The corrosion behavior of Mg-10Gd-xZn(x=2,6 wt.%)alloys in 0.5 wt.%NaCl solution was investigated.Microstructures of both the alloys consisted of(Mg,Zn)_(3) Gd phase and lamellar long period stacking ordered(LPSO)phas...The corrosion behavior of Mg-10Gd-xZn(x=2,6 wt.%)alloys in 0.5 wt.%NaCl solution was investigated.Microstructures of both the alloys consisted of(Mg,Zn)_(3) Gd phase and lamellar long period stacking ordered(LPSO)phase.The morphology of the second phase at the grain boundary differed in both alloys:it was a continuous network structure in Mg-10Gd-6Zn,whereas it was relatively discrete in Mg-10Gd-2Zn.The dendrites were finer in size and highly branched in Mg-10Gd-6Zn.The corrosion results indicated that the increase in Zn content increased the corrosion rate in Mg-10Gd-xZn alloys.Micro-galvanic corrosion occurred near the grain boundary in both alloys initially as the grain boundary phase was stable and acted as a cathode,however,filiform corrosion dominated in the later stage,which was facilitated by the LPSO phase in the matrix.Severe micro-galvanic corrosion occurred in Mg-10Gd-6Zn due to the higher volume of second phase.The stability of the second phase at the grain boundary was altered and dissolved after the long immersion times.Probably the NaCl solution chemically reacted with the grain boundary phase and de-stabilized it during the long immersion times,and was removed by the chromic acid used for the corrosion product removal.展开更多
Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in f...Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCNNclassifierwas 96.5%for the images acquired from various hospitals as well as databases.A comparison between the accuracy rates of BSN andDCNN approaches showed thatDCNNwith fundus images decreased the error rate to 4%.展开更多
The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if t...The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.展开更多
文摘The corrosion behavior of Mg-10Gd-xZn(x=2,6 wt.%)alloys in 0.5 wt.%NaCl solution was investigated.Microstructures of both the alloys consisted of(Mg,Zn)_(3) Gd phase and lamellar long period stacking ordered(LPSO)phase.The morphology of the second phase at the grain boundary differed in both alloys:it was a continuous network structure in Mg-10Gd-6Zn,whereas it was relatively discrete in Mg-10Gd-2Zn.The dendrites were finer in size and highly branched in Mg-10Gd-6Zn.The corrosion results indicated that the increase in Zn content increased the corrosion rate in Mg-10Gd-xZn alloys.Micro-galvanic corrosion occurred near the grain boundary in both alloys initially as the grain boundary phase was stable and acted as a cathode,however,filiform corrosion dominated in the later stage,which was facilitated by the LPSO phase in the matrix.Severe micro-galvanic corrosion occurred in Mg-10Gd-6Zn due to the higher volume of second phase.The stability of the second phase at the grain boundary was altered and dissolved after the long immersion times.Probably the NaCl solution chemically reacted with the grain boundary phase and de-stabilized it during the long immersion times,and was removed by the chromic acid used for the corrosion product removal.
文摘Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCNNclassifierwas 96.5%for the images acquired from various hospitals as well as databases.A comparison between the accuracy rates of BSN andDCNN approaches showed thatDCNNwith fundus images decreased the error rate to 4%.
文摘The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.
