Yangkang Chen focuses on Algorithm, Noise reduction, Mathematical optimization, Signal processing and Artificial intelligence. His research integrates issues of Noise, Hilbert–Huang transform, Source data and Thresholding in his study of Algorithm. His Noise reduction research includes themes of Singular value decomposition, Reduction, Rank, Hankel matrix and Attenuation.
The study incorporates disciplines such as Dither and Regularization in addition to Mathematical optimization. His biological study spans a wide range of topics, including Speech recognition, Noise and Synthetic data. His research in Artificial intelligence intersects with topics in Computer vision and Pattern recognition.
Yangkang Chen spends much of his time researching Algorithm, Artificial intelligence, Noise reduction, Pattern recognition and Attenuation. The various areas that Yangkang Chen examines in his Algorithm study include Signal, Noise, Median filter, Mathematical optimization and Noise. His Noise research is multidisciplinary, incorporating perspectives in Acoustics, Reflection and Noise measurement.
His Mathematical optimization research is multidisciplinary, incorporating elements of Regularization, Thresholding and Interpolation. While the research belongs to areas of Artificial intelligence, Yangkang Chen spends his time largely on the problem of Microseism, intersecting his research to questions surrounding Waveform. His Noise reduction study which covers Reduction that intersects with Rank.
His scientific interests lie mostly in Algorithm, Artificial intelligence, Deep learning, Pattern recognition and Expression. His Algorithm research is multidisciplinary, relying on both Constraint, Noise reduction and Inverse problem. Yangkang Chen has included themes like Matrix decomposition, Sparse matrix, Adaptive filter, Noise and Synthetic data in his Noise reduction study.
His Artificial intelligence research incorporates elements of Machine learning and Generalization. His Deep learning study integrates concerns from other disciplines, such as Porosity, Simulation and Nonlinear system. His Pattern recognition research incorporates themes from Signal and Facies.
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Pattern recognition, Deep learning and Random noise. The concepts of his Artificial intelligence study are interwoven with issues in Generalization and Facies. His Algorithm research integrates issues from Noise reduction and Inverse problem.
Synthetic data is closely connected to Slope field in his research, which is encompassed under the umbrella topic of Noise reduction. His research integrates issues of Waveform, Microseism and Identification in his study of Pattern recognition. Yangkang Chen combines subjects such as Porosity, Simulation and Nonlinear system with his study of Deep learning.
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Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization
Yangkang Chen;Sergey B Fomel;Jingwei Hu.
Random noise attenuation using local signal-and-noise orthogonalization
Yangkang Chen;Sergey B Fomel.
Random noise attenuation by f-x empirical mode decomposition predictive filtering
Yangkang Chen;Jitao Ma.
Applications of variational mode decomposition in seismic time-frequency analysis
Wei Liu;Siyuan Cao;Yangkang Chen.
Damped multichannel singular spectrum analysis for 3D random noise attenuation
Weilin Huang;Runqiu Wang;Yangkang Chen;Huijian Li.
Seismic Time–Frequency Analysis via Empirical Wavelet Transform
Wei Liu;Siyuan Cao;Yangkang Chen.
IEEE Geoscience and Remote Sensing Letters (2016)
Simultaneous denoising and reconstruction of 5-D seismic data via damped rank-reduction method
Yangkang Chen;Dong Zhang;Zhaoyu Jin;Xiaohong Chen.
Geophysical Journal International (2016)
Double Sparsity Dictionary for Seismic Noise Attenuation
Yangkang Chen;Jianwei Ma;Sergey B Fomel.
Seismic imaging of incomplete data and simultaneous-source data using least-squares reverse time migration with shaping regularization
Zhiguang Xue;Yangkang Chen;Sergey B Fomel;Junzhe Sun.
Fast dictionary learning for noise attenuation of multidimensional seismic data
Geophysical Journal International (2017)
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