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Non-Destructive Radio Wave Scanning Method for the Inspection of Composite Materials

Non-Destructive Radio Wave Scanning Method for the Inspectio...

2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information technology, USBEREIT 2023

作者： Vakhitov, Maksim Klygach, Denis Razevig, Vladimir Olivenko, Nikita Kudryavtsev, Oleg School of Electronic Engineering and Computer Science South Ural State University Chelyabinsk Russia Bauman Moscow State Technical University Remote Sensing Laboratory Moscow Russia Institute of Engineering and Technology South Ural State University Chelyabinsk Russia

ISBN: (纸本)9798350336054

Polymer composites, for example, carbon or aramid fibre-reinforced plastics, are widely used in aviation technology, aircraft engines for the manufacture of load-bearing elements. The main advantage of composite materials based on synthetic reinforcing fibers is a high specific strength, the ability to control the strength and rigidity of a structure over a wide range, achieving its high weight perfection. A significant disadvantage of traditional layered composites is the low resistance to transversal impact. Low-velocity impacts are commonly occurred in the operation of aircraft structures: tool drops during maintenance, hail, gravel from the runway, bird strikes, etc. Intra- and interlaminar damages (delamination, fibre fracture) that occur during impacts significantly affect the residual strength of the composite element and can lead to its sudden failure during normal operation. In this regard, an important part of health monitoring of composite structures is the identification of impact defects for their further repair or the decision to replace the entire element. In this regard, it is of considerable interest to determine the location of a possible defect in a semi-automatic mode using remote control methods, in particular, radar and radio waves. The paper presents the results of state control for a sample of an aramid plate with defects, obtained microwave holograms in the frequency range of 15-24 GHz, and reconstructed microwave images in three-dimensional planes. The proposed method of non-destructive testing can be successfully used to determine damage in composite structures reinforced with glass and organic fibers, etc. © 2023 IEEE.

关键词： Defects

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Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

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Advances in Atmospheric sciences 2024年第8期41卷 1465-1482页

作者： Jiazhen ZHAO Shengping HE Ke FAN Huijun WANG Fei LI Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China Geophysical Institute University of Bergen and Bjerknes Centre for Climate ResearchBergen 5007Norway School of Atmospheric Science Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research Bergen 5007Norway Nansen-Zhu International Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519082China

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions ***, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

关键词： wintertime newly formed Arctic sea ice model democracy model weighting scheme model performance model independence

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INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS DATA FOR UNDERSTANDING HEAVY PRECIPITATION AND FLOODING IN AN GIANG PROVINCE, VIET NAM 44

INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS D...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Minh, Ton Binh Chiang, Shou-Hao International Ph.D Program in Environmental Science and Technology University System of Taiwan No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Understanding the mechanisms of heavy precipitation and flooding is essential for effective climate change adaptation and response. The cloud-penetrating ability of microwaves makes Synthetic Aperture Radar (SAR) an ideal tool for providing weather-independent images for flood mapping. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a land-only rainfall dataset that incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for various analysis applications. This study employs intensity images from Sentinel-1 satellites to generate flood maps and utilizes CHIRPS to derive heavy rainfall frequency. The segmentation technique is applied to distinguish between pre-existing standing water, recently flooded areas, and non-flooded regions within the study area. By combining SAR and CHIRPS data, this research yields valuable insights into the relationship between heavy rainfall and flood occurrences in An Giang province, Viet Nam. The escalating frequency of heavy precipitation correlates with an augmentation in local rainfall amounts, and a positive correlation emerges between heavy rainfall and the extension of flood-prone areas. While recognizing that flooding influences water flow from the upstream of the Mekong River, flooding discharges from neighboring areas, and tidal levels, the accelerated trend of urbanization has also played a significant role. The expansion of impervious surfaces contributes to the severity of deep floods and elevates flood potential in the region. Furthermore, this research affirms the effectiveness of SAR intensity image mapping for flood detection, and the integration of SAR and CHIRPS data furthers our understanding of heavy precipitation mechanisms. As the implications of climate change persist, the findings are crucial for developing sustainable flood management strategies, as well as enhancing resilience to climate-induced challenges in the Mekong River Del

关键词： CHIRPS climate change Flood Mekong River Delta Sentinel-1

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Extreme Wind Variability and Wind Map Development in Western Java,Indonesia

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International Journal of Disaster Risk science 2022年第3期13卷 465-480页

