A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis.

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge.

[Spatial Heterogeneity of Soil Respiration in a Planted Larch Forest in Shanxi Plateau].

Based on the data from a planted larch forest in Panquangou Natural Reserve of Shanxi Province, at three sampling scales (4, 2, and 1 m, respectively), soil respiration (Rs) and its affecting factors including soil temperature at 5 cm (T5), 10 cm (T10), and 15 cm (T15) depths, soil water content (Ws), litter mass (Lw), litter moisture (Lm), soil total carbon (C), and soil total nitrogen ( N) were determined. The spatial heterogeneities of Rs and the environmental factors were further analyzed and their intrinsic correlations were established. The results of traditional statistics showed that the spatial variations of Rs and the all measured factors were in the middle range; Rs were highly significantly positively correlated with T10, T15, and N (P < 0.