Electrical resistance tomography image reconstruction based on one-dimensional multi-branch convolutional neural network combined with attention mechanism

Tang, Hao; Xu, Chao; Han, XW

doi:10.1016/j.flowmeasinst.2022.102140

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…The implementation of the Attention Mechanism is achieved by preserving the intermediate output results of the CNN encoder on the input sequence, and then training a model to selectively learn these inputs and associate the output sequence with them when the model outputs [28][29]. Although the model may increase computational complexity after using this mechanism, its performance level can be improved.…”

Section: ) Attention Mechanism and Model Trainingmentioning

confidence: 99%

Analysis and Application of Antibacterial Drug Resistance Based on Deep Learning

Zhang,

Zhang

et al. 2023

IJACSA

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The continuous improvement of deep learning technology has led to its deeper application in related fields, especially in the detection of antimicrobial resistance in the medical field. In drug resistance detection, the CNN-ATT-TChan model based on the fusion of CNN algorithm and attention mechanism can classify and organize a large amount of antimicrobial resistance data, achieving standardized processing. Based on mature chemical analysis and testing methods, drug resistance test data was obtained, and the training duration and classification accuracy F of the model were discussed in combination with the test data. At the same time, based on relevant research literature, the changes in ROC curves and AUC values between different models were compared. The results showed that the CNN algorithm using fusion attention mechanism can improve the training time of the model and also improve the classification accuracy of the model. Therefore, the application of CNN-ATT-TChan model combined with attention mechanism in the detection of antimicrobial resistance provides more support for the development of antimicrobial resistance testing.

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Section: ) Attention Mechanism and Model Trainingmentioning

confidence: 99%

Analysis and Application of Antibacterial Drug Resistance Based on Deep Learning

Zhang,

Zhang

et al. 2023

IJACSA

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“…The Web GUI image recognition algorithm based on deep learning can well adapt to the illumination and angle changes of the scene, and can quickly and robustly learn image features and complete accurate recognition. Therefore, a more effective depth learning algorithm should be proposed according to the features of the image, so as to improve the accuracy of Web GUI image recognition [3][4][5][6][7]. BP neural network belongs to an effective Web GUI image recognition technology.…”

Section: Related Workmentioning

confidence: 99%

Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

Zhao

2022

JWE

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In this paper, a MobileNet V2 convolutional neural network depending on L2 regularization method, a amended particle swarm model and Dropout method are constructed in a bid for enhancing the accuracy and speed of Web GUI image recognition for establishing Web Web GUI image recognition system of Web GUI test. Firstly, an improved MobileNet V2 convolutional neural network is constructed. Secondly, the basic models of L2 regularization method, improved particle swarm method and Dropout method are studied, the improved MobileNet V2 convolution neural network optimization algorithm is proposed, and the basic model of image processing is designed. Finally, simulation analysis is carried out on Web GUI image recognition through using the amended convolutional neural network on basis of MINIST data set as the research object. The simulation results illustrate that the amended convolutional neural network proposed in this study is more accurate and efficient in Web GUI image recognition.

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“…To solve the problem of difficulty and long time, a method of active and reactive power coordination optimization for ADN based on 1D-CNN [4] was proposed. According to the historical load distribution network and distributed power generating capacity data, this study uses the MISOCP [5] method to generate the corresponding control strategy and coding of power equipment, and through the training of 1D-CNN fitting history load and distributed power distribution network capacity and the nonlinear relationship between the active reactive power optimization strategy.…”

Section: Introductionmentioning

confidence: 99%

Active and Reactive Power Coordination Optimization of the Active Distribution Network

Deng

Wang²,

Gao³

2023

J. Phys.: Conf. Ser.

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The operation and control of the active distribution network are faced with great challenges due to a mass of tunable and controllable devices connected to the network, resulting in large active power loss and voltage deviation. In this paper, a method of active and reactive power coordination optimization for the active distribution network based on a one-dimensional convolutional neural network (1D-CNN) is proposed. This method can mine valuable information from the historical data of distribution networks, and use one-dimensional convolutional neural networks to map the complex nonlinear relationship between node load and optimization strategy. The simulation results of the modified IEEE33 node distribution network system show that the active power loss and the node voltage deviation of the proposed method are significantly reduced.

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Electrical resistance tomography image reconstruction based on one-dimensional multi-branch convolutional neural network combined with attention mechanism

Cited by 7 publications

References 22 publications

Analysis and Application of Antibacterial Drug Resistance Based on Deep Learning

Analysis and Application of Antibacterial Drug Resistance Based on Deep Learning

Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

Active and Reactive Power Coordination Optimization of the Active Distribution Network

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