• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.205-211
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• 2012

The retrieved documents have to be transformed into proper data structure for the clustering algorithms of statistics and machine learning. A popular data structure for document clustering is document-term matrix. This matrix has the occurred frequency value of a term in each document. There is a sparsity problem in this matrix because most frequencies of the matrix are 0 values. This problem affects the clustering performance. The sparseness of document-term matrix decreases the performance of clustering result. So, this research uses the factor score by factor analysis to solve the sparsity problem in document clustering. The document-term matrix is transformed to document-factor score matrix using factor scores in this paper. Also, the document-factor score matrix is used as input data for document clustering. To compare the clustering performances between document-term matrix and document-factor score matrix, this research applies two typed matrices to self organizing map (SOM) clustering.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.09a
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• pp.115-119
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• 2001

Contaminant retardation factor is derived from the colloidal and contaminant transport equations for a four-phase porous medium: an aqueous phase, two mobile colloidal phases, and a solid matrix. It is assumed that the contaminant sorption to solid matrix and colloidal particles and the colloidal deposition on solid matrix follow the linear isotherms. The behavior of the contaminant retardation factor in response to the change of model parameters is examined employing the experimental data of Magee et al. (1991) and Jenkins and Lion (1993). In the four-phase system, the contaminant retardation factor is determined by both the contaminant association with solid matrix and colloidal particles and the colloidal deposition on solid matrix. The contaminant mobility is enhanced when the affinity of contaminants to mobile colloids increases. In addition, as the affinity of colloids to solid matrix decreases, the contaminant mobility increases.

• Steel and Composite Structures
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• v.18 no.6
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• pp.1465-1478
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• 2015

The effect of cutting off fibers on transient load in a polymeric matrix composite lamina was studied in this paper. The behavior of fibers was considered to be linear elastic and the matrix behavior was considered to be linear viscoelastic. To model the viscoelastic behavior of matrix, a three parameter solid model was employed. To conduct this research, finite difference method was used. The governing equations were obtained using Shear-lag theory and were solved using boundary and initial conditions before and after the development of break. Using finite difference method, the governing integro-differential equations were developed and normal stress in the fibers is obtained. Particular attention is paid the dynamic overshoot resulting when the fibers are suddenly broken. Results show that considering viscoelastic properties of matrix causes a decrease in dynamic load concentration factor and an increase in static load concentration factor. Also with increases the number of broken fibers, trend of increasing load concentration factor decreases gradually. Furthermore, the overshoot of load in fibers adjacent to the break in a polymeric matrix with high transient time is lower than a matrix with lower transient time, but the load concentration factor in the matrix with high transient time is lower.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.727-733
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• 2011

To understand the stock market structure it is very important to extract meaningful information by analyzing the correlation matrix between stock returns. Recently there has been many studies on the correlation matrix using the Random Matrix Theory. In this paper we adopt this random matrix methodology to a single-factor model and we obtain meaningful information on the correlation matrix. In particular we observe the analysis of the correlation matrix using the single-factor model explains the real market data and as a result we confirm the usefulness of the single-factor model.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.679-682
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• 2004

Air void systems in hardened concrete has an important influence on concrete durability such as freeze-thaw resistance, water permeability, surface scaling resistance. Linear traverse method and point count method described at ASTM is the routine analysis of the air void system that have been widely used to estimate the spacing factor in hardened concrete. Recently, many concretes often have a spacing factor higher than the generally accepted $200-250{\mu}m$ limit for the usual range of air contents. This study is proposed to estimate the matrix spacing factor by calculation of simplicity. The matrix spacing factor needs two parameters that are air content and numbers of air voids in the hardened concrete. Those are obtained from the standard air-void system analysis of the ASTM C 457. The equation is valid for all values of paste-to-air ratio because the estimation of paste content is unnecessary at the using ASTM C 457. The matrix spacing factor yields a similar estimate of the standard spacing factor.

• Bulletin of the Korean Mathematical Society
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• v.42 no.3
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• pp.501-507
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• 2005

A Boolean matrix with rank 1 is factored as a left factor and a right factor. The perimeter of a rank-1 Boolean matrix is defined as the number of nonzero entries in the left factor and the right factor of the given matrix. We obtain new characterizations of rank preservers, in terms of perimeter, of Boolean matrices.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.81-96
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• 2004

The level-m scaled circulant factor matrix over the complex number field is introduced. Its diagonalization and spectral decomposition and representation are discussed. An explicit formula for the entries of the inverse of a level-m scaled circulant factor matrix is presented. Finally, an algorithm for finding the inverse of such matrices over the quaternion division algebra is given.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.227-242
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• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• The Korean Journal of Pain
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• v.36 no.1
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• pp.72-83
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• 2023

Background: Globally, spinal cord injury (SCI) results in a big burden, including 90% suffering permanent disability, and 60%-69% experiencing neuropathic pain. The main causes are oxidative stress, inflammation, and degeneration. The efficacy of the stem cell secretome is promising, but the role of human neural stem cell (HNSC)-secretome in neuropathic pain is unclear. This study evaluated how the mechanism of HNSC-secretome improves neuropathic pain and locomotor function in SCI rat models through antioxidant, anti-inflammatory, anti-matrix degradation, and neurotrophic activities. Methods: A proper experimental study investigated 15 Rattus norvegicus divided into normal, control, and treatment groups (30 µL HNSC-secretome, intrathecal in the level of T10, three days post-traumatic SCI). Twenty-eight days post-injury, specimens were collected, and matrix metalloproteinase (MMP)-9, F2-Isoprostanes, tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-β, and brain derived neurotrophic factor (BDNF) were analyzed. Locomotor recovery was evaluated via Basso, Beattie, and Bresnahan scores. Neuropathic pain was evaluated using the Rat Grimace Scale. Results: The HNSC-secretome could improve locomotor recovery and neuropathic pain, decrease F2-Isoprostane (antioxidant), decrease MMP-9 and TNF-α (anti-inflammatory), as well as modulate TGF-β and BDNF (neurotrophic factor). Moreover, HNSC-secretomes maintain the extracellular matrix of SCI by reducing the matrix degradation effect of MMP-9 and increasing the collagen formation effect of TGF-β as a resistor of glial scar formation. Conclusions: The present study demonstrated the mechanism of HNSC-secretome in improving neuropathic pain and locomotor function in SCI through antioxidant, anti-inflammatory, anti-matrix degradation, and neurotrophic activities.

• Korean Journal of Cognitive Science
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• v.30 no.3
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• pp.133-156
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• 2019

To investigate how personality test items are understood by participants, their semantic representations were explored by Latent Semantic Analysis, In this thesis, Semantic Similarity Matrix was proposed, which contains cosine similarity of semantic representations between test items and personality traits. The matrix was compared to traditional factor loading matrix. In preliminary study, semantic space was constructed from the passages describing the five traits, collected from 154 undergraduate participants. In study 1, positive correlation was observed between the factor loading matrix of Korean shorten BFI and its semantic similarity matrix. In study 2, short personality test was constructed from semantic similarity matrix, and observed that its factor loading matrix was positively correlated with the semantic similarity matrix as well. In conclusion, the results implies that the factor structure of personality test can be inferred from semantic similarity between the items and factors.