Статті в журналах: "Acoustic source location"

1

Holford, Karen M., and D. C. Carter. "Acoustic Emission Source Location." Key Engineering Materials 167-168 (June 1999): 162–71. http://dx.doi.org/10.4028/www.scientific.net/kem.167-168.162.

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2

O’Keefe, Ed, and Russ Graves. "Noise Source Location Optimization." Shock and Vibration 1, no. 5 (1994): 431–37. http://dx.doi.org/10.1155/1994/839439.

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This article describes a method to determine locations of noise sources that minimize modal coupling in complex acoustic volumes. Using the acoustic source scattering capabilities of the boundary element method, predictions are made of mode shape and pressure levels due to various source locations. Combining knowledge of the pressure field with a multivariable function minimization technique, the source location generating minimum pressure levels can be determined. The analysis also allows for an objective comparison of “best/worst” locations. The technique was implemented on a personal computer for the U.S. Space Station, predicting 5–10 dB noise reduction using optimum source locations.

3

Coulter, John E., Robert S. Evans, and Michael O. Robertson. "Acoustic emission leak source location." Journal of the Acoustical Society of America 92, no. 6 (December 1992): 3453–54. http://dx.doi.org/10.1121/1.404160.

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4

Huang, Chao, Liang-Guo Dong, Yu-Zhu Liu, and Ji-Zhong Yang. "Waveform-based source location method using a source parameter isolation strategy." GEOPHYSICS 82, no. 5 (September 1, 2017): KS85—KS97. http://dx.doi.org/10.1190/geo2017-0062.1.

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We have developed a novel acoustic-wave-equation-based full-waveform source location method to locate microseismic events. With the acoustic-wave equation and source signature independent inversion strategy, source location parameters (hypocenter locations) can be isolated from others and can then be retrieved independently and accurately, even when the origin time and source signature are not correct. Based on the acoustic-wave equation, new Fréchet derivatives of seismic waveforms with respect to the location parameters are derived to better accelerate the inversion process. To ease the cycle-skipping problem, a correlation is applied to select the best starting source positions. Some 2D and 3D numerical examples are presented to demonstrate the validity of our method. Compared with the waveform-based grid-search method, our method is effective in isolating the hypocenter locations from the source signature and origin time. The computational cost is nearly negligible compared with the waveform-based grid-search method. The robustness of our method is also tested for cases with inaccurate velocity models or using microseismic data with a signal-to-noise ratio of ≥[Formula: see text]. Finally, field data are used to indicate the practical applicability of our method.

5

Chernov, Dmitriy V., Igor E. Vasil′ev, Artem Yu Marchenkov, Tatyana Yu Kovaleva, Ekaterina A. Kulikova, Ivan V. Mishchenko, and Mariya V. Goryachkina. "The Influence of Acoustic Signal Amplitude on the Acoustic Emission Source Detection Probability." Vestnik MEI, no. 1 (2022): 130–36. http://dx.doi.org/10.24160/1993-6982-2022-1-130-136.

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The article discusses the results of using a standard algorithm for linearly locating the sources of acoustic emission signals generated by a broadband acoustic emission (AE) transducer mounted on the surface of a steel plate with sizes 1000×650×7 mm. To generate AE impulses with an amplitude of um = 55–100 dB, the electronic simulator’s difference of potentials was varied in the range of 10–300 V. As a result of laboratory experiments, the reduced error γ of the standard linear location algorithm was calculated. The maximum error equal to γ = 16.3% was recorded at the coordinate X = 100 mm in locating the source of acoustic signals with an amplitude of less than 60 dB and the antenna array basic size B = 800 mm. The minimum error equal to γ = 2.69% was recorded with the electronic simulator installed at the coordinate X = 400 mm. It is shown that the maximum error of the standard algorithm is observed in locating the sources of low-amplitude AE signals situated near the antenna array receiving transducers. The AE source detection probability as a function of recorded impulse amplitude is quantified. For determining the AE source detection probability p, the flow of recorded signals was divided into three amplitude ranges: 40–60 dB, 60–75 dB, and 75–100 dB. For the sources of acoustic signals with an amplitude of less than 60 dB and located at the coordinates X = 100, 200, 600, and 700 mm, the parameter p value tends to zero. It has been revealed in processing the experimental study results that the AE source detection probability increases with a growth in the maximum amplitude of the recorded signals. For AE impulses with an amplitude above 75 dB, the parameter p value approaches unity regardless of the source location. It has been determined that the error of the standard linear location algorithm depends on the distance between the AE source and the antenna array receiving transducers. The dependence p(X, um) has been demonstrated as a numerical assessment of the way in which the above-mentioned factors influence the obtained location picture results.

