基于Matlab和BP神经网络的角点检测方法研究

角点检测在图像匹配、形状描述、边缘检测中具有重要作用。鉴于神经网络所具有的自组织、自学习和分布式存储信息的特点,提出了一种基于神经网络的角点检测方法,利用Matlab神经网络工具箱建立角点检测模型,检测图像中的角点。实验证明,神经网络为计算机视觉中的角点检测提供了一个很好的工具。     

主族元素掺杂对SmCo5合金结构和磁性能影响的第一性原理计算

建立了以Al、Ga、In、Sn等元素为例的主族元素掺杂SmCo₅合金的计算模型,基于第一性原理结合统计热力学方法研究了添加元素本征特性、掺杂浓度和温度对合金物相结构和磁学性能的影响。计算结果表明,主族元素的优先占位受元素理化性质和掺杂体系占位空间大小两方面的影响;Al和Ga的添加有利于SmCo₅体系保持结构稳定性,且Al的占位概率随温度变化不明显,适用于较宽的温度范围。几种主族元素添加均削弱SmCo₅体系的总磁矩,而In掺杂体系具有相对较大的总磁矩,主要原因是In原子半径较大,引起掺杂体系晶格畸变,使In周围次近邻的Co原子出现磁矩增大的现象,对体系的总磁矩下降具有弥补作用。基于计算结果分析优选出利于SmCo₅体系结构稳定性和磁性能的主族元素Al和In,且预测了Al和In的最佳掺杂浓度范围。     

Conicity error evaluation using sequential quadratic programming algorithm

Form error evaluation plays an important role in processing quality evaluation. Conicity error is evaluated as a typical example in this paper based on sequential quadratic programming (SQP) algorithm. The evaluation is carried out in three stages. Signed distance function from the measured points to conical surface is defined and the cone is located roughly by the method of traditional least-squares (LS) firstly; the fitted cone and the measured point coordinates are transformed to simplify the optimal mathematical model of conicity error evaluation secondly; and then optimization problem on conicity error evaluation satisfying the minimum zone criterion is solved by means of SQP algorithm and kinematic geometry, where approximate linear differential movement model of signed distance function is deduced in order to reduce the computational complexity. Experimental results show that the conicity error evaluation algorithm is more accurate, and has good robustness and high efficiency. The obtained conicity error is effective.     

Active label-denoising algorithm based on broad learning for annotation of machine health status

Deep learning has led to tremendous success in machine maintenance and fault diagnosis.However,this success is predicated on the correctly annotated datasets.Labels in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis models.The emerging concept of broad learning shows the potential to address the label noise problem.Compared with existing deep learning algorithms,broad learning has a simple architecture and high training efficiency.An active label denois...