Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air tr...Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity labels.However,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity evaluation.This paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label noise.The confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio datasets.Experiments are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.展开更多
The author tries to point out the right class teaching patterns and the important roles of teachers in class compared with the traditional teaching method---teacher-centered way. Examples are also employed to show the...The author tries to point out the right class teaching patterns and the important roles of teachers in class compared with the traditional teaching method---teacher-centered way. Examples are also employed to show the correct ways for students to learn more and better, to have more confidence and ease in English acquisition.展开更多
Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation.However,existing methods often fall into...Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation.However,existing methods often fall into what we call interactive misunderstanding,the essence of which is the dilemma in trading off short-and long-term interaction information.To better use the interaction information at various timescales,we propose an interactive segmentation framework,called interactive MEdical image segmentation with self-adaptive Confidence CAlibration(MECCA),which combines action-based confidence learning and multi-agent reinforcement learning.A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information.A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation,thus directly correcting the model’s interactive misunderstanding.MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance,respectively.Numerical experiments on different segmentation tasks show that MECCA can significantly improve short-and long-term interaction information utilization efficiency with remarkably fewer labeled samples.The demo video is available at https://bit.ly/mecca-demo-video.展开更多
基金This work was supported by the Na⁃tional Natural Science Foundation of China(No.61903187)Nanjing University of Aeronautics and Astronautics Graduate Innovation Base(Laboratory)Open Fund(No.kfjj20190732)。
文摘Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity labels.However,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity evaluation.This paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label noise.The confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio datasets.Experiments are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.
文摘The author tries to point out the right class teaching patterns and the important roles of teachers in class compared with the traditional teaching method---teacher-centered way. Examples are also employed to show the correct ways for students to learn more and better, to have more confidence and ease in English acquisition.
基金Project supported by the Science and Technology Commission of Shanghai Municipality,China(No.22511106004)the Postdoctoral Science Foundation of China(No.2022M723039)+1 种基金the National Natural Science Foundation of China(No.12071145)the Shanghai Trusted Industry Internet Software Collaborative Innovation Center,China。
文摘Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation.However,existing methods often fall into what we call interactive misunderstanding,the essence of which is the dilemma in trading off short-and long-term interaction information.To better use the interaction information at various timescales,we propose an interactive segmentation framework,called interactive MEdical image segmentation with self-adaptive Confidence CAlibration(MECCA),which combines action-based confidence learning and multi-agent reinforcement learning.A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information.A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation,thus directly correcting the model’s interactive misunderstanding.MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance,respectively.Numerical experiments on different segmentation tasks show that MECCA can significantly improve short-and long-term interaction information utilization efficiency with remarkably fewer labeled samples.The demo video is available at https://bit.ly/mecca-demo-video.
