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The accuracy of dynamic threshold is low by using the weighted average method. In order to solve this problem,a dynamics threshold calculation method for swashplate bearing based on Bayesian inference is proposed by using the characteristics of the probability density of normal data and different fault type data. Firstly,the singular value decomposition was used to preprocess the vibration signal,and the influence of noise on the dynamic threshold calculation result was filtered out.Secondly,the fault frequency energy feature extracting method was used to extract the features of normal data and inner ring,outer ring and ball fault data. Finally,Bayesian inference method was used to calculate the dynamic threshold of normal data and inner ring,outer ring and ball fault data respectively. The data were obtained by using the fault implantation method,and the dynamic threshold experiments were carried out. The experimental results show that the proposed method which has higher true positive rate and lower false positive rate can correctly find the dividing point between the probability density curve of normal data and different fault type data. The proposed method improves the calculation accuracy of dynamic threshold.

For the problem of Local Mean Decomposition（ LMD） was easily affected by noise interference when in the extraction of fault features,a rolling bearing fault diagnosis method which based on LMD and Independent Component Analysis（ ICA） was proposed. Firstly,original signal was decomposed into a series of production functions（ PF） by the LMD method.Secondly,the estimate of PF was obtained after the PF components had been separated by ICA method,and the noise was effectively removed. Then,mutual information,correlation coefficient and approximate entropy which were extracted from the estimate of PF components were grouped together as a feature vector. Finally,the fault feature vectors were classified by SVM.The results of the feature extraction and fault diagnosis experiments show that the fault recognition rate of LMD-ICA method is significantly better than the traditional LMD method.

Abstract Background As a result of the growing body of protein phosphorylation sites data, the number of phosphoprotein databases is constantly increasing, and dozens of tools are available for predicting protein phosphorylation sites to achieve fast automatic results. However, none of the existing tools has been developed to predict protein phosphorylation sites in rice. Results In this paper, the phosphorylation site predictors, NetPhos 2.0, NetPhosK, Kinasephos, Scansite, Disphos and Predphosphos, were integrated to construct meta-predictors of rice-specific phosphorylation sites using several methods, including unweighted voting, unreduced weighted voting, reduced unweighted voting and weighted voting strategies. PhosphoRice, the meta-predictor produced by using weighted voting strategy with parameters selected by restricted grid search and conditional random search, performed the best at predicting phosphorylation sites in rice. Its Matthew's Correlation Coefficient (MCC) and Accuracy (ACC) reached to 0.474 and 73.8%, respectively. Compared to the best individual element predictor (Disphos_default), PhosphoRice archieved a significant increase in MCC of 0.071 (P < 0.01), and an increase in ACC of 4.6%. Conclusions PhosphoRice is a powerful tool for predicting unidentified phosphorylation sites in rice. Compared to the existing methods, we found that our tool showed greater robustness in ACC and MCC. PhosphoRice is available to the public at http://bioinformatics.fafu.edu.cn/PhosphoRice.

In order to improve the efficiency of protein spots detection, a fast detection method based on CUDA was proposed. Firstly, the parallel algorithms of the three most time-consuming parts in the protein spots detection algorithm: image preprocessing, coarse protein point detection and overlapping point segmentation were studied. Then, according to single instruction multiple threads executive model of CUDA to adopted data space strategy of separating two-dimensional (2D) images into blocks, various optimizing measures such as shared memory and 2D texture memory are adopted in this study. The results show that the operative efficiency of this method is obviously improved compared to CPU calculation. As the image size increased, this method makes more improvement in efficiency, such as for the image with the size of 2,048 x 2,048, the method of CPU needs 52,641 ms, but the GPU needs only 4,384 ms.

To separate the overlapped protein spots in two-dimensional gel electrophoresis (2-DE) images, we proposed an auto-separating algorithm based on valley characteristics. Firstly, the marker-controlled watershed algorithm was used to detect the initial outlines of the object regions. Secondly, medial axis transform and hierarchical branch pruning method were applied to the main skeletons of the object regions, and each main skeleton was fitted into line segments to describe the overlap directions. Then, the 3-dimensional model of the object region was scanned on the normal planes of the line segments to find the valley locations. And finally, a validation model was adopted to construct separation lines. The experiments on 2 real scanned 2-DE images showed that the true overlap separate (TOSs) were 78.95% and 85.71%, respectively. The results indicated that the proposed algorithm was better than the existing algorithms and could be used in engineering practice.

To reduce the mismatching and non-matching in the protein two-dimension electrophoresis (2-DE) images, we proposed an auto-matching algorithm based on gray hierarchical and geometric blocking in this study. Firstly, protein spots in the gel images were divided into groups by gray level and geometric position, and then a method based on shape context and normalized correlation was used for coarse matching in protein spots. Secondly, matched pairs in coarse matching were set as feature points, and the precise matching in the rest of not matched protein spots was accomplished by the method of geometric correlation and similarity criterion. Finally, local affine transformation was used in the verification of matching results to remove non-matching and mis-matching points. The algorithm was applied to different 2-DE gel images. The results showed that the new matching algorithm could reduce the non-matching and mis-matching spots, and increase the matching accuracy.

In order to detect the overlapped and clustered protein spots in protein gel images, we suggest an auto-separating algorithm based on the shape characteristics in this paper. Firstly, we used a marker-controlled watershed algorithm to detect the initial outline of the protein spots in application of the method. Secondly, we extracted shape markers adaptively by combining local minima depth and shape characteristics information, and calculated the shape distance image according to these shape markers. Then, we imposed these shape markers on the shape distance image as its local minima. Finally, we applied the watershed algorithm again on the modified shape distance image to separate the overlapped spots accurately. The experiments on the synthetic gel image and the different types of real gel images showed that compared with other improved watershed algorithm, the proposed one had a higher correct separation rate with an error less than 6%, and better separation results of overlapped spots.