scholarly journals An efficient peak-separation algorithm for a personal computer. I. Accurate peak-finding and comparison of nonlinear optimization algorithms.

1986 ◽  
Vol 35 (2) ◽  
pp. 142-153 ◽  
Author(s):  
Yasuhiro SENGA ◽  
Sigeo MINAMI
Keyword(s):  
Nonlinear Optimization ◽  
Personal Computer ◽  
Optimization Algorithms ◽  
Separation Algorithm ◽  
Peak Separation ◽  
Peak Finding

scholarly journals Film Thickness-Profile Measurement Using Iterative Peak Separation Structured Illumination Microscopy

2021 ◽  
Vol 11 (7) ◽  
pp. 3023
Author(s):  
Kejun Yang ◽  
Chenhaolei Han ◽  
Jinhua Feng ◽  
Yan Tang ◽  
Zhongye Xie ◽  
...  
Keyword(s):  
Modulation Depth ◽  
Thickness Distribution ◽  
Detection Threshold ◽  
Profile Measurement ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Separation Algorithm ◽  
Peak Separation ◽  
Lower Detection ◽  
Depth Response

The surface and thickness distribution measurement for transparent film is of interest for electronics and packaging materials. Structured illumination microscopy (SIM) is a prospective technique for measuring film due to its high accuracy and efficiency. However, when the distance between adjacent layers becomes close, the peaks of the modulation depth response (MDR) start to overlap and interfere with the peak extraction, which restricts SIM development in the field of film measurement. In this paper, an iterative peak separation algorithm is creatively applied in the SIM-based technique, providing a precise peak identification even as the MDR peaks overlap and bend into one. Compared with the traditional method, the proposed method has a lower detection threshold for thickness. The experiments and theoretical analysis are elaborated to demonstrate the feasibility of the mentioned method.

scholarly journals AUC-Maximizing Ensembles through Metalearning

2016 ◽  
Vol 12 (1) ◽  
pp. 203-218 ◽  
Author(s):  
Erin LeDell ◽  
Mark J. van der Laan ◽  
Maya Petersen
Keyword(s):  
Nonlinear Optimization ◽  
Roc Curve ◽  
Optimization Problem ◽  
Binary Classification ◽  
Optimization Algorithms ◽  
Training Data ◽  
Classification Problems ◽  
Nonlinear Optimization Problem ◽  
Auc Maximization ◽  
Super Learner

Abstract Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

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