Research and Implementation of Intelligent Risk Recognition Model Based on Engineering Construction of Neural Network

2017 ◽  
pp. 319-326
Author(s):  
Jihong Luo ◽  
Rong Pan
Keyword(s):  
Neural Network ◽  
Risk Recognition ◽  
Engineering Construction ◽  
Recognition Model ◽  
Model Based

scholarly journals Recognition Method of Drinking-driving Behaviors Based on PCA and RBF Neural Network

2018 ◽  
Vol 30 (4) ◽  
pp. 407-417
Author(s):  
Yifan Sun ◽  
Jinglei Zhang ◽  
Xiaoyuan Wang ◽  
Zhangu Wang ◽  
Jie Yu
Keyword(s):  
Neural Network ◽  
Traffic Safety ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Physiological Data ◽  
Recognition Model ◽  
Driving Behaviors ◽  
Model Based ◽  
Safety Research

Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers’ physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF  neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and  driving and traffic safety maintenance.

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.

scholarly journals FRMN - A Face Recognition Model Based on Convolutional Neural Network

2019 ◽  
Vol 585 ◽  
pp. 012101
Author(s):  
Shifeng Shang ◽  
Haiyan Liu ◽  
Qiang Qu ◽  
Guannan Li ◽  
Jie Cao
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Model ◽  
Model Based

scholarly journals Improvement of Ancient Shui Character Recognition Model Based on Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 33080-33087
Author(s):  
Hongshuai Zhao ◽  
Haozhen Chu ◽  
Yuanyuan Zhang ◽  
Yu Jia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Recognition Model ◽  
Model Based
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