Thinning Out Redundant Empirical Data

Thinning Out Redundant Empirical Data

Given a set X of “empirical” points, whose coordinates are perturbed by errors, we analyze whether it contains redundant information, that is whether some of its elements could be represented by a single equivalent point. If this is the case, the empirical information associated to X could be described by fewer points, chosen in a suitable way. We present two different methods to reduce the car...

Thinning Algorithms in Scattered Data

Reducing Trials by Thinning-Out in Skill Discovery

In this paper, we propose a new concept, thinning-out, for reducing the number of trials in skill discovery. Thinning-out means to skip over such trials that are unlikely to improve discovering results, in the same way as “pruning” in a search tree. We show that our thinningout technique significantly reduces the number of trials. In addition, we apply thinning-out to the discovery of good phys...

Thinning Algorithms in Scattered Data Modelling

Configuration determination for k-out-of-n partially redundant systems

The k-out-of-n configuration is a widely adopted structure for partially redundant safety systems. This configuration ensures a high level of reliability and safety with limited financial and space resources. It also facilitates on-line Test and Maintenance (T&M) without having to shut the system down. One question a decision maker needs to answer when adopting k-out-of-n systems is: what is th...