6

Richarz, Werner G. "Jet noise source location via acoustic intensity." Journal of the Acoustical Society of America 78, S1 (November 1985): S26. http://dx.doi.org/10.1121/1.2022720.

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7

Bhatt, Tarun, Esther T. Ososanya, and Corinne M. Darvennes. "Acoustic source location using a neural network." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3057. http://dx.doi.org/10.1121/1.418656.

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8

Lympertos, Efstratios M., and Evangelos S. Dermatas. "Acoustic emission source location in dispersive media." Signal Processing 87, no. 12 (December 2007): 3218–25. http://dx.doi.org/10.1016/j.sigpro.2007.05.010.

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9

Baxter, Matthew Geoffrey, Rhys Pullin, Karen M. Holford, and Sam L. Evans. "Delta T source location for acoustic emission." Mechanical Systems and Signal Processing 21, no. 3 (April 2007): 1512–20. http://dx.doi.org/10.1016/j.ymssp.2006.05.003.

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10

Chang, Pi Sheng. "Acoustic source location using a microphone array." Journal of the Acoustical Society of America 113, no. 6 (2003): 2957. http://dx.doi.org/10.1121/1.1588801.

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11

Zhang, Zhong Ning, and Jian Tian. "A New Acoustic Emission Source Location Method Based on the Linear Layout of Sensors." Advanced Materials Research 267 (June 2011): 561–64. http://dx.doi.org/10.4028/www.scientific.net/amr.267.561.

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For the problem of acoustic emission source location, it has always been one of the important issues to look for simple and convenient layout of sensors. This paper presents a new time difference method for locating acoustic emission source in a plate, which the sensors for locating are arranged in a straight line, and does not need the pre-determining of the acoustic wave propagation velocity. The method makes the acoustic emission source locating task simplified.

12

Moriya, Hirokazu, Koji Nagano, and Hiroaki Niitsuma. "Precise source location of AE doublets by spectral matrix analysis of triaxial hodogram." GEOPHYSICS 59, no. 1 (January 1994): 36–45. http://dx.doi.org/10.1190/1.1443532.

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We have developed a precise relative source location technique using acoustic emission doublets (AE doublets) in the triaxial hodogram method to evaluate the direction and distance of subsurface extension cracks. An AE doublet is a pair of acoustic emissions with similar waveforms and adjacent locations on the same crack but which occur at different times. The relative source location is estimated by an analysis in the frequency domain. The relative distance between two AE sources is determined from the difference of P-S arrival time delays by cross‐spectrum analysis. The relative direction is derived using a spectral matrix from the difference in P‐wave polarization directions. We also propose a method to optimize the estimated relative location by using a group of AE doublets. The accuracy of the estimated source location was confirmed by performing field experiments. The relative locations of artificial wave sources about 150 m from a triaxial detector can be estimated with distance errors of less than 1 m, and direction errors of less than 3.8 degrees in both azimuth and inclination. Results of the application of this analysis on AE doublets in a geothermal field demonstrate its ability to evaluate deeper subsurface fractures.

13

Kloepper, Laura N., Meike Linnenschmidt, Zelda Blowers, Brian Branstetter, Joel Ralston, and James A. Simmons. "Estimating colony sizes of emerging bats using acoustic recordings." Royal Society Open Science 3, no. 3 (March 2016): 160022. http://dx.doi.org/10.1098/rsos.160022.

