Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics

[1]  Jian Shen,et al.  A Method of Data Recovery Based on Compressive Sensing in Wireless Structural Health Monitoring , 2014 .

[2]  Qiushuang Lin,et al.  Kriging based sequence interpolation and probability distribution correction for gaussian wind field data reconstruction , 2020 .

[3]  Yandong Yang,et al.  Power load probability density forecasting using Gaussian process quantile regression , 2017 .

[4]  F. Cecinato,et al.  CFD-Based Framework for Analysis of Soil–Pipeline Interaction in Reconsolidating Liquefied Sand , 2020, Journal of Engineering Mechanics.

[5]  G. Matheron Principles of geostatistics , 1963 .

[6]  Kan Wang,et al.  A new power mapping method based on ordinary kriging and determination of optimal detector location strategy , 2014 .

[7]  Francesco Cadini,et al.  Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis , 2019, Structural Control and Health Monitoring.

[8]  N. Altman An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[9]  Giorgio Serino,et al.  Study of wire rope devices for improving the re‐centering capability of base isolated buildings , 2017 .

[10]  R. Koenker,et al.  Regression Quantiles , 2007 .

[11]  Masayuki Kohiyama,et al.  Detection method of unlearned pattern using support vector machine in damage classification based on deep neural network , 2020, Structural Control and Health Monitoring.

[12]  Slawomir Koziel,et al.  Accelerated design optimization of miniaturized microwave passives by design reusing and Kriging interpolation surrogates , 2020 .

[13]  Yl L. Xu,et al.  Structural damage identification via response reconstruction under unknown excitation , 2017 .

[14]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[15]  Gao Fan,et al.  Lost data recovery for structural health monitoring based on convolutional neural networks , 2019, Structural Control and Health Monitoring.

[16]  David V. Rosowsky,et al.  Nonlinear seismic response reconstruction and performance assessment of instrumented wood‐frame buildings—Validation using NEESWood Capstone full‐scale tests , 2019, Structural Control and Health Monitoring.

[17]  Chao Wang,et al.  Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network , 2019, Applied Energy.

[18]  K. Sarma,et al.  Mapping spatial distribution of traffic induced criteria pollutants and associated health risks using kriging interpolation tool in Delhi , 2020 .

[19]  Yuequan Bao,et al.  A novel distribution regression approach for data loss compensation in structural health monitoring , 2018 .

[20]  Haiyan Li,et al.  Probability density forecasting of wind power using quantile regression neural network and kernel density estimation , 2018 .

[21]  A G Davenport,et al.  NOTE ON THE DISTRIBUTION OF THE LARGEST VALUE OF A RANDOM FUNCTION WITH APPLICATION TO GUST LOADING. , 1964 .

[22]  Fuyou Xu,et al.  Interpolation of wind pressures using Gaussian process regression , 2019, Journal of Wind Engineering and Industrial Aerodynamics.

[23]  Y. C. Wang,et al.  Field measurement system based on a wireless sensor network for the wind load on spatial structures: Design, experimental, and field validation , 2018 .

[24]  H. Muller,et al.  Functional data analysis for density functions by transformation to a Hilbert space , 2016, 1601.02869.

[25]  Ahsan Kareem,et al.  An efficient space–time based simulation approach of wind velocity field with embedded conditional interpolation for unevenly spaced locations , 2016 .

[26]  Chenyang Lu,et al.  Benchmark problem in active structural control with wireless sensor network , 2016 .

[27]  Hui Li,et al.  LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data , 2018, Mechanical Systems and Signal Processing.

[28]  Zhicong Chen,et al.  A low‐noise, real‐time, wireless data acquisition system for structural monitoring applications , 2014 .

[29]  James W. Taylor A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns , 2000 .

[30]  Forrest J. Masters,et al.  Low-rise gable roof buildings pressure prediction using deep neural networks , 2020 .

[31]  Li Mo,et al.  Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest , 2020 .

[32]  Ying Lei,et al.  Synthesize identification and control for smart structures with time‐varying parameters under unknown earthquake excitation , 2020, Structural Control and Health Monitoring.

[33]  G. Hu,et al.  Non-Gaussian properties and their effects on extreme values of wind pressure on the roof of long-span structures , 2019, Journal of Wind Engineering and Industrial Aerodynamics.

[34]  L. Chen,et al.  Multi-scale correlation analyses of two lateral profiles of full-scale downburst wind speeds , 2006 .

[35]  Zilong Zou,et al.  Compressive sensing‐based lost data recovery of fast‐moving wireless sensing for structural health monitoring , 2015 .

[36]  Qing-shan Yang,et al.  Investigation of wind load on 1,000 m‐high super‐tall buildings based on HFFB tests , 2018 .

[37]  V. Lakshmikantham,et al.  Stability of conditionally invariant sets and controlleduncertain dynamic systems on time scales , 1995 .

[38]  T. Kármán Progress in the Statistical Theory of Turbulence , 1948 .

[39]  Yongchao Yang,et al.  Harnessing data structure for recovery of randomly missing structural vibration responses time history: Sparse representation versus low-rank structure , 2016 .

[40]  J. Holmes,et al.  Probability distributions of extreme pressure coefficients , 2003 .

[41]  C. Li,et al.  Fast simulation of non-stationary wind velocity based on time-frequency interpolation , 2019, Journal of Wind Engineering and Industrial Aerodynamics.

[42]  Zhu Xue,et al.  Prediction of wind loads on high-rise building using a BP neural network combined with POD , 2017 .

[43]  Yongchao Yang,et al.  Robust data transmission and recovery of images by compressed sensing for structural health diagnosis , 2017 .

[44]  Xinzhong Chen,et al.  Analysis of Alongwind Tall Building Response to Transient Nonstationary Winds , 2008 .

[45]  R. Webster,et al.  Kriging: a method of interpolation for geographical information systems , 1990, Int. J. Geogr. Inf. Sci..

[46]  Franklin T. Lombardo,et al.  Thunderstorm characteristics of importance to wind engineering , 2014 .