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Progress in Electromagnetic Research Symposium (PIERS)

作者： Chun Xin Li Zi He Da Zhi Ding Fan Ding Xin Rui Jiang Yu Peng Wang Nanjing University of Science and Technology Nanjing China The China Ship Development and Design Center Wuhan China Beijing Institute of Remote Sensing Equipment Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

It is instructive for aerial target interception to exploit the point/broadband electromagnetic scattering characteristics of particles cloud. The particles cloud is generated by the explosive targets after being struck in the air. Complex point scattering source approximation, as the common method, is usually used to calculate the backscattering characteristics of clouds. However, it often ignores the coupling effect as the distance between particles is too close. Therefore, in order to solve this problem, a method based on vector radiative transfer theory is proposed. It is used to better predict the backscattering characteristics of the cloud. In this process, the absorption, scattering and propagation of electromagnetic waves inside the cloud is all analyzed. First, the Rayleigh-Gans approximate is introduced to simulate the particles in the explosive cloud. Secondly, vector radiative transfer equation based on conservation of energy is presented. In this process, Monte Carlo method is adopted to simulate the absorption, scattering and propagation of electromagnetic waves inside the cloud to better predict the backscattering characteristics of the cloud. Finally, the whole process is analyzed in this paper, including early stage and late stage. The backscattering characteristics of dense clouds can be predicted by using the method based on VRT theory in the early stage of explosion, and it also can be predicted by using the method, which is based on complex point scattering source approximation in the late stage. Numerical results show that the results of our method are consistent with those of the FEKO-MLFMM in the most scattering direction.

关键词： Couplings Monte Carlo methods Absorption Electromagnetic scattering Energy conservation Vectors Mathematical models

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ANTI-JAMMING PERFORMANCE ANALYSIS OF MULTIOAM-MODE VORTEX RADAR BASED ON WAVEFORM DIVERSITY

ANTI-JAMMING PERFORMANCE ANALYSIS OF MULTIOAM-MODE VORTEX RA...

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2022 International Conference on Radar Systems, RADAR 2022

作者： Hu, Dunfa Liu, Lin Duan, Yaohui Jiang, Xinrui Lv, Kun Ma, Hui National Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing Institute of Remote-sensing Equipment Science and Technology on Millimeter-wave Laboratory Beijing China Guangzhou Institute of Technology Xidian University Guangzhou China

ISBN: (纸本)9781839537776

This paper exploits the electromagnetic vortex (EMV) radar system in anti-jamming applications of digital radio frequency memory (DRFM), considering the orthogonal-waveform-based uniform circular array (UCA) system specifically. Compared to conventional multiple-input-multiple-output (MIMO) system, the EMV radar has a novel dimension of EMV mode proportional to the orbital angular momentum (OAM), providing a new dimension for the orthogonal decomposition of the transmitted waveform, as well as a new dimension for distinguish the DRFM and a real target. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Vortex flow

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Evaluating spatial accessibility to healthcare services from the lens of emergency hospital visits based on floating car data

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International Journal of Digital Earth 2022年第1期15卷 108-133页

作者： Wei Jiao Wei Huang Hongchao Fan School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China College of Surveying and Geo-Informatics Tongji UniversityShanghaiPeople’s Republic of China Department of Civil Engineering Ryerson UniversityTorontoCanada Department of Civil and Environmental Engineering Norwegian University of Science and TechnologyTrondheimNorway

Public medical facilities that are closely related to the health of residents have been recognised as one of the most crucial elements in sustainable urban *** the sake of social equality of medical services(especially for emergency medical conditions),the spatial distributions of medical resources need to be accurately measured and continuously *** study presents an effective method to examine night emergency hospital visit and analyse its spatiotemporal characteristics using float car data(FCD).By extracting the hospital service areas,the two-step floating catchment area(2SFCA)methodology was improved to calculate hospital ***,the balance between hospital accessibility and population density was *** addition,we investigated the relationship between individual hospital choice preferences and hospital level and analysed several factors that affect individual *** results help us understand the special requirements and need of emergency hospital travel in cities and identify areas where medical resources are *** can be used as guidance for urban hospital planning and *** the approach of hospital access behaviour investigation and the improved 2SFCA method can also provide insights for other activity-based travel behaviour research.

