arXiv subjects

Jinhao Xie

Publications and source records attributed to Jinhao Xie.

3 recordsLinked to original sources

arXiv2026

Nonlinear Dynamics and Performance Optimization Based on Primary Resonance of an Electromechanically Coupled Magnetic Levitation Energy Harvester

This research paper explores the potential of nonlinear magnetic levitation systems for energy harvesting by developing a modified system that incorporates a more realistic energy harvesting circuit, enabling a better representation of practical operating conditions. Methodologically, approximate solutions for the system dynamics were obtained using the method of multiple scales, complemented by numerical simulations to capture parameter variations visualized through phase planes and parameter variation plots. The results demonstrate that by adjusting capacitance to induce internal and primary resonances, an extended detuning formulation (utilizing parameters sigma3 and sigma4) is innovatively introduced to capture the coupled dynamic interaction between the harvesting circuit and the mechanical system under diverse conditions. Periodic variations in circuit charge and intermediate magnet dis placement were thoroughly analyzed. Comparisons with legacy models demonstrate that the proposed coupling mechanism effectively suppresses undesirable nonlinear behaviors, such as chaos and multi-stability, resulting in a more stable and predictable energy harvesting process. Ultimately, a critical trade-off between energy harvesting efficiency and dynamical stability is identified, providing a valuable new design perspective for practical energy harvesting systems.

math.AP↗

arXiv2026

Slack More, Predict Better: Proximal Relaxation for Probabilistic Latent Variable Model-based Soft Sensors

Nonlinear Probabilistic Latent Variable Models (NPLVMs) are a cornerstone of soft sensor modeling due to their capacity for uncertainty delineation. However, conventional NPLVMs are trained using amortized variational inference, where neural networks parameterize the variational posterior. While facilitating model implementation, this parameterization converts the distributional optimization problem within an infinite-dimensional function space to parameter optimization within a finite-dimensional parameter space, which introduces an approximation error gap, thereby degrading soft sensor modeling accuracy. To alleviate this issue, we introduce KProxNPLVM, a novel NPLVM that pivots to relaxing the objective itself and improving the NPLVM's performance. Specifically, we first prove the approximation error induced by the conventional approach. Based on this, we design the Wasserstein distance as the proximal operator to relax the learning objective, yielding a new variational inference strategy derived from solving this relaxed optimization problem. Based on this foundation, we provide a rigorous derivation of KProxNPLVM's optimization implementation, prove the convergence of our algorithm can finally sidestep the approximation error, and propose the KProxNPLVM by summarizing the abovementioned content. Finally, extensive experiments on synthetic and real-world industrial datasets are conducted to demonstrate the efficacy of the proposed KProxNPLVM.

cs.LG↗

arXiv2023

CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market

In recent years, great advances in pre-trained language models (PLMs) have sparked considerable research focus and achieved promising performance on the approach of dense passage retrieval, which aims at retrieving relative passages from massive corpus with given questions. However, most of existing datasets mainly benchmark the models with factoid queries of general commonsense, while specialised fields such as finance and economics remain unexplored due to the deficiency of large-scale and high-quality datasets with expert annotations. In this work, we propose a new task, policy retrieval, by introducing the Chinese Stock Policy Retrieval Dataset (CSPRD), which provides 700+ prospectus passages labeled by experienced experts with relevant articles from 10k+ entries in our collected Chinese policy corpus. Experiments on lexical, embedding and fine-tuned bi-encoder models show the effectiveness of our proposed CSPRD yet also suggests ample potential for improvement. Our best performing baseline achieves 56.1% MRR@10, 28.5% NDCG@10, 37.5% Recall@10 and 80.6% Precision@10 on dev set.

cs.CL↗