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Full-Text Articles in Engineering
Rgb-D Data-Based Action Recognition: A Review, Muhammad Bilal Shaikh, Douglas Chai
Rgb-D Data-Based Action Recognition: A Review, Muhammad Bilal Shaikh, Douglas Chai
Research outputs 2014 to 2021
Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The increase in the number of action recognition datasets intersects with advances in deep learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality—such as RGB, depth, skeleton, and infrared (IR)—has distinct characteristics; therefore, it is important to exploit the value of …
Deep Learning Control For Digital Feedback Systems: Improved Performance With Robustness Against Parameter Change, Nuha A. S. Alwan, Zahir M. Hussain
Deep Learning Control For Digital Feedback Systems: Improved Performance With Robustness Against Parameter Change, Nuha A. S. Alwan, Zahir M. Hussain
Research outputs 2014 to 2021
Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a …