期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到91篇文章
< 1 2 … 5 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Enhancing microseismic/acoustic emission source localization accuracy with an outlier-robust kernel density estimation approach 认领 引用 被引量：8
1
作者 Jie Chen Huiqiong Huang +4 位作者 Yichao Rui Yuanyuan Pu Sheng Zhang Zheng Li Wenzhong Wang 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2024年第7期943-956,共14页
Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust l... Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications. 展开更多
关键词 Microseismic source/acoustic emission(MS/AE) Kernel density estimation(KDE) Damping linear correction Source location Abnormal arrivals
暂未订购 下载PDF
Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation 认领 引用 被引量：5
2
作者 Peizhe Xin Ying Liu +2 位作者 Nan Yang Xuankun Song Yu Huang 《Global Energy Interconnection》 EI 2020年第3期247-258,共12页
In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling met... In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE. 展开更多
关键词 Moving average method Signal decomposition Wind power fluctuation characteristics Kernel density estimation Constrained order optimization
暂未订购 下载PDF
Statistical Characteristics Analysis Based on F/A-XX Fighter Using Adapative Kernel Density Estimation Algorithm 认领 引用 被引量：3
3
作者 FU Li JIANG Guanwu HUANG Quanjun 《Journal of Shanghai Jiaotong university(Science)》 EI 2024年第6期1202-1210,共9页
The sixth-generation fighter has superior stealth performance,but for the traditional kernel density estimation(KDE),precision requirements are difficult to satisfy when dealing with the fluctuation characteristics of... The sixth-generation fighter has superior stealth performance,but for the traditional kernel density estimation(KDE),precision requirements are difficult to satisfy when dealing with the fluctuation characteristics of complex radar cross section(RCS).To solve this problem,this paper studies the KDE algorithm for F/AXX stealth fighter.By considering the accuracy lack of existing fixed bandwidth algorithms,a novel adaptive kernel density estimation(AKDE)algorithm equipped with least square cross validation and integrated squared error criterion is proposed to optimize the bandwidth.Meanwhile,an adaptive RCS density estimation can be obtained according to the optimized bandwidth.Finally,simulations verify that the estimation accuracy of the adaptive bandwidth RCS density estimation algorithm is more than 50%higher than that of the traditional algorithm.Based on the proposed algorithm(i.e.,AKDE),statistical characteristics of the considered fighter are more accurately acquired,and then the significant advantages of the AKDE algorithm in solving cumulative distribution function estimation of RCS less than 1 m2 are analyzed. 展开更多
关键词 radar cross section(RCS) kernel density estimation(KDE) statistical properties
暂未订购 下载PDF
An Independent Component Analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation 认领 引用 被引量：2
4
作者 薛云峰 王宇嘉 杨杰 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第2期204-209,共6页
A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introdu... A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm. 展开更多
关键词 independent component analysis blind source separation gradient method kernel density estimation
暂未订购 下载PDF
Static Frame Model Validation with Small Samples Solution Using Improved Kernel Density Estimation and Confidence Level Method 认领 引用 被引量：6
5
作者 ZHANG Baoqiang CHEN Guoping GUO Qintao 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2012年第6期879-886,共8页
An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only smal... An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples. 展开更多
关键词 model validation small samples uncertainty analysis kernel density estimation confidence level prediction
暂未订购 下载PDF
Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking 认领 引用 被引量：3
6
作者 张路平 王鲁平 +1 位作者 李飚 赵明 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第3期956-965,共10页
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ... In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD. 展开更多
关键词 particle filter with probability hypothesis density marginalized particle filter meanshift kernel density estimation multi-target tracking
暂未订购 下载PDF
Diversity Sampling Based Kernel Density Estimation for Background Modeling 认领 引用
7
作者 毛燕芬 施鹏飞 《Journal of Shanghai University(English Edition)》 2005年第6期506-509,共4页
A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for ... A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems. 展开更多
关键词 background subtraction diversity sampling kernel density estimation multi-modal background model
暂未订购 下载PDF
AN EFFECTIVE IMAGE RETRIEVAL METHOD BASED ON KERNEL DENSITY ESTIMATION OF COLLAGE ERROR AND MOMENT INVARIANTS 认领 引用 被引量：1
8
作者 Zhang Qin Huang Xiaoqing +2 位作者 Liu Wenbo Zhu Yongjun Le Jun 《Journal of Electronics(China)》 2013年第4期391-400,共10页
