Research on condition monitoring system of high speed railway catenary based on image processing

A contactless detection method based on the image processing algorithm is proposed to detect the geometric parameters of catenary. Aiming at the other obstacles in the image, the image edge is detected and enhanced by Canny algorithm, then the catenary image is extracted gradually through target tracking, image segmentation and breakpoint continuation. The corresponding relationship between the coordinates of contact line feature point and the 3D space coordinates measured by the binocular triangulation method is established to get the conductor height and the stagger value. According to the relevant theory, a catenary condition monitoring system is designed, which realizes the working state monitoring and the dynamic measurement of geometrical parameters for catenary

Research on parallel nonlinear control system of PD and RBF neural network based on U model

The modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system

Template effect in TiN/AlN multilayered coatings from first principles

Multilayered TiN/AlN coatings find many technological applications where superhardness is suspected to be affected by AlN structures and template effect. Here, we demonstrate, by first-principles calculations on alternative adsorptions of Al and N atoms on Ti- and N-terminated TiN surfaces, that the preferred stacking sequences (i.e., having the largest adsorption energy) transform from fcc- to hcp- mode in first a few AlN layers. Using several analytic methods, we identify that for the T-terminated surface, the third added N layer is critical to inducing the structural transition of AlN, weakening the interaction between the second added Al and first added N atoms. The findings provide insight to the complicated template effects in TiN/AlN multilayered coatings, which are practically relevant for further improving property of multilayered coatings at the atomic scale

Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers to take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs, and it is still an open question. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed. First, we reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we reconstructed the adjacency tensor via Tucker decomposition, wherein more informative and global spatial-temporal dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning are assembled to aggregate and learn the comprehensive spatial-temporal dependencies of the road network. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of the prediction performance and computational cost.Comment: 11 pages, 8 figure

Detection of the deep-sea plankton community in marine ecosystem with underwater robotic platform.

Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5–2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches

Volatile Component Analysis of Michelia alba Leaves and Their Effect on Fumigation Activity and Worker Behavior of Solenopsis invicta

Volatile compounds from mashed (fresh, fallen, and dried) leaves ofMichelia alba were collected via solid-phase microextraction and werethen identified via gas chromatography-mass spectrometry. The resultsshowed that linalool was the dominant component in different leaves,together with caryophyllene, β-elemene, and selinene, the contents ofwhich vary across the samples. The fumigation bioassay results showedthat the volatiles from M. alba leaves exhibited insecticidal activity againstred imported fire ant workers, and the mortality of workers could reachup to 100% after the fallen leaves were treated for 16 h. Mashed freshleaves could effectively reduce the aggregation and drinking ability ofworkers. The volatile substances released from the mashed leaves mightkill the ants, or affect their behavior and weaken the activity by interferingtransmit information between ants. A comprehensive consideration ofthe economic and ecological value of M. alba shows that fallen leavesmight be a good resource to control red imported fire ant