Minimization of classification samples for supercritical and subcritical patterns of supersonic inlet

Chang, Juntao; Zheng, Risheng; Yu, Daren; Bao, Wen; Chen, Fu; Jiang, Wanshun; Zhu, Shoumei; Zheng, Rong

doi:10.1007/s11630-014-0720-8

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…9 There are also some relevant studies on the monitoring of inlet flow patterns in . [10][11][12][13][14][15] . Trapier et al 10 proposed using the cumulative sum method and the generalized likelihood ratio method for early warning of the inlet buzz.…”

Section: Introductionmentioning

confidence: 99%

“…16 To reduce the number of ground tests and total cost for the classification of hypersonic inlet start/unstart modes, the variation of classification accuracy with different training set size and minimization of samples were studied in. 14,15 However, most research studies of Chang et al deal with CFD simulation data that only contain hundreds of steady samples under different conditions, thus forming an obvious gap between different flow patterns and making the recognition tasks very easy. In practice, the data collected by the measurement system or sensors are almost always dynamic, which means that they are variant with time and may have some transient characteristics that cannot be captured by steady data.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid of fast K-nearest neighbor and improved directed acyclic graph support vector machine for large-scale supersonic inlet flow pattern recognition

Zhao

Tan

2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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Inlet flow pattern recognition is one of the most crucial issues and also the foundation of protection control for supersonic air-breathing propulsion systems. This article proposes a hybrid algorithm of fast K-nearest neighbors (F-KNN) and improved directed acyclic graph support vector machine (I-DAGSVM) to solve this issue based on a large amount of experimental data. The basic idea behind the proposed algorithm is combining F-KNN and I-DAGSVM together to reduce the classification error and computational cost when dealing with big data. The proposed algorithm first finds a small set of nearest samples from the training set quickly by F-KNN and then trains a local I-DAGSVM classifier based on these nearest samples. Compared with standard KNN which needs to compare each test sample with the entire training set, F-KNN uses an efficient index-based strategy to quickly find nearest samples, but there also exists misclassification when the number of nearest samples belonging to different classes is the same. To cope with this, I-DAGSVM is adopted, and its tree structure is improved by a measure of class separability to overcome the sequential randomization in classifier generation and to reduce the classification error. In addition, the proposed algorithm compensates for the expensive computational cost of I-DAGSVM because it only needs to train a local classifier based on a small number of samples found by F-KNN instead of all training samples. With all these strategies, the proposed algorithm combines the advantages of both F-KNN and I-DAGSVM and can be applied to the issue of large-scale supersonic inlet flow pattern recognition. The experimental results demonstrate the effectiveness of the proposed algorithm in terms of classification accuracy and test time.

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