In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The pro... In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The proposed method is called CHK (KDE of Collage error and Hu moment) and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average Retrieval Rate (ARR) can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time. 展开更多
关键词 Fractal Coding （FC） Hu moment invariant Kernel Density Estimation （KDE） Variableoptimized bandwidth Image retrieval
暂未订购 下载PDF
Bayesian Classifier Based on Robust Kernel Density Estimation and Harris Hawks Optimisation 认领 引用
9
作者 Bi Iritie A-D Boli Chenghao Wei 《International Journal of Internet and Distributed Systems》 2024年第1期1-23,共23页
In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate pr... In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers. 展开更多
关键词 Classification Robust Kernel Density Estimation M-Estimation Harris Hawks Optimisation Algorithm Complete Cross-Validation
暂未订购 下载PDF
ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION 认领 引用 被引量：2
10
作者 陈斌 李斌 +1 位作者 冯爱民 潘志松 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第4期275-281,共7页
One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of t... One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships. 展开更多
关键词 one-class support vector machine（OCSVM） support vector data description（SVDD） kernel density estimation
暂未订购 下载PDF
A KERNEL ESTIMATOR OF A DENSITY FUNCTION IN MULTIVARIATE CASE FROM RANDOMLY CENSORED DATA 认领 引用
11
作者 周勇 《Acta Mathematica Scientia》 SCIE 1996年第2期170-180,共11页
A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error ... A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied. 展开更多
关键词 Kernel density estimator asymptotic normality product-limit estimator,mean square error and censored data.
暂未订购 下载PDF
ASYMPTOTIC NORMALITY OF KERNEL ESTIMATES OF A DENSITY FUNCTION UNDER ASSOCIATION DEPENDENCE 认领 引用
12
作者 林正炎 《Acta Mathematica Scientia》 SCIE 2003年第3期345-350,共6页
Let {Xn, n≥1} be a strictly stationary sequence of random variables, which are either associated or negatively associated, f(.) be their common density. In this paper, the author shows a central limit theorem for a k... Let {Xn, n≥1} be a strictly stationary sequence of random variables, which are either associated or negatively associated, f(.) be their common density. In this paper, the author shows a central limit theorem for a kernel estimate of f(.) under certain regular conditions. 展开更多
关键词 Associated random variables negatively associated random variables kernel estimate of a density function central limit theorem
暂未订购 下载PDF
Large Deviations for a Test of Symmetry Based on Kernel Density Estimator of Directional Data 认领 引用
13
作者 Mingzhou XU Kun CHENG 《Journal of Mathematical Research with Applications》 CSCD 2021年第6期639-647,共9页
Assume that fnis the nonparametric kernel density estimator of directional data based on a kernel function K and a sequence of independent and identically distributed random variables taking values in d-dimensional... Assume that fnis the nonparametric kernel density estimator of directional data based on a kernel function K and a sequence of independent and identically distributed random variables taking values in d-dimensional unit sphere Sd-1.We established that the large deviation principle for{supx∈Sd-1|fn(x)-fn(-x)|,n≥1}holds if the kernel function is a function with bounded variation,and the density function f of the random variables is continuous and symmetric. 展开更多
关键词 symmetry test kernel density estimator directional data large deviations
暂未订购 下载PDF
Optimization Strategy of Commercial Space in Xianyukou Hutong Based on Kernel Density and Space Syntax 认领 引用
14
作者 Qiqi Xiong Yi Zheng Bo Zhang 《Journal of World Architecture》 2022年第6期40-48,共9页
Beijing Xianyukou Hutong(hutong refers to historical and cultural block in Chinese)occupies an important geographical location with unique urban fabric,and after years of renewal and protection,the commercial space of... Beijing Xianyukou Hutong(hutong refers to historical and cultural block in Chinese)occupies an important geographical location with unique urban fabric,and after years of renewal and protection,the commercial space of Xianyukou Street and has gained some recognition.This article Xianyukou takes commercial hutong in Beijing as an example,spatial analysis was carried out using methods like GIS kernel density method,space syntax after site investigation and research.Based on the street space problems found,this paper then puts forward strategies to improve and upgrade Xianyukou Street’s commercial space and improve businesses in Xianyukou Street and other similar hutong. 展开更多
关键词 Hutong Xianyukou Street Commercial space Space syntax Kernel density estimation
暂未订购 下载PDF
Dynamic parameters prediction of the spatial deployable mechanism:a hybrid approach combining physics-based and data-driven models 认领 引用
15
作者 Yanhe Tao Qintao Guo +4 位作者 Jin Zhou Cheng Yi You Zhang Xiaofei Liu Ruiqi Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期535-549,共15页