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The decline of bats demands more widespread monitoring of populations for conservation and management. Current censusing methods are either prone to bias or require costly equipment. Here, we report a new method using passive acoustics to determine bat count census from overall acoustic amplitude of the emerging bat stream. We recorded the video and audio of an emerging colony of Mexican free-tailed bats from two cave locations across multiple nights. Instantaneous bat counts were calculated from the video frames, and the bat stream’s acoustic amplitude corresponding to each video frame was determined using three different methods for calculating acoustic intensity. We found a significant link between all three acoustic parameters and bat count, with the highest R 2 of 0.742 linking RMS pressure and bat count. Additionally, the relationship between acoustics and population size at one cave location could accurately predict the population size at another cave location. The data were gathered with low-cost, easy-to-operate equipment, and the data analysis can be easily accomplished using automated scripts or with open-source acoustic software. These results are a potential first step towards creating an acoustic model to estimate bat population at large cave colonies worldwide.

14

Blahacek, Michal, M. Chlada, and Z. Prevorovský. "Acoustic Emission Source Location Based on Signal Features." Advanced Materials Research 13-14 (February 2006): 77–82. http://dx.doi.org/10.4028/www.scientific.net/amr.13-14.77.

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Good knowledge of acoustic emission (AE) source location is the basic requirement for further damage mechanism characterization. Calculation of the AE source location is mostly based on arrival time differences of the signals recorded by different transducers. Error free arrival time determination is the crucial factor for the localization results accuracy together with the exact elastic wave velocity measurement. In the paper difficulties and limitations of the elastic wave velocity computation are shown. To solve the velocity and the time differences problems, new approach to AE source localization is described. The new method estimates the AE source coordinates using artificial neural network (ANN) processing extracted signal parameters. The ANN do not uses neither arrival time differences nor elastic wave velocities as input data. The new approach advantages are discussed in cases of both numerical and practical experiments. The experiments results are promising for the use of designed localization method in praxis.

15

Madarshahian, Ramin, Paul Ziehl, and Juan M. Caicedo. "Acoustic emission Bayesian source location: Onset time challenge." Mechanical Systems and Signal Processing 123 (May 2019): 483–95. http://dx.doi.org/10.1016/j.ymssp.2019.01.021.

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16

Maji, A. K., D. Satpathi, and T. Kratochvil. "Acoustic Emission Source Location Using Lamb Wave Modes." Journal of Engineering Mechanics 123, no. 2 (February 1997): 154–61. http://dx.doi.org/10.1061/(asce)0733-9399(1997)123:2(154).

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17

Nivesrangsan, P., J. A. Steel, and R. L. Reuben. "Source location of acoustic emission in diesel engines." Mechanical Systems and Signal Processing 21, no. 2 (February 2007): 1103–14. http://dx.doi.org/10.1016/j.ymssp.2005.12.010.

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18

Barat, P., P. Kalyanasundaram, and Baldev Raj. "Acoustic emission source location on a cylindrical surface." NDT & E International 26, no. 6 (December 1993): 295–97. http://dx.doi.org/10.1016/0963-8695(93)90004-e.

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19

Bi, Hai Sheng, Zi Li Li, Yuan Peng Cheng, Isaac Isaac, and Jun Wang. "The Corrosion Acoustic Emission Source Location Technique and its New Trend." Advanced Materials Research 694-697 (May 2013): 1167–72. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.1167.

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The corrosion acoustic emission (AE) source location is one of the main purposes of acoustic emission testing (AET), corrosion detection and location can guarantee the safety and integrity of pipeline, storage tank and other equipment in the petrochemical industry. The computed source location and zonal location methods are reviewed in this paper, and also new source location method based on modal acoustic emission (MAE) is introduced and this new method will be more widely used in the field of corrosion detection in future.

20

Ding, Hao, Yumei Bao, Qi Huang, Chunxiao Li, and Guozhong Chai. "Three-dimensional localization of point acoustic sources using a planar microphone array combined with beamforming." Royal Society Open Science 5, no. 12 (December 2018): 181407. http://dx.doi.org/10.1098/rsos.181407.

