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10th international workshop on Structural Health Monitoring (IWSHM)

作者： Abdel-Jaber, Hiba Glisic, Branko Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781605952758

Monitoring temperature in structures is essential for understanding their thermal behavior and performing thermal compensation for any other type of sensors used for structural health monitoring (e.g. strain). this is particularly important for long-term monitoring applications. Nevertheless, a universal procedure for the validation of temperature data from a monitoring system has not been created and implemented. In this paper, preliminary research on two methods that aim to validate temperature readings from temperature sensors is presented. the methods differ in the type of data available for validation: (1) data from two independent monitoring systems are available, and (2) data from a monitoring system and a nearby weather tower are available. the methods use linear regression and confidence intervals on the linear regression model parameters to quantify annual changes in readings, which are then used to characterize long-term performance of the sensor such as changes in sensitivity or stability, or other unusual behaviors. the methods were applied to data collected over five years from fiber optic sensors based on Fiber Bragg Gratings (FBG) installed on Streicker Bridge on the Princeton University campus, and similar qualitative conclusions were derived from the two methods at instrumented locations where two independent monitoring systems are installed. this validation of the two methods was used as a confirmation that the second method, for which data is generally available, can be used in lieu of the first in cases where two independent monitoring systems do not exist. Based on this validation, the second method was independently used to test data at other locations on the bridge, where only one type of sensors is installed. the validation procedure showed and quantified minor changes in sensitivity and stability in measurements from some instrumented locations after the first two years.

关键词： Temperature sensors

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Coidentification of Group-Level Hole structures in Brain Networks via Hodge Laplacian 22nd

Coidentification of Group-Level Hole Structures in Brain Net...

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Lee, Hyekyoung Chung, Moo K. Kang, Hyejin Choi, Hongyoon Ha, Seunggyun Huh, Youngmin Kim, Eunkyung Lee, Dong Soo Seoul Natl Univ Hosp Seoul South Korea Seoul Natl Univ Seoul South Korea Univ Wisconsin Madison WI 53706 USA Catholic Univ Korea Seoul ST Marys Hosp Seoul South Korea

ISBN: (纸本)9783030322519;9783030322502

One of outstanding issues in brain network analysis is to extract common topological substructure shared by a group of individuals. Recently, methods to detect group-wise modular structure on graph Laplacians have been introduced. From the perspective of algebraic topology, the modules or clusters are the zeroth topology information of a topological space. Higher order topology information can be found in holes. In this study, we extend the concept of graph Laplacian to higher order Hodge Laplacian of weighted networks, and develop a group-level hole identification method via the Stiefel optimization. In experiments, we applied the proposed method to three synthetic data and Alzheimer's disease neuroimaing initiative (ADNI) database. Experimental results showed that the coidentification of group-level hole structures helped to find the underlying topology information of brain networks that discriminate groups well.

关键词： Hole structure Group analysis Hodge Laplacian Stiefel optimization ADNI

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Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images 22nd

Annotation-Free Cardiac Vessel Segmentation via Knowledge Tr...

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Yu, Fei Zhao, Jie Gong, Yanjun Wang, Zhi Li, Yuxi Yang, Fan Dong, Bin Li, Quanzheng Zhang, Li Peking Univ Ctr Data Sci Beijing 100871 Peoples R China Peking Univ Ctr Data Sci Hlth & Med Beijing 100871 Peoples R China Peking Univ First Hosp Dept Cardiol Beijing 100034 Peoples R China Peking Univ Beijing Int Ctr Math Res BICMR Beijing 100871 Peoples R China MGH BWH Ctr Clin Data Sci Boston MA 02115 USA

ISBN: (纸本)9783030322458;9783030322441

Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible. To solve this problem, we propose a knowledge transfer based shape-consistent generative adversarial network (SC-GAN), which is an annotation-free approach that uses the knowledge from publicly available annotated fundus dataset to segment coronary arteries. the proposed network is trained in an end-to-end fashion, generating and segmenting synthetic images that maintain the background of coronary angiography and preserve the vascular structures of retinal vessels and coronary arteries. We train and evaluate the proposed model on a dataset of 1092 digital subtraction angiography images, and experiments demonstrate the supreme accuracy of the proposed method on coronary arteries segmentation.

关键词： Coronary artery segmentation Knowledge transfer Generative adversarial network Deep learning

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Advanced treatment and care for patients receiving Ventricular Assist Device (VAD) therapy through efficient monitoring and intelligent decision support algorithms

Advanced treatment and care for patients receiving Ventricul...

