arXiv subjects

Ziqing Xia

Publications and source records attributed to Ziqing Xia.

5 recordsLinked to original sources

arXiv2026

Closed-loop domain generation enhances battery health diagnosis

Reliable electrode-level degradation diagnosis is essential for accurate health estimation and non-destructive monitoring in advanced battery management. Diagnosis models developed under controlled laboratory conditions can fail when operating conditions change, while acquiring sufficient full-lifespan data in new target domains remains prohibitively time-consuming and practically unfeasible. Here, we propose a closed-loop domain generation framework to enable cross-domain battery health diagnosis under dynamic conditions. Physics-constrained domain generation is achieved through Variational Autoencoder-based latent extrapolation, which maps the source data and few-shot early-life target domain data into a shared latent space and generates cross-domain samples within physical boundaries. Bayesian optimization closes the generation-diagnosis loop by automatically calibrating the sampling, latent regularization, extrapolation, perturbation, and generation-volume hyperparameters using leakage-free development validation, thereby strictly excluding later-life target data from optimization and ensuring the quality and quantity of generated data. Numerical results demonstrate that, across four cross-domain cases, the proposed framework achieves average root mean square errors of 1.53%, 2.15%, and 2.02% for positive electrode active material loss, negative electrode active material loss, and lithium inventory loss, respectively, whereas traditional methods suffer from notably higher errors or even complete failure. This framework provides a general pathway for onboard and physics-consistent electrode-level degradation diagnosis, independent of downstream diagnosis models.

eess.SY↗

arXiv2026

KUPAS MASTER: Distilling the Tacit Expertise of Master Practitioners into Agent-Ready Experience Corpora

Experienced professionals know more than just facts and conclusions. They know which cues matter, why a judgment is reasonable, and which action to take. Routine work records often leave out this tacit knowledge, making it difficult for Large Language Model (LLM) agents to use professional experience effectively. We introduce KUPAS MASTER, an experience engineering platform built around nine-layer cognitive corpus construction. It turns heterogeneous work records and practitioner interviews into traceable, reusable experience corpora for agents. Six case elements preserve the task process: context, cues, judgment, action, boundaries, and outcomes. Nine-layer cognitive corpus construction organizes tacit experience along nine extraction dimensions and stores the resulting assets in six libraries: rules, constraints, best practices, negative examples, corner cases, and skills. Semantic alignment, individual experience distillation, organizational consolidation, and cross-review preserve source evidence, conditions of use, and unresolved disagreements. The platform packages these assets into callable skills with explicit inputs, steps, dependencies, and stopping conditions, connecting experience collection to task execution and evaluation feedback. Using authorized samples from 20 randomly selected practitioners, the platform processed 1,576 source files into 23,024 individual experience records and 13,113 organizational assets. The evaluation spans multiple professional domains. Under common task inputs and scoring criteria, the base model, raw corpus retrieval-augmented generation (RAG), and KUPAS MASTER agent scored 70.63, 79.75, and 89.58, respectively. The KUPAS MASTER agent improved on raw-corpus RAG in all seven scoring dimensions. The platform provides a practical path from individual tacit experience to organizational knowledge and agent capabilities.

cs.AI↗

arXiv2026

When are supercapacitors practically feasible in electric vehicles? A multi-dimensional HESS techno-economic evaluation

While the hybrid energy storage system (HESS) can theoretically mitigate battery degradation in electric vehicles, its practical implementation remains highly limited. To delineate the specific scenarios and application boundaries where supercapacitors remain feasible, this study proposes a multi-dimensional techno-economic feasibility evaluation framework. First, a cross-vehicle sizing method based on dynamic programming is established to quantify physical mass-volume packaging constraints and identify feasible supercapacitor candidates across different vehicle types. Building upon the optimal sizing parameters derived from the battery aging Pareto front, an expert-guided deep reinforcement learning energy management strategy is integrated to yield near-optimal online performance, ensuring a fair life-cycle economic assessment. Finally, a comprehensive feasibility matrix is constructed to systematically evaluate mass, volume, battery lifespan, additional supercapacitor costs, operation cost, future energy storage prices, and the influence of emerging solid-state batteries. Results reveal that city buses remain the most promising vehicle type for HESS due to minimal additional costs and sufficient packaging space. Current mass-volume penalties and limited economic benefits hinder HESS application in passenger vehicles and heavy-duty trucks, respectively. This situation may only improve if supercapacitor prices drop significantly in the future. Quantified analysis shows that higher load-frequency characteristics generally favor HESS benefits within the same vehicle platform, while the overall techno-economic feasibility across vehicles is jointly influenced by load characteristics and vehicle configuration. Furthermore, looking toward the 2030+ solid-state battery era, we highlight that integrating increasingly affordable supercapacitors can provide substantial asset protection leverage.

eess.SY↗

arXiv2026

Breaking the Horizontal Prior: From Long-Tailed Orientation Bias to Roll-Robust Monocular Depth Estimation

Despite recent advances in Monocular Depth Estimation, state-of-the-art depth foundation models remain vulnerable to robustness issues. Particularly, even slight camera rolls can result in substantial degradation in depth estimations. We attribute this problem to a previously overlooked phenomenon, termed the Horizontal Prior, which is a manifestation of long-tailed distribution bias: most training images are captured in approximately horizontal orientations due to human visual preferences and photographic habits. While intuitive remedies such as re-balanced data augmentation and horizon leveling provide partial improvements, they fail to fully address the issue. In this paper, we introduce Invariant Depth Constraint (ID-Constraint), a training-time supervision strategy that improves roll robustness by fine-tuning and jointly regularizing the depth backbone with a series of geometric and spatial reasoning tasks. These auxiliary objectives encourage the backbone to learn rotation-stable, depth-relevant representations, while the auxiliary prediction heads are discarded after training, leaving the original inference architecture unchanged. Extensive experiments on five benchmark datasets across four roll settings demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2021

Testing the ALP-photon coupling with polarization measurements of Sagittarius A*

Ultra-light bosons such as axions or axion-like particles (ALPs), are promising candidates to solve the dark matter problem. A unique way to detect such ALPs is to search for the periodic oscillation feature of the position angles of linearly polarized photons emitted from the regions where ALP dark matter forms a solitonic core. In this work, we use the high-resolution polarimetric measurements of the radiation near the super-massive black hole (SMBH) in the center of the Milky Way, i.e., Sagittarius A$^\star$ (Sgr A$^\star$), by a sub-array of the Event Horizon Telescope to search for the ultra-light ALPs. We derive upper limits on the ALP-photon coupling of $\sim 10^{-12}{\rm GeV^{-1}}$ for ALP masses of $m\sim (10^{-19}-10^{-18})$eV. Our results are stronger than that derived from the observations of SN1987A and a population of supernovae in the mass window of ($10^{-19}-10^{-17}$)eV. Improved polarimetric measurements with the full Event Horizon Telescope can further strengthen the constraints.

astro-ph.HE↗