The ocean's “biological pump” significantly modulates atmospheric carbon dioxide levels. However, the complexity and variability of processes involved introduces uncertainty in interpretation transient observations future climate projections. Much research has focused on “parametric uncertainty,” particularly determining exponent(s) a power-law relationship sinking particle flux with depth. Var...

Di Crescenzo and Longobardi (2002) has been proposed a measure of uncertainty related to past life namely past entropy. The present paper addresses the question of extending this concept to bivariate set-up and study some properties of the proposed measure. It is shown that the proposed measure uniquely determines the distribution function. Characterizations for some bivariate lifetime models a...

Simple parametric models (e.g. [1, 2, 3, 4, 5]) suffice to describe many idealized galaxy shapes, but severely misfit actual galaxies: they are not sufficiently flexible [5]. The popular program GALFIT copes with the limitations of simple parametric models by allowing users to fit an arbitrary number of mixture components [6, 7]. These mixtures are not learned from actual galaxies, so the model...

This paper considers the estimation and control of systems with parametric uncertainty. An approach that combines moving horizon estimation and model predictive control into a single min-max optimization is employed to estimate past and current values of the state, compute a sequence of optimal future control inputs, predict future values of the state, as well as estimate current values of unce...

Most of the practical systems are having uncertainty which makes it difficult to maintain stability and better performance. In this paper a robust controller design, based on the quantitative feedback theory (QFT) is proposed for a nonlinear process like Spherical tank system and bioreactor. The QFT controller is designed to ensure the robust stability and performance (input and output disturba...

Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to large-scale data sets comes at the cost of approximating the uncertainty estimat...

In this work, we address short-term batch process scheduling under uncertainty in which the scheduling model is contaminated with uncertain data in the objective function, the right-hand side vector and in the constraint matrix, introduced by price, demand, and processing time or conversion rate uncertainty, respectively. We apply a two-stage robust optimization/multi-parametric programming pro...

This paper presents a solution to the global adaptive partial state feedback control problem for rigid-link, flexible-joint (RLFJ) robots. The proposed tracking controller adapts for parametric uncertainty throughout the entire mechanical system while only requiring link and actuator position measurements. A nonlinear filter is employed to eliminate the need for link velocity measurements while...

The concepts of system load and capacity are pivotal in risk analysis. The complexity in risk analysis increases when the input parameters are either stochastic (aleatory uncertainty) and/or missing (epistemic uncertainty). The aleatory and epistemic uncertainties related to input parameters are handled through simulation-based parametric and non-parametric probabilistic techniques. The complex...

The permanent magnet synchronous motors (PMSM) are used as servo motor for precise motion control and are used as generator to generate electrical energy driven by wind energy. There is large variation in inertia due to varying load and parametric uncertainty in PMSM. The design objective of this paper is to analytically determine the relative robust stability of PMSM with parametric uncertaint...