Convolutional neural network‐based data anomaly detection method using multiple information for structural health monitoring
暂无分享,去创建一个
Hui Li | Yuequan Bao | Zhicheng Chen | Zhiyi Tang | Hui Li | Y. Bao | Zhicheng Chen | Zhiyi Tang
[1] Hui Li,et al. Computer vision and deep learning–based data anomaly detection method for structural health monitoring , 2019 .
[2] Mohammad R. Jahanshahi,et al. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection , 2018 .
[3] Álvaro Cunha,et al. Continuous dynamic monitoring of a prestressed concrete bridge based on strain, inclination and crack measurements over a 14-year span , 2018 .
[4] Oral Büyüköztürk,et al. Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types , 2018, Comput. Aided Civ. Infrastructure Eng..
[5] Joo-Ho Choi,et al. Gear fault diagnosis using transmission error and ensemble empirical mode decomposition , 2018, Mechanical Systems and Signal Processing.
[6] Francesc Pozo,et al. Wind turbine fault detection and classification by means of image texture analysis , 2018, Mechanical Systems and Signal Processing.
[7] Xiaowei Luo,et al. Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images , 2018 .
[8] Xiaochun Luo,et al. Recognizing Diverse Construction Activities in Site Images via Relevance Networks of Construction-Related Objects Detected by Convolutional Neural Networks , 2018, J. Comput. Civ. Eng..
[9] Kaige Zhang,et al. Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning , 2018, J. Comput. Civ. Eng..
[10] Hyeran Byun,et al. Detecting Construction Equipment Using a Region-Based Fully Convolutional Network and Transfer Learning , 2018, J. Comput. Civ. Eng..
[11] Guowei Ma,et al. Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Hangingwall Stability , 2018, J. Comput. Civ. Eng..
[12] Hui Li,et al. Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer‐grade camera images , 2018 .
[13] Peter E.D. Love,et al. A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory , 2018 .
[14] Li Deng,et al. Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives] , 2018, IEEE Signal Processing Magazine.
[15] M. Sami Fadali,et al. Active neural predictive control of seismically isolated structures , 2018 .
[16] Yi-Zhou Lin,et al. Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..
[17] Yun-Lai Zhou,et al. Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure , 2017 .
[18] Hao Zhang,et al. Quantitative analysis of pit defects in an automobile engine cylinder cavity using the radial basis function neural network–genetic algorithm model , 2017 .
[19] Mustafa Gul,et al. Damage detection under varying temperature using artificial neural networks , 2017 .
[20] Yang Liu,et al. Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..
[21] Oral Büyüköztürk,et al. Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..
[22] Yongchao Yang,et al. Robust data transmission and recovery of images by compressed sensing for structural health diagnosis , 2017 .
[23] Yongchao Yang,et al. Modeling and harnessing sparse and low‐rank data structure: a new paradigm for structural dynamics, identification, damage detection, and health monitoring , 2017 .
[24] Lior Rokach,et al. Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems , 2016, DLRS.
[25] Jerome P. Lynch,et al. Structural health monitoring: technological advances to practical implementations [scanning the issue] , 2016, Proc. IEEE.
[26] Jerome P. Lynch,et al. Three-Tier Modular Structural Health Monitoring Framework Using Environmental and Operational Condition Clustering for Data Normalization: Validation on an Operational Wind Turbine System , 2016, Proceedings of the IEEE.
[27] Heng-Tze Cheng,et al. Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.
[28] Yongchao Yang,et al. Harnessing data structure for recovery of randomly missing structural vibration responses time history: Sparse representation versus low-rank structure , 2016 .
[29] Jinping Ou,et al. The state of the art in structural health monitoring of cable-stayed bridges , 2016 .
[30] Kemal Kilic,et al. An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance , 2015, Appl. Soft Comput..
[31] Chengqi Zhang,et al. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2015, KDD.
[32] Jianxiong Xiao,et al. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[33] Reinhold Huber-Mörk,et al. Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images , 2014, ISVC.
[34] Yongchao Yang,et al. Data Compression of Structural Seismic Responses via Principled Independent Component Analysis , 2014 .
[35] Satish Nagarajaiah,et al. Blind denoising of structural vibration responses with outliers via principal component pursuit , 2013, Structural Control and Health Monitoring.
[36] Jyrki Kullaa,et al. Detection, identification, and quantification of sensor fault in a sensor network , 2013 .
[37] K. Yuen,et al. A novel probabilistic method for robust parametric identification and outlier detection , 2012 .
[38] Jyrki Kullaa,et al. Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring , 2011 .
[39] Sven Behnke,et al. Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.
[40] Yann LeCun,et al. Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[41] Jyrki Kullaa,et al. Sensor validation using minimum mean square error estimation , 2010 .
[42] Mustafa Gul,et al. Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications , 2009 .
[43] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[44] Kai Goebel,et al. Correcting Sensor Drift and Intermittency Faults With Data Fusion and Automated Learning , 2008, IEEE Systems Journal.
[45] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[46] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[47] Gaëtan Kerschen,et al. Sensor validation using principal component analysis , 2005 .
[48] Dean Pomerleau,et al. Efficient Training of Artificial Neural Networks for Autonomous Navigation , 1991, Neural Computation.
[49] Azriel Rosenfeld,et al. Computer Vision , 1988, Adv. Comput..
[50] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[51] Xiaochun Luo,et al. Detecting non-hardhat-use by a deep learning method from far-field surveillance videos , 2018 .
[52] Michael A. Arbib,et al. The handbook of brain theory and neural networks , 1995, A Bradford book.