Browsing by Author "Yen, C. H."

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Charge-transfer energy in iridates: A hard x-ray photoelectron spectroscopy study
(2020-07-13)
Takegami, D.
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Kasinathan, D.
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Wolff, K. K.
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Altendorf, S. G.
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Chang, C. F.
We have investigated the electronic structure of iridates in the double perovskite crystal structure containing either Ir4+ or Ir5+ using hard x-ray photoelectron spectroscopy. The experimental valence band spectra can be well reproduced using tight-binding calculations including only the Ir 5d, O 2p, and O 2s orbitals with parameters based on the downfolding of the density-functional band structure results. We found that, regardless of the A and B cations, the A(2)BIrO(6) iridates have essentially zero O 2p to Ir 5d charge-transfer energies. Hence double perovskite iridates turn out to be extremely covalent systems with the consequence being that the magnetic exchange interactions become very long ranged, thereby hampering the materialization of the long-sought Kitaev physics. Nevertheless, it still would be possible to realize a spin-liquid system using the iridates with a proper tuning of the various competing exchange interactions.
Measuring production yield for processes with multiple quality characteristics
(2006-11-01)
Pearn, W. L.
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Wang, F. K.
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Yen, C. H.
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工業工程與管理學系
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Department of Industrial Engineering and Management
Process yield is an important criterion used in the manufacturing industry for measuring process performance. Methods for measuring yield for processes with single characteristic have been investigated extensively. However, methods for measuring yield for processes with multiple characteristics have been comparatively neglected. In this paper, we develop a generalized yield index, called TSpk, PC, based on the index S-pk introduced by Boyles (Journal of Quality Technology, 23, 17-26, 1991) using the principal component analysis (PCA) technique. We obtained a lower confidence bound (LCB) for the true process yield. The proposed method can be used to determine whether a process meets the preset yield requirement, and make reliable decisions. Examples are provided to demonstrate the proposed methodology.
Multivariate capability indices: Distributional and inferential properties
(2007)
Pearn, W. L.
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Wang, F. K.
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Yen, C. H.
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工業工程與管理學系
;
Department of Industrial Engineering and Management
Process capability indices have been widely used in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications. Properties of the univariate processes have been investigated extensively, but are comparatively neglected for multivariate processes where multiple dependent characteristics are involved in quality measurement. In this paper, we consider two commonly used multivariate capability indices MCp and MCpm, to evaluate multivariate process capability. We investigate the statistical properties of the estimated MCp and obtain the lower confidence bound for MCp. We also consider testing MCp, and provide critical values for testing if a multivariate process meets the preset capability requirement. In addition, an approximate confidence interval for MCpm is derived. A simulation study is conducted to ascertain the accuracy of the approximation. Three examples are presented to illustrate the applicability of the obtained results.
Select better suppliers based on manufacturing precision for processes with multivariate data
(2009)
Yen, C. H.
;
Pearn, W. L.
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Process capability indices have been widely used in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications. Processes with univariate data have been investigated extensively, but are comparatively neglected for processes with multivariate data. Chou (Chou, Y.M., 1994. Selecting a better supplier by testing process capability indices. Quality Engineering, 6, 427-438) developed a procedure using univariate Cp to determine whether or not two processes are equally capable, which allows one to select the supplier with better quality. However, for processes with multiple characteristics, no methods are available for comparing two processes with multivariate data. In this paper, we consider the supplier selection problem based on manufacturing precision in which the processes involve multiple quality characteristics. We derive the distribution of the corresponding test statistic, and calculate critical values required for the comparison purpose. A real-world application is presented for justification.