目的探究成果导向教育(Outcome Based Education,OBE)理论下TPA阶梯教学对神经科医师培训效果及教学满意度的影响。方法随机选取2020年5月—2023年5月潍坊医学院附属医院接受规范化培训的52名神经科医师为研究对象,于2020年5月—2021年1...目的探究成果导向教育(Outcome Based Education,OBE)理论下TPA阶梯教学对神经科医师培训效果及教学满意度的影响。方法随机选取2020年5月—2023年5月潍坊医学院附属医院接受规范化培训的52名神经科医师为研究对象,于2020年5月—2021年12月培训中采用传统教学法的26名医师为A组、于2022年1月—2023年5月培训中采用OBE理论下TPA阶梯教学的26名医师为B组,对比两组培训效果。结果教学后,B组理论考核成绩、实践能力考核成绩均高于A组,差异有统计学意义(P均<0.05);B组教学满意度(100.00%)高于A组(76.92%),差异有统计学意义(χ^(2)=4.710,P<0.05)。结论OBE理论下TPA阶梯教学的应用效果更理想,有助于提高神经科医师理论及实践能力考核成绩、教学满意度。展开更多
Soil nonlinear behavior displays noticeable effects on the site seismic response.This study proposes a new functional expression of the skeleton curve to replace the hyperbolic skeleton curve.By integrating shear modu...Soil nonlinear behavior displays noticeable effects on the site seismic response.This study proposes a new functional expression of the skeleton curve to replace the hyperbolic skeleton curve.By integrating shear modulus and combining the dynamic skeleton curve and the damping degradation coefficient,the constitutive equation of the logarithmic dynamic skeleton can be obtained,which considers the damping effect in a soil dynamics problem.Based on the finite difference method and the multi-transmitting boundary condition,a 1D site seismic response analysis program called Soilresp1D has been developed herein and used to analyze the time-domain seismic response in three types of sites.At the same time,this study also provides numerical simulation results based on the hyperbolic constitutive model and the equivalent linear method.The results verify the rationality of the new soil dynamic constitutive model.It can analyze the mucky soil site nonlinear seismic response,reflecting the deformation characteristics and damping effect of the silty soil.The hysteresis loop area is more extensive,and the residual strain is evident.展开更多
The reduced weight and improved efficiency of modern aeronautical structures result in a decreasing separation of frequency ranges of rigid and elastic modes.Particularly,a high-aspect-ratio flexible flying wing is pr...The reduced weight and improved efficiency of modern aeronautical structures result in a decreasing separation of frequency ranges of rigid and elastic modes.Particularly,a high-aspect-ratio flexible flying wing is prone to body freedomflutter(BFF),which is a result of coupling of the rigid body short-periodmodewith 1st wing bendingmode.Accurate prediction of the BFF characteristics is helpful to reflect the attitude changes of the vehicle intuitively and design the active flutter suppression control law.Instead of using the rigid body mode,this work simulates the rigid bodymotion of the model by using the six-degree-of-freedom(6DOF)equation.A dynamicmesh generation strategy particularly suitable for BFF simulation of free flying aircraft is developed.An accurate Computational Fluid Dynamics/Computational Structural Dynamics/six-degree-of-freedom equation(CFD/CSD/6DOF)-based BFF prediction method is proposed.Firstly,the time-domain CFD/CSD method is used to calculate the static equilibrium state of the model.Based on this state,the CFD/CSD/6DOF equation is solved in time domain to evaluate the structural response of themodel.Then combinedwith the variable stiffnessmethod,the critical flutter point of the model is obtained.This method is applied to the BFF calculation of a flyingwing model.The calculation results of the BFF characteristics of the model agree well with those fromthe modalmethod andNastran software.Finally,the method is used to analyze the influence factors of BFF.The analysis results show that the flutter speed can be improved by either releasing plunge constraint or moving the center ofmass forward or increasing the pitch inertia.展开更多
