作为一种非线性维数约减算法,高斯过程隐变量模型(Gaussian process latent variable model,GPLVM)由于其适合处理小样本、高维数据,因而在模式识别、计算机视觉等领域得到了广泛应用.基于此,提出一种基于改进GPLVM的SAR图像目标特征提...作为一种非线性维数约减算法,高斯过程隐变量模型(Gaussian process latent variable model,GPLVM)由于其适合处理小样本、高维数据,因而在模式识别、计算机视觉等领域得到了广泛应用.基于此,提出一种基于改进GPLVM的SAR图像目标特征提取及自动识别方法,其中利用改进的GPLVM进行特征提取,高斯过程分类进行目标识别.传统GPLVM使用共轭梯度法对似然函数进行优化,为避免梯度估值易受噪声干扰、步长对算法影响严重等缺点,提出基于免疫克隆选择算法的GPLVM,利用其具有快速收敛到全局最优的特性提高算法性能.实验结果表明,该算法不仅降低了特征维数,且提高了识别精度,从而验证了算法用于SAR图像目标识别的有效性.展开更多
In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical j...In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.展开更多
文摘作为一种非线性维数约减算法,高斯过程隐变量模型(Gaussian process latent variable model,GPLVM)由于其适合处理小样本、高维数据,因而在模式识别、计算机视觉等领域得到了广泛应用.基于此,提出一种基于改进GPLVM的SAR图像目标特征提取及自动识别方法,其中利用改进的GPLVM进行特征提取,高斯过程分类进行目标识别.传统GPLVM使用共轭梯度法对似然函数进行优化,为避免梯度估值易受噪声干扰、步长对算法影响严重等缺点,提出基于免疫克隆选择算法的GPLVM,利用其具有快速收敛到全局最优的特性提高算法性能.实验结果表明,该算法不仅降低了特征维数,且提高了识别精度,从而验证了算法用于SAR图像目标识别的有效性.
基金Supported by the Basic Scientific Research Projects of the Central University of China(ZXH2010D010)the National Natural Science Foundation of China(60979021/F01)~~
文摘In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.
