CAJ | 학술논문

在云理论的基础上提出了一种无需人工干预的垩白识别方法.在此方法中,把垩白与非垩白定义为两个定性概念,以一个不对称云和一个对称云来表达垩白与非垩白,以两组数字特征分别描述垩白云与非垩白云.首先,利用动态阈值算法获得训练样本,然后设计逆向云发生器实现定量到定性的转换,最后根据两个云的隶属度函数,用极大值判定法来实现垩白区与非垩白区的分离.试验结果表明,云分类法分类精度高于传统的硬分类法.
재운이론적기출상제출료일충무수인공간예적성백식별방법.재차방법중,파성백여비성백정의위량개정성개념,이일개불대칭운화일개대칭운래표체성백여비성백,이량조수자특정분별묘술성백운여비성백운.수선,이용동태역치산법획득훈련양본,연후설계역향운발생기실현정량도정성적전환,최후근거량개운적대속도함수,용겁대치판정법래실현성백구여비성백구적분리.시험결과표명,운분류법분류정도고우전통적경분류법.
A method based on the cloud theory was developed to improve the automatic degree and accuracy of chalkiness detection. In this method, without man's intervention, chalkiness and non-chalkiness were defined as two qualitative concepts. An asymmetrical cloud was used to represent chalkiness, and a symmetrical cloud was used to represent non-chalkiness. These two clouds were respectively described by two groups of digital characters. Firstly, dynamic threshold program was designed to acquire training samples for the two clouds. Secondly, backward cloud generators were developed to implement the transformation from the quantities to the qualitatives. Finally, maximum value judgment method was used to separate the chalky region from non-chalky region according to the membership function of each cloud. The result shows that the classification accuracy of cloud classifier is higher than the classification accuracy of traditional hard classifiers.