The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult ...The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult problem.This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance.Compared with previous blockchain transactions,the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance.Finally,the effectiveness of the proposed method is verified by experiments.展开更多
Wheat is the most widely grown crop in the world,and its yield is closely related to global food security.The number of ears is important for wheat breeding and yield estimation.Therefore,automated wheat ear counting ...Wheat is the most widely grown crop in the world,and its yield is closely related to global food security.The number of ears is important for wheat breeding and yield estimation.Therefore,automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain yield.However,all existing methods require position-level annotation for training,implying that a large amount of labor is required for annotation,limiting the application and development of deep learning technology in the agricultural field.To address this problem,we propose a count-supervised multiscale perceptive wheat counting network(CSNet,count-supervised network),which aims to achieve accurate counting of wheat ears using quantity information.In particular,in the absence of location information,CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear features.We conduct comparative experiments on a publicly available global wheat head detection dataset,showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error(MAE)and root mean square error(RMSE).This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs,demonstrating its great potential for agricultural counting tasks.The code is available at .展开更多
基金This research was funded by science and technology project of State Grid JiangSu Electric Power Co.,Ltd.(Research on Key Technologies of power network security digital identity authentication and management and control based on blockchain,Grant No.is J2021021).
文摘The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult problem.This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance.Compared with previous blockchain transactions,the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance.Finally,the effectiveness of the proposed method is verified by experiments.
基金supported by the National Natural Science Foundation of China(no.62162008)Guizhou Provincial Science and Technology Projects(CXTD[2023]027)Guiyang Guian Science and Technology Talent Training Project([2024]2-15).
文摘Wheat is the most widely grown crop in the world,and its yield is closely related to global food security.The number of ears is important for wheat breeding and yield estimation.Therefore,automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain yield.However,all existing methods require position-level annotation for training,implying that a large amount of labor is required for annotation,limiting the application and development of deep learning technology in the agricultural field.To address this problem,we propose a count-supervised multiscale perceptive wheat counting network(CSNet,count-supervised network),which aims to achieve accurate counting of wheat ears using quantity information.In particular,in the absence of location information,CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear features.We conduct comparative experiments on a publicly available global wheat head detection dataset,showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error(MAE)and root mean square error(RMSE).This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs,demonstrating its great potential for agricultural counting tasks.The code is available at .
