Turbulence,a complex multi-scale phenomenon inherent in fluid flow systems,presents critical challenges and opportunities for understanding physical mechanisms across scientific and engineering domains.Although high-r...Turbulence,a complex multi-scale phenomenon inherent in fluid flow systems,presents critical challenges and opportunities for understanding physical mechanisms across scientific and engineering domains.Although high-resolution(HR)turbulence data remain indispensable for advancing both theoretical insights and engineering solutions,their acquisition is severely limited by prohibitively high computational costs.While deep learning architectures show transformative potential in reconstructing high-fidelity flow representations from sparse measurements,current methodologies suffer from two inherent constraints:strict reliance on perfectly paired training data and inability to perform multi-scale reconstruction within a unified framework.To address these challenges,we propose HADF,a hash-adaptive dynamic fusion implicit network for turbulence reconstruction.Specifically,we develop a low-resolution(LR)consistency loss that facilitates effective model training under conditions of missing paired data,eliminating the conventional requirement for fully matched LR and HR datasets.We further employ hash-adaptive spatial encoding and dynamic feature fusion to extract turbulence features,mapping them with implicit neural representations for reconstruction at arbitrary resolutions.Experimental results demonstrate that HADF achieves superior performance in global reconstruction accuracy and local physical properties compared to state-of-the-art models.It precisely recovers fine turbulence details for partially unpaired data conditions and diverse resolutions by training only once while maintaining robustness against noise.展开更多
Detection and counting of abalones is one of key technologies of abalones breeding density estimation.The abalones in the breeding stage are small in size,densely distributed,and occluded between individuals,so the ex...Detection and counting of abalones is one of key technologies of abalones breeding density estimation.The abalones in the breeding stage are small in size,densely distributed,and occluded between individuals,so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage.To solve this problem,a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research.The innovation points of this method are:Firstly,the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size;secondly,the multiscale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage;finally,the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size.The experimental results show that the AP@0.5 value,AP@0.7 value and AP@0.75 value of the detection results of the proposed method are 91.14%,89.90% and 80.14%,respectively.The precision and recall rates of the counting results are 99.59% and 97.74%,respectively,which are superior to the counting results of SSD,FSSD,MutualGuide,EfficientDet and VarifocalNet models.The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.展开更多
基金Project supported by the National Natural Science Foundation of China(No.12402349)the Natural Science Foundation of Hunan Province(No.2024JJ6468)+1 种基金the Youth Foundation of the National University of Defense Technology(No.ZK2023-11)the National Key Research and Development Program of China(No.2021YFB0300101)。
文摘Turbulence,a complex multi-scale phenomenon inherent in fluid flow systems,presents critical challenges and opportunities for understanding physical mechanisms across scientific and engineering domains.Although high-resolution(HR)turbulence data remain indispensable for advancing both theoretical insights and engineering solutions,their acquisition is severely limited by prohibitively high computational costs.While deep learning architectures show transformative potential in reconstructing high-fidelity flow representations from sparse measurements,current methodologies suffer from two inherent constraints:strict reliance on perfectly paired training data and inability to perform multi-scale reconstruction within a unified framework.To address these challenges,we propose HADF,a hash-adaptive dynamic fusion implicit network for turbulence reconstruction.Specifically,we develop a low-resolution(LR)consistency loss that facilitates effective model training under conditions of missing paired data,eliminating the conventional requirement for fully matched LR and HR datasets.We further employ hash-adaptive spatial encoding and dynamic feature fusion to extract turbulence features,mapping them with implicit neural representations for reconstruction at arbitrary resolutions.Experimental results demonstrate that HADF achieves superior performance in global reconstruction accuracy and local physical properties compared to state-of-the-art models.It precisely recovers fine turbulence details for partially unpaired data conditions and diverse resolutions by training only once while maintaining robustness against noise.
基金jointly supported by the National Key R&D Project(2020YFD0900204)the Yantai Key R&D Project(2019XDHZ084).
文摘Detection and counting of abalones is one of key technologies of abalones breeding density estimation.The abalones in the breeding stage are small in size,densely distributed,and occluded between individuals,so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage.To solve this problem,a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research.The innovation points of this method are:Firstly,the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size;secondly,the multiscale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage;finally,the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size.The experimental results show that the AP@0.5 value,AP@0.7 value and AP@0.75 value of the detection results of the proposed method are 91.14%,89.90% and 80.14%,respectively.The precision and recall rates of the counting results are 99.59% and 97.74%,respectively,which are superior to the counting results of SSD,FSSD,MutualGuide,EfficientDet and VarifocalNet models.The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.
