The seismic behavior of frames with semi rigid connections and rotational dampers is examined.The ground acceleration due to earthquake is regarded as a stochastic process,and a pseudo excitation algorithm in frequen...The seismic behavior of frames with semi rigid connections and rotational dampers is examined.The ground acceleration due to earthquake is regarded as a stochastic process,and a pseudo excitation algorithm in frequency domain is implemented in a computer program to handle non orthogonal damping properties of the system.The computer program which incorporates detailed connection models and rotational damping models is used to investigate the effect of the connection of the semi rigid frame.It is shown from analytical studies that semi rigid frames with rotational dampers improve the seismic response of the building and may provide an effective and reliable earthquake resistant design solution.展开更多
通常对于大数据的学习问题,需要选择一个训练集的子集来进行学习,以降低问题本身的时间和空间复杂性。有很多学者从样本的近邻出发来选择样本,根据样本的近邻特点寻找位于靠近分类面的样本。对于SVDD(Support Vector Data Description)...通常对于大数据的学习问题,需要选择一个训练集的子集来进行学习,以降低问题本身的时间和空间复杂性。有很多学者从样本的近邻出发来选择样本,根据样本的近邻特点寻找位于靠近分类面的样本。对于SVDD(Support Vector Data Description)算法而言,只有位于数据集边缘区域的样本对学习结果有影响。提出了通过估计样本领域样本概率的方式来判断样本在数据集里的位置,位于数据集边缘区域的样本概率要明显小于位于数据集内部样本的概率,通过删除位于数据集内部的样本可以大大降低数据集的规模,在不降低算法的性能时,降低训练模型的复杂度,提高识别速度和算法的学习速度。并在实时性要求比较高的电能扰动信号识别方面,得到了很好的应用。展开更多
文摘The seismic behavior of frames with semi rigid connections and rotational dampers is examined.The ground acceleration due to earthquake is regarded as a stochastic process,and a pseudo excitation algorithm in frequency domain is implemented in a computer program to handle non orthogonal damping properties of the system.The computer program which incorporates detailed connection models and rotational damping models is used to investigate the effect of the connection of the semi rigid frame.It is shown from analytical studies that semi rigid frames with rotational dampers improve the seismic response of the building and may provide an effective and reliable earthquake resistant design solution.
文摘通常对于大数据的学习问题,需要选择一个训练集的子集来进行学习,以降低问题本身的时间和空间复杂性。有很多学者从样本的近邻出发来选择样本,根据样本的近邻特点寻找位于靠近分类面的样本。对于SVDD(Support Vector Data Description)算法而言,只有位于数据集边缘区域的样本对学习结果有影响。提出了通过估计样本领域样本概率的方式来判断样本在数据集里的位置,位于数据集边缘区域的样本概率要明显小于位于数据集内部样本的概率,通过删除位于数据集内部的样本可以大大降低数据集的规模,在不降低算法的性能时,降低训练模型的复杂度,提高识别速度和算法的学习速度。并在实时性要求比较高的电能扰动信号识别方面,得到了很好的应用。