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This paper presents a beamforming-based acoustic imaging (BBAI) method employing a two-dimensional (2D) microphone array that not only can locate an acoustic source in the XY plane parallel to the array, but can also identify the distance between the source and array in the Z direction, denoted as the source depth, and thus provides three-dimensional (3D) localization ability. In this method, the acoustic field is reconstructed on virtual XY planes at different distances along the Z direction. The source depth is then determined according to the virtual plane providing the maximum response of the acoustic field. The location of the source in the X and Y directions of the identified virtual plane can then be easily determined based on the standard beamforming principles of a planar array. The proposed BBAI method is evaluated based on simulations involving single- and multiple-point sources, and corresponding experimental evaluations are similarly conducted in an anechoic chamber. Both simulation and experimental results demonstrate that the proposed method is capable of locating acoustic sources in 3D space.

21

Eaton, Mark J., Rhys Pullin, and Karen M. Holford. "Towards improved damage location using acoustic emission." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 226, no. 9 (May 31, 2012): 2141–53. http://dx.doi.org/10.1177/0954406212449582.

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The acoustic emission technique is a passive non-destructive testing technique that has significant potential for use as a structural health monitoring technique for many large-scale structures, allowing continuous global monitoring. The location capability of the acoustic emission technique is its most beneficial attribute; however, the location accuracy can often be limited in complex materials and structures. This article discusses recent advances in the location of acoustic emission signals. The key sources of errors are identified as signal arrival time measurement and processing algorithm limitations. A series of strategies for reducing the effects of both causes of error are presented. Additionally, the results of a case study are used to demonstrate a novel mapping technique for acoustic emission source location of fatigue crack signals in an aircraft landing gear component. Improvements in location accuracy of up to 87.5% were observed when compared with standard location calculation algorithms.

22

Zhou, Xiang, Yan Gao, and Zi Jia Shu. "Sound Pressure Distribution and Acoustic Parameters Estimation by Simulation." Advanced Materials Research 889-890 (February 2014): 161–64. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.161.

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A study on the sound pressure distribution and related acoustic parameters estimation bymeans of the acoustics simulation software EASE is presented in this paper. By defining the sound-absorbing material, the types of sound source, the location of sound source, and the related factors, a more accurate model for the specified acoustic field distribution is obtained. It provides certain theory reference for the design of indoor electroacoustics, which saves the cost and time for the development of construction engineering.

23

Go, Yeong-Ju, and Jong-Soo Choi. "An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array." Drones 5, no. 3 (August 6, 2021): 75. http://dx.doi.org/10.3390/drones5030075.

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Currently, the detection of targets using drone-mounted imaging equipment is a very useful technique and is being utilized in many areas. In this study, we focus on acoustic signal detection with a drone detecting targets where sounds occur, unlike image-based detection. We implement a system in which a drone detects acoustic sources above the ground by applying a phase difference microphone array technique. Localization methods of acoustic sources are based on beamforming methods. The background and self-induced noise that is generated when a drone flies reduces the signal-to-noise ratio for detecting acoustic signals of interest, making it difficult to analyze signal characteristics. Furthermore, the strongly correlated noise, generated when a propeller rotates, acts as a factor that degrades the noise source direction of arrival estimation performance of the beamforming method. Spectral reduction methods have been effective in reducing noise by adjusting to specific frequencies in acoustically very harsh situations where drones are always exposed to their own noise. Since the direction of arrival of acoustic sources estimated from the beamforming method is based on the drone’s body frame coordinate system, we implement a method to estimate acoustic sources above the ground by fusing flight information output from the drone’s flight navigation system. The proposed method for estimating acoustic sources above the ground is experimentally validated by a drone equipped with a 32-channel time-synchronized MEMS microphone array. Additionally, the verification of the sound source location detection method was limited to the explosion sound generated from the fireworks. We confirm that the acoustic source location can be detected with an error performance of approximately 10 degrees of azimuth and elevation at the ground distance of about 150 m between the drone and the explosion location.

24

Li, Qing, Qi Yin Shi, Zhi Yu Jin, Fan Yang, and Bao Bing Liu. "Study on Self Judgment of Location Lave Speed of Acoustic Emission on Concrete Members." Applied Mechanics and Materials 578-579 (July 2014): 1118–24. http://dx.doi.org/10.4028/www.scientific.net/amm.578-579.1118.

