Three Dimensional Parametric Electromagnetic Part Model based SAR ATR using Convolution Neural Network

2019 ◽  
Author(s):  
CongHui Ma ◽  
GongJian Wen ◽  
Min Hu
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Convolution Neural Network ◽  
Model Based ◽  
Part Model

scholarly journals A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 7948
Author(s):  
Seungro Lee ◽  
Luca Quagliato ◽  
Donghwi Park ◽  
Inwoo Kwon ◽  
Juhyun Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Design Methodology ◽  
Three Dimensional ◽  
Convolution Neural Network ◽  
Design System ◽  
Preform Design ◽  
Plane Symmetric ◽  
Innovative Methodology ◽  
Quarter Plane ◽  
3D Cnn

This study presents an innovative methodology for preform design in metal forging processes based on the convolution neural network (CNN) algorithm. The proposed approach extracts the features of inputted forging product geometries and utilizes them to derive the corresponding preform shapes by employing weight arrays (filters) determined during the convolutional operations. The filters are progressively updated during the training process, emulating the learning steps of a process engineer responsible for the design of preform shapes for the forging processes. The design system is composed of multiple three-dimensional (3D) CNN sub-models, which can automatically derive individual 3D preform design candidates. It also implies that the 3D surfaces of preforms are easily acquired, which is important for the forging industry. The proposed preform design methodology was validated by applying it to two-dimensional (2D) axisymmetric shapes, one-quarter plane-symmetric 3D shapes, and two other industrial cases. In all the considered cases, the design methodology achieved substantial reductions in the forging load without forging defects, proving its reliability and effectiveness for application in metal forging processes.

scholarly journals Core looseness fault identification model based on Mel spectrogram-CNN

2021 ◽  
Vol 2137 (1) ◽  
pp. 012060
Author(s):  
Ping He ◽  
Yong Li ◽  
Shoulong Chen ◽  
Hoghua Xu ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Recognition Model ◽  
Model Based ◽  
Fault Recognition ◽  
Transformer Core ◽  
Identification Model

Abstract In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

Three-Dimensional Parallel Convolution Neural Network Brain Tumor Segmentation Based on Dilated Convolution

2020 ◽  
Vol 57 (14) ◽  
pp. 141009
Author(s):  
冯博文 Feng Bowen ◽  
吕晓琪 Lü Xiaoqi ◽  
谷宇 Gu Yu ◽  
李菁 Li Qing ◽  
刘阳 Liu Yang
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Three Dimensional ◽  
Convolution Neural Network ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Dilated Convolution
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