The present work deals with accurately estimating wall-skin friction from near-wall mean velocity by means of PIV measurement.The estimation accuracy relies on the spatial resolution and the precision of the resolved ...The present work deals with accurately estimating wall-skin friction from near-wall mean velocity by means of PIV measurement.The estimation accuracy relies on the spatial resolution and the precision of the resolved velocity profile inside the viscous sublayer,which is a big challenge for conventional window-based correlation method(K?hler C J,et al.Exp Fluids,2012,52:1641–1656).With the help of single-pixel ensemble correlation,the ensemble-averaged velocity vector can be resolved at significant spatial resolution,thus improving the measurement accuracy.To demonstrate the feasibility of this single-pixel ensemble correlation method,we first study the velocity estimation precision in a case of steady near-wall flow.Synthetic particle images are used to investigate the effect of different image parameters.It is found that the velocity RMS-uncertainty level of the single-pixel ensemble correlation method can be equivalent to the conventional window correlation method once the effective particle number used for the ensemble correlation is large enough.Furthermore,a canonical turbulent boundary layer is synthetically simulated based on velocity statistics resolved by previous Direct Numerical Simulation(DNS)work(Schlatter P,et al.J Fluid Mech,2010,659:116–126).The relative error of wall skin friction coefficient is shown to be one-order smaller than that of the window correlation method.And the optimization strategy to further minimize the measurement uncertainty is discussed in the last part.展开更多
Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliabl...Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliable method to accurately measure traffic complexity is important. Considering that many factors correlate with traffic complexity in complicated nonlinear ways,researchers have proposed several complexity evaluation methods based on machine learning models which were trained with large samples. However, the high cost of sample collection usually results in limited training set. In this paper, an ensemble learning model is proposed for measuring air traffic complexity within a sector based on small samples. To exploit the classification information within each factor, multiple diverse factor subsets（FSSs） are generated under guidance from factor noise and independence analysis. Then, a base complexity evaluator is built corresponding to each FSS. The final complexity evaluation result is obtained by integrating all results from the base evaluators. Experimental studies using real-world air traffic operation data demonstrate the advantages of our model for small-sample-based traffic complexity evaluation over other stateof-the-art methods.展开更多
The Extreme Learning Machine（ELM） is an effective learning algorithm for a Single-Layer Feedforward Network（SLFN）. It performs well in managing some problems due to its fast learning speed. However, in practical a...The Extreme Learning Machine（ELM） is an effective learning algorithm for a Single-Layer Feedforward Network（SLFN）. It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning（NCL） to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs（HE2 LM） for classification has different ELM algorithms including the Regularized ELM（RELM）, the Kernel ELM（KELM）, and the L2-norm-optimized ELM（ELML2）. The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers.展开更多
This paper presents a new correction method, "instant correction method(ICM)", to improve the accuracy of numerical prediction products(NPP) and provide weather variables at grid cells. The ICM makes use of ...This paper presents a new correction method, "instant correction method(ICM)", to improve the accuracy of numerical prediction products(NPP) and provide weather variables at grid cells. The ICM makes use of the continuity in time of the forecast errors at different forecast times to improve the accuracy of large scale NPP. To apply the ICM in China, an ensemble correction scheme is designed to correct the T213 NPP(the most popular NPP in China) through different statistical methods. The corrected T213 NPP(ICM T213 NPP) are evaluated by four popular indices: Correlation coefficient, climate anomalies correlation coefficient, root-mean-square-errors(RMSE), and confidence intervals(CI). The results show that the ICM T213 NPP are more accurate than the original T213 NPP in both the training period(2003–2008) and the validation period(2009–2010). Applications in China over the past three years indicate that the ICM is simple, fast, and reliable. Because of its low computing cost, end users in need of more accurate short-range weather forecasts around China can benefit greatly from the method.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.11327202 and 11372001)
