Partial discharge (PD) pattern classification has recently become popular since the automated acquisition of PD signals has become vital and cogent. A novel method for identification of defects due to partial discharge is described in this paper. Starting from different PD families of specimen, several sets of characteristic vectors are determined and then used as input variables to the propose...

INTRODUCTION In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD The proposed solution ess...

The number of vehicles in the urban areas is rising at high pace. The critical issues are arising with the rise in the number of vehicles for the traffic analysis. The analysis of the vehicle running across the roads is usually done for the density analysis, traffic shaping and many other similar applications. The vehicle detection in the rushed areas produces the real challenge of independent ...

This paper is focusing on the application of a probabilistic neural network-based model in diagnosing hepatic diseases. In the diagnose process, the physicians compare numerical medical data against prior knowledge in order to determine the right diagnostic. Neural networks are ideal in recognizing diseases using representative examples since there is no need to provide a specific algorithm on ...

A probabilistic neural network (PNN) is presented for predicting the magnitude of the largest earthquake in a pre-defined future time period in a seismic region using eight mathematically computed parameters known as seismicity indicators. The indicators considered are the time elapsed during a particular number (n) of significant seismic events before the month in question, the slope of the Gu...

OBJECTIVE In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. METHODS Twenty-two healthy females participated ...

A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs), namely, multilayer perceptron (MLP), radial basis function (RBF) network, and probabilistic neural network (PNN). The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features ...

In this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD). Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing sta...

-By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network ( PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any numb...

Proactive programs as condition monitoring justify the most extreme demands of plastic industry as safety, reliability and cost-competitiveness with other ones. It is a less expensive and precautionary way, rather than the reactive one. Condition monitoring program is utilizing different emerging technologies for better results. The aim of this practical research work, carried out on a plastic ...