What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Statistics
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.
His most cited work include:
- Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization (181 citations)
- Random noise attenuation by f-x empirical mode decomposition predictive filtering (177 citations)
- Random noise attenuation using local signal-and-noise orthogonalization (157 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Algorithm (50.97%)
- Artificial intelligence (24.51%)
- Noise reduction (17.90%)
What were the highlights of his more recent work (between 2019-2021)?
- Algorithm (50.97%)
- Artificial intelligence (24.51%)
- Deep learning (6.23%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Deep-Learning Inversion of Seismic Data (49 citations)
- Deep denoising autoencoder for seismic random noise attenuation (15 citations)
- Automatic waveform-based source-location imaging using deep learning extracted microseismic signalsMicroseismic signals extraction (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Statistics
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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