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A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot
A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper.
Deep Learning Training with Simulated Approximate Multipliers
The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy.
Using Deep Learning to Automate the Detection of Flaws in Nuclear Fuel Channel UT Scans
The proposed CNN model achieves this target by automatically identifying at least a portion of each flaw where further manual analysis is performed to identify the width, the length, and the type of the flaw.
Subtractor-Based CNN Inference Accelerator
This paper presents a novel method to boost the performance of CNN inference accelerators by utilizing subtractors.
Managing the Impact of Sensor's Thermal Noise in Machine Learning for Nuclear Applications
This paper lists some applications for Canada Deuterium Uranium (CANDU) reactors where such sensors are used and therefore can be impacted by the thermal noise issue if machine learning is utilized.
