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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding

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Journal of Hydrology 2025年 661卷

作者： Chunxiao Zhang Heng Li Yuqian Hu Dingtao Shen Bingli Xu Min Chen Wenhao Chu Rongrong Li School of Information Engineering China University of Geosciences Beijing Beijing China Hebei Key Laboratory of Geospatial Digital Twin and Collaborative Optimization China University of Geosciences Beijing Beijing China Chinese Academy of Surveying and Mapping Beijing China Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan China College of Urban and Environmental Sciences Central China Normal University Wuhan China Department of Information and Communication Academy of Army Armored Forces Beijing China Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC) Nanjing Normal University Nanjing Jiangsu China International Research Center of Big Data for Sustainable Development Goals Beijing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Jiangsu China Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin New Territories Hong Kong China

Differentiable parameterized learning (dPL) represents a cutting-edge advancement in synergizing process-based models (PBMs) and machine learning to improve both the model prediction and interpretability. However, dPL mainly focuses on refining parameters within a predefined process-based structure, which limits its performance and process understanding to the constraints of existing PBM frameworks. To address this limitation, we propose an extended framework: Differentiable Process and Parameter Learnings (dP2Ls). dP2Ls incorporate regionalized neural networks (NNs) to simultaneously improve both the modeling of process variables and parameter learnings. Within dP2Ls, the EXP-HYDRO model serves as a differentiable physical backbone, where the potential evapotranspiration (PET) process is modeled using a regionalized long short-term memory (LSTM), while the dPL strategy is independently applied to learning hydrological parameters. Experiments with runoff prediction in ungauged catchments across the contiguous United States revealed that: (1) The median Nash-Sutcliffe efficiency (NSE) of regionalized dP2Ls in ungauged catchments surpassed that of all models using NNs for either process learning or parameter learning and approached the LSTM model (the median NSE differs by only 0.021); (2) Regionalized dP2Ls enhanced the interpretability of some intermediate variables by jointly learning the process variables and parameters, resulting in the correlation coefficients of snowpack and ET fitting the estimation products increased by 0.022 and 0.026, respectively; (3) The embedded regionalized LSTM outputs more interpretable PET than EXP-HYDRO, demonstrating the capability of NNs to improve traditional process understanding. In summary, this study demonstrates the advantages of dP2Ls in regionalized modeling, overcoming the limitations of traditional process-based structures through flexible NN configurations, and providing a reliable pathway for the future in-depth diagno

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Lump-type solutions of a generalized Kadomtsev–Petviashvili equation in(3+1)-dimensions

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Chinese Physics B 2019年第10期28卷 245-252页

作者： Xue-Ping Cheng Wen-Xiu Ma Yun-Qing Yang Physics MathematicsInformation College of Zhejiang Ocean UniversityZhoushan 316004China Key Laboratory of Oceanographic Big Data Mining&Application of Zhejiang Province Zhoushan 316022China Department of Mathematics and Statistics University of South FloridaTampaFL 33620-5700USA Department of Mathematics King Abdulaziz UniversityJeddahSaudi Arabia College of Mathematics and Systems Science Shandong University of Science and TechnologyQingdao 266590China International Institute for Symmetry Analysis and Mathematical Modelling Department of Mathematical SciencesNorth-West UniversityMafikeng CampusPrivate Bag X2046Mmabatho 2735South Africa

Through the Hirota bilinear formulation and the symbolic computation software Maple, we construct lump-type solutions for a generalized(3+1)-dimensional Kadomtsev-Petviashvili(KP) equation in three cases of the coefficients in the equation. Then the sufficient and necessary conditions to guarantee the analyticity of the resulting lump-type solutions(or the positivity of the corresponding quadratic solutions to the associated bilinear equation) are discussed. To illustrate the generality of the obtained solutions, two concrete lump-type solutions are explicitly presented, and to analyze the dynamic behaviors of the solutions specifically, the three-dimensional plots and contour profiles of these two lump-type solutions with particular choices of the involved free parameters are well displayed.

