can enlarge inter-speaker variation by searching the individual model with world model at the same time. During decision procedure, we can maximize inter-speaker variation by using the proposed mixed model scheme. We also make use of a symbol probability weighting function in this system so as to reduce vector quantization errors by measuring symbol probability derived from the distance rate of between the world codebook and individual codebook. As the result of our experiment using this method, we could halve the Detection Cost Function (DCF) of the system from $2.37\%\;to\;1.16\%$.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.75-81
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• 2004

Gaussian selection (GS) is a popular approach in the continuous density hidden Markov model for fast decoding. It enables fast likelihood computation by reducing the number of Gaussian components calculated. In this paper, we propose a new GS method for the phonetic tied-mixture (PTM) hidden Markov models. The PTM model can represent each state of the same topological location with a shared set of Gaussian mixture components and contort dependent weights. Thus the proposed method imposes constraint on the weights as well as the number of Gaussian components to reduce the computational load. Experimental results show that the proposed method reduces the percentage of Gaussian computation to 16.41%, compared with 20-30% for the conventional GS methods, with little degradation in recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.355-360
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• 2015

In this paper, we propose the design of Radial Basis Function Neural Network based on PCA in order to recognize handwritten digits. The proposed pattern classifier consists of the preprocessing step of PCA and the pattern classification step of pRBFNNs. In the preprocessing step, Feature data is obtained through preprocessing step of PCA for minimizing the information loss of given data and then this data is used as input data to pRBFNNs. The hidden layer of the proposed classifier is built up by Fuzzy C-Means(FCM) clustering algorithm and the connection weights are defined as linear polynomial function. In the output layer, polynomial parameters are obtained by using Least Square Estimation (LSE). MNIST database known as one of the benchmark handwritten dataset is applied for the performance evaluation of the proposed classifier. The experimental results of the proposed system are compared with other existing classifiers.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.71-89
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• 2003

Although many studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective in the classification problems. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques. This study proposes the linearly combining methodology of different classification techniques. The methodology is developed to find the optimal combining weight and compute the weighted-average of different techniques' outputs. The proposed methodology is represented as the form of mixed integer programming. The objective function of proposed combining methodology is to minimize total misclassification cost which is the weighted-sum of two types of misclassification. To simplify the problem solving process, cutoff value is fixed and threshold function is removed. The form of mixed integer programming is solved with the branch and bound methods. The result showed that proposed methodology classified more accurately than any of techniques individually did. It is confirmed that Proposed methodology Predicts significantly better than individual techniques and the other combining methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1953-1962
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• 1999

While acquiring the image, the shaking of the image capturing equipment or the object seriously damages the image quality. This phenomenon, which degrades the clarity and the resolution of the image is called motion blur. In this paper, a newly defined function is introduced for finding the degree and the length of the motion blur. The domain of this function defined as Peak-trace domain. In The Peak-trace domain, the noise dominant region for calculating the noise variance and the signal dominant region for extracting the degree and the length of the motion blur are defined and analyzed. Using the information of the Peak-trace in the signal dominant region, we can find the direction of the motion regardless of the noise corruption. Weighted least mean square method helps extracting the Peak-trace more precisely. After getting the direction of the motion blur, we can find the length of the motion blur based on one dimensional Cepstrum. In the experiment, we could efficiently restore the degraded image using the information obtained by the proposed algorithm.