摘要
为了合理区分和有效识别不同航班号,首先提出基于多标准的判断准则,使用主成分分析法量化得到统一的相似度;其次建立改进的Siamese网络模型,获得文本的语义信息;最后采用文本之间的Jaro-Winkler距离客观修正网络对比损失函数,综合网络输出判定2个航班号的相似情况。研究结果表明:多标准准则判定方法速度快且通用性强,改进后的Siamese网络虽受到训练样本的直接影响,但收敛速度明显提高,识别率比原网络平均提高约2.7%,比多标准判断准则提高约3%。研究结果可为相似航班号识别与预警提供理论依据。
In order to reasonably distinguish and effectively identify different call signs,firstly,the judgment criteria based on multi standards were proposed,and the unified similarity degree was quantified using the principal component analysis method.Secondly,an improved Siamese network model was established to obtain the semantic information of texts,and the Jaro-Winkler distance between texts was used to objectively modify the network contrast loss function,and the integrated network output was used to determine the similarity of two call signs.The results showed that the judgment method of multi standards criteria was fast and general,while the improved Siamese network,although directly influenced by the training samples,had a significantly higher convergence speed,and the recognition rate was on average about 2.7%higher than the original network and about 3%higher than the multi standards judgment criteria.The research results can provide theoretical basis for the identification and early warning of similar call signs.
作者
孙禾
陈一新
SUN He;CHEN Yixin(College of Air Traffic Management,Civil Aviation University of China,Tianjin 300300,China;CAAC Xiamen Air Traffic Control Station,Xiamen Fujian 361006,China)
出处
《中国安全生产科学技术》
CAS
CSCD
北大核心
2023年第1期47-53,共7页
Journal of Safety Science and Technology
基金
国家重点研发计划项目(2021YFF0603902)
中央高校基本科研业务费项目(3122019133)。
关键词
航空运输
空中交通管理
主成分分析法
Siamese网络
航班号
aviation transportation
air traffic management
principal component analysis
Siamese network
call sign
similarity
