1,721,025 research outputs found
Gaussian Basis Representation FIR Filtering for the Numerical Differentiation of Laplacian FE Solutions
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Guaranteed in-control control chart performance with cautious parameter learning
Full text linkParameter estimation has a large impact on control chart performance. Recently, widened control limits have been suggested to guarantee an acceptable in-control behavior.
However, the consequence is a reduced ability to detect a real change in the process. In order to overcome this undesired effect, we explore an alternative design based on a
delayed updating of parameter estimates. We consider an application to the Shewhart X, EWMA, and CUSUM control charts for the process mean. This approach is simple to implement, reduces the variation of the in-control average run lengths, and significantly improves the out-of-control performance
Alternative parameter learning schemes for monitoring process stability
No full textIn statistical process control, accurately estimating in-control (IC) parameters is crucial for effective monitoring. This typically requires a Phase I analysis to obtain estimates before monitoring commences. The traditional "fixed" estimate (FE) approach uses these estimates exclusively, while the "adaptive" estimate (AE) approach updates the estimates with each new observation. Such extreme criteria reflect the traditional bias-variance tradeoff in the framework of the sequential parameter learning schemes. This paper proposes an intermediate update rule that generalizes two ad hoc criteria for monitoring univariate Gaussian data, by giving a lower probability to parameter updates when an out-of-control (OC) situation is likely, therefore updating more frequently when there is no evidence of an OC scenario. The simulation study shows that this approach improves the detection power for small and early shifts, which are commonly regarded as a weakness of control charts based on fully online adaptive estimation. The paper also shows that the proposed method performs similarly to the fully adaptive procedure for larger or later shifts. The proposed method is illustrated by monitoring the sudden increase in ICU counts during the 2020 COVID outbreak in New York
Artificial Neural Networks for the Representation of Axisymmetric Magnetic Fields in TWT Collectors
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A new approach for Lead-Acid batteries modeling by local cosine
No full textIn this paper a new approach based on the Local Cosine Bases is proposed in order to obtain an easy and improved Lead-Acid battery modeling so avoiding the training process of RNN and the need of big amount of relative data training sets. The wavelet packet analysis give us a tools to achieve major improvements on data discrimination and analysis. In particular the Local Cosine Bases transform allows us to sensitively reduce the number of significant coefficients, it is useful to synthesize a complex signal with an high degree of approximation of the original signal
Automated diagnosis of encephalitis in pediatric patients using EEG rhythms and slow biphasic complexes
Full text linkSlow biphasic complexes (SBC) have been identified in the EEG of patients suffering for inflammatory brain diseases. Their amplitude, location and frequency of appearance were found to correlate with the severity of encephalitis. Other characteristics of SBCs and of EEG traces of patients could reflect the grade of pathology. Here, EEG rhythms are investigated together with SBCs for a better characterization of encephalitis. EEGs have been acquired from pediatric patients: ten controls and ten encephalitic patients. They were split by neurologists into five classes of different severity of the pathology. The relative power of EEG rhythms was found to change significantly in EEGs labeled with different severity scores. Moreover, a significant variation was found in the last seconds before the appearance of an SBC. This information and quantitative indexes characterizing the SBCs were used to build a binary classification decision tree able to identify the classes of severity. True classification rate of the best model was 76.1% (73.5% with leave-one-out test). Moreover, the classification errors were among classes with similar severity scores (precision higher than 80% was achieved considering three instead of five classes). Our classification method may be a promising supporting tool for clinicians to diagnose, assess and make the follow-up of patients with encephalitis
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
No full textThe investigation of solar-like oscillations for probing the star interiors has encountered a tremendous growth in the last decade. For ground based observations the most important difficulties in properly identifying the true oscillation frequencies of the stars are produced by the gaps in the observation time-series and the presence of atmospheric plus the intrinsic stellar granulation noise, unavoidable also in the case of space observations. In this paper an innovative neuro-wavelet method for the reconstruction of missing data from photometric signals is presented. The prediction of missing data was done by using a composite neuro-wavelet reconstruction system composed by two neural networks separately trained. The combination of these two neural networks obtains a "forward and backward" reconstruction. This technique was able to provide reconstructed data with an error greatly lower than the absolute a priori measurement error. The reconstructed signal frequency spectrum matched the expected spectrum with high accuracy
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