Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one. In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system. Our proposed autoencoder communication system can better reduce the signal noise by adding an “attention mechanism” and “soft thresholding” modules and has better performance at various signal-to-noise ratios (SNR). Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs. It has been shown to work efficiently in the AWGN channel. Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.

Specific Emitter Identification for IoT Devices Based on Deep Residual Shrinkage Networks

Specific emitter identification can distin-guish individual transmitters by analyzing received signals and extracting inherent features of hard-ware circuits.Feature extraction is a key part of traditional machine learning-based methods,but manual extrac-tion is generally limited by prior professional knowl-edge.At the same time,it has been noted that the per-formance of most specific emitter identification meth-ods degrades in the low signal-to-noise ratio(SNR)environments.The deep residual shrinkage network(DRSN)is proposed for specific emitter identification,particularly in the low SNRs.The soft threshold can preserve more key features for the improvement of performance,and an identity shortcut can speed up the training process.We collect signals via the receiver to create a dataset in the actual environments.The DRSN is trained to automatically extract features and imple-ment the classification of transmitters.Experimental results show that DRSN obtains the best accuracy un-der different SNRs and has less running time,which demonstrates the effectiveness of DRSN in identify-ing specific emitters.

Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment,a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network(IADRSN)is proposed.Firstly,the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets.Then,a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established.To improve the ability of fault feature extraction of the model,the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer.To further improve the anti-noise ability of the model,the first layer of the model was set as the Drop-block layer.Finally,the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results.The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.

基于GRU-DRSN的双通道人体活动识别

人体活动识别(human activity recognizition, HAR)在医疗、军工、智能家居等领域有很大的应用空间。传统机器学习方法特征提取难度较大且精度不高。针对上述问题并结合传感器时序特性,提出了一种融合CBAM(convolutional block attention module)注意力机制的GRU-DRSN双通道并行模型,有效避免了传统串行模型因网络深度加深引起梯度爆炸和消失问题。同时并行结构使得两条支路具有相同的优先级,使用深度残差收缩网络(deep residual shrinkage network, DRSN)提取数据的深层空间特征,同时使用门控循环结构(gated recurrent unit, GRU)学习活动样本在时间序列上的特征,同时进行提取样本不同维度的特征,并通过CBAM模块进行特征的权重分配,最后通过Softmax层进行识别,实现了端对端的人体活动识别。使用公开数据集(wireless sensor data mining, WISDM)进行验证,模型平均精度达到了97.6%,与传统机器学习模型和前人所提神经网络模型相比,有更好的识别效果。

Improved deep residual shrinkage network for a multi-cylinder heavy-duty engine fault detection with single channel surface vibration

The health monitoring and fault diagnosis of heavy-duty engines are increasingly important for energy storage ecosystem. During operation, vibration characters corresponding to the specific fault need to be extracted from the overall system vibration. Faulty characteristics emanating from one single cylinder are also mixed with those from other cylinders. Besides, the change of working condition brings strong nonlinearities in surface vibration. To solve these problems, an improved deep residual shrinkage network (IDRSN) is developed for detecting diverse engine faults at various degrees using single channel surface vibration signal. Within IDRSN, a wide convolution kernel is utilized in first convolution layer to capture the long-term fault-related impacts and eliminate the short-time random impact. The residual network module is adopted to enhance the focus the relevant components of vibration signals. Mini-batch training strategy is used to improve the model stability. Meanwhile, Gradient-weighted class activation map is adopted to assess the consistency between the learned knowledge and the fault-related information. The IDRSN is implemented to diagnosing a diesel engine under various faults, faulty degrees and operating speeds. Comparisons with existing models are analyzed in terms of hyper-parameters, training samples, noise resistance, and visualization. Results demonstrate the proposed IDRSN's superior performance on fault diagnosis accuracy, stability, anti-noise performance, and anti-interference performance. An average accuracy rate of 98.38 % was achieved by the proposed IDRSN, in comparison to 96.64 % and 93.56 % achieved by the DRSN and the wide-kernel deep convolutional neural network respectively. These results highlight the proposed IDRSN's superiority in diagnosing multiple faults under various working conditions, offering a low-cost, highly effective, and applicable approach for complex fault diagnosis tasks.

