• Geomechanics and Engineering
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• v.7 no.3
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• pp.317-333
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• 2014

The study of the mechanical behavior of rockfill materials under three-dimensional loading conditions is a current research focus area. This paper presents a microscale numerical study of rockfill deformation and strength characteristics using the Combined Finite-Discrete Element Method (FDEM). Two features unique to this study are the consideration of irregular particle shapes and particle crushability. A polydisperse assembly of irregular polyhedra was prepared to reproduce the mechanical behavior of rockfill materials subjected to axial compression at a constant mean stress for a range of intermediate principal stress ratios in the interval [0, 1]. The simulation results, including the stress-strain characteristics, relationship between principal strains, and principal deviator strains are discussed. The stress-dilatancy behavior is described using a linear dilatancy equation with its material constants varying with the intermediate principal stress ratio. The failure surface in the principal stress space and its traces in the deviatoric and meridian plane are also presented. The modified Lade-Duncan criterion most closely describes the stress points at failure.

• Journal of the Ergonomics Society of Korea
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• v.10 no.1
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• pp.29-40
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• 1991

This study deals with a quantitative analysis of the effects of colors on learning effectiveness. First, sensuous or emotional factors that school children feel about colors of teachers' clothes are measured by SD method and analyzed by factor analysis. Second, sensuous or emotional factors to enhance learning effectiveness are measured by SD method from teachers, and principal factors are extracted by factor analysis. Finally, the analysis of interaction between the effects of colors and the learning effectiveness is done using the sensuous or emotional factors found from the previous two analyses. The results are as follows: (1) For in-class concentration, the principal factors are "stable", and "near" feelings. The colors related to these feelings are black, red, and blue. (2) For question inducing, the first principal factors are "soft" and "stable" feelings, and the colors are white and black. The second principal factors are "gentle" and "refined" feelings, and the colors are orange and black. (3) For extra-curricular activity, the principal factors are "artless" and "plain" feelings, and the color is blue.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.215-218
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• 2014

In hashing-based methods for approximate nearest neighbors(ANN) search, by mapping data points to k-bit binary codes, nearest neighbors are searched in a binary embedding space. In this paper, we present a hashing method using a PCA-based clustering method, Principal Direction Divisive Partitioning(PDDP). PDDP is a clustering method which repeatedly partitions the cluster with the largest variance into two clusters by using the first principal direction. The proposed hashing method utilizes the first principal direction as a projective direction for binary coding. Experimental results demonstrate that the proposed method is competitive compared with other hashing methods.

• Bulletin of the Korean Chemical Society
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• v.29 no.4
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• pp.833-841
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• 2008

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.6
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• pp.64-73
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• 1996

The three-dimensional strength behavior of compacted decomposed granite soil was studied using cubical triaxial tests with independent control of the three principal stresses. All specimens were loaded under conditions of principal stress direction fixed and aligned with the directions of compacted plane. For comparable test conditions, the major principal strain and volume strain to failure were smallest when the major principal stress acted perpendicular to the compacted plane. The opposite extremes were obtained when the major principal stress acted parallel to the compacted plane. In cubical triaxial tests with same b values and with ${\theta}$ values in one of three sectors of the octahedral plane, independent of the range of ${\theta}$, higher friction angles are obtained in tests with b greater than in triaxial compression tests in which b 0.0, Comparison between the results of the drained cubical triaxial tests on lksan compacted decomposed granite soil and the cross section of the Mohr-Coulomb failure surface as well as the cross section of the Mohr-Coulomb failure surface were made. Lade's isotropic failure criterion based on vertical specimens overestimates the strengths for tests performed with values of 0 between 90˚ and 1 50˚ the Mohr-Coulomb criterion generally underestimates the strengths of tests performed with values of ${\theta}$ between $0^{\circ}$ and $180^{\circ}$ except around the $120^{\circ}$.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• IE interfaces
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• v.24 no.1
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• Structural Engineering and Mechanics
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• v.73 no.5
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• pp.515-527
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• 2020

This paper presents a stress field approach for the shear capacity of stirrup-reinforced concrete beams that explicitly incorporates the contribution of principal tensile stresses in concrete. This formulation represents an extension of the variable strut inclination method adopted in the Eurocode 2. In this model, the stress fields in web concrete consist of principal compressive stresses inclined at an angle θ combined with principal tensile stresses oriented along a direction orthogonal to the former (the latter being typically neglected in other formulations). Three different failure mechanisms are identified, from which the strut inclination angle and the corresponding shear strength are determined through equilibrium principles and the static theorem of limit analysis, similar to the EC-2 approach. It is demonstrated that incorporating the contribution of principal tensile stresses of concrete slightly increases the ultimate inclination angle of the compression struts as well as the shear capacity of reinforced concrete beams. The proposed stress field approach improves the prediction of the shear strength in comparison with the Eurocode 2 model, in terms of both accuracy (mean) and precision (CoV), as demonstrated by a broad comparison with more than 200 published experimental results from the literature.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.