According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
This paper uses the TSA （therrnoelastic stress analysis） technique to determine the stress concentration factor （Kt） of a U-notch in an aluminum plate, and then compares the results with those obtained from a FEA ...This paper uses the TSA （therrnoelastic stress analysis） technique to determine the stress concentration factor （Kt） of a U-notch in an aluminum plate, and then compares the results with those obtained from a FEA （finite elements analysis） of the same specimen. In order to do so, it devises a calculation procedure to extrapolate the thermoelastic data near the tip of the notch and then applies the resulting algorithm to seven distinct experiments that had different loading frequencies, mean loads and load ranges. The overall positive results suggest that the technique may be suitable for Kt measurements in real-world structures. A discussion about the calibration factor of the thermoelastic data is included by confronting the calibration results using independent tensile uniaxial tests and using the U-notch TSA and FEA paired specimen data.展开更多
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
文摘This paper uses the TSA （therrnoelastic stress analysis） technique to determine the stress concentration factor （Kt） of a U-notch in an aluminum plate, and then compares the results with those obtained from a FEA （finite elements analysis） of the same specimen. In order to do so, it devises a calculation procedure to extrapolate the thermoelastic data near the tip of the notch and then applies the resulting algorithm to seven distinct experiments that had different loading frequencies, mean loads and load ranges. The overall positive results suggest that the technique may be suitable for Kt measurements in real-world structures. A discussion about the calibration factor of the thermoelastic data is included by confronting the calibration results using independent tensile uniaxial tests and using the U-notch TSA and FEA paired specimen data.