Accurate prediction of the spatial mechanism's dynamic parameters in microgravity deployment simulations is crucial for identifying potential faults and ensuring precise gravitational compensation.Traditional engi... Accurate prediction of the spatial mechanism's dynamic parameters in microgravity deployment simulations is crucial for identifying potential faults and ensuring precise gravitational compensation.Traditional engineering models are often inaccurate,primarily because of insufficient experimental data and incomplete understanding of physical phenomena,which impedes model bias reduction in information-poor scenarios.We present a novel hybrid approach aimed at improving the predictive accuracy of the dynamic behavior of spatial deployable mechanisms.The graph convolutional network-temporal convolutional network(GCN-TCN)model,a type of deep learning architecture,is utilized for its expertise in forecasting spatio-temporal data through multi-step predictions.Next,the adaptive bandwidth kernel density estimation technique is applied to estimate the probability density function of residuals from the testing set of the GCN-TCN,quantifying predictive uncertainty.The predictive information is further refined using Bayesian inference,integrating a priori knowledge from physics-based models with data from data-driven models to yield robust posterior predictions.The proposed methodology is validated and shown to be robust through rigorous numerical simulations and experimental validation,demonstrating its ability to provide accurate and reliable predictions for the deployment of spatial mechanisms. 展开更多
关键词 Deep learning Kernel density estimation Data fusion Spatial deployable mechanisms Uncertainty quantification Hybrid approach Bayesian
暂未订购 下载PDF
Spatial-temporal correlation between surface distress and internal damage in pavement structure 认领 引用
16
作者 Yuhui Zhang Peiguo Yuan +5 位作者 Zhongping Wang Zepeng Fan Haotian Lyu Fujiao Tang Binglei Xie Dawei Wang 《Journal of Road Engineering》 EI CAS 2026年第2期232-244,共13页
A recurrent phenomenon is the reappearance of distress conditions on the same road section,both before and after maintenance interventions.The maintenance work essentially addresses the superficial symptoms rather tha... A recurrent phenomenon is the reappearance of distress conditions on the same road section,both before and after maintenance interventions.The maintenance work essentially addresses the superficial symptoms rather than the root causes,since the internal relationships between various forms of distress remain unclear.This study quantitatively evaluates the correlation between surface distress and internal defects based on field detection data and statistical methods,effectively complementing existing qualitative analytical method.Approximately 200 defect locations data were collected from the RIOHTrack full-scale ring road,and targeted evaluation metrics reflecting pavement performance were proposed.Then,the Ripley's K-function was employed to analyze the spatial aggregation of surface and internal cracks,and to further verify their macroscopic correlation during the spatio-temporal evolution process.Next,kernel density estimation and relative risk assessment were used to investigate the relationships between the surface distress and internal defects.Experimental results reveal that the loading position significantly affects surface distress,but exhibits no obvious correlation with hidden damage,and there is also no spatial aggregation phenomenon between them.However,for semi-rigid base asphalt pavement,internal cracks and surface cracks show a strong correlation,while demonstrating only a weak association with loading position.Finally,a sensitivity analysis was performed based on the results obtained at different distance thresholds,and r=0.5 m was designated as the optimal spatial correlation distance threshold.This threshold was then used to determine the pavement structure offering the best crack resistance performance,providing a key reference for the design and maintenance of heavy-duty highway pavements.This study provides a reference for road active maintenance and supports the transformation of maintenance strategies from passive response to active intervention. 展开更多
关键词 Pavement structure RIOHTrack Ripley's K-function Kernel density estimation Surface distress Internal defects
暂未订购 下载PDF
Multidimensional seismic fragility analysis of subway station structures using the adaptive bandwidth kernel density estimation and Copula function 认领 引用 被引量：2
17
作者 Chunyi Cui Jingtong Zhao +3 位作者 Minze Xu Chengshun Xu Hailong Liu Kunpeng Wang 《Underground Space》 SCIE EI CSCD 2025年第3期110-123,共14页
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
关键词 Multidimensional seismic fragility Subway station structure Adaptive bandwidth kernel density estimation Gaussian kernel function Copula function
