arXiv research

Two approaches detect EV charging patterns at stations.

Develops a combinatorial semi-bandit method for electric vehicle charging station selection.

Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks.…

Challenge forecasts EV charging station usage accurately.

A federated graph learning approach improves EV charging demand forecasting while protecting against cyberattacks.

Kernel-based mean-field games use MMD penalties for interaction and target costs.

PIML uses physics equations in machine learning for better forecasting.

In this paper, we prove conformal positive mass theorems for asymptotically flat manifolds with charge. We apply conformal relations to show that if the conformal sum of scalar curvature is not less than the norm square of electric field and electric density, the sum of the mass will not less than the modulus of total …

Bayesian model for energy consumption helps electric vehicles navigate efficiently.

The Lorentz force equations provide a partial description of the geodesic motion of a charged particle on a four-manifold. Under the hypothesis that Maxwell's equations express symmetry properties of the Ricci tensor, the full electromagnetic connection is determined. From this connection, the fourth equation of the ge…

The article proves charged quasi-local Penrose inequalities for compact manifolds with boundary.

FedGAN trains GANs across distributed data sources with reduced communication.

Bayesian model for energy-efficient EV navigation.

Study on implied certainty equivalent rates in financial markets and electric vehicles.

This paper constructs charged Riemannian manifolds to test Penrose inequality.

Initial DR studies mainly adopt model predictive control and thus require accurate models of the control problem (e.g., a customer behavior model), which are to a large extent uncertain for the EV scenario. Hence, model-free approaches, especially based on reinforcement learning (RL) are an attractive alternative. In t…

The paper explores how topological methods can reveal insights into electric charge distributions on knots.

Deep learning solves EV routing with time windows for EV fleets.

Positive energy theorems for spin initial data with charge in higher dimensions.

Throughout the literature on the charged Riemannian Penrose inequality, it is generally assumed that there is no charged matter present; that is, the electric field is divergence-free. The aim of this article is to clarify when the charged Riemannian Penrose inequality holds in the presence of charged matter, and when …

A necessary and sufficient condition for energy-momentum conservation is proved within a topological, pre-metric approach to classical electrodynamics including magnetic as well as electric charges. The extended Lorentz force, consisting of mutual actions by F=(E, B) on the electric current and G=(H, D) on the magnetic…

Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form o…

In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy th…

Paper models and forecasts intra-day electricity price spreads.

We establish a class of area-angular momentum-charge inequalities satisfied by stable marginally outer trapped surfaces in 5-dimensional minimal supergravity which admit a $U(1)^2$ symmetry. A novel feature is the fact that such surfaces can have the nontrivial topologies $S^1 \times S^2$ and $L(p,q)$. In addition to t…

Survey of reinforcement learning for sustainable energy challenges.

Adversarial attacks degrade DRL-based EV energy management systems.

No time-periodic Majorana fermions found in Kerr-Newman spacetimes with nontrivial charge.

CapOptix uses options theory to price capacity in electricity markets.

We prove existence of all possible bi-axisymmetric near-horizon geometries of 5-dimensional minimal supergravity. These solutions possess the cross-sectional horizon topology $S^3$, $S^1\times S^2$, or $L(p,q)$ and come with prescribed electric charge, two angular momenta, and a dipole charge (in the ring case). Moreov…

Study electric field and potential of torus knots, focusing on z-axis.

An analytic extension of the Reissner-Nordstrom solution at and beyond the singularity is presented. The extension is obtained by using new coordinates in which the metric becomes degenerate at $r=0$. The metric is still singular in the new coordinates, but its components become finite and smooth. Using this extension …

Deep Q-learning optimizes same-day delivery with vehicles and drones.

The paper explores rigid geometric structures near surfaces with equality in area-charge inequalities.

Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.

Constructs solutions to Einstein-Maxwell-current system using Sasakian manifolds.

Let A be the space of irreducible connections (vector potentials) over a SU(n)-principal bundle on a three-dimensional manifold M. Let T be the fiber product of the tangent and cotangent bundles of A. We endow T with a symplectic structure Ωwhich is represented by a vortex formula. The corresponding Poisson bracket wil…

Adapts model-based advice to stabilize black-box policies for nonlinear control.

Making use of the Kerr theorem for shear-free null congruences and of Newman's representation for a virtual charge ``moving'' in complex space-time, we obtain an axisymmetric time-dependent generalization of the Kerr congruence, with a singular ring uniformly contracting to a point and expanding then to infinity. Elect…

Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods…

Study electric-magnetic duality in M-theory compactifications.

Closed and broken electromagnetic orbits in Kerr-Newman spacetime

A novel controller for wheeled robots handles joystick inputs for smooth steering.

This paper uses t-SNE to visualize multi-objective electric machine optimization at various operating points.

In the paper [4] is presented a theory which unifies the gravitation theory and the mechanical effects, which is different from the Riemannian theories like GTR. Moreover it is built in the style of the electomagnetic field theory. This paper is a continuation of [4] such that the complex variant of that theory yields …

We construct a covariant functor from a category of Abelian principal bundles over globally hyperbolic spacetimes to a category of *-algebras that describes quantized principal connections. We work within an appropriate differential geometric setting by using the bundle of connections and we study the full gauge group,…

We count the supersymmetric bound states of many distinct BPS monopoles in N=4 Yang-Mills theories and in pure N=2 Yang-Mills theories. The novelty here is that we work in generic Coulombic vacua where more than one adjoint Higgs fields are turned on. The number of purely magnetic bound states is again found to be cons…

In this paper we present an econophysic model for the description of shares transactions in a capital market. For introducing the fundamentals of this model we used an analogy between the electrical field produced by a system of charges and the overall of economic and financial information of the shares transactions fr…