曲巍崴,高 峰,杜发荣,周 煜,李雪雪.叶片类截面数据特征点精确识别方法[J].农业机械学报,2010,41(1):195-199.
Qu Weiwei,Gao Feng,Du Farong,Zhou Yu,Li Xuexue.Accurate Recognition Method for Cross section Data Feature Points of Blades[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(1):195-199.
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叶片类截面数据特征点精确识别方法   [下载全文]
Accurate Recognition Method for Cross section Data Feature Points of Blades   [Download Pdf][in English]
  
DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  叶片 特征识别 小波模极大值 特征尺度因子
基金项目:航空推进技术验证基金资助项目(0406022)
作者单位
曲巍崴 北京航空航天大学交通科学与工程学院, 北京 100191 
高 峰 北京航空航天大学交通科学与工程学院, 北京 100191 
杜发荣 北京航空航天大学交通科学与工程学院, 北京 100191 
周 煜 北京航空航天大学交通科学与工程学院, 北京 100191 
李雪雪 北京航空航天大学交通科学与工程学院, 北京 100191 
中文摘要: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。
Qu Weiwei  Gao Feng  Du Farong  Zhou Yu  Li Xuexue
School of Transportation Science and Engineering, Beihang University , Beijing 100191, China;School of Transportation Science and Engineering, Beihang University , Beijing 100191, China;School of Transportation Science and Engineering, Beihang University , Beijing 100191, China;School of Transportation Science and Engineering, Beihang University , Beijing 100191, China;School of Transportation Science and Engineering, Beihang University , Beijing 100191, China
Key Words:Blade, Feature recognition, Wavelet modulus maximum, Characteristic scale factor
Abstract:Wavelet modulus maximum method can be used to recognize the features and restrai n the noise. However, some important local features would disappear in the fines t 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 sc ale 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 se ctions to be recognized respectively when the features of the cross section dat a differed considerably. And the feature points of each section would be integra ted finally. The experimental results show that the shape scale factor can refl ect the information contained in the signal well and guide features recognition. This recognition method can ensure the integrity of the feature numbers effecti vely. 

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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