• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.10
• /
• pp.2201-2210
• /
• 2002

The accurate analysis of ultrasonic wave propagation and scattering plays an important role in many aspects of nondestructive evaluation. A numerical analysis makes it possible to perform parametric studies, and in this way the probability of detection and reliability of test results can be improved. In this paper, a finite element method was employed for the analysis of ultrasonic wave propagation in anisotropic materials, and the accuracy of results was checked by comparing with analytical predictions. The element size and the integral time step, which are the critical components for the convergence of finite element solutions, were determined using a commercial finite element code. Some differences for wave propagation in anisotropic media were illustrated when plane waves are propagating in a unidirectionally reinforced composite materials. When plane waves are propagating in nonsymmetric directions in a symmetric plane, deviation angles between the wave vector and the energy vector were found from finite element analyses and the results agreed well with analytical calculations.

• Geophysics and Geophysical Exploration
• /
• v.23 no.1
• /
• pp.55-60
• /
• 2020

The closed-form expressions of gravity, magnetic, gravity gradient tensor, and magnetic gradient tensor due to a rectangular prism are derived. The vertical gravity is derived via triple integration of a rectangular prism in Cartesian coordinates, and the two horizontal components of vector gravity are then derived via cycle permutation of the axis variables of vertical gravity through the axial symmetry of the rectangular prism. The gravity gradient tensor is obtained by differentiating the vector gravity with respect to each coordinate. Using Poisson's relation, a vector magnetic field with constant magnetic direction can be obtained from the gravity gradient tensor. Finally, the magnetic gradient tensor is derived by differentiating the vector magnetic with respect to appropriate coordinates.

• Journal of Magnetics
• /
• v.6 no.2
• /
• pp.53-56
• /
• 2001

We have developed a three-axis configurational in situ SMOKE apparatus by which three-dimensional vector magnetization reversal processes are studied for ultrathin Co films grown on a Pd (111) single crystal in the thickness range of spin reorientation transition. This study provides a better understanding of magnetization reversal motions with the knowledge of three components of the magnetization vector at the transition of an easy axis of magnetization from the film normal at 5 ML Co to in-plane at 6 ML Co (ML denotes monolayer). For a 5.25 ML Co, it was observed that a slightly canted magnetization vector from the film normal rotated in the film plane under an applied field direction parallel to the film normal.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
• /
• v.24 no.3
• /
• pp.67-72
• /
• 2013

• Bulletin of the Korean Mathematical Society
• /
• v.52 no.1
• /
• pp.149-157
• /
• 2015

We show that $C^1$-generically, the shadowable chain component of a $C^1$-vector field containing a hyperbolic periodic orbit is hyperbolic if it is locally maximal.

• Geophysics and Geophysical Exploration
• /
• v.27 no.2
• /
• pp.119-128
• /
• 2024

Marine magnetic surveys provide a rapid and cost-effective method for pioneer geophysical survey for many purposes. Sea-surface magnetometers offer high accuracy but are limited to measuring the scalar total magnetic field and require dedicated cruise missions. Shipboard three-component magnetometers, on the other hand, can collect vector three components and applicable to any cruise missions. However, correcting for the ship's magnetic field, particularly viscous magnetization, still remains a challenge. This study proposes a new additional correction method for ship's viscous magnetization effect in vector data acquired by shipboard three-component magnetometer. This method utilizes magnetic data collected simultaneously with a sea-surface magnetometer providing total magnetic field measurements. Our method significantly reduces deviations between the two datasets, resulting in corrected vector anomalies with errors as low as 7-25 nT. These tiny errors are possibly caused by the vector magnetic anomaly and its related viscous magnetization. This method is expected to significantly improve the accuracy of shipborne magnetic surveys by providing corrected vector components. This will enhance magnetic interpretations and might be useful for understanding plate tectonics, geological structures, hydrothermal deposits, and more.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.11 no.2
• /
• pp.172-183
• /
• 2006

This paper analyzes instantaneous power compensation theory through comparing p-q theory and cross-vector theory which were proposed by Akagi and Nabae respectively in three-phase four-wire systems. The two compensation theories are identical when there is no zero-sequence voltage component in three-phase three-wire systems, However, when the zero-sequence voltage and/or current components exist in three-phase four-wire systems, the two compensation theories we different in definition on instantaneous real power and instantaneous imaginary power. Based on the analysis, this paper presents instantaneous power compensation method that can eliminate neutral current completely without using energy storage element when the zero-sequence current and voltage components exist in three-phase four-wire systems.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.1
• /
• pp.31-39
• /
• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• Journal of IKEEE
• /
• v.17 no.3
• /
• pp.241-247
• /
• 2013

This paper presents the design of a System on Programmable Chip (SoPC) based on Field Programmable Gate Array (FPGA) for speech recognition in which Mel-Frequency Cepstral Coefficients (MFCC) for speech feature extraction and Vector Quantization for recognition are used. The implementing process of the speech recognition system undergoes the following steps: feature extraction, training codebook, recognition. In the first step of feature extraction, the input voice data will be transformed into spectral components and extracted to get the main features by using MFCC algorithm. In the recognition step, the obtained spectral features from the first step will be processed and compared with the trained components. The Vector Quantization (VQ) is applied in this step. In our experiment, Altera's DE2 board with Cyclone II FPGA is used to implement the recognition system which can recognize 64 words. The execution speed of the blocks in the speech recognition system is surveyed by calculating the number of clock cycles while executing each block. The recognition accuracies are also measured in different parameters of the system. These results in execution speed and recognition accuracy could help the designer to choose the best configurations in speech recognition on SoPC.

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