Search

Your search keyword '"Liu, Haijun"' showing total 1,784 results
Start Over Author "Liu, Haijun"
1,784 results on '"Liu, Haijun"'

201. Complex genetic architecture underlying the plasticity of maize agronomic traits

Author

Jin, Minliang, primary, Liu, Haijun, additional, Liu, Xiangguo, additional, Guo, Tingting, additional, Guo, Jia, additional, Yin, Yuejia, additional, Ji, Yan, additional, Li, Zhenxian, additional, Zhang, Jinhong, additional, Wang, Xiaqing, additional, Qiao, Feng, additional, Xiao, Yingjie, additional, Zan, Yanjun, additional, and Yan, Jianbing, additional

Published
2023
Full Text
View/download PDF

205. Controlled Adhesion of Ice—Toward Ultraclean 2D Materials (Adv. Mater. 14/2023)

Author

Liu, Haijun, primary, Thi, Quoc Huy, additional, Man, Ping, additional, Chen, Xin, additional, Chen, Tianren, additional, Wong, Lok Wing, additional, Jiang, Shan, additional, Huang, Lingli, additional, Yang, Tiefeng, additional, Leung, Ka Ho, additional, Leung, Tsz Tung, additional, Gao, Shan, additional, Chen, Honglin, additional, Lee, Chun‐Sing, additional, Kan, Min, additional, Zhao, Jiong, additional, Deng, Qingming, additional, and Ly, Thuc Hue, additional

Published
2023
Full Text
View/download PDF

209. MSNet: Self-Supervised Multiscale Network With Enhanced Separation Training for Hyperspectral Anomaly Detection

Author

Liu, Haijun, Su, Xi, Shen, Xiangfei, and Zhou, Xichuan

Abstract

Hyperspectral anomaly detection (HAD) has attracted increasing attention due to its economical and efficient applications. The main challenge lies in the data-starved problem of hyperspectral images (HSIs) and the costliness of manual annotation, making it heavily reliant on the model’s adaptability and robustness to unseen scenes under limited samples. Self-supervised learning offers a solution to this urgency via mining meaningful representations from the data itself. One promising paradigm is leveraging untrained neural networks to reconstruct the background component for revealing anomalous information. Its capability stems from the network architecture and the training process rather than learning from expensive and strongly domain-dependent data, which is naturally applicable to HAD. In this article, to handle the urgent requirement for self-supervised learning in HAD, we propose a multiscale network (termed MSNet) that detects anomalies with enhanced separation training. The network architecture consists of several multiscale convolutional encoder-decoder (CED) layers, considering the spatial characteristics of the anomalies. To suppress the anomalies during background reconstruction, we adopt a new separation training strategy by introducing a soft separator for better practicality on larger datasets. Extensive experiments conducted on five commonly used datasets and the HAD100 dataset, demonstrate the superiority of our method over its counterparts. Our code is available at https://github.com/enter-i-username/MSNet.

Published
2024
Full Text
View/download PDF

210. AirSOD: A Lightweight Network for RGB-D Salient Object Detection

Author

Zeng, Zhihong, Liu, Haijun, Chen, Fenglei, and Tan, Xiaoheng

Abstract

Salient object detection (SOD) aims to identify the most prominent regions in images. However, the large model sizes, high computational costs, and slow inference speeds of existing RGB-D SOD models have hindered their deployment on real-world embedded devices. To address this issue, we propose a novel method named AirSOD, which is committed to lightweight RGB-D SOD. Specifically, we first design a hybrid feature extraction network, which includes the first three stages of MobileNetV2 and our Parallel Attention-Shift convolution (PAS) module. Using the novel PAS module enables capturing both long-range dependencies and local information to enhance the representation learning while significantly reducing the number of parameters and computational complexity. Secondly, we propose a Multi-level and Multi-modal feature Fusion (MMF) module to facilitate feature fusion, and a Multi-path enhancement for Feature Refinement (MFR) decoder for feature integration. The proposed method significantly reduces the model size by 63%, decreases the computational complexity by 43%, and improves the inference speed by 43% compared with the cutting-edge model (MobileSal). We test our AirSOD on six widely-used RGB-D SOD datasets. Extensive experimental results demonstrate that our method obtains satisfactory performance. The source codes will be made available.

Published
2024
Full Text
View/download PDF

212. Tailoring the Pt/ionomer interface for enhancing the local oxygen transport in proton exchange membrane fuel cells.

Author

Sun, Fengman, Liu, Haijun, Di, Qian, Xu, Keyi, Chen, Ming, and Wang, Haijiang

Abstract

In order to realize the high-power operation of low-cost proton exchange membrane fuel cells, it is urgent to solve the problem of oxygen mass transfer in a low Pt loading cathode at high current density. More exactly, the high local oxygen transport resistance (Rionomer) originating from the dense ionomer film on the Pt surface results in significant voltage losses. Herein, this paper proposes an exceptionally simple and cost-effective strategy to precisely regulate a Pt/ionomer interface to decrease Rionomer only by introducing hydrophobic nano-SiO2 into a cathode catalyst layer. As a result, the cathode catalyst layer modified by nano-SiO2 with a particle size of 10 nm and a content of 10% exhibits optimal mass transfer characteristics, and a specific Pt/ionomer interface with low Rionomer is established. The microstructure characterization of the catalyst layer confirms that the majority of ionomer adheres to nano-SiO2 particles and evenly distributes around the Pt/C agglomerates. Moreover, the stronger affinity of nano-SiO2 to ionomer is verified by molecular dynamics simulation. Collectively, the ability of nano-SiO2 to capture ionomer effectively reduces the ionomer density on the Pt surface, thus establishing a highly efficient oxygen-delivering Pt/ionomer interface. This work highlights an extremely promising method for Pt/ionomer interface construction of catalyst layer with high oxygen mass-transfer characteristics. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

219. Controlled Adhesion of Ice – Towards Ultra‐Clean 2D Materials

Author

Liu, Haijun, primary, Thi, Quoc Huy, additional, Man, Ping, additional, Chen, Xin, additional, Chen, Tianren, additional, Wong, Lok‐Wing, additional, Jiang, Shan, additional, Huang, Lingli, additional, Yang, Tiefeng, additional, Leung, Ka‐Ho, additional, Leung, Tsz‐Tung, additional, Gao, Shan, additional, Chen, Honglin, additional, Lee, Chun‐Sing, additional, Kan, Min, additional, Zhao, Jiong, additional, Deng, Qingming, additional, and Ly, Thuc Hue, additional

Published
2023
Full Text
View/download PDF

226. Oral drug delivery platforms for biomedical applications

Author

Ouyang, Jiang, primary, Zhang, Zhongyang, additional, Deng, Bo, additional, Liu, Jinggong, additional, Wang, Liqiang, additional, Liu, Haijun, additional, Koo, Seyoung, additional, Chen, Shuying, additional, Li, Yongjiang, additional, Yaremenko, Alexey V., additional, Huang, Xiangang, additional, Chen, Wei, additional, Lee, Yuhan, additional, and Tao, Wei, additional

Published
2023
Full Text
View/download PDF

237. Multistate Capability Improvement of BEOL Compatible FeFET by Introducing an Al2O3 Interlayer

Author

Yu, Shihao, Wang, Qin, Zhang, Yefan, Yang, Peng, Luo, Xiaopeng, Liu, Haijun, Chen, Changlin, Li, Qingjiang, and Liu, Sen

Abstract

The capability of multistate is critical for electronic synapses in neuromorphic computing. In this article, the multistate performance of the ferroelectric field-effect transistors (FeFETs) is investigated by inserting an Al $_{\mathbf {{2}}}~\text{O}_{\mathbf {{3}}}$ interlayer (IL) into Hf $_{\mathbf {{0}.{5}}}$ Zr0.5O2 film. The experimental results of characterization indicate that the grain sizes of the ferroelectric film can be effectively reduced by the Al $_{\mathbf {{2}}}~\text{O}_{\mathbf {{3}}}$ IL. Benefiting from the refined grain sizes, both the proposed ferroelectric capacitor and FeFET show excellent multistate performance and linearity. The benchmark with other emerging synaptic devices demonstrates that the back-end-of-line (BEOL) compatible FeFETs behave noticeable synaptic performances, such as 160 conductance states, low nonlinearity ( $\alpha _{\mathbf {p}}$ = −1.83 and $\alpha _{\mathbf {d}}$ = −1.39), high $G_{\mathbf {\textit {max}}}$ / $G_{\mathbf {\textit {min}}}$ (46.1), and low energy consumption (1.13 pJ).

Published
2023
Full Text
View/download PDF

239. Primary Cylindroma of the Breast with Brisk Mitosis: A Case Report and Review of Literature.

Author

Liu, Haijun, Wei, Chun'e, Ding, Huaye, and Zhang, Hongkai

Subjects
*LITERATURE reviews, *ADENOID cystic carcinoma, *BREAST, *MITOSIS
Abstract

Primary cylindroma of the breast is extremely rare. To the best of our knowledge, there have been only 18 tumors reported. All of them were benign and had no mitosis. However, the rarity of this entity made the correct diagnosis preoperatively very difficult. Here we present a unique cylindroma with focal brisk mitosis and largest diameter of 5cm in an 80-year woman, she was misdiagnosed as adenoid cystic carcinoma in biopsy and then underwent a radical breast resection. We reviewed the related literature and emphasized differential diagnosis. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results