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Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

100%
Boran S., Diren D.
Acta Physica Polonica A
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2017
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vol. 132
|
issue 3
1054-1057
EN
Control charts that are used for monitoring the process and detecting the out-of-control signals are important tools for statistical process control. It is simple to estimate source(s) for out-of-control signals in the univariate process, whereas it is difficult to identify the source(s) in the multivariate processes. The reason is that these kinds of processes require monitoring and controlling of more than one quality characteristics simultaneously. In this study, the proposed model is expected to detect the source(s) for out-of-control signals without help of an expert in the process, by using a multilayer neural network. This model was implemented in furniture fasteners manufacturing. Time gain was obtained while detecting source(s) for out-of-control signals.
2
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Labyrinthine pathways towards supercycle attractors in unimodal maps

76%
Moyano L., Silva D., Robledo A.
Open Physics
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2009
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vol. 7
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issue 3
591-600
EN
As an important preceding step for the demonstration of an uncharacteristic (q-deformed) statisticalmechanical structure in the dynamics of the Feigenbaum attractor we uncover previously unknown properties of the family of periodic superstable cycles in unimodal maps. Amongst the main novel properties are the following: i) The basins of attraction for the phases of the cycles develop fractal boundaries of increasing complexity as the period-doubling structure advances towards the transition to chaos. ii) The fractal boundaries, formed by the pre-images of the repellor, display hierarchical structures organized according to exponential clusterings that manifest in the dynamics as sensitivity to the final state and transient chaos. iii) There is a functional composition renormalization group (RG) fixed-point map associated with the family of supercycles. iv) This map is given in closed form by the same kind of q-exponential function found for both the pitchfork and tangent bifurcation attractors. v) There is final-stage ultra-fast dynamics towards the attractor, with a sensitivity to initial conditions which decreases as an exponential of an exponential of time. We discuss the relevance of these properties to the comprehension of the discrete scale-invariance features, and to the identification of the statistical-mechanical framework present at the period-doubling transition to chaos. This is the first of three studies (the other two are quoted in the text) which together lead to a definite conclusion about the applicability of q-statistics to the dynamics associated to the Feigenbaum attractor.
3
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Comprehensive Analyses of Gaussian Graphical Model under Different Biological Networks

76%
Dokuzoğlu D., Purutçuoğlu V.
Acta Physica Polonica A
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2017
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vol. 132
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issue 3
1106-1111
EN
Naturally, genes interact with each other by forming a complicated network and the relationship between groups of genes can be shown by different functions as gene networks. Recently, there has been a growing concern in uncovering these complex structures from gene expression data by modeling them mathematically. The Gaussian graphical model is one of the very popular parametric approaches for modelling the underlying types of biochemical systems. In this study, we evaluate the performance of this probabilistic model via different criteria, from the change in dimension of the systems to the change in the distribution of the data. Hereby, we generate high dimensional simulated datasets via copulas and apply them in Gaussian graphical model to compare sensitivity, specificity, F-measure and various other accuracy measures. We also assess its performance under real datasets. We consider that such comprehensive analyses can be helpful for assessing the limitation of this common model and for developing alternative approaches, to overcome its disadvantages.
4
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Gauss’ law of error revisited in the framework of Sharma-Taneja-Mittal information measure

64%
Scarfone A., Suyari H., Wada T.
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EN
We reformulate the Gauss’ law of error in presence of correlations which are taken into account by means of a deformed product arising in the framework of the Sharma-Taneja-Mittal measure. Having reviewed the main proprieties of the generalized product and its related algebra, we derive, according to the Maximum Likelihood Principle, a family of error distributions with an asymptotic power-law behavior.
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