The lithium battery is an essential component of electric cars;prompt and accurate problem detection is vital in guaranteeing electric cars’safe and dependable functioning and addressing the limitations of Back Propa...The lithium battery is an essential component of electric cars;prompt and accurate problem detection is vital in guaranteeing electric cars’safe and dependable functioning and addressing the limitations of Back Propagation(BP)neural networks in terms of vanishing gradients and inability to effectively capture dependencies in time series,and the limitations of Long-Short Term Memory(LSTM)neural network models in terms of risk of overfitting.A method based on LSTM-BP is put forward for power battery fault diagnosis to improve the accuracy of lithium battery fault diagnosis.First,a lithium battery model is constructed based on the second-order RC equivalent circuit and the electro-thermal coupling model,and various lithium battery failures are simulated to examine the fault characteristics.Then,the lithium battery charging and discharging experiments collect,clean,and process the battery data.By constructing a neural network LSTM-BP model,we verified the superiority and accuracy of the LSTM-BP neural network model by comparing the LSTM model and BP model vertically and by comparing the Recurrent Neural Network(RNN)model,the Gated Recurrent Unit(GRU)model,and the Residual Neural Network(ResNet)model of a more advanced architecture horizontally.Finally,the lithium battery fault diagnosis process is summarized through the threshold quantitative criteria,and different faults are diagnosed and analyzed.Theresults show that the LSTM-BP neural network not only overcomes the limitations of the LSTMneural network and BP neural network but also improves the ability to process sequence data and reduces the risk of overfitting.展开更多
β-ray-induced X-ray spectroscopy(BIXS)is a promising method for tritium detection in solid materials because of its unique advantages,such as large detection depth,nondestructive testing capabilities,and low requirem...β-ray-induced X-ray spectroscopy(BIXS)is a promising method for tritium detection in solid materials because of its unique advantages,such as large detection depth,nondestructive testing capabilities,and low requirements for sample preparation.However,high-accuracy reconstruction of the tritium depth profile remains a significant challenge for this technique.In this study,a novel reconstruction method based on a backpropagation(BP)neural network algorithm that demonstrates high accuracy,broad applicability,and robust noise resistance is proposed.The average reconstruction error calculated using the BP network(8.0%)was much lower than that obtained using traditional numerical methods(26.5%).In addition,the BP method can accurately reconstruct BIX spectra of samples with an unknown range of tritium and exhibits wide applicability to spectra with various tritium distributions.Furthermore,the BP network demonstrates superior accuracy and stability compared to numerical methods when reconstructing the spectra,with a relative uncertainty ranging from 0 to 10%.This study highlights the advantages of BP networks in accurately reconstructing the tritium depth profile from BIXS and promotes their further application in tritium detection.展开更多
This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga...This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.展开更多
文摘The lithium battery is an essential component of electric cars;prompt and accurate problem detection is vital in guaranteeing electric cars’safe and dependable functioning and addressing the limitations of Back Propagation(BP)neural networks in terms of vanishing gradients and inability to effectively capture dependencies in time series,and the limitations of Long-Short Term Memory(LSTM)neural network models in terms of risk of overfitting.A method based on LSTM-BP is put forward for power battery fault diagnosis to improve the accuracy of lithium battery fault diagnosis.First,a lithium battery model is constructed based on the second-order RC equivalent circuit and the electro-thermal coupling model,and various lithium battery failures are simulated to examine the fault characteristics.Then,the lithium battery charging and discharging experiments collect,clean,and process the battery data.By constructing a neural network LSTM-BP model,we verified the superiority and accuracy of the LSTM-BP neural network model by comparing the LSTM model and BP model vertically and by comparing the Recurrent Neural Network(RNN)model,the Gated Recurrent Unit(GRU)model,and the Residual Neural Network(ResNet)model of a more advanced architecture horizontally.Finally,the lithium battery fault diagnosis process is summarized through the threshold quantitative criteria,and different faults are diagnosed and analyzed.Theresults show that the LSTM-BP neural network not only overcomes the limitations of the LSTMneural network and BP neural network but also improves the ability to process sequence data and reduces the risk of overfitting.
基金supported by the National Key Research and Development Program of China(No.2022YFE03170003)the National Natural Science Foundation of China(Nos.12305403 and 12275243).
文摘β-ray-induced X-ray spectroscopy(BIXS)is a promising method for tritium detection in solid materials because of its unique advantages,such as large detection depth,nondestructive testing capabilities,and low requirements for sample preparation.However,high-accuracy reconstruction of the tritium depth profile remains a significant challenge for this technique.In this study,a novel reconstruction method based on a backpropagation(BP)neural network algorithm that demonstrates high accuracy,broad applicability,and robust noise resistance is proposed.The average reconstruction error calculated using the BP network(8.0%)was much lower than that obtained using traditional numerical methods(26.5%).In addition,the BP method can accurately reconstruct BIX spectra of samples with an unknown range of tritium and exhibits wide applicability to spectra with various tritium distributions.Furthermore,the BP network demonstrates superior accuracy and stability compared to numerical methods when reconstructing the spectra,with a relative uncertainty ranging from 0 to 10%.This study highlights the advantages of BP networks in accurately reconstructing the tritium depth profile from BIXS and promotes their further application in tritium detection.
基金supported by the National Natural Science Foundation of China Grant No.30771858Jiangsu Provincial Natural Science Foundation Grant No.BK2007229Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.
