Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displ...Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.展开更多
目的探讨卒中患者上肢运动过程中躯干肌用力疲劳情况。方法前瞻性连续纳入2019年4-9月于中山大学附属第三医院康复医学科住院的卒中患者作为卒中组,并招募年龄、性别匹配的健康志愿者作为对照组。使用木插板分别让卒中患者的健侧和患侧...目的探讨卒中患者上肢运动过程中躯干肌用力疲劳情况。方法前瞻性连续纳入2019年4-9月于中山大学附属第三医院康复医学科住院的卒中患者作为卒中组,并招募年龄、性别匹配的健康志愿者作为对照组。使用木插板分别让卒中患者的健侧和患侧上肢、健康志愿者利手侧上肢进行前后向置物-返回运动各15次,分3组完成,每组5次,组间间隔30 s。受试者在运动前后根据改良版Borg自觉用力程度量表对其自身疲劳程度进行评价,对比受试者运动前后的量表评分结果。利用表面肌电技术采集进行前后向置物-返回运动时躯干肌(运动侧斜方肌、双侧腹直肌、双侧腹外斜肌、双侧胸段及腰段竖脊肌)的肌电频域指标[平均功率频率(mean power frequency,MPF)]平均值,分别比较卒中患者健侧、患侧运动时与健康志愿者利手侧肌肉MPF的差异。结果卒中组患侧和健侧上肢运动后改良版Borg自觉用力程度量表评分均较运动前升高(均P<0.001),而对照组运动前后差异无统计学意义。使用表面肌电图技术分析发现,在前后向置物-返回运动过程中,相比对照组,卒中组患者不论用患侧还是健侧上肢运动,其健侧腹直肌、患侧腹直肌、健侧腹外斜肌、患侧胸段竖脊肌的MPF均较低,以上差异均具有统计学意义;其余肌肉两组差异无统计学意义。结论卒中患者双上肢活动时躯干肌群容易出现疲劳。在进行上肢前后向置物-返回动作过程中,双侧腹直肌、健侧腹外斜肌和患侧胸段竖脊肌的疲劳程度更加明显。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.51674169)Department of Education of Hebei Province of China(Grant No.ZD2019140)+1 种基金Natural Science Foundation of Hebei Province of China(Grant No.F2019210243)S&T Program of Hebei(Grant No.22375413D)School of Electrical and Electronics Engineering。
文摘Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.
文摘目的探讨卒中患者上肢运动过程中躯干肌用力疲劳情况。方法前瞻性连续纳入2019年4-9月于中山大学附属第三医院康复医学科住院的卒中患者作为卒中组,并招募年龄、性别匹配的健康志愿者作为对照组。使用木插板分别让卒中患者的健侧和患侧上肢、健康志愿者利手侧上肢进行前后向置物-返回运动各15次,分3组完成,每组5次,组间间隔30 s。受试者在运动前后根据改良版Borg自觉用力程度量表对其自身疲劳程度进行评价,对比受试者运动前后的量表评分结果。利用表面肌电技术采集进行前后向置物-返回运动时躯干肌(运动侧斜方肌、双侧腹直肌、双侧腹外斜肌、双侧胸段及腰段竖脊肌)的肌电频域指标[平均功率频率(mean power frequency,MPF)]平均值,分别比较卒中患者健侧、患侧运动时与健康志愿者利手侧肌肉MPF的差异。结果卒中组患侧和健侧上肢运动后改良版Borg自觉用力程度量表评分均较运动前升高(均P<0.001),而对照组运动前后差异无统计学意义。使用表面肌电图技术分析发现,在前后向置物-返回运动过程中,相比对照组,卒中组患者不论用患侧还是健侧上肢运动,其健侧腹直肌、患侧腹直肌、健侧腹外斜肌、患侧胸段竖脊肌的MPF均较低,以上差异均具有统计学意义;其余肌肉两组差异无统计学意义。结论卒中患者双上肢活动时躯干肌群容易出现疲劳。在进行上肢前后向置物-返回动作过程中,双侧腹直肌、健侧腹外斜肌和患侧胸段竖脊肌的疲劳程度更加明显。
