Because single trigger system is unreliable for shock wave overpressure test, this paper presents a multi-trigger overpressure test system. The large memory capacity is divided into parts to achieve data acquisition a...Because single trigger system is unreliable for shock wave overpressure test, this paper presents a multi-trigger overpressure test system. The large memory capacity is divided into parts to achieve data acquisition and storage with multiple triggers. Compared with conventional single-shot storage test system, this system can prevent false trigger and improve reliability of the test. By using explosion time to extract valid signal segments, it improves the efficiency of data recovery. These characteristics of the system contribute to multi-point test. After the dynamic characteristics of the system are calibrated, the valid data can be obtained in explosion experiments. The results show that the multi-trigger test system has higher reliability than single trigger test system.展开更多
The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam,and an accurate prediction of valley deformation(VD)remains a challenging part of risk mitiga...The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam,and an accurate prediction of valley deformation(VD)remains a challenging part of risk mitigation.In order to enhance the accuracy of VD prediction,a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising(EEMD-ITD),Differential evolutions—Shuffled frog leaping algorithm(DE-SFLA)and Least squares support vector machine(LSSVM)is proposed.The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD;then,ITD is applied for redundant information denoising on special sub-series,and the denoised deformation is divided into the trend and periodic deformation components.Meanwhile,several relevant triggering factors affecting the VD are considered,from which the input features are extracted by Grey relational analysis(GRA).After that,DE-SFLA-LSSVM is separately performed to predict the trend and periodic deformation with the optimal inputs.Ultimately,the two individual forecast components are reconstructed to obtain the final predicted values.Two VD series monitored in Xiluodu reservoir region are utilized to verify the proposed model.The results demonstrate that:(1)Compared with Discrete wavelet transform(DWT),better denoising performance can be achieved by EEMD-ITD;(2)Using GRA to screen the optimal input features can effectively quantify the deformation response relationship to the triggering factors,and reduce the model complexity;(3)The proposed hybrid model in this study displays superior performance on some compared models(e.g.,LSSVM,Backward Propagation neural network(BPNN),and DE-SFLA-BPNN)in terms of forecast accuracy.展开更多
文摘Because single trigger system is unreliable for shock wave overpressure test, this paper presents a multi-trigger overpressure test system. The large memory capacity is divided into parts to achieve data acquisition and storage with multiple triggers. Compared with conventional single-shot storage test system, this system can prevent false trigger and improve reliability of the test. By using explosion time to extract valid signal segments, it improves the efficiency of data recovery. These characteristics of the system contribute to multi-point test. After the dynamic characteristics of the system are calibrated, the valid data can be obtained in explosion experiments. The results show that the multi-trigger test system has higher reliability than single trigger test system.
基金the National Key R&D Program of China(No.2018YFC0407004)the National Natural Science Foundation Project of China(No.11772118).
文摘The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam,and an accurate prediction of valley deformation(VD)remains a challenging part of risk mitigation.In order to enhance the accuracy of VD prediction,a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising(EEMD-ITD),Differential evolutions—Shuffled frog leaping algorithm(DE-SFLA)and Least squares support vector machine(LSSVM)is proposed.The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD;then,ITD is applied for redundant information denoising on special sub-series,and the denoised deformation is divided into the trend and periodic deformation components.Meanwhile,several relevant triggering factors affecting the VD are considered,from which the input features are extracted by Grey relational analysis(GRA).After that,DE-SFLA-LSSVM is separately performed to predict the trend and periodic deformation with the optimal inputs.Ultimately,the two individual forecast components are reconstructed to obtain the final predicted values.Two VD series monitored in Xiluodu reservoir region are utilized to verify the proposed model.The results demonstrate that:(1)Compared with Discrete wavelet transform(DWT),better denoising performance can be achieved by EEMD-ITD;(2)Using GRA to screen the optimal input features can effectively quantify the deformation response relationship to the triggering factors,and reduce the model complexity;(3)The proposed hybrid model in this study displays superior performance on some compared models(e.g.,LSSVM,Backward Propagation neural network(BPNN),and DE-SFLA-BPNN)in terms of forecast accuracy.
