Harnessing energy from ocean waves presents a promising solution to combating global
climate change in the marine environment, significantly contributing to mitigation efforts …

Abstract Convolutional Neural Networks (CNNs) are state-of-the-art models for computer
vision tasks such as image classification, object detection, and segmentation. However …

A measure of robustness against naturally occurring distortions is key to safety, success, and
trustworthiness of machine learning models on deployment. We propose an adversarial …

As machine learning workloads are significantly increasing energy consumption,
sustainable data centers with low carbon emissions are becoming a top priority for …

The rapid growth of machine learning (ML) has led to an increased demand for
computational power, resulting in larger data centers (DCs) and higher energy consumption …

In the field of wave energy extraction, employing active control strategies amplifies the Wave
Energy Converter's (WEC) response to wave motion. In this regard, a numerical simulation …

The transition to sustainable energy is a key challenge of our time, requiring modifications in
the entire pipeline of energy production, storage, transmission, and consumption. At every …

We present a novel framework for generating adversarial benchmarks to evaluate the
robustness of image classification models. The RLAB framework allows users to customize …

This paper proposes a robust learning-based model predictive control (MPC) strategy
tailored for sea wave energy converters (WECs). The control algorithm aims to maximize …

Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to
maximize energy generation. Traditional controllers have shown limitations to capture …