Toward smart carbon capture with machine learning

Toward carbon dioxide capture using nanoporous materials*

The development of more efficient processes for CO2 capture from the flue streams of power plants is considered a key to the reduction of greenhouse gas emissions implicated in global warming. Indeed, several U.S. and international climate change initiatives have identified the urgent need for improved materials and methods for CO2 capture. Conventional CO2 capture processes employed in power p...

Machine Learning for Image Based Motion Capture

Image based motion capture is a problem that has recently gained a lot of attention in the domain of understanding human motion in computer vision. The problem involves estimating the 3D configurations of a human body from a set of images and has applications that include human computer interaction, smart surveillance, video analysis and animation. This thesis takes a machine learning based app...

Toward “smart tubes” using iterative learning control

In this paper, we present our progress toward designing a “smart” high-peak power microwave (HPM) tube. We use iterative learning-control (ILC) methodologies in order to control a repetitively pulsed high-power backward-wave oscillator (BWO). The learning-control algorithm is used to drive the error between the actual output and its desired value to zero. The desired output may be a given power...

Effect of Cryogenic Carbon Capture (CCC) on Smart Power Grids

Human and Smart Machine Co-Learning with Brain Computer Interface

Machine learning has become a very popular approach for cybernetics systems, and it has always been considered important research in the Computational Intelligence area. Nevertheless, when it comes to smart machines, it is not just about the methodologies. We need to consider systems and cybernetics as well as include human in the loop. The purpose of this article is as follows: (1) To integrat...