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
Carbon brushes with a resin binder were prepared according to an industrial process and the effects of the molding pressure, grains size and cure temperature on the properties of brush samples were discussed. The resu...Carbon brushes with a resin binder were prepared according to an industrial process and the effects of the molding pressure, grains size and cure temperature on the properties of brush samples were discussed. The results show that the bulk density, bending strength and Rockwell hardness increase, while resistivity decreases with increasing molding pressure. Cure temperature has much more influence on the properties of brushes than molding pressure and grains size. Isothermal differential scanning calorimetry(DSC) was used to estimate the degree of cure of resin binder and a novel method of using the true density to measure the degree of cure of resin binder was presented and discussed briefly. Based on optimal process parameters carbon brushes were manufactured, durability tests for brushes were carried out on an alternate current motor and scanning electron microscope(SEM) was adopted to observe the morphology of worn surface of brushes. The results show that a luster oxide film can be formed on the surface of brushes and their service life reaches 380 h.展开更多
Thermosetting acrylic coatings were prepared by using carboxyl acid group-containing acrylic oligomer and curing with titanium-oxo-clusters which were first pre-hydrolyzed from titanium n-butoxide.The curing ability o...Thermosetting acrylic coatings were prepared by using carboxyl acid group-containing acrylic oligomer and curing with titanium-oxo-clusters which were first pre-hydrolyzed from titanium n-butoxide.The curing ability of the titanium-oxo-cluster was examined using a microdielectric analytical(DEA)curing monitor,Fourier transformed infrared spectroscopy(FTIR),and Soxhlet extraction experiments,and the properties of the resulted coatings were investigated with pendulum hardness tester,dynamic mechanical analysis(DMA),thermogravimetric analysis(TGA)and ultraviolet-visible spectrometer.The effect of titania-oxo-cluster in leading acrylic oligomers to form thermosetting acrylic coatings was confirmed.An increasing pendulum hardness and modulus of acrylic coatings with increasing titania content was observed, which resulted from the increment of crosslinking degree rather than of the titania content.The thermosetting acrylic/titania coatings also showed better thermal stability and higher UV-blocking properties than those coatings using organic curing agent.展开更多
Thermosetting materials are widely used as encapsulation in the electrical packaging to protect the core electronic components from external force, moisture, dust, and other factors. However, the spreading and curing ...Thermosetting materials are widely used as encapsulation in the electrical packaging to protect the core electronic components from external force, moisture, dust, and other factors. However, the spreading and curing behaviors of such kind of fluid on a heated surface have been rarely explored. In this study, we experimentally and numerically investigated the spreading and curing behaviors of the silicone(OE6550 A/B, which is widely used in the light-emitting diode packaging) droplet with diameter of ~2.2 mm on a heated surface with temperature ranging from 25 ℃ to 250 ℃. For the experiments, we established a setup with high-speed camera and heating unit to capture the fast spreading process of the silicone droplet on the heated surface. For the numerical simulation, we built a viscosity model of the silicone by using the Kiuna’s model and combined the viscosity model with the Volume of Fluid(VOF) model by the User Defined Function(UDF) method. The results show that the surface temperature significantly affected the spreading behaviors of the silicone droplet since it determines the temperature and viscosity distribution inside the droplet. For surface temperature varied from 25 ℃ to 250 ℃, the final contact radius changed from ~2.95 mm to ~1.78 mm and the total spreading time changed from ~511 s to ~0.15 s. By further analyzing the viscosity evolution of the droplet, we found that the decreasing of the total spreading time was caused by the decrease of the viscosity under high surface temperature at initial spreading stage, while the reduction of the final contact radius was caused by the curing of the precursor film. This study supplies a strategy to tuning the spreading and curing behavior of silicone by imposing high surface temperature, which is of great importance to the electronic packaging.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
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
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。
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。
基金Project(59972009) supported by the National Natural Science Foundation of China
文摘Carbon brushes with a resin binder were prepared according to an industrial process and the effects of the molding pressure, grains size and cure temperature on the properties of brush samples were discussed. The results show that the bulk density, bending strength and Rockwell hardness increase, while resistivity decreases with increasing molding pressure. Cure temperature has much more influence on the properties of brushes than molding pressure and grains size. Isothermal differential scanning calorimetry(DSC) was used to estimate the degree of cure of resin binder and a novel method of using the true density to measure the degree of cure of resin binder was presented and discussed briefly. Based on optimal process parameters carbon brushes were manufactured, durability tests for brushes were carried out on an alternate current motor and scanning electron microscope(SEM) was adopted to observe the morphology of worn surface of brushes. The results show that a luster oxide film can be formed on the surface of brushes and their service life reaches 380 h.
基金supported by the National Natural Science Foundation of China(No.20774023)Shanghai Leading Academic Discipline Project(No.B113).
文摘Thermosetting acrylic coatings were prepared by using carboxyl acid group-containing acrylic oligomer and curing with titanium-oxo-clusters which were first pre-hydrolyzed from titanium n-butoxide.The curing ability of the titanium-oxo-cluster was examined using a microdielectric analytical(DEA)curing monitor,Fourier transformed infrared spectroscopy(FTIR),and Soxhlet extraction experiments,and the properties of the resulted coatings were investigated with pendulum hardness tester,dynamic mechanical analysis(DMA),thermogravimetric analysis(TGA)and ultraviolet-visible spectrometer.The effect of titania-oxo-cluster in leading acrylic oligomers to form thermosetting acrylic coatings was confirmed.An increasing pendulum hardness and modulus of acrylic coatings with increasing titania content was observed, which resulted from the increment of crosslinking degree rather than of the titania content.The thermosetting acrylic/titania coatings also showed better thermal stability and higher UV-blocking properties than those coatings using organic curing agent.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.51606074,51625601,and 51576078)the Ministry of Science and Technology of the People’s Republic of China(Grant No.2017YFE0100600)the Creative Research Groups Funding of Hubei Province(Grant No.2018CFA001)
文摘Thermosetting materials are widely used as encapsulation in the electrical packaging to protect the core electronic components from external force, moisture, dust, and other factors. However, the spreading and curing behaviors of such kind of fluid on a heated surface have been rarely explored. In this study, we experimentally and numerically investigated the spreading and curing behaviors of the silicone(OE6550 A/B, which is widely used in the light-emitting diode packaging) droplet with diameter of ~2.2 mm on a heated surface with temperature ranging from 25 ℃ to 250 ℃. For the experiments, we established a setup with high-speed camera and heating unit to capture the fast spreading process of the silicone droplet on the heated surface. For the numerical simulation, we built a viscosity model of the silicone by using the Kiuna’s model and combined the viscosity model with the Volume of Fluid(VOF) model by the User Defined Function(UDF) method. The results show that the surface temperature significantly affected the spreading behaviors of the silicone droplet since it determines the temperature and viscosity distribution inside the droplet. For surface temperature varied from 25 ℃ to 250 ℃, the final contact radius changed from ~2.95 mm to ~1.78 mm and the total spreading time changed from ~511 s to ~0.15 s. By further analyzing the viscosity evolution of the droplet, we found that the decreasing of the total spreading time was caused by the decrease of the viscosity under high surface temperature at initial spreading stage, while the reduction of the final contact radius was caused by the curing of the precursor film. This study supplies a strategy to tuning the spreading and curing behavior of silicone by imposing high surface temperature, which is of great importance to the electronic packaging.
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Acknowledgements: The authors would like to express their appreciation to the National Natural Science Foundation of China (No. 50373008) and Key Project of Heilongjiang Province of China (No. GC04A216).
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.
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.