Extended Abstract. When a new treatment is being considered, trials are carried out to estimate the increase in performance which is likely to result if the new treatment were to replace the treatment in current use. Many authors have looked at this problem and many procedures have been introduced to solve it. An important feature of the analysis in this work is that account is taken of the fac...

In this paper a novel tuning procedure for Two-Degree-of-Freedom (2-DOF) PID controllers is proposed. The methodology based on an Uncertainty and Disturbance Estimator. As result, equivalent 2-DOF controller with simpler rules obtained. One advantage of the proposed method that tracking performance disturbance rejection can be accommodated separately in control synthesis. Moreover, trade-off be...

state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

This paper is about the task-directed updating of an incomplete and inaccurate geometric model of a nuclear environment, using only robust radiation-resistant sensors installed on a robot that is remotely controlled by a human operator. In this problem, there are many sources of uncertainty and ambiguity. This paper proposes a probabilistic solution under Gaussian assumptions. Uncertainty is re...

One of the difficulties in interpreting experimental results is in assessing the uncertainty to be associated with the theoretical calculation. This is particularly true in QCD where the coupling is quite strong and one expects higher order corrections to be significant. Typically, one characterizes theoretical uncertainty by the dependence on the renormalization scale μ. Since we don’t actuall...

In this paper, a methodology for propagation of uncertainty in stochastic nonlinear dynamical systems is investigated. The process noise is approximated using KarhunenLoève (KL) expansion. Perron-Frobenius (PF) operator is used to predict the evolution of uncertainty. A multivariate Kolmogorov-Smirnov test is used to verify the proposed framework. The method is applied to predict uncertainty ev...

An on-the-fly self-localization system is developed for mobile robot which operates in a 3D environment with elaboratec 3D landmarks. The robot estimates its pose recursively through a MAP estimator that incorporates the information collected from odometry and unidirectional camera. We build the nonlinear models for these two sensors and, maintain that the uncertainty manipulation of robot moti...

Bayesian analysis is especially useful to apply to lowcount medical imaging data, such as gated cardiac SPECT, because it allows one to solve the nonlinear, ill-posed, inverse problems associated with such data. One advantage of the Bayesian approach is that it quantifies the uncertainty in estimated parameters through the posterior probability. We compare various approaches to exploring the un...

Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator...