House of Quality

/pdf/citizen-science-a-study-of-people-expertise-and-sustainable-3po1jwi9qc.pdf

Citizen Science: A Study of People, Expertise and Sustainable Development

International Joint Conference on Neural Networks

Computational methods in optimization considering uncertainties – An overview

Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles

Visualizing and analyzing multiple Pareto-optimal solutions obtained using an evolutionary multi- or many-objective optimization algorithm is as important a task as the task of finding them. Besides helping to choose a single preferred solution, they provide a better understanding of the trade-off among the objectives and also reveal key insights about interactions among variables and objectives for the Pareto-optimal solutions. Existing visualization methods do not provide a comprehensive account of both visualization and analysis of Pareto-optimal solutions. In this paper, we present an interpretable self-organizing map (iSOM) method that produces a more simplistic mapping of higher-dimensional variable spaces into two dimensions. Multiple iSOM plots, one for each objective, allows an easier visual understanding of trade-off among objectives. By identifying high trade-off Pareto-optimal solutions and marking them on the iSOM plots, we also provide decision-makers a comprehensive method to locate critical and likely preferred solutions on the Pareto-optimal front. The visualization and analysis of Pareto-optimal solutions using iSOMs are demonstrated on 11 problems involving three to five objectives. As discussed, the approach is generically applicable to higher-dimensional and constrained problems. 

Visualization and analysis of Pareto-optimal fronts using interpretable self-organizing map (iSOM)

Dealing with the unknown-but-bounded uncertainty and insufficient or scarce data is an often-encountered challenge in real life engineering. In this study, we develop an efficient convexity approach to construct the uncertainty model in the form of bounds to address such a challenge. Approaches that use probabilistic models require comprehensive information about the uncertainty, which is often difficult or expensive to obtain, whereas the convex models that approximate the uncertain region using different bounding geometries can operate with scarce data or parameter bounds. The novelty of the current work is to use (i) convex hull as the bounding geometry in the uncertain design space (ii) Chebyshev inequality to inflate the convex hull that can include future data points which will then be used in design. Convex hull provides the least volume compared to other geometries reported in literature, such as interval, ellipse, super ellipse, and parallelepiped that are used in convexity approaches. In addition, to obtain the bounds of linear limit states, it is sufficient to evaluate the limit states only at the vertices of the convex hull, thereby saving on expensive simulations at nonvertex points. The proposed method is demonstrated on engineering examples with different coefficient of variation and random variables following different distributions. Results reveal the superiority of the proposed approach over the existing convexity approaches for uncertainty quantification. Also, the optima obtained by proposed method are always conservative to ones from a large sample Monte Carlo simulation and thus avoids under- or over-design associated with safety factor approach.

Palaniappan Ramu

Papers

Visualization and analysis of Pareto-optimal fronts using interpretable self-organizing map (iSOM)

Chebyshev inequality–based inflated convex hull for uncertainty quantification and optimization with scarce samples

ECG Steganography Using Contourlet Transform For Transmission of Secured Patient Identity.

A dual surrogate driven L-moments based robust design with scarce samples in the presence of extremes

L-moments and Chebyshev inequality driven convex model for uncertainty quantification