• The Korean Journal of Applied Statistics
• /
• v.33 no.1
• /
• pp.61-74
• /
• 2020

Interval-valued data, one type of symbolic data, are observed in the form of intervals rather than single values. Each interval-valued observation has an internal variation. Principal component analysis reduces the dimension of data by maximizing the variance of data. Therefore, the principal component analysis of the interval-valued data should account for the variance between observations as well as the variation within the observed intervals. In this paper, three principal component analysis methods for interval-valued data are summarized. In addition, a new method using a truncated normal distribution has been proposed instead of a uniform distribution in the conventional quantile method, because we believe think there is more information near the center point of the interval. Each method is compared using simulations and the relevant data set from the OECD. In the case of the quantile method, we draw a scatter plot of the principal component, and then identify the position and distribution of the quantiles by the arrow line representation method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.5
• /
• pp.1093-1102
• /
• 2010

Principal component analysis (PCA) is a well-known method for dimension reduction while maintaining most of the variation in data. Although PCA has been applied to many areas successfully, it is sensitive to outliers. Several variants of PCA have been proposed to resolve the problem and, among the variants, robust fuzzy PCA (RF-PCA) demonstrated promising results. RF-PCA uses fuzzy memberships to reduce the noise sensitivity. However, there are also problems in RF-PCA and the convergence property is one of them. RF-PCA uses two different objective functions to update memberships and principal components, which is the main reason of the lack of convergence property. The difference between two functions also slows the convergence and deteriorates the solutions of RF-PCA. In this paper, a variant of RF-PCA, called RF-PCA2, is proposed. RF-PCA2 uses an integrated objective function both for memberships and principal components. By using alternating optimization, RF-PCA2 is guaranteed to converge on a local optimum. Furthermore, RF-PCA2 converges faster than RF-PCA and the solutions found are more similar to the desired solutions than those of RF-PCA. Experimental results also support this.

• International Journal of Contents
• /
• v.3 no.2
• /
• pp.1-5
• /
• 2007

This paper proposes a technique for principal component analysis (PCA) to raise the recognition rate of a front face in a low dimension by hierarchical image and parallel processing structure. The conventional PCA shows a recognition rate of less than 50% in a low dimension (dimensions 1 to 6) when used for facial recognition. In this paper, a face is formed as images of 3 fixed-size levels: the 1st being a region around the nose, the 2nd level a region including the eyes, nose, and mouth, and the 3rd level image is the whole face. PCA of the 3-level images is treated by parallel processing structure, and finally their similarities are combined for high recognition rate in a low dimension. The proposed method under went experimental feasibility study with ORL face database for evaluation of the face recognition function. The experimental demonstration has been done by PCA and the proposed method according to each level. The proposed method showed high recognition of over 50% from dimensions 1 to 6.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.698-701
• /
• 2002

Subspace analysis (which includes PCA) seeks for feature subspace (which corresponds to the eigenspace), given multivariate input data and has been widely used in computer vision and pattern recognition. Typically data space belongs to very high dimension, but only a few principal components need to be extracted. In this paper I present a fast sequential algorithm for subspace analysis or tracking. Useful behavior of the algorithm is confirmed by numerical experiments.

• Proceedings of the Korean Society For Composite Materials Conference
• /
• 1999.11a
• /
• pp.272-276
• /
• 1999

The present paper proposes design and manufacturing methods of the carbon/epoxy square grid structure with near zero-CTE in three geometrical principal directions. Bonding strength of the grid structure is examined for different bonding methods. Numerical examples show that maximum displacement of the composite grid structure is almost zero comparing with that of aluminum grid structure with same dimension under thermal loading.

