一种基于同步传输机制的混合保护方式研究

随着同步数字体系（SDH）多业务平台的日趋发展,传统的SDH保护方式已无法满足现在的需要。提出了一种混合保护方式,在同一个环内实现子网连接保护和两纤双向复用段保护,可实现对高阶和低阶业务的同时兼顾,解决SDH多业务平台的保护问题。该方法在实践中取得了良好的效果。     

钢管混凝土柱-钢梁节点的力学性能分析

基于弹塑性有限元理论建立了钢管混凝土柱-钢梁节点荷载-位移全过程非线性有限元模型,在单元分析中采用改进的AUL表述推导得到梁柱单元刚度矩阵方程,同时考虑了材料的物理非线性和单元的几何非线性,并编制了非线性有限元程序NLFEACFST。采用该模型对相关研究者和作者进行的节点试验进行了分析,理论计算结果与试验结果比较表明,该模型具有很好的适用性和精度。在理论分析模型得到试验结果验证的基础上,对典型的中柱节点进行了荷载-位移全过程非线性特性分析,并对影响节点承载力和荷载-位移骨架曲线的因素进行了参数分析,为进一步从理论研究钢管混凝土框架结构的力学性能创造了条件。     

Certain Curvature Conditions on P-Sasakian Manifolds Admitting a Quater-Symmetric Metric Connection

The authors consider a quarter-symmetric metric connection in a P-Sasakian manifold and study the second order parallel tensor in a P-Sasakian manifold with respect to the quarter-symmetric metric connection. Then Ricci semisymmetric P-Sasakian manifold with respect to the quarter-symmetric metric connection is considered. Next the authors study ξ-concircularly flat P-Sasakian manifolds and concircularly semisymmetric P-Sasakian manifolds with respect to the quarter-symmetric metric connection. ...     

Multiscale Hybrid Convolutional Deep Neural Networks with Channel Attention

Attention mechanisms can improve the performance of neural networks, but the recent attention networks bring a greater computational overhead while improving network performance. How to maintain model performance while reducing complexity is a hot research topic. In this paper, a lightweight Mixture Attention (MA) module is proposed to improve network performance and reduce the complexity of the model. Firstly, the MA module uses multi-branch architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Secondly, in order to reduce the number of parameters, each branch uses group convolution independently, and the feature maps extracted by different branches are fused along the channel dimension. Finally, the fused feature maps are processed using the channel attention module to extract statistical information on the channels. The proposed method is efficient yet effective, e.g., the network parameters and computational cost are reduced by 9.86% and 7.83%, respectively, and the Top-1 performance is improved by 1.99% compared with ResNet50. Experimental results on common-used benchmarks, including CIFAR-10 for classification and PASCAL-VOC for object detection, demonstrate that the proposed MA outperforms the current SOTA methods significantly by achieving higher accuracy while having lower model complexity.     

Distinguished Connections on Finsler Algebroids

Considering the prolongation of a Lie algebroid,the authors introduce Finsler algebroids and present important geometric objects on these spaces.Important endomorphisms like conservative and Barthel,Cartan tensor and some distinguished connections like Berwald,Cartan,Chern-Rund and Hashiguchi are introduced and studied.     

Odd-dimensional counterparts of abelian complex and hypercomplex structures

We introduce the notion of abelian almost contact structures on an odd-dimensional real Lie algebra $\mathfrak{g}$. We investigate correspondences with even-dimensional Lie algebras endowed with an abelian complex structure, and with Kähler Lie algebras when $\mathfrak{g}$ carries a compatible inner product. The classification of 5-dimensional Sasakian Lie algebras with abelian structure is obtained. Later, we introduce abelian almost 3-contact structures on real Lie algebras of dimension $4n+3$, obtaining the classification of these Lie algebras in dimension 7. Finally, we deal with the geometry of a Lie group G endowed with a left invariant abelian almost 3-contact metric structure. We determine conditions for G to admit a canonical metric connection with skew torsion, which plays the role of the Bismut connection for hyperKähler with torsion (HKT) structures arising from abelian hypercomplex structures. We provide examples and discuss the parallelism of the torsion of the canonical connection.