Let f : S → P 1 be a semistable family of curves of genus g 2. We prove that if f admits exactly 5 singular fibers and 4 of them have non-compact Jacobian, then g = 2.
针对光纤振动信号有噪声干扰、识别信号类型准确率不高且识别时间长的问题,提出了基于奇异值分解(singular value decomposition,SVD)和改进粒子群优化支持向量机(modified particle swarm optimization support vector machine,MPSO-S...针对光纤振动信号有噪声干扰、识别信号类型准确率不高且识别时间长的问题,提出了基于奇异值分解(singular value decomposition,SVD)和改进粒子群优化支持向量机(modified particle swarm optimization support vector machine,MPSO-SVM)的识别方法。首先,采用SVD对信号去噪,根据奇异值序列二阶差分谱单边极小值原则确定信号重构秩阶次。其次,提取振动信号特征,利用串行特征融合(serial feature fusion,SFF)方法组建特征向量组。最后,利用MPSO-SVM进行分类识别,提高识别精度和算法效率。采用实测信号进行验证,结果表明,信噪比有明显提升,信号平均识别率较粒子群优化支持向量机(particle swarm optimization support vector machine,PSO-SVM)提升5%。该方法较传统神经网络识别方法有较好的效果,具有实际应用价值。展开更多
文摘Let f : S → P 1 be a semistable family of curves of genus g 2. We prove that if f admits exactly 5 singular fibers and 4 of them have non-compact Jacobian, then g = 2.