Rough grammar for efficient and fault-tolerant computing on a distributed system

Wójcik, Zbigniew M.; Wójcik, Barbara E.

doi:10.1109/32.83902

Cited by 5 publications

(1 citation statement)

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“…When a failure status is approached, the positive performance reports will decrease and at the same time the negative reports will substantially increase. The parameters are collected in a similar way as performance is measured for computing systems [7]. Latent variables describing object's health degradation over some recent time periods are collected into matrices.…”

Section: Fusion Of Degradation Factors Sequenced By Timementioning

confidence: 99%

System Health Prognostic Model Using Rough Sets

Wójcik¹

2005

Lecture Notes in Computer Science

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Abstract.A new rough sets data fusion model is presented fusing measured health degradation levels and influences on these degradations. The data fusion model is a system of matrix inequalities of the rough sets covariances. Rough sets variance allows to explicitly assess only health degradations assuring increased signal-to-noise ratio, thus high accuracy of processing. The matrices of inequalities fuse measured health degradation levels and influences on these degradations. Adaptations mechanisms are by a new machine learning approach determining weights of the terms of the inequalities at the time of key events found in the historical data. Prognostic is always time-sequenced, therefore methods based on time sequences are incorporated, e.g. a new data fusion model exploiting time-dependency of events, assuring high quality of prediction. Deterministic prognostic is by estimating the pattern of health degradation in question, finding the match with degradation pattern in historical data, and then tracing this historical degradation pattern up to its conclusion. The model is hierarchical: the right sides of the data fusion expressions substitute for endogenous variables of higher-level expressions.

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Section: Fusion Of Degradation Factors Sequenced By Timementioning

confidence: 99%