Wire Mismatch Detection Using a Convolutional Neural Network and Fault Localization Based on Time–Frequency-Domain Reflectometry

2019 ◽  
Vol 66 (3) ◽  
pp. 2102-2110 ◽  
Author(s):  
Seung Jin Chang ◽  
Jin Bae Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Fault Localization ◽  
Time Frequency ◽  
Mismatch Detection ◽  
Frequency Domain Reflectometry

scholarly journals Design and Implementation of Opportunity Signal Perception Unit Based on Time-Frequency Representation and Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7871
Author(s):  
Zhongliang Deng ◽  
Hang Qi ◽  
Yanxu Liu ◽  
Enwen Hu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Utilization Efficiency ◽  
Prototype System ◽  
Positioning System ◽  
Signal Recognition ◽  
Positioning Systems ◽  
Time Frequency ◽  
Signal Perception

The traditional signal of opportunity (SOP) positioning system is equipped with dedicated receivers for each type of signal to ensure continuous signal perception. However, it causes a low equipment resources utilization and energy waste. With increasing SOP types, problems become more serious. This paper proposes a new signal perception unit for SOP positioning systems. By extracting the perception function from the positioning system and operating independently, the system can flexibly schedule resources and reduce waste based on the perception results. Through time-frequency joint representation, time-frequency image can be obtained which provides more information for signal recognition, and is difficult for traditional single time/frequency-domain analysis. We also designed a convolutional neural network (CNN) for signal recognition and a negative learning method to correct the overfitting to noisy data. Finally, a prototype system was built using USRP and LabVIEW for a 2.4 GHz frequency band test. The results show that the system can effectively identify Wi-Fi, Bluetooth, and ZigBee signals at the same time, and verified the effectiveness of the proposed signal perception architecture. It can be further promoted to realize SOP perception in almost full frequency domain, and improve the integration and resource utilization efficiency of the SOP positioning system.

Ultrasonic feature imaging of a multi-layered structure beyond a thin, highly reflective layer using a convolutional neural network

2021 ◽  
Vol 63 (4) ◽  
pp. 219-228
Author(s):  
Chuanyu Lu ◽  
Minghui Lu ◽  
Yiting Chen ◽  
Yongdong Pan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Layered Structure ◽  
Composite Structures ◽  
Service Failure ◽  
Joint Analysis ◽  
Echo Signal ◽  
Training Time ◽  
Time Frequency

A helicopter propeller is a kind of multi-layered composite material bonding structure. Ensuring that composite structures are free from defects can reduce the risk of in-service failure and hence improve safety. As a common non-destructive testing (NDT) technology, ultrasonic testing is often used in the inspection of composite structures. However, a composite structure made of multiple thin-layer materials bonded together can cause a serious aliasing problem for echo signals when inspecting with ultrasound. In this study, the frequency-domain characteristics of an aliasing echo signal were analysed using the spectrum of the acoustic pressure reflection coefficient. Furthermore, the time-frequency joint analysis results of the echo signal were obtained using a continuous wavelet transform. Finally, the obtained time-frequency features of the echo signal were used to classify and image with a convolutional neural network (CNN). The results revealed that, as opposed to the direct imaging of the time- and frequency-domain features, the time-frequency wavelet map of a thin-walled multi-layered structure that was classified and imaged with a CNN exhibited greater clarity and better defect recognition ability. In addition, the training time of the CNN was 17 s and the classification accuracy of the verification set was high, reaching 97.8%.

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.

Offline Fault Localization Technique on HVDC Submarine Cable via Time–Frequency Domain Reflectometry

2017 ◽  
Vol 32 (3) ◽  
pp. 1626-1635 ◽  
Author(s):  
Gu-Young Kwon ◽  
Chun-Kwon Lee ◽  
Geon Seok Lee ◽  
Yeong Ho Lee ◽  
Seung Jin Chang ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Fault Localization ◽  
Submarine Cable ◽  
Localization Technique ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

scholarly journals Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 733
Author(s):  
Dalal A. AlDuwaile ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Recognition System ◽  
Ecg Signal ◽  
Biometric Recognition ◽  
Short Segment ◽  
Time Frequency ◽  
Learning Techniques ◽  
Proposed Model

The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.

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