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Full-Text Articles in Physical Sciences and Mathematics
End-To-End Convolutional Neural Network Model For Gear Fault Diagnosis Based On Sound Signals, Yong Yao, Honglei Wang, Shaobo Li, Zhongnhao Liu, Gui Gui, Yabo Dan, Jianjun Hu
End-To-End Convolutional Neural Network Model For Gear Fault Diagnosis Based On Sound Signals, Yong Yao, Honglei Wang, Shaobo Li, Zhongnhao Liu, Gui Gui, Yabo Dan, Jianjun Hu
Faculty Publications
Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused …
An Ensemble Stacked Convolutional Neural Network Model For Environmental Event Sound Recognition, Shaobo Li, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, Jianjun Hu
An Ensemble Stacked Convolutional Neural Network Model For Environmental Event Sound Recognition, Shaobo Li, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, Jianjun Hu
Faculty Publications
Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three …
Optimizing Fpga-Based Cnn Accelerator For Energy Efficiency With An Extended Roofline Model, Sayed Omid Ayat, Mohamed Khalil-Hani, Ab Al-Hadi Ab Rahman
Optimizing Fpga-Based Cnn Accelerator For Energy Efficiency With An Extended Roofline Model, Sayed Omid Ayat, Mohamed Khalil-Hani, Ab Al-Hadi Ab Rahman
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, the convolutional neural network (CNN) has found wide acceptance in solving practical computer vision and image recognition problems. Also recently, due to its flexibility, faster development time, and energy efficiency, the field-programmable gate array (FPGA) has become an attractive solution to exploit the inherent parallelism in the feedforward process of the CNN. However, to meet the demands for high accuracy of today's practical recognition applications that typically have massive datasets, the sizes of CNNs have to be larger and deeper. Enlargement of the CNN aggravates the problem of off-chip memory bottleneck in the FPGA platform since there …
Estimating Left Ventricular Volume With Roi-Based Convolutional Neural Network, Feng Zhu
Estimating Left Ventricular Volume With Roi-Based Convolutional Neural Network, Feng Zhu
Turkish Journal of Electrical Engineering and Computer Sciences
The volume of the human left ventricular (LV) chamber is an important indicator for diagnosing heart disease. Although LV volume can be measured manually with cardiac magnetic resonance imaging (MRI), the process is difficult and time-consuming for experienced cardiologists. This paper presents an end-to-end segmentation-free method that estimates LV volume from MRI images directly. The method initially uses Fourier transform and a regression filter to calculate the region of interest that contains the LV chambers. Then convolutional neural networks are trained to estimate the end-diastolic volume (EDV) and end-systolic volume (ESV). The resulting models accurately estimate the EDV and ESV …