A Multi-Scale Fusion Convolutional Neural Network based on Attention Mechanism for the Visualization Analysis of EEG Signals Decoding

Visible image quality is very susceptible to changes in illumination, and there are limitations in ship classification using images acquired by a single sensor. This study proposes a ship classification method based on an attention mechanism and multi-scale convolutional neural network (MSCNN) for visible and infrared images. First, the features of visible and infrared images are extracted by a two-stream symmetric multi-scale convolutional neural network module, and then concatenated to make full use of the complementary features present in multi-modal images. After that, the attention mechanism is applied to the concatenated fusion features to emphasize local details areas in the feature map, aiming to further improve feature representation capability of the model. Lastly, attention weights and the original concatenated fusion features are added element by element and fed into fully connected layers and Softmax output layer for final classification output. Effectiveness of the proposed method is verified on a visible and infrared spectra (VAIS) dataset, which shows 93.81% accuracy in classification results. Compared with other state-of-the-art methods, the proposed method could extract features more effectively and has better overall classification performance.

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Track Prediction for HF Radar Vessels Submerged in Strong Clutter Based on MSCNN Fusion with GRU-AM and AR Model

Remote Sensing ◽

10.3390/rs13112164 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2164

Author(s):

Ling Zhang ◽

Jingzhi Zhang ◽

Jiong Niu ◽

Q. M. Jonathan Wu ◽

Gangsheng Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Attention Mechanism ◽

Hf Radar ◽

Ar Model ◽

Trajectory Prediction ◽

Long Distance ◽

Multi Scale ◽

Wave Radar ◽

Wide Range

High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong clutter and interference, causing the echo of vessels completely submerged by clutter. As a result, the target cannot be detected and tracked for a period of time under the influence of strong clutter, which causes broken trajectories. To solve this problem, we propose an HF radar-vessel trajectory-prediction method based on a multi-scale convolutional neural network (MSCNN) that combines a gated recurrent unit and attention mechanism (GRU-AM) and a fusion with an autoregressive (AR) model. The vessel’s latitude and longitude information obtained by the HF radar is sent into the convolutional neural network (CNN) with different window lengths in parallel, and feature fusion is performed on the extracted multi-scale features. The deep GRU model is built to learn the time series with the GRU structure to preserve historical information. Different weights are given to the features using the temporal attention mechanism (AM), which helps the network learn the key information. The linear information on latitude and longitude at the current timestep is forecast by combining the AR model with the trajectory output from the AM to achieve a combination of linear and nonlinear prediction models. To make full use of the HF radar tracking information, the broken trajectory prediction is carried out by forward and backward computation using data from before and after the fracture, respectively. Weights are then assigned to the two predicted results by the entropy-value method to obtain the final ship trajectory by weighted summation. Field experiments show that the proposed method can accurately forecast the trajectories of vessels concealed in clutter. In comparison with other mainstream methods, the new method performs better in estimation accuracy for HF radar vessels concealed in clutter.

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Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism

ISA Transactions ◽

10.1016/j.isatra.2020.10.054 ◽

2020 ◽

Author(s):

Zifei Xu ◽

Chun Li ◽

Yang Yang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Attention Mechanism ◽

Rolling Bearings ◽

Multi Scale ◽

Feature Attention

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Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Energies ◽

10.3390/en12203937 ◽

2019 ◽

Vol 12 (20) ◽

pp. 3937 ◽

Cited By ~ 4

Author(s):

Tengda Huang ◽

Sheng Fu ◽

Haonan Feng ◽

Jiafeng Kuang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Time Domain ◽

Attention Mechanism ◽

Intelligent Fault Diagnosis ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale

Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.

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Classification of hyperspectral image based on multi-scale convolutional neural network and attention mechanism

10.1117/12.2604557 ◽

2021 ◽

Author(s):

Chang Liu ◽

Han Jiang ◽

Yuhui Shi ◽

Panpan Xun ◽

Renhao Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hyperspectral Image ◽

Attention Mechanism ◽

Multi Scale

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A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

10.21203/rs.3.rs-554671/v1 ◽

2021 ◽

Author(s):

QINGHUA ZHONG ◽

Haibo Lei ◽

Qianru Chen ◽

Guofu Zhou

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sleep Disorder ◽

Eye Movement ◽

Cross Validation ◽

Single Channel ◽

Rapid Eye Movement Sleep ◽

Sleep Staging ◽

Eeg Signals ◽

Multi Scale

Abstract Sleep disorder is a serious public health problem. Non hospital sleep monitoring system for monitoring sleep quality can effectively support the screening of sleep disorder related diseases. A new algorithm of multi-scale residual convolutional neural network (MS-RESCNN) was proposed to discover the feature of electroencephalography (EEG) signals detected with wearable system and staging the sleep stage. EEG signals were analyzed by this algorithm every 30 seconds, and then sleep staging results of wake-up (W), rapid eye movement sleep (REM) and non-rapid eye movement sleep (NREM) were outputed. NREM can also be subdivided into N1, N2 and N3 stages. 5-fold cross validation and independent subject cross validation were performed on the dataset with Kappa cofficients 0.7360 and 0.7001, respectively. The accuracy rates of those methods were 92.06% and 91.13%, respectively. Compared with the other methods, our proposed method can obtain the information of sleep stages from single channel EEG signals without special feature extraction. It has a good performance and can provide support for clinical application based on automatic sleep staging.

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