几何模型分类器具有坚实的几何统计基础和良好的泛化能力,因此在旋转机械故障诊断中取得了较高的分类精度。与仿射包和凸包相比,超圆盘(Hyperdisk,HD)对样本分布区域的估计更加合理。但超圆盘模型属于浅层学习模型,对复杂函数的表示能...几何模型分类器具有坚实的几何统计基础和良好的泛化能力,因此在旋转机械故障诊断中取得了较高的分类精度。与仿射包和凸包相比,超圆盘(Hyperdisk,HD)对样本分布区域的估计更加合理。但超圆盘模型属于浅层学习模型,对复杂函数的表示能力有限,存在学习能力和泛化能力差等缺点。针对这个问题提出一种深度超圆盘分类器(Deep Hyperdisk Large Margin Classifier,DHD),该方法通过模块叠加的方式将超圆盘分类器深度化,利用特征提取公式从每层模块的输入样本中自主提取新的特征值,并将其应用在下一层模块的训练学习中。将所提方法应用到旋转机械故障诊断当中,实验结果表明该方法对故障样本的分类准确率高于其他模型算法,且对不均衡样本和强噪声背景下的故障样本均具有良好的分类能力。展开更多
This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden node...This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility o...Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility of EMS has received considerable attention in health and transport geography studies.^([3])One of the optimal gauges for evaluating the accessibility of EMS is the response time,which is defined as the time from receiving an emergency call to the arrival of an ambulance.^([4])Beijing has already reduced the response time to approximately12 min,and the next goal is to ensure that the response time across Beijing does not exceed 12 min (the information comes from the Beijing Emergency Medical Center).展开更多
文摘几何模型分类器具有坚实的几何统计基础和良好的泛化能力,因此在旋转机械故障诊断中取得了较高的分类精度。与仿射包和凸包相比,超圆盘(Hyperdisk,HD)对样本分布区域的估计更加合理。但超圆盘模型属于浅层学习模型,对复杂函数的表示能力有限,存在学习能力和泛化能力差等缺点。针对这个问题提出一种深度超圆盘分类器(Deep Hyperdisk Large Margin Classifier,DHD),该方法通过模块叠加的方式将超圆盘分类器深度化,利用特征提取公式从每层模块的输入样本中自主提取新的特征值,并将其应用在下一层模块的训练学习中。将所提方法应用到旋转机械故障诊断当中,实验结果表明该方法对故障样本的分类准确率高于其他模型算法,且对不均衡样本和强噪声背景下的故障样本均具有良好的分类能力。
基金supported by National key research and development program(No.2022YFA1602404)the National Natural Science Foundation of China(Nos.12388102,12275338,12005280)the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C152)。
文摘This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
基金supported by National Key Research & Development Program of China (2022YFC3006201)。
文摘Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility of EMS has received considerable attention in health and transport geography studies.^([3])One of the optimal gauges for evaluating the accessibility of EMS is the response time,which is defined as the time from receiving an emergency call to the arrival of an ambulance.^([4])Beijing has already reduced the response time to approximately12 min,and the next goal is to ensure that the response time across Beijing does not exceed 12 min (the information comes from the Beijing Emergency Medical Center).
