一类洛伦兹流形的2-调和类空超曲面的一个拼挤定理

研究局部对称共形平坦洛伦兹流形中的2-调和类空超曲面,得到它对外围空间的一个拼挤定理.     

算子方程组的正规能解性及应用

借助局部凸空间和广义函数论的观点和方法，讨论了算子方程组的正规能解性，并得出了一些定理和推理。将这些应用于微分方程组，证明微分方程组具有正确性解性。     

Hybrid artificial neural network and locally weighted regression models for lane-based short-term urban traffic flow forecasting

ABSTRACT

In recent years, there has been considerable research interest in short-term traffic flow forecasting. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g. 1 or 5?min) and lane level are still rare. In this study, a combination of genetic algorithm, neural network and locally weighted regression is used to achieve optimal prediction under various input and traffic settings. The genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) models are developed and tested, with the former forecasting traffic flow every 5-min within a 30-min period and the latter for forecasting traffic flow of a particular 5-min period of each for four lanes of an urban arterial road in Beijing, China. In particular, for morning peak and off-peak traffic flow prediction, the GA-ANN 5-min traffic flow model results in average errors of 3–5% and most 95th percentile errors of 7–14% for each of the four lanes; for the peak and off-peak time traffic flow predictions, the GA-LWR 5-min traffic flow model results in average errors of 2–4% and most 95th percentile errors are lower than 10% for each of the four lanes. When compared to previous models that usually offer average errors greater than 6–15%, such empirical findings should be of interest to and instrumental for transportation authorities to incorporate in their city- or state-wide Advanced Traveller Information Systems (ATIS).     

基于LLE和LS_SVM的胃粘膜肿瘤细胞图像分类

胃粘膜肿瘤细胞图像的复杂性,组织器官形状的不规则性以及不同细胞的差异性,使得采用一般的线性分类方法对其进行分类很困难,结合局部线性嵌入（LLE）在处理非线性数据及最小二乘支持向量机（LS_SVM）在处理小样本、高维数及泛化问题方面的优势,文章提出一种基于LLE＋LS_SVM的胃粘膜肿瘤细胞图像分类方法,并采用LS_SVM的线性拟合误差来判断实验效果,最后比较本文方法与其他分类方法的优越性。实验结果表明,该方法在分类准确率和运行时间方面都有很大的优势。     

Infinite clusters in locally dependent percolation on ℤ d

Techniques of Burton-Keane, developed earlier for independent percolation on ℤ ^d , are adapted to the setting of locally dependent percolation on ℤ ^d for d ⩾ 2. The main result of this paper is a uniqueness theorem, that there exists almost surely a unique infinite out-cluster in locally dependent percolation on ℤ ^d , under the finite energy condition.