L
Li Chen
Researcher at Xi'an Jiaotong University
Publications - 136
Citations - 4929
Li Chen is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Lattice Boltzmann methods & Proton exchange membrane fuel cell. The author has an hindex of 34, co-authored 100 publications receiving 3527 citations. Previous affiliations of Li Chen include Chinese Ministry of Education & ETH Zurich.
Papers
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Journal ArticleDOI
Numerical study of the dehydration and hydration processes of the Ca(OH)2/CaO system in an indirect-direct reactor
TL;DR: In this article , a hybrid indirect-direct reactor of the CaO/Ca(OH)2 system is proposed with the physicochemical model developed to study the dehydration and hydration processes under different operating and structural conditions, and five indicators are defined to evaluate the overall reaction performance.
Proceedings ArticleDOI
A 320 FPS Pixel-Level Pipelined Stereo Vision Accelerator with Regional Optimization and Multi-direction Hole Filling
TL;DR: In this paper , a region-optimized semi-global matching (SGM) algorithm is proposed, which can alleviate memory consumption bottleneck and strike a balance among power dissipation, processing speed, and resource consumption.
Journal ArticleDOI
Experimental determination of the role of roughness and wettability on pool-boiling heat transfer of refrigerant
TL;DR: In this article , an experimental rig was established and verified in order to investigate pool boiling heat transfer characteristics of refrigerants on different surfaces, and the results indicated that higher roughness is favorable to boiling heat transfers.
Proceedings ArticleDOI
Post-Processing Refinement for Semi-Global Matching Algorithm Based on Real-Time FPGA
TL;DR: This work presents a pixel-level pipeline architecture for the post-processing of SGM, which refines disparity through a left-right check, and multi-directional occlusion filling refinement.
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Live Demonstration: Supervised-learning-based Visual Quantification for Image Enhancement
TL;DR: In this article , a framework of visual quantification for image enhancement where multivariate Gaussian (MVG) models are trained to assess image visibility is presented, where the visibility of an image is depicted by statistical features such as the contrast energy of the gray channel, yellow-blue channel, and red-green channel, average saturation, and gradients.