arXiv subjects

Richard Nguyen

Publications and source records attributed to Richard Nguyen.

2 recordsLinked to original sources

arXiv2023

Flat bands and multi-state memory devices from chiral domain wall superlattices in magnetic Weyl semimetals

We propose a novel analog memory device utilizing the gigantic magnetic Weyl semimetal (MWSM) domain wall (DW) magnetoresistance. We predict that the nucleation of domain walls between contacts will strongly modulate the conductance and allow for multiple memory states, which has been long sought-after for use in magnetic random access memories or memristive neuromorphic computing platforms. We motivate this conductance modulation by analyzing the electronic structure of the helically-magnetized MWSM Hamiltonian, and report tunable flat bands in the direction of transport in a helically-magnetized region of the sample for Bloch and Neel-type domain walls via the onset of a local axial Landau level spectrum within the bulk of the superlattice. We show that Bloch devices also provide means for the generation of chirality-polarized currents, which provides a path towards nanoelectronic utilization of chirality as a new degree of freedom in spintronics.

cond-mat.mes-hall↗

arXiv2022

Feature Construction and Selection for PV Solar Power Modeling

Using solar power in the process industry can reduce greenhouse gas emissions and make the production process more sustainable. However, the intermittent nature of solar power renders its usage challenging. Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages and further design proper operations. The solar power output is time-series data dependent on many factors, such as irradiance and weather. A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data. Our method extends the input dataset into higher dimensional Chebyshev polynomial space. Then, a feature selection scheme is developed with constrained linear regression to construct the predictor for different weather types. Several tests show that the proposed approach yields lower mean squared error than classical machine learning methods, such as support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT).

eess.SY↗