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Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy ( Cho et al., 2021 ). However, the SE estimation procedure has yet to be fully addressed. To tackle this issue, the present study proposed an updated supplemented expectation maximization (USEM) method and a bootstrap method for SE estimation. These two methods were compared in terms of SE recovery accuracy. The simulation results demonstrated that the GVEM algorithm with bootstrap and item priors (GVEM-BSP) outperformed the other methods, exhibiting less bias and relative bias for SE estimates under most conditions. Although the GVEM with USEM (GVEM-USEM) was the most computationally efficient method, it yielded an upward bias for SE estimates.
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Parameter Optimization Algorithm of a Discrete Energy-Averaged Model for Galfenol Alloys
An optimization algorithm is proposed to determine the parameters of a discrete energy-averaged (DEA) model for Galfenol alloys. A new numerical approximation approach for partial derivative expressions is developed, which improves computational speed of the DEA model by 61% relative to existing partial derivative expressions. Initial estimation of model parameters and a two-step optimization procedure, including anhysteresis and hysteresis steps, are performed to improve accuracy and efficiency of the algorithm. Initial estimation of certain material properties such as saturation magnetization, saturation magnetostriction, Young’s modulus, and anisotropy energies can improve the convergence and enhance efficiency by 41% compared to the case where these parameters are not estimated. The two-step optimization improves efficiency by 28% while preserving accuracy compared to one-step optimization. Proposed algorithm is employed to find the material properties of Galfenol samples with different compositions and heat treatments. The trends obtained from these optimizations can guide future Galfenol modeling studies.
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- Award ID(s):
- 1738723
- PAR ID:
- 10060438
- Date Published:
- Journal Name:
- Proceedings of the ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
- Volume:
- SMASIS2017
- Issue:
- 3906
- Page Range / eLocation ID:
- V001T02A005
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/SMASIS2017-3906
