Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring

In structural health monitoring, data quality is crucial to the performance of data-driven methods for structural damage identification, condition assessment, and safety warning. However, structura...

[1]  Michael Beer,et al.  Compressive sensing with an adaptive wavelet basis for structural system response and reliability analysis under missing data , 2017 .

[2]  Hui Li,et al.  Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring , 2018, Structural Control and Health Monitoring.

[3]  Xuelong Li,et al.  Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Feng Han,et al.  A Study on Data Loss Compensation of WiFi-Based Wireless Sensor Networks for Structural Health Monitoring , 2016, IEEE Sensors Journal.

[5]  Yuequan Bao,et al.  Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach , 2018, Structural Health Monitoring.

[6]  Hui Li,et al.  Computer vision and deep learning–based data anomaly detection method for structural health monitoring , 2019 .

[7]  Manus P. Henry,et al.  The self-validating sensor: rationale, definitions and examples , 1993 .

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

[9]  Shengchang Chen,et al.  Seismic data two-step recovery approach combining sparsity-promoting and hyperbolic Radon transform methods , 2015 .

[10]  Yuejie Chi,et al.  Low-Rank Matrix Completion [Lecture Notes] , 2018, IEEE Signal Processing Magazine.

[11]  Mohamed-Jalal Fadili,et al.  Inpainting and Zooming Using Sparse Representations , 2009, Comput. J..

[12]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[13]  Nick Gregor,et al.  NGA Project Strong-Motion Database , 2008 .

[14]  Massimiliano Pontil,et al.  Convex multi-task feature learning , 2008, Machine Learning.

[15]  Hui Li,et al.  The State of the Art of Data Science and Engineering in Structural Health Monitoring , 2019, Engineering.

[16]  James H. McClellan,et al.  Joint Seismic Data Denoising and Interpolation with Double-Sparsity Dictionary Learning , 2017, 1703.02461.

[17]  Michael B. Wakin,et al.  Modal Analysis With Compressive Measurements , 2014, IEEE Transactions on Signal Processing.

[18]  Hassan Mansour,et al.  Efficient matrix completion for seismic data reconstruction , 2015 .

[19]  Gul Agha,et al.  Next Generation Wireless Smart Sensors Toward Sustainable Civil Infrastructure , 2017 .

[20]  Gul Agha,et al.  Reliable multi-hop communication for structural health monitoring , 2010 .

[21]  Hui Li,et al.  Compressive sensing of wireless sensors based on group sparse optimization for structural health monitoring , 2018 .

[22]  D. Newland Harmonic wavelet analysis , 1993, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

[23]  Yan Yu,et al.  Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring , 2013 .

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

[25]  Hui Li,et al.  Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach , 2019, Structural Health Monitoring.

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