文摘The present work deals with accurately estimating wall-skin friction from near-wall mean velocity by means of PIV measurement.The estimation accuracy relies on the spatial resolution and the precision of the resolved velocity profile inside the viscous sublayer,which is a big challenge for conventional window-based correlation method(K?hler C J,et al.Exp Fluids,2012,52:1641–1656).With the help of single-pixel ensemble correlation,the ensemble-averaged velocity vector can be resolved at significant spatial resolution,thus improving the measurement accuracy.To demonstrate the feasibility of this single-pixel ensemble correlation method,we first study the velocity estimation precision in a case of steady near-wall flow.Synthetic particle images are used to investigate the effect of different image parameters.It is found that the velocity RMS-uncertainty level of the single-pixel ensemble correlation method can be equivalent to the conventional window correlation method once the effective particle number used for the ensemble correlation is large enough.Furthermore,a canonical turbulent boundary layer is synthetically simulated based on velocity statistics resolved by previous Direct Numerical Simulation(DNS)work(Schlatter P,et al.J Fluid Mech,2010,659:116–126).The relative error of wall skin friction coefficient is shown to be one-order smaller than that of the window correlation method.And the optimization strategy to further minimize the measurement uncertainty is discussed in the last part.
基金co-supported by the State Key Program of National Natural Science Foundation of China (No. 91538204)the National Science Fund for Distinguished Young Scholars (No. 61425014)the National Key Technologies R&D Program of China (No. 2015BAG15B01)
文摘Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliable method to accurately measure traffic complexity is important. Considering that many factors correlate with traffic complexity in complicated nonlinear ways,researchers have proposed several complexity evaluation methods based on machine learning models which were trained with large samples. However, the high cost of sample collection usually results in limited training set. In this paper, an ensemble learning model is proposed for measuring air traffic complexity within a sector based on small samples. To exploit the classification information within each factor, multiple diverse factor subsets（FSSs） are generated under guidance from factor noise and independence analysis. Then, a base complexity evaluator is built corresponding to each FSS. The final complexity evaluation result is obtained by integrating all results from the base evaluators. Experimental studies using real-world air traffic operation data demonstrate the advantages of our model for small-sample-based traffic complexity evaluation over other stateof-the-art methods.
基金supported by the National Natural Science Foundation of China(Nos.61174103 and61603032)the National Key Technologies R&D Program of China(No.2015BAK38B01)+2 种基金the National Key Research and Development Program of China(No.2017YFB0702300)the China Postdoctoral Science Foundation(No.2016M590048)the University of Science and Technology Beijing–Taipei University of Technology Joint Research Program(TW201705)
文摘The Extreme Learning Machine（ELM） is an effective learning algorithm for a Single-Layer Feedforward Network（SLFN）. It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning（NCL） to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs（HE2 LM） for classification has different ELM algorithms including the Regularized ELM（RELM）, the Kernel ELM（KELM）, and the L2-norm-optimized ELM（ELML2）. The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers.
基金partially supported by the National Natural Science Foundation of China(Grant No.91125010)
文摘This paper presents a new correction method, "instant correction method(ICM)", to improve the accuracy of numerical prediction products(NPP) and provide weather variables at grid cells. The ICM makes use of the continuity in time of the forecast errors at different forecast times to improve the accuracy of large scale NPP. To apply the ICM in China, an ensemble correction scheme is designed to correct the T213 NPP(the most popular NPP in China) through different statistical methods. The corrected T213 NPP(ICM T213 NPP) are evaluated by four popular indices: Correlation coefficient, climate anomalies correlation coefficient, root-mean-square-errors(RMSE), and confidence intervals(CI). The results show that the ICM T213 NPP are more accurate than the original T213 NPP in both the training period(2003–2008) and the validation period(2009–2010). Applications in China over the past three years indicate that the ICM is simple, fast, and reliable. Because of its low computing cost, end users in need of more accurate short-range weather forecasts around China can benefit greatly from the method.
