The redundancy allocation problem (RAP) is one of the most commonly used approaches for improving reliability systems. present study investigates recent variant RAP, namely, RAP with heterogeneous components under mixed strategy. An exact branch-and-price (BP) algorithm proposed that solves in less than CPU second all benchmark instances reported literature. not only first a strategy but also a...

In this study, a new strategic model called Markov-based cold standby combined with components mixing (RRAP-CM-MCCS), is proposed to improve system reliability. The optimization method used here simplified swarm (SSO), in which SSO adopts multi-role resource sharing strategy provide the diverse required by RRAP-CM-MCCS. diversity lies providing every component subsystem its own failure rate as ...

Redundancy has been referred to as a state of no longer being needed or useful. Microbiologists often theorize that the only case of true redundancy in a haploid organism would be a recent gene duplication event, prior to divergence through selective pressure. However, a growing number of examples exist where an organism encodes two genes that appear to perform the same function. For example, m...

considering the increasingly high attention to quality, promoting the reliability of products during designing process has gained significant importance. in this study, we consider one of the current models of the reliability science and propose a non-linear programming model for redundancy allocation in the series-parallel systems according to the redundancy strategy and considering the assump...

Distributed control systems in power plants are the latest trends to optimize and control performance. Reliability prediction of input-output components of distributed control systems have a key role to play in the power plant scenario. Triple modular redundancy is a means of increasing reliability of systems. Triple modular redundancy of three modules each containing m cards is studied in this...

Non-volatile memories are increasingly important for big-data storage. However, their long-term data reliability has significant challenges. This work studies how to use the natural redundancy in data for error correction. The natural redundancy can be combined with error-correcting codes to effectively improve data reliability. This work studies several aspects of natural redundancy: effective...

We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filters — a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimenta...