“Reinforcing a Monitoring System of a Regulated River using LSTM”
ICAI'23 and CSCE'23 — Published in IEEE Xplore
Abstract— Accurate methods predicting flow in a river require high performance computing systems providing only short-term early warnings. To address these limitations, we propose an optimized Long Short Term Memory layer combined with a Fully connected layer within a six-layer architecture to estimate flows based on readings of three neighboring river gage heights. This study assessed the number of cells in the LSTM and the number of neurons in the Fully connected layer as well as the probability in the Dropout layer to avoid overfitting. Identification of the best settings by means of the RMSE evaluated six different input datasets to estimate flow. Our study proved that relying on the river gage height from neighboring sensors is possible to predict flow which is crucial when a sensor may fail and a reliable neural network at low computational cost can handle that.
“Intelligent Upper Limbs Prosthetics with 1D Convolutional Neural Networks and Quick Training”
BIOENG'23 and CSCE'23 — Published in IEEE Xplore
Abstract— Upper limb amputation can severely restrict the mobility and ability of amputees to perform daily activities. In addressing this issue, deep learning algorithms and electromyography pattern recognition have emerged as promising clinical solutions for functional upper-limb prosthetics. This article presents the use of EMG sensors to capture muscle movement signals and applies the pattern recognition function of a 1D convolutional neural network to identify these signals and control the movement of prosthetics. Experimental results demonstrate that the convolutional neural network exhibits fast training and high-precision recognition capabilities enabling it to accurately identify muscle signals and effectively control prosthetic movements.