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The wave speed of acoustic emission in the medium is uncertain, which is influenced by the source characteristics and the relative position between the acoustic emission source and the sensors. Due to this difference, the results of TDOA location method determination of wave speed in advance are very discrete. As to liner location ,the more farther the distance between two acoustic emission source sensor are, the more serious the discrete error are. Any of the two sensors, a location line can be obtained by setting the wave speed as a horizontal coordinate and the location as the vertical coordinate. The horizontal coordinate of location line of the different sensors is the real wave speed of acoustic emission events. This method has lower computational complexity, which can overcome the influence on acoustic emission location which wave speed setting error brings, having some practical value in Engineering.

25

Li, Li, Ji Li, and Xin Liang Yu. "Acoustic Emission Activity Detector for Crack Location in Steel Structures." Applied Mechanics and Materials 325-326 (June 2013): 1301–4. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1301.

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In acoustic emission (AE) testing for steel structures, location of AE source is an important problem because the source is often related to micro-structure change or defects. In the paper, an activity detector was proposed to locate AE sources based on clustering analysis and AE statistical parameters. Firstly, a location plane, which may contain AE sources, was obtained by the technique of time difference of arrival (TDOA). And the plane was divided into some sub-planes base on clustering analysis. Then, the activity detector was established based on two AE statistical parameters of energy counts and ring-down counts. Finally, the detector value in each sub-plane was calculated and the sub-plane with the maximum value was identified the position of AE sources. Further, by applying the method to test crack sources in a steel structure experiment, the crack position was located correctly compared with actual crack sources. The results demonstrated that the method based on the AE activity detector can reduce ambiguity and locate AE sources accurately and effectively.

26

Grigg, S., C. A. Featherston, M. Pearson, and R. Pullin. "Advanced Acoustic Emission Source Location in Aircraft Structural Testing." IOP Conference Series: Materials Science and Engineering 1024, no. 1 (January 1, 2021): 012029. http://dx.doi.org/10.1088/1757-899x/1024/1/012029.

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27

Zárate, Boris A. "A Bayesian Approach to Modal Acoustic Emission Source Location." Eco Matemático 10, no. 1 (January 1, 2019): 6–18. http://dx.doi.org/10.22463/17948231.2536.

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Modal Acoustic Emission (MAE) is a branch of Acoustic Emission (AE) with proven capabilities for Structural Health Monitoring (SHM) of plate-like structures. MAE differences from AE in that MAE uses the understanding of the wave propagation to characterize and locate the source. The analysis of the waveform includes the use of time frequency techniques to determine the Time Of Arrival (TOA) of the different modes. This paper proposes the use of Bayesian inference to quantify the uncertainty in the source location for two different MAE location techniques. The first technique uses only the TOA of the extensional (symmetric) mode, while the second technique uses the TOA of both extensional and flexural (antisymmetric) modes. The Morlet wavelet is used to determine the scalogram of the waveform. The scalogram is reassigned and Markov Chain Monte Carlo (MCMC) is used to sample the posterior distribution built through Bayesian inference. Results are presented from location of Pencil Lead Breaks (PLBs) in an aluminum plate of 1/8in of thickness and 36in by 36in. Results show that using the TOA of only the symmetric mode leads to a lower level of uncertainty compared to using both extensional and flexural modes, because of the difficulty in assessing the time of arrival of the flexural mode.

28

Poole, Travis L., and George V. Frisk. "Determining low‐frequency source location from acoustic phase measurements." Journal of the Acoustical Society of America 112, no. 5 (November 2002): 2224. http://dx.doi.org/10.1121/1.4778796.

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29

Hardin, Jay C., Mikhail Gilinsky, and Vitali Khaikine. "Estimation of the location of a farfield acoustic source." Journal of the Acoustical Society of America 118, no. 1 (July 2005): 45–50. http://dx.doi.org/10.1121/1.1926007.

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30

Forero, Pedro A., and Paul A. Baxley. "Reweighted sparse source-location acoustic mapping in shallow water." Journal of the Acoustical Society of America 133, no. 5 (May 2013): 3577. http://dx.doi.org/10.1121/1.4806571.

