This study aimed to investigate the task demand of intelligent unmanned fertilizer application in seedling stage of corn planted in full-film double-ditch seedbed,a film identification method based on improved DeepLab...This study aimed to investigate the task demand of intelligent unmanned fertilizer application in seedling stage of corn planted in full-film double-ditch seedbed,a film identification method based on improved DeepLabv3+ identification method for full-film double-ditch corn seedbed was proposed.The differences in performance indicators of the original Deeplabv3+ network taking Xception as the backbone network and the network model that replaced three lightweight backbone networks,MobileNetV2,MobileNetV3 and GhostNet were tested.At the same time,the network models,classical semantic segmentation was introduced to PSPNet and UNet for comparative test.The MIoU of DeepLabv3+ network model that replaced its backbone network increased by 5.01%,and FPS improved by 206%compared with original network,and the model size reduced by 90.3%.The three DeepLabv3+ models after replacing the backbone network were further compressed,and the two-layer expansion convolution with low expansion rate in ASPP was deleted,and the common convolution after feature fusion was replaced by the depthwise separable convolution to obtain a lightweight network model.After testing the improved network model,it was found that the average decline of precision indicators was only 0.17%,FPS raised to 66.5,with an average increase of 25.5%,and the size of the model was compressed to 10.53 MB.Test results showed that,the improved model showed excellent performance,and could provide important technology and method support for the research and development of intelligent topdressing and field management on full-film double-ditch corn seedbed during seedling stage.展开更多
Swept blades are widely utilized in transonic compressors/fans and provide high load,high through-flow,high efficiency,and adequate stall margin.However,there is limited quantitative research on the mechanism of the e...Swept blades are widely utilized in transonic compressors/fans and provide high load,high through-flow,high efficiency,and adequate stall margin.However,there is limited quantitative research on the mechanism of the effect of swept blades on the flow field,resulting in a lack of direct quantitative guidance for the design and analysis of swept blades in fans/compressors.To better understand this mechanism,this study employs a reduced-dimensional force equilibrium method to analyze more than 1500 swept cascades data.Results verify that circumferential fluctuation terms are responsible for inducing radial migration in the inlet airflow field of the swept blade,resulting in variations in the incidence angle and consequently leading to changes in the characteristics of the swept blade.Thus,a combination of simple functions and machine learning is utilized to model the circumferential fluctuation terms and quantify the sweep mechanism.The prediction accuracy of the model is high,with coefficient of determination greater than 0.95 on the test set.When the model is applied in a meridional flow analysis program,the calculation accuracy of the program for the incidence angle is improved by 0.4°and 0.6°at the design and off-design conditions respectively,compensating for the program’s original deficiencies.Meanwhile,the model can also provide quantitative guidance for the design of swept blades,thereby reducing the number of design iterations and improving design efficiency.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.5206500552365029)Outstanding Youth Foundation of Gansu Province(Grant No.20JR10RA560),China Postdoctoral Science Foundation(Grant No.2021M700741).
文摘This study aimed to investigate the task demand of intelligent unmanned fertilizer application in seedling stage of corn planted in full-film double-ditch seedbed,a film identification method based on improved DeepLabv3+ identification method for full-film double-ditch corn seedbed was proposed.The differences in performance indicators of the original Deeplabv3+ network taking Xception as the backbone network and the network model that replaced three lightweight backbone networks,MobileNetV2,MobileNetV3 and GhostNet were tested.At the same time,the network models,classical semantic segmentation was introduced to PSPNet and UNet for comparative test.The MIoU of DeepLabv3+ network model that replaced its backbone network increased by 5.01%,and FPS improved by 206%compared with original network,and the model size reduced by 90.3%.The three DeepLabv3+ models after replacing the backbone network were further compressed,and the two-layer expansion convolution with low expansion rate in ASPP was deleted,and the common convolution after feature fusion was replaced by the depthwise separable convolution to obtain a lightweight network model.After testing the improved network model,it was found that the average decline of precision indicators was only 0.17%,FPS raised to 66.5,with an average increase of 25.5%,and the size of the model was compressed to 10.53 MB.Test results showed that,the improved model showed excellent performance,and could provide important technology and method support for the research and development of intelligent topdressing and field management on full-film double-ditch corn seedbed during seedling stage.
基金supported by the National Natural Science Foundation of China(No.52376021)。
文摘Swept blades are widely utilized in transonic compressors/fans and provide high load,high through-flow,high efficiency,and adequate stall margin.However,there is limited quantitative research on the mechanism of the effect of swept blades on the flow field,resulting in a lack of direct quantitative guidance for the design and analysis of swept blades in fans/compressors.To better understand this mechanism,this study employs a reduced-dimensional force equilibrium method to analyze more than 1500 swept cascades data.Results verify that circumferential fluctuation terms are responsible for inducing radial migration in the inlet airflow field of the swept blade,resulting in variations in the incidence angle and consequently leading to changes in the characteristics of the swept blade.Thus,a combination of simple functions and machine learning is utilized to model the circumferential fluctuation terms and quantify the sweep mechanism.The prediction accuracy of the model is high,with coefficient of determination greater than 0.95 on the test set.When the model is applied in a meridional flow analysis program,the calculation accuracy of the program for the incidence angle is improved by 0.4°and 0.6°at the design and off-design conditions respectively,compensating for the program’s original deficiencies.Meanwhile,the model can also provide quantitative guidance for the design of swept blades,thereby reducing the number of design iterations and improving design efficiency.
