Image recognition model based on deep learning for remaining oil recognition from visualization experiment

Fuel ◽  
2021 ◽  
Vol 291 ◽  
pp. 120216
Author(s):  
Yanwei Wang ◽  
Huiqing Liu ◽  
Mingzhe Guo ◽  
Xudong Shen ◽  
Bailu Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Recognition Model ◽  
Model Based ◽  
Visualization Experiment ◽  
Remaining Oil

Design of MPCANet fire image recognition model for deep learning

2018 ◽  
Vol 47 (2) ◽  
pp. 203006
Author(s):  
张秀玲 Zhang Xiuling ◽  
侯代标 Hou Daibiao ◽  
张逞逞 Zhang Chengcheng ◽  
周凯旋 Zhou Kaixuan ◽  
魏其珺 Wei Qijun
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Recognition Model

scholarly journals Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Lejun Gong ◽  
Zhifei Zhang ◽  
Shiqi Chen
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Clinical Entity ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Recognition Model ◽  
Named Entity ◽  
Model Based

Background. Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods. Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. Then BiLSTM and Transformer are, respectively, used as feature extractors to identify four types of clinical entities including diseases, symptoms, drugs, and operations from the text of Chinese electronic medical records. Results. 75.06% Macro-P, 76.40% Macro-R, and 75.72% Macro-F1 aiming at test dataset could be achieved. These experiments show that the Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition effect. Conclusions. These experiments show that the proposed Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition performance.

The optimization of parallel convolutional RBM based on Spark

2019 ◽  
Vol 17 (02) ◽  
pp. 1940011 ◽  
Author(s):  
Yun Jiang ◽  
Junyu Zhuo ◽  
Juan Zhang ◽  
Xiao Xiao
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Image Classification ◽  
Image Recognition ◽  
Restricted Boltzmann Machine ◽  
Sequential Algorithm ◽  
Boltzmann Machine ◽  
X Ray ◽  
Model Based ◽  
Traditional Algorithm

With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditional sequential algorithm. We train the CRBM with the method and apply it to breast X-ray image classification. The experiments show that it can improve the precision and the speed of training compared with traditional algorithm.

GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

2021 ◽  
Author(s):  
Jian Zhao ◽  
ZhiWei Zhang ◽  
Jinping Qiu ◽  
Lijuan Shi ◽  
Zhejun KUANG ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Rapid Development ◽  
Semantic Features ◽  
Hemispheric Differences ◽  
Recognition Model ◽  
Model Based ◽  
Spatial Features ◽  
Convolution Kernels

Abstract With the rapid development of deep learning in recent years, automatic electroencephalography (EEG) emotion recognition has been widely concerned. At present, most deep learning methods do not normalize EEG data properly and do not fully extract the features of time and frequency domain, which will affect the accuracy of EEG emotion recognition. To solve these problems, we propose GTScepeion, a deep learning EEG emotion recognition model. In pre-processing, the EEG time slicing data including channels were pre-processed. In our model, global convolution kernels are used to extract overall semantic features, followed by three kinds of temporal convolution kernels representing different emotional periods, followed by two kinds of spatial convolution kernels highlighting brain hemispheric differences to extract spatial features, and finally emotions are dichotomy classified by the full connected layer. The experiments is based on the DEAP dataset, and our model can effectively normalize the data and fully extract features. For Arousal, ours is 8.76% higher than the current optimal emotion recognition model based on Inception. For Valence, the best accuracy of our model reaches 91.51%.

scholarly journals Analysis and Recognition Method of Internet Image Public Opinion Based on Partial Differential Equation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Zhang
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Diffusion Coefficient ◽  
Public Opinion ◽  
Image Recognition ◽  
Opinion Mining ◽  
Diffusion Method ◽  
Partial Differential ◽  
Recognition Model ◽  
Model Based

This article comprehensively and systematically expounds the development trends and basic theory of partial differential methods, analyzes the characteristics of sampling multiscale transformation in detail, and deeply studies the network image denoising and network image restoration methods that perform partial differential diffusion in the pixel domain and the transform domain. An adaptive diffusion method of partial differential equations is proposed. Among them, the key parameters can be adaptively changed according to the curvature and gradient of the local geometric information of the network image, and the diffusion direction and intensity of the diffusion can be controlled. First, using the principle of variation, we derive the Euler equation corresponding to the diffusion method of partial differential equations and analyze its diffusion ability using the local orthogonal coordinate system of the network image. Based on the theoretical analysis of public opinion, this article applies opinion mining technology to the online public opinion early warning system to achieve the purpose of grasping the opinions of netizens in time and guiding the trend of public opinion. Opinion mining is the use of natural language processing technology to automatically extract the emotional tendencies and evaluation objects contained in the subjective text. In the edge area of the network image, the diffusion along the edge direction should have a large diffusion coefficient, and the diffusion along the vertical edge direction should have a small diffusion coefficient; in the flat area of the network image, it diffuses to the surrounding with equal intensity, and the diffusion intensity value is relatively high. Secondly, based on the analysis of the adaptive partial differential equation diffusion method, using the half-point difference format, a numerical method for network image recognition is designed. Both theoretical analysis and experimental results show that the network image recognition model based on adaptive partial differential equation diffusion is more effective than the model based on partial differential equation recognition; at the same time, experiments show that the network image recognition model based on adaptive partial differential equation diffusion is more effective than the network image recognition model based on ordinary diffusion. The network image recognition model based on constant partial differential equation diffusion is more effective in improving the quality of network image recognition.

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