Optimization-based robustness enhancement of compact microwave component designs with response feature regression surrogates

2022 ◽  
pp. 108161
Author(s):  
Anna Pietrenko-Dabrowska ◽  
Slawomir Koziel
Keyword(s):  
Microwave Component ◽  
Response Feature

Digital enhancement of microwave component performance

2003 ◽  
Author(s):  
H. Endler ◽  
A.M. Madni ◽  
P. Vuong
Keyword(s):  
Microwave Component

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

A Feature-Segmentation Model of Short-Term Visual Memory

Perception ◽  
10.1068/p3320 ◽  
2002 ◽  
Vol 31 (5) ◽  
pp. 579-589 ◽  
Author(s):  
Koji Sakai ◽  
Toshio Inui
Keyword(s):  
Visual Memory ◽  
Random Variable ◽  
Short Term ◽  
Pattern Complexity ◽  
Convex Part ◽  
Good Account ◽  
Feature Segmentation ◽  
Short Term Visual Memory ◽  
The Relationship ◽  
Response Feature

A feature-segmentation model of short-term visual memory (STVM) for contours is proposed. Memory of the first stimulus is maintained until the second stimulus is observed. Three processes interact to determine the relationship between stimulus and response: feature encoding, memory, and decision. Basic assumptions of the model are twofold: (i) the STVM system divides a contour into convex parts at regions of concavity; and (ii) the value of each convex part represented in STVM is an independent Gaussian random variable. Simulation showed that the five-parameter fits give a good account of the effects of the four experimental variables. The model provides evidence that: (i) contours are successfully encoded within 0.5 s exposure, regardless of pattern complexity; (ii) memory noise increases as a linear function of retention interval; (iii) the capacity of STVM, defined by pattern complexity (the degree that a pattern can be handled for several seconds with little loss), is about 4 convex parts; and (iv) the confusability contributing to the decision process is a primary factor in deteriorating recognition of complex figures. It is concluded that visually presented patterns can be retained in STVM with considerable precision for prolonged periods of time, though some loss of precision is inevitable.

scholarly journals A network-based response feature matrix as a brain injury metric

2019 ◽  
Vol 19 (3) ◽  
pp. 927-942 ◽  
Author(s):  
Shaoju Wu ◽  
Wei Zhao ◽  
Bethany Rowson ◽  
Steven Rowson ◽  
Songbai Ji
Keyword(s):  
Brain Injury ◽  
Response Feature

Investigation on Impact Response Feature of Railway Vehicles With Wheel Flat Fault Under Variable Speed Conditions

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Yang Jianwei ◽  
Yue Zhao ◽  
Jinhai Wang ◽  
Yongliang Bai ◽  
Chuan Liu
Keyword(s):  
Fourier Transform ◽  
Variable Speed ◽  
Dynamic Features ◽  
Modeling And Analysis ◽  
Railway Vehicles ◽  
Safety Issues ◽  
Time Frequency ◽  
Wheel Flat ◽  
Short Time ◽  
Response Feature

Abstract Wheel faults are the main causes of safety issues in railway vehicles. The modeling and analysis of wheel faults is crucial for determining and studying the dynamic characteristics of railway vehicles under variable speed conditions. Hence, a vehicle–track coupled dynamics model was established for analysis and calculations. The results showed that the dynamic features of the wheel with a flat fault were more pronounced under traction and braking conditions, whereas the variations in the features under coasting conditions were insignificant. In this paper, a short-time fast Fourier transform and reassignment method was used to process the signals, because the results were unclear when the time–frequency graph was processed only by short time Fourier transform, especially under braking conditions. The variation in the fault frequency under variable speed conditions was determined. Finally, statistical indicators were used to describe the vibration behaviors caused by the wheel flat fault.

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