The accurate estimation of lithium battery state of health(SOH)plays an important role in the health management of battery systems.In order to improve the prediction accuracy of SOH,this paper proposes a stochastic co...The accurate estimation of lithium battery state of health(SOH)plays an important role in the health management of battery systems.In order to improve the prediction accuracy of SOH,this paper proposes a stochastic configuration network based on a multi-converged black-winged kite search algorithm,called SBKA-CLSCN.Firstly,the indirect health index(HI)of the battery is extracted by combining it with Person correlation coefficients in the battery charging and discharging cycle point data.Secondly,to address the problem that the black-winged kite optimization algorithm(BKA)falls into the local optimum problem and improve the convergence speed,the Sine chaotic black-winged kite search algorithm(SBKA)is designed,which mainly utilizes the Sine mapping and the golden-sine strategy to enhance the algorithm’s global optimality search ability;secondly,the Cauchy distribution and Laplace regularization techniques are used in the SCN model,which is referred to as CLSCN,thereby improving the model’s overall search capability and generalization ability.Finally,the performance of SBKA and SBKA-CLSCN is evaluated using eight benchmark functions and the CALCE battery dataset,respectively,and compared in comparison with the Long Short-Term Memory(LSTM)model and the Gated Recurrent Unit(GRU)model,and the experimental results demonstrate the feasibility and effectiveness of the SBKA-CLSCN algorithm.展开更多
Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are ...Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are integrated in this research responsibly and effectively to develop the Mobile Neuron Attention Stage-by-Stage Network(MNASNet)model,which is the integration of both Mobile Network(MobileNet)and Neuron Attention Stage-by-Stage.The steps followed to detect ASD with privacy-preserved data are data normalization,data augmentation,and K-Anonymization.The clinical data of individuals are taken initially and preprocessed using the Z-score Normalization.Then,data augmentation is performed using the oversampling technique.Subsequently,K-Anonymization is effectuated by utilizing the Black-winged Kite Algorithm to ensure the privacy of medical data,where the best fitness solution is based on data utility and privacy.Finally,after improving the data privacy,the developed approach MNASNet is implemented for ASD detection,which achieves highly accurate results compared to traditional methods to detect autism behavior.Hence,the final results illustrate that the proposed MNASNet achieves an accuracy of 92.9%,TPR of 95.9%,and TNR of 90.9%at the k-samples of 8.展开更多
为了深度挖掘电价序列中所蕴含的特征与信息,进一步提升日前电价的预测准确率,提出一种基于改进互信息特征选取(improve mutual information feature selection,IMIFS)、变分模态分解(variational mode decomposition,VMD)和红鸢优化算...为了深度挖掘电价序列中所蕴含的特征与信息,进一步提升日前电价的预测准确率,提出一种基于改进互信息特征选取(improve mutual information feature selection,IMIFS)、变分模态分解(variational mode decomposition,VMD)和红鸢优化算法(red kite optimization algorithm,ROA)优化长短记忆网络(long short term memory,LSTM)相结合的混合日前电价预测模型。首先,通过IMIFS对原始多元特征集进行降维,提取出包含维度最小且电价信息丰富的特征集,同时,利用VMD对电价序列进行有效分解,减轻电价序列的波动性;其次,引入ROA对LSTM中阈值与权重进行优化,提升算法的全局搜索与局部寻优能力;最后,通过算例验证IMIFS-VMD和ROA-LSTM日前电价预测模型效果,结果表明所提模型X_(RMSE)、X_(MAE)和R^(2)分别为2.532元/(MW·h)、1.956元/(MW·h)和98.06%,较其他电价预测模型具有较高的预测准确率。展开更多
文摘The accurate estimation of lithium battery state of health(SOH)plays an important role in the health management of battery systems.In order to improve the prediction accuracy of SOH,this paper proposes a stochastic configuration network based on a multi-converged black-winged kite search algorithm,called SBKA-CLSCN.Firstly,the indirect health index(HI)of the battery is extracted by combining it with Person correlation coefficients in the battery charging and discharging cycle point data.Secondly,to address the problem that the black-winged kite optimization algorithm(BKA)falls into the local optimum problem and improve the convergence speed,the Sine chaotic black-winged kite search algorithm(SBKA)is designed,which mainly utilizes the Sine mapping and the golden-sine strategy to enhance the algorithm’s global optimality search ability;secondly,the Cauchy distribution and Laplace regularization techniques are used in the SCN model,which is referred to as CLSCN,thereby improving the model’s overall search capability and generalization ability.Finally,the performance of SBKA and SBKA-CLSCN is evaluated using eight benchmark functions and the CALCE battery dataset,respectively,and compared in comparison with the Long Short-Term Memory(LSTM)model and the Gated Recurrent Unit(GRU)model,and the experimental results demonstrate the feasibility and effectiveness of the SBKA-CLSCN algorithm.
文摘Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are integrated in this research responsibly and effectively to develop the Mobile Neuron Attention Stage-by-Stage Network(MNASNet)model,which is the integration of both Mobile Network(MobileNet)and Neuron Attention Stage-by-Stage.The steps followed to detect ASD with privacy-preserved data are data normalization,data augmentation,and K-Anonymization.The clinical data of individuals are taken initially and preprocessed using the Z-score Normalization.Then,data augmentation is performed using the oversampling technique.Subsequently,K-Anonymization is effectuated by utilizing the Black-winged Kite Algorithm to ensure the privacy of medical data,where the best fitness solution is based on data utility and privacy.Finally,after improving the data privacy,the developed approach MNASNet is implemented for ASD detection,which achieves highly accurate results compared to traditional methods to detect autism behavior.Hence,the final results illustrate that the proposed MNASNet achieves an accuracy of 92.9%,TPR of 95.9%,and TNR of 90.9%at the k-samples of 8.
文摘为了深度挖掘电价序列中所蕴含的特征与信息,进一步提升日前电价的预测准确率,提出一种基于改进互信息特征选取(improve mutual information feature selection,IMIFS)、变分模态分解(variational mode decomposition,VMD)和红鸢优化算法(red kite optimization algorithm,ROA)优化长短记忆网络(long short term memory,LSTM)相结合的混合日前电价预测模型。首先,通过IMIFS对原始多元特征集进行降维,提取出包含维度最小且电价信息丰富的特征集,同时,利用VMD对电价序列进行有效分解,减轻电价序列的波动性;其次,引入ROA对LSTM中阈值与权重进行优化,提升算法的全局搜索与局部寻优能力;最后,通过算例验证IMIFS-VMD和ROA-LSTM日前电价预测模型效果,结果表明所提模型X_(RMSE)、X_(MAE)和R^(2)分别为2.532元/(MW·h)、1.956元/(MW·h)和98.06%,较其他电价预测模型具有较高的预测准确率。
