The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition.In this study,a new method is proposed based on ensemble deep transfer lear...The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition.In this study,a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions.First,a novel stacked autoencoder(NSAE)is constructed using a denoising autoencoder,batch normalization,and the Swish activation function.Second,a series of source-domain NSAEs with multisensor vibration signals is pretrained.Third,the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs.Finally,a modified voting fusion strategy is designed to obtain a comprehensive result.The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method.The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample,thereby outperforming the existing methods.展开更多
Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challeng...Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challenge. In this paper we propose a novel patch-based segmentation method combining a local weighted voting strategy with Bayesian inference. Multiple atlases are registered to a target image by an advanced normalization tools(ANTs) algorithm. To obtain a segmentation of the target, labels of the atlas images are propagated to the target image. We first adopt intensity prior and label prior as two key metrics when implementing the local weighted voting scheme, and then compute the two priors at the patch level. Further, we analyze the label fusion procedure concerning the image background and take the image background as an isolated label when estimating the label prior. Finally, by taking the Dice score as a criterion to quantitatively assess the accuracy of segmentations, we compare the results with those of other methods, including joint fusion, majority voting, local weighted voting, majority voting based on patch, and the widely used Free Surfer whole-brain segmentation tool. It can be clearly seen that the proposed algorithm provides better results than the other methods. During the experiments, we make explorations about the influence of different parameters(including patch size, patch area, and the number of training subjects) on segmentation accuracy.展开更多
To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to ...To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.展开更多
基金the National Natural Science Foundation of China(Grant No.51905160)the Natural Science Foundation of Hunan Province(Grant No.2020JJ5072)the Fundamental Research Funds for the Central Universities(Grant No.531118010335)。
文摘The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition.In this study,a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions.First,a novel stacked autoencoder(NSAE)is constructed using a denoising autoencoder,batch normalization,and the Swish activation function.Second,a series of source-domain NSAEs with multisensor vibration signals is pretrained.Third,the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs.Finally,a modified voting fusion strategy is designed to obtain a comprehensive result.The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method.The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample,thereby outperforming the existing methods.
基金Project supported by the National Natural Science Foundation of China(No.61203224)the Science and Technology Innovation Foundation of Shanghai Municipal Education Commission,China(No.13YZ101)
文摘Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challenge. In this paper we propose a novel patch-based segmentation method combining a local weighted voting strategy with Bayesian inference. Multiple atlases are registered to a target image by an advanced normalization tools(ANTs) algorithm. To obtain a segmentation of the target, labels of the atlas images are propagated to the target image. We first adopt intensity prior and label prior as two key metrics when implementing the local weighted voting scheme, and then compute the two priors at the patch level. Further, we analyze the label fusion procedure concerning the image background and take the image background as an isolated label when estimating the label prior. Finally, by taking the Dice score as a criterion to quantitatively assess the accuracy of segmentations, we compare the results with those of other methods, including joint fusion, majority voting, local weighted voting, majority voting based on patch, and the widely used Free Surfer whole-brain segmentation tool. It can be clearly seen that the proposed algorithm provides better results than the other methods. During the experiments, we make explorations about the influence of different parameters(including patch size, patch area, and the number of training subjects) on segmentation accuracy.
基金This project was supported by the National Basic Research Programof China (2001CB309403)
文摘To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.
