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
The structure and hydrogenation properties of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce(x=0,1,3,5;at%) alloys with M=Cr,Mo,Al were investigated.All the alloys show the same phase composition consisting of BCC matric and CeO...The structure and hydrogenation properties of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce(x=0,1,3,5;at%) alloys with M=Cr,Mo,Al were investigated.All the alloys show the same phase composition consisting of BCC matric and CeO_(2)phases which distribute along the grain boundary.The hydrogen capacities of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce change little with M,and none of them exceeds 2.0 wt%.But the plateau pressure shows remarkable linearly variation with the amount of M and the lattice parameter of BCC matric.The additions of Cr,Mo and A1 raise the plateau,but the linear relation of plateau versus BCC lattice parameter of Mo/Al-added alloys is opposite to that of Cr-added alloys and many other conventional hydrogen storage alloys.The radius of hydrogen site is introduced to explain the inconsistent.The plateau pressure of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce with any kind of M added increases with the contraction of the hydrogen site.The plateau pressure of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce can also be reflected by the stability of hydride which is judged by the enthalpy change during the dehydrogenation.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financially supported by the National High Technology Research and Development Program of China (No.2012AA051503)。
文摘The structure and hydrogenation properties of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce(x=0,1,3,5;at%) alloys with M=Cr,Mo,Al were investigated.All the alloys show the same phase composition consisting of BCC matric and CeO_(2)phases which distribute along the grain boundary.The hydrogen capacities of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce change little with M,and none of them exceeds 2.0 wt%.But the plateau pressure shows remarkable linearly variation with the amount of M and the lattice parameter of BCC matric.The additions of Cr,Mo and A1 raise the plateau,but the linear relation of plateau versus BCC lattice parameter of Mo/Al-added alloys is opposite to that of Cr-added alloys and many other conventional hydrogen storage alloys.The radius of hydrogen site is introduced to explain the inconsistent.The plateau pressure of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce with any kind of M added increases with the contraction of the hydrogen site.The plateau pressure of (V_(0.85)Fe_(0.15))_(100-x)M_(x)-Ce can also be reflected by the stability of hydride which is judged by the enthalpy change during the dehydrogenation.
