scholarly journals Mechanism for Measuring System Complexity Applying Sensitivity Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Viviane M. Gomes ◽  
Joao R. B. Paiva ◽  
Marcio R. C. Reis ◽  
Gabriel A. Wainer ◽  
Wesley P. Calixto
Keyword(s):  
Sensitivity Analysis ◽  
System Dynamics ◽  
High Performance ◽  
Measuring System ◽  
System Complexity ◽  
System Sensitivity ◽  
Output Variability ◽  
Sensitive Parameters ◽  
Natural Complexity ◽  
Internal Connections

This work proposes a complexity metric which maps internal connections of the system and its relationship with the environment through the application of sensitivity analysis. The proposed methodology presents (i) system complexity metric, (ii) system sensitivity metric, and (iii) two models as case studies. Based on the system dynamics, the complexity metric maps the internal connections through the states of the system and the metric of sensitivity evaluates the contribution of each parameter to the output variability. The models are simulated in order to quantify the complexity and the sensitivity and to analyze the behavior of the systems leading to the assumption that the system complexity is closely linked to the most sensitive parameters. As findings from results, it may be observed that systems may exhibit high performance as a result of optimized configurations given by their natural complexity.

scholarly journals Application of Sensitivity Analysis to the Correction of Static Characteristics of a Phase Angle Modulator

2011 ◽  
Vol 18 (2) ◽  
pp. 249-260 ◽  
Author(s):  
Ryszard Sroka
Keyword(s):  
Sensitivity Analysis ◽  
Phase Angle ◽  
Static Characteristic ◽  
Real System ◽  
Measuring System ◽  
Static Characteristics ◽  
Nonlinearity Error ◽  
System Sensitivity ◽  
Error Sensitivity ◽  
Error Sensitivity Analysis

Application of Sensitivity Analysis to the Correction of Static Characteristics of a Phase Angle ModulatorThe paper presents definitions and relative measures of the system sensitivity and sensitivity of its errors. The model of a real system and model of an ideal measuring system were introduced. It allows to determine the errors of the system. The paper presents also how to use the error sensitivity analysis carried out on the models of the measuring system to the correction of the nonlinearity error of its static characteristic. The corrective function is determined as a relation between the input variable of the tested system and its chosen parameter. The use of the proposed method has been presented on the example of a phase angle modulator. The obtained results have been compared with the results of analytic calculations. The idea of a phase angle modulator is also presented.

scholarly journals Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

2021 ◽  
Author(s):  
Sebastian Brandstaeter ◽  
Sebastian L. Fuchs ◽  
Jonas Biehler ◽  
Roland C. Aydin ◽  
Wolfgang A. Wall ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Computational Studies ◽  
Growth And Remodeling ◽  
Global Sensitivity ◽  
Output Variability ◽  
Constrained Mixture ◽  
The One ◽  
Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

Multiple CMOS Intersection Measuring System Modeling and Analysis

2012 ◽  
Vol 614-615 ◽  
pp. 1299-1302
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Guang Liang Cheng
Keyword(s):  
High Speed ◽  
High Performance ◽  
High Reliability ◽  
System Modeling ◽  
Position Effect ◽  
Field Of View ◽  
Measuring System ◽  
Price Ratio ◽  
Matlab Simulation ◽  
Modeling And Analysis

Multiple high speed CMOS cameras composing intersection system to splice large effect field of view(EFV). The key problem of system is how to locate multiple CMOS cameras in suitable position. Effect field of view was determined according to size, quantity and dispersion area of objects, so to determine camera position located on below, both sides and ahead to moving targets. This paper analyzes effect splicing field of view, operating range etc through establishing mathematical model and MATLAB simulation. Location method of system has advantage of flexibility splicing, convenient adjustment, high reliability and high performance-price ratio.

