This paper presents a probabilistic model for fatigue life estimation of composite laminates using a high fidelity multi-scale approach called M-LaF (Micromechanics based approach for Fatigue Life Failure).
The increase in the peak-to-average power ratio (PAPR) is a well known but not sufficiently addressed problem with data-dependent superimposed training (DDST) based approaches for channel estimation and synchronization in digital communication links.
The "model-based approach for estimation of expression variation" [ 27], supported by the NormFinder software, estimates intra- and intergroup variation, and thus subdivision of the sample set in at least two coherent groups is required for the correct application of this approach.
This program allows analyzing large combined data sets and uses a GTR model based approach with internal estimation of all free model parameters for all data partitions.
The CRLB for model parameters are compared with simulation results using a grid-based approach for estimation.
This paper discusses the use of general simulation-based approach for estimation of the stochastic performance of deteriorating engineering systems.
Motivated by this phenomenon, we propose a Random Forest (RF) Out-of-Bag (OB) estimation based approach to extract discriminative parts for each action.
The proposed method is on the basis of maximal deviation based approach (MDBA) for dealing with the corporations' performance estimations of ECs, and it integrates AHP and scale method for analyzing the priority rating of achieving the improvement goal of performance evaluation (IGPE) of each EC.
The statistical behavior of the state estimation process, through cumulative sum based approach (CUSUM), was used for FDI attacks detection in [31].
In a series of papers on sample-size estimation to detect gene-environment interaction, which is a controversial role for sample-size formulas, comparisons have been made between regression based approaches and the stratified analysis approach [ 13, 15].
The PowerAtlas tool is developed based on a mixture model approach for estimation of power and sample size of high-dimensional data (Gadbury et al., 2004).
