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Your search keyword '"Yongmei Cheng"' showing total 25 results
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25 results on '"Yongmei Cheng"'

2. SVM-based land/sea clutter identification with multi-features.

Author

Zhenlu, Jin, Quan, Pan, Yan, Liang, Yongmei, Cheng, and Wentian, Zhou

Abstract

For target location error correction of over-the-horizon radar (OTHR) based on passive beacon reference source, SVM-based land/sea clutter identification with multi-features was proposed in this paper by establishing signal model and analyzing the three typical features of land/sea clutter. Experiments were carried out for different clutter-noise-ratios (CNR), and the results showed that the proposed algorithm has achieved a higher recognition rate of land/sea clutter than methods based on single feature of the backscatter amplitude and linear discriminant analysis. The proposed algorithm could provide theoretical guidance for improving target location accuracy of OTHR. [ABSTRACT FROM PUBLISHER]

Published
2012

16. A New Hybrid Approach to Predict Subcellular Localization by Incorporating Protein Evolutionary Conservation Information.

Author

Istrail, Sorin, Pevzner, Pavel, Waterman, Michael S., Kang Li, Xin Li, Irwin, George William, Gusen He, ShaoWu Zhang, YunLong Zhang, JunHui Li, HuiFeng Yang, YongMei Cheng, and GuoPing Zhou

Abstract

The rapidly increasing number of sequence entering into the genome databank has created the need for fully automated methods to analyze them. Knowing the cellular location of a protein is a key step towards understanding its function. The development in statistical prediction of protein attributes generally consists of two cores: one is to construct a training dataset and the other is to formulate a predictive algorithm. The latter can be further separated into two subcores: one is how to give a mathematical expression to effectively represent a protein and the other is how to find a powerful algorithm to accurately perform the prediction. Here, an improved evolutionary conservation algorithm was proposed to calculate per residue conservation score. Then, each protein can be represented as a feature vector created with multi-scale energy (MSE). In addition, the protein can be represented as other feature vectors based on amino acid composition (AAC), weighted auto-correlation function and Moment descriptor methods. Finally, a novel hybrid approach was developed by fusing the four kinds of feature classifiers through a product rule system to predict 12 subcellular locations. Compared with existing methods, this new approach provides better predictive performance. High success accuracies were obtained in both jackknife cross-validation test and independent dataset test, suggesting that introducing protein evolutionary information and the concept of fusing multi-features classifiers are quite promising, and might also hold a great potential as a useful vehicle for the other areas of molecular biology. [ABSTRACT FROM AUTHOR]

Published
2007
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20. Anomaly Detection in Hyperspectral Imagery by Fuzzy Integral Fusion of Band-subsets.

Author

Wei Di, Quan Pan, Lin He, and Yongmei Cheng

Subjects
ANOMALY detection (Computer security), MULTISPECTRAL imaging, FREE-space optical technology, DETECTORS, ANALYSIS of covariance, FUZZY systems, MULTISENSOR data fusion, SIGNAL processing, SENSOR networks
Abstract

Anomaly detection in hyperspectral imagery is gaining increasing interest. However, most covariance matrix-based detectors are applied directly to all the hyperspectral data bands without considering the spectral variation and their possible different contribution to detection. Besides, the limited sample number as compared with the high dimensionality of the data can lead to the imprecise estimation of the covariance matrix and even the singularity problem. In this paper, a band-subset fuzzy integral fusion (BS-FI) detection method is presented to solve these problems. The complete set of hyperspectral data bands is first partitioned into several lower dimensional band-subsets, whose detection results are obtained separately and finally merged by a fuzzy integral fusion method. We adopt a non-parametric fuzzy support function which can utilize more statistical information and avoid the model discrepancy that might be brought in by a fixed distribution model. In addition, the fuzzy density is assigned by the ratio between the target signal and noise, which is the key to the target detection problem through an adaptive eigenvalue-based approach. In the experiments on real OMIS-I hyperspectral imagery, the proposed method outperforms the Rx detector both on the complete set of bands and on each band-subset, and other band-subset fusion detectors. [ABSTRACT FROM AUTHOR]

Published
2008
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21. Tracking and length estimation of underwater acoustic target.

Author

Liang Yu, Yongmei Cheng, Song Li, Yan Liang, and Xiaoxu Wang

Subjects
UNDERWATER acoustics, ACOUSTICS, ECHO, SOUND, TRANSMISSION of sound
Abstract

Underwater acoustic multi-target tracking using bearing, Doppler and a conventional filtering method to determine the target location has problems such as low precision and inaccurate identification. Target echo time broadening is used first for the tracking process. The length of the target can be derived from the echo broadening, and the target length is obtained as a parameter by expectation-maximisation derivation, thereby increasing the dimension of the measurement for the estimation. Simulation results show that this method has higher tracking accuracy and target recognition accuracy than do other feature-aided tracking methods. [ABSTRACT FROM AUTHOR]

Published
2017
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