Clustering-based unsupervised domain adaptive (UDA) person re-identification (ReID) reduces exhaustive annotations. However, owing to unsatisfactory feature embedding and imperfect clustering, pseudo labels for target data inherently contain an unknown proportion of wrong ones, which would mislead learning. In this paper, we propose approach named probabilistic uncertainty guided progressive la...

A control-oriented identification and uncertainty estimation approach from input-output data is presented, for use in the design of control systems for paper machines. The application of this approach is demonstrated on a high fidelity simulator. An estimate of the process model along with the uncertainty bounds that describe the confidence limits of the model, consistent with the robust contro...

For two decades, environmental economists have generally maintained that benefit uncertainty is irrelevant for choosing between price and quantity instruments, but that cost uncertainty matters, with the identity of the efficient instrument depending upon the relative slopes of the marginal benefit and cost functions. But, in the presence of simultaneous, correlated uncertainty, such policy ins...

This work takes up the challenge of deriving ‘uncertainty law’ for close modes, i.e., closed form analytical expressions remaining uncertainty modal parameters identified using (output-only) ambient vibration data. In principle law can be obtained from inverse Fisher information matrix parameters. The key mathematical challenges stem treatment entangled stochastic dynamics with a large number d...

The Monte Carlo technique can be used to propagate input variable uncertainty and parameter uncertainty through a model to determine output uncertainty. However, to carry out Monte Carlo simulations, the uncertainty distributions or the probability density functions (PDFs) of the model parameters and input variables must be known. This remains one of the bottlenecks in current uncertainty resea...

The ease of picture naming in children was assessed as a function of two stimulus characteristics: (a) the number of available correct names for a picture (referential uncertainty) and (b) the degree to which a picture realistically represents the depicted object (stimulus realism). Two experiments employing different methods demonstrated that: (a) children named low uncertainty objects (those ...

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the c...

The aim of this paper is to identify the nominal model and associated uncertainty bound of a lightly damped flexible beam in order to be utilized in robust controller design methods. Our approach is based on Set Membership theory where the system’s uncertainties assumed to be unknown but bounded (UBB). Both parametric and non-parametric uncertainties have been accounted in the robust identifica...