Výsledky vyhledávání - "Chen JiXiang"

Akademický článek

Enhancement on SBR treatment for oily wastewater by three strains of highly efficient petroleum-degrading bacteria

Autor: LI Qian, CHEN Jixiang, YANG Xuan, PENG Cheng, WANG Tianfeng

Publikováno v: Gongye shui chuli, Vol 44, Iss 11, Pp 52-60 (2024)

This paper investigated the effect of three efficient petroleum-degrading bacterial strains on oily wastewater treatment by sequencing batch reactor activated sludge process (SBR). The results showed that the average removal rates of COD were 90.8%,

Externí odkaz: https://doaj.org/article/d885a236ee694faa8d40ea73fd7bfc42

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Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays

Autor: Chen, Jixiang, Lin, Yiqun, Sun, Haoran, Li, Xiaomeng

Retrieving 3D bone anatomy from biplanar X-ray images is crucial since it can significantly reduce radiation exposure compared to traditional CT-based methods. Although various deep learning models have been proposed to address this complex task, the

Externí odkaz: http://arxiv.org/abs/2407.15433

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Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation

Autor: Elbatel, Marawan, Wang, Hualiang, Chen, Jixiang, Wang, Hao, Li, Xiaomeng

Federated semi-supervised learning (FedSemi) refers to scenarios where there may be clients with fully labeled data, clients with partially labeled, and even fully unlabeled clients while preserving data privacy. However, challenges arise from client

Externí odkaz: http://arxiv.org/abs/2407.10327

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Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction

Autor: Lin, Yiqun, Wang, Hualiang, Chen, Jixiang, Li, Xiaomeng

Cone-Beam Computed Tomography (CBCT) is an indispensable technique in medical imaging, yet the associated radiation exposure raises concerns in clinical practice. To mitigate these risks, sparse-view reconstruction has emerged as an essential researc

Externí odkaz: http://arxiv.org/abs/2407.01090

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Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition

Autor: Chang, Haochen, Chen, Jing, Li, Yilin, Chen, Jixiang, Zhang, Xiaofeng

Publikováno v: IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2024, Seoul (Korea), South Korea

Skeleton-based action recognition has attracted much attention, benefiting from its succinctness and robustness. However, the minimal inter-class variation in similar action sequences often leads to confusion. The inherent spatiotemporal coupling cha

Externí odkaz: http://arxiv.org/abs/2402.02210

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