Pipeline Acoustic Pre-Warning System and Method of Signal Analysis

2008 ◽  
Author(s):  
Pengchao Chen ◽  
Yongjun Cai ◽  
Dongjie Tan ◽  
Yi Sun ◽  
Muyang Ai ◽  
...  
Keyword(s):  
Background Noise ◽  
Signal Analysis ◽  
Warning System ◽  
Acoustic Signals ◽  
Third Party ◽  
Feature Subset ◽  
Long Distance ◽  
Long Distance Transmission ◽  
Optimal Feature Subset ◽  
Optimal Feature

This paper describes a new acoustic pre-warning system for pipelines, aimed at preventing third party damage by monitoring the pipeline acoustic signals. Many environmental factors, such as the by-passing of vehicles and pedestrians, could introduce background noise into long distance transmission of pipeline acoustic signals. As a result, normal pipeline acoustic pre-warning system is disturbed to identify abnormal events. In this work, statistical methods were applied to signal analysis in order to extract feature parameters of different events. Then the optimal feature subset was obtained by gene arithmetic to differentiate hazardous events between normal events effectively. PetroChina has applied the new pre-warning system to their long distance transmission pipelines and the system operates well.

scholarly journals Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 478 ◽  
Author(s):  
Jiajin Qi ◽  
Xu Gao ◽  
Nantian Huang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Time Domain ◽  
High Efficiency ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Feature Subset ◽  
High Voltage Circuit Breaker ◽  
Optimal Feature Subset ◽  
Optimal Feature

The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

2017 ◽  
Vol 8 (3) ◽  
pp. 90-111 ◽  
Author(s):  
Hui Wang ◽  
Li Li Guo ◽  
Yun Lin
Keyword(s):  
Feature Selection ◽  
Information Entropy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Digital Multimedia ◽  
Optimal Feature Subset ◽  
Optimal Feature

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

scholarly journals A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Chen ◽  
Tao Lin ◽  
Ningjiu Tang ◽  
Xin Xia
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Coarse Grained ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Subset ◽  
Parallel Genetic Algorithm ◽  
Support Vectors ◽  
Optimal Feature Subset ◽  
Optimal Feature

The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.

scholarly journals Optimal Feature Subset Selection for Imbalanced Class Data using SMOTE and Binary ALO Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 344-349
Keyword(s):  
Feature Selection ◽  
Class Imbalance ◽  
Classification Performance ◽  
Selection Model ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Spatial Features ◽  
Imbalanced Classes ◽  
Optimal Feature Subset ◽  
Optimal Feature

Feature selection in multispectral high dimensional information is a hard labour machine learning problem because of the imbalanced classes present in the data. The existing Most of the feature selection schemes in the literature ignore the problem of class imbalance by choosing the features from the classes having more instances and avoiding significant features of the classes having less instances. In this paper, SMOTE concept is exploited to produce the required samples form minority classes. Feature selection model is formulated with the objective of reducing number of features with improved classification performance. This model is based on dimensionality reduction by opt for a subset of relevant spectral, textural and spatial features while eliminating the redundant features for the purpose of improved classification performance. Binary ALO is engaged to solve the feature selection model for optimal selection of features. The proposed ALO-SVM with wrapper concept is applied to each potential solution obtained during optimization step. The working of this methodology is tested on LANDSAT multispectral image.

scholarly journals Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

2019 ◽  
Vol 8 (2) ◽  
pp. 910-916
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Mutation Operator ◽  
Knn Classifier ◽  
Optimal Feature Subset ◽  
Optimal Feature

The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

scholarly journals A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction

2020 ◽  
Vol 17 (5) ◽  
pp. 721-730
Author(s):  
Kamal Bashir ◽  
Tianrui Li ◽  
Mahama Yahaya
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Feature Selection ◽  
Maximum Likelihood ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Optimal Feature Subset ◽  
Optimal Feature

The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datasets in their sampled and unsampled forms to select useful features for classification in the context of Software Defect Prediction (SDP). The Support Vector Machine (SVM) and Random Forest (RaF) classifiers are applied on the FS subsets that are based on sampled and unsampled datasets. The performance of the models captured using Area Ander Receiver Operating Characteristics Curve (AUC) metrics are compared for all FS methods considered. The Analysis Of Variance (ANOVA) F-test results validate the superiority of the proposed method over all the FS techniques, both in sampled and unsampled data. The results confirm that the MLLR can be useful in selecting optimal feature subset for more accurate prediction of defective modules in software development process

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