Noise cancelling algorithms for FPGA-based time-domain EMI measurements in real-time

2011 ◽  
Author(s):  
Arnd Frech ◽  
Steffen Limmer ◽  
Peter Russer
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Noise Cancelling

A real-time sound rendering system based on the finite-difference time-domain algorithm

2014 ◽  
Vol 53 (7S) ◽  
pp. 07KC14 ◽  
Author(s):  
Tan Yiyu ◽  
Yasushi Inoguchi ◽  
Yukinori Sato ◽  
Makoto Otani ◽  
Yukio Iwaya ◽  
...  
Keyword(s):  
Finite Difference ◽  
Real Time ◽  
Time Domain ◽  
Finite Difference Time Domain ◽  
Sound Rendering ◽  
Difference Time

Real-Time Gait Phase Recognition Based on Time Domain Features of Multi-MEMS Inertial Sensors

2021 ◽  
Vol 70 ◽  
pp. 1-12
Author(s):  
Meiyan Zhang ◽  
Qisong Wang ◽  
Dan Liu ◽  
Boqi Zhao ◽  
Jiaze Tang ◽  
...  
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Inertial Sensors ◽  
Gait Phase ◽  
Phase Recognition

scholarly journals A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3521 ◽  
Author(s):  
Funa Zhou ◽  
Po Hu ◽  
Shuai Yang ◽  
Chenglin Wen
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Real Time ◽  
Time Domain ◽  
Feature Fusion ◽  
Rotating Machinery ◽  
Time Domain Data ◽  
Diagnosis Method ◽  
The Time Domain ◽  
Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

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