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365 results on '"Chen, Chengcheng"'

26. The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study

Author

Yang, Guangjie, Nie, Pei, Yan, Lei, Zhang, Mingxin, Wang, Yangyang, Zhao, Lianzi, Li, Mingyao, Xie, Fei, Xie, Haizhu, Li, Xianjun, Xiang, Fawei, Wang, Nan, Cheng, Nan, Zhao, Xia, Wang, Ning, Wang, Yicong, Chen, Chengcheng, Yun, Canhua, Cui, Jingjing, Duan, Shaofeng, Zhang, Ran, Hao, Dapeng, Wang, Ximing, Wang, Zhenguang, and Niu, Haitao

Published
2022
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35. Similarities and Differences between Gas Diffusion Layers Used in Proton Exchange Membrane Fuel Cell and Water Electrolysis for Material and Mass Transport.

Author

Zhang, Tao, Meng, Ling, Chen, Chengcheng, Du, Lei, Wang, Ning, Xing, Lixing, Tang, Chunmei, Hu, Jian, and Ye, Siyu

Subjects
CARBON-based materials, MASS transfer, HYDROGEN as fuel, FUEL cells
Abstract

Proton‐exchange membrane fuel cells (PEMFCs) and water electrolysis (PEMWE) are rapidly developing hydrogen energy conversion devices. Catalyst layers and membranes have been studied extensively and reviewed. However, few studies have compared gas diffusion layers (GDLs) in PEMWE and PEMFC. This review compares the differences and similarities between the GDLs of PEMWE and PEMFC in terms of their material and mass transport characteristics. First, the GDL materials are selected based on their working conditions. Carbon materials are prone to rapid corrosion because of the high anode potential of PEMWEs. Consequently, metal materials have emerged as the primary choice for GDLs. Second, the mutual counter‐reactions of the two devices result in differences in mass transport limitations. In particular, water flooding and the effects of bubbles are major drawbacks of PEMFCs and PEMWE, respectively; well‐designed structures can solve these problems. Imaging techniques and simulations can provide a better understanding of the effects of materials and structures on mass transfer. Finally, it is anticipated that this review will assist research on GDLs of PEMWE and PEMFC. [ABSTRACT FROM AUTHOR]

Published
2024
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39. S<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>O-Det: A Semisupervised Oriented Object Detection Network for Remote Sensing Images

Author

Fu, Ronghao, Yan, Shuang, Chen, Chengcheng, Wang, Xianchang, Heidari, Ali Asgher, Li, Jing, and Chen, Huiling

Abstract

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the detection performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted to the detection of arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, this article proposes a semisupervised oriented object detection framework (S$^{2}$O-Det) to reduce annotation costs while improving detection performance in a semisupervised manner. Initially, the proposed task-consistent learning aims to alleviate the inconsistencies between classification and localization, which provides consistent confidence for the pseudolabels. Subsequently, the introduced coarse-to-fine sample mining employs dense prediction for pseudolabel assignment, adopting a divide-and-conquer approach to independently identify consistent and reliable labels for both classification and localization tasks. Finally, a probabilistic distillation loss ensures the harmonization of the probability distributions across the teacher and student feature domains, thereby reciprocally enhancing the learning competencies. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that S$^{2}$O-Det achieves promising performance across different labeling ratios.

Published
2024
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40. FADL-Net: Frequency-Assisted Dynamic Learning Network for Oriented Object Detection in Remote Sensing Images

Author

Fu, Ronghao, Chen, Chengcheng, Yan, Shuang, Zhang, Rui, Wang, Xianchang, and Chen, Huiling

Abstract

In the field of Earth observation and computer vision, oriented object detection for remotesensing images is a crucial task that aims to locate objects more accurately in complex scenes containing a large number of densely arranged, large aspect ratio, and arbitrarily oriented objects. Although recently proposed methods have achieved remarkable performance, there are still several challenges to address: 1) interference from complex backgrounds, 2) imbalanced and mismatched label assignments caused by tiny objects and objects with large aspect ratios, and 3) misalignment between the tasks of classification and localization. In this article, we propose a frequency-assisted dynamic learning network (FADL-Net) to overcome the crucial challenges. Concretely, we introduce a spatial-spectral feature pyramid network to adaptively capture global long-range dependency feature representations containing various frequency domains. Meanwhile, to produce more reliable training samples for objects with extreme shapes, we design a geometric aware dynamic label assignment to dynamically mitigate the imbalance and mismatch in label assignment in a coarse-to-fine manner, thereby achieving more stable optimization during the training process. Moreover, we propose a joint-learning rotated quality loss that addresses the inconsistency between classification and localization by dynamically adapting the weights of different samples in the training stage. Extensive experiments on several public remote sensing datasets demonstrate that our method performs favorably against state-of-the-art detection approaches.

Published
2024
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