• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.48-54
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• 2011

As personal or compact devices with image acquisition functionality are becoming easily available for common users, the voluminous images that need to be managed by image related services or systems demand efficient and effective methods in the perspective of image identification. The objective of image identification is to associate an image with a unique identifier. Moreover, whenever an image identifier needs to be regenerated, the newly generated identifier should be consistent. In this paper, we propose three image identifier generation methods utilizing image features: linear component, luminance area, and combination of both features. The linear component based method exploits the information of distribution of partial lines over an image, while the luminance area based method utilizes the partition of an image into a number of small areas according to the same luminance degree. The third method is proposed in order to take advantage of both former methods. In this paper, we also demonstrate the experimental evaluations for uniqueness and similarity analysis that have shown favorable results.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.393-397
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• 2001

A linear ultrasonic motor was designed by a combination of the first longitudinal and fourth bending mode, the motor consisted of a straight aluminum alloys bar bonded with a piezoelectric ceramics element as a driving element. That is, L$_1$-B$_4$ linear ultrasonic motor can be constructed using a multi-mode vibrator of longitudinal and bending modes. The simulation with variation of material characteristics of piezoceramic were performed as use of finite element analysis ANSYS 5.5, such as elastic compliance, piezoelectric constant, electro-mechanical coupling coefficient, poisson's ratio and density. The results of simulation, elastic compliance constant s$_{11}$ and piezoelectric constant d$_{31}$ had the most of influence on the elliptic-motion. This results consist with using transverse effect of material. The used motor were piezoceramics of 4 layers, and the dimensions were 65$\times$5$\times$3.5mm(LxWxt).).

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• Journal of Industrial Technology
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• v.24 no.A
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• pp.37-46
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• 2004

A performance analysis for newly being considered electric rail car office has been made in this study. The major purpose is to examine a proposed design in terms of its capacity and the proposed number of travels (82-84) per day. For this study we first examined the overall system configuration with detailed operational processes of cleaning and inspection. We then developed a simulation model using ARENA and designed input data from 12 selected factors and their interaction effects. A simulation run for each treatment combination of $L_{16}(2^{15})$ orthogonal array was run and 20 batch means were obtained. Through careful analyses of the results obtained, we drew a diversity of suggestions including the best factor level combination. Our confirmation experiments at the optimal level combination further validate the possibility of 82 runs and the consistency in the results.

• MALSORI
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• no.66
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.80-92
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• 1994

The dynamic response characteristics of a floating ocean city are examined for presenting the basic data for the design of huge offshore structures supported by a large number of floating bodies in waves. The numerical approach which is accurate in linear system is based on combination of a three dimensional source distribution method, wave interaction theory and the finite element method of using the space frame element. The hydrodynamic interactions among the floating bodies are taken into account in their exact form within the context of linear potential theory in the motion and structural analysis. The method is applicable to an arbitrary number of three dimensional bodies having any individual body geometries and geometrical arrangement with the restriction that the circumscribed, bottom-mounted. Imaginary vertical cylinder for each body does not contain any part of the other body. The validity of this procedure was verified by comparing with experimental results obtained in the literature.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.1060-1062
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• 1993

This paper presents the dynamic analysis method of a linear induction motor by finite element method. For simulation of dynamic performance, a step by step process with respect to time is used with external voltage source and motional equation. Movement is taken into account by a combination of mesh distortion and remeshing technique.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.721-731
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• 2017

In this study, principal component analysis methods using Lasso penalty are introduced. There are two popular methods that apply Lasso penalty to principal component analysis. The first method is to find an optimal vector of linear combination as the regression coefficient vector of regressing for each principal component on the original data matrix with Lasso penalty (elastic net penalty in general). The second method is to find an optimal vector of linear combination by minimizing the residual matrix obtained from approximating the original matrix by the singular value decomposition with Lasso penalty. In this study, we have reviewed two methods of principal components using Lasso penalty in detail, and shown that these methods have an advantage especially in applying to data sets that have more variables than cases. Also, these methods are compared in an application to a real data set using R program. More specifically, these methods are applied to the crime data in Ahamad (1967), which has more variables than cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.533-541
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• 2013

In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.