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Chen, Gong

Publications and source records attributed to Chen, Gong.

Reducing crystal symmetry to generate out-of-plane Dzyaloshinskii–Moriya interaction

The Dzyaloshinskii-Moriya antisymmetric exchange interaction (DMI) stabilises topological spin textures with promising future spintronics applications. According to crystal symmetry, the DMI can be categorized as four different types that favour different chiral textures. Unlike the other three extensively-investigated types, out-of-plane DMI, as the last type that favours in-plane chirality, remained missing so far. Here we apply point-group-dependent DMI matrix analysis to show that out-of-plane DMI exists under reduced crystal symmetry. Through strain and structure engineering, we show how C s symmetry is realized in ultrathin magnets and observe the out-of-plane DMI stabilised in-plane chirality using spin-polarized electron microscopy. Our results show that extremely low out-of-plane DMI strengths at µeV/atom are sufficient to stabilise topological spin textures, including merons and bimerons. We also demonstrate field-induced reversible control of the in-plane chirality and merons. Our findings open up untapped paths on topological magnetic textures and their potential applications.

36 MATERIALS SCIENCE

A Deep Learning Approach for In-Network Synchrophasor Missing Data Recovery Using Programmable Network Switches

Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today’s electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machine learning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. Performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.

Phasor Measurement Unit, Machine Learning, Program