Ping An

Shanghai University

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Pengliang Yang

Hefei University of Technology

Luo Hong

Beijing Forestry University

Fuqiang He

Xiamen University of Technology

Xuelian Yao

Shanghai Jiao Tong University

Guan Qiming

Xinpeng Huang

Shanghai University

Ran Ma

Zhengzhou University

A N Rencz

Natural Resources Canada

Xin Liu

Fudan University

Cong Shan

Yantai University

Cooperative Institutions

Shanghai University

Shenyang University of Chemical Technology

Tianjin University

Ministry of Education of the People's Republic of China

University of Chinese Academy of Sciences

China Coal Research Institute (China)

China Coal Technology and Engineering Group Corp (China)

Tsinghua University

Chinese Academy of Sciences

Harbin Institute of Technology

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Unsupervised blind image quality assessment via joint spatial and transform features

Scientific Reports (2023)

Chao Yang Qinglin He Ping An

A novel unsupervised blind image quality assessment (BIQA) method, which requires no mean opinion scores for model training is presented in this paper. The method employs joint spatial and transform features as quality degradation metrics, specifically, phase congruency, gradient magnitude (GM), and GM and Laplacian of Gaussian response and local normalized coefficient are extracted as spatial features, and Karhunen-Loéve transform coefficient and discrete cosine transform coefficient are modeled as transform features. Both spatial and transform features are well analyzed to remove the redundancy, and then fitted to the multivariate Gaussian model for no-reference image quality assessment. Extensive experiments conducted on seven IQA databases demonstrate the superiority of the proposed method over the state-of-the-art both supervised and unsupervised BIQA methods.

Karhunen–Loève theorem

Ground truth

10.1038/s41598-023-38099-5

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Citations (7)

Digital lithology mapping from airborne geophysical and remote sensing data in the Melville Peninsula, Northern Canada, using a neural network approach

Remote Sensing of Environment (1995)

Ping An C. F. Chung A N Rencz

Outcrop

Bedrock

Lithology

Geologic map

Peninsula

10.1016/0034-4257(95)00041-x

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Citations (25)

Methanation of CO₂ over Yb-Promoted Ni/Al₂O₃ Catalysts Prepared by Solution Combustion

Energy & Fuels (2022)

Luqman Abdullahi Sani Chao Wang Mengjuan Zhang Haolong Bai Ping An

Introducing a proper promoter into Ni-based catalysts for CO2 methanation is an effective method to improve their catalytic performance. In this work, we prepare a series of Yb-modified Ni/Al2O3 catalysts by a solution combustion approach and characterize them using various techniques, including nitrogen adsorption, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), temperature-programmed hydrogen reduction (H2-TPR), hydrogen desorption (H2-TPD), carbon dioxide desorption (CO2-TPD), Fourier transform infrared (FTIR), and Thermogravimetric/differential thermal (TG/DTA), and test their CO2 methanation activity in a fixed-bed reactor under conditions of 200–500 °C, 0.1–2 MPa, space velocities ranging from 7200 to 16,200 h–1, and feed molar ratios of H2/CO2 from 1 to 4. We found that Yb modification promotes the dispersion of reducible NiO on Al2O3 and simultaneously leads to the creation of new Yb-involved CO2 chemisorption sites responsible for the improved catalytic activity and stability of Ni/Al2O3 catalysts for CO2 methanation so that the Yb-modified Ni/Al2O3 catalysts exhibit higher catalytic activities than Ni/Al2O3 catalysts without Yb.

Methanation

Thermogravimetric analysis

Temperature-programmed reduction

Non-blocking I/O

Chemisorption

10.1021/acs.energyfuels.2c00180

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Citations (7)

Context-adaptive based CU processing for 3D-HEVC

PLoS ONE (2017)

Liquan Shen Ping An Zhi Liu

The 3D High Efficiency Video Coding (3D-HEVC) standard aims to code 3D videos that usually contain multi-view texture videos and its corresponding depth information. It inherits the same quadtree prediction structure of HEVC to code both texture videos and depth maps. Each coding unit (CU) allows recursively splitting into four equal sub-CUs. At each CU depth level, it enables 10 types of inter modes and 35 types of intra modes in inter frames. Furthermore, the inter-view prediction tools are applied to each view in the test model of 3D-HEVC (HTM), which uses variable size disparity-compensated prediction to exploit inter-view correlation within neighbor views. It also exploits redundancies between a texture video and its associated depth using inter-component coding tools. These achieve the highest coding efficiency to code 3D videos but require a very high computational complexity. In this paper, we propose a context-adaptive based fast CU processing algorithm to jointly optimize the most complex components of HTM including CU depth level decision, mode decision, motion estimation (ME) and disparity estimation (DE) processes. It is based on the hypothesis that the optimal CU depth level, prediction mode and motion vector of a CU are correlated with those from spatiotemporal, inter-view and inter-component neighboring CUs. We analyze the video content based on coding information from neighboring CUs and early predict each CU into one of five categories i.e., DE-omitted CU, ME-DE-omitted CU, SPLIT CU, Non-SPLIT CU and normal CU, and then each type of CU adaptively adopts different processing strategies. Experimental results show that the proposed algorithm saves 70% encoder runtime on average with only a 0.1% BD-rate increase on coded views and 0.8% BD-rate increase on synthesized views. Our algorithm outperforms the state-of-the-art algorithms in terms of coding time saving or with better RD performance.

