CAJ | 학술논문

利用小波模极大值方法可很好地对特征进行识别,并能抑制噪声的影响.利用该方法对叶片截面数据进行特征识别时,某些重要的局部特征在细尺度下会消失,导致数据特征点不能完整识别,针对该情况提出特征尺度因子的概念,以了解数据中所含特征的差异性.特征尺度因子越大意味着可分解尺度数越多,相反则意味着可分解尺度数越少,当数据点的特征差异较大时,可将其分为几段分别进行识别,最后再将各段特征点汇总.实验证明,特征尺度因子可以很好地体现特征的信息并指导特征识别,有效保证了数据中特征个数的完整性.
이용소파모겁대치방법가흔호지대특정진행식별,병능억제조성적영향.이용해방법대협편절면수거진행특정식별시,모사중요적국부특정재세척도하회소실,도치수거특정점불능완정식별,침대해정황제출특정척도인자적개념,이료해수거중소함특정적차이성.특정척도인자월대의미착가분해척도수월다,상반칙의미착가분해척도수월소,당수거점적특정차이교대시,가장기분위궤단분별진행식별,최후재장각단특정점회총.실험증명,특정척도인자가이흔호지체현특정적신식병지도특정식별,유효보증료수거중특정개수적완정성.
Wavelet modulus maximum method can be used to recognize the features and restrain the noise. However, some important local features would disappear in the finest scales when using this method to recognize the whole cross-section data. The concept of characteristic scale factor was proposed to find out the differences of the features contained in the cross-section data. When the characteristic scale factor was larger, the numbers of the decomposable scales would become more and more, and vice versa. The cross-section data can be divided into several sections to be recognized respectively when the features of the cross-section data differed considerably. And the feature points of each section would be integrated finally. The experimental results show that the shape-scale factor can reflect the information contained in the signal well and guide features recognition. This recognition method can ensure the integrity of the feature numbers effectively.