作者： Muhammad Rais Abdillah Prasanti Widyasih Sarli Hafidz Rizky Firmansyah Anjar Dimara Sakti Faiz Rohman Fajary Robi Muharsyah Gian Gardian Sudarman Atmospheric Science Research Group Faculty of Earth Science and TechnologyInstitut Teknologi BandungBandung40132Indonesia Structural Engineering Research Group Faculty of Civil and Environmental EngineeringInstitut Teknologi BandungBandung40132Indonesia Center for Infrastructures and Built Environment Institut Teknologi BandungBandung40132Indonesia Civil Engineering Department Faculty of Civil and Environmental EngineeringInstitut Teknologi BandungBandung40132Indonesia Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung40132Indonesia Center for Remote Sensing Institut Teknologi BandungBandung40132Indonesia Center for Climate Change and Information The Agency for MeteorologyClimatology and Geophysics of the Republic of Indonesia(BMKG)Jakarta10720Indonesia Subdivision for Executive Secretary Administration The Agency for MeteorologyClimatology and Geophysics of the Republic of Indonesia(BMKG)Jakarta10720Indonesia Earth Science Department Faculty of Earth Science and TechnologyInstitut Teknologi BandungBandung40132Indonesia

Wind-related disasters are one of the most frequent disasters in *** can cause severe damages of residential construction,especially in the world's most populated island of *** the characteristics of extreme winds is crucial for mitigating the disasters and for defining structural design *** study investigated the spatiotemporal variations of extreme winds and pioneered a design wind map in Indonesia by focusing on western *** on gust data observed in recent years from 24 stations,the extreme winds exhibit a clear annual cycle where northwestern and southeastern sides of western Java show out-of-phase relationship due to reversal ***,extreme wind occurrences are mostly affected by small-scale weather systems,regardless of seasons and *** build the wind map,we used bias-corrected gust from ERA5 and applied the Gumbel method to predict extreme winds with different return *** wind map highlights some drawbacks of the current national design standards,which use single wind speed values regardless of location and return *** a fundamental improvement for wind design,this study will benefit disaster risk mapping and other applications that require extreme wind speed distribution.

关键词： ERA5 reanalysis Extreme wind Mixed weather system Tropical climate

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POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNING LAND COVER CLASSIFICATION USING VERY-HIGH RESOLUTION SATELLITE IMAGERY 44

POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNI...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Hakim, Yofri Furqani Tsai, Fuan International Ph.D. Program in Environmental Science and Technology National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Land cover is one of the fundamental data utilized in spatial analysis for a range of applications, including climate, environment, natural resources, agriculture, forestry, planning, health, and even social issues. The demand for land cover data is diverse, ranging from global scale, regional scale, and detailed scale. Furthermore, data updates also become a necessity for users. The current trend is the requirement for more accurate and up-to-date land cover data. The growing demand for accurate and up-to-date land cover data is in sync with improvements in satellite imagery data acquisition technology, which now offers improved spatial resolution and more effective satellite imagery data processing. Satellite imagery data processing technology is also growing rapidly with the advance of artificial intelligence for semantic classification or segmentation. This research uses a deep learning approach to classify land cover on Pleiades very high-resolution satellite imagery. A post-classification enhancement is also carried out to improve the consistency and accuracy of the deep learning classification results. The preliminary research results show that post-classification enhancement with the algorithms proposed in this study can increase the accuracy of classification results using deep learning-based approaches by approximately 2%. The original Overall Accuracy and Kappa of the deep learning classification results were 0.84 and 0.79. After the post-classification enhancement process, the Overall Accuracy and Kappa values increased to 0.86 and 0.81. © 2023 ACRS. All Rights Reserved.

关键词： Deep Learning Image Morphology Land Cover Classification Post-Classification Enhancement Semantic Segmentation U-Net

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Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

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地球空间信息科学学报（英文版） 2024年第3期27卷 602-623页

作者： Roberto Tomás Qiming Zeng Juan M.Lopez-Sanchez Chaoying Zhao Zhenhong Li Xiaojie Liu María I.Navarro-Hernández Liuru Hu Jiayin Luo Esteban Díaz William T.Szeibert José Luis Pastor Adrián Riquelme Chen Yu Miguel Cano Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencePeking UniversityBeijingChina Institute for Computer Research(IUII) University of AlicanteAlicanteSpain College of Geological Engineering and Geomatics Chang'an UniversityXi'anChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain School of Civil Engineering Lanzhou University of TechnologyLanzhouChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Land Satellite Remote Sensing Application Center(LASAC) Ministry of Natural Resources of P.R.ChinaBeijingChina The First Topographic Surveying Brigade of Ministry of Natural Resources of the People's Republic of China Xi'anChina Sixense Departamento SatéliteL'Hospitalet de LlobregatBarcelonaSpain

