A statistical model of gear mesh vibration signals is proposed in this paper. It is seen that the sample mean is first removed from the gear mesh vibration signal, and the remaining signal components are approximated with a set of sinusoidal functions in the signal model. The signal model parameters can be estimated by the least-square method and the optimal model order is determined based on t...

nowadays, by the expansion of information technology in human life and dependence of business upon it, data protection, as one the most valuable and critical assets of the organization, has become the vital tool of modern industry and prerequisite for sustaining business process. in order to face the challenges and to take advantage of new opportunities brought forth by it advances we suggest o...

In this paper I present a Minimum Description Length Estimator for number of sources in an anechoic mixture of sparse signals. The criterion is roughly equal to the sum of negative normalized maximum log-likelihood and the logarithm of number of sources. Numerical evidence supports this approach and compares favorabily to both the Akaike (AIC) and Bayesian (BIC) Information Criteria. 1 Signal a...

The mixed model approach to the analysis of repeated measurements allows users to model the covariance structure of their data. That is, rather than using a univariate or a multivariate test statistic for analyzing effects, tests that assume a particular form for the covariance structure, the mixed model approach allows the data to determine the appropriate structure. Using the appropriate cova...

The author introduced the “geometric AIC” and the “geometric MDL” as model selection criteria for geometric fitting problems. These correspond to Akaike’s “AIC” and Rissanen’s “BIC”, respectively, well known in the statistical estimation framework. Another criterion well known is Schwarz’ “BIC”, but its counterpart for geometric fitting has been unknown. This paper introduces the corresponding ...

We discuss how to characterize entanglement sources with finite sets of measurements. The measurements do not have to be tomographically complete and may consist of POVMs rather than von Neumann measurements. Our method yields a probability that the source generates an entangled state as well as estimates of any desired calculable entanglement measures, including their error bars. We apply two ...

In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in which time series are forecasted by feed forward neural networks is examined. Various model selection criteria have been used for the determining architecture. In addition, a ne...

In this paper, we propose a clustering method by SOM and information criteria. In this method, initial cluster-candidates are derived by SOM, and then these candidates are merged appropriately based on information criterion such as BIC or AIC (Akaike Information Criterion). Through the clustering experiments for the artificial datasets and UCI Machine Learning Repository’s datasets, we confirm ...

Current advances in observational cosmology suggest that our Universe is flat and dominated by dark energy. There are several different theoretical ideas invoked to explain the dark energy with relatively little guidance of which one of them might be right. Therefore the emphasis of ongoing and forthcoming research in this field shifts from estimating specific parameters of cosmological model t...