Magnetic Shielding Analysis for Arrayed Eddy Current Testing

Wang

et al. 2021

Plant Methods

Self Cite

Background Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. Method In this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML’s gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness. Results The average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model. The loss value of the meta-learning optimization model in the iterative process drops the fastest and steadily compared to the MAML model and the LSTM model. In cross-domain experiments, the average accuracy of the meta-learning optimized model can still reach 0.833. Conclusions In the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model. And in the training of cross-domain datasets, this model performs best.

Section: Methodsmentioning

confidence: 99%

Meta-learning prediction of physical and chemical properties of magnetized water and fertilizer based on LSTM

Wang

et al. 2021

Plant Methods

Self Cite

“…It is necessary to build the LSTM network and the dense layer, and place the dense layer as the last one. Non-linear changes process the previously extracted features and sort out the weights in the LSTM network [34]. At the same time, cooperate with the dropout layer to avoid the over-fitting phenomenon due to the excessive number of hidden layer nodes [35,36].…”

Section: Meta-learner Lstmmentioning

confidence: 99%

Meta-learning Prediction of Physical and Chemical Properties of Magnetized Water and Fertilizer Based on LSTM

Wang

et al. 2021

Preprint

Self Cite

BackgroundDue to the high cost of data collection and labeling for magnetization detection of medium, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer(PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. MethodIn this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML's gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness.ResultsThe average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model.ConclusionsIn the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model.

“…On the other hand, their ability for accurate recognition and classification of targets with a small number of samples is gradually attracting researchers' attention [25][26][27][28][29]. However, the tradeoff between data quality and quantity is a complex problem that cannot be ignored [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Identification Method for Cone Yarn Based on the Improved Faster R-CNN Model

Zhao

et al. 2022

Processes

Self Cite

To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and Faster R-CNN + VGG16. The results showed that the Faster R-CNN + ResNet50 algorithm had the highest mean average precision rate for the five types of cone yarn at 99.95%, as compared with the YOLOv3 + DarkNet-53 algorithm with a mean average precision rate that was 2.24% higher and the Faster R-CNN + VGG16 algorithm with a mean average precision that was 1.19% higher. Regarding cone yarn defects, shielding, and wear, the Faster R-CNN + ResNet50 algorithm can correctly identify these issues without misdetection occurring, with an average precision rate greater than 99.91%.