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Quantitative comparison of the total focusing method, reverse time migration, and full waveform inversion for ultrasonic imaging

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Ultrasonics 2025年 155卷 107705页

作者： Tim Bürchner Simon Schmid Lukas Bergbreiter Ernst Rank Stefan Kollmannsberger Christian U. Grosse Technical University of Munich TUM School of Engineering and Design Chair of Computing in Civil and Building Engineering Arcisstrasse 21 Munich 80333 Germany Technical University of Munich TUM School of Engineering and Design Department of Materials Engineering Chair of Non-destructive Testing Franz-Langinger-Str. 10 Munich 81245 Germany Technical University of Munich Institute for Advanced Study Lichtenbergstrasse 2a Garching 85748 Germany Bauhaus-Universität Weimar Chair of Data Science in Civil Engineering Coudraystraße 13 Weimar 99423 Germany

Phased array ultrasound is a widely used technique in non-destructive testing. Using piezoelectric elements as both sources and receivers provides a significant gain in information and enables more accurate defect detection. The process is called full matrix capture when all source–receiver combinations are used. The total focusing method (TFM) exploits such datasets and relies on a delay and sum algorithm to sum up the signals on a pixel grid. However, TFM only uses the first arriving p-waves, making it challenging to size complex-shaped defects. By contrast, more advanced methods such as reverse time migration (RTM) and full waveform inversion (FWI) use full waveforms to reconstruct defects. Both methods compare measured signals with ultrasound simulations. While RTM identifies defects by convolving forward and backward wave fields once, FWI iteratively updates material models to reconstruct the actual distributions of material properties. This study compares TFM, RTM, and FWI for six specimens featuring circular defects or Y-shaped notches. The reconstructed results are first evaluated qualitatively by visual assessment, then different defect segmentation approaches are studied, and finally, they are evaluated quantitatively using metrics such as AUPRC, AUROC, and F1-score. The results show that a two-step FWI framework performs best in most of the six samples studied, both qualitatively and quantitatively.

关键词： Ultrasound Reconstruction Phased array Full matrix capture Wave simulation Total focusing method Reverse time migration Full waveform inversion

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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science China(Life sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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Low-Rank Laplacian-Uniform Mixed Model for Robust Face Recognition

Low-Rank Laplacian-Uniform Mixed Model for Robust Face Recog...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Jiayu Dong Huicheng Zheng Lina Lian School of Data and Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

ISBN: (纸本)9781728132945

Sparse representation based methods have successfully put forward a general framework for robust face recognition through linear reconstruction and sparsity constraints. However, residual modeling in existing works is not yet robust enough when dealing with dense noise. In this paper, we aim at recognizing identities from faces with varying levels of noises of various forms such as occlusion, pixel corruption, or disguise, and take improving the fitting ability of the error model as the key to addressing this problem. To fully capture the characteristics of different noises, we propose a mixed model combining robust sparsity constraint and low-rank constraint, which can deal with random errors and structured errors simultaneously. For random noises such as pixel corruption, we adopt a Laplacian-uniform mixed function for fitting the error distribution. For structured errors like continuous occlusion or disguise, we utilize robust nuclear norm to constrain the rank of the error matrix. An effective iterative reweighted algorithm is then developed to solve the proposed model. Comprehensive experiments were conducted on several benchmark databases for robust face recognition, and the overall results demonstrate that our model is most robust against various kinds of noises, when compared with state-of-the-art methods.

关键词： Facial Recognition Noise error model random noise error matrix Robustness error distribution Random error vandalism corruption

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Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition

arXiv

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arXiv 2022年

作者： Qin, Rong Huangfu, Luwen Hood, Devon Ma, James Huang, Sheng School of Big Data & Software Engineering Chongqing University Chongqing400044 China Fowler College of Business San Diego State University San DiegoCA92182 United States Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States College of Science San Diego State University San DiegoCA92182 United States Department of Business Analysis University of Colorado Colorado SpringsCO80918 United States Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing400044 China

Pavement Distress Recognition (PDR) is an important step in pavement inspection and can be powered by image-based automation to expedite the process and reduce labor costs. Pavement images are often in high-resolution with a low ratio of distressed to non-distressed areas. Advanced approaches leverage these properties via dividing images into patches and explore discriminative features in the scale space. However, these approaches usually suffer from information loss during image resizing and low efficiency due to complex learning frameworks. In this paper, we propose a novel and efficient method for PDR. A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information across different feature granularities and scales. The mined information is passed along to the resized images to yield an informative image pyramid to assist the image classification network for PDR. We applied our method to three well-known Convolutional Neural Networks (CNNs), and conducted an evaluation on a large-scale pavement image dataset named CQU-BPDD. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of these CNN models and show that the simple combination of KIPRN and EfficientNetB3 significantly outperforms the state-of-the-art patch-based method in both performance and efficiency. © 2022, CC BY-NC-SA.