Landslides are destructive geohazards to people and infrastructure,resulting in hundreds of deaths and billions of dollars of damage every ***,mapping the rate of deforma-tion of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated *** this paper,the main outcomes relevant to the joint European Space Agency(ESA)and the Chinese Ministry of science and technology(MOST)Dragon-5 initiative cooperation project ID 59,339"Earth observation for seismic hazard assessment and landslide early warning system"are *** primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks,detect potential landslides in wide regions,and demonstrate EO-based landslide early warning system over selected *** work only focuses on the landslide hazard content of the project,and thus,in order to achieve these objectives,the following tasks were developed up to now:a)a procedure for phase unwrap-ping errors and tropospheric delay correction;b)an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements;c)the application of polarimetric SAR interferometry(PolInSAR)to increase the number and quality of monitoring points in landslide-prone areas;d)the semiautomatic mapping and preliminary classification of active displacement areas on wide regions;e)the modeling and identification of landslides in order to identify triggering factors or predict future displacements;and f)the application of an InSAR-based landslide early warning system on a selected *** achieved results,which mainly focus on specific sensitive regions,provide essential assets for planning present and future scientific activities devoted to identifying,mapping,characterizing,monitoring and predicting landslides,as well as for the implementation of early warning systems.

关键词： InSAR SAR offset tracking PolInSAR landslide mapping modeling early warning monitoring

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

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Environmental Advances 2025年 19卷

作者： Virtriana, Riantini Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Deliar, Albertus Harto, Agung Budi Riqqi, Akhmad Sakti, Anjar Dimara Remote Sensing and GIS Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung 40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung 40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung 40132 Indonesia Geographic Information Science Universitas Pendidikan Indonesia Bandung 40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung 40132 Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. © 2024

关键词： GIS Health Facility Machine Learning remote sensing

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DeepLCZChange: A remote sensing DEEP LEARNING MODEL ARCHITECTURE FOR URBAN CLIMATE RESILIENCECRediT

DeepLCZChange: A REMOTE SENSING DEEP LEARNING MODEL ARCHITEC...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Wenlu Sun Yao Sun Chenying Liu Conrad M Albrecht Data Science in Earth Observation Technical University of Munich Remote Sensing Technology Institute German Aerospace Center

Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite’s surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.

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Sparse Nonnegative Matrix Factorization based on Double Low-Rank and Sparse Matrix Factorization Model for Spectral Variability

Sparse Nonnegative Matrix Factorization based on Double Low-...

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International Symposium on Computer science and Intelligent Controls (ISCSIC)

作者： Tingting Yang Meiping Song Sen Li the Center of Hyperspectral Imaging in Remote Sensing Information Science and Technology College Dalian Maritime University Dalian China

To reduce the interference of complex imaging environment on spectral unmixing, we introduce a double low-rank and sparse matrix factorization model (LRSMD) (DLRSMD) to better characterize the intrinsic and variant endmember components in the scene, and propose a sparse NMF based on DLRSMD (DLRSNMF) by accounting for the sparsity of intrinsic and variant abundances. The sparsity of abundances not only satisfies the practical significance of abundances, but also effectively promotes the representation ability of endmembers. The experimental results also demonstrate the better performance of DLRSNMF, which can provide reliable endmembers and abundances.

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Collapse of Meilong Expressway as Seen from Space:Detecting Precursors of Failure with Satellite remote sensing

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Journal of Earth science 2025年第2期36卷 835-838页

作者： Zhuge Xia Chao Zhou Wandi Wang Mimi Peng Dalu Dong Xiufeng He Guangchao Tan School of Earth Sciences and Engineering Hohai UniversityNanjing 211100China School of Geography and Information Engineering China University of Geosciences(Wuhan)Wuhan 430074China GFZ German Research Centre for Geosciences Potsdam 14473Germany Department of Remote Sensing Science and Technology Xidian UniversityXi'an 710071China Nanjing Research Institute of Surveying Mapping and Geotechnical Investigation LTDNanjing 210019China Hydrogeology and Engineering Geology Institute of Hubei Geological Bureau Jingzhou 434020China

*** May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″*** collapse resulted in... 详细信息

关键词： failure detection satellite remote sensing pavement failure Meilong Expressway meilong expressway collapse precursors

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