Manufacturing feature recognition with a 2D convolutional neural network

2020 ◽  
Vol 30 ◽  
pp. 36-57
Author(s):  
Yang Shi ◽  
Yicha Zhang ◽  
Ramy Harik
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Recognition ◽  
Manufacturing Feature

scholarly journals A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengze Wu ◽  
Yongdi Lu ◽  
Wenli Yang ◽  
Shen Yuong Wong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Recognition ◽  
Cardiac Activity ◽  
Medical Monitoring ◽  
Denoising Method ◽  
Medical Resources ◽  
Training Time ◽  
Complex Models ◽  
Electrocardiogram Ecg

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

scholarly journals Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bin Hu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Feature Recognition ◽  
Semantic Segmentation ◽  
Landscape Patterns ◽  
Image Feature ◽  
Accuracy Rate ◽  
Landscape Elements ◽  
Landscape Element

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.

scholarly journals The Recognition of Rice Area Images by UAV Based on Deep Learning

2018 ◽  
Vol 232 ◽  
pp. 02057 ◽  
Author(s):  
Hongyuan Wei ◽  
Jian Mao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Recognition ◽  
Rice Field ◽  
Training Model ◽  
Practical Application ◽  
Training Set ◽  
Large Size ◽  
Rice Area ◽  
Python Programming

Aiming at the target detection of remote sensing rice field of uav, the image of large-size uav is firstly segmented, and the type of each image is manually identified, and the image training set and verification set are made. Then, the training model of convolutional neural network is realized by python programming. The advantage and disadvantage of the two-layer convolutional neural network and ResNet50 are compared, and it is found that the training set is less and the picture feature complexity is not high in practical application. In the end, the feature recognition of rice field is realized, which has certain application value.

scholarly journals Multilayer Extreme Learning Convolutional Feature Neural Network Model for the Weak Feature Classification and Status Identification of Planetary Bearing

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Rui Yang ◽  
Zenghui An ◽  
Shijun Song
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Recognition ◽  
Optimization Problems ◽  
Self Regulation ◽  
Training Model ◽  
Feedback Mechanism ◽  
Model Parameters

A convolutional neural network has the characteristics of sharing information between layers, which can realize high-dimensional data processing. In general, the convolutional neural network uses a feedback mechanism to realize parameter self-regulation, which solves the disadvantages of manual parameter adjustment. However, it is unable to determine the iteration number with the best calculation accuracy. Calculation efficiency cannot be guaranteed while achieving the best accuracy. In this paper, a multilayer extreme learning convolutional neural network model is proposed for feature recognition and classification. Firstly, two-dimensional spatial characteristics of planetary bearing status data were enhanced. Then, extreme learning machine is embedded in a convolution layer to solve convex optimization problems. Finally, the parameters obtained from the training model were nested into a network to initialize the model parameters to separate each status feature. Planetary bearing experimental cases show the effectiveness and superiority of the proposed model in the recognition and classification of weak signals.

ANN-Based Feature Recognition to Integrate CAD and CAM

2011 ◽  
Vol 55-57 ◽  
pp. 1269-1274
Author(s):  
Yong Tao Hao ◽  
Yong Min Chi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Feature Recognition ◽  
Intelligent Manufacturing ◽  
Recognition Method ◽  
Feature Extraction Method ◽  
The Subject ◽  
Artificial Neural ◽  
Manufacturing Feature

This paper presents an intelligent manufacturing feature extraction method employing artificial neural network techniques. It discuss the subject about how to represent the features as the input expression of the ANN(Artificial Neural Network), how to determine the structure of ANN and the ANN-based feature recognition method. This method is mainly used pre-trained BP neural network to identify the B-rep model representation of the product. Through a lot testing, the validity of the system was verified.

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