Global sensitivity analysis for the design of nonlinear identification experiments

Lund, Alana; Dyke, Shirley J.; Song, Wei; Bilionis, Ilias

doi:10.1007/s11071-019-05199-9

Cited by 15 publications

(6 citation statements)

References 58 publications

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“…Finite difference approximation or dynamic sensitivity (e.g., direct, adjoint) methods may be used to estimate the partial derivatives

{(|, \frac{\partial {\overset{y}{true}}_{r}}{\partial θ_{i}})}_{t_{m}}

evaluated at the nominal values of θ . Recent works 57–59 consider global sensitivity analysis methods such as Fourier amplitude sensitivity test 60–62 and Sobol methods 63 to approximate Q . In this work, we focus on the local sensitivity analysis, which is currently the most popular approach.…”

Section: Mathematical Primer On Model‐based Design Of Experimentsmentioning

confidence: 99%

Pyomo.DOE: An open‐source package for model‐based design of experiments in Python

Wang

Dowling

2022

AIChE Journal

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Predictive mathematical models are a cornerstone of science and engineering. Yet selecting, calibrating, and validating said science‐based models often remains an art in practice. Model‐based design of experiments (MBDoE) provides a systematic framework to maximize information gain from experiments while minimizing time and resource costs. But MBDoE remains limited to niche application areas, in part because practitioners must integrate expertise in statistics, computational optimization, and modeling. To help reduce this barrier, we introduce Pyomo.DOE, an open‐source package for MBDoE. Pyomo.DOE uses a nonlinear sensitivity analysis code k_aug to quickly approximate the Fisher information matrix and leverages a new stochastic programming abstraction. We demonstrate Pyomo.DOE with the first application of MBDoE to fixed‐bed breakthrough experiments, which highlights the power of Pyomo.DOE to quantify the value of experimental modifications a priori for large‐scale partial differential‐algebraic equation (PDAE) models. We also provide a mathematical primer on MBDoE targeted at general chemical engineers.

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“…Finite difference approximation or dynamic sensitivity (e.g., direct, adjoint) methods may be used to estimate the partial derivatives

{(|, \frac{\partial {\overset{y}{true}}_{r}}{\partial θ_{i}})}_{t_{m}}

Section: Mathematical Primer On Model‐based Design Of Experimentsmentioning

confidence: 99%

Pyomo.DOE: An open‐source package for model‐based design of experiments in Python

Wang

Dowling

2022

AIChE Journal

View full text Add to dashboard Cite

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“…The main function of micro control unit is to control business logic and implement wireless communication protocol and network communication protocol. Among them, the role of wireless module is to complete the wireless signaling and data transceiver function [17,18]. The wireless module and wireless protocol interact with WIS to complete the transmission of image data from WIS to WNC.…”

Section: Hardware Design Of Wireless Image Transmission Interference Signal Recognition System Based On Deep Learningmentioning

confidence: 99%

Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

Zhi-jun

Liu

2021

Wireless Communications and Mobile Computing

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In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time-domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP-CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.

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“…The Sobol indices, established in [44], are based on the variance method, quantifying the contribution of each parameter concerning the total variance of the model. It has recently been used in many works, with high impact research, as in [2,35]. One of its main advantages is dealing with nonlinear and non-parameterized models and providing a quantitative and qualitative classification.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Global sensitivity analysis of asymmetric energy harvesters

Norenberg¹,

Cunha²,

Silva³

et al. 2021

Preprint

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Parametric variability is inevitable in actual energy harvesters and can define crucial aspects of the system performance, especially in susceptible systems to small perturbations. In this way, this work aims to identify the most critical parameters in the dynamics of (a)symmetric bistable energy harvesters with nonlinear piezoelectric coupling, considering the variability of their physical and excitation parameters. For this purpose, a global sensitivity analysis based on the Sobol' indices is performed by an orthogonal decomposition in terms of conditional variances to access the dependence of the recovered power concerning the harvester parameters. This technique quantifies the variance concerning each parameter individually and jointly regarding the total variation of the model. The results indicate that the frequency and amplitude of excitation, asymmetric bias angle, and piezoelectric coupling at the electrical domain are the most influential parameters that affect the mean power harvested. It has also been shown that the order of importance of the parameters can change from stable conditions. In possession of this, a better

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Global sensitivity analysis for the design of nonlinear identification experiments

Cited by 15 publications

References 58 publications

Pyomo.DOE: An open‐source package for model‐based design of experiments in Python

Pyomo.DOE: An open‐source package for model‐based design of experiments in Python

Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

Global sensitivity analysis of asymmetric energy harvesters

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