为探讨芸豆蛋白提取的最佳工艺条件,在单因素试验的基础上,根据Box-Behnken的中心组合试验设计原理,设计4因素3水平试验,研究了p H、提取时间、液料比和提取温度对芸豆蛋白提取率的影响。结果表明,在芸豆蛋白提取工艺参数为p H 10.4、...为探讨芸豆蛋白提取的最佳工艺条件,在单因素试验的基础上,根据Box-Behnken的中心组合试验设计原理,设计4因素3水平试验,研究了p H、提取时间、液料比和提取温度对芸豆蛋白提取率的影响。结果表明,在芸豆蛋白提取工艺参数为p H 10.4、提取时间1.5 h、液料比20︰1(m L/g)、提取温度40℃时,测得芸豆蛋白提取率为68.55%。展开更多
The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decisi...The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.展开更多
基金supported by the National Natural Science Foundation of China(No.61501229)the Fundamental Research Funds for the Central Universities(Nos.2019054,2020045)。
文摘The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.