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31

OMORI, Takahiro, Takashi USUI, Kazuo WATABE, Minh-Dung NGUYEN, and Isao SHIMOYAMA. "Acoustic Emission source location by a MEMS AE Sensor." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2017 (2017): I—04. http://dx.doi.org/10.1299/jsmeiip.2017.i-04.

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32

Ozevin, Didem. "Geometry-based spatial acoustic source location for spaced structures." Structural Health Monitoring: An International Journal 10, no. 5 (September 21, 2010): 503–10. http://dx.doi.org/10.1177/1475921710384906.

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33

Pang, Yan Rong, Zhi Hui Lv, Xiao Min Liang, Han Chang Chai, Ruo Chen Liu, Shao Qing You, and Wei Zhou. "Acoustic Emission Attenuation and Source Location of Resin Matrix for Wind Turbine Blade Composites." Advanced Materials Research 912-914 (April 2014): 36–39. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.36.

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In recent years, acoustic emission (AE) testing technology is the one of the most important non-destructive testing (NDT) methods. The characteristics can be described by AE signals, including the location, nature and severity. In order to obtain the basic data for monitoring the wind turbine blade composite structure, the experiment adopted Φ0.5 mm lead pencil as artificial acoustic emission source and measured AE parameters, attenuation and source location of resin matrix for wind turbine blade. This paper introduced linear location and two-dimensional positioning technology of time arrival location method about the burst AE signal. The result shows that the location of AE source basically reflects the location of stimulation AE source, the location of AE source for resin matrix can agree well with the simulated location of AE source, the more close to the middle area, the more accurate location.

34

Wang, Xing Wang, Bing Yi Sun, Bin Li, Li Li He, and Cheng Quan Hu. "An Acoustic Source Localization Method Based on Equal Distances Multi-Sensors Array." Applied Mechanics and Materials 214 (November 2012): 856–61. http://dx.doi.org/10.4028/www.scientific.net/amm.214.856.

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The traditional acoustic source is sensitive to time. A novel sound source location method using linear intersection spacing multi-sensors array is provided in this paper. Each array is composed of three spaced nodes, and least squares method is used to calculate the final position according to ternary array results. Multi-arrays method is more robust than the ternary one, and much wider scope is covered. Location scope extends from 120m to 800m when the relative positioning error is 10%. A multi-array group based on linear intersection sound source localization method is provided in this paper too. Experiment results show that the proposed method has higher precision on angle locating than distance locating.

35

Ramachandran, T., and M. C. Lenin Babu. "Optimization of the Location of Secondary Sources for the Active Engine Vibration Acoustic Noise Control in the Generator Room." Applied Mechanics and Materials 766-767 (June 2015): 968–73. http://dx.doi.org/10.4028/www.scientific.net/amm.766-767.968.

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The noise acoustic control in the interior of a diesel engine generator room model is studied and optimized. The finite element modelling and discretization of the engine room is carried out and the noise control is achieved using global active control of sound. The Genetic algorithm (GA) is used to find the optimized location of secondary sources to minimize the sound pressure level at receiver’s location. The secondary sources strengths for the active noise control system are computed using quadratic minimization acoustic potential energy. It is found that the sound pressure level at receiver’s location has been significantly reduced with changing the secondary source positions from arbitrarily to optimal location.

36

Cho, Hideo, Takashi Naruse, Takuma Matsuo, and Mikio Takemoto. "Development of Novel Optical Fiber AE Sensor with Multi-Sensing Function." Key Engineering Materials 321-323 (October 2006): 71–76. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.71.

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A novel optical fiber acoustic emission (AE) system with multi-sensing function in single long fiber was developed and utilized for the estimation of AE sources of model steel plate and jointed pipes. Multi-sensing function was achieved by dividing the single sensing fiber into several sensor portions with different resonance frequencies. The resonance frequencies were provided by winding the sensing fiber around the solid rods (sensor holders) with different diameters. The monitoring system with three sensors in a 10 m long fiber was demonstrated to detect three wave packets with different frequencies and correctly estimate the source locations of AEs from artificial (Nelson-Sue) sources on a 0.9 wide x 1.8 m long steel plate. Here the arrival times of AEs for the source location were determined by the continuous wavelet transform. Source locations on the steel plate were determined within a distance error of 53 mm. The system also makes the location of the pipe with damage possible.

