改进K-means聚类算法行驶工况及油耗研究

为解决传统聚类算法初始中心易陷入局部最优、耗时长的问题，提出一种改进的K-means聚类优化算法。该算法引入最大最小距离和加权欧氏距离，从剩余聚类点距离均值和出发，避免孤立点和边缘数据的影响。利用比重法对主成分进行改进，以由此获得的特征影响因子作为初始特征权重，构建一种加权欧氏距离度量。根据特征贡献率对聚类的影响，筛选具有代表性的特征因子凸显聚类效果，最终合成汽车行驶工况，分析瞬时油耗。结果表明，所提算法构建行驶工况的速度-加速度联合分布差异值仅为105%，比传统K-means聚类省时44.2%，行驶工况拟合度较高，能反映实际车辆的运行特征及油耗。 

An automatic fine crack recognition algorithmfor airport pavement under significant noises

In order to solve the problem that the initial center of traditional clustering algorithm is easy to fall into local optimum and time-consuming. An improved K-means clustering algorithm is proposed. In this algorithm, the maximum minimum distance and weighted Euclidean distance are introduced to avoid the influence of outliers and edge data. The weight method is used to improve the principal component, and the feature influence factor is used as the initial feature weight to construct a weight- ed Euclidean distance measure. According to the influence factors of feature contribution rate on cluster- ing, a clustering method of feature weight influence factors is proposed, which selects representative feature factors to highlight clustering effect, and finally synthesizes driving cycle and analyzes instantaneous fuel consumption. The results show that: the difference value of speed acceleration joint distribution of the proposed method is only 1.05%, which saves 44.2% of the time, compared with the traditional K-means clustering. The driving cycle fitting degree is high, which can reflect the actual vehicle operation characteristics and fuel consumption.