Three-dimensional fingerprint recognition by using convolution neural network

2018 ◽  
Author(s):  
Qianyu Tian ◽  
Zonghua Zhang ◽  
Nan Gao
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Convolution Neural Network ◽  
Fingerprint Recognition

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.

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

Pulmonary Nodule Recognition Based on Three-Dimensional Convolution Neural Network

2019 ◽  
Vol 39 (6) ◽  
pp. 0615006 ◽  
Author(s):  
冯雨 Yu Feng ◽  
易本顺 Benshun Yi ◽  
吴晨玥 Chenyue Wu ◽  
章云港 Yungang Zhang
Keyword(s):  
Neural Network ◽  
Pulmonary Nodule ◽  
Three Dimensional ◽  
Convolution Neural Network

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

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