[1] 刘爱华, 董竞成.运动心肺功能试验的应用进展[J].医学综述, 2013, 19(22):4125-4128. LIU A H, DONG J C.The application progress of cardiopulmonary exercise testing[J].Medical Recapitulate, 2013, 19(22):4125-4128.(in Chinese) [2] GUAZZI M, BANDERA F, OZEMEK C, et al.Cardiopulmonary exercise testing:what is its value?[J].Journal of the American College of Cardiology, 2017, 70(13):1618-1636. [3] 孙兴国.生命整体调控新理论体系与心肺运动试验[J].医学与哲学, 2013, 34(3):22-27. SUN X G.New theoretical system of holistic control and regulation for life and cardiopulmonary exercise testing[J].Medicine & Philosophy, 2013, 34(3):22-27.(in Chinese) [4] 陈海燕, 刘晨晖, 孙博.时间序列数据挖掘的相似性度量综述[J].控制与决策, 2017, 32(1):1-11. CHEN H Y, LIU C H, SUN B.Survey on similarity measurement of time series data mining[J].Control and Decision, 2017, 32(1):1-11.(in Chinese) [5] LI H L.Time works well:dynamic time warping based on time weighting for time series data mining[J].Information Sciences, 2021, 547:592-608. [6] 李海林, 梁叶.基于关键形态特征的多元时间序列降维方法[J].控制与决策, 2020, 35(3):629-636. LI H L, LIANG Y.Dimension reduction for multivariate time series based on crucial shape features[J].Control and Decision, 2020, 35(3):629-636.(in Chinese) [7] LI Z X, ZHANG F M, NIE F P, et al.Speed up dynamic time warping of multivariate time series[J].Journal of Intelligent & Fuzzy Systems, 2019, 36(3):2593-2603. [8] 陈海兰, 高学东.基于波动特征的时间序列相似性度量及聚类分析[J].统计与决策, 2019, 35(11):17-22. CHEN H L, GAO X D.Similarity measurement and cluster analysis of time series based on fluctuation features[J].Statistics & Decision, 2019, 35(11):17-22.(in Chinese) [9] 陶洋, 邓行, 杨飞跃, 等.基于DTW距离度量的层次聚类算法[J].计算机工程与设计, 2019, 40(1):116-121. TAO Y, DENG H, YANG F Y, et al.Hierarchical clustering algorithm based on DTW distance measurement[J].Computer Engineering and Design, 2019, 40(1):116-121.(in Chinese) [10] D'URSO P, GIOVANNI L, MASSARI R.Trimmed fuzzy clustering of financial time series based on dynamic time warping[J].Annals of Operations Research, 2021, 299(1/2):1379-1395. [11] DE LUCA G, ZUCCOLOTTO P.Dynamic tail dependence clustering of financial time series[J].Statistical Papers, 2017, 58(3):641-657. [12] 黎新华, 李俊辉, 黎景壮.基于改进DTWAGNES的网约车需求量时间序列聚类研究[J].重庆交通大学学报(自然科学版), 2019, 38(8):13-19. LI X H, LI J H, LI J Z.Clustering research on time series of online car-hailing demand based on the improved DTWAGNES[J].Journal of Chongqing Jiaotong University (Natural Science), 2019, 38(8):13-19.(in Chinese) [13] WANG J C, WU J Y, NI J H, et al.Relationship between urban road traffic characteristics and road grade based on a time series clustering model:a case study in Nanjing, China[J].Chinese Geographical Science, 2018, 28(6):1048-1060. [14] CHEN R J, ZHANG J Y, RAVISHANKER N, et al.Clustering activity-travel behavior time series using topological data analysis[J].Journal of Big Data Analytics in Transportation, 2019, 1(2/3):109-121. [15] GUO H Y, LIU X D.Dynamic programming-based optimization for segmentation and clustering of hydrometeorological time series[J].Stochastic Environmental Research and Risk Assessment, 2016, 30(7):1875-1887. [16] GÜLER DINCER N, AKKUŞ Ö.A new fuzzy time series model based on robust clustering for forecasting of air pollution[J].Ecological Informatics, 2018, 43:157-164. [17] 杨清浩, 胡雄玉, 陈子全.基于时间序列聚类和LSSVM的隧道拱顶位移预测[J].公路工程, 2019, 44(1):9-15, 31. YANG Q H, HU X Y, CHEN Z Q.Predicting tunnel's vault displacements based on time-series clustering and LSSVM[J].Highway Engineering, 2019, 44(1):9-15, 31.(in Chinese) [18] 饶卫振, 徐丰, 朱庆华, 等.依托平台协作配送成本分摊的有效方法研究[J].管理科学学报, 2021, 24(9):105-126. RAO W Z, XU F, ZHU Q H, et al.Fair and effective cost-sharing method for collaborative distribution based on a third-party platform[J].Journal of Management Sciences in China, 2021, 24(9):105-126.(in Chinese) [19] 李海林, 梁叶.标签传播时间序列聚类的股指期货套期保值策略研究[J].智能系统学报, 2019, 14(2):288-295. LI H L, LIANG Y.Research on the stock index futures hedging strategy using label propagation time series clustering[J].CAAI Transactions on Intelligent Systems, 2019, 14(2):288-295.(in Chinese) [20] HULSEGGE B, DE GREEF K H.A time-series approach for clustering farms based on slaughterhouse health aberration data[J].Preventive Veterinary Medicine, 2018, 153:64-70. [21] MOTLAGH O, BERRY A, O'NEIL L.Clustering of residential electricity customers using load time series[J].Applied Energy, 2019, 237:11-24. [22] 钟丽.我国优秀中跑运动员板块训练特征研究[J].湖北体育科技, 2016, 35(10):887-890, 872. ZHONG L.Research on training characteristics of elite middle distance runner in China[J].Hubei Sports Science, 2016, 35(10):887-890, 872.(in Chinese) [23] MEZZANI A.Cardiopulmonary exercise testing:basics of methodology and measurements[J].Annals of the American Thoracic Society, 2017, 14(1):3-11. [24] SANDFORD G N, KILDING A E, ROSS A, et al.Maximal sprint speed and the anaerobic speed reserve domain:the untapped tools that differentiate the world's best male 800 m runners[J].Sports Medicine, 2019, 49(6):843-852. [25] SAKAMOTO A, CHOW C, NAITO H.End-tidal partial pressure of CO2 and minute ventilation:new measures to distinguish elite long-distance runners[J].Journal of Science and Medicine in Sport, 2019, 22:S31. [26] 许晋.中长跑训练方法的研究述评[J].体育世界(学术版), 2018(7):28, 39. XU J.Review on the research of middle and long distance running training methods[J].Sports world (Academic Edition), 2018(7):28, 39.(in Chinese) |