Power plant performance can decrease along with its life span, and move away from the design commissioning targets. Maintenance issues, operational practices, market restrictions, financial objectives may lead to that behavior, knowledge of appropriate actions could support system retake original performance. This paper applies unsupervised machine learning techniques identify operating pattern...

Random forests are a commonly used tool for classification with high-dimensional data as well as for ranking candidate predictors based on the so-called variable importance measures. There are different importance measures for ranking predictor variables, the two most common measures are the Gini importance and the permutation importance. The latter has been found to be more reliable than the G...

The research in the field of data mining has widely addressed the problem of variable selection and several variable importance measures have been proposed in the literature. This paper deals with a frequently used variable importance measure defined in the context of decision trees and tree-based ensemble models like Random Forests and Treeboost. The aim of this paper is to show the existence ...

Recent developments in the area of smart structures indicate that variable geometry / stiffness truss network is of fundamental importance in designing smart transformable structures and systems for space applications. This paper presents the conceptual design and dynamic modeling of a cooperative re-configurabel dual-arm robotic structure called Dual-Arm Cam-Lock Manipulator. The Manipulator i...

Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration problem. To make matters worse, it is often hard to measure the accuracy of one’s ML estimates. We present bidirectional Monte Carlo, a technique for obtaining accurate log-ML estimates on data simulated from a model. This...

The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is qui...