China
Africa
Explore chapters and articles related to this topic
Analysis of Particle Morphology Data
Published in John Keith Beddow, T. P. Meloy, Advanced Particulate Morphology, 1980
Robert Ehrlich, P. Jeffrey Brown, Jeffrey M. Yarus, Duane T. Eppler
The variables used in Q-mode factor analysis are the relative proportions of grains in each category of the shape frequency histograms at a given harmonic. For instance, given an array of samples, each amplitude frequency histogram for the 16th harmonic was divided into 20 intervals. The relative proportion of grains in each interval was determined (Figure 10). From the data matrix, similarity coefficients between all pairs of samples were determined, thus yielding a similarity matrix.13 The similarity coefficient used, the cos theta coefficient is analogous to the correlation coefficient. It is this matrix that is analyzed in the factor analysis. The algorithm Extended CABFAC18 was expressly designed for this kind of analysis, and was therefore used in this study.
Expressing Robot’s Understanding of Human Preference Based on Successive Estimations during Dialog
Published in International Journal of Human–Computer Interaction, 2023
Kazuki Sakai, Yutaka Nakamura, Yuichiro Yoshikawa, Shingo Kano, Hiroshi Ishiguro
The similarities of the items paired in each HIT (i.e., the likelihood that both items of each pair belong to the same category) is calculated by the IRM. Because IRM identifies the underlying structure (categorization) of the data, the subjective similarity of the model person is supposedly captured through this process. IRM takes a random clustering as the initial value and probabilistically updates the likelihood while clustering step by step. Sampling is performed from the point of sufficient convergence. Based on the sampled data, we defined the similarity as the number of times the data belonged to the same category divided by the total number of data. In this study, Gibbs sampling that repeated for 1000 steps was used. Sampling was performed every 10 steps (at a resolution of 0.02) starting from the 501st step, when sufficient convergence was confirmed. This result is preserved as a matrix (similarity matrix K), as shown in Figure 2, and is used in the similarity model of the robot.
Transient thermal stress analysis of functionally graded annular fin with free base
Published in Journal of Thermal Stresses, 2020
Ali Yildirim, Durmuş Yarimpabuç, Kerimcan Celebi
The transient thermal stress in an annular fin is investigated analytically by Wu [26]. The temperature distribution is obtained on Laplace domain and inverse Laplace transformation is achieved by combination of complex contour integration and residual method. The same problem is solved by using modified Durbin method for inverse Laplace transformation [30]. The transient thermal stresses for convention–conduction–radiation case in an annular fin are determined by using a hybrid method that is combination of Taylor transformation and finite difference approximation [31]. Transient thermoelastic analysis of an annular fin with thermomechanical coupling effect is analyzed on Laplace domain and Fourier transformation is used in order to transform the real domain [32]. The transient thermal stresses in an annular fin with thermomechanical coupling effect for the variable heat transfer coefficient are obtained on the Laplace domain by using a hybrid method that is a combination of Laplace transform and finite difference method. The matrix similarity transformation and Fourier series techniques are used in order to transform the real domain [33]. The transient temperature distribution of an annular fin with temperature-dependent thermal conductivity is solved by using the Adomian decomposition method under the periodic boundary conditions and then the transient thermal stresses are obtained by direct integration [34].
The mise en scéne of memristive networks: effective memory, dynamics and learning
Published in International Journal of Parallel, Emergent and Distributed Systems, 2018
First we work out a simple exercise which will turn out to be useful later. Specifically, this will be in the case , for which . One key element of the proof which follows below is the analysis of matrix similarity, for which a matrix has similar eigenvalues to another matrix. For instance, although is not a symmetric matrix, it has always real eigenvalues. In order to see this, we note that the eigenvalues of any matrix product has the same eigenvalues of the matrix . In this case, since W is diagonal and positive, the square root of the matrix is simply the square root of the diagonal elements. This is due to the fact that any matrix , for any invertible matrix Q, has the same eigenvalues as those of M. In the following, we use the symbol for similarity, i.e. matrices with similar eigenvalues (). If is symmetric and real, then has real eigenvalues as it is a symmetric matrix. This implies that also has real eigenvalues. In fact, we have that