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Damage detection and localization in Pipeline Using Sparse Estimation of Ultrasonic Guided Waves Signals

IFAC-PapersOnLine 2018年第24期51卷 941-948页

作者： Mountassir, Mahjoub El Mourot, Gilles Yaacoubi, Slah Maquin, Didier Institut de soudure Plateforme RDI CND 4 Bvd Henri BecquerelYutz57970 France Centre de Recherche en Automatique de Nancy CNRS Université de Lorraine 2 Avenue de la Forêt de Haye TSA 60604 54 518 France

This paper focuses on the development of a method for damage detection and localization in pipeline structures. These structures are subject to variation of environmental and operational conditions (EOCs) which have an impact on the collected signals. Since damage detection is generally based on comparison between the reference signals and the current signals acquired from the structure, the effects of EOCs will give rise to false alarm. This issue is addressed by selecting from the database of reference signals those with similar or very close EOCs. Such an operation can be performed by calculating a sparse estimation of the current signal. The estimation error is used as an indication of the presence of damage. Actually, a damage signal will be characterized by a high estimation error compared to that of a healthy signal. The position of the detected damage is obtained by calculating the estimation error on a sliding window over the damaged signal. This method was tested on signals collected on a small scale pipeline placed in laboratory conditions. Results have shown that the created damage was successfully detected and localized. © 2018

关键词： Damage detection Errors Guided electromagnetic wave propagation Pipelines Signal processing Structural health monitoring Ultrasonic applications detection and localization environmental Laboratory conditions localization Operational conditions Pipeline structure Sparse estimation Ultrasonic guided wave

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Hierarchical Progressive Image Forgery detection and localization Method Based on UNet

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BIG DATA AND COGNITIVE COMPUTING 2024年第9期8卷 119页

作者： Liu, Yang Li, Xiaofei Zhang, Jun Li, Shuohao Hu, Shengze Lei, Jun Natl Univ Def Technol Lab Big Data & Decis Changsha 410000 Peoples R China

The rapid development of generative technologies has made the production of forged products easier, and AI-generated forged images are increasingly difficult to accurately detect, posing serious privacy risks and cognitive obstacles to individuals and society. Therefore, constructing an effective method that can accurately detect and locate forged regions has become an important task. This paper proposes a hierarchical and progressive forged image detection and localization method called HPUNet. This method assigns more reasonable hierarchical multi-level labels to the dataset as supervisory information at different levels, following cognitive laws. Secondly, multiple types of features are extracted from AI-generated images for detection and localization, and the detection and localization results are combined to enhance the task-relevant features. Subsequently, HPUNet expands the obtained image features into four different resolutions and performs detection and localization at different levels in a coarse-to-fine cognitive order. To address the limited feature field of view caused by inconsistent forgery sizes, we employ three sets of densely cross-connected hierarchical networks for sufficient interaction between feature images at different resolutions. Finally, a UNet network with a soft-threshold-constrained feature enhancement module is used to achieve detection and localization at different scales, and the reliance on a progressive mechanism establishes relationships between different branches. We use ACC and F1 as evaluation metrics, and extensive experiments on our method and the baseline methods demonstrate the effectiveness of our approach.

关键词： computer vision fake images detection and localization hierarchical network UNet

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detection and localization of debonding beneath concrete pavement using transmissibility function analysis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 159卷 107802-107802页

作者： Wu, Difei Zeng, Mengyuan Zhao, Hongduo Wang, Yang Du, Yuchuan Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai Peoples R China Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

This paper proposes a novel method to detect and localize debonding beneath the concrete pavement using transmissibility function (TF) analysis. The layout of the measurement node array, the signal processing approach, and the testing procedures are proposed. Two matrices of transmissibility damage indicator (TDI) are proposed to describe the distribution of debonding. This method is a response-only approach and can offer advantages in easy and low-cost implementation. The theoretical deduction, finite element analysis (FEA), and field tests are conducted to demonstrate and verify detection and localization performance. It is first observed that the arrangement of measurement nodes and the frequency range are crucial to TF analysis. TFs at higher frequencies range are more sensitive to the debonding but may result in higher errors. The density of the measurement node array and the noise's effect are investigated in the FEA. Using a denser node array and maintaining a lower noise level helps localize the debonding more precisely. Field tests are conducted on a precast concrete pavement plate using a specially designed accelerometer array. The test results demonstrate the feasibility of the TF-based method for debonding detection and localization. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Concrete pavement Debonding Transmissibility function Transmissibility damage indicator detection and localization

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A 3D Simulation Model for Experimental Analysis from Moving Target detection and localization using UWB Radar System 6

A 3D Simulation Model for Experimental Analysis from Moving ...

