Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithm...Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithms for average reward problems, a novel incremental algorithm, called R( λ ) learning, was proposed. Results and Conclusion The proposed algorithm is a natural extension of the Q( λ) learning, the multi step discounted reward reinforcement learning algorithm, to the average reward cases. Simulation results show that the R( λ ) learning with intermediate λ values makes significant performance improvement over the simple R learning.展开更多
To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is proposed.Firstly,large boxes are used to label a large area covering t...To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is proposed.Firstly,large boxes are used to label a large area covering the range of corrosion,provided that the area is visually continuous and adjacent to corrosion that cannot be clearly divided.Secondly,in each labeling box established in the first step,regions with distinct corrosion and relative independence are labeled to form a second layer of nested boxes.Finally,a series of comparative experiments are conducted with other common annotation methods to validate the effectiveness of HAM.The experimental results show that,with the help of HAM,the recall of YOLOv5 increases from 50.79%to 59.41%;the recall of Faster R-CNN+VGG16 increases from 66.50%to 78.94%;the recall of Faster R-CNN+Res101 increases from 78.32%to 84.61%.Therefore,HAM can effectively improve the detection ability of mainstream models in detecting metal corrosion.展开更多
This study based on the conclusion demonstrated in Asher＇s studies that display oral practice with actions brings considerable effectiveness. TPR would be an appropriate and effective teaching method that will promot...This study based on the conclusion demonstrated in Asher＇s studies that display oral practice with actions brings considerable effectiveness. TPR would be an appropriate and effective teaching method that will promote acquisition of comprehensible input in a natural way; it is a good way to learn a second language, not just for children, but also for adults as well. At the same time, it＇s a great helper to the teachers, who can use it in their classes to make the studying environment active and dynamic. Thus it can help teachers solve many problems in English class, help young children learning English, make them found English learning very interesting. They love English class. It＇s a good beginning to learn English in their future.展开更多
文摘Aim To investigate the model free multi step average reward reinforcement learning algorithm. Methods By combining the R learning algorithms with the temporal difference learning (TD( λ ) learning) algorithms for average reward problems, a novel incremental algorithm, called R( λ ) learning, was proposed. Results and Conclusion The proposed algorithm is a natural extension of the Q( λ) learning, the multi step discounted reward reinforcement learning algorithm, to the average reward cases. Simulation results show that the R( λ ) learning with intermediate λ values makes significant performance improvement over the simple R learning.
基金The National Key R&D Program of China(No.2018YFC0830200)the Open Research Fund from State Key Laboratory of Smart Grid Protection and Control(No.NARI-T-2-2019189)+1 种基金Rapid Support Project(No.61406190120)the Fundamental Research Funds for the Central Universities(No.2242021k10011).
文摘To solve the ambiguity and uncertainty in the labeling process of power equipment corrosion datasets,a novel hierarchical annotation method(HAM)is proposed.Firstly,large boxes are used to label a large area covering the range of corrosion,provided that the area is visually continuous and adjacent to corrosion that cannot be clearly divided.Secondly,in each labeling box established in the first step,regions with distinct corrosion and relative independence are labeled to form a second layer of nested boxes.Finally,a series of comparative experiments are conducted with other common annotation methods to validate the effectiveness of HAM.The experimental results show that,with the help of HAM,the recall of YOLOv5 increases from 50.79%to 59.41%;the recall of Faster R-CNN+VGG16 increases from 66.50%to 78.94%;the recall of Faster R-CNN+Res101 increases from 78.32%to 84.61%.Therefore,HAM can effectively improve the detection ability of mainstream models in detecting metal corrosion.
文摘This study based on the conclusion demonstrated in Asher＇s studies that display oral practice with actions brings considerable effectiveness. TPR would be an appropriate and effective teaching method that will promote acquisition of comprehensible input in a natural way; it is a good way to learn a second language, not just for children, but also for adults as well. At the same time, it＇s a great helper to the teachers, who can use it in their classes to make the studying environment active and dynamic. Thus it can help teachers solve many problems in English class, help young children learning English, make them found English learning very interesting. They love English class. It＇s a good beginning to learn English in their future.
