Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

Chen, Gongfa; Liu, Zongchao; Li, Cheng; Sun, Xiaoyan

doi:10.3390/s21123950

Cited by 42 publications

(19 citation statements)

References 43 publications

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“…This multisensor system needs to use new methods to process many multidimensional data. In this application background, a new data processing method, that is, the multisensor data fusion technology method, came into being [17].…”

Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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This study is aimed at improving the utilization efficiency of resources and enhancing the experiments’ effect of various composite membrane research. Firstly, the meaning and preparation process of Metal-Organic Frameworks (MOFs) are discussed. Then, the theoretical knowledge of fusing machine learning and multisensor technology is outlined. Finally, based on the controllable fabrication concept of MOF [UIO- (Universitetet I Oslo-) 66]/ZrAl ceramic composite membranes, a multisensor model incorporating machine learning is designed. The results show that the designed radial sensor backpropagation (RS-BP) fusion multisensor model has the highest error rate of about 0.87. When the number of training is about 100 times, the model’s error rate tends to be stable, and the minimum error rate is about 0.01. Secondly, the maximum adsorption capacity of the composite membrane under the controllable preparation of the model is 800 cm3/g Spanning Tree Protocol (STP). Additionally, the adsorption capacity decreases slowly, and the overall adsorption energy is higher than that of the traditional preparation method. Finally, the catalytic efficiency of membranes prepared by fusing multiple sensors is 90%-97%. The research achieves innovation in technology and improves the feasibility of rational application of MOF (UIO-66)/ZrAl ceramic composite membranes. This study not only provides technical support for the development of machine learning fusion multisensing technology but also contributes to the comprehensive improvement of the resource utilization effect.

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Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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“…The DLF models fuse multichannel/multiscale information and typically produce more consistent and better prediction performance than individual models, have good noise immunity, can handle high-dimensional data, provide complete and detailed object information, and are simple to implement and fast to train [32,33]. These models are extensively used in the fields of injury detection, artificial intelligence, and image processing [34][35][36]. Based on previous studies, machine learning and hyperspectral imagery have been used successfully in many applications, but the strategy based on DLF model fusion has not yet been applied to crop yield prediction [37,38].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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“…CNN is a kind of deep feedforward neural network, which is specialized in processing data with network structure. 24 It has the ability of feature extraction and weight sharing. CNN includes 3D CNN, 2D CNN, and 1D CNN, the differences are as follows:…”

Section: Hybrid Neural Networkmentioning

confidence: 99%

A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery

Gao

Liu

Zhu

et al. 2022

Measurement and Control

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For accelerating the technology development and facilitating the reliable operation of lithium-ion batteries, accurate prediction for battery remaining useful life (RUL) are both critical. In this paper, a 1D CNN-BiLSTM method is proposed to extract the RUL prediction of lithium-ion battery of Electric Vehicles (EVs). By using one dimensional convolutional neural network (1D CNN) and bidirectional long short-term memory (BiLSTM) neural network simultaneously, selecting the ELU activation function to apply to the convolutional layer, a hybrid neural network is proposed to improve the accuracy and stability of lithium-ion battery RUL prediction. The 1D CNN is used to fully mine the deep features of lithium-ion SOH data, while the BiLSTM is adopted to study the deep features in two directions, and the RUL prediction of lithium-ion battery is output through dense layer. To verify the effectiveness of the proposed method, the battery data of the National Aeronautics and Space Administration (NASA) are utilized to make some comparisons among the RNN model, LSTM model, BiLSTM model and hybrid neural network model. The results show that the hybrid one has higher generalization ability and prediction accuracy than the others.

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Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network

Cited by 42 publications

References 43 publications

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery

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