• Proceedings of the KSPS conference
• /
• 2005.04a
• /
• pp.103-106
• /
• 2005

The goal of our research is to build a text independent speaker recognition system that can be used in any condition without any additional adaptation process. The performance of speaker recognition systems can be severally degraded in some unknown mismatched microphone and noise conditions. In this paper, we show that PCA(Principal component analysis) without dimension reduction can greatly increase the performance to a level close to matched condition. The error rate is reduced more by the proposed augmented PCA, which augment an axis to the feature vectors of the most confusable pairs of speakers before PCA

• Journal of applied mathematics & informatics
• /
• v.28 no.3_4
• /
• pp.1017-1023
• /
• 2010

We prove that a maximal ideal M of D[x] has two generators and is of the form where p is an irreducible element in a PID D having infinitely many nonassociate irreducible elements and q(x) is an irreducible non-constant polynomial in D[x]. Moreover, we find how minimal generators of maximal ideals of a polynomial ring D[x] over a DVR D consist of and how many generators those maximal ideals have.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.1
• /
• pp.195-201
• /
• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.4 no.2
• /
• pp.220-235
• /
• 1998

The revolution of nursing curriculum has been focused on clinical competency for nursing graduates to flexibly respond to changes in societal health needs and disciplinary requirements. In this trend, the study was designed to identify basic concepts of nursing education that reflects the changes in societal needs and nursing discipline, and to develop the instrument to measure performance level in each dimension of clinical competency. The study was conducted in two phases. In phase 1, principal concepts consisted of nursing education were determined through literature review as well as series of discussion sessions on nursing philosophies and educational objectives among researchers. Though the process, the conceptual framework of competency based nursing curriculum was constructed with nursing process and professional role as horizontal threads, client, health needs, and nursing interventions as vertical threads. Then, items were developed to represent each dimension of competency : client and health need, nursing process, professional role, and nursing interventions. The total of 273 items were included as to represent clinical competency required for BSN graduates. In phase 2, questionnaires were distributed to nursing faculties of 41 BSN programs to validate the 273-item Instrument developed to measure competency. The total of 34 subjects returned the questionnaire with 81% of response rates. The subjects of the study had an average of 42 months of clinical experience and 13 years of education experience in various nursing areas with an age range of 30 to 52 years. The data were analyzed by utilizing SPSSWIN and the results are as follows. 1) The mean score of the nursing process dimension was supported most with the mean of 3.60(SD=0.32) compared to client and health need dimension(M=3.49, SD=.40), professional role(M=3.41, SD=.44), and nursing interventions(M=3.57, SD=.34). 2) The dimensions of competency were moderately correlated to each other with a range of r=.433 to r=.829, confirming that four dimensions of competency were related but distinct concepts. 3) The items of each dimension were analyzed based on its appropriateness. 'Assessing risk factors of the clients' were most highly supported in client and health need dimension. Most items of nursing process dimension were considered appropriate, while items related to efficient communication were well supported in professional role dimension. In nursing intervention dimension, items on basic nursing skills were highly supported while items on specific nursing interventions such as music therapy or art therapy were considered relatively inappropriate to competency for BSN graduates. The findings clearly showed that the current nursing education more emphasizes nursing interventions based on nursing process than other dimensions of competency. There is a need to reconceptualize nursing curriculum that is able to reflect more of nursing professional role and client/health need dimensions. Further research to validate the instrument by confirming competency dimensions of nursing graduates who are currently working at the hospital has been suggested.

• Journal of Digital Convergence
• /
• v.11 no.12
• /
• pp.201-211
• /
• 2013

This study compares Customer Satisfaction Index(CSI) and the weight for each dimension by applying various methods of weight calculation and attempts to suggest some implications. For the purpose, the study classified the methods of weight calculation into the subjective method and the statistical method. Constant sum scale was used for the subjective method, and the statistical method was again segmented into correlation analysis, principal component analysis, factor analysis, structural equation model. The findings showed that there is difference between the weights from the subjective method and the statistical method. The order of the weights by the analysis methods were classified with similar patterns. Besides, the weight for each dimension by different methods of weight calculation showed considerable deviation and revealed the difference of discrimination and stability among the dimensions. Lastly, the CSI calculated by various methods of weight calculation showed to be the highest in structural equation model, followed by in the order of regression analysis, correlation analysis, arithmetic mean, principal component analysis, constant sum scale and factor analysis. The CSI calculated by each method showed to have statistically significant difference.