Flexible Coupling in Joint Inversions: A Bayesian Structure Decoupling Algorithm

Learning Bayesian Network Structure using Markov Blanket in K2 Algorithm

‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node...

Cartesian Impedance Control of Flexible Joint Robots: A Decoupling Approach

Bayesian Joint Inversions for the Exploration of Earth Resources

We propose a machine learning approach to geophysical inversion problems for the exploration of earth resources. Our approach is based on nonparametric Bayesian methods, specifically, Gaussian processes, and provides a full distribution over the predicted geophysical properties whilst enabling the incorporation of data from different modalities. We assess our method both qualitatively and quant...

Decoupling based Cartesian impedance control of flexible joint robots

This paper addresses the impedance control problem for flexible joint manipulators. An impedance controller structure is proposed, which is based on an exact decoupling of the torque dynamics from the link dynamics. A formal stability analysis of the proposed controller is presented for the general tracking case. Preliminary experimental results are given for a single flexible joint.

Bayesian inversions of a dynamic vegetation model

Introduction Conclusions References