关键词： Floating car data hospital emergency travels hospital service areas hospital accessibility

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Geoscience model service integrated workflow for rainstorm waterlogging analysis

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International Journal of Digital Earth 2021年第7期14卷 851-873页

作者： Xicheng Tan Jingguo Jiao Nengcheng Chen Fang Huang Liping Di Jinchuan Wang Zongyao Sha Jin Liu School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China School of Resources&Environment University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Center for Spatial Information Science and Systems George Mason UniversityFairfaxVAUSA School of Computer Science Wuhan UniversityWuhanPeople’s Republic of China

This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis *** this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed *** proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources *** method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.

关键词： Geoscience model workflow waterlogging OGC geospatial service

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Extreme Weather Event (Cyclone) Detection in India Using Advanced Deep Learning Techniques 9

Extreme Weather Event (Cyclone) Detection in India Using Adv...

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9th International Conference on Orange technology, ICOT 2021

作者： Dabhade, Atharv Roy, Samshitha Moustafa, Marwa S. Mohamed, Sayed A. El Gendy, Reda Barma, Shovan Indian Institute of Information Technology Guwahati Assam India National Authority of Remote Sensing and Space Science Cairo Egypt

ISBN: (纸本)9781665478427

Detection and Prediction of extreme weather events have always been important and challenging task, which has become very significant while the whole world is experiencing adverse effect of climate change. The cyclone, one of the extreme weather events has become very often and intense in Indian sub-continent areas in last few decades and its accurate detection and prediction has become very relevant. In this direction, machine learning (ML) based approached has become very popular than other conventional methods. However, the performance of the traditional ML based approaches is highly dependent on hand crafted feature engineering. In contrast, deep learning, an advance ML eliminates such barrier and selects the feature set automatically in hierarchical way, which has already displayed significant performance in various domain of research including weather forecasting. In this purpose, convolutional long-short term memory (ConvLSTM) can efficiently extract spatial and temporal information, which are very important for accurate detection of extreme weather events. Therefore, a model with ConvLSTM has been proposed in this work to detect and predict the cyclone in India. For validation, Community Atmospheric Model (CAM5.1) image data considering seven variables have been generated and employed. Two matrices, peak signal to noise ratio (PSNR) and structural index similarity (SSIM) have been used to validate the model performance. The results and analysis show that the proposed idea can effectively improve the cyclone detection and predication. © 2021 IEEE.

关键词： Storms

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H-/H∞UIO-Based Robust Fault Diagnosis Design in Finite Frequency Domain for Discrete-Time Systems 41

H-/H∞UIO-Based Robust Fault Diagnosis Design in Finite Freq...

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41st Chinese Control Conference, CCC 2022

作者： Gao, Sheng Wang, Jian Lu, Zhihui Ma, Guangfu Guo, Yanning Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Beijing Institute of Remote Sensing Equipment Beijing100005 China Aerospace System Engineering Shanghai Shanghai201109 China

ISBN: (纸本)9789887581536

This study investigates the problem of robust fault diagnosis for discrete-time systems subjected to actuator faults and unknown input disturbances. A novel H-/L-{infty} unknown input observer (UIO) based on the generalized Kalman-Yakubovich-Popov (GKYP) lemma is presented by decoupling the partial unknown input disturbances and attenuating the unknown input disturbances that cannot be decoupled. Then, the H- performance index in the finite frequency domain is used to measure the fault sensitivity and the L-{infty} performance index is used to reduce the influence of unknown input disturbances on fault diagnosis results and ensure the robustness performance. A residual evaluation function and a time-varying threshold are presented based on the L-{alpha} performance index. In addition, sufficient conditions for designing the H-/L-{infty} UIO are proposed and transformed into linear matrix inequalities (LMIs) that can be easily solved. Finally, simulations of a simple discrete-time system are used to validate the effectiveness of the developed H-/L-{infty} UIO. © 2022 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Linear matrix inequalities

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Modeling of Spring Phenology of Boreal Forest by Coupling Machine Learning and Diurnal Temperature Indicators

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Chinese Geographical science 2025年第1期35卷 38-54页

作者： DENG Guorong ZHANG Hongyan HONG Ying GUO Xiaoyi YI Zhihua EHSAN BINIYAZ School of Hydraulic Engineering Nanchang Institute of TechnologyNanchang 330099China Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of EducationSchool of Geographical SciencesNortheast Normal UniversityChangchun 130024China Urban Remote Sensing Application Innovation Center School of Geographical SciencesNortheast Normal UniversityChangchun 130024China School of Geographical Sciences Inner Mongolia Normal UniversityHohhot 010022China Landscape Ecology and Geoinformation Science Department of GeographyKiel UniversityKiel 24118Germany

The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology *** previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains *** this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal *** estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario *** sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime *** LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN *** incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is ***,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study *** simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate *** study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.

