What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mechanical engineering
- Electrical engineering
Robert X. Gao mainly focuses on Artificial intelligence, Bearing, Wavelet, Pattern recognition and Condition monitoring.
His Artificial intelligence research incorporates elements of Machine learning and Process.
His Bearing research is multidisciplinary, relying on both Vibration, Signal processing, Algorithm, Feature extraction and Electronic engineering.
His work in Stationary wavelet transform, Discrete wavelet transform, Second-generation wavelet transform, Wavelet packet decomposition and Wavelet transform are all subfields of Wavelet research.
His work on Activity recognition as part of general Pattern recognition study is frequently connected to Acceleration, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies deal with areas such as Computer-integrated manufacturing, Control engineering, Risk analysis, Scheduling and Continuous wavelet transform as well as Condition monitoring.
His most cited work include:
- Wavelets for fault diagnosis of rotary machines: A review with applications (738 citations)
- Deep learning and its applications to machine health monitoring (630 citations)
- Deep learning for smart manufacturing: Methods and applications (440 citations)
What are the main themes of his work throughout his whole career to date?
Robert X. Gao mostly deals with Artificial intelligence, Electronic engineering, Wavelet, Pattern recognition and Vibration.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His study on Electronic engineering also encompasses disciplines like
- Acoustics which is related to area like Pressure measurement,
- Signal which connect with Energy.
His study in Wavelet concentrates on Wavelet packet decomposition, Wavelet transform, Discrete wavelet transform, Second-generation wavelet transform and Lifting scheme.
Robert X. Gao interconnects Fault, Structural engineering, Control theory and Bearing in the investigation of issues within Vibration.
His studies examine the connections between Bearing and genetics, as well as such issues in Condition monitoring, with regards to Control engineering.
He most often published in these fields:
- Artificial intelligence (22.44%)
- Electronic engineering (15.34%)
- Wavelet (13.92%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (22.44%)
- Deep learning (5.97%)
- Fault (7.10%)
In recent papers he was focusing on the following fields of study:
Robert X. Gao mainly investigates Artificial intelligence, Deep learning, Fault, Convolutional neural network and Pattern recognition.
He has included themes like Machine learning and Layer in his Artificial intelligence study.
The concepts of his Machine learning study are interwoven with issues in Smart manufacturing and Tool wear.
As a part of the same scientific family, he mostly works in the field of Deep learning, focusing on Process and, on occasion, Data mining.
His study in Fault is interdisciplinary in nature, drawing from both Vibration, Relevance, Transfer of learning, Algorithm and Bearing.
His research in Convolutional neural network intersects with topics in Recurrent neural network, Wavelet, Robustness and Time series.
Between 2016 and 2021, his most popular works were:
- Deep learning and its applications to machine health monitoring (630 citations)
- Deep learning for smart manufacturing: Methods and applications (440 citations)
- A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests (150 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Electrical engineering
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Feature extraction.
His research on Artificial intelligence focuses in particular on Random forest.
Robert X. Gao has researched Deep learning in several fields, including Dynamical systems theory, Key and Data mining, Data analysis.
His work on Feature learning and Deep belief network as part of general Machine learning study is frequently linked to Structure and Health condition, bridging the gap between disciplines.
The study incorporates disciplines such as Recurrent neural network, Robustness and Condition monitoring in addition to Convolutional neural network.
His Feature extraction study necessitates a more in-depth grasp of Pattern recognition.
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