关键词： lump-type solution generalized(3+1)-dimensional Kadomtsev-Petviashvili equation Hirota bilinear form symbolic computation

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Wheat yellow rust monitoring method based on Sentinel-2 image and BPNN model

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2019年第17期35卷 178-185页

作者： Huang, Linsheng Jiang, Jing Huang, Wenjiang Ye, Huichun Zhao, Jinling Ma, Huiqin Ruan, Chao National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University 230601 China Sanya Institute of Remote Sensing Sanya572029 China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100094 China

Wheat yellow rust is a deadly disease of winter wheat and its timely and accurate detection at regional scale is critical to ameliorate infectious yield loss and safeguard wheat production. With the development in remote sensing technology, satellite imagery with high spatial resolution and high revisiting frequency has become increasingly accessible. Remote sensing data has a unique advantage over traditional method in detecting crop diseases and controlling their spreading, including simple operation, real-time detection, high spatiotemporal resolution and targeting specific-disease, especially the multispectral satellite imagery which covers a wide range of wave bands and provides rich information related to crop diseases at regional scale. Compared to conventional broad band satellite imagery, the Sentinel-2 is a sensor with three wave bands within the edge of the red light which are sensitive to crop diseases. In this study, a Sentinel-2 image acquired in May 12, 2018 was used to extract 26 characteristic variables related to wheat yellow rust, including 3 visible bands (blue, green and red) reflectance variables, one near infrared band, 3 red-edge bands, 14 broad-bands and 5 red-edge vegetation indices. An approach combining K-means and ReliefF algorithm is proposed to filter these features. We first used the RelieF algorithm to calculate the weight of each feature and filter out 10 features most relevant to the disease. The feature with maximum weight was then taken as the initial center of the K-Means algorithm, and other features were added to form a cluster in descending order of their weight. The combination of features with the highest clustering accuracy was taken as the final input variable to the model. The optimal features, including enhanced vegetation index (EVI), structure intensive pigment index (SIPI), simple ratio index (SR), normalized red-edge2 index(NREDI2), normalized red-edge3 index (NREDI3), three wide-band vegetation indices and 2 red ed

关键词： Remote sensing

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A new runoff calculation model based on multi-source radar remote sensing technology 6

A new runoff calculation model based on multi-source radar r...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Zhao, Jianhui Wang, Ning Wu, Lin Min, Lin College of Computer and Information Engineering Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

River runoff changing will directly affect the safety of the surrounding areas. In theory, river runoff can be calculated from water depth, river width and flow velocity. But in the actual monitoring, the observation of water depth, river width and flow velocity is complex. Radar remote sensing can observe target all-time and all-weather, and has the ability to penetrate clouds, rain and fog. The common Radar remote sensing runoff calculation model is depth-runoff relationship model, which is constructed by fitting water depth and in-situ runoff. The river depth is derived from Radar Altimetry (RA) data. But this model has only one input parameter, the accuracy of this model inverted runoff is not high. In view of the above problems, this paper proposed the Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM). Firstly, river width and relative water depth were derived from Synthetic Aperture Radar (SAR) images and RA data. Secondly, based on the Manning's equation, the flow velocity were described from river width and relative water depth. Thirdly, river width relative water depth, and in-situ runoff were fitted to construct MRRS-RCM. Experiments show the accuracy of MRRS-RCM estimated runoff is better than depth-runoff relationship model, the RRMSE values of MRRS-RCM reach 13%. © 2019 IEEE.

关键词： Synthetic aperture radar

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Exploiting cognitive structure for adaptive learning

arXiv

引用

arXiv 2019年

作者： Liu, Qi Tong, Shiwei Liu, Chuanren Zhao, Hongke Chen, Enhong Ma, Haiping Wang, Shijin Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology School of Data Science University of Science and Technology of China Business Analytics and Statistics University of Tennessee College of Management and Economics Tianjin University IFLYTEK Research IFLYTEK CO. LTD. State Key Laboratory of Cognitive Intelligence

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes

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A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

arXiv

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arXiv 2020年

作者： Yang, Jun Li, Qing Sun, Yixuan Wang, Wei Li, Linchao Wang, Xuwei Jia, Shengyao Tong, Renyuan National and Local Joint Engineering research center for Disaster Monitoring Technologies and Instruments China Jiliang University Hangzhou310051 China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability Analysis and Application China Jiliang University Hangzhou310051 China Zhejiang PeckerAI Technology. Ltd Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Tailings ponds are places for storing industrial waste. The saturation line is the key factor of quantifying the safety of tailings pond. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. In this paper, we presents a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into CNN-LSTM model to enhance the feature extracting ability in channel and temporal dimension. Consequently, our proposed model is shown to outperform classic models on five real-world datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Award for Outstanding Comprehensive Mini-Challenge 2 Solution - RadiationMonitor: An Interactive System for Visualizing and Exploring Spatial-Temporal data

Award for Outstanding Comprehensive Mini-Challenge 2 Solutio...

