Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.

Link Prediction Based on the Relational Path Inference of Triangular Structures

Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.

Maximizing the mechanical performance of Ti_(3)AlC_(2)-based MAX phases with aid of machine learning

Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly dependent on the strength of M–X and M–A bonds.In this study,a novel strategy based on the crystal graph convolution neural network(CGCNN)model has been successfully employed to tune these mechanical properties of Ti_(3)AlC_(2)-based MAX phases via the A-site substitution(Ti_(3)(Al1-xAx)C_(2)).The structure–property correlation between the A-site substitution and mechanical properties of Ti_(3)(Al1-xAx)C_(2)is established.The results show that the thermodynamic stability of Ti_(3)(Al1-xAx)C_(2)is enhanced with substitutions A=Ga,Si,Sn,Ge,Te,As,or Sb.The stiffness of Ti_(3)AlC_(2)increases with the substitution concentration of Si or As increasing,and the higher thermal shock resistance is closely associated with the substitution of Sn or Te.In addition,the plasticity of Ti_(3)AlC_(2)can be greatly improved when As,Sn,or Ge is used as a substitution.The findings and understandings demonstrated herein can provide universal guidance for the individual synthesis of high-performance MAX phases for various applications.