H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 32 Citations 7,144 178 World Ranking 5169 National Ranking 60

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Statistics
  • Computer vision

Irene Yu-Hua Gu mainly focuses on Artificial intelligence, Electronic engineering, Electric power system, Computer vision and Power quality. Her work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with her field of interest in Pooling. Her Electronic engineering research integrates issues from Emphasis, Estimation theory, Power electronics and Electrical network.

The Electric power system study combines topics in areas such as Control theory, Expert system, Waveform, Signal processing and Data analysis. Her Computer vision research is multidisciplinary, relying on both Robustness and Pattern recognition. Her work on Feature extraction as part of general Pattern recognition study is frequently linked to Color histogram, bridging the gap between disciplines.

Her most cited work include:

  • Statistical modeling of complex backgrounds for foreground object detection (904 citations)
  • Signal processing of power quality disturbances (584 citations)
  • Foreground object detection from videos containing complex background (366 citations)

What are the main themes of her work throughout her whole career to date?

Irene Yu-Hua Gu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Video tracking and Electronic engineering. Her study in Feature extraction, Object detection, Deep learning, Image segmentation and Segmentation falls under the purview of Artificial intelligence. Her work deals with themes such as Image processing and Background subtraction, which intersect with Object detection.

She works mostly in the field of Computer vision, limiting it down to concerns involving Geodesic and, occasionally, Riemannian manifold. As a part of the same scientific study, she usually deals with the Pattern recognition, concentrating on Contextual image classification and frequently concerns with AdaBoost. Irene Yu-Hua Gu combines subjects such as Power quality, Voltage, Electric power system, Electrical network and Signal processing with her study of Electronic engineering.

She most often published in these fields:

  • Artificial intelligence (62.82%)
  • Computer vision (41.88%)
  • Pattern recognition (31.20%)

What were the highlights of her more recent work (between 2015-2021)?

  • Artificial intelligence (62.82%)
  • Pattern recognition (31.20%)
  • Deep learning (8.97%)

In recent papers she was focusing on the following fields of study:

Irene Yu-Hua Gu mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Feature extraction and Computer vision. Her Convolutional neural network and Segmentation study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Fall detection, bridging the gap between disciplines. Her biological study spans a wide range of topics, including Salient and Recurrent neural network.

Her research in Deep learning intersects with topics in Feature learning, Feature and Overfitting. Her study focuses on the intersection of Feature extraction and fields such as Image segmentation with connections in the field of Object detection. Her Computer vision study incorporates themes from False alarm and Motion.

Between 2015 and 2021, her most popular works were:

  • Deepside: A general deep framework for salient object detection (63 citations)
  • On waveform distortion in the frequency range of 2 kHz–150 kHz—Review and research challenges (47 citations)
  • An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images (33 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Computer vision

Irene Yu-Hua Gu mostly deals with Artificial intelligence, Deep learning, Pattern recognition, Feature extraction and Convolutional neural network. Irene Yu-Hua Gu has included themes like Machine learning, Geodesic and Computer vision in her Artificial intelligence study. Her study brings together the fields of Segmentation and Deep learning.

In her study, which falls under the umbrella issue of Segmentation, Feature is strongly linked to Salience. Her work carried out in the field of Feature extraction brings together such families of science as Image segmentation, Conditional random field, Object detection, Feature learning and Voltage. The study incorporates disciplines such as Classification methods, Convolution and Constant false alarm rate in addition to Convolutional neural network.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Signal processing of power quality disturbances

Math H. J. Bollen;Irene Yu-Hua Gu.
(2006)

1357 Citations

Statistical modeling of complex backgrounds for foreground object detection

Liyuan Li;Weimin Huang;Irene Yu-Hua Gu;Qi Tian.
IEEE Transactions on Image Processing (2004)

1144 Citations

Foreground object detection from videos containing complex background

Liyuan Li;Weimin Huang;Irene Y. H. Gu;Qi Tian.
acm multimedia (2003)

626 Citations

Expert System for Classification and Analysis of Power System Events

E. Styvaktakis;M.H.J. Bollen;I.Y.H. Gu.
IEEE Power & Energy Magazine (2002)

298 Citations

Support Vector Machine for Classification of Voltage Disturbances

P.G.V. Axelberg;Irene Yu-Hua Gu;M.H.J. Bollen.
IEEE Transactions on Power Delivery (2007)

215 Citations

Estimating Interharmonics by Using Sliding-Window ESPRIT

I.Y.-H. Gu;M.H.J. Bollen.
IEEE Transactions on Power Delivery (2008)

187 Citations

Categorization and analysis of power system transients

M.H.J. Bollen;E. Styvaktakis;Irene Yu-Hua Gu.
IEEE Transactions on Power Delivery (2005)

154 Citations

Robust Visual Object Tracking Using Multi-Mode Anisotropic Mean Shift and Particle Filters

Z H Khan;I Y Gu;A G Backhouse.
IEEE Transactions on Circuits and Systems for Video Technology (2011)

153 Citations

Bridging the gap between signal and power

M.H.J. Bollen;I.Y.H. Gu;S. Santoso;M.F. Mcgranaghan.
IEEE Signal Processing Magazine (2009)

144 Citations

Automatic classification of power system events using RMS voltage measurements

E. Styvaktakis;M.H.J. Bollen;I.Y.H. Gu.
power engineering society summer meeting (2002)

126 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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