[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
Introduction Cancer is an attractive target of gene therapy and currently represents the disease in most clinical trials[1]. Strategies for cancer gene therapy include: (1) stimulation of immune responses to tumor cel...Introduction Cancer is an attractive target of gene therapy and currently represents the disease in most clinical trials[1]. Strategies for cancer gene therapy include: (1) stimulation of immune responses to tumor cells,(2) delivery of specific enzymes展开更多
在水稻株型设计系统(Rice Plant Type Design System,RPTDS)基础上,以冠层光分布为指标,利用改造后的非自然群体,通过田间实验和虚拟实验的比较研究,对RPTDS系统中水稻冠层三维模型的准确性进行了检验,结果表明,虚拟实验结果与田间实验...在水稻株型设计系统(Rice Plant Type Design System,RPTDS)基础上,以冠层光分布为指标,利用改造后的非自然群体,通过田间实验和虚拟实验的比较研究,对RPTDS系统中水稻冠层三维模型的准确性进行了检验,结果表明,虚拟实验结果与田间实验结果虽然在数值上存在一定差异,但具有极显著的正向相关关系,同时,虚拟实验结果更能够有效反映冠层结构差异,冠层模型具有较高的准确性。说明RPTDS系统基本能够准确反映群体冠层结构和光分布态势。展开更多
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
基金supported by a predoctoral fellowship from the National Institutes of Health and a research grant from the National Science Foundation
文摘Introduction Cancer is an attractive target of gene therapy and currently represents the disease in most clinical trials[1]. Strategies for cancer gene therapy include: (1) stimulation of immune responses to tumor cells,(2) delivery of specific enzymes
文摘在水稻株型设计系统(Rice Plant Type Design System,RPTDS)基础上,以冠层光分布为指标,利用改造后的非自然群体,通过田间实验和虚拟实验的比较研究,对RPTDS系统中水稻冠层三维模型的准确性进行了检验,结果表明,虚拟实验结果与田间实验结果虽然在数值上存在一定差异,但具有极显著的正向相关关系,同时,虚拟实验结果更能够有效反映冠层结构差异,冠层模型具有较高的准确性。说明RPTDS系统基本能够准确反映群体冠层结构和光分布态势。
