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Relationship of Uncertainty Between Polygon Segment and Line Segment for Spatial Data in GIS 被引量:1
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作者 LIU Chun TONG Xiaohua 《Geo-Spatial Information Science》 2005年第3期183-188,共6页
The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quali... The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed. 展开更多
关键词 spatial datas error bands polygon segments uncertainty
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A local spectrum enhancement-based method and its application in incipient fault diagnosis of rotating machinery 被引量:1
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作者 Jiancong Shi Baoming Xu +1 位作者 Xinglong Wang Jun Zhang 《International Journal of Mechanical System Dynamics》 2023年第2期162-172,共11页
Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectr... Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals.For this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands.Then,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency bands.Furthermore,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost.Finally,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis.Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method.The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner.Furthermore,the advantages of the proposed method are highlighted by comparison with other methods. 展开更多
关键词 fault diagnosis frequency band segmentation adaptive stochastic resonance improved grasshopper optimization algorithm synthetic quantitative index
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Orthogonal Wavelet Packet Analysis Based Chaos Recognition Method 被引量:1
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作者 JIANG Wan-lu 《Frontiers of Electrical and Electronic Engineering in China》 CSCD 2006年第1期13-19,共7页
The chaotic motion characteristics are expounded by taking the Duffing equation system as an example.The frequency band segmentation ability and the frequency resolution of the orthogonal multiresolution analysis and ... The chaotic motion characteristics are expounded by taking the Duffing equation system as an example.The frequency band segmentation ability and the frequency resolution of the orthogonal multiresolution analysis and the orthogonal wavelet packet analysis are compared.A new orthogonal wavelet packet analysis-based chaos recognition method for chaotic motion characteristics is put forward.The chaotic,random,and periodic motions are identified effectively by use of the subfrequency band energy distribution in the signal spectrum.The characteristic frequency of chaotic motion is thus extracted. 展开更多
关键词 chaos recognition wavelet packet analysis frequency band segmentation
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