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IEEE Symposium on Visual Analytics Science and Technology (VAST)

作者： Datong Wei Hanning Shao Zijing Tan Chenlu Li Zhixian Lin Xiaoju Dong Xiaoru Yuan Key Laboratory of Machine Perception Ministry of Education School of EECS Peking University Weinberg College of Arts and Sciences Northwestern University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University National Engineering Laboratory for Big Data Analysis and Application Peking University

In this paper, we proposed RadiationMonitor, an interactive system for visualizing and exploring spatial-temporal data from static and mobile sensors. We use pixel-based time-series visualization for event presentation, and the trajectories of mobile sensors are discretized to display the spatial distribution of sensor readings. Our system provides cross-filtering and detail-on-demand interactions, which allow users to investigate data from both spatial and temporal aspects.

关键词： Sensor systems Pollution measurement data visualization Uncertainty Image color analysis Measurement uncertainty Awards

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Author Correction: Mapping annual 10-m soybean cropland with spatiotemporal sample migration

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Scientific data 2024年第1期11卷 565页

作者： Hongchi Zhang Zihang Lou Dailiang Peng Bing Zhang Wang Luo Jianxi Huang Xiaoyang Zhang Le Yu Fumin Wang Linsheng Huang Guohua Liu Shuang Gao Jinkang Hu Songlin Yang Enhui Cheng Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. University of Chinese Academy of Sciences Beijing 100094 China. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. pengdl@***. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. pengdl@***. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. University of Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. Jiangxi Nuclearindustry Surveying and Mapping Institute Group Co. Ltd Nanchang 330038 China. College of Land Science and Technology China Agricultural University Beijing 100083 China. Geospatial Sciences Center of Excellence Department of Geography Geospatial Sciences South Dakota State University Brookings SD 57007 USA. Department of Earth System Science Tsinghua University Beijing 100084 China. Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou 310058 China. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei 230601 China. Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai 200120 China.

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Fiber-spinning Asymmetric Assembly for Janus-structured Bifunctional Nanofiber Films towards All-Weather Smart Textile

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Angewandte Chemie 2022年第40期134卷

作者： Dr. An-Quan Xie Prof. Liangliang Zhu Yunzheng Liang Jian Mao Prof. Yijiang Liu Prof. Su Chen State Key Laboratory of Materials-Oriented Chemical Engineering and College of Chemical Engineering Nanjing Tech University Nanjing 210009 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Project administration (lead) Writing - original draft (lead) Contribution: Conceptualization (equal) Funding acquisition (lead) Investigation (equal) Resources (lead) Supervision (equal) Writing - review & editing (equal) Contribution: Investigation (supporting) Methodology (supporting) Validation (supporting) Contribution: Data curation (supporting) Investigation (supporting) Methodology (supporting) College of Chemistry Key Laboratory of Environmentally Friendly Chemistry and Application of Ministry of Education Xiangtan University Xiangtan 411105 Hunan Province P. R. China Contribution: Supervision (supporting) Writing - review & editing (supporting)

Given health threat by global warming and increased energy consumption in regulating body temperature, it is an urgent need to construct smart temperature-regulating materials. Herein, a novel fiber-spinning asymmetric chemical assembly (FACA) method is proposed to construct nanofiber materials with asymmetric photothermal properties. The silver nanowires (AgNWs) and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with opposite thermal radiation are assembled on reduced graphene oxide (rGO) film, imparting AgNW/rGO/PVDF-HFP film with Janus structure that can realize the AgNWs side consistently keeps temperature of ca. 11 °C lower than the side of PVDF-HFP nanofiber regardless of the irradiation directions under 1 sun, suggesting the adjustable photothermal regulation. Such photothermally selective hybrid nanofiber film provides great potential as fabrics to achieve all-weather smart clothes, promoting controllable and comprehensive utilization of solar energy.

关键词： Electrospinning Janus Film Passive Cooling Photothermal Management Thermoregulatory Textile

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