The feature extraction of plant electrical signal based on wavelet packet and neural network

2012 ◽  
Author(s):  
Lu Jingxia ◽  
Ding Weimin
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Packet ◽  
Electrical Signal

AUTOMATIC FROG CALLS MONITORING SYSTEM: A MACHINE LEARNING APPROACH

2001 ◽  
Vol 01 (02) ◽  
pp. 165-186 ◽  
Author(s):  
GARY G. YEN ◽  
QIANG FU
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Environmental Monitoring ◽  
Monitoring System ◽  
Wavelet Packet ◽  
Digital Signal ◽  
Automatic Monitoring ◽  
Individual Identification ◽  
Biological Research ◽  
Automatic Monitoring System

Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recognize the vocalizations of four species of frogs and can identify different individuals within the species of interest, is proposed. For the desired monitoring system, species identification is performed first with the proposed filtering and grouping algorithm. Individual identification, which can estimate frog population within the specific species, is performed in the second stage. Digital signal pre-processing, feature extraction, dimensionality reduction, and neural network pattern classification are performed step by step in this stage. Wavelet Packet feature extraction together with two different dimension reduction algorithms are synergistically integrated to produce final feature vectors, which are to be fed into a neural network classifier. The simulation results show the promising future of deploying an array of continuous, on-line environmental monitoring systems based upon nonintrusive analysis of animal calls.

scholarly journals An Approach of Neural Network For Electrocardiogram Classification

2016 ◽  
Vol 1 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Mayank Kumar Gautam ◽  
Vinod Kumar Giri
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Graphical Representation ◽  
Vital Role ◽  
Electrical Signal ◽  
Physiological Parameter ◽  
Ecg Signal ◽  
Poincaré Plot ◽  
Cardiac Muscles

ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.

Fault Diagnosis on Transmission System of Wind Turbines Based on Wavelet Packet Transform and RBF Neural Networks

2013 ◽  
Vol 373-375 ◽  
pp. 1102-1105 ◽  
Author(s):  
Xiao Yun Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Transmission System ◽  
Wavelet Packet Transform ◽  
Fault Classification

Wind turbine transmission system with abundant fault feature and variable types, the vibration signal was a carrier of fault features and it can reflect most of the fault information in the wind turbine transmission system. As there were a large number of transient and non-stationary signals accompany with the vibration signals, so wavelet packet transform was adopted for feature extraction. As RBF Neural network has a strong nonlinear mapping ability and self-adaptability, so it was introduced to the diagnosis system for network training, the neural networks structure and learning algorithm was presented, which could enhance the accuracy of diagnosis. The two-level neural networks recognition method was proposed, first level for fault classification and second level for fault diagnosis. The example shows that this method can be effectively applied to transmission system of wind turbine fault diagnosis with wavelet packet algorithm for fault feature extraction and RBF neural network for pattern recognition.

Airborne Oxygen-Making System Drift Oxygen Sensor Characteristics for Fault Diagnosis Strategy

2013 ◽  
Vol 347-350 ◽  
pp. 371-375
Author(s):  
Xiang Yan Luo ◽  
Jun Bin Cao ◽  
Jun Qing Cao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Software Design ◽  
Oxygen Sensor ◽  
Wavelet Packet ◽  
Wavelet Neural Network ◽  
Wavelet Packet Analysis ◽  
Neural Network Method ◽  
Network Method

This paper focuses on airborne oxygen-making system shortcomings of oxygen sensor characteristic drift in, proposes a method of fault diagnosis. Oxygen sensor with a Wavelet packet analysis of feature extraction, based on wavelet neural network method to determine whether the sensor has failed, and sensor to detect hardware and software design are given.

scholarly journals An Approach of Neural Network For Electrocardiogram Classification

2020 ◽  
Vol 1 (3) ◽  
pp. 119-127
Author(s):  
Mayank Kumar Gautam ◽  
Vinod Kumar Giri
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Graphical Representation ◽  
Vital Role ◽  
Electrical Signal ◽  
Ecg Signal ◽  
Poincaré Plot ◽  
Cardiac Muscles ◽  
Almost All

ECG is basically the graphical representation of the electrical activity of cardiac muscles duringcontraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to thisearly detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated bythe cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiologicalparameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG playsa vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of patternrecognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes ofpredefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generatedwaveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier for identifying the abnormalities of heart disease.

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