Optimal Data-Driven Modeling-Free Differential-Inversion-Based Iterative Control: A Wafer Stage Example

2020 ◽  
Author(s):  
Zezhou Zhang ◽  
Qingze Zou
Keyword(s):  
System Dynamics ◽  
High Performance ◽  
Random Noise ◽  
High Accuracy ◽  
Data Driven ◽  
Modeling Process ◽  
Random Disturbances ◽  
Noise Disturbances ◽  
Data Driven Modeling ◽  
Iterative Control

Abstract In this paper, an optimal data-driven modeling-free differential-inversion-based iterative control (OMFDIIC) method is proposed for both high performance and robustness in the presence of random disturbances. Achieving high accuracy and fast convergence is challenging as the system dynamics behaviors vary due to the external uncertainties and the system bandwidth is limited. The aim of the proposed method is to compensate for the dynamics effect without modeling process and achieve both high accuracy and robust convergence, by extending the existed modeling-free differential-inversion-based iterative control (MFDIIC) method through a frequency- and iteration-dependent gain. The convergence of the OMFDIIC method is analyzed with random noise/disturbances considered. The developed method is applied to a wafer stage, and shows a significant improvement in the performance.

High-Fidelity AeroAcoustic Optimization Tool for Flexible Rotors

2020 ◽  
Author(s):  
Li Wang ◽  
Boris Diskin ◽  
Leonard V. Lopes ◽  
Eric J. Nielsen ◽  
Elizabeth Lee-Rausch ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Complex Variable ◽  
High Performance ◽  
Computational Cost ◽  
Geometric Constraints ◽  
General Level ◽  
High Fidelity ◽  
Variable Approach ◽  
Multidisciplinary Analysis ◽  
Tightly Coupled

A high-fidelity multidisciplinary analysis and gradient-based optimization tool for rotorcraft aero-acoustics is presented. Tightly coupled discipline models include physics-based computational fluid dynamics, rotorcraft comprehensive analysis, and noise prediction and propagation. A discretely consistent adjoint methodology accounts for sensitivities of unsteady flows and unstructured, dynamically deforming, overset grids. The sensitivities of structural responses to blade aerodynamic loads are computed using a complex-variable approach. Sensitivities of acoustic metrics are computed by chain-rule differentiation. Interfaces are developed for interactions between the discipline models for rotorcraft aeroacoustic analysis and the integrated sensitivity analysis. The multidisciplinary sensitivity analysis is verified through a complex-variable approach. To verify functionality of the multidisciplinary analysis and optimization tool, an optimization problem for a 40% Mach-scaled HART-II rotor-and-fuselage configuration is crafted with the objective of reducing thickness noise subject to aerodynamic and geometric constraints. The optimized configuration achieves a noticeable noise reduction, satisfies all required constraints, and produces thinner blades as expected. Computational cost of the optimization cycle is assessed in a high-performance computing environment and found to be acceptable for design of rotorcraft in general level-flight conditions.

scholarly journals End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

2019 ◽  
Vol 33 ◽  
pp. 3387-3395 ◽  
Author(s):  
Richard Cheng ◽  
Gábor Orosz ◽  
Richard M. Murray ◽  
Joel W. Burdick
Keyword(s):  
Reinforcement Learning ◽  
System Dynamics ◽  
Learning Process ◽  
High Performance ◽  
Continuous Control ◽  
Barrier Functions ◽  
Synthesis Algorithm ◽  
Vehicle Communication ◽  
Model Free ◽  
Vehicle To Vehicle

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous carfollowing with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

Towards a Methodology for Monitoring and Analyzing the Supply Chain Behavior

2011 ◽  
pp. 186-208
Author(s):  
Reinaldo Moraga ◽  
Luis Rabelo ◽  
Alfonso Sarmiento
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Supply Chain ◽  
System Dynamics ◽  
Eigenvalue Analysis ◽  
Background Information ◽  
Short And Long Term ◽  
And Performance

In this chapter, the authors present general steps towards a methodology that contributes to the advancement of prediction and mitigation of undesirable supply chain behavior within short- and long- term horizons by promoting a better understanding of the structure that determines the behavior modes. Through the integration of tools such as system dynamics, neural networks, eigenvalue analysis, and sensitivity analysis, this methodology (1) captures the dynamics of the supply chain, (2) detects changes and predicts the behavior based on these changes, and (3) defines needed modifications to mitigate the unwanted behaviors and performance. In the following sections, some background information is given from literature, the general steps of the proposed methodology are discussed, and finally a case study is briefly summarized.

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