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Research papers, competition projects, and independent side projects. Click a thumbnail on the left to view details, or click a title or link below to visit the corresponding page.Research papers, competition projects, and independent side projects. Click a title or link below to visit the corresponding website.

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VaseMuseum: Digital Intelligent Museum for Ancient Greek Pottery

Qiushi Xie^†, Jiazi Wang^†, Nonghai Zhang^†, Zeyu Zhang^†^‡, Yang Zhao, Ling Shao, Hao Tang^*

In arXiv, 2026.07

A digital intelligent museum for ancient Greek pottery that combines multimodal perception, 3D reasoning, external knowledge retrieval, and inference-time reliability control to deliver more trustworthy and verifiable cultural-heritage explanations.

arXiv2607.06374 GitHubCODE WebsitePaper CiteBibTeX

VaseMuseum · Methods and EvaluationKey figures from the research paper

VaseMuseum system architecture — System Architecture · Perception, retrieval, and reliability control

Virtual museum interaction pipeline — Interaction Pipeline · From artifact ingestion to digital exhibition

Virtual museum interface — System Interface · 3D exhibits and verifiable question answering

VaseAgent reliability framework — Reliability Framework · Source / Response Control

VaseAgent main experimental results — Main Results · Accuracy, hallucination rate, and link validity

VaseAgent ablation and scalability experiments — Further Evaluation · Ablation, scalability, and multidimensional comparisons

Optical-sensing-oriented remaining useful life estimation of lithium-ion batteries using ALA-tuned VMD and a BiTCN-AM model

Qiushi Xie

In Proc. SPIE, 2026.09

Lithium-ion battery remaining-useful-life estimation with ALA-optimized VMD decomposition and a bidirectional temporal convolutional attention model, validated across the NASA and CALCE datasets.

PDFVol. 14327 GitHubCODE WebsitePaper CiteBibTeX

Lithium-ion Battery Remaining Useful Life EstimationALA-VMD · BiTCN-AM · NASA / CALCE

0:00 / 0:00

ALA-VMD-BiTCN-AM battery RUL prediction framework — Method · ALA optimization, VMD decomposition, and BiTCN-AM prediction

Standard VMD and ALA-VMD battery capacity decomposition comparison — Adaptive Decomposition · Standard VMD vs. ALA-VMD

NASA B0005 battery capacity and RUL prediction results — NASA B0005 · Capacity trajectory and RUL prediction

CALCE CS2_38 battery capacity and RUL prediction results — CALCE CS2_38 · Cross-dataset validation

0.01137NASA RMSE0 cycleNASA RUL absolute error0.00490CALCE RMSE99.93%CALCE R²