Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Soil Science submitted time 2023-02-27 Cooperative journals: 《干旱区地理》
Abstract： Soil salinization can hinder agricultural development. In this study, the degree of regional soil salinization was obtained to provide a theoretical reference for improving agricultural land quality. Using Yinchuan Plain of China as the study area with a grid size of 5 km×5 km, the soil salinity data of 166 sampling points at different depths were obtained. Combined with the Landsat 8 OLI image corresponding to the sampling time, the salt influence factor and salt index were used as input parameters, respectively, and soil salinity at field sampling points was used as output layer parameters. Support vector machine, back propagation neural network, and Bayesian neural network (BNN) were established as soil salinity inversion models. The determination coefficient and root mean square error of the different models were compared to screen the best model. Finally, soil salinization inversion at different depths was performed in the study area. The following results were obtained: (1) In the 0-20 cm soil salinity inversion model, the BNN model based on the influence factor variable group of salinization was the best, with a coefficient of determination (R2 ) and root mean square error (RMSE) of 0.618 and 2.986, respectively; the best inversion result of 20-40 cm soil salinity was the BNN model based on the salt index variable group (R2 =0.651; RMSE=1.947); the comparative analysis of the modeling and verification effects of different variables of the selected algorithms revealed that the BNN model was the best inversion model with a better fitting degree than the other two models, and the introduction of a neural network had certain advantages in the model construction. (2) Non- salinized and mildly salinized soils were the main soil types in Yinchuan Plain. Soil salinization showed a low trend in the south and a high trend in the north. The 20-40 cm soil salinization was found to be lighter than the 0-20 cm soil salinization.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-18 Cooperative journals: 《计算机应用研究》
Abstract： Bone age assessment is a common method to detect endocrine and growth abnormalities in children. But in deep learning methods, low-quality hand X-ray images reduce the final evaluation accuracy. To solve this problem, this paper proposed an alignment network that increases the area of interest in hand X-ray images. This network uses the Swin Transformer structure as the backbone network to learn image hand similarity and obtain affine coefficients and does not require large-scale hand annotation during the training process. In the bone age assessment network, for the improvement of efficient channel attention and spatial attention mechanism. This paper proposed dual-pool efficient channel attention and asymmetric convolution spatial attention method and combines these two methods in the form of dual attention and Xception network proposes DA-Xception. This paper tested the RSNA dataset, which achieved a mean absolute error of 5.37 months for this bone age assessment method. Compared with other deep learning methods, this method can fully extract features and optimize the evaluation results.
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