Wenjun Xi | Searcharxiv

arXiv2025

Cross-Axis Weighted Harmonic Method: A Frequency-Domain Approach for Enhanced Resolution in Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is a promising imaging modality that tracks magnetic nanoparticles (MNPs) to generate real time, high-resolution images. However, achieving an optimal balance between strong signal strength and sharp image clarity remains challenging. Higher drive field frequencies improve the signal-to-noise ratio (SNR), but also risk image blurring due to nanoparticle relaxation effects. To address this, we developed an end-to-end MPI simulation framework that models MNPs behavior, magnetic field dynamics, signal acquisition, and image reconstruction across a wide frequency range (20 to 85 kHz). Central to this framework is Cross-Axis Harmonic Analysis (CAHA), a novel, frequency-domain signal processing technique that adaptively extracts high-SNR harmonics from the x, y, and z directions for improved signal reconstruction. Using a simulated 3D vascular phantom, CAHA significantly enhanced image quality, achieving sub-millimeter resolution (0.8 mm FWHM at 85 kHz), strong noise suppression (nRMSE as low as 0.01), and structural fidelity (SSIM up to 0.94 at 55 kHz). The peak SNR reached 29.7 dB at 85 kHz. The signal processed with CAHA was also tested with other reconstruction methods; when combined with total variation regularization, CAHA achieved a pSNR of 37.91 dB. Evaluation on the Open MPI dataset further demonstrated up to 20% resolution improvement, confirming CAHA's robustness on real-world data. Although minor blurring was observed at the highest frequency due to relaxation, CAHA consistently maintained image clarity. By leveraging directional harmonic content rather than the full signal, CAHA sets a new benchmark for sharper, faster, and more robust MPI imaging.

physics.med-ph↗

arXiv2025

Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting

Accurately predicting the wind power output of a wind farm across various time scales utilizing Wind Power Forecasting (WPF) is a critical issue in wind power trading and utilization. The WPF problem remains unresolved due to numerous influencing variables, such as wind speed, temperature, latitude, and longitude. Furthermore, achieving high prediction accuracy is crucial for maintaining electric grid stability and ensuring supply security. In this paper, we model all wind turbines within a wind farm as graph nodes in a graph built by their geographical locations. Accordingly, we propose an ensemble model based on graph neural networks and reinforcement learning (EMGRL) for WPF. Our approach includes: (1) applying graph neural networks to capture the time-series data from neighboring wind farms relevant to the target wind farm; (2) establishing a general state embedding that integrates the target wind farm's data with the historical performance of base models on the target wind farm; (3) ensembling and leveraging the advantages of all base models through an actor-critic reinforcement learning framework for WPF.

cs.LG↗