Structural damages during an earthquake are typically controlled by seismic demands,which are represented by the combination of amplitude of ground motion and cyclic load effects.Since traditional methods normally ass...Structural damages during an earthquake are typically controlled by seismic demands,which are represented by the combination of amplitude of ground motion and cyclic load effects.Since traditional methods normally assume the lognormal distributions of seismic demands and resistance parameters,uncertainties are inevitably induced in the seismic fragility analysis.In this paper,the Copula function and adaptive bandwidth kernel density estimation method(ABKDE)are used to establish a novel multidimensional seismic fragility analysis framework.Based on the results of incremental dynamic analysis for subway station structures,ABKDE is adopted to establish single-parameter seismic fragility curves for both the maximum inter-story drift ratio(MIDR)and cumulated dissipated hysteretic energy(CDHE),respectively.Subsequently,the Copula function is used to formulate a bivariate seismic fragility function considering the correlations among seismic demand measures and establish the corresponding fragility curves.Finally,comparative analyses are conducted to evaluate seismic fragility curves using Copula-based dual and single-parameter damage models as well as the traditional damage models.It is found that the seismic fragility analysis method using the Copula function has the ability to gain a comprehensive consideration of the MIDR and CDHE during the damage process of subway station structures.Moreover,this newly developed seismic fragility analysis framework can capture the influence of the correlation between deformation and energy under various peak ground accelerations on structural damage.Thus,this framework can provide a scientific basis for predicting structural damage in subway stations subjected to varying intensities of ground motion while considering multiple damage indicators.展开更多
The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle co...The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.展开更多
When analyzing and evaluating risks in insurance, people are often confronted with the situation of incomplete information and insufficient data, which is known as a small-sample problem. In this paper, a one-dimensio...When analyzing and evaluating risks in insurance, people are often confronted with the situation of incomplete information and insufficient data, which is known as a small-sample problem. In this paper, a one-dimensional small-sample problem in insurance was investigated using the kernel density estimation method （KerM） and general limited information diffusion method （GIDM）. In particular, MacCormack technique was applied to get the solutions of GIDM equations and then the optimal diffusion solution was acquired based on the two optimization principles. Finally, the analysis introduced in this paper was verified by treating some examples and satisfying results were obtained.展开更多
The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computi...The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.展开更多
A novel particle filter bandwidth adaption for kernel particle filter(BAKPF)is proposed.Selection of the kernel bandwidth is a critical issue in kernel density estimation(KDE).The plug-in method is adopted to get the ...A novel particle filter bandwidth adaption for kernel particle filter(BAKPF)is proposed.Selection of the kernel bandwidth is a critical issue in kernel density estimation(KDE).The plug-in method is adopted to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error(AMISE)firstly.Then,particle-driven bandwidth selection is invoked in the KDE.To get a more effective allocation of the particles,the KDE with adap-tive bandwidth in the BAKPF is used to approximate the posterior probability density function(PDF)by moving particles toward the posterior.A closed-form expression of the true distribution is given.The simulation results show that the proposed BAKPF performs better than the standard particle filter(PF),unscented particle filter(UPF)and the kernel particle filter(KPF)both in efficiency and estimation precision.展开更多
Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction an...Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.展开更多
提出了一种自适应的核密度估计(Kernel density estimation,KDE)运动检测算法.算法首先提出一种自适应前景、背景阈值的双阈值选择方法,用于像素分类.该方法用双阈值克服了单阈值分类存在的不足,阈值的选择能自适应进行,且能适应不同的...提出了一种自适应的核密度估计(Kernel density estimation,KDE)运动检测算法.算法首先提出一种自适应前景、背景阈值的双阈值选择方法,用于像素分类.该方法用双阈值克服了单阈值分类存在的不足,阈值的选择能自适应进行,且能适应不同的场景.在此基础上,本文提出了基于概率的背景更新模型,按照像素的概率来更新背景,并利用帧间差分背景模型和KDE分类结果解决背景更新中的死锁问题,同时检测背景的突然变化.实验证明了所提出方法的适应性和可靠性.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52178315,and 51578100)the Fundamental Research Funds for the Central Universities(Grant No.3132023504)+1 种基金the Dalian Science and Technology Innovation Fund(Grant No.2022JJ12GX031)the Project of Shenyang Key Laboratory of Safety Evaluation and Disaster Prevention of Engineering Structures(Grant No.S230184).
