With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impu...With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.展开更多
It is quite common that both categorical and continuous covariates appear in the data. But, most feature screening methods for ultrahigh-dimensional classification assume the covariates are continuous. And applicable ...It is quite common that both categorical and continuous covariates appear in the data. But, most feature screening methods for ultrahigh-dimensional classification assume the covariates are continuous. And applicable feature screening method is very limited;to handle this non-trivial situation, we propose a model-free feature screening for ultrahigh-dimensional multi-classification with both categorical and continuous covariates. The proposed feature screening method will be based on Gini impurity to evaluate the prediction power of covariates. Under certain regularity conditions, it is proved that the proposed screening procedure possesses the sure screening property and ranking consistency properties. We demonstrate the finite sample performance of the proposed procedure by simulation studies and illustrate using real data analysis.展开更多
Due to the advantages of LCC-VSC (line-commutated converter-voltage source converter) three-terminal DC technology in terms of new energy consumption, long-distance transmission, reliability and economy, China has suc...Due to the advantages of LCC-VSC (line-commutated converter-voltage source converter) three-terminal DC technology in terms of new energy consumption, long-distance transmission, reliability and economy, China has successively constructed a number of hybrid DC transmission projects. However, there is very little research on the protection principles of this topology. And there are problems, such as different control strategies, different line boundary elements, and different operating time requirements. A technology based on the Gini impurity of line mode voltage is proposed as the main protection scheme. This protection principle uses Gini impurity to describe the degree of confusion of fault information caused by boundary elements and further identifies internal and external faults. Finally, different faults are set to verify the reliability and superiority of the proposed protection scheme. A large number of results show that the protection scheme based on Gini impurity can identify the fault type within 1ms under an interference of 600 Ω resistance and 10dB noise.展开更多
基金the Science and Technology Commission of Shanghai Municipality(No.19030501100)the Technical Service Platform for Vibration and Noise Testing and Control of New Energy Vehicles(No.18DZ2295900)。
文摘With the rapid development of machine learning,the prediction of the performance of acoustic meta-materials using neural networks is replacing the traditional experiment-based testing methods.In this paper,a Gini impurity-based artificial neural network structural optimizer(GIASO)is proposed to optimize the neural network structure,and the effects of five different initialization algorithms on the model performance and struc-ture optimization are investigated.Two physically guided models with additional resonant frequencies and sound transmission loss formula are achieved to further improve the prediction accuracy of the model.The results show that GIASO utilizing the gray wolf optimizer as the initialization method can significantly improve the prediction performance of the model.Simultaneously,the physical guidance model with additional resonant frequencies has the best performance and can better predict the edge data points.Eventually,the effect of each input parameter on the sound transmission loss is explained by combining sensitivity analysis and theoretical formulation.
文摘It is quite common that both categorical and continuous covariates appear in the data. But, most feature screening methods for ultrahigh-dimensional classification assume the covariates are continuous. And applicable feature screening method is very limited;to handle this non-trivial situation, we propose a model-free feature screening for ultrahigh-dimensional multi-classification with both categorical and continuous covariates. The proposed feature screening method will be based on Gini impurity to evaluate the prediction power of covariates. Under certain regularity conditions, it is proved that the proposed screening procedure possesses the sure screening property and ranking consistency properties. We demonstrate the finite sample performance of the proposed procedure by simulation studies and illustrate using real data analysis.
基金supported by the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U2066210).
文摘Due to the advantages of LCC-VSC (line-commutated converter-voltage source converter) three-terminal DC technology in terms of new energy consumption, long-distance transmission, reliability and economy, China has successively constructed a number of hybrid DC transmission projects. However, there is very little research on the protection principles of this topology. And there are problems, such as different control strategies, different line boundary elements, and different operating time requirements. A technology based on the Gini impurity of line mode voltage is proposed as the main protection scheme. This protection principle uses Gini impurity to describe the degree of confusion of fault information caused by boundary elements and further identifies internal and external faults. Finally, different faults are set to verify the reliability and superiority of the proposed protection scheme. A large number of results show that the protection scheme based on Gini impurity can identify the fault type within 1ms under an interference of 600 Ω resistance and 10dB noise.
