The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning al...The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.展开更多
The fiber length trait （FLT） of 538 individuals from nature birch population in Maorshan region, Heilongjang, China were measured, of which 100 individuals were selected as representative variety of correlated fragm...The fiber length trait （FLT） of 538 individuals from nature birch population in Maorshan region, Heilongjang, China were measured, of which 100 individuals were selected as representative variety of correlated fragments screening with random amplified polymorphism DNA （RAPD） technique. In total of 20 RAPD primers were tested through multiple regression analysis between amplified strip and the character behaviors, and a correlative segment BFLR-16 was obtained. The correlation coefficient between BFLI-16 and FLT was 0.6144, with the significant level of 1%. BFLI-16 was then cloned, sequenced and transformed into SCAR marker. The percentage of identifying long fiber birches by this SCAR was more than 92. The result indicates that the SCAR markers has high specificity for the long fiber individuals and is highly linked with the gene controlling the character of fiber length, and its existence is significantly correlative with the increase in the fiber length.展开更多
基金supported by the Shaanxi Province Natural Science Basic Research Plan Project(2023-JC-YB-244).
文摘The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.
基金supported by the National 863 Program (2002BA515B0401)National Natural Science Foundation of China (30571513)Foundation of Heilongjiang Province (GA06B301)
文摘The fiber length trait （FLT） of 538 individuals from nature birch population in Maorshan region, Heilongjang, China were measured, of which 100 individuals were selected as representative variety of correlated fragments screening with random amplified polymorphism DNA （RAPD） technique. In total of 20 RAPD primers were tested through multiple regression analysis between amplified strip and the character behaviors, and a correlative segment BFLR-16 was obtained. The correlation coefficient between BFLI-16 and FLT was 0.6144, with the significant level of 1%. BFLI-16 was then cloned, sequenced and transformed into SCAR marker. The percentage of identifying long fiber birches by this SCAR was more than 92. The result indicates that the SCAR markers has high specificity for the long fiber individuals and is highly linked with the gene controlling the character of fiber length, and its existence is significantly correlative with the increase in the fiber length.
