arXiv subjects

Yong Qian

Publications and source records attributed to Yong Qian.

4 recordsLinked to original sources

arXiv2026

Experimental measurements and modeling of characteristic time scales in single iron particle ignition

Recyclable metal fuels such as iron are promising carbon-free energy carriers for heat and power. In such systems, particle ignition characteristics strongly affect combustion efficiency and combustor stability, making them critical for burner and reactor design. However, predictive ignition modelling remains limited by the lack of time-resolved data for single-particle solid-phase oxidation and phase transitions. In this work, digital in-line holography combined with ultra-high-speed single-color pyrometry is used to resolve characteristic solid-phase oxidation times of spherical micron-sized iron particles burning in well-defined hot oxidizing environments. Three temperature plateaus are identified, corresponding to FeO melting, the γ-Fe to δ-Fe transition, and Fe melting, from which pre-melting oxidation times and melting durations are extracted. An ignition model based on solid-phase iron oxidation kinetics following a parabolic rate law, coupled with external-oxygen-transport-limited description, is used to simulate these characteristic times. The model accurately captures the FeO-scale pre-melting oxidation time, which is nearly independent of oxygen concentration, while the FeO, γ-Fe to δ-Fe, and Fe melting stages show strong oxygen-concentration dependence consistent with external-oxygen-transport-limited reaction rates. These measurements and simulations provide the first diameter-resolved dataset for FeO and Fe melting processes and show that this modelling framework can quantitatively predict characteristic times for single iron particles in metal-fuel applications.

cond-mat.mtrl-sci↗

arXiv2023

Exchanging-based Multimodal Fusion with Transformer

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of multimodalities into different low-dimensional spaces and cannot be applied to the sequential input data. To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer. We first use two encoders to separately map multimodal inputs into different low-dimensional spaces. Then we employ two decoders to regularize the embeddings and pull them into the same space. The two decoders capture the correlations between texts and images with the image captioning task and the text-to-image generation task, respectively. Further, based on the regularized embeddings, we present CrossTransformer, which uses two Transformer encoders with shared parameters as the backbone model to exchange knowledge between multimodalities. Specifically, CrossTransformer first learns the global contextual information of the inputs in the shallow layers. After that, it performs inter-modal exchange by selecting a proportion of tokens in one modality and replacing their embeddings with the average of embeddings in the other modality. We conduct extensive experiments to evaluate the performance of MuSE on the Multimodal Named Entity Recognition task and the Multimodal Sentiment Analysis task. Our results show the superiority of MuSE against other competitors. Our code and data are provided at https://github.com/RecklessRonan/MuSE.

cs.CV↗

arXiv2021

SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis

Previous studies show effective of pre-trained language models for sentiment analysis. However, most of these studies ignore the importance of sentimental information for pre-trained models.Therefore, we fully investigate the sentimental information for pre-trained models and enhance pre-trained language models with semantic graphs for sentiment analysis.In particular, we introduce Semantic Graphs based Pre-training(SGPT) using semantic graphs to obtain synonym knowledge for aspect-sentiment pairs and similar aspect/sentiment terms.We then optimize the pre-trained language model with the semantic graphs.Empirical studies on several downstream tasks show that proposed model outperforms strong pre-trained baselines. The results also show the effectiveness of proposed semantic graphs for pre-trained model.

cs.CL↗

arXiv2008

Generation of Narrow-Band Polarization-Entangled Photon Pairs for Atomic Quantum Memories

We report an experimental realization of a narrow-band polarization-entangled photon source with a linewidth of 9.6 MHz through cavity-enhanced spontaneous parametric down-conversion. This linewidth is comparable to the typical linewidth of atomic ensemble based quantum memories. Single-mode output is realized by setting a reasonable cavity length difference between different polarizations, using of temperature controlled etalons and actively stabilizing the cavity. The entangled property is characterized with quantum state tomography, giving a fidelity of 94% between our state and a maximally entangled state. The coherence length is directly measured to be 32 m through two-photon interference.

quant-ph↗