An Integrated Attention-BiLSTM Approach for Probabilistic Remaining Useful Life Prediction 认领 引用 被引量：1
18
作者 Bo Zhu Enzhi Dong +3 位作者 Zhonghua Cheng Kexin Jiang Chiming Guo Shuai Yue 《Computers, Materials & Continua》 SCIE EI 2026年第4期966-984,共19页
Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction pla... Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk. 展开更多
关键词 Bidirectional long short-term memory network attention mechanism kernel density estimation remaining useful life prediction
暂未订购 下载PDF
Stratospheric airship flight trajectory envelope prediction based on wind field uncertainty model 认领 引用
19
作者 Lele QI Xixiang YANG +2 位作者 Xiaolong DENG Fangchao BAI Zhenyu MA 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期389-405,共17页
Stratospheric airships,as typical low-speed near space aircraft,exhibit great potential in the fields of communication relay and high-altitude monitoring.However,due to the considerable uncertainty of the external win... Stratospheric airships,as typical low-speed near space aircraft,exhibit great potential in the fields of communication relay and high-altitude monitoring.However,due to the considerable uncertainty of the external wind field environment,the deviation of stratospheric airship flight trajectory increases.To effectively predict the envelope of stratospheric airship flight trajectory,this paper proposes an innovative trajectory envelope prediction method to offer a new perspective on related research.By establishing wind field uncertainty model including both wind speed uncertainty and wind direction uncertainty,stratospheric airship trajectories are obtained under four extreme wind fields,and the trajectory envelope is generated using the Alpha-Shape method.Furthermore,the adaptive bandwidth kernel density estimation method is introduced to quantify the trajectory distribution probability,providing an intuitive description of the trajectory distribution probability.The simulation results show that the proposed methods can effectively predict the stratospheric airship flight trajectory envelope and describe trajectory distribution characteristics,supporting mission planning,risk assessment,and energy-management strategy selection for stratospheric airship flight tests. 展开更多
关键词 Alpha-Shape Flight trajectory envelope Kernel density estimation Stratospheric airship Trajectory distribution probability Worst-to-worst
暂未订购 下载PDF
A physics-guided explainable machine learning framework for residual-based performance deviation detection and probabilistic severity assessment in photovoltaic systems 认领 引用
20
作者 Roby Mohajon Shuva Chandra Sarker +5 位作者 Md Amzad Sadik Abid Simanta Datta Ronobir Abid Kamal Rumi Shimul Chakraborty Adiba Juoairia Nur Mohammad 《Clean Energy》 EI CSCD 2026年第3期143-163,共21页
Monitoring photovoltaic plants entails the use of techniques that are physically meaningful for detecting deviations in the observed behavior from what is expected.Data-driven methods frequently suffer from poor physi... Monitoring photovoltaic plants entails the use of techniques that are physically meaningful for detecting deviations in the observed behavior from what is expected.Data-driven methods frequently suffer from poor physical consistency,whereas physics-based models may fail to account for practical variations in operational behavior.In this study,we present a physics-driven machine learning methodology for estimating fault severity in grid-connected photovoltaic systems.The proposed methodology combines a calibrated PVsyst-based expected power generation model and supervisory control and data acquisition data collected from two 56.32 kilowatt-peak photovoltaic plants.Linear bias correction of the simulation results enhanced the correlation between the predicted and actual power generation levels,with coefficient of determination values of 0.685 and 0.566 for Plants 1 and 2,respectively.The Gaussian mixture model approach coupled with Bayesian information criterion tuning revealed the statistical fault-severity threshold,Rnorm=−0.161,for the discrimination between normal and severe states.For the prediction task,a random forest classifier was trained on seven physics-aware variables and achieved accuracy and macro-F1 scores of 0.920 and 0.907,respectively,through walk-forward validation.Comparative benchmarking with other classifiers,including XGBoost,support vector machine,and decision tree algorithms,showed a better classification performance balance.In addition,the explainability study verified the importance of irradiance,expected power,and conversion efficiency metrics. 展开更多
关键词 photovoltaic performance monitoring physics-Guided machine learning bias-Corrected residual analysis Gaussian mixture model walk-Forward cross-Validation SHAP explainability MDI feature importance kernel density estimation
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 5 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