• Smart Structures and Systems
• /
• v.33 no.2
• /
• pp.133-143
• /
• 2024

Piezoelectric (PZT) actuators have been widely used in precision positioning fields for their excellent displacement resolution. However, due to the inherent characteristics of piezoelectric actuators, hysteresis has been proven to greatly reduce positioning performance. In this paper, five mathematical hysteretic models based on activation function are proposed to characterize the nonlinear hysteresis characteristics of piezoelectric actuators. Then the performance of the proposed models is verified by particle swarm optimization (PSO) algorithm and the experiment data. Thirdly, the fitting performance of the proposed models is compared with the classical Bouc-Wen model. Finally, the performance of the five proposed models in modelling hysteresis nonlinearity of piezoelectric drivers is compared, in terms of RMSE, MAPE, SAPE and operation efficiency, and relevant suggestions are given.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1155-1163
• /
• 2016

This paper deals with an estimation procedure of variance components in a mixed effects model by projections. Projections are used to obtain sums of squares instead of using reductions in sums of squares due to fitting both the assumed model and sub-models in the fitting constants method. A projection matrix can be obtained for the residual model at each step by a stepwise procedure to test the hypotheses. A weighted least squares method is used for the estimation of fixed effects. Satterthwaite's approximation is done for the confidence intervals for variance components.

• Communications for Statistical Applications and Methods
• /
• v.17 no.4
• /
• pp.527-540
• /
• 2010

Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.1
• /
• pp.9-19
• /
• 2016

Accurate models of the characteristics of typical inverse-time overcurrent (OC) protective devices play an important role in the protective coordination schemes. This paper presents a novel approach to determine the OC protective device parameters. The approach is based on the Eigensystem Realization Algorithm which generates a state space model to fit the characteristics of OC protective devices. Instead of the conventional characteristic curves, the dynamic state space model gives a more exact fit of the OC protective device characteristics. This paper demonstrates the feasibility of decomposing the characteristic curve into smooth components and oscillation components. 19 characteristic curves from 13 typical and 6 non-typical OC protective devices are chosen for curve-fitting. The numbers of fitting components required are determined by the maximum absolute values of errors for the fitted equation. All fitted equations are replaced by a versatile equation for the characteristics of OC protective devices which represents the characteristic model of a novel flexible OC relay, which in turn may be applied to improve the OC coordination problems in the sub-transmission and distribution systems.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.47 no.6
• /
• pp.1080-1095
• /
• 2023

Three-dimensional virtual fitting has become a trending practice in the fashion industry because of its productivity benefits, allowing garments to be virtually worn by avatar models without physical production. This study analyzed the variables influencing clothing pressure in both real and virtual fittings to expand the potential utility of pressure data derived from the latter. For this purpose, six sets of compression garments were created by combining two types of tricot fabrics and three types of reduced-pattern tops, with the clothing for real and virtual fittings having identical dimensions. Focus was directed to analyzing the correlation among clothing pressure, surface area deformation, and the mechanical properties of the fabrics. In real fittings, clothing pressure was influenced by multiple factors, including garment design, pattern reduction ratio, body shape, and fabric properties, consistent with existing knowledge. In virtual fittings, however, only the digital mechanical characteristics of the fabrics significantly influenced clothing pressure. The findings suggest that a more reliable implementation of clothing pressure in virtual fitting programs necessitates an approach that considers the complex structural information of garments.

• Smart Structures and Systems
• /
• v.22 no.2
• /
• pp.175-183
• /
• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• Korean Journal of Computational Design and Engineering
• /
• v.12 no.1
• /
• pp.27-38
• /
• 2007

This paper describes a multiresidual approximation method for scattered volumetric data modeling. The approximation method employs a volumetric NURBS or VNURBS as a data interpolating function and proposes two multiresidual methods as a data modeling algorithm. One is called as the residual series method that constructs a sequence of VNURBS functions and their algebraic summation produces the desired approximation. The other is the residual merging method that merges all the VNURBS functions mentioned above into one equivalent function. The first one is designed to construct wavelet-type multiresolution models and also to achieve more accurate approximation. And the second is focused on its improvement of computational performance with the save fitting accuracy for more practical applications. The performance results of numerical examples demonstrate the usefulness of VNURBS approximation and the effectiveness of multiresidual methods. In addition, several graphical examples suggest that the VNURBS approximation is applicable to various applications such as surface modeling and fitting problems.

• Food Science and Preservation
• /
• v.6 no.1
• /
• pp.37-42
• /
• 1999

The equilibrium moisture content(EMC) of Shiitake mushroom is and important factor because it has a close relationship to storage and drying problems. The determination of the EMC for Shiitake mushroom was made in atmospheres of various constant humidities at four different constant temperatures and the fitting of the five selected EMC models were performed with the experimental EMC data. The desorption equilibrium moisture contents for Shiitake mushroom were increased as the temperature was decreased and the relative humidity was increased. The significant difference of the equilibrium moisture content was appeared between the cap and the stipe of Shiitake mushroom. The Henderson-Thompson model was fitter than the others with the experimental data.

• Journal of The Korean Astronomical Society
• /
• v.25 no.1
• /
• pp.11-21
• /
• 1992

Applying mass model to disk galaxy NGC 300, since the observed rotation curve of NGC 300 is flatter than Toomre's mass model n = 1, two cases are used; obtaining parameters $a^n$ and $b^n$ from the polynomial fitting of the observed rotation curve (case A) and from the least square fitting between the observed rotation curve and model rotation curve (case B). In any case, n bas a fixed value of 1. Brandt's mass model is also discussed. which has a shape parameter n = 1.4. Calculated total mass and total mass to luminosity ratio are $3.3{\times}10^{10}M_{\odot}$, l2.1 for case A and $2.8{\times}10^{10}M_{\odot}$, 10.3 for case B. In case of Brandt's model, the values are $4.2{\times}10^{10}M_{\odot}$ and 15.4. The rise in the local mass to luminosity ratio in the outer part of NGC 300 implies existence of massive halo. Other dynamical properties are also discussed.

• The Korean Journal of Applied Statistics
• /
• v.13 no.1
• /
• pp.197-206
• /
• 2000

For estimating missing cells in contingency table, we suggest an iterative method which extends IPF (Iterative Proportional Fitting) method. The suggested m~thod is not restricted by the number and the location of missing cells, and does not distort the given quasi-independency.