Rotor fault classification technique and precision analysis with kernel principal component analysis and multi-support vector machines

To solve the diagnosis problem of fault classification for aero-engine vibration over standard during test, a fault diagnosis classification approach based on kernel principal component analysis (KPCA) feature extraction and multi-support vector machines (SVM) is proposed, which extracted the feature of testing cell standard fault samples through exhausting the capability of nonlinear feature extraction of KPCA. By computing inner product kernel functions of original feature space, the vibration signal of rotor is transformed from principal low dimensional feature space to high dimensional feature spaces by this nonlinear map. Then, the nonlinear principal components of original low dimensional space are obtained by performing PCA on the high dimensional feature spaces. During muti-SVM training period, as eigenvectors, the nonlinear principal components are separated into training set and test set, and penalty parameter and kernel function parameter are optimized by adopting genetic optimization algorithm. A high classification accuracy of training set and test set is sustained and over-fitting and under-fitting are avoided. Experiment results indicate that this method has good performance in distinguishing different aero-engine fault mode, and is suitable for fault recognition of a high speed rotor

Action Recognition of Human’s Lower Limbs Based on a Human Joint

In order to recognize the actions of human’s lower limbs, a novel action recognition method based on a human joint was proposed. Firstly, hip joint was chosen as the recognition object, its y coordinates were as recognition parameter, and human action characteristics were achieved based on filtering and wavelet transform. Secondly, an improved self-organizing competitive neural network was proposed, which could classify the action characteristics automatically according to the classification number. The classification results of motion capture data proved the validity of the neural network. Finally,an action recognition method based on hidden Markov model (HMM) was introduced to realize the recognition of classification results of human action characteristicswith the change direction of y coordinates. The proposed action recognition method needs less action information and has a fast calculation speed. Experiments proved the method hada high recognition rate and a good application prospect

Genome-wide characterization and analysis of Golden 2-Like transcription factors related to leaf chlorophyll synthesis in diploid and triploid Eucalyptus urophylla

Golden 2-Like (GLK) transcription factors play a crucial role in chloroplast development and chlorophyll synthesis in many plant taxa. To date, no systematic analysis of GLK transcription factors in tree species has been conducted. In this study, 40 EgrGLK genes in the Eucalyptus grandis genome were identified and divided into seven groups based on the gene structure and motif composition. The EgrGLK genes were mapped to 11 chromosomes and the distribution of genes on chromosome was uneven. Phylogenetic analysis of GLK proteins between E. grandis and other species provided information for the high evolutionary conservation of GLK genes among different species. Prediction of cis-regulatory elements indicated that the EgrGLK genes were involved in development, light response, and hormone response. Based on the finding that the content of chlorophyll in mature leaves was the highest, and leaf chlorophyll content of triploid Eucalyptus urophylla was higher than that of the diploid control, EgrGLK expression pattern in leaves of triploid and diploid E. urophylla was examined by means of transcriptome analysis. Differential expression of EgrGLK genes in leaves of E. urophylla of different ploidies was consistent with the trend in chlorophyll content. To further explore the relationship between EgrGLK expression and chlorophyll synthesis, co-expression networks were generated, which indicated that EgrGLK genes may have a positive regulatory relationship with chlorophyll synthesis. In addition, three EgrGLK genes that may play an important role in chlorophyll synthesis were identified in the co-expression networks. And the prediction of miRNAs targeting EgrGLK genes showed that miRNAs might play an important role in the regulation of EgrGLK gene expression. This research provides valuable information for further functional characterization of GLK genes in Eucalyptus

RCLane: Relay Chain Prediction for Lane Detection

Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution. In this paper, we present a new method for lane detection based on relay chain prediction. Specifically, our model predicts a segmentation map to classify the foreground and background region. For each pixel point in the foreground region, we go through the forward branch and backward branch to recover the whole lane. Each branch decodes a transfer map and a distance map to produce the direction moving to the next point, and how many steps to progressively predict a relay station (next point). As such, our model is able to capture the keypoints along the lanes. Despite its simplicity, our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.Comment: ECCV 202