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In this paper, we consider a heuristic iterated-subspace minimization method with pattern search for solving such unconstrained optimization problems.
This paper proposes an online adaptive condition-based maintenance method with pattern discovery and fault learning capabilities for mechanical systems.
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Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy.
This work, which will begin at the awarded universities in mid-2015, will focus on developing probabilistic methods for dealing with pattern evidence and digital evidence and providing training materials with these new methods.
bExploratory factor analysis with Maximum likelihood parameter extraction method with oblique rotation (pattern matrix).
The distributions of ratings showed reasonable discriminatory power of the SBEI exposure prevention rating method, with a general pattern towards low Potential and high Protection ratings, and a distribution of overall SBEI scores that was strongly skewed towards the favourable end.
Keeping low false positive rates, our method detects accurate TR with pattern lengths varying within a significant interval.
The results point towards a new doping method with sub-nanoscale patterning precision on graphene and its electronic applications based on electronic junction properties.
These include coefficients of wavelet analysis (Ebrahimi et al. 2008; Gabran et al. 2008; Sinha 2008; Fraiwan et al. 2010, 2011; Garces Correa & Laciar Leber 2010; Agrawal et al. 2011), bispectral density (Acharya et al. 2010; Swarnkar et al. 2010), parameters of multichannel autoregressive modeling (Zhovna & Shallom 2008) and matching pursuit method with slow wave patterns (Picot et al. 2011).
The crystal loop fine recognition procedure based on Hooke-Jeeves pattern search method with an ellipse as a fitting pattern has been designed and developed.
In this section, we compare our proposed RNS-FH pattern design method with the technique presented in [11] (which also considers RNS as the design metric).
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