Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Th...Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Thus,a prediction model that performs well in one case might underperform in another.To address this shortcoming,this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another important and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncertainty,which should be handled by operating reserve.Operating reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account.Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.展开更多
The computational uncertainty principle states that the numerical computation of nonlinear ordinary differential equations（ODEs） should use appropriately sized time steps to obtain reliable solutions.However,the int...The computational uncertainty principle states that the numerical computation of nonlinear ordinary differential equations（ODEs） should use appropriately sized time steps to obtain reliable solutions.However,the interval of effective step size（IES） has not been thoroughly explored theoretically.In this paper,by using a general estimation for the total error of the numerical solutions of ODEs,a method is proposed for determining an approximate IES by translating the functions for truncation and rounding errors.It also illustrates this process with an example.Moreover,the relationship between the IES and its approximation is found,and the relative error of the approximation with respect to the IES is given.In addition,variation in the IES with increasing integration time is studied,which can provide an explanation for the observed numerical results.The findings contribute to computational step-size choice for reliable numerical solutions.展开更多
Aeolian deposits from the deserts in northern China have been used for palaeoenvironmental research to understand aeolian sedimentology and its dynamic connection to past climate conditions. The Tengger Desert in Chin...Aeolian deposits from the deserts in northern China have been used for palaeoenvironmental research to understand aeolian sedimentology and its dynamic connection to past climate conditions. The Tengger Desert in China is sensitive to the waxing and waning of the monsoonal system. In response to past climate change, the southern margin of the Tengger Desert has evolved significantly since the last glacial period. However, previous attempts to date aeolian deposits in this region were mainly based on radiocarbon dating, which has problems when applied to aeolian deposits. Moreover, sedimentary records are limited. Accordingly, past aeolian activity in this desert remains poorly understood. In the present study, we dated sand samples from Gulang county at the southern margin of the Tengger Desert using optically stimulated luminescence (OSL) to understand the history of aeolian activity in this region. Our samples represented well-sorted aeolian sands and sandy loess. Aeolian sands are evidence of dune field buildup and sparse vegetation cover whereas sandy loess is evidence of improved stabilization of sand dunes resulting from ameliorated vegetation cover. Certain samples showed a decline in the equivalent dose (D<sub> e </sub>) values when successive integration intervals were applied, which resulted from unstable OSL signals from non-fast components in the initial part of the decay curve. In order to obtain reliable D<sub> e </sub> estimates, we investigated component-resolved and different background subtraction approaches, and compared the resultant D<sub> e </sub> estimates. We adopted the early background subtraction method to derive D<sub> e </sub> values. Luminescence chronologies and sedimentary records indicated that sand dunes accumulation occurred before 10 ka, and sandy loess developed between 9.5 and 7.6 ka when sand dunes were stabilized as a result of increased effective moisture levels. The transition between sand dune mobilization and stabilization emphasizes the significance of an effective展开更多
Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range target...Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.展开更多
为了解决不均衡数据集的分类问题和一般的代价敏感学习算法无法扩展到多分类情况的问题,提出了一种基于 K 最近邻( K NN)样本平均距离的代价敏感算法的集成方法。首先,根据最大化最小间隔的思想提出一种降低决策边界样本密度的重采样方...为了解决不均衡数据集的分类问题和一般的代价敏感学习算法无法扩展到多分类情况的问题,提出了一种基于 K 最近邻( K NN)样本平均距离的代价敏感算法的集成方法。首先,根据最大化最小间隔的思想提出一种降低决策边界样本密度的重采样方法;接着,采用每类样本的平均距离作为分类结果的判断依据,并提出一种符合贝叶斯决策理论的学习算法,使得改进后的算法具备代价敏感性;最后,对改进后的代价敏感算法按 K 值进行集成,以代价最小为原则,调整各基学习器的权重,得到一个以总体误分代价最低为目标的代价敏感AdaBoost算法。实验结果表明,与传统的 K NN算法相比,改进后的算法在平均误分代价上下降了31.4个百分点,并且代价敏感性能更好。展开更多
基金supported in part by the Natural Sciences and Engineering Research Council(NSERC)of Canada and the Saskatchewan Power Corporation(SaskPower).
