Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Empirical mode decomposition and neural network for the classification of electroretinographic data
2013
The processing of biosignals is increasingly being utilized in ambulatory situations in order to extract significant signals' features that can help in clinical diagnosis. However, this task is hampered by the fact that biomedical signals exhibit a complex behaviour characterized by strong non-linear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose to apply a signal processing method, based on empirical mode decomposition and artificial neural networks, to analyse electroretinograms, i.e. the retinal response to a light flash, with the aim to detect and classify retinal diseases…
Structural Health Monitoring Procedure for Composite Structures through the use of Artificial Neural Networks
2015
In this paper different architectures of Artificial Neural Networks (ANNs) for structural damage detection are studied. The main objective is to investigate an ANN able to detect and localize damage without any prior knowledge on its characteristics so as to serve as a real-time data processor for Structural Health Monitoring (SHM) systems. Two different architectures are studied: the standard feed-forward Multi Layer Perceptron (MLP) and the Radial Basis Function (RBF) ANNs. The training data are given, in terms of a Damage Index ℑD, properly defined using a piezoelectric sensor signal output to obtain suitable information on the damage position and dimensions. The electromechanical respon…
Neural based MRAS sensorless techniques for high performance linear induction motor drives.
2010
This paper proposes a neural based MRAS (Model reference Adaptive System) speed observer suited for linear induction motors (LIM). Starting from the dynamical equation of the LIM in the synchronous reference frame in literature, the so-called voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been deduced. Then, while the inductor equations have been used as reference model of the MRAS observer, the induced part equations have been discretized and rearranged so to be represented by a linear neural network (ADALINE). On this basis, the so called TLS EXIN neuron has been used to compute on-line, in recursive form, the machi…
Load forecast on intelligent buildings based on temporary occupancy monitoring
2016
The modeling of energy consumption in buildings must consider occupancy as a relevant input, since it plays a very important role in the overall building's energy consumption. Frequently, buildings lack of permanent occupancy monitoring solutions. However, they may include data sources that are correlated with real building occupancy. This study proposes a new methodology for energy consumption modeling, supported by these alternative data sources, such as the number of vehicles in a parking lot. The aim is to mitigate investment in permanent occupancy monitoring solutions. The proposed methodology makes use of short-term real occupancy monitoring for model fitting, to enable the developmen…
A massive lesion detection algorithm in mammography
2004
A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by avera…
Eddy current flaw detection with neural network applications
2005
Abstract Eddy current inspection is a fast and effective method for detecting and sizing most of the flaws in conducting materials. The inverse problem solution, i.e., inferencing about possible defect, based on the measurement signal from the eddy current probe, is a difficult task. In this paper an implementation of artificial neural networks for multi-frequency eddy current testing on conducting layers (aluminum, copper) and ferrous tubes has been presented.
A methodology for sequencing batch reactor identification with artificial neural networks: A case study
2009
This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR) identification. The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. The proposed approach makes optimal use of the available data during the training stage and it is aimed at achieving high generalization ability. For this purpose, a wide range of experimental conditions, including different solids retention times and influent characteristics, has been used. The methodology is successfully applied to develop a soft-sensor for monitoring a laboratory-scale SBR operated for enhanced biological phosphorus…
Integrated emitter local loss prediction using artificial neural networks.
2010
This paper describes an application of artificial neural networks (ANNs) to the prediction of local losses from integrated emitters. First, the optimum input-output combination was determined. Then, the mapping capability of ANNs and regression models was compared. Afterwards, a five-input ANN model, which considers pipe and emitter internal diameter, emitter length, emitter spacing, and pipe discharge, was used to develop a local losses predicting tool which was obtained from different training strategies while taking into account a completely independent test set. Finally, a performance index was evaluated for the test emitter models studied. Emitter data with low reliability were removed…
New Procedures of Pattern Classification for Vibration-Based Diagnostics via Neural Network
2014
In this paper, the new distance-based embedding procedures of pattern classification for vibration-based diagnostics of gas turbine engines via neural network are proposed. Diagnostics of gas turbine engines is important because of the high cost of engine failure and the possible loss of human life. Engine monitoring is performed using either ‘on-line’ systems, mounted within the aircraft, that perform analysis of engine data during flight, or ‘off-line’ ground-based systems, to which engine data is downloaded from the aircraft at the end of a flight. Typically, the health of a rotating system such as a gas turbine is manifested by its vibration level. Efficiency of gas turbine monitoring s…
An Intelligent Tool to Predict Fracture in Sheet Metal Forming Operations
2007
One of the main issues in sheet metal forming operations design is the determination of formability limits in order to prevent necking and fracture. In fact, the ability to predict fracture represents a powerful tool to improve the production quality in mechanical industry. Many researchers investigated the problem here addressed, mainly studying forming limit diagrams (FLD) or developing fracture criteria which are able to foresee fracture defects for different processes. In this paper, the author present some early results of a research project focused on the application of artificial intelligence (AI) for ductile fracture prediction in sheet metal forming operations. The main advantage o…