37

Zhang, Liangyong, Xubin Liang, Tongdong Wang, Houlin Fang, Shiying Tang, Yunzhe Liu, and Dezhi Zhang. "Application of the Bayesian Source Location Using Seismic and Acoustic Observations in Inversion of Surface Explosion Source Locations." Journal of Physics: Conference Series 2035, no. 1 (September 1, 2021): 012026. http://dx.doi.org/10.1088/1742-6596/2035/1/012026.

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38

Alkhalifah, Tariq. "Acoustic wavefield evolution as a function of source location perturbation." Geophysical Journal International 183, no. 3 (October 6, 2010): 1324–31. http://dx.doi.org/10.1111/j.1365-246x.2010.04800.x.

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39

Mostafapour, Amir, and Saman Davoodi. "A method for acoustic source location in plate-type structures." Mechanical Systems and Signal Processing 93 (September 2017): 92–103. http://dx.doi.org/10.1016/j.ymssp.2017.02.006.

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40

Yang, M. H., and C. P. Chou. "A LAB-SCALE DIGITAL ACOUSTIC EMISSION SYSTEM FOR SOURCE LOCATION." Experimental Techniques 23, no. 4 (July 1999): 32–35. http://dx.doi.org/10.1111/j.1747-1567.1999.tb01507.x.

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41

Li, Jihui, and Gang Qi. "Improving Source Location Accuracy of Acoustic Emission in Complicated Structures." Journal of Nondestructive Evaluation 28, no. 1 (February 5, 2009): 1–8. http://dx.doi.org/10.1007/s10921-009-0042-z.

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42

Jingpin, Jiao, Wu Bin, and He Cunfu. "Acoustic emission source location methods using mode and frequency analysis." Structural Control and Health Monitoring 15, no. 4 (2008): 642–51. http://dx.doi.org/10.1002/stc.220.

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43

Wang, Xing Guo. "Quality Testing of the Chrome Coating Based on Acoustic Emission Technology." Applied Mechanics and Materials 253-255 (December 2012): 399–402. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.399.

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A assessment method on quality detected system was developed on cylinders inner wall coating through the hydraulic radial expansive load system and acoustic emission testing technology,. Put double probes linear position principle and uniform motion of load system together, and make curved positioning come true based on the technical principle of locating acoustic emission source. The disadvantage was solved that double probes can’t make acoustic emission source planar positioning. The result showed that this system can provide a precise identification and location; it has fast testing velocity and portable device.

44

Lin, Yi-Wei, and Gee-Pinn James Too. "A Parametric Study of Sound Focusing in Shallow Water by Using Acoustic Contrast Control." Journal of Computational Acoustics 22, no. 04 (September 18, 2014): 1450012. http://dx.doi.org/10.1142/s0218396x1450012x.

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Acoustic contrast control is a sound focusing technique applied to personal audio system devices to provide the optimal sound contrast for increasing or decreasing the potential sound energy of a specific area. In this study, acoustic contrast control was developed for sound focusing in shallow water. The advantage of this technique is the establishment of two zones: a bright zone around the user and a dark zone for other regions. In the acoustic contrast control process, computational ocean acoustics are used to calculate the Green's function between the source point and the field point. The effects of environmental parameters, which comprised the number of control sources, transmission frequency, control distances between sources and control zone of a geometric location were simulated. The results show that acoustic contrast control is an effective approach for sound focusing in shallow water that can increase the potential sound energy of a specific area. Employing this technique can also enhance underwater communications by using frequency-shift keying modulation for cross-talking applications.

45

Day, Mitchell L., Kanthaiah Koka, and Bertrand Delgutte. "Neural encoding of sound source location in the presence of a concurrent, spatially separated source." Journal of Neurophysiology 108, no. 9 (November 1, 2012): 2612–28. http://dx.doi.org/10.1152/jn.00303.2012.

