基于应变模态振型和误差补偿的形状重构方法

形状传感技术是近年来传感领域备受关注的新研究方向,具有广阔的应用前景。文章提出了一种基于应变模态振型和误差补偿的形状重构方法,通过测量物体部分位置点的应变数据,采用模态理论实现应变-位移转换,进而重构物体形状。以长宽高分别为1000,1000和0.5mm的钛合金板作为研究对象,通过ANSYS workbench18有限元仿真软件获取位移模态振型及应变模态振型,依据有限元仿真中位置点模态的相似性,采用K-means++聚类算法对应变测量点位置进行优化,在合金板上表面施加400N的力使其产生形变。该算法的形状重构误差小于常规的均匀分布算法。采用径向基函数神经网络对误差和重构位移数据集进行了训练,依据拟合重构误差和重构位移的关系,补偿误差后重构误差不大于3.5%。     

一种基于溢出值的局部拥塞消除技术

随着芯片的集成度越来越高,物理设计布局阶段的拥塞问题越发严重.提出了一种基于溢出值的局部拥塞消除技术,根据溢出值选择出拥塞密度最高的拥塞区域,然后基于模拟退火算法对该区域内的高引脚单元设置合适大小的隔离区域,以缓解局部拥塞.将提出的方法应用于SMIC 180 nm工艺的四万门设计和SMIC 55 nm工艺的七千门设计进...     

An evolutive framework for server placement optimization to digital twin networks

Digital twin network (DTN) is a foremost enabler for efficient optimization in modern networks, as it owns massive real-time data and requires interaction with the physical network in real-time. When constructing a DTN, it is necessary to deploy many servers in the physical network for digital models' storage, calculation, and communication. Evolutionary algorithms show outstanding global optimization capabilities compared to the constructive heuristic method in such an optimization problem. However, due to the high dimensionality of the problem and the complicated evaluation of the deployment plan, evolutionary algorithms easily fall into the optimum local at a high computational cost, given that the server placement problem is an NP-hard combinatorial optimization problem. In this research, we propose an evolutionary framework for server layout optimization that significantly improves the optimization efficiency of evolutionary algorithms and reduces the algorithm's computational cost. An offline-learning-based approach is used to reduce the search space, and a self-examining guided local search method is proposed to improve the search efficiency. Additionally, a look-up table-based hybrid approach is used for solution evaluation, reducing computational overhead. Experimental results show that the proposed framework and optimization strategy can significantly improve the evolutionary algorithm search efficiency and achieve excellent convergence performance.     

改进的泡泡布点方法研究

泡泡布点方法能够在复杂区域内生成高质量的节点集，但是其计算效率仍有待提高，为此本文做了两方面的改进。一是种子填充算法思想应用于节点的初始布置中，从而生成数目合适的节点集，省去了原布点方法中的节点增删过程，有效地节约模拟时间；二是简化了泡泡运动模拟的控制方程，使得运动模拟更加简单。数值算例表明，改进的泡泡布点方法生成的网格平均质量均高于0.94,且计算效率相比原布点方法提高90%以上。因此，改进的泡泡布点方法是一种高质量、高效率且适宜求解大规模问题的节点布置方法。