The Experts below are selected from a list of 67161 Experts worldwide ranked by ideXlab platform
Jinkyu Yang - One of the best experts on this subject based on the ideXlab platform.
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Visualization of Stress Wave propagation via air-coupled acoustic emission sensors
Smart Materials and Structures, 2017Co-Authors: Joshua C. Rivey, Jinkyu YangAbstract:We experimentally demonstrate the feasibility of visualizing Stress Waves propagating in plates using air-coupled acoustic emission sensors. Specifically, we employ a device that embeds arrays of microphones around an optical lens in a helical pattern. By implementing a beamforming technique, this remote sensing system allows us to record Wave propagation events in situ via a single-shot and full-field measurement. This is a significant improvement over the conventional Wave propagation tracking approaches based on laser doppler vibrometry or digital image correlation techniques. In this paper, we focus on demonstrating the feasibility and efficacy of this air-coupled acoustic emission technique by using large metallic plates exposed to external impacts. The visualization results of Stress Wave propagation will be shown under various impact scenarios. The proposed technique can be used to characterize and localize damage by detecting the attenuation, reflection, and scattering of Stress Waves that occurs at damage locations. This can ultimately lead to the development of new structural health monitoring and nondestructive evaluation methods for identifying hidden cracks or delaminations in metallic or composite plate structures, simultaneously negating the need for mounted contact sensors.
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Stress Wave isolation by purely mechanical topological phononic crystals
Scientific Reports, 2016Co-Authors: Rajesh Chaunsali, Feng Li, Jinkyu YangAbstract:We present an active, purely mechanical Stress Wave isolator that consists of short cylindrical particles arranged in a helical architecture. This phononic structure allows us to change inter-particle stiffness dynamically by controlling the contact angles of the cylinders. We use torsional travelling Waves to control the contact angles, thereby imposing a desired spatio-temporal stiffness variation to the phononic crystal along the longitudinal direction. Such torsional excitation is a form of parametric pumping in the system, which results in the breakage of the time-reversal symmetry. We report that, in quasi-static sense, the system shows topologically non-trivial band-gaps. However, in a dynamic regime where the pumping effect is significant, these band-gaps become asymmetric with respect to the frequency and Wavenumber domains in the dispersion relationship. By using numerical simulations, we show that such asymmetry has a direct correspondence to the topological invariant, i.e., Chern number, of the system. We propose that this asymmetry, accompanied by selective inter-band transition, can be utilized for directional isolation of the Stress Wave propagating along the phononic crystal.
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visualization of Stress Wave propagation via air coupled acoustic emission sensors
arXiv: Pattern Formation and Solitons, 2016Co-Authors: Joshua C. Rivey, Jinkyu Yang, Gilyong Lee, Youngkey Kim, Sungchan KimAbstract:We experimentally demonstrate the feasibility of visualizing Stress Waves propagating in plates using air-coupled acoustic emission sensors. Specifically, we employ a device that embeds arrays of microphones around an optical lens in a helical pattern. By implementing a beamforming technique, this remote sensing system allows us to record Wave propagation events in situ via a single-shot and full-field measurement. This is a significant improvement over the conventional Wave propagation tracking approaches based on laser doppler vibrometry or digital image correlation techniques. In this paper, we focus on demonstrating the feasibility and efficacy of this air-coupled acoustic emission technique using large metallic plates exposed to external impacts. The visualization results of Stress Wave propagation will be shown under various impact scenarios. Such Wave visualization capability is of tremendous importance from a structural health monitoring and nondestructive evaluation (SHM/NDE) standpoint. The proposed technique can be used to characterize and localize damage by detecting the attenuation, reflection, and scattering of Stress Waves that occurs at damage locations. This can ultimately lead to the development of new SHM/NDE methods for identifying hidden cracks or delaminations in metallic or composite plate structures simultaneously negating the need for mounted contact sensors.
Hailin Feng - One of the best experts on this subject based on the ideXlab platform.
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Stress Wave velocity patterns in the longitudinal radial plane of trees for defect diagnosis
Computers and Electronics in Agriculture, 2016Co-Authors: Xiang Weng, Xiping Wang, Hailin FengAbstract:We developed an analytical model of Stress Wave propagation velocity in the longitudinal-radial (LR) plane of sound healthy trees.The presented Stress Wave velocity model was verified by the experiments in the LR planes of live trees of different species.The Stress Wave velocities show different characters in live trees and logs with decay.The presented Stress Wave velocity model can be used to diagnosis the internal defects in urban trees and improve the accuracy of 3D tomographic images in tree inspection applications. Acoustic tomography for urban tree inspection typically uses Stress Wave data to reconstruct tomographic images for the trunk cross section using interpolation algorithm. This traditional technique does not take into account the Stress Wave velocity patterns along tree height. In this study, we proposed an analytical model for the Wave velocity in the longitudinal-radial (LR) plane of a live tree. Both field and laboratory Stress Wave testing were conducted to determine the Stress Wave velocity patterns in healthy and defective trees. The results showed that the ratio of the Wave velocity at a propagation path angle ? (with respect to the radial direction) to the radial velocity in healthy trees approximated a second-order parabolic curve with respect to the symmetric axis (?=0). Our analysis of the velocity patterns indicated that the measured velocities in healthy trees were in a good agreement with the theoretical models. The results of this preliminary study indicated that the Stress Wave velocity patterns can be used to diagnosis internal defects in urban trees and improve the accuracy of 3D tomographic images in tree inspection applications.
