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中文摘要：
为准确描述与识别时间序列曲线形态，根据曲线段类型定义了5个语素和1个通配符，进而定义语素向量及通配符向量，使得对曲线的描述具有层次性．据此将任意时间序列曲线转变为对应的二维表，二维表第二列组成的字符串能够进行初级的模板匹配，二维表各行所代表的向量增强了语素关系运算的能力，可实现较深入的模式识别．在经典回溯法的启发下设计了一种属性约束下的带通配符字符串匹配算法，并以基于感应线圈信号曲线的车型分类为例，验证了所提出的方法的有效性和合理性．
英文摘要：
In order to correctly describe and recognize the configuration of a time series crave （TSC） , five morphemes and one wildcard are defined according to the curve segment types, and the corresponding morpheme vector and the wildcard vector are defined for the hierarchical description of the curve. Then, one TSC is transformed into a 2D table. The second colmnn of the table makes up of a string that can effect the elementary pattern matching and the rows of the table represent the attribute vectors that can enhance the morpheme-relational operation, thus reali- zing a more complex pattern matching at the next step. Moreover, a string matching arithmetic with attribute re- straints and wildcard strings is proposed based on the classical backtracking method. The method is finally em- ployed to classify vehicles using the TSCs collected from inductive loops in a real traffic scene. The results show that the proposed method is effective and reasonable.
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