相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN)
Mengying Li et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)
Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals
Chao Qun et al.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2022)
Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring
Mohammad Hassan Daneshvar et al.
ENGINEERING STRUCTURES (2022)
SHM under varying environmental conditions: an approach based on model order reduction and deep learning
Matteo Torzoni et al.
COMPUTERS & STRUCTURES (2022)
Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling
Hassan Sarmadi et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)
Detection and segmentation of underwater objects from forward-looking sonar based on a modified Mask RCNN
Zhimiao Fan et al.
SIGNAL IMAGE AND VIDEO PROCESSING (2021)
Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
Shuai Teng et al.
APPLIED SCIENCES-BASEL (2021)
RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting
Kiran Kumar Chandriah et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2021)
Damage identification by wavelet analysis of modal rotation differences
Andrzej Katunin et al.
STRUCTURES (2021)
Structural damage detection using convolutional neural networks combining strain energy and dynamic response
Shuai Teng et al.
MECCANICA (2020)
Deep learning for data anomaly detection and data compression of a long-span suspension bridge
FuTao Ni et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2020)
Best optimizer selection for predicting bushfire occurrences using deep learning
Malka N. Halgamuge et al.
NATURAL HAZARDS (2020)
Entropy Measures in Machine Fault Diagnosis: Insights and Applications
Zhiqiang Huo et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
Kefeng Zhong et al.
APPLIED SCIENCES-BASEL (2020)
A Nonparametric Method for Identifying Structural Damage in Bridges Based on the Best-Fit Auto-Regressive Models
Tung Khuc et al.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS (2020)
Towards semi-supervised and probabilistic classification in structural health monitoring
L. A. Bull et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks
Serkan Kiranyaz et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Fault Classification Decision Fusion System Based on Combination Weights and an Improved Voting Method
Fanliang Zeng et al.
PROCESSES (2019)
Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring
Zhiyi Tang et al.
STRUCTURAL CONTROL & HEALTH MONITORING (2019)
Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks
Onur Avci et al.
JOURNAL OF SOUND AND VIBRATION (2018)
Active learning for semi-supervised structural health monitoring
L. Bull et al.
JOURNAL OF SOUND AND VIBRATION (2018)
An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis
Shaobo Li et al.
SENSORS (2017)
Structural Damage Detection with Automatic Feature-Extraction through Deep Learning
Yi-zhou Lin et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2017)
A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function
Ramin Ghiasi et al.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL (2016)
Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization
Young-Jin Cha et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2015)
Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks
Pang-jo Chun et al.
SHOCK AND VIBRATION (2015)
Phone recognition with hierarchical convolutional deep maxout networks
Laszlo Toth
EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING (2015)
Modal flexibility-based damage detection of cantilever beam-type structures using baseline modification
S. H. Sung et al.
JOURNAL OF SOUND AND VIBRATION (2014)
A multi-stage data-fusion procedure for damage detection of linear systems based on modal strain energy
Ernesto Grande et al.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING (2014)
Damage classification and estimation in experimental structures using time series analysis and pattern recognition
Oliver R. de Lautour et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2010)
Deep, Big, Simple Neural Nets for Handwritten Digit Recognition
Dan Claudiu Ciresan et al.
NEURAL COMPUTATION (2010)
Damage identification in timber bridges utilising the damage index method and neural network ensembles
U. Dackermann et al.
AUSTRALIAN JOURNAL OF STRUCTURAL ENGINEERING (2009)
Seismic damage identification in buildings using neural networks and modal data
Maria P. Gonzalez et al.
COMPUTERS & STRUCTURES (2008)
Damage detection of truss bridge joints using Artificial Neural Networks
M. Mehrjoo et al.
EXPERT SYSTEMS WITH APPLICATIONS (2008)
The PolyMAX frequency-domain method: a new standard for modal parameter estimation?
B Peeters et al.
SHOCK AND VIBRATION (2004)
Vibration-based damage detection for composite structures using wavelet transform and neural network identification
LH Yam et al.
COMPOSITE STRUCTURES (2003)
Multiple damage location with flexibility curvature and relative frequency change for beam structures
Q Lu et al.
JOURNAL OF SOUND AND VIBRATION (2002)