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| 1 | Electropolishing of NiTi for Improving Biocompatibility显示文摘A modified electrolyte (CH_3COOH-HClO_4-A-B) for electropolishing (EP) of NiTi was presented for improving the corrosion resistance and biocompatibility of the alloy.Using the proposed parameters,a homogeneous and uniform surface was obtained.Atomic force microscopy (AFM) revealed that the surface roughness (Ra) for EP sample (23.21 nm) was close to mechanical polishing (MP) sample (19.36 nm).Analysis by X-ray photoelectron spectroscopy (XPS) showed that Ti/Ni ratio increased from 3.1 for MP sample to 27.6 for EP sample.Measurements using potentiodynamic polarization in Hanks' solution showed that no pitting occurred for EP sample even though the applied potential increased up to 1500 mV (vs SCE),while the MP sample was broken down at 650 mV.The present study indicates that electropolishing NiTi with this modified electrolyte contributes to the improved biocompatibility of NiTi. | Wei WU Xinjie LIU Huimin HAN Dazhi YANG Shoudong LU | 2008 | Journal of Materials Science & Technology2008，24，6： | 4 |
| 2 | Image segmentation based on grabcut frame integrating multi-scale nonlinear structure tensor显示文摘 | Han Shoudong Tao Wenbing Wang Desheng | 2009 | IEEE Transactions on image processing2009，18，10： | 1 |
| 3 | Image Segmentation Based on Grabcut Framework Integrating Multiscale Nonlinear s-tructure Tensor显示文摘 | Han Shoudong Tao Wenbing Wang Desheng | 2009 | IEEE Transactions on Image Processing2009，18，10： | 1 |
| 4 | Fast Image Segmentation Based on Multilevel Banded Closed-Form Method显示文摘 | Han Shoudong Tao Wenbing Wu Xianglin | 2010 | Pattern Recognition Letters2010，31，3： | 1 |
| 5 | Image Segmenta- tion Based on GrabCut Framework Integrating Multiseale Nonlinear Structure Tensor显示文摘 | Shoudong Han Wenbing Tao Desheng Wang | 2009 | IEEE Transactions on Image Processing2009，，18： | 1 |
| 6 | Structural characteristics and antioxidant activities of the extracellular polysaccharides produced by marine bacterium Edwardsiella tarda显示文摘 | GUO Shoudong MAO Wenjun HAN Yin | 2010 | Bioresource Technology2010，101，： | 1 |
| 7 | Structural characteris-tics and antioxidant activities of the extracellular polysaccharides pro-duced by marine bacterium Edwardsiella tarda显示文摘 | GUO Shoudong MAO Wenjun HAN Yin | 2010 | BioresourceTechnology2010，101，： | 1 |
| 8 | The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit显示文摘Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment.At present,the diagnosis of various kinds of bearing fault information,such as the occurrence,location and degree of fault,can be carried out by machine learning and deep learning and realized through the multiclassification method.However,the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information.To improve the above shortcomings,an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approach-In this model,the labels of each bearing are binarized by using the binary relevance method.Then,the integrated convolutional neural network and gated recurrent unit(CNN-GRU)is employed to classify faults.Different from the general CNN networks,the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.Findings-The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model.The experimental results show that the average accuracy in test set is 99.7%,and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing,and the multilabel classification method is superior to the multiclassification method.Consequently,the model can intuitively classify faults with higher accuracy.Originality/value-The fault labels of each bearing are labeled according to the failure or not,the fault location,the damage mode and the damage degree,and then the binary value is obtained.The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method,and the predicted probability value of each fault label is directly output in the output layer,which visually distinguishes different fault conditions. | Shanling Han Shoudong Zhang Yong Li Long Chen | 2022 | International Journal of Intelligent Computing and Cybernetics2022，15，3： | 0 |
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