Recognition method research on rough handling of express parcels based on acceleration features and CNN

Ding, Ao; Zhang, Yuan; Zhu, Lei; Du, Yangping; Ma, Luping

doi:10.1016/j.measurement.2020.107942

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…CNN begins with the input layer, which is used to input a three-dimensional matrix of an image and transmits it to the next layer. Generally, the information of visible image data (RGB image data) includes three parameters, the image pixels and color [13]. Image conversion is usually completed in data preprocessing, which aims to reduce the image to a form that is easy to process without losing feature information for training.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

Yang

et al. 2021

IEEE Access

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Electrical discharge is a sign of insulation damage of power equipment and an essential part of power inspection. For the recognition of plasma discharge patterns, the accuracy rate of traditional manual inspection relies heavily on the experience of the staff, and misjudgment is inevitable. The emerging method based on manual feature extraction of discharge signals also has its limitations. Thus, a high-accurate recognition method based on convolutional neural network (CNN) and visible images is proposed in this paper. Under the deep learning framework-TensorFlow, CNN model is established and pre-trained weights are loaded through transfer learning. The visible image dataset used for training and evaluation includes four discharge patterns: arc discharge, corona discharge, creeping discharge and plasma jet. In data processing, data amplification and data enhancement are used to expand the sample. Besides, typical network models with fully connected layers are adopted and compared. Three optimization methods of ReLU, BN and Dropout are applied to solve the overfitting problem, and the corresponding effects are discussed in the model of VGG16 and ResNet152. The test accuracy of the optimized VGG16 reaches 99.6% without overfitting, which indicates the promising potential in the recognition of plasma discharge patterns in power inspection. INDEX TERMS electrical discharge, plasma discharge patterns, visible images, image recognition, convolutional neural network, transfer learningThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: A Convolutional Neural Networkmentioning

confidence: 99%

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

Yang

et al. 2021

IEEE Access

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Signals classification based on IA-optimal CNN

Zhang

et al. 2021

Neural Comput & Applic

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The versatility of the existing A-optimal-based CNN for solving multiple types of signals classification problems has not been verified by different signals datasets. Moreover, the existing A-optimal-based CNN uses a simplified approximate function as the optimization objective function instead of precise analytical function, which affects the signals classification accuracy to a certain extent. In this paper, a classification method called IA-optimal CNN is proposed. To improve the stability of the classifier, the trace of the covariance matrix of the weights of the fully connected layer is used as the optimization objective function, and the parameter optimization model is established without any simplification of the optimization objective function. In addition, to avoid the difficulty of not being able to obtain the analytical expression formula of the partial derivative of the inverse matrix with regard to the networks parameters, a novel dual function is introduced to transform the optimization problem into an equivalent binary function optimization problem. Furthermore, based on the above analytical solution results, the parameters are updated using the alternate iterative optimization method and the accurate weight update formula is deduced in detail. Five signals datasets are used to test the universality of the IAoptimal CNN in signals classification fields. The performance of IA-optimal CNN is showed, and the experimental results are compared with the existing A-optimal-based classification algorithm. Lastly, the following conclusion is proved theoretically: For the A-optimal-based CNN, the trace of the covariance matrix will continue to decrease and approach a convergence value in the iterative process, but it is impossible for the networks to strictly reach the A-optimal state.

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