What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of study are Artificial intelligence, Computer vision, Data compression, Signal and Neuroscience.
His Artificial intelligence research integrates issues from Subtraction and Pattern recognition.
In most of his Computer vision studies, his work intersects topics such as Adaptive filter.
His Data compression research includes themes of Image processing, Image quality and Discrete cosine transform.
His research in Signal intersects with topics in Encoding, Pixel, Pixel based, Boundary and Residual.
His research integrates issues of Visual perception, Visualization and Human visual system model in his study of Stereoscopy.
His most cited work include:
- A method and an apparatus for processing a video signal (219 citations)
- Method and an apparatus for processing a video signal (213 citations)
- A parallel MR imaging method using multilayer perceptron. (92 citations)
What are the main themes of his work throughout his whole career to date?
HyunWook Park spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Magnetic resonance imaging.
His study in Artificial intelligence concentrates on Discrete cosine transform, Data compression, Segmentation, Iterative reconstruction and Interpolation.
Image processing, Motion estimation, Image, Image quality and Pixel are among the areas of Computer vision where he concentrates his study.
His work deals with themes such as Encoder and Coding, which intersect with Algorithm.
The concepts of his Magnetic resonance imaging study are interwoven with issues in Phase, Signal and Nuclear magnetic resonance.
His biological study spans a wide range of topics, including Imaging phantom and Optics.
He most often published in these fields:
- Artificial intelligence (51.40%)
- Computer vision (44.97%)
- Algorithm (12.01%)
What were the highlights of his more recent work (between 2018-2021)?
- Signal integrity (3.91%)
- Artificial intelligence (51.40%)
- Pattern recognition (11.45%)
In recent papers he was focusing on the following fields of study:
HyunWook Park mainly investigates Signal integrity, Artificial intelligence, Pattern recognition, Electronic engineering and Artificial neural network.
His study ties his expertise on Computer vision together with the subject of Artificial intelligence.
His Computer vision research incorporates elements of Visualization, Metric and Sensitivity.
His Pattern recognition study integrates concerns from other disciplines, such as Healthy volunteers, Metal Artifact, Overfitting and Mr images.
His Electronic engineering study incorporates themes from Wireless power transfer, Electromagnetic coil and Electromagnetic shielding.
His Artificial neural network research is multidisciplinary, incorporating elements of Reflectometry and Computer hardware.
Between 2018 and 2021, his most popular works were:
- Improvement of combustion and emissions with exhaust gas recirculation in a natural gas-diesel dual-fuel premixed charge compression ignition engine at low load operations (27 citations)
- Comparisons of advanced combustion technologies (HCCI, PCCI, and dual-fuel PCCI) on engine performance and emission characteristics in a heavy-duty diesel engine (23 citations)
- Expansion of low-load operating range by mixture stratification in a natural gas-diesel dual-fuel premixed charge compression ignition engine (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Combustion, NOx, Thermal efficiency, Diesel fuel and Artificial intelligence are his primary areas of study.
HyunWook Park has included themes like Particulates, Exhaust gas recirculation and Natural gas in his NOx study.
His work carried out in the field of Particulates brings together such families of science as Carbon monoxide, Automotive engineering, Homogeneous charge compression ignition and Single-cylinder engine.
His work in Exhaust gas recirculation addresses subjects such as Analytical chemistry, which are connected to disciplines such as Mean effective pressure and Gasoline.
Much of his study explores Artificial intelligence relationship to Pattern recognition.
His Pattern recognition research includes elements of Ripple and Metal Artifact.
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