Given a linear system in a real or complex domain, linear regression aims to recover the model parameters from a set of observations. Recent studies in compressive sensing have successfully shown that under certain co...
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Acoustic emission measurement results of acoustically induced cavitation bubbles in insulating oil are presented in this paper. Spectral analysis of acquired by a broadband transducer acoustic emission signals is prop...
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ISBN:
(纸本)1424415853
Acoustic emission measurement results of acoustically induced cavitation bubbles in insulating oil are presented in this paper. Spectral analysis of acquired by a broadband transducer acoustic emission signals is proposed as a diagnostic tool for estimation of aging properties of mineral insulating oils. The article describes the main parts of the designed and built measurement apparatus used for acoustic cavitation investigation in insulating oils. In this note representative results of the experimental data taken from the apparatus are shown.
Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the res...
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In this paper an anti-slip predictive controller is designed and implemented in order to control the rear wheels of a V-PRA (Variable Powered Rear Axle) vehicle. The control algorithm is EPSAC, a Model based Predictiv...
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In the current industrial environment, which is shifting towards an event-based control prone paradigm, sampling strategies in use must be revisited. Both temporal and magnitude dependent sampling strategies have been...
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Expressing user's preferences in database querying is best achieved by fuzzy modeling of linguistic terms included in selection criteria. This paper deals with temporal criteria, for querying date/time columns in ...
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In this paper, we put forth distributed algorithms for solving loosely coupled unconstrained and constrained optimization problems. Such problems are usually solved using algorithms that are based on a combination of ...
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A model reference adaptive controller (MRAC) for the effective control of the calcination temperature on a rotary cement kiln was developed. Using the tools of identification systems was obtained a mathematical model ...
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One of the objectives of event-based control is the reduction of generated events to perform an appropriate process control. Among the event generation techniques used to this end, those quantifying the error signal c...
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This study presents an algorithm for multi-channel electroencephalographic (EEG) spike prescreening based on radial basis function (RBF) artificial neural network. Sixteen-channel EEG records from twenty-eight patient...
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This study presents an algorithm for multi-channel electroencephalographic (EEG) spike prescreening based on radial basis function (RBF) artificial neural network. Sixteen-channel EEG records from twenty-eight patients with epilepsy were examined online. The single-channel recognition network consisted of input-layer neurons receiving three EEG waveform parameters (peak angle, amplitude, and velocity), ten hidden-layer neurons, and one output neuron. An optimal network model was chosen according to receiver operating characteristics analysis. Identification of multi-channel geometric correlation was performed with an incidence matrix. Sensitivity and selectivity cross-over value was found to be 80% for the single-channel RBF network. Compared with error back-propagation, a substantial improvement in training efficiency by at least two to three orders of magnitude was obtained for the RBF network. Validation with visual analysis showed 87.3% sensitivity using the proposed online multi-channel classification algorithm. The computation time required for spike detection was significantly less than that needed for online display of the EEG record. The proposed algorithm was able to achieve classification accuracy similar to previous artificial neural network schemes based on error back-propagation, while the training needed for RBF network was substantially less. Therefore, the RBF neural network is potentially an effective tool in real-time prescreening for epileptic spikes.
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