文摘Structural damages during an earthquake are typically controlled by seismic demands,which are represented by the combination of amplitude of ground motion and cyclic load effects.Since traditional methods normally assume the lognormal distributions of seismic demands and resistance parameters,uncertainties are inevitably induced in the seismic fragility analysis.In this paper,the Copula function and adaptive bandwidth kernel density estimation method(ABKDE)are used to establish a novel multidimensional seismic fragility analysis framework.Based on the results of incremental dynamic analysis for subway station structures,ABKDE is adopted to establish single-parameter seismic fragility curves for both the maximum inter-story drift ratio(MIDR)and cumulated dissipated hysteretic energy(CDHE),respectively.Subsequently,the Copula function is used to formulate a bivariate seismic fragility function considering the correlations among seismic demand measures and establish the corresponding fragility curves.Finally,comparative analyses are conducted to evaluate seismic fragility curves using Copula-based dual and single-parameter damage models as well as the traditional damage models.It is found that the seismic fragility analysis method using the Copula function has the ability to gain a comprehensive consideration of the MIDR and CDHE during the damage process of subway station structures.Moreover,this newly developed seismic fragility analysis framework can capture the influence of the correlation between deformation and energy under various peak ground accelerations on structural damage.Thus,this framework can provide a scientific basis for predicting structural damage in subway stations subjected to varying intensities of ground motion while considering multiple damage indicators.
基金co-supported by the National Natural Science Foundation of China(Nos.52272403,52402506)Natural Science Basic Research Program of Shaanxi,China(Nos.2022JC-27,2023-JC-QN-0599)。
文摘The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.
基金Project supported by the National Natural Science Foundation of China （Grant No.10271072）
文摘When analyzing and evaluating risks in insurance, people are often confronted with the situation of incomplete information and insufficient data, which is known as a small-sample problem. In this paper, a one-dimensional small-sample problem in insurance was investigated using the kernel density estimation method （KerM） and general limited information diffusion method （GIDM）. In particular, MacCormack technique was applied to get the solutions of GIDM equations and then the optimal diffusion solution was acquired based on the two optimization principles. Finally, the analysis introduced in this paper was verified by treating some examples and satisfying results were obtained.
基金supported by the National Natural Science Foundation of China under Grant Nos.52105136,51975028China Postdoctoral Science Foundation under Grant[No.2021M690290]the National Science and TechnologyMajor Project under Grant No.J2019-IV-0002-0069.
文摘The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.
基金supported by the National Natural Science Foundation of China(60736043,60805012)the Fundamental Research Funds for the Central Universities(K50510020032)
文摘A novel particle filter bandwidth adaption for kernel particle filter(BAKPF)is proposed.Selection of the kernel bandwidth is a critical issue in kernel density estimation(KDE).The plug-in method is adopted to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error(AMISE)firstly.Then,particle-driven bandwidth selection is invoked in the KDE.To get a more effective allocation of the particles,the KDE with adap-tive bandwidth in the BAKPF is used to approximate the posterior probability density function(PDF)by moving particles toward the posterior.A closed-form expression of the true distribution is given.The simulation results show that the proposed BAKPF performs better than the standard particle filter(PF),unscented particle filter(UPF)and the kernel particle filter(KPF)both in efficiency and estimation precision.
基金supported by the National Key Research and Development Plan(Grant No.2023YFB3712400)the National Key Research and Development Plan(Grant No.2020YFB1713600).
文摘Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.
文摘提出了一种自适应的核密度估计(Kernel density estimation,KDE)运动检测算法.算法首先提出一种自适应前景、背景阈值的双阈值选择方法,用于像素分类.该方法用双阈值克服了单阈值分类存在的不足,阈值的选择能自适应进行,且能适应不同的场景.在此基础上,本文提出了基于概率的背景更新模型,按照像素的概率来更新背景,并利用帧间差分背景模型和KDE分类结果解决背景更新中的死锁问题,同时检测背景的突然变化.实验证明了所提出方法的适应性和可靠性.
