Information about dairy cow ruminating is closely associated with the health status of dairy cows.Therefore,it is of great significance to recognize and make statistics of dairy cows’ruminating and feeding behavior.C...Information about dairy cow ruminating is closely associated with the health status of dairy cows.Therefore,it is of great significance to recognize and make statistics of dairy cows’ruminating and feeding behavior.Concerning conventional recognition methods which are dependent on contact type devices,they have some defects of poor instantaneity and strong stress responses.As for recognition based on machine vision,it needs to transmit masses of data and raises high requirements for the cloud server and network performance.According to principles of edge computing,the model is deployed via Tensorflow.js in an edge device in the present study,constructing a recognition and statistical system for ruminating and feeding behavior of dairy cows.Through the application programming interface(API)of the browser,an edge device is able to invoke a camera and acquire dairy cow images.Then,the images can be inputted in the SSD MobileNet V2 model,which is followed by inference based on browser hashrate.Moreover,the edge device merely uploads recognition results to the cloud server for statistics,which features high instantaneity and compatibility.In terms of recognizing ruminating and feeding behavior of dairy cows,the proposed system has a precision ratio of 96.50%,a recall rate of 91.77%,an F1-score of 94.08%,specificity of 91.36%,and accuracy of 91.66%.This suggests that the proposed method is effective in recognizing dairy cow behavior.展开更多
已有基于深度强化学习(Deep Reinforcement Learning,DRL)的云数据中心任务调度算法存在有效经验利用率低造成训练成本高、状态空间维数不固定和维度较高导致学习震荡,以及策略更新步长固定造成的收敛速度慢等问题。为解决以上问题,基...已有基于深度强化学习(Deep Reinforcement Learning,DRL)的云数据中心任务调度算法存在有效经验利用率低造成训练成本高、状态空间维数不固定和维度较高导致学习震荡,以及策略更新步长固定造成的收敛速度慢等问题。为解决以上问题,基于云数据中心场景构建并行任务调度框架,并以时延、能耗和负载均衡为目标研究云任务调度问题。在DRL算法A2C(Advantage Actor Critic)的基础上,提出了一种基于自适应状态优选和动态步长的云数据中心任务调度算法(Adaptive state Optimization and Dynamic Step size A2C,AODS-A2C)。首先,使用准入控制和优先级策略对队列任务进行筛选和排序,提高有效经验的利用率;其次,将动态高维状态以自适应的方式进行快速优选处理,保持相对稳定的状态空间,避免训练过程中出现震荡问题;最后,使用JS(Jensen Shannon)散度度量新旧策略的概率分布差异,并根据这种差异动态地匹配调整Actor网络和Critic网络的学习步长,从而将当前学习状态迅速调整为最佳值,提高算法的收敛速度。仿真实验结果表明,所提出的AODS-A2C算法具有收敛速度快、鲁棒性高等特点,相较于其他对比算法在时延方面降低了1.2%到34.4%,在能耗方面降低了1.6%到57.2%,并可以实现良好的负载均衡。展开更多
基金supported by the National Key Research and Development Program of China(2019YFE0125600)Northeast Agricultural University“East Agricultural Scholar Program(Academic Backbone)”project(20XG37)+1 种基金Research on Intelligent Non-contact Monitoring of Ruminating and Feeding Behavior of Dairy Cows,Heilongjiang Natural Science Foundation(LH2019C025)the China Agricultural Research System(CARS-36).
文摘Information about dairy cow ruminating is closely associated with the health status of dairy cows.Therefore,it is of great significance to recognize and make statistics of dairy cows’ruminating and feeding behavior.Concerning conventional recognition methods which are dependent on contact type devices,they have some defects of poor instantaneity and strong stress responses.As for recognition based on machine vision,it needs to transmit masses of data and raises high requirements for the cloud server and network performance.According to principles of edge computing,the model is deployed via Tensorflow.js in an edge device in the present study,constructing a recognition and statistical system for ruminating and feeding behavior of dairy cows.Through the application programming interface(API)of the browser,an edge device is able to invoke a camera and acquire dairy cow images.Then,the images can be inputted in the SSD MobileNet V2 model,which is followed by inference based on browser hashrate.Moreover,the edge device merely uploads recognition results to the cloud server for statistics,which features high instantaneity and compatibility.In terms of recognizing ruminating and feeding behavior of dairy cows,the proposed system has a precision ratio of 96.50%,a recall rate of 91.77%,an F1-score of 94.08%,specificity of 91.36%,and accuracy of 91.66%.This suggests that the proposed method is effective in recognizing dairy cow behavior.
文摘已有基于深度强化学习(Deep Reinforcement Learning,DRL)的云数据中心任务调度算法存在有效经验利用率低造成训练成本高、状态空间维数不固定和维度较高导致学习震荡,以及策略更新步长固定造成的收敛速度慢等问题。为解决以上问题,基于云数据中心场景构建并行任务调度框架,并以时延、能耗和负载均衡为目标研究云任务调度问题。在DRL算法A2C(Advantage Actor Critic)的基础上,提出了一种基于自适应状态优选和动态步长的云数据中心任务调度算法(Adaptive state Optimization and Dynamic Step size A2C,AODS-A2C)。首先,使用准入控制和优先级策略对队列任务进行筛选和排序,提高有效经验的利用率;其次,将动态高维状态以自适应的方式进行快速优选处理,保持相对稳定的状态空间,避免训练过程中出现震荡问题;最后,使用JS(Jensen Shannon)散度度量新旧策略的概率分布差异,并根据这种差异动态地匹配调整Actor网络和Critic网络的学习步长,从而将当前学习状态迅速调整为最佳值,提高算法的收敛速度。仿真实验结果表明,所提出的AODS-A2C算法具有收敛速度快、鲁棒性高等特点,相较于其他对比算法在时延方面降低了1.2%到34.4%,在能耗方面降低了1.6%到57.2%,并可以实现良好的负载均衡。
