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        Advanced signal processing for enhanced damage detection with piezoelectric wafer active sensors

        Yu, Lingyu,Giurgiutiu, Victor Techno-Press 2005 Smart Structures and Systems, An International Jou Vol.1 No.2

        Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

      • KCI등재

        Tight Focusing Characteristics of Circularly Polarized Bessel-Gauss Beams with Fractional-order Vortex Modulation

        Lingyu Wang,Yu Miao,Mingzhu Xu,Xiumin Gao 한국광학회 2023 Current Optics and Photonics Vol.7 No.2

        Radially polarized beams with the ability to generate a sub-wavelength sized spot in a longitudinal field provides significant applications in microscopic imaging, optical tweezers, lithography and so on. However, this excellent property can also be achieved based on conventional circularly polarized beams. Here, we demonstrate its ability to create a strong longitudinal field by comparing the tight focusing characteristics of fractional-order vortex modulated radial polarized and left-handed circular polarized Bessel-Gauss beams. Additionally, the possibility of generating arbitrary fractional-order vortex modulated Bessel-Gauss beams with a strong longitudinal field is demonstrated. A special modulation method of left-handed circularly polarized Bessel-Gauss beams modulated by a fractional-order vortex is adopted creatively and a series of regulation laws are obtained. Specifically, the fractional-order phase modulation parameter n can accurately control the number of optical lobes. The ratio of the pupil radius to the incident beam waist β 1 can control the radius of the optical lobes. The first-order Bessel function amplitude modulation parameter β 2 can control the number of layers of optical lobes. This work not only adds a new modulation method for optical micromanipulation and optical communication, but also enriches the research on fractional vortex beams which has very important academic significance.

      • KCI등재후보

        Advanced signal processing for enhanced damage detection with piezoelectric wafer active sensors

        Victor Giurgiutiu,Lingyu Yu 국제구조공학회 2005 Smart Structures and Systems, An International Jou Vol.1 No.2

        Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal? frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

      • KCI등재

        Attentional Connectivity-based Prediction of Autism Using Heterogeneous rs-fMRI Data from CC200 Atlas

        Yaya Liu,Lingyu Xu,Jun Li,Jie Yu,Xuan Yu 한국뇌신경과학회 2020 Experimental Neurobiology Vol.29 No.1

        Autism spectrum disorder (ASD) is a developmental syndrome characterized by obvious drawbacks in sociality and communication. It has crucial significance to exactly discern the individuals with ASD and typical controls (TC). Previous imaging studies on ASD/TC identification have made remarkable progress in the exploration of objective as well as crucial biomarkers associated with ASD. However, glaring deficiency is manifested by the investigation on solely homogeneous and small datasets. Thus, we attempted to unveil some replicable and robust neural patterns of autism using a heterogeneous multi-site brain imaging dataset from ABIDE (Autism Brain Imaging Data Exchange). Experiments were carried out with an attention mechanism based on Extra-Trees algorithm, taking the study object of brain connectivity measured with the resting-state functional magnetic resonance imaging (fMRI) data of CC200 atlas. With cross-validation strategy, our proposed method resulted in a mean classification accuracy of 72.2% (sensitivity=68.6%, specificity=75.4%). It raised the precision of ASD prediction by about 2% and specificity by 3.2% in comparison with the most competitive reported effort. Connectivity analysis on the optimal model highlighted informative regions strongly involved in the social cognition as well as interaction, and manifested lower correlation between the anterior and posterior default mode network (DMN) in autistic individuals than controls. This observation is concordant with previous studies, which enables our proposed method to effectively identify the individuals with risk of ASD.

      • KCI등재

        Assessment of temperature effect in structural health monitoring with piezoelectric wafer active sensors

        Tuncay Kamas,Banibrata Poddar,Bin Lin,Lingyu Yu 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.16 No.5

        This paper presents theoretical and experimental evaluation of the structural health monitoring (SHM) capability of piezoelectric wafer active sensors (PWAS) at elevated temperatures. This is important because the technologies for structural sensing and monitoring need to account for the thermal effect and compensate for it. Permanently installed PWAS transducers have been One of the extensively employed sensor technologies for in-situ continuous SHM. In this paper, the electro-mechanical impedance spectroscopy (EMIS) method has been utilized as a dynamic descriptor of PWAS behavior and as a high frequency standing wave local modal technique. Another SHM technology utilizes PWAS as far-field transient transducers to excite and detect guided waves propagating through the structure. This paper first presents how the EMIS method is used to qualify and quantify circular PWAS resonators in an increasing temperature environment up to 230 deg C. The piezoelectric material degradation with temperature was investigated and trends of variation with temperature were deduced from experimental measurements. These effects were introduced in a wave propagation simulation software called Wave Form Revealer (WFR). The thermal effects on the substrate material were also considered. Thus, the changes in the propagating guided wave signal at various temperatures could be simulated. The paper ends with summary and conclusions followed by suggestions for further work.

      • KCI등재

        Nonlinear damage detection using higher statistical moments of structural responses

        Ling Yu,Jun-Hua Zhu 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2

        An integrated method is proposed for structural nonlinear damage detection based on time seriesanalysis and the higher statistical moments of structural responses in this study. It combines the time seriesanalysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clusteringtechniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean ofthe higher statistical moments, and are classified by using the FCM clustering method to achieve nonlineardamage detection. A series of the measured response data, downloaded from the web site of the Los AlamosNational Laboratory (LANL) USA, from a three-storey building structure considering the environmentalvariety as well as different nonlinear damage cases, are analyzed and used to assess the performance of thenew nonlinear damage detection method. The effectiveness and robustness of the new proposed method arefinally analyzed and concluded.

      • KCI등재
      • KCI등재

        A MOM-based algorithm for moving force identification: Part II – Experiment and comparative studies

        Ling Yu,Tommy H.T. Chan,Jun-hua Zhu 국제구조공학회 2008 Structural Engineering and Mechanics, An Int'l Jou Vol.29 No.2

        A MOM-based algorithm (MOMA) has been developed for moving force identification from dynamic responses of bridge in the companion paper. This paper further evaluates and investigates the properties of the developed MOMA by experiment in laboratory. A simply supported bridge model and a few vehicle models were designed and constructed in laboratory. A series of experiments have then been conducted for moving force identification. The bending moment and acceleration responses at several measurement stations of the bridge model are simultaneously measured when the model vehicle moves across the bridge deck at different speeds. In order to compare with the existing time domain method (TDM), the best method for moving force identification to date, a carefully comparative study scheme was planned and conducted, which includes considering the effect of a few main parameters, such as basis function terms, mode number involved in the identification calculation, measurement stations, executive CPU time, Nyquist fraction of digital filter, and two different solutions to the ill-posed system equation of moving force identification. It was observed that the MOMA has many good properties same as the TDM, but its CPU execution time is just less than one tenth of the TDM, which indicates an achievement in which the MOMA can be used directly for real-time analysis of moving force identification in field.

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