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Stress Wave tomography of wood internal defects using ellipse based spatial interpolation and velocity compensation
Bioresources, 2015Co-Authors: Hailin Feng, Shengyong ChenAbstract:In this paper, a novel Stress Wave tomography method, using spatial interpolation and velocity compensation, is proposed for the detection of internal defects in wood, based on the measured time of flight data and the assumption that Stress Waves propagate in straight lines in the cross-sectional area of wood. First, an improved ellipse-based spatial interpolation method is proposed, which could be used to estimate the velocity value of a grid cell by the elliptic affected zones corresponding to the nearby velocity rays. Second, because of the anisotropic property of wood, a velocity compensation method was applied to obtain more accurate input data for spatial interpolation. Then, the internal graph of the cross-section of a wood trunk could be reconstructed by the proposed algorithm. Four wood samples, with different defects, were used to test the proposed tomography method in the experiment. The results showed that the proposed method performed well and was able to resist signal interference caused by the density variation of the defective area.
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Stress Wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin FengAbstract:Stress-Wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary Stress Wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to Stress Wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The Waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the Waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real Stress Wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered Stress Waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
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Stress Wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin Feng, Jian Li, Guanghui LiAbstract:Stress-Wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary Stress Wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to Stress Wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The Waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the Waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real Stress Wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered Stress Waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
Yiming Fang - One of the best experts on this subject based on the ideXlab platform.
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Stress Wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin FengAbstract:Stress-Wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary Stress Wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to Stress Wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The Waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the Waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real Stress Wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered Stress Waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
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Stress Wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin Feng, Jian Li, Guanghui LiAbstract:Stress-Wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary Stress Wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to Stress Wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The Waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the Waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real Stress Wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered Stress Waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
Guanghui Li - One of the best experts on this subject based on the ideXlab platform.
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Stress Wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin Feng, Jian Li, Guanghui LiAbstract:Stress-Wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary Stress Wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to Stress Wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The Waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the Waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real Stress Wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered Stress Waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
Joshua C. Rivey - One of the best experts on this subject based on the ideXlab platform.
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Visualization of Stress Wave propagation via air-coupled acoustic emission sensors
Smart Materials and Structures, 2017Co-Authors: Joshua C. Rivey, Jinkyu YangAbstract:We experimentally demonstrate the feasibility of visualizing Stress Waves propagating in plates using air-coupled acoustic emission sensors. Specifically, we employ a device that embeds arrays of microphones around an optical lens in a helical pattern. By implementing a beamforming technique, this remote sensing system allows us to record Wave propagation events in situ via a single-shot and full-field measurement. This is a significant improvement over the conventional Wave propagation tracking approaches based on laser doppler vibrometry or digital image correlation techniques. In this paper, we focus on demonstrating the feasibility and efficacy of this air-coupled acoustic emission technique by using large metallic plates exposed to external impacts. The visualization results of Stress Wave propagation will be shown under various impact scenarios. The proposed technique can be used to characterize and localize damage by detecting the attenuation, reflection, and scattering of Stress Waves that occurs at damage locations. This can ultimately lead to the development of new structural health monitoring and nondestructive evaluation methods for identifying hidden cracks or delaminations in metallic or composite plate structures, simultaneously negating the need for mounted contact sensors.
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visualization of Stress Wave propagation via air coupled acoustic emission sensors
arXiv: Pattern Formation and Solitons, 2016Co-Authors: Joshua C. Rivey, Jinkyu Yang, Gilyong Lee, Youngkey Kim, Sungchan KimAbstract:We experimentally demonstrate the feasibility of visualizing Stress Waves propagating in plates using air-coupled acoustic emission sensors. Specifically, we employ a device that embeds arrays of microphones around an optical lens in a helical pattern. By implementing a beamforming technique, this remote sensing system allows us to record Wave propagation events in situ via a single-shot and full-field measurement. This is a significant improvement over the conventional Wave propagation tracking approaches based on laser doppler vibrometry or digital image correlation techniques. In this paper, we focus on demonstrating the feasibility and efficacy of this air-coupled acoustic emission technique using large metallic plates exposed to external impacts. The visualization results of Stress Wave propagation will be shown under various impact scenarios. Such Wave visualization capability is of tremendous importance from a structural health monitoring and nondestructive evaluation (SHM/NDE) standpoint. The proposed technique can be used to characterize and localize damage by detecting the attenuation, reflection, and scattering of Stress Waves that occurs at damage locations. This can ultimately lead to the development of new SHM/NDE methods for identifying hidden cracks or delaminations in metallic or composite plate structures simultaneously negating the need for mounted contact sensors.
