Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generali...Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.展开更多
In this study,the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised.Silica fume was used at concentrations of 0%,5%,10%,and 20...In this study,the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised.Silica fume was used at concentrations of 0%,5%,10%,and 20%.Cube specimens(100 mm×100 mm×100 mm)were prepared for testing the compressive strength and ultrasonic pulse velocity.They were cured at 20℃zb2℃ in a standard cure for 7,28,and 90 d.After curing,they were subjected to temperatures of 20℃,200℃,400℃,600℃,and 800℃.Two well-known deep learning approaches,i.e.,stacked autoencoders and long short-term memory(LSTM)networks,were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures.The forecasting experiments were carried out using MATLAB deep learning and neural network tools,respectively.Various statistical measures were used to validate the prediction performances of both the approaches.This study found that the LSTM network achieved better results than the stacked autoencoders.In addition,this study found that deep learning,which has a very good prediction ability with little experimental data,was a convenient method for civil engineering.展开更多
文摘Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.
基金supported by the National Natural Science Foundation of China(No.51605309)the Aeronautical Science Foundation of China(Nos.201933054002,20163354004)。
基金The experimental part of this study was supported by the Firat University BAPYB(Project No.TEF.12.04)he authors gratefully acknowledge the Firat University of BAPYB.
文摘In this study,the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised.Silica fume was used at concentrations of 0%,5%,10%,and 20%.Cube specimens(100 mm×100 mm×100 mm)were prepared for testing the compressive strength and ultrasonic pulse velocity.They were cured at 20℃zb2℃ in a standard cure for 7,28,and 90 d.After curing,they were subjected to temperatures of 20℃,200℃,400℃,600℃,and 800℃.Two well-known deep learning approaches,i.e.,stacked autoencoders and long short-term memory(LSTM)networks,were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures.The forecasting experiments were carried out using MATLAB deep learning and neural network tools,respectively.Various statistical measures were used to validate the prediction performances of both the approaches.This study found that the LSTM network achieved better results than the stacked autoencoders.In addition,this study found that deep learning,which has a very good prediction ability with little experimental data,was a convenient method for civil engineering.
