What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Computer vision
Grantham K. H. Pang mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Wavelet transform.
His Artificial intelligence research includes themes of Optical engineering and Machine learning.
His research on Computer vision often connects related topics like Intelligent transportation system.
Grantham K. H. Pang combines subjects such as Image resolution, Background subtraction and Feature with his study of Feature extraction.
The Wavelet transform study combines topics in areas such as Classifier, Quadratic classifier and Backpropagation.
His Image processing research includes elements of Acoustics and Yarn.
His most cited work include:
- Defect detection in textured materials using Gabor filters (378 citations)
- Fabric defect detection by Fourier analysis (316 citations)
- Review article: Automated fabric defect detection-A review (260 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Expert system, Pattern recognition and Control engineering.
Feature extraction, Image segmentation, Segmentation, Wavelet transform and Wavelet are the subjects of his Artificial intelligence studies.
His work in Image processing, Object detection, Feature, Texture and Pixel are all subfields of Computer vision research.
His research integrates issues of Control system, Systems engineering, Process control, Software engineering and Design knowledge in his study of Expert system.
His research links Visual inspection with Pattern recognition.
The concepts of his Control engineering study are interwoven with issues in Robustness, Control theory and Fuzzy logic.
He most often published in these fields:
- Artificial intelligence (40.00%)
- Computer vision (21.82%)
- Expert system (16.97%)
What were the highlights of his more recent work (between 2012-2020)?
- Artificial intelligence (40.00%)
- Electric vehicle (4.24%)
- Mathematical optimization (6.06%)
In recent papers he was focusing on the following fields of study:
Grantham K. H. Pang mostly deals with Artificial intelligence, Electric vehicle, Mathematical optimization, Optimization problem and Pattern recognition.
The various areas that Grantham K. H. Pang examines in his Artificial intelligence study include Partition and Computer vision.
His work on Rotation, Texture, Image processing and Wavelet as part of general Computer vision research is often related to Bollinger Bands, thus linking different fields of science.
In his work, Automotive engineering and Genetic algorithm is strongly intertwined with Battery, which is a subfield of Electric vehicle.
Grantham K. H. Pang works mostly in the field of Mathematical optimization, limiting it down to topics relating to Wireless sensor network and, in certain cases, Evolution strategy, Pareto principle and Range, as a part of the same area of interest.
His Pattern recognition research incorporates themes from Visual inspection, Point, Computer graphics and Image.
Between 2012 and 2020, his most popular works were:
- Fabric inspection based on the Elo rating method (44 citations)
- An Optimization Model for Electric Vehicle Battery Charging at a Battery Swapping Station (34 citations)
- A decision support system for optimizing dynamic courier routing operations (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Electrical engineering
Grantham K. H. Pang focuses on Optimization problem, Differential evolution, Electric vehicle, Particle swarm optimization and Artificial intelligence.
As a part of the same scientific study, Grantham K. H. Pang usually deals with the Optimization problem, concentrating on Heuristics and frequently concerns with Electricity, Fuzzy control system and Scheduling.
His Differential evolution study combines topics from a wide range of disciplines, such as Evolutionary algorithm, Genetic algorithm and Battery, Electric-vehicle battery.
His work carried out in the field of Electric-vehicle battery brings together such families of science as Schedule, Automotive engineering and Simulation.
He interconnects Machine learning and Computer graphics in the investigation of issues within Artificial intelligence.
His work deals with themes such as Range, Wireless sensor network, Algorithm design and Crossover, which intersect with Mathematical optimization.
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