关键词： spring phenology diurnal temperature machine learning future climate scenarios boreal forest

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Multispectral and Hyperspectral Image Fusion Based on Coupled Non-Negative Block Term Tensor Decomposition with Joint Structured Sparsity

Multispectral and Hyperspectral Image Fusion Based on Couple...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Hao Guo Wenxing Bao Wei Feng Shasha Sun Lei Yang Kewen Qu Xuan Ma Xiaowu Zhang School of Computer Science and Engineering North Minzu University Yinchuan China Department of Remote Sensing Science and Technology School of Electronic Engineering Xidian University Xi’an China

Multispectral and hyperspectral image fusion (MHF) aims to reconstruct high-resolution hyperspectral images by fusing spatial and spectral information. The block-item tensor fusion model is able to use endmember and abundance information to improve the quality of hyperspectral images. This paper implements image fusion based on a coupled non-negative block term tensor decomposition model. Firstly, the two abundance matrices are formed into a chunking matrix and L 2,1 -parametric is added as well, promoting structured sparsity and eliminating the scaling effect present in the model. Immediately after, the counter-scaling effect present in the model is eliminated by adding a L 2 -parametric number to the endmember matrix. Finally, the focus is on solving the noise/artifacts generated by the no exact estimation of rank in the model, and over-estimation of rank by coupling the chunking matrix and the endmember matrix together to reconstruct the matrix, adding L 2,1 -parameters to it to facilitate the elimination of chunks, and solving the problems using an extended iteratively reweighted least squares (IRLS) method. The experiments on the University of Pavia dataset show that the proposed algorithm works better compared to the state of the art methods.

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Hyperspectral Images Super-Resolution Algorithms Based On Spectral Subspace Sparse Tensor Factorization

Hyperspectral Images Super-Resolution Algorithms Based On Sp...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Shasha Sun Wenxing Bao Hao Guo Kewen Qu Wei Feng School of Computer Science and Engineering North Minzu University Yinchuan China Department of Remote Sensing Science and Technology School of Electronic Engineering Xidian University Xi’an China

Hyperspectral image super resolution (HSI-SR) problem aims to fuse a low-resolution hyperspectral image (HSI) with its corresponding multispectral image (MSI) to obtain a high-resolution hyperspectral image (HSR). However, the commonly used methods have some limitations. For example, the matrix decomposition method does not preserve the spatial or spectral information of the image well, and the tensor decomposition method has a high computational complexity. This paper proposes a method based on spectral subspace sparse tensor factorization (SSTF), which learns the spectral subspace from hyperspectral images, constrains this model using sparse tensor regularisation, transforms it to solve a convex optimisation problem, and iteratively optimises the problem using the alternating direction method of multipliers (ADMM). The computational complexity of the algorithm is effectively reduced while retaining spatial and spectral features. Compared with the state-of-the-art methods, experimental results demonstrate the effectiveness of the SSTF method.

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Settlement Mapping for Population Density Modelling in Disease Risk Spatial Analysis 6

Settlement Mapping for Population Density Modelling in Disea...

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6th International Workshop on Big Data and Information Security, IWBIS 2021

作者： Mutasodirin, Mirza Alim Rizqullah, Rafi Dwi Setiyoko, Andie Arymurthy, Aniati Murni Universitas Indonesia Faculty of Computer Science Depok Indonesia LAPAN Remote Sensing Technology and Data Center Jakarta Indonesia

ISBN: (数字)9781665424516

ISBN: (纸本)9781665424516

In disease risk spatial analysis, many researchers especially in Indonesia are still modelling population density as the ratio of total population to administrative area extent. This model oversimplifies the problem, because it covers large uninhabited areas, while the model should focus on inhabited areas. This study uses settlement mapping against satellite imagery to focus the model and calculate settlement area extent. As far as our search goes, we did not find any specific studies comparing the use of settlement mapping with administrative area to model population density in computing its correlation to a disease case rate. This study investigates the comparison of both models using data on Tuberculosis (TB) case rate in Central and East Java Indonesia. Our study shows that using administrative area density the Spearman's $\rho$ was considered as 'Fair' $(0.566, p © 2021 IEEE.

关键词： Satellite imagery

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