[1] 李军会,朱金福,陈欣.基于航班延误分布的机位鲁棒指派模型[J].交通运输工程学报,2014,14(6):74-82.(LI J H, ZHU J F, CHEN X. Robust assignment model of airport gate based on flight delay distribution[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6):74-82.) [2] 邢志伟,唐云霄,罗谦.基于贝叶斯网络的航班保障服务时间动态估计[J].计算机应用,2017,37(1):299-304.(XING Z W, TANG Y X, LUO Q. Dynamic estimation about service time of flight support based on Bayesian network[J]. Journal of Computer Applications, 2017, 37(1):299-304.) [3] 丁建立,王曼,曹卫东,等.面向机场时段差异的航班延误免疫预测算法[J].计算机工程与设计,2015,36(4):1037-1041.(DING J L, WANG M, GAO W D, et al. Immune prediction algorithm of flight delay for hub airport in different periods[J]. Computer Engineering and Design, 2015, 36(4):1037-1041.) [4] BASPINAR B, URE N K, KOYUNCU E, et al. Analysis of delay characteristics of European air traffic through a data-driven airport-centric queuing network model[J]. International Federation of Automatic Control, 2016, 49(3):359-364. [5] 邵荃,朱燕,贾萌,等.基于复杂网络理论的航班延误波及分析[J].航空计算技术,2015,45(4):24-28.(SHAO Q, ZHU Y, JIA M, et al. Analysis of flight delay propagation based on complex network theory[J]. Aeronautical Computing Technique, 2015, 45(4):24-28.) [6] 马正平,崔德光.机场航班延误优化模型[J].清华大学学报(自然科学版),2004,44(4):474-477.(MA Z P, CUI D G. Optimizing airport flight delays[J]. Journal of Tsinghua University (Science & Technology), 2004, 44(4):474-477.) [7] 罗赟骞,陈志杰,汤锦辉,等.采用支持向量机回归的航班延误预测研究[J].交通运输系统工程与信息,2014,15(1):143-149.(LUO Y Q, CHEN Z J, TANG J H, et al. Flight delay prediction using support vector machine regression[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 15(1):143-149.) [8] 程华,李艳梅,罗谦,等.基于C4.5决策树方法的到港航班延误预测问题研究[J].系统工程理论与实践,2014,34(s1):239-247.(CHENG H, LI Y M, LUO Q, et al. Study on flight delay with C4.5 decision tree based prediction method[J]. System Engineering-Theory & Practice, 2014, 34(s1):239-247.) [9] 曹卫东,贺国光.连续航班延误与波及的贝叶斯网络分析[J].计算机应用,2009,29(2):606-610.(CAO W D, HE G G. Bayesian networks analysis for sequence flight delay and propagation[J]. Journal of Computer Applications, 2009, 29(2):606-610.) [10] KHANMOHAMMADI S, TUTUN S, KUCUK Y. A new multilevel input layer artificial neural network for predicting flight delays at JFK airport[J]. Procedia Computer Science, 2016, 95:237-244. [11] REBOLLO J J, BALAKRISHNAN H. Characterization and prediction of air traffic delays[J]. Transportation Research Part C:Emerging Technologies, 2014, 44:231-241. [12] LAWSON D, CASTILLO W. Predicting flight delays[EB/OL].[2018-01-05]. http://cs229.stanford.edu/proj2016/report/MenonMovva-PredictingFlightDelays-report.pdf. [13] KIM Y J, CHOI S, BRICENO S, et al. A deep learning approach to flight delay prediction[C]//DASC 2016:Proceedings of the 2016 IEEE/AIAA Digital Avionics Systems Conference. Piscataway, NJ:IEEE, 2016:1-6. [14] YOO D, PARK S, LEE J Y, et al. AttentionNet:aggregating weak directions for accurate object detection[C]//ICCV 2015:Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:2659-2667. [15] 董峻妃,郑伯川,杨泽静.基于卷积神经网络的车牌字符识别[J].计算机应用,2017,37(7):2014-2018.(DONG J F, ZHENG B C, YANG Z J. Character recognition of license plate based on convolution neural network[J]. Journal of Computer Applications, 2017, 37(7):2014-2018.) [16] GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks[C]//CVPR 2016:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:2414-2423. [17] SRIVASTAVA R K, GREFF K, SCHMIDHUBER J. Training very deep networks[EB/OL]. (2015-12-23)[2017-10-20]. https://arxiv.org/pdf/1507.06228.pdf. [18] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//CVPR 2016:Proceedings of the 2016 IEEE International Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:770-778. [19] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C]//AISTATS 2011:Proceedings of the 4nd International Conference on Artificial Intelligence and Statistics. New York:JMLR, 2011:315-323. [20] ZAHARIA M, CHOWDHURY M, DAS T, et al. Resilient distributed datasets:a fault-tolerant abstraction for in-memory cluster computing[C]//USENIX 2012:Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. Berkeley, CA:USENIX Association, 2012:2-2. [21] 民航局运行监控中心.2016年全国民航航班运行效率报告[R].北京:空管行业管理办公室,2016:26-53.(Civil Aviation Authority Operation Monitoring Center. National civil aviation flight operation efficiency report 2016[R]. Beijing:Air Traffic Control Industry Management Office, 2016:26-53.) [22] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//ICML 2015:Proceedings of the 32nd International Conference on Machine Learning. New York:ICML, 2015:448-456. [23] DUMOULIN V, VISIN F. A guide to convolution arithmetic for deep learning[EB/OL].[2018-01-11]. https://arxiv.org/pdf/1603.07285.pdf. [24] JIA Y Q, SHELHAMER E, DONAHUE J, et al. Caffe:convolutional architecture for fast feature embedding[C]//ACM 2014:Proceedings of the 2014 ACM International Conference on Multimedia. New York:ACM, 2014:675-678. [25] HE K, ZHANG X, REN S, et al. Delving deep into rectifiers:surpassing human-level performance on ImageNet classification[C]//CVPR 2015:Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:1026-1034. |