关键词： Convolution

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A path-deformation framework for determining weighted genome rearrangement distance

arXiv

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arXiv 2020年

作者： Bhatia, Sangeeta Egri-Nagy, Attila Serdoz, Stuart Praeger, Cheryl E. Gebhardt, Volker Francis, Andrew Centre for Research in Mathematics and Data Science Western Sydney University SydneyNSW Australia School of Physics Mathematics and Computing University of Western Australia PerthWA Australia MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London United Kingdom Akita International University Japan

Measuring the distance between two bacterial genomes under the inversion process is usually done by assuming all inversions to occur with equal probability. Recently, an approach to calculating inversion distance using group theory was introduced, and is effective for the model in which only very short inversions occur. In this paper, we show how to use the group-theoretic framework to establish minimal distance for any weighting on the set of inversions, generalizing previous approaches. To do this we use the theory of rewriting systems for groups, and exploit the Knuth–Bendix algorithm, the first time this theory has been introduced into genome rearrangement *** central idea of the approach is to use existing group theoretic methods to find an initial path between two genomes in genome space (for instance using only short inversions), and then to deform this path to optimality using a confluent system of rewriting rules generated by the Knuth–Bendix algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Group theory

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NERank+: a graph-based approach for entity ranking in document collections

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计算机科学前沿 2018年第3期12卷 504-517页

作者： Chengyu WANG Guomin ZHOU Xiaofeng HE Aoying ZHOU Shanghai Key Laboratory of Trustworthy Computing School of Computer Science and Software EngineeringEast China Normal University Shanghai 200062 China Department of Computer and Information Technology Zhejiang Police College Hangzhou 310053 China School of Data Science and Engineering East China Normal University Shanghai 200062 China

Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user *** rank order of entities is determined by the relevance between the query and contexts of ***,entities can be ranked directly based on their relative importance in a document collection,independent of any *** this paper,we introduce an entity ranking algorithm named NERank+.Given a document collection,NERank+ first constructs a graph model called Topical Tripartite Graph,consisting of document,topic and entity *** design separate ranking functions to compute the prior ranks of entities and topics,respectively.A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph *** evaluate NERank+ over real-life datasets and compare it with *** results illustrate the effectiveness of our approach.

关键词： entity ranking Topical Tripartite Graph prior rank estimation meta-path constrained random walk

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An implementation of nDGP gravity in Pinocchio

arXiv

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arXiv 2023年

作者： Song, Yanling Hu, Bin Ruan, Cheng-Zong Moretti, Chiara Monaco, Pierluigi Center for Gravitation and Cosmology College of Physical Science and Technology Yangzhou University Yangzhou225009 China Institute for Frontier in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China Institute of Theoretical Astrophysics University of Oslo Oslo0315 Norway SISSA - International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Centro Nazionale "High Performance Computer Big Data and Quantum Computing" Italy INAF Osservatorio Astronomico di Trieste Via Tiepolo 11 TriesteI-34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Dipartimento di Fisica Universitá di Trieste Sezione di Astronomia via Tiepolo 11 TriesteI-34143 Italy INFN Sezione di Trieste Italy

In this paper we investigate dark matter structure formation in the normal branch of the Dvali-Gabadadze-Porrati (nDGP) model using the PINOCCHIO algorithm. We first present 2nd order Lagrangian perturbation theory for the nDGP model, which shows that the 1st- and 2nd-order growth functions in nDGP are larger than those in ΛCDM. We then examine the dynamics of ellipsoidal collapse in nDGP, which is accelerated compared to ΛCDM due to enhanced gravitational interactions. Running the nDGP-PINOCCHIO code with a box size of 512 Mpc h−1 and 10243 particles, we analyze the statistical properties of the output halo catalogs, including the halo power spectrum and halo mass function. The calibrated PINOCCHIO halo power spectrum agrees with N-body simulations within 5% in the comoving wavenumber range k −1) at redshift z = 0. The agreement is extended to smaller scales for higher redshifts. For the cumulative halo mass function, the agreement between N-body and PINOCCHIO is also within the simulation scatter. Copyright © 2023, The Authors. All rights reserved.

关键词： Power spectrum

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A secure federated learning framework for 5G networks

arXiv

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arXiv 2020年

作者： Liu, Yi Peng, Jialiang Kang, Jiawen Iliyasu, Abdullah M. Niyato, Dusit Abd El-Latif, Ahmed A. School of Data Science and Technology Heilongjiang University Harbin150080 China Energy Research Institute Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Mathematics and Computer Science Department Faculty of Science Menoufia University Shebin El-Koom32511 Egypt School of Information Technology and Computer Science Nile University Egypt

Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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Explicit Linear Dual-Multistep Methods Applied to ZNN Illustrated via Discrete Time-Dependent Linear and Nonlinear Inequalities System Solving

Explicit Linear Dual-Multistep Methods Applied to ZNN Illust...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jinjin Guo Binbin Qiu Liangjie Ming Yunong Zhang School of Data and Computer Science Sun Yat-sen University Guangzhou China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this work, time-dependent linear and nonlinear inequalities system (TDLNIS) is studied and solved. First, using zeroing neural network (ZNN) method twice, a continuous time-dependent ZNN (CTDZNN) model is proposed to solve the continuous TDLNIS. Subsequently, explicit linear dual-multistep methods, i.e., explicit linear dual-4-step, dual-3-step, and dual-2-step methods, are presented and studied. Afterwards, by applying the explicit linear dual-4-step method to the proposed CTDZNN model, a 4-step discrete time-dependent ZNN (4S-DTDZNN) model is proposed to solve the discrete TDLNIS. For comparison, 3-step discrete time-dependent ZNN (3S-DTDZNN) and 2-step discrete time-dependent ZNN (2S-DTDZNN) models are also developed for solving the discrete TDLNIS. In addition, theoretical analyses and results indicate the effectiveness and superiority of the proposed 4S-DTDZNN model. Finally, numerical experimental results further substantiate the effectiveness and superiority of the proposed 4S-DTDZNN model.

关键词： Numerical models Mathematical model Trajectory Neural networks Analytical models Computational modeling Finite wordlength effects

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