Model reduction of high order linear-in-parameters discrete-time systems is considered. The main novelty of the paper is that the coefficients of the original system model are assumed to be known only within given intervals, and the coefficients of the derived reduced order model are also obtained in intervals, such that the complex value sets of the uncertain original and reduced models will b...

This paper is'on sensor based control for guiding a robot to correct gripping of objects having a large position uncertainty. An eye-in-hand mounted range camera is considered. A probabilistic problem formulation based on the requestedposture at gripping and corresponding tolerances is presented. The problem is solved approximately using dynamic programming for a 1-degree-of-freedom manipulatol...

Many studies have found that stimuli can be discriminated more accurately at attended locations than at unattended locations, and such results have typically been taken as evidence for the hypothesis that attention operates by allocating limited perceptual processing resources to attended locations. An alternative proposal, however, is that attention acts to reduce uncertainty about target loca...

Ever-changing production campaigns complicate the management of recovery and treatment options for unavoidable effluents at pharmaceutical plants. Each campaign produces large amounts of by-products differing in their number, amount as well as composition. Future business strategies designed to address changing market demands add uncertainty to this already challenging design problem. In such a...

A challenge in big data classification is the design of highly parallelized learning algorithms. One solution to this problem is applying parallel computation to different components of a learning model. In this paper, we first propose an extreme learning machine tree (ELM-Tree) model based on the heuristics of uncertainty reduction. In the ELM-Tree model, information entropy and ambiguity are ...

Optimization of expensive computer models with the help of Gaussian process emulators in now commonplace. However, when several (competing) objectives are considered, choosing an appropriate sampling strategy remains an open question. We present here a new algorithm based on stepwise uncertainty reduction principles to address this issue. Optimization is seen as a sequential reduction of the vo...