• Journal of KSNVE
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• v.6 no.4
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• pp.481-491
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• 1996

In this paper, we formulated algorithm for free vibration analysis of cylindrical shells with stiffeners by applying the transfer influence coefficient method. This was developed as a vibration analysis method suitable for using personal computer(PC). The simple computational results form PC demonstrated the validity of the present algorithm, that is, the computational high accuracy and speed, and the flexibility of programming. We compared with results of the transfer matrix method and the reference. We also confirmed that the present algorithm could provide the solutions of high accuracy for system with a lots of intermediate rigid supports and stiffeners. And all boundary conditions and the intermediate stiff supports between shell and foundation could be treated only by adequately varying the values of the spring constants.

• Journal of the Korean Wood Science and Technology
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• v.37 no.2
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• pp.137-140
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• 2009

Chromated copper arsenate (CCA), a long history of successful preservative, have raised environmental concerns. Adsorption characteristics of domestic soils for chromium, copper, and arsenic were assessed by measuring distribution coefficient ($K_d$) values of these metal components in a laboratory scale. The results revealed that $K_d$ values were higher in chromium, followed by arsenic and copper in soil matrix. Different soil matrixes resulted in varying mobilities of CCA components. The values of $K_d$ for all three metals increased with organic matter contents. The results suggest that the mobility of metal components may be very limited to the surface area adjacent to CCA-treated wood due to their fairly large distribution coefficient ($K_d$). However, the metal components would be persistent and accumulated in the soil, resulting in high chemical concentration in service area of treated wood.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.5-16
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• 2016

Generally, methods using transfer techniques, like the transfer matrix method and the transfer stiffness coefficient method, find natural frequencies using the sign change of frequency determinants in searching frequency region. However, these methods may omit some natural frequencies when the initial frequency interval is large. The Sylvester-transfer stiffness coefficient method ("S-TSCM") can always obtain all natural frequencies in the searching frequency region even though the initial frequency interval is large. Because the S-TSCM obtain natural frequencies using the number of natural frequencies existing under a searching frequency. In this paper, the algorithm for the free vibration analysis of axisymmetric conical shells was formulated with S-TSCM. The effectiveness of S-TSCM was verified by comparing numerical results of S-TSCM with those of other methods when analyzing free vibration in two computational models: a truncated conical shell and a complete (not truncated) conical shell.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.287-294
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• 2001

In static analysis of a variety of structures, the matrix method of structural analysis is the most widely used and powerful analysis method. However, this method has drawback requiring high-performance computers with many memory units and fast processing units in the case of analyzing complex and large structures accurately. Therefore, it's very difficult to analyze these structures accurately in personal computers. For overcoming the drawback of the matrix method of structural analysis, authors suggest transfer stiffness coefficient method(TSCM). The TSCM is very suitable to a personal computer because the concept of the TSCM is based on the transfer of the stiffness coefficient for an analytical structure. In this paper, the static analysis algorithm for frame structures is formulated by the TSCM. We confirm the validity of the proposed method through the compare of computation results by the TSCM and the NASTRAN.

• Journal of applied mathematics & informatics
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• v.32 no.5_6
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• pp.807-816
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• 2014

The asymptotic behavior of solutions of Lyapunov type matrix Volterra integro differential equation, in which the coefficient matrices are not stable, is studied by the method of reduction.

• Structural Engineering and Mechanics
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• v.67 no.5
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• pp.457-464
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• 2018

This paper presents a method for detecting damage in irregular 2D and 3D continuum structures based on combination of wavelet transform (WT) with fuzzy inference system (FIS) and particle swarm optimization (PSO). Many damage detection methods study regular structures. This method studies irregular structures and doesn't need response of healthy structures. First the damaged structure is analyzed with finite element methods, and damage response is obtained at the finite element points that have irregular distance, secondly the FIS, which is optimized by PSO is used to obtain responses at points, having equal distance by response at those points that previously obtained by the finite element methods. Then a 2D (for 2D continuum structures) or a 3D (for 3D continuum structures) matrix is performed by equal distance point response. Thirdly, by applying 2D or 3D wavelet transform on 2D or 3D matrix that previously obtained by FIS detail matrix coefficient of WT is obtained. It is shown that detail matrix coefficient can determine the damage zone of the structure by perturbation in the damaged area. In order to illustrate the capability of proposed method some examples are considered.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.385-392
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• 2015

Principal component analysis (PCA), a dimension-reduction technique, is usually implemented after the variables are standardized when the measurement unit of variables are different. To standardize a variable we divide it by its standard deviation. But there is another way to transform a variable to be independent of its measurement unit. It is to divide it by its mean rather than standard deviation. Implementing PCA on standardized variables is equivalent to implementing PCA with a correlation matrix of original variables. Similarly, implementing PCA on the transformed variables divided by their means is equivalent to implementing PCA with a matrix related to the coefficients of variation of the original variables. We explain why we need to implement PCA on the variables transformed by their means.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.522-539
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• 2021

In order to solve the problems of the existing audio fingerprint method when extracting audio fingerprints from long speech segments, such as too large fingerprint dimension, poor robustness, and low retrieval accuracy and efficiency, a robust audio fingerprint retrieval method based on feature dimension reduction and feature combination is proposed. Firstly, the Mel-frequency cepstral coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) of the original speech are extracted respectively, and the MFCC feature matrix and LPCC feature matrix are combined. Secondly, the feature dimension reduction method based on information entropy is used for column dimension reduction, and the feature matrix after dimension reduction is used for row dimension reduction based on energy feature dimension reduction method. Finally, the audio fingerprint is constructed by using the feature combination matrix after dimension reduction. When speech's user retrieval, the normalized Hamming distance algorithm is used for matching retrieval. Experiment results show that the proposed method has smaller audio fingerprint dimension and better robustness for long speech segments, and has higher retrieval efficiency while maintaining a higher recall rate and precision rate.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.625-632
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• 2006

Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.

• Korean Management Science Review
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• v.14 no.2
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• pp.69-79
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• 1997

The computational speed of interior point method of linear programming depends on the speed of Cholesky factorization. If the coefficient matrix A has dense columns then the matrix A.THETA. $A^{T}$ becomes a dense matrix. This causes Cholesky factorization to be slow. We study an efficient implementation method of the dense column splitting among dense column resolving technique and analyze the relation between dense column splitting and order methods to improve the sparsity of Cholesky factoror.