In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
The multi-stage development strategy is often adopted in the gas field.However,when the productivity decline occurs,many large processing stations will be severely idle and underutilized,significantly reducing operati...The multi-stage development strategy is often adopted in the gas field.However,when the productivity decline occurs,many large processing stations will be severely idle and underutilized,significantly reducing operating efficiency and revenue.This study proposes a novel operation mode of multiple gathering production systems for gas field multi-stage development,integrating the decisions about processing capacity allocation and infrastructure construction to share processing stations and improve multi-system operating efficiency.A multi-period mixed integer linear programming model for multisystem operation optimization is established to optimize the net present value(NPV),considering the production of gas wells,time-varying gas prices,and the capacity of processing stations.The decision of processing capacity,location,construction timing,and capacity expansion of processing stations,as well as transmission capacity of pipelines and processing capacity allocation schemes,can be obtained to meet long-term production demand.Furthermore,a real case study indicates that the proposed processing capacity allocation approach not only has a shorter payback period and increases NPV by 4.8%,but also increases the utilization efficiency of processing stations from 27.37% to 48.94%.This work demonstrates that the synergy between the processing capacity allocation and infrastructure construction can hedge against production fluctuations and increase potential profits.展开更多
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ23E040004。
文摘The multi-stage development strategy is often adopted in the gas field.However,when the productivity decline occurs,many large processing stations will be severely idle and underutilized,significantly reducing operating efficiency and revenue.This study proposes a novel operation mode of multiple gathering production systems for gas field multi-stage development,integrating the decisions about processing capacity allocation and infrastructure construction to share processing stations and improve multi-system operating efficiency.A multi-period mixed integer linear programming model for multisystem operation optimization is established to optimize the net present value(NPV),considering the production of gas wells,time-varying gas prices,and the capacity of processing stations.The decision of processing capacity,location,construction timing,and capacity expansion of processing stations,as well as transmission capacity of pipelines and processing capacity allocation schemes,can be obtained to meet long-term production demand.Furthermore,a real case study indicates that the proposed processing capacity allocation approach not only has a shorter payback period and increases NPV by 4.8%,but also increases the utilization efficiency of processing stations from 27.37% to 48.94%.This work demonstrates that the synergy between the processing capacity allocation and infrastructure construction can hedge against production fluctuations and increase potential profits.