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international workshop on Biomedical Engineering

作者： Evaggelos C. Karvounis Nikolaos S. Katertsidis themis P. Exarchos Dimitrios I. Fotiadis Biomedical Research Institute-FORTH Ioannina Greece

the scope of this paper is to present in detail the Specialist's main components of the SensorART platform, specifically the Monitoring Application and the Decision Support System (SDSS). the former provides to the specialists tele-monitoring and tele-controlling functionalities, while the latter assists the specialists on deciding the best treatment strategy for a specific patient.

关键词： Monitoring Medical treatment Heart Decision support systems Biomedical monitoring data mining Prototypes

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Index structures for information filtering under the vector space model

Index structures for information filtering under the vector ...

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international Conference on data Engineering

作者： T.W. Yan H. Garcia-Molina Department of Computer Science University of Stanford Stanford CA USA

the authors study what data structures and algorithms can be used to efficiently perform large-scale information filtering under the vector space model, a retrieval model established as being effective. they apply the idea of the standard inverted index to index user profiles. they devise an alternative to the standard inverted index, in which they, instead of indexing every term in a profile, select only the significant ones to index. they evaluate their performance and show that the indexing methods require orders of magnitude fewer I/Os to process a document than when no index is used. they also show that the proposed alternative performs better in terms of I/O and CPU processing time in many cases.< >

关键词： Information filtering Information filters data structures Information retrieval Large-scale systems Filtering algorithms Network servers Computer science Information systems databases

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Combined model-based and machine learning approach for damage identification in bridge type structures 10

Combined model-based and machine learning approach for damag...

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10th international Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021

作者： Fernández-Navamuel, Ana Zamora-Sánchez, Diego Armijo-Prieto, Alberto Varona-Poncela, Tomás García-Sánchez, David García-Villena, Francisco Ruiz-Cuenca, Francisco Mikeletegi Pasealekua 2 San Sebastián20009 Spain Bilbao Spain Leioa Spain CEMOSA INGENIERIA Y CONTROL SA Calle Benaque 9 Málaga29004 Spain MAGTEL OPERACIONES SL Calle Gabriel Ramos Bejarano 114 Córdoba14014 Spain

In this work, we propose a combined approach of model-based and machine learning techniques for damage identification in bridge structures. First, a finite element model is calibrated with real data from experimental vibration modes for the undamaged or baseline state. Second, generic synthetic damage scenarios based on modal parameters are automatically generated with the model to train machine learning algorithms for damage classification (Support Vector Machine, SVM) and damage location and quantification (Neural Network, NN). For an initial validation of the method we use a lab scale truss bridge model, proving that specific damage scenarios can be assessed by the Supervised Machine Learning algorithms trained with generic damage scenarios including a certain variability. the NN provides an assessment in terms of damage location and quantification, whereas the SVM provides a damage severity classification with graphical indication of the damage location and quantification through a reduced dimension plot. © 2021 international Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Structural health monitoring

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Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation 10th

Unsupervised Multi-modal Style Transfer for Cardiac MR Segme...

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10th international workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM)

作者： Chen, Chen Ouyang, Cheng Tarroni, Giacomo Schlemper, Jo Qiu, Huaqi Bai, Wenjia Rueckert, Daniel Imperial Coll London Biomed Image Anal Grp London England Imperial Coll London Data Sci Inst London England Imperial Coll London Dept Med London England

ISBN: (纸本)9783030390747;9783030390730

In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. the multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. this set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.

关键词： Image segmentation

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Use of Deep Learning Techniques for Damage Localization in Aeronautical Composite structures 10th

Use of Deep Learning Techniques for Damage Localization in A...

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10th European workshop on Structural Health Monitoring (EWSHM)

作者： Azuara, Guillermo Ruiz, Mariano Barrera, Eduardo Cui, Ranting di Scalea, Francesco Lanza Univ Politecn Madrid Instrumentat & Appl Acoust Res Grp C Nikola Tesla S-N Madrid 28031 Spain Univ Utah Dept Civil & Environm Engn 110 Cent Campus Dr116 CME Salt Lake City UT 84112 USA Univ Calif San Diego Expt Mech NDE & SHM Lab Dept Struct Engn 9500 Gilman DrMC 0085 La Jolla CA 92093 USA

