Energy sector is undergoing a massive transformation that includes key aspects such as
integrating renewables, improving operational efficiency, leveraging smart grid …

Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from
spectral variability, making it difficult for spectral unmixing to accurately estimate abundance …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of
the added perturbation is usually used to measure the similarity between original image and …

In the traditional power system, the end users are independent individuals without any
interaction. While the development of communication and information technology brings …

Tasks across diverse application domains can be posed as large-scale optimization
problems, these include graphics, vision, machine learning, imaging, health, scheduling …

In this paper, a distributed multi-objective optimization model is proposed to coordinate the
fast-dispatch of photovoltaic (PV) inverters with the slow-dispatch of on-load tap changer …

This paper presents a distributed Model Predictive Control (MPC) approach for multi-zone
building energy systems based on nonlinear Modelica controller models. The method …

This paper develops a new class of nonconvex regularizers for low-rank matrix recovery.
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-
the-art DNNs raises security concerns of DNNs in many application domains requiring high …