arXiv subjects

Zhoulu Yu

Publications and source records attributed to Zhoulu Yu.

2 recordsLinked to original sources

arXiv2025

Magnetic signal scan imaging system based on giant magnetoimpedance (GMI) differential sensor

This paper presents the design and implementation of a magnetic signal scanning and imaging system based on the giant magnetoimpedance (GMI) effect. The system employs a pair of performance-matched GMI sensing elements configured as a differential probe structure. Through co-optimized low-noise electronic and probe design, the system effectively suppresses both intrinsic sensor common-mode drift and external environmental magnetic noise, enabling high signal-to-noise ratio detection of nono-tesla to micro-tesla-level magnetic signals without magnetic shielding. Experimental results demonstrate that the differential system achieves significantly lower noise spectral density in unshielded environments compared to conventional GMI sensors (\SI{46}{pT}/$\sqrt{\text{Hz}}$ versus \SI{286}{pT}/$\sqrt{\text{Hz}}$ at \SI{1}{Hz}), with a sensitivity of 186,790 V/T and spatial resolution better than 200 micrometers. The system's excellent performance in weak magnetic field detection and spatial resolution was verified through scanning experiments of magnetic ink on US banknotes and magnetic reference samples. Compared to SQUID scanning systems, which requiring liquid helium cooling, this system based on the GMI effect offers advantages of room-temperature operation, compact structure, and low cost. Relative to conventional single-element GMI microscopes, it achieves significant improvements in signal-to-noise ratio and environmental adaptability. This research provides a practical solution for high-resolution magnetic field imaging at room temperature with broad application potential in materials magnetism, biomagnetic imaging, and nanomagnetic detection.

physics.ins-det↗

arXiv2019

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification

The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence of the aforementioned technique on local geometry. In this study, focusing on binary imbalanced data classification, a novel dynamic ensemble method, namely adaptive ensemble of classifiers with regularization (AER), is proposed, to overcome the stated limitations. The method solves the overfitting problem through implicit regularization. Specifically, it leverages the properties of stochastic gradient descent to obtain the solution with the minimum norm, thereby achieving regularization; furthermore, it interpolates the ensemble weights by exploiting the global geometry of data to further prevent overfitting. According to our theoretical proofs, the seemingly complicated AER paradigm, in addition to its regularization capabilities, can actually reduce the asymptotic time and memory complexities of several other algorithms. We evaluate the proposed AER method on seven benchmark imbalanced datasets from the UCI machine learning repository and one artificially generated GMM-based dataset with five variations. The results show that the proposed algorithm outperforms the major existing algorithms based on multiple metrics in most cases, and two hypothesis tests (McNemar's and Wilcoxon tests) verify the statistical significance further. In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the research on the regularization for dynamic ensemble methods.

cs.LG↗