文摘Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and tested on specific case studies.However,wind behavior and characteristics can vary significantly across regions.Thus,a prediction model that performs well in one case might underperform in another.To address this shortcoming,this paper proposes an ensemble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another important and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncertainty,which should be handled by operating reserve.Operating reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-layer optimization approach that takes both WPPI quality and reserve requirements into account.Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.
基金supported by the National Natural Science Foundation of China[grant numbers 41375110,11471244]
文摘The computational uncertainty principle states that the numerical computation of nonlinear ordinary differential equations（ODEs） should use appropriately sized time steps to obtain reliable solutions.However,the interval of effective step size（IES） has not been thoroughly explored theoretically.In this paper,by using a general estimation for the total error of the numerical solutions of ODEs,a method is proposed for determining an approximate IES by translating the functions for truncation and rounding errors.It also illustrates this process with an example.Moreover,the relationship between the IES and its approximation is found,and the relative error of the approximation with respect to the IES is given.In addition,variation in the IES with increasing integration time is studied,which can provide an explanation for the observed numerical results.The findings contribute to computational step-size choice for reliable numerical solutions.
基金funded by the National Basic Research Program of China(2013CB956000,2012CB426501)
文摘Aeolian deposits from the deserts in northern China have been used for palaeoenvironmental research to understand aeolian sedimentology and its dynamic connection to past climate conditions. The Tengger Desert in China is sensitive to the waxing and waning of the monsoonal system. In response to past climate change, the southern margin of the Tengger Desert has evolved significantly since the last glacial period. However, previous attempts to date aeolian deposits in this region were mainly based on radiocarbon dating, which has problems when applied to aeolian deposits. Moreover, sedimentary records are limited. Accordingly, past aeolian activity in this desert remains poorly understood. In the present study, we dated sand samples from Gulang county at the southern margin of the Tengger Desert using optically stimulated luminescence (OSL) to understand the history of aeolian activity in this region. Our samples represented well-sorted aeolian sands and sandy loess. Aeolian sands are evidence of dune field buildup and sparse vegetation cover whereas sandy loess is evidence of improved stabilization of sand dunes resulting from ameliorated vegetation cover. Certain samples showed a decline in the equivalent dose (D<sub> e </sub>) values when successive integration intervals were applied, which resulted from unstable OSL signals from non-fast components in the initial part of the decay curve. In order to obtain reliable D<sub> e </sub> estimates, we investigated component-resolved and different background subtraction approaches, and compared the resultant D<sub> e </sub> estimates. We adopted the early background subtraction method to derive D<sub> e </sub> values. Luminescence chronologies and sedimentary records indicated that sand dunes accumulation occurred before 10 ka, and sandy loess developed between 9.5 and 7.6 ka when sand dunes were stabilized as a result of increased effective moisture levels. The transition between sand dune mobilization and stabilization emphasizes the significance of an effective
文摘Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.
文摘为了解决不均衡数据集的分类问题和一般的代价敏感学习算法无法扩展到多分类情况的问题,提出了一种基于 K 最近邻( K NN)样本平均距离的代价敏感算法的集成方法。首先,根据最大化最小间隔的思想提出一种降低决策边界样本密度的重采样方法;接着,采用每类样本的平均距离作为分类结果的判断依据,并提出一种符合贝叶斯决策理论的学习算法,使得改进后的算法具备代价敏感性;最后,对改进后的代价敏感算法按 K 值进行集成,以代价最小为原则,调整各基学习器的权重,得到一个以总体误分代价最低为目标的代价敏感AdaBoost算法。实验结果表明,与传统的 K NN算法相比,改进后的算法在平均误分代价上下降了31.4个百分点,并且代价敏感性能更好。
