Yue Lei

Zeynep Duygu Tekler, Yue Lei, Xilei Dai et al.

Developing personalised thermal comfort models to inform occupant-centric controls (OCC) in buildings requires collecting large amounts of real-time occupant preference data. This process can be highly intrusive and labour-intensive for large-scale implementations, limiting the practicality of real-world OCC implementations. To address this issue, this study proposes a thermal preference-based HVAC control framework enhanced with Active Learning (AL) to address the data challenges related to real-world implementations of such OCC systems. The proposed AL approach proactively identifies the most informative thermal conditions for human annotation and iteratively updates a supervised thermal comfort model. The resulting model is subsequently used to predict the occupants' thermal preferences under different thermal conditions, which are integrated into the building's HVAC controls. The feasibility of our proposed AL-enabled OCC was demonstrated in an EnergyPlus simulation of a real-world testbed supplemented with the thermal preference data of 58 study occupants. The preliminary results indicated a significant reduction in overall labelling effort (i.e., 31.0%) between our AL-enabled OCC and conventional OCC while still achieving a slight increase in energy savings (i.e., 1.3%) and thermal satisfaction levels above 98%. This result demonstrates the potential for deploying such systems in future real-world implementations, enabling personalised comfort and energy-efficient building operations.

Gang Yang, Yue Lei, Wenxin Tai et al.

Diffusion and flow matching (FM) models have achieved remarkable progress in speech enhancement (SE), yet their dependence on multi-step generation is computationally expensive and vulnerable to discretization errors. Recent advances in one-step generative modeling, particularly MeanFlow, provide a promising alternative by reformulating dynamics through average velocity fields. In this work, we present COSE, a one-step FM framework tailored for SE. To address the high training overhead of Jacobian-vector product (JVP) computations in MeanFlow, we introduce a velocity composition identity to compute average velocity efficiently, eliminating expensive computation while preserving theoretical consistency and achieving competitive enhancement quality. Extensive experiments on standard benchmarks show that COSE delivers up to 5x faster sampling and reduces training cost by 40%, all without compromising speech quality. Code is available at https://github.com/ICDM-UESTC/COSE.