ISBN: (纸本)9783031073229;9783031073212

Damage localization is one of the most challenging topics within Structural Health Monitoring (SHM) in aeronautics, especially when the structure is manufactured out of carbon fiber-reinforced composite materials. Using ultrasonic guided waves (particularly Lamb waves), generated and recorded with piezoelectric transducers, is also challenging in this type of material. Otherwise, traditional methods used for this task are subjected to physics-based knowledge of the problem, such as damage imaging algorithms like delay-and-sum and RAPID. this paper presents an entirely data-driven approach, based on the ability of Deep Learning (DL) techniques (particularly those based on Convolutional Neural Networks - CNNs -) to extract features of interest for damage imaging from a pre-dataset. In this work, the selected feature to be estimated is the normal distance from the propagation path of the guided wave to a simulated damage, which allows, in combination with an especially designed positioning algorithm, to locate with high accuracy defects, even in different positions than the used for the training of the network (a fixed grid of points over the analysis zone). this paper presents the application of the method to a real composite material specimen, as well as the recorded results obtained from additional datasets recorded with the simulated damage (a piece of blutack) attached to different random positions other than those of the training grid.

关键词： Structural Health Monitoring Ultrasonic guided waves Damage imaging Deep learning Neural network

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EMatch: Discovery of high resolution structural homologues of protein domains in intermediate resolution cryo-EM maps

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007年第1期4卷 28-39页

作者： Lasker, Keren Dror, Oranit Shatsky, Maxim Nussinov, Ruth Wolfson, Haim J. Tel Aviv Univ Raymond & Beverly Sackler Fac Exact Sci Sch Comp Sci IL-69978 Tel Aviv Israel Tel Aviv Univ Sackler Fac Med Dept Human Genet & Mol Med IL-69978 Tel Aviv Israel NCI Frederick Canc Res & Dev Ctr Ctr Canc Res Nanobiol Program SAIC FrederickBasic Res Program Frederick MD 21702 USA

Cryo-EM has become an increasingly powerful technique for elucidating the structure, dynamics, and function of large flexible macromolecule assemblies that cannot be determined at atomic resolution. However, due to the relatively low resolution of cryo-EIM data, a major challenge is to identify components of complexes appearing in cryo-EM maps. Here, we describe EMatch, a novel integrated approach for recognizing structural homologues;of protein domains present in a 6-10 angstrom resolution cryo-EM map and constructing a quasi-atomic structural model of their assembly. the method is highly efficient and has been successfully validated on various simulated data. the strength of the method is demonstrated by a domain assembly of an experimental cryo-EM map of native GroEL at 6 angstrom resolution.

关键词： structural bioinformatics intermediate resolution cryo-EM maps 3D alignment of secondary structures macromolecular assemblies cyclic symmetry

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10th international Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2024

10th International Work-Conference on the Interplay Between ...

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10th international Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2024

ISBN: (纸本)9783031611391

the proceedings contain 99 papers. the special focus in this conference is on Interplay Between Natural and Artificial Computation. the topics include: Improved Surface Defect Classification from a Simple Convolutional Neural Network by Image Preprocessing and data Augmentation;prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis;enhanced Cellular Detection Using Convolutional Neural Networks and Sliding Window Super-Resolution Inference;exploring Text-Driven Approaches for Online Action Detection;deep Learning for Assistive Decision-Making in Robot-Aided Rehabilitation therapy;text-Driven data Augmentation Tool for Synthetic Bird Behavioural Generation;deep Learning for Enhanced Risk Assessment in Home Environments;Lightweight CNNs for Advanced Bird Species Recognition on the Edge;learning Adaptable Utility Models for Morphological Diversity;deep Learning-Based Classification of Invasive Coronary Angiographies with Different Patch-Generation Techniques;Refinement of Protein structures with a Memetic Algorithm. Examples with SARS-CoV-2 Proteins;evolutionary algorithms for Bin Packing Problem with Maximum Lateness and Waste Minimization;Stationary Wavelet Entropy and Cat Swarm Optimization to Detect COVID-19;private Inference on Layered Spiking Neural P Systems;cooperative Multi-fitness Evolutionary Algorithm for Scientific Workflows Scheduling;a Genetic Approach to Green Flexible Job Shop Problem Under Uncertainty;AI Emmbedded in Drone Control;dual-System Recommendation Architecture for Adaptive Reading Intervention Platform for Dyslexic Learners;Accurate LiDAR-Based Semantic Classification for Powerline Inspection;RESISTO Project: Automatic Detection of Operation Temperature Anomalies for Power Electric Transformers Using thermal Imaging;RESISTO Project: Safeguarding the Power Grid from Meteorological Phenomena;Multi-UAV System for Power-Line Failure Detection Within the RESISTO Project;machin

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