A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a tr...A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.展开更多
基金supported by the National Key R&D Program of China under Grant 2021YFB1407001the National Natural Science Foundation of China (NSFC) under Grants 62001269 and 61960206006+2 种基金the State Key Laboratory of Rail Traffic Control and Safety (under Grants RCS2022K009)Beijing Jiaotong University, the Future Plan Program for Young Scholars of Shandong Universitythe EU H2020 RISE TESTBED2 project under Grant 872172
文摘A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.
文摘目的:探讨亚临床甲状腺功能减退(SCH)对胰岛素抵抗和胰岛B细胞功能的影响。方法:共有92名中学生志愿者行75 g口服葡萄糖刺激胰岛素释放试验(OGIRT),同时测定甲状腺功能。采用HOMA-IR、松田指数(MI)等指标评价胰岛素抵抗。胰岛B细胞功能的评估包括HOMA-β,血胰岛素/葡萄糖之值,以及多种由OGIRT计算出的参数。结果:SCH组较甲状腺功能正常(ET)组OGIRT后120 min血糖显著升高[(7.39±1.07)mmol·L-1 vs(6.53±1.24)mmol·L-1,P=0.021],糖耐量异常的发生率显著升高(53.85 vs 18.99,P=0.017)。SCH组的MI显著降低(33.33±10.32 vs 59.59±27.18,P=0.001),HOMA-IR显著升高[7.61(5.62~9.66)vs 4.04(2.96~5.87),P=0.002]。SCH组较ET组HOMA-β显著升高[318.52(285.87~387.69)vs 217.69(143.79~302.01),P=0.001],两组间糖负荷后胰岛素曲线下面积较空腹的增量(ΔAUCins),以及其与血糖曲线下面积增量的比值(ΔAUCins/ΔAUCglu)差异无统计学意义,反映胰岛素早相分泌的ΔAUCins30/ΔAUCglu30差异也无统计学意义。结论:SCH较ET者血糖升高,糖耐量异常发生率升高,存在着更为严重的胰岛素抵抗,空腹胰岛素分泌增加,而糖负荷后的胰岛素分泌没有差异。
