scholarly journals A TIME-FREQUENCY FEATURE FUSION ALGORITHM BASED ON NEURAL NETWORK FOR HRRP

2017 ◽  
Vol 55 ◽  
pp. 63-71
Author(s):  
Lele Yuan
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Frequency Feature

scholarly journals Extended Segmented Beat Modulation Method for Cardiac Beat Classification and Electrocardiogram Denoising

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1178
Author(s):  
Amnah Nasim ◽  
Agnese Sbrollini ◽  
Micaela Morettini ◽  
Laura Burattini
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Modulation Method ◽  
Signal To Noise ◽  
Predictive Values ◽  
Time Frequency ◽  
Beat Classification ◽  
Ecg Denoising ◽  
Frequency Feature

Beat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.

scholarly journals Tool Wear Prediction Based on Multidomain Feature Fusion by Attention-Based Depth-Wise Separable Convolutional Neural Network in Manufacturing

2021 ◽  
Author(s):  
Guofa Li ◽  
Yanbo Wang ◽  
Jialong He ◽  
Yongchao Huo
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Prediction Accuracy ◽  
Feature Fusion ◽  
Prediction Method ◽  
Wear Prediction ◽  
Time Frequency ◽  
Tool Wear Prediction

Abstract Tool wear during machining has a great influence on the quality of machined surface and dimensional accuracy. Tool wear monitoring is extremely important to improve machining efficiency and workpiece quality. Multidomain features (time domain, frequency domain and time-frequency domain) can accurately characterise the degree of tool wear. However, manual feature fusion is time consuming and prevents the improvement of monitoring accuracy. A new tool wear prediction method based on multidomain feature fusion by attention-based depth-wise separable convolutional neural network is proposed to solve these problems. In this method, multidomain features of cutting force and vibration signals are extracted and recombined into feature tensors. The proposed hypercomplex position encoding and high dimensional self-attention mechanism are used to calculate the new representation of input feature tensor, which emphasizes the tool wear sensitive information and suppresses large area background noise. The designed depth-wise separable convolutional neural network is used to adaptively extract high-level features that can characterize tool wear from the new representation, and the tool wear is predicted automatically. The proposed method is verified on three sets of tool run-to-failure data sets of three-flute ball nose cemented carbide tool in machining centre. Experimental results show that the prediction accuracy of the proposed method is remarkably higher than other state-of-art methods. Therefore, the proposed tool wear prediction method is beneficial to improve the prediction accuracy and provide effective guidance for decision making in processing.

scholarly journals Research on bogie traction seat crack identification based on OvO multi-feature fusion

2021 ◽  
Author(s):  
Qi Chang ◽  
Yi Liu ◽  
Lin Li ◽  
Junfeng Man ◽  
Cheng Peng ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Time Domain ◽  
Feature Fusion ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Time Frequency ◽  
Feature Sets ◽  
Optimal Feature

Abstract Aimed at the problems of unsatisfactory feature extraction and low diagnostic accuracy in the identification process during train bogie traction seat operation status , an optimized method of the neural network model of back propagation based on OvO multi-feature fusion is proposed. Firstly, a simulated experimental platform is built to collect vibration signals, and the collected original vibration data are analyzed in time domain, frequency domain and time-frequency. Secondly, the principal component analysis is carried out on the proposed high-dimensional feature sets. Finally, the reduced dimensional features are fused into the optimal feature set based on OvO algorithm. The back propagation neural network model is constructed, and the time domain, frequency domain, time-frequency and optimal feature sets are taken as inputs respectively, and the results of output recognition are compared. The experimental results show that the recognition accuracy of back propagation neural network model based on OvO multi-feature fusion is higher, and the running state of bogie traction seat can be recognized accurately.

scholarly journals Time–Frequency Feature Fusion for Noise Robust Audio Event Classification

2019 ◽  
Vol 39 (3) ◽  
pp. 1672-1687
Author(s):  
Ian McLoughlin ◽  
Zhipeng Xie ◽  
Yan Song ◽  
Huy Phan ◽  
Ramaswamy Palaniappan
Keyword(s):  
Feature Fusion ◽  
Event Classification ◽  
Time Frequency ◽  
Audio Event ◽  
Noise Robust ◽  
Frequency Feature

scholarly journals Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2524
Author(s):  
Huibin Zhu ◽  
Zhangming He ◽  
Juhui Wei ◽  
Jiongqi Wang ◽  
Haiyin Zhou
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Feature Fusion ◽  
Singular Value ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Frequency Feature

Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

Sign in / Sign up

Export Citation Format

Share Document