Search Results for author:
Found 7 papers, 1 papers with code
Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting
By comparing our spintronic physical RC approach with state-of-the-art energy load forecasting algorithms, such as LSTMs and RNNs, we conclude that the proposed framework presents good performance in achieving high predictions accuracy, while also requiring low memory and energy both of which are at a premium in hardware resource and power constrained edge applications.
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data
Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/communications.
Self-Supervised and Interpretable Anomaly Detection using Network Transformers
Monitoring traffic in computer networks is one of the core approaches for defending critical infrastructure against cyber attacks.
Physics Enhanced Data-Driven Models with Variational Gaussian Processes
Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning models.
An Adversarial Approach for Explainable AI in Intrusion Detection Systems
Adversarial machine learning offers an approach to increase our understanding of these models.
Building Energy Load Forecasting using Deep Neural Networks
Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data.
Fast Trajectory Simplification Algorithm for Natural User Interfaces in Robot Programming by Demonstration
While most current trajectory simplification algorithms are tailored for GPS trajectories, our approach focuses on smooth trajectories for robot programming by demonstration recorded using motion capture systems. Two variations of the algorithm are presented: 1. aims to preserve shape and temporal information; 2. preserves only shape information.
