What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Gordon Lightbody focuses on Control theory, Nonlinear system, Artificial intelligence, Artificial neural network and Neonatal seizure.
His Control theory research includes themes of Wind power, Quadratic programming, Model predictive control and Power control.
His study in Nonlinear system is interdisciplinary in nature, drawing from both Control theory, Mathematical optimization and Feed forward.
Gordon Lightbody studied Artificial intelligence and Linear model that intersect with Energy and Renewable energy.
His work on Backpropagation as part of general Artificial neural network research is frequently linked to Gaussian process, thereby connecting diverse disciplines of science.
The Neonatal seizure study combines topics in areas such as Classifier, Support vector machine and Pattern recognition.
His most cited work include:
- Modeling of the wind turbine with a doubly fed induction generator for grid integration studies (581 citations)
- Wind-turbine fault ride-through enhancement (214 citations)
- EEG-based neonatal seizure detection with Support Vector Machines. (214 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Electroencephalography, Artificial intelligence, Control theory, Pattern recognition and Neonatal seizure.
The various areas that Gordon Lightbody examines in his Electroencephalography study include Speech recognition, Support vector machine and Feature extraction.
His work carried out in the field of Artificial intelligence brings together such families of science as Hypoxic Ischemic Encephalopathy and Machine learning.
The concepts of his Control theory study are interwoven with issues in Control engineering and Model predictive control.
His work on Classifier, Convolutional neural network and Principal component analysis as part of general Pattern recognition research is often related to Intensive care, thus linking different fields of science.
His studies in Neonatal seizure integrate themes in fields like Detector, Gold standard, Channel, Neonatal intensive care unit and Probabilistic framework.
He most often published in these fields:
- Electroencephalography (39.67%)
- Artificial intelligence (31.52%)
- Control theory (27.17%)
What were the highlights of his more recent work (between 2017-2021)?
- Electroencephalography (39.67%)
- Artificial intelligence (31.52%)
- Pattern recognition (22.83%)
In recent papers he was focusing on the following fields of study:
His main research concerns Electroencephalography, Artificial intelligence, Pattern recognition, Control theory and Distributed generation.
His research integrates issues of Audiology, Grading, Support vector machine, Hypoxic Ischemic Encephalopathy and Feature extraction in his study of Electroencephalography.
His Support vector machine study frequently intersects with other fields, such as Neonatal seizure.
His study deals with a combination of Artificial intelligence and Term.
His work deals with themes such as Neonatal eeg and Confidence interval, which intersect with Pattern recognition.
Gordon Lightbody has included themes like Converters and Topology in his Control theory study.
Between 2017 and 2021, his most popular works were:
- Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations (126 citations)
- An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory (71 citations)
- Framework for smart transactive energy in home-microgrids considering coalition formation and demand side management (62 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Electroencephalography, Pattern recognition, Neonatal seizure, Artificial intelligence and Support vector machine.
His Electroencephalography study integrates concerns from other disciplines, such as Internal medicine, Blood pressure, Mutual information and Cardiology.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Receptive field, Detector and Detection performance.
His research in Feature extraction intersects with topics in Time domain, Deep learning, Convolutional neural network and Training set.
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