In this paper we address one of the most important issues for autonomous mobile robots, namely their ability to localize themselves safely and reliably within their environments. We propose a probabilistic framework for modelling the robot’s state and sensory information based on a Switching State-Space Model. The proposed framework generalizes two of the most successful probabilistic model fam...

Based on 3-D elasticity approach, differential quadrature method (DQM) in axial direction is adopted along with Globalized Nelder–Mead (GNM) algorithm to optimize the stacking sequence of a laminated cylindrical shell. The anisotropic cylindrical shell has finite length with simply supported boundary conditions. The elasticity approach, combining the state space method and DQM is used to obtain...

A state-space formulation of classical scalar Preisach model (CSPM) of hysteresis is proposed. The introduced state dynamics and memory interface allow to use the state equation, which is rapid in calculation, instead of the original Preisach equation. The main benefit of the proposed modeling approach is the reduced computational effort which requires only a single integration over the instant...

This paper utilizes descriptor state-space formulation for computation of energy amplification in incompressible channel flows. The dynamics of velocity and pressure fluctuations in these flows are described by a system of partial differential-algebraic equations. Typically, the evolution model is obtained by projecting the velocity fluctuations on a divergence-free subspace which eliminates pr...

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting. Our formulation includes as special cases Vector-valued Manifold Regularization and Multi-view Learning, thus provides in particular a...

In presence of incomplete information about the world we need to distinguish between the state of the world and the state of the agent's knowledge about the world. In such a case the agent may need to have at its disposal sensing actions that change its state of knowledge about the world and may need to construct more general plans consisting of sensing actions and conditional statements to ach...

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multileve...