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6th International Conference on Computational Intelligence and Communication Networks

作者： Nguyen, N. T. Minh Int Univ VNU HCMC Sch Elect Engn Ho Chi Minh City Vietnam

ISBN: (纸本)9781479969296

In this paper, we propose a 3D simulation model to characterize the scattered far-field by a scenario. The scenario is composed of a metallic plate representing wall and a trihedral, which is located behind the wall in non-line-of-sight (NLOS) areas. This model is based on a ray-tracing (RT) technique combined with the asymptotic method Uniform Theory of Diffraction (UTD). Two ultra-wideband (UWB) radars are configured as transmitter and receiver, able to work in bistatic or monostatic mode. The modeling is useful not only in explicating the mechanisms of wave propagation through the observed area, but also in evaluating the amplitude and the phase of any given point in the far-zone above the ground. Furthermore, our model is tested against 33-37GHz (Ka-band) indoor measurements conducted in the anechoic chamber. These comparisons have shown that the model gives a good agreement with the experimental results.

关键词： detection and localization moving target simulation model ultra-wideband

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Deep Faster R-CNN-based automated detection and localization of multiple types of damage

Deep Faster R-CNN-based automated detection and localization...

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Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Suh, Gahyun Cha, Young-Jin Univ Manitoba Dept Civil Engn Winnipeg MB Canada

ISBN: (数字)9781510616936

ISBN: (纸本)9781510616936

The primary method of structural health monitoring is human-based visual inspection, which-despite its limitations of consistency and accessibility-can warn about changes in a bridge's condition. To improve the visual inspection of civil infrastructure and address these drawbacks of human-oriented inspection, computer vision-based techniques have been developed to detect structural damage in images. Most of these methods, however, detect only specific types of damage, such as cracks in concrete or steel. Another drawback is that the traditional convolutional neural network-based damage detection method is not able to provide the location of the detected damage. To provide quasi-realtime simultaneous detection and localization of multiple types of damage, a structural damage detection method based on Faster Region-based Convolutional Neural Network (Faster R-CNN) is proposed(1). The original architecture of Faster R-CNN is modified, trained, validated, and tested for this study. The robustness of the trained Faster R-CNN is evaluated and demonstrated using seven new images taken of various structures.

关键词： Computer vision Automated monitoring Non-contact Deep learning Convolutional neural network Region-based network detection and localization

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Subspace-Based detection and localization in Distributed MIMO Radars 12

Subspace-Based Detection and Localization in Distributed MIM...

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IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Lai, Yangming Venturino, Luca Grossi, Emanuele Yi, Wei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Univ Cassino & Southern Lazio Dept Elect & Informat Engn Cassino Italy Natl Interunivs Consortium Telecommun Parma Italy

ISBN: (纸本)9781665406338

In this paper, we consider a distributed multipleinput multiple-output (MIMO) radar which radiates waveforms with non-ideal cross- and auto-correlation functions and derive a novel subspace-based procedure to detect and localize multiple prospective targets. The proposed solution solves a sequence of composite binary hypothesis testing problems by resorting to the generalized information criterion (GIC);in particular, at each step, it aims to detect and localize one additional target, upon removing the interference caused by the previously-detected targets. An illustrative example is provided.

关键词： detection and localization distributed MIMO radar generalized information criterion non-ideal correlation.

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SPECT image system optimization using ideal observer for detection and localization - art. no. 69171D

SPECT image system optimization using ideal observer for det...

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Medical Imaging 2008 Conference

作者： Zhou, Lili Gindi, Gene SUNY Stony Brook Dept Radiol Stony Brook NY 11794 USA

ISBN: (纸本)9780819471017

We consider the problem of optimizing collimator characteristics for a simple emission tomographic imaging system. We use the performance of two different ideal observers to carry out the optimization. The first ideal observer applies to signal detection when signal location is unknown and background is variable, and the second ideal observer (one proposed previously by our group) to the more realistic task of signal detection and localization with signal location unknown and background variable. The two observers operate on sinogram data to deliver scalar figures of merit AROC and ALROC, respectively. We considered three different collimators that span a range of efficiency-resolution tradeoffs. Our central question is this: For optimizing the collimator in an emission tomographic system, does adding a localization requirement to a detection task yield an efficiency-resolution tradeoff that differs from that for the detection-only task? Our simulations with a simple SPECT imaging system show that as the localization requirement becomes more stringent, the optimal collimator shifts from a low-resolution, high efficiency version toward higher resolution, lower efficiency version. We had previously observed such behavior for a planar pinhole imaging system. In our simulations, we used a simplified model of tornographic imaging and a simple model for object background variability. This allowed us to avoid the severe computational complexity associated with ideal-observer performance calculations. Thus the more realistic task (i.e. localization included) resulted for this case in a different optimal collimator.

