Pulmonary rehabilitation training is one of the important therapeutic measures for patients with chronic obstructive pulmonary disease (COPD) during the clinical remission period. It aims to improve lung function through a series of respiratory muscle training exercises. Common respiratory muscle training includes pursed-lip breathing and diaphragmatic breathing, which can effectively improve lung ventilation efficiency. This paper innovatively applies RFID technology to pulmonary function breathing training, proposing the use of Gramian Angular Field (GAF) and Markov Transition Field (MTF) to convert one-dimensional RSSI breathing signal data into multi-channel image data, more comprehensively reflecting the dynamic changes and characteristics of breathing signals. By classifying respiratory muscle actions through the Mnasnet network model, and inspired by Mnasnet, we propose a Mnasnet-3Block-SVM network model with fewer parameters and shorter inference time. This model achieves a classification accuracy of 96.59% for respiratory actions, an improvement of 2.09% over the baseline model.
The haploid breeding technology of maize can shorten the breeding cycle and is an important technology for modern crop improvement. However, selection of maize haploid seeds is often done manually, resulting in loss of time and labor. It is of scientific value to study the selection algorithm of maize haploid seeds with high accuracy and strong generalization ability. In this paper, CNN and SVM were combined, and CNN-SVM model was used to classify maize seeds. The optimal CNN model is obtained by adjusting the number of convolution layers through experiments. In the training process of SVM, the cuckoo search algorithm is used to optimize the value of hyperparameter C and hyperparameter gamma, so as to improve the training efficiency and classification performance of SVM. The performance of CNN-SVM model was compared with CNN-KNN, CNN, HOG-KNN, HOG-SVM, SURF-KNN, SURF-SVM, Pixel histogram-KNN, Pixel histogram-SVM. Experimental results show that the CNN-SVM model is superior to other models, and the accuracy of maize seeds classification is 98.5%.
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