This paper proposedmethod that combined transmission path analysis(TPA)and empirical mode decomposition(EMD)envelope analysis to solve the vibration problemof an industrial robot.Firstly,the deconvolution filter timed...This paper proposedmethod that combined transmission path analysis(TPA)and empirical mode decomposition(EMD)envelope analysis to solve the vibration problemof an industrial robot.Firstly,the deconvolution filter timedomain TPA method is proposed to trace the source along with the time variation.Secondly,the TPA method positioned themain source of robotic vibration under typically different working conditions.Thirdly,independent vibration testing of the Rotate Vector(RV)reducer is conducted under different loads and speeds,which are key components of an industrial robot.The method of EMD and Hilbert envelope was used to extract the fault feature of the RV reducer.Finally,the structural problems of the RV reducer were summarized.The vibration performance of industrial robots was improved through the RV reducer optimization.From the whole industrial robot to the local RV Reducer and then to the internal microstructure of the reducer,the source of defect information is traced accurately.Experimental results showed that the TPA and EMD hybrid methods were more accurate and efficient than traditional time-frequency analysis methods to solve industrial robot vibration problems.展开更多
台风条件下海上风电场风速变化大、无明显周期性,这对海上风电场的风速预测造成了极大的困难。针对此问题,提出台风条件下海上风电场风速多步预测方法。首先,针对台风预报信息与风电场风速数据时间尺度不统一的问题,提出用嵌入层网络对...台风条件下海上风电场风速变化大、无明显周期性,这对海上风电场的风速预测造成了极大的困难。针对此问题,提出台风条件下海上风电场风速多步预测方法。首先,针对台风预报信息与风电场风速数据时间尺度不统一的问题,提出用嵌入层网络对台风预报信息进行动态插值。其次,基于Holland气压场模型和Batts梯度风模型构建融合物理信息的神经网络,将Holland模型和Batts模型中的经验参数替换成网络可学习的参数,并针对网络训练过程中可能出现的数值问题引入适当的近似方法。最后,对含时序模式注意力机制的长短期记忆网络(temporal pattern attention long short-term memory,TPA-LSTM)进行改进,嵌入融合物理信息的神经网络,利用近40年台风期间的数据进行训练和测试。结果表明,在引入较少参数的情况下,物理信息神经网络能减少TPA-LSTM网络的训练迭代次数以及提高预测精度,所提模型相比序列到序列(sequence to sequence,Seq2Seq)模型和TPA-LSTM网络具有更高的预测精度。展开更多
The time-domain inverse technique based on the time-domain rotating equivalent source method has been proposed to localize and quantify rotating sound sources. However, this technique encounters two problems to be add...The time-domain inverse technique based on the time-domain rotating equivalent source method has been proposed to localize and quantify rotating sound sources. However, this technique encounters two problems to be addressed: one is the time-consuming process of solving the transcendental equation at each time step, and the other is the difculty of controlling the instability problem due to the time-varying transfer matrix. In view of that, an improved technique is proposed in this paper to resolve these two problems. In the improved technique, a de-Dopplerization method in the time-domain rotating reference frame is frst applied to eliminate the Doppler efect caused by the source rotation in the measured pressure signals, and then the restored pressure signals without the Doppler efect are used as the inputs of the time-domain stationary equivalent source method to locate and quantify sound sources. Compared with the original technique, the improved technique can avoid solving the transcendental equation at each time step, and facilitate the treatment of the instability problem because the transfer matrix does not change with time. Numerical simulation and experimental results show that the improved technique can eliminate the Doppler efect efectively, and then localize and quantify the rotating nonstationary or broadband sources accurately. The results also demonstrate that the improved technique can guarantee a more stable reconstruction and compute more efciently than the original one.展开更多
Terahertz time-domain spectroscopy(THz-TDS)system,as a new means of spectral analysis and detection,plays an increasingly pivotal role in basic scientific research.However,owing to the long scanning time of the tradit...Terahertz time-domain spectroscopy(THz-TDS)system,as a new means of spectral analysis and detection,plays an increasingly pivotal role in basic scientific research.However,owing to the long scanning time of the traditional THz-TDS system and the complex control of the asynchronous optical scanning(ASOPS)system,which requires frequent calibration,we combine traditional THz-TDS and ASOPS systems to form a composite system and propose an all-fiber trigger signal generation method based on the time overlapping interference signal generated by the collinear motion of two laser pulses.Finally,the time-domain and frequency-domain spectra are obtained by using two independent systems in the integrated systems.It is found that the full width at half maximum(FWHM)of the time-domain spectra and the spectral width of the frequency-domain spectra are almost the same,but the sampling speed of the ASOPS system is significantly faster than that of the traditional THz-TDS system,which conduces to the study of the transient characteristics of substances.展开更多
Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural ne...Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.展开更多
文摘风速变化的间歇性和波动性给风功率的精准预测带来极大挑战,充分挖掘风电功率与风速等关键因素的内在规律是提高风电功率预测精度的有效途径。提出一种结合时间模式注意力(time pattern attention,TPA)机制的多层堆叠双向长短期记忆网络的超短期风电功率预测方法。首先,利用基于密度的含噪声空间聚类方法(den⁃sity based spatial clustering with noise,DBSCAN)和线性回归算法进行风功率数据集的异常值检测,利用k最邻近(k⁃nearest neighbor,KNN)插值法重构异常点数据;其次,综合考虑风电功率与各气象特征的内在关联性,在MBLSTM网络中引入TPA机制合理分配时间步长权重,捕捉风电功率时间序列潜在逻辑规律;最后,利用实验仿真数据进行分析验证本文方法的有效性,该方法能够充分挖掘风功率与风速影响因素的关系,从而提高其预测精度。
文摘目的探究成果导向教育(Outcome Based Education,OBE)理论下TPA阶梯教学对神经科医师培训效果及教学满意度的影响。方法随机选取2020年5月—2023年5月潍坊医学院附属医院接受规范化培训的52名神经科医师为研究对象,于2020年5月—2021年12月培训中采用传统教学法的26名医师为A组、于2022年1月—2023年5月培训中采用OBE理论下TPA阶梯教学的26名医师为B组,对比两组培训效果。结果教学后,B组理论考核成绩、实践能力考核成绩均高于A组,差异有统计学意义(P均<0.05);B组教学满意度(100.00%)高于A组(76.92%),差异有统计学意义(χ^(2)=4.710,P<0.05)。结论OBE理论下TPA阶梯教学的应用效果更理想,有助于提高神经科医师理论及实践能力考核成绩、教学满意度。
基金Major Program of the National Natural Science Foundation of China under Grant No.52192675 and the 111 Project of China under Grant No.D21001。
文摘Soil nonlinear behavior displays noticeable effects on the site seismic response.This study proposes a new functional expression of the skeleton curve to replace the hyperbolic skeleton curve.By integrating shear modulus and combining the dynamic skeleton curve and the damping degradation coefficient,the constitutive equation of the logarithmic dynamic skeleton can be obtained,which considers the damping effect in a soil dynamics problem.Based on the finite difference method and the multi-transmitting boundary condition,a 1D site seismic response analysis program called Soilresp1D has been developed herein and used to analyze the time-domain seismic response in three types of sites.At the same time,this study also provides numerical simulation results based on the hyperbolic constitutive model and the equivalent linear method.The results verify the rationality of the new soil dynamic constitutive model.It can analyze the mucky soil site nonlinear seismic response,reflecting the deformation characteristics and damping effect of the silty soil.The hysteresis loop area is more extensive,and the residual strain is evident.
基金This work was supported by the National Natural Science Foundation of China(No.11872212)and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘The reduced weight and improved efficiency of modern aeronautical structures result in a decreasing separation of frequency ranges of rigid and elastic modes.Particularly,a high-aspect-ratio flexible flying wing is prone to body freedomflutter(BFF),which is a result of coupling of the rigid body short-periodmodewith 1st wing bendingmode.Accurate prediction of the BFF characteristics is helpful to reflect the attitude changes of the vehicle intuitively and design the active flutter suppression control law.Instead of using the rigid body mode,this work simulates the rigid bodymotion of the model by using the six-degree-of-freedom(6DOF)equation.A dynamicmesh generation strategy particularly suitable for BFF simulation of free flying aircraft is developed.An accurate Computational Fluid Dynamics/Computational Structural Dynamics/six-degree-of-freedom equation(CFD/CSD/6DOF)-based BFF prediction method is proposed.Firstly,the time-domain CFD/CSD method is used to calculate the static equilibrium state of the model.Based on this state,the CFD/CSD/6DOF equation is solved in time domain to evaluate the structural response of themodel.Then combinedwith the variable stiffnessmethod,the critical flutter point of the model is obtained.This method is applied to the BFF calculation of a flyingwing model.The calculation results of the BFF characteristics of the model agree well with those fromthe modalmethod andNastran software.Finally,the method is used to analyze the influence factors of BFF.The analysis results show that the flutter speed can be improved by either releasing plunge constraint or moving the center ofmass forward or increasing the pitch inertia.
基金supported by Natural Science Foundation of Hunan Province,(Grant No.2022JJ30147)the National Natural Science Foundation of China (Grant No.51805155)the Foundation for Innovative Research Groups of National Natural Science Foundation of China (Grant No.51621004).
