Adaptive Uncertainty Propagation for Coupled Multidisciplinary Systems

Ghoreishi, Seyede Fatemeh; Allaire, Douglas

doi:10.2514/1.j055893

Cited by 37 publications

(17 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This threshold may be varied based on the observed people, however, we find that it works in our experiment. The articles (Ghoreishi and Allaire, 2017;Xie et al, 2018;Imani and Braga-Neto, 2017; have mentioned a number of effective ways to estimate parameters and state in dynamic environment. Although they are effective, we did not consider this in the proposed method, as the hardcoded threshold used in the proposed method provides a better result compared to the state-of-the-art method.…”

Section: Proposed Solutionmentioning

confidence: 99%

Elderly People Fall Detection System Using Skeleton Tracking and Recognition

Bansal¹,

Alsadoon²,

Prasad³

et al. 2018

American Journal of Applied Sciences

View full text Add to dashboard Cite

The fall detection systems use a variety of technologies like sensors, wearable devices, color camera, thermal camera etc. With the use of Microsoft Kinect camera for non-gaming purposes, depth images have started being utilized for fall detection. Various authors have made an attempt at using Kinect for fall detection in combination with a variety of techniques like ellipse analysis, bounding box analysis etc. However, most of these attempts fail to differentiate between human subjects and other inanimate objects and fail to identify the person who has fallen while assuming that there is only one person who needs to be monitored. This paper proposes a new system that is based on the depth images captured by Microsoft Kinect, skeleton tracking and bounding box analysis. The key novelty of this system is that it wraps the moving object into the bounding box and determines the change of size of the moving object by analysis the motion over the time to distinguish the human moving object and non-human moving object. The system stores the joint measurements of the known people in a database and compares the joint measurements of the detected person with the values in the database to identify the person. The proposed solution provides a significantly higher accuracy rate as compared to the current best solution and especially when the person carrying an object, sweeping the floor, dropping an object and picking an object from the floor.

show abstract

Section: Proposed Solutionmentioning

confidence: 99%

Elderly People Fall Detection System Using Skeleton Tracking and Recognition

Bansal¹,

Alsadoon²,

Prasad³

et al. 2018

American Journal of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Generally, UQ incorporates the detection of uncertainty sources and the development of corresponding appropriate mathematical approaches to calculate the error bounds for any quantity of interest in models [2][3][4]. The uncertainty can arise from different sources, which are categorized as natural uncertainty (NU) due to the random nature of a physical system, model parameter uncertainty (MPU) resulting from the lack of sufficient and/or accurate data for model parameters, propagated uncertainty (PU) in the case of multiscale modeling, and model structure uncertainty (MSU) owing to any simplifications, assumptions, and/or incomplete physics in the model [5].…”

Section: Introductionmentioning

confidence: 99%

Bayesian uncertainty quantification and information fusion in CALPHAD-based thermodynamic modeling

et al. 2019

View full text Add to dashboard Cite

Calculation of phase diagrams is one of the fundamental tools in alloy design-more specifically under the framework of Integrated Computational Materials Engineering. Uncertainty quantification of phase diagrams is the first step required to provide confidence for decision making in property-or performance-based design. As a manner of illustration, a thorough probabilistic assessment of the CALPHAD model parameters is performed against the available data for a Hf-Si binary case study using a Markov Chain Monte Carlo sampling approach. The plausible optimum values and uncertainties of the parameters are thus obtained, which can be propagated to the resulting phase diagram. Using the parameter values obtained from deterministic optimization in a computational thermodynamic assessment tool (in this case Thermo-Calc) as the prior information for the parameter values and ranges in the sampling process is often necessary to achieve a reasonable cost for uncertainty quantification. This brings up the problem of finding an appropriate CALPHAD model with high-level of confidence which is a very hard and costly task that requires considerable expert skill. A Bayesian hypothesis testing based on Bayes' factors is proposed to fulfill the need of model selection in this case, which is applied to compare four recommended models for the Hf-Si system. However, it is demonstrated that information fusion approaches, i.e., Bayesian model averaging and an error correlation-based model fusion, can be used to combine the useful information existing in all the given models rather than just using the best selected model, which may lack some information about the system being modelled.

show abstract

“…A likelihood-based approach is proposed to estimate the probability density function of coupling variables [11] and is further extended to handle the model uncertainty [12] and the uncertainty propagation in high dimensional coupled systems [13]. Gibbs sampling and sequential importance resampling techniques are introduced to reduce the computational cost for decoupled multidisciplinary uncertainty analysis [14,15]. These MDSA methods under uncertainty generally guarantee statistical 2 Mathematical Problems in Engineering multidisciplinary consistence, rather functional consistence.…”

Section: Introductionmentioning

confidence: 99%

A Method for Multidisciplinary System Analysis Based on Minimal Feedback Variables

Yin

Wang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

As modern engineering design usually involves dependence of one discipline on another, multidisciplinary system analysis (MDSA) plays an important role in the multidisciplinary simulation and design optimization on coupled systems. The paper proposes an MDSA method based on minimal feedback variables (MDSA MF) to enhance the solving efficiency. There are two phases in the method. In phase 1, design structural matrix (DSM) is introduced to represent a coupled system, and each off-diagonal element is denoted by a coupling variable set; then an optimal sequence model is built to obtain a reordered DSM with minimal number of feedback variables. In phase 2, the feedback in the reordered DSM is broken, so that the coupled system is transformed into one directed acyclic graph; then, regarding the inputs depending on the broken feedback as independent variables, a least-squares problem is constructed to minimize the residuals of these independents and corresponding outputs to zero, which means the multidisciplinary consistence is achieved. Besides, the MDSA MF method is implemented in a multidisciplinary platform called FlowComputer. Several examples of coupled systems are modeled and solved in the platform using several MDSA methods. The results demonstrate that the proposed method could enhance the solving efficiency of coupled systems.

show abstract

Adaptive Uncertainty Propagation for Coupled Multidisciplinary Systems

Cited by 37 publications

References 29 publications

Elderly People Fall Detection System Using Skeleton Tracking and Recognition

Elderly People Fall Detection System Using Skeleton Tracking and Recognition

Bayesian uncertainty quantification and information fusion in CALPHAD-based thermodynamic modeling

A Method for Multidisciplinary System Analysis Based on Minimal Feedback Variables

Contact Info

Product

Resources

About