arXiv subjects

Chi Xiao

Publications and source records attributed to Chi Xiao.

4 recordsLinked to original sources

arXiv2025

A large-scale complexity-graded dataset of neuronal images and annotations

Accurate reconstruction of neuronal morphology is essential for classifying cell types and understanding brain connectivity. Recent advances in imaging and reconstruction techniques have greatly expanded the scale and quality of neuronal data. However, large-scale, standardized annotated datasets remain limited. Here, we present an open, multi-level neuronal dataset covering the whole mouse brain. Using a hierarchical strategy, we divided imaging data from 237 mouse brains into about 13,570,000 standardized blocks, classified into four levels of reconstruction difficulty. With the custom-developed reconstruction platform, we achieved high-precision three-dimensional reconstructions of 9,676 neurons at the whole-brain scale. This dataset will be made publicly available, providing a valuable resource for algorithm development and brain circuit modeling in neuroscience research.

q-bio.NC↗

arXiv2025

Research on Core Loss of Direct-drive 75kW Tidal Current Generator Using Machine Learning and Multi-objective Optimization Algorithms

This paper presents a classification of generator excitation waveforms using principal component analysis (PCA) and machine learning models, including logistic regression, random forest, and gradient boosting decision trees (GBDT). Building upon the traditional Steinmetz equation, a temperature correction term is introduced. Through nonlinear regression and least squares parameter fitting, a novel temperature correction equation is proposed, which significantly reduces the prediction error for core losses under high-temperature conditions. The average relative error is decreased to 16.03%, thereby markedly enhancing the accuracy. Using GBDT and random forest regression models, the independent and combined effects of temperature, excitation waveforms, and magnetic materials on core loss are analyzed. The results indicate that the excitation waveform has the most significant impact, followed by temperature, while the magnetic core material exhibits the least influence. The optimal combination for minimizing core loss is identified, achieving a core loss value of 35,310.9988 under the specified conditions. A data-driven predictive model for core loss is developed, demonstrating excellent performance with an R*R value of 0.9703 through machine learning regression analysis, indicating high prediction accuracy and broad applicability. Furthermore, a multi-objective optimization model considering both core loss and transmission magnetic energy is proposed. Genetic algorithms are employed for optimization, resulting in an optimal design scheme that minimizes core loss and maximizes transmission magnetic energy. Based on this model, the magnetic core compensation structure of a direct-drive 75kW tidal energy generator is optimized in practical applications, yielding satisfactory results.

physics.app-ph↗

arXiv2025

Hydraulic performance study of hollow adaptive variable pitch tidal energy turbine

To address the challenges of bidirectional tidal energy utilization efficiency and operational reliability of tidal turbines under low-flow conditions, this paper presents a novel hollow adaptive variable-pitch tidal energy generator based on symmetric airfoil design.In this document, the hydrodynamic performance of the device is analyzed by CFD method, and the energy capture and thrust load characteristics of the turbine are analyzed and discussed. The simulation results show that the power coefficient of the three-bladed turbine is better than that of the four-bladed and five-bladed turbine in the range of 2-4 tip speed ratios at a pitch angle of 10{\deg}, and the optimum power coefficient is 36.8%, which is higher than that of the ordinary axisymmetric wing turbine in terms of its energy acquisition efficiency. Its optimal power coefficient is 36.8%, and its energy efficiency is higher than that of ordinary axial symmetrical wing turbines.

physics.app-ph↗

arXiv2020

Non-iterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissue

In this paper, we propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images, which is a key component in volume reconstruction of serial sections of biological tissue. In order to avoid error accumulation and propagation caused by current algorithms, we add extra condition that the position of the first and the last section images should remain unchanged. This constrained simultaneous registration problem has not been solved before. Our algorithm method is non-iterative, it can simultaneously compute rigid transformation for a large number of serial section images in a short time. We prove that our algorithm gets optimal solution under ideal condition. And we test our algorithm with synthetic data and real data to verify our algorithm's effectiveness.

cs.CV↗