Quadtree

Algorithmic efficiency

10.1371/journal.pone.0171018

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Citations (7)

UW-CycleGAN: Model-Driven CycleGAN for Underwater Image Restoration

IEEE Transactions on Geoscience and Remote Sensing (2023)

Haorui Yan Zhenwei Zhang Jing Xu Tingting Wang Ping An

The formation of underwater images is a complex physical process that often suffers from various degradation factors, such as blurriness, low contrast, and color casts, which pose challenges for underwater object detection and recognition tasks. Because of the absence of reference images, learning-based methods that rely on unpaired images have been employed to enhance the underwater images. However, these methods may lose their effectiveness in real-world complex underwater environments. In this paper, we propose a model-driven cycle-consistent generative adversarial network (CycleGAN) model, which is inspired by the underwater image formation model to estimate the background light, transmission map, scene depth, and attenuation coefficient directly. Comprehensive experiments have demonstrated that our approach surpasses the compared underwater image restoration methods in both qualitative and quantitative aspects, providing restored images with satisfactory color saturation and brightness. We also conduct experiments on underwater object detection to illustrate the effectiveness of our CycleGAN in improving the detection accuracy. All our source codes and data are available at https://github.com/Duanlab123/UW-CycleGAN.

10.1109/tgrs.2023.3315772

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Citations (14)

Tu1112 CORRELATION OF PHYSICAL ACTIVITY LEVEL WITH QUALITY OF LIFE AND EMOTION IN ADULT IBD PATIENTS: A STRUCTURED QUESTIONNAIRE SURVEY

Gastroenterology (2024)

Zhishun Tang Jing Wang Wei Wang Ping An

Questionnaire

10.1016/s0016-5085(24)03301-8

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Citations (0)

真空スイング吸着を用いたシリカゲルによるo‐キシレンの除去と回収【Powered by NICT】

Chemical Engineering Journal (2017)

Hong Sui Ping An Xingang Li Cong Shan He Lin

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A Novel Capacitor Deionization Performance Study Based on Carbon Nanorods/MnO2 Composite Material

Sustainability (2025)

Zhifu Wei Yu-rong Shi Huajian Gao Fan Wu Huilin Xu

The Earth abounds in water resources; however, only 0.4% of the freshwater resources are suitable for drinking. The scarcity of freshwater resources has a severe impact on the sustainable development of human society. Desalination is regarded as one of the most effective solutions. In this study, a research approach integrating materials and devices was utilized to synthesize manganese oxide-coated carbon nanospheres (CS@MnO2). Experimental results demonstrated that the system, by combining the distinctive performance merits of the CS@MnO2 material and the balanced desalination features, exhibited outstanding desalination performance. In the EDS elemental mapping analysis, the relatively feeble signal of carbon was ascribed to the encapsulation of MnO2 on the outer surface of CS. Through computational TGA analysis, the mass fraction of carbon in CS@MnO2-2 was determined to be approximately 51.2%. The excellent hydrophilicity of the material facilitated the permeation of the salt solution throughout the electrode, thereby enhancing the capacitance. CS@MnO2-2 manifested a high salt adsorption capacity of 27.42 mg g⁻¹ and the fastest electrosorption rate of 7.81 mg g⁻¹ min⁻¹. During 50 adsorption desorption cycles, the adsorption capacity showed good results. The adsorption kinetics and adsorption isotherm fitting indicated that the desalination process involved electrostatic and multilayer adsorption. This study holds great significance for reducing the cost of desalinated water and guaranteeing a sustainable supply of freshwater resources.

Nanorod

Capacitive Deionization

Carbon fibers

10.3390/su17031004

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A deep learning approach for underwater feature extraction

A.A. Balkema eBooks (2025)

M. V. Baiju Nimmi R. Nair Sumam Mary Idicula Norman L. Thomas Ferose Babu

Feature (linguistics)

10.1201/9781003587392-78

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