文摘This paper proposedmethod that combined transmission path analysis(TPA)and empirical mode decomposition(EMD)envelope analysis to solve the vibration problemof an industrial robot.Firstly,the deconvolution filter timedomain TPA method is proposed to trace the source along with the time variation.Secondly,the TPA method positioned themain source of robotic vibration under typically different working conditions.Thirdly,independent vibration testing of the Rotate Vector(RV)reducer is conducted under different loads and speeds,which are key components of an industrial robot.The method of EMD and Hilbert envelope was used to extract the fault feature of the RV reducer.Finally,the structural problems of the RV reducer were summarized.The vibration performance of industrial robots was improved through the RV reducer optimization.From the whole industrial robot to the local RV Reducer and then to the internal microstructure of the reducer,the source of defect information is traced accurately.Experimental results showed that the TPA and EMD hybrid methods were more accurate and efficient than traditional time-frequency analysis methods to solve industrial robot vibration problems.
文摘台风条件下海上风电场风速变化大、无明显周期性,这对海上风电场的风速预测造成了极大的困难。针对此问题,提出台风条件下海上风电场风速多步预测方法。首先,针对台风预报信息与风电场风速数据时间尺度不统一的问题,提出用嵌入层网络对台风预报信息进行动态插值。其次,基于Holland气压场模型和Batts梯度风模型构建融合物理信息的神经网络,将Holland模型和Batts模型中的经验参数替换成网络可学习的参数,并针对网络训练过程中可能出现的数值问题引入适当的近似方法。最后,对含时序模式注意力机制的长短期记忆网络(temporal pattern attention long short-term memory,TPA-LSTM)进行改进,嵌入融合物理信息的神经网络,利用近40年台风期间的数据进行训练和测试。结果表明,在引入较少参数的情况下,物理信息神经网络能减少TPA-LSTM网络的训练迭代次数以及提高预测精度,所提模型相比序列到序列(sequence to sequence,Seq2Seq)模型和TPA-LSTM网络具有更高的预测精度。
基金Supported by National Natural Science Foundation of China(Grant Nos.51875147,12174082,51675149)。
文摘The time-domain inverse technique based on the time-domain rotating equivalent source method has been proposed to localize and quantify rotating sound sources. However, this technique encounters two problems to be addressed: one is the time-consuming process of solving the transcendental equation at each time step, and the other is the difculty of controlling the instability problem due to the time-varying transfer matrix. In view of that, an improved technique is proposed in this paper to resolve these two problems. In the improved technique, a de-Dopplerization method in the time-domain rotating reference frame is frst applied to eliminate the Doppler efect caused by the source rotation in the measured pressure signals, and then the restored pressure signals without the Doppler efect are used as the inputs of the time-domain stationary equivalent source method to locate and quantify sound sources. Compared with the original technique, the improved technique can avoid solving the transcendental equation at each time step, and facilitate the treatment of the instability problem because the transfer matrix does not change with time. Numerical simulation and experimental results show that the improved technique can eliminate the Doppler efect efectively, and then localize and quantify the rotating nonstationary or broadband sources accurately. The results also demonstrate that the improved technique can guarantee a more stable reconstruction and compute more efciently than the original one.
基金Project supported by the National Key Research and Development Program of China(Grant No.2021YFB3200100)the National Natural Science Foundation of China(Grant No.61575131)。
文摘Terahertz time-domain spectroscopy(THz-TDS)system,as a new means of spectral analysis and detection,plays an increasingly pivotal role in basic scientific research.However,owing to the long scanning time of the traditional THz-TDS system and the complex control of the asynchronous optical scanning(ASOPS)system,which requires frequent calibration,we combine traditional THz-TDS and ASOPS systems to form a composite system and propose an all-fiber trigger signal generation method based on the time overlapping interference signal generated by the collinear motion of two laser pulses.Finally,the time-domain and frequency-domain spectra are obtained by using two independent systems in the integrated systems.It is found that the full width at half maximum(FWHM)of the time-domain spectra and the spectral width of the frequency-domain spectra are almost the same,but the sampling speed of the ASOPS system is significantly faster than that of the traditional THz-TDS system,which conduces to the study of the transient characteristics of substances.
基金supported by the Major Project of Basic and Applied Research in Guangdong Universities (2017WZDXM012)。
文摘Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting,we propose a bidirectional long short-term memory(BiLSTM)neural network model based on the temporal pattern attention(TPA)mechanism.Firstly,based on the grey relational analysis,datasets similar to forecast day are obtained.Secondly,thebidirectional LSTM layermodels the data of thehistorical load,temperature,humidity,and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network,so that the influencing factors(with different characteristics)can select relevant information from different time steps to reduce the prediction error of the model.Simultaneously,the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism,so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input.Finally,the chaotic sparrow search algorithm(CSSA)is used to optimize the hyperparameter selection of the model.The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238,respectively,which is lower than other forecastingmethods,demonstrating the accuracy and stability of our model.