关键词： ideal observer ALROC AROC SPECT detection and localization

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Optimization of pinhole imaging system using ideal observer for detection and localization

Optimization of pinhole imaging system using ideal observer ...

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IEEE Nuclear Science Symposium/Medical Imaging Conference

作者： Zhou, Lili Khurd, Parmeshwar Kulkarni, Santosh Rangarajan, Anand Gindi, Gene SUNY Stony Brook Dept Radiol Stony Brook NY 11790 USA Philips Med Syst Highland Hts OH 44143 USA SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY USA Univ Florida Dept C I S E Gainesville FL 32611 USA SUNY Stony Brook Dept ELEC Stony Brook NY 11790 USA SUNY Stony Brook Dept Comp Engn Stony Brook NY 11790 USA SUNY Stony Brook Dept Radiol Stony Brook NY 11790 USA

ISBN: (纸本)9781424409228

For the familiar 2-class detection problem (signal present/absent), ideal observers have been applied to optimization of pinhole and collimator parameters in planar emission imaging. Given photon noise and background and signal variability, such experiments show how to optimize an aperture to maximize detectability of the signal. Here, we consider a fundamentally different, more realistic task in which the observer is required to both detect and localize a signal. The signal is embedded in a variable background and is known except for location. We inquire whether the addition of a localization requirement changes conclusions on aperture optimization. We have previously formulated an ideal observer for this joint detection/localization task, and here apply it to the classic problem of determining an optimal pinhole diameter in a planar emission imaging system. We conclude that as search tolerance on localization decreases, the optimal pinhole diameter shrinks from that required by detection alone, and in addition, task performance becomes more sensitive to fluctuations about the optimal pinhole diameter. As in the case for detection only, the optimal pinhole diameter shrinks as the amount of background variability grows, and in addition, conspicuity limits can be observed. Unlike the case for detection only, our task leads to a finite aperture size in the absence of background variability. For both tasks, the inclusion of background variability yields a finite aperture size.

关键词： ideal observer LROC ROC planar emission imaging pinhole detection and localization

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Review on detection and localization of underwater target

Review on detection and localization of underwater target

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2nd International Conference on Civil Engineering, Architecture and Building Materials (CEABM 2012)

作者： Yang Guang Zheng Yi Wang Shitao Xiang Xu Yu Yanting Shandong Prov Key Lab Ocean Environm Monitoring T Qingdao 266001 Peoples R China Shandong Acad Sci Inst Oceanograph Instrumentat Qingdao 266001 Peoples R China

ISBN: (纸本)9783037854228

Europe, the United States and China have conducted a lot of research on the detection and localization of underwater target. The United States, the typical representative among Europe and America, pays much attention to marine research and has a profound foundation. In China, Harbin Engineering University, Institute of Acoustics of Chinese Academy of Sciences and Northwestern Polytechnical University also have done a lot of research on target detection and localization. Overall, the United States of America and other developed countries temporarily precede China in underwater target detection and localization.

关键词： Underwater Target detection and localization Research Status

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Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

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Global Energy Interconnection 2024年第3期7卷 362-376页

作者： Xinyu Wang Xiangjie Wang Xiaoyuan Luo Xinping Guan Shuzheng Wang School of Electrical Engineering Yanshan UniversityQinhuangdao066004P.R.China School of Electrical Engineering Jiangsu Collaborative Innovation Center for Smart Distribution NetworkNanjing636600P.R.China School of Electronic and Electrical Engineering Shanghai Jiaotong UniversityShanghai200240P.R.China

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power ***,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy ***,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy *** proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical ***,a data-driven method was proposed to predict the DLAA sequence in the cyber *** designing a double radial basis function network,the influence of disturbances on attack prediction can be *** on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical *** addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the ***,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and *** with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

关键词： Smart energy grids Cyber-physical system Dynamic load altering attacks Attack prediction detection and localization

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