A novel residual shrinkage block-based convolutional neural network for improving the recognition of motor imagery EEG signals

Purpose-Recently,the convolutional neural network(ConvNet)has a wide application in the classification of motor imagery EEG signals,However,the low sigalto-noise electroencephalogram(EEG)signals are ollectede under the interference of noises.However,the conventional ConvNet model cannot directly solve this problem.This study aims to discuss the aforementioned issues.Design/methodology/approach-To solve this problem,this paper adopted a novel residual shrinkage block(RSB)to construct the ComvNet model(RSBConvNet).During the feature extraction from EEG simnals,the proposed RSBConvNet prevented the noise component in EEG signals,and improved the classification accuracy of motor imagery.In the construction of RSBConvNet,the author applied the soft thresholding strategy to prevent the non-related.motor imagery features in EEG sigmals.The soft thresholding was inserted into the residual block(RB),and the suitable threshold for the curent EEG signals distribution can be learned by minimizing the loss function.Therefore,during the feature extraction of motor imagery,the proposed RSBConvNet de noised the EEG signals and improved the discriminative of dassifiation features.Findings-Comparative experiments and ablation studies were done on two public benchumark datasets.Compared with conventionalConvNet models,the proposed RSBConvNet model has olbvious improvements in motor imagery classification accuracy and Kappa officient.Ablation studies have also shown the de noised abilities of the RSBConvNet modeL Morbover,different parameters and computational methods of the RSBConvNet model have been tested om the dassificatiton of motor imagery.Originality/value-Based ou the experimental results,the RSBComvNet constructed in this paper has an excellent reogmition accuracy of M-BCI which can be used for further appications for the online MI-BCI.

Data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network

A control valve is one of the most widely used machines in hydraulic systems.However,it often works in harsh environments and failure occurs from time to time.An intelligent and robust control valve fault diagnosis is therefore important for operation of the system.In this study,a fault diagnosis based on the mathematical model(MM)imputation and the modified deep residual shrinkage network(MDRSN)is proposed to solve the problem that data-driven models for control valves are susceptible to changing operating conditions and missing data.The multiple fault time-series samples of the control valve at different openings are collected for fault diagnosis to verify the effectiveness of the proposed method.The effects of the proposed method in missing data imputation and fault diagnosis are analyzed.Compared with random and k-nearest neighbor(KNN)imputation,the accuracies of MM-based imputation are improved by 17.87%and 21.18%,in the circumstances of a20.00%data missing rate at valve opening from 10%to 28%.Furthermore,the results show that the proposed MDRSN can maintain high fault diagnosis accuracy with missing data.

Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks

This study explored the potential of using probabilistic neural networks (PNN) to predict shrinkage of thermal insulation mortar.Probabilistic results were obtained from the PNN model with the aid of Parzen non-parametric estimator of the probability density functions (PDF).Five variables,water-cementitious materials ratio,content of cement,fly ash,aggregate and plasticizer,were employed for input variables,while a category of 56-d shrinkage of mortar was used for the output variable.A total of 192 groups of experimental data from 64 mixtures designed using JMP7.0 software were collected,of which 120 groups of data were used for training the model and the other 72 groups of data for testing.The simulation results showed that the PNN model with an optimal smoothing parameter determined by the curves of the mean square error (MSE) and the number of unrecognized probability densities (UPDs) exhibited a promising capability of predicting shrinkage of mortar.

Capillary shrinkage of graphene oxide hydrogels

Conventional carbon materials cannot combine high density and high porosity,which are required in many applications,typically for energy storage under a limited space.A novel highly dense yet porous carbon has previously been produced from a three-dimensional(3D)reduced graphene oxide(r-GO)hydrogel by evaporation-induced drying.Here the mechanism of such a network shrinkage in r-GO hydrogel is specifically illustrated by the use of water and 1,4-dioxane,which have a sole difference in surface tension.As a result,the surface tension of the evaporating solvent determines the capillary forces in the nanochannels,which causes shrinkage of the r-GO network.More promisingly,the selection of a solvent with a known surface tension can precisely tune the microstructure associated with the density and porosity of the resulting porous carbon,rendering the porous carbon materials great potential in practical devices with high volumetric performance.