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
Aim and Method A novel three-dimensional quantitative structure-activityrelationship (3D-QSAR) method, self-organizing molecular field analysis (SOMFA) , was used toinvestigate the correlation between the molecular pr...Aim and Method A novel three-dimensional quantitative structure-activityrelationship (3D-QSAR) method, self-organizing molecular field analysis (SOMFA) , was used toinvestigate the correlation between the molecular properties and a class of chromanol analogs asI_(Ks) blockers. Results The cross-validated correlation coefficient q^2 values (0.698) and noncross-validated correlation coefficient r^2 values (0.701) proved a good conventional statisticalcorrelation. Conclusion The final SOMFA model has therefore good predictive activity for the furthermolecular design of chromanol I_(Ks) potassium channel blockers.展开更多
For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated back...For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated background and current frame is obtained by using background subtraction, and then an edge-based shadow removal algorithm is implemented. Moreover, a tbresholding segmentation method for the region detection of moving vehicle based on lo- cation search is developed. At the estimation stage, a registration line is set up in the detection area, then the vehicle length is estimated with the horizontal projection technique as soon as the vehicle leaves the registration line. Lastly, the vehicle is classified according to its length and the classification threshold. The proposed method is different from traditional methods that require complex camera calibrations. It calculates the pixel-based vehicle length by using uncalibrated traffic video sequences at lower computational cost. Furthermore, only one registration line is set up, which has high flexibility. Experimental results of three traffic video sequences show that the classification accuracies for the large and small vehicles are 97.1% and 96.7% respectively, which demonstrates the effectiveness of the proposed method.展开更多
Femoral head fractures without disloca- tion or subluxation are extremely rare injuries. We report a neglected case of isolated comminuted fracture of femoral head without hip dislocation or subluxation of one year du...Femoral head fractures without disloca- tion or subluxation are extremely rare injuries. We report a neglected case of isolated comminuted fracture of femoral head without hip dislocation or subluxation of one year duration in a 36-year-old patient who sustained a high en- ergy trauma due to road traffic accident. He presented with painful right hip and inability to bear full weight on right lower limb with Harris hip score of 39. He received cementless total hip replacement. At latest follow-up of 2.3 years, functional outcome was excellent with Harris hip score of 95. Such isolated injuries have been described only once in the literature and have not been classified till now. The purpose of this report is to highlight the extreme rarity, pos- sible mechanism involved and a novel classification system to classify such injuries.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.
文摘Aim and Method A novel three-dimensional quantitative structure-activityrelationship (3D-QSAR) method, self-organizing molecular field analysis (SOMFA) , was used toinvestigate the correlation between the molecular properties and a class of chromanol analogs asI_(Ks) blockers. Results The cross-validated correlation coefficient q^2 values (0.698) and noncross-validated correlation coefficient r^2 values (0.701) proved a good conventional statisticalcorrelation. Conclusion The final SOMFA model has therefore good predictive activity for the furthermolecular design of chromanol I_(Ks) potassium channel blockers.
基金Supported by Key Natural Science Foundation of Hebei Education Department (No.ZD200911)Technology R&D Program of Hebei Province(No.11213518d)
文摘For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated background and current frame is obtained by using background subtraction, and then an edge-based shadow removal algorithm is implemented. Moreover, a tbresholding segmentation method for the region detection of moving vehicle based on lo- cation search is developed. At the estimation stage, a registration line is set up in the detection area, then the vehicle length is estimated with the horizontal projection technique as soon as the vehicle leaves the registration line. Lastly, the vehicle is classified according to its length and the classification threshold. The proposed method is different from traditional methods that require complex camera calibrations. It calculates the pixel-based vehicle length by using uncalibrated traffic video sequences at lower computational cost. Furthermore, only one registration line is set up, which has high flexibility. Experimental results of three traffic video sequences show that the classification accuracies for the large and small vehicles are 97.1% and 96.7% respectively, which demonstrates the effectiveness of the proposed method.
文摘Femoral head fractures without disloca- tion or subluxation are extremely rare injuries. We report a neglected case of isolated comminuted fracture of femoral head without hip dislocation or subluxation of one year duration in a 36-year-old patient who sustained a high en- ergy trauma due to road traffic accident. He presented with painful right hip and inability to bear full weight on right lower limb with Harris hip score of 39. He received cementless total hip replacement. At latest follow-up of 2.3 years, functional outcome was excellent with Harris hip score of 95. Such isolated injuries have been described only once in the literature and have not been classified till now. The purpose of this report is to highlight the extreme rarity, pos- sible mechanism involved and a novel classification system to classify such injuries.
