The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the va...The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the variational modal decomposition(VMD)method is introduced into the bolt detection signal analysis.On the basis of morphological filtering(MF)and the VMD method,a VMD?combined MF principle is established into a bolt detection signal analysis method(MF?VMD).MF?VMD is used to analyze the vibration and actual bolt detection signals of the simulation.Results show that MF?VMD effectively separates intrinsic mode function,even under strong interference.In comparison with conventional VMD method,the proposed method can remove noise interference.An intrinsic mode function of the field detection signal can be effectively identified by reflecting the signal at the bottom of the bolt.展开更多
随着西藏地区旅游业的蓬勃发展,人群荷载对藏式古建筑结构安全的影响日益显著。为了量化人群荷载对结构的影响,需要从结构健康监测数据中分离出人群荷载引起的应变响应,提出了一种基于灰狼优化(grey wolf optimizer,GWO)算法优化变分模...随着西藏地区旅游业的蓬勃发展,人群荷载对藏式古建筑结构安全的影响日益显著。为了量化人群荷载对结构的影响,需要从结构健康监测数据中分离出人群荷载引起的应变响应,提出了一种基于灰狼优化(grey wolf optimizer,GWO)算法优化变分模态分解(variational mode decomposition,VMD)算法并结合GG(Gath-Geva,GG)聚类算法的人群荷载效应分离方法,简称GWO-VMD-GG。首先,利用GWO算法以最小包络熵为适应度函数来确定VMD参数模态分解层数K和二次惩罚因子α;其次,采用优化后的VMD算法对实测应变信号进行分解;最后,以相关系数为特征参数,采用GG聚类算法对分解得到的本征模态函数(intrinsic mode function,IMF)分量进行聚类,将快变应变分量重构,得到人群荷载引起的应变响应。简述了某藏式古建筑游客分布特征,并通过对藏式古建筑木结构应变监测数据的分析,成功分离出游客日、周、年分布特征人群荷载效应,验证了所提方法在工程实践中的有效性。结果表明,该方法能够有效避免VMD参数选择和IMF分量划分过程中的人为干预,实现从大规模监测数据中自动分离人群荷载效应,为藏式古建筑的结构安全评估提供了一种有效手段。展开更多
Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking co...Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.展开更多
基金supported by the Key Project of the National Natural Science Foundation of China (No.51739006)the Open Research Fund of the Fundamental Science on Radioactive Geology and Exploration Technology Laboratory (No.RGET1502)+1 种基金the Open Research Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering (No.2017SDSJ05)the Project of the Hubei Foundation for Innovative Research Groups (No.2015CFA025)
文摘The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the variational modal decomposition(VMD)method is introduced into the bolt detection signal analysis.On the basis of morphological filtering(MF)and the VMD method,a VMD?combined MF principle is established into a bolt detection signal analysis method(MF?VMD).MF?VMD is used to analyze the vibration and actual bolt detection signals of the simulation.Results show that MF?VMD effectively separates intrinsic mode function,even under strong interference.In comparison with conventional VMD method,the proposed method can remove noise interference.An intrinsic mode function of the field detection signal can be effectively identified by reflecting the signal at the bottom of the bolt.
基金The National Natural Science Foundation of China (No.62262011)The Natural Science Foundation of Guangxi (No.2021JJA170130).
文摘Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.