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In the presence of multiple, spatially separated sound sources, the binaural cues used for sound localization in the horizontal plane become distorted from the cues from each sound in isolation, yet localization in everyday multisource acoustic environments remains robust. We examined changes in the azimuth tuning functions of inferior colliculus (IC) neurons in unanesthetized rabbits to a target broadband noise when a concurrent broadband noise interferer was presented at different locations in virtual acoustic space. The presence of an interferer generally degraded sensitivity to target azimuth and distorted the shape of the tuning function, yet most neurons remained significantly sensitive to target azimuth and maintained tuning function shapes somewhat similar to those for the target alone. Using binaural cue manipulations in virtual acoustic space, we found that single-source tuning functions of neurons with high best frequencies (BFs) were primarily determined by interaural level differences (ILDs) or monaural level, with a small influence of interaural time differences (ITDs) in some neurons. However, with a centrally located interferer, the tuning functions of most high-BF neurons were strongly influenced by ITDs as well as ILDs. Model-based analysis showed that the shapes of these tuning functions were in part produced by decorrelation of the left and right cochlea-induced envelopes that occurs with source separation. The strong influence of ITD on the tuning functions of high-BF neurons poses a challenge to the “duplex theory” of sound localization and suggests that ITD may be important for localizing high-frequency sounds in multisource environments.

46

Carlson, Edward A., Pierre-Philippe J. Beaujean, and Edgar An. "Location-Aware Source Routing Protocol for Underwater Acoustic Networks of AUVs." Journal of Electrical and Computer Engineering 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/765924.

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Acoustic networks of autonomous underwater vehicles (AUVs) cannot typically rely on protocols intended for terrestrial radio networks. This work describes a new location-aware source routing (LASR) protocol shown to provide superior network performance over two commonly used network protocols—flooding and dynamic source routing (DSR)—in simulation studies of underwater acoustic networks of AUVs. LASR shares some features with DSR but also includes an improved link/route metric and a node tracking system. LASR also replaces DSR's shortest-path routing with the expected transmission count (ETX) metric. This allows LASR to make more informed routing decisions, which greatly increases performance compared to DSR. Provision for a node tracking system is another novel addition: using the time-division multiple access (TDMA) feature of the simulated acoustic modem, LASR includes a tracking system that predicts node locations, so that LASR can proactively respond to topology changes. LASR delivers 2-3 times as many messages as flooding in 72% of the simulated missions and delivers 2–4 times as many messages as DSR in 100% of the missions. In 67% of the simulated missions, LASR delivers messages requiring multiple hops to cross the network with 2–5 times greater reliability than flooding or DSR.

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Jekosch, Simon, and Ennes Sarradj. "An Inverse Microphone Array Method for the Estimation of a Rotating Source Directivity." Acoustics 3, no. 3 (June 22, 2021): 462–72. http://dx.doi.org/10.3390/acoustics3030030.

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Microphone arrays methods are useful for determining the location and magnitude of rotating acoustic sources. This work presents an approach to calculating a discrete directivity pattern of a rotating sound source using inverse microphone array methods. The proposed method is divided into three consecutive steps. Firstly, a virtual rotating array method that compensates for motion of the source is employed in order to calculate the cross-spectral matrix. Secondly, the source locations are determined by a covariance matrix fitting approach. Finally, the sound source directivity is calculated using the inverse method SODIX on a reduced focus grid. Experimental validation and synthetic data from a simulation are used for the verification of the method. For this purpose, a rotating parametric loudspeaker array with a controllable steering pattern is designed. Five different directivity patterns of the rotating source are compared. The proposed method compensates for source motion and is able to reconstruct the location as well the directivity pattern of the rotating beam source.

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Yang, Han, Bin Wang, Stephen Grigg, Ling Zhu, Dandan Liu, and Ryan Marks. "Acoustic Emission Source Location Using Finite Element Generated Delta-T Mapping." Sensors 22, no. 7 (March 24, 2022): 2493. http://dx.doi.org/10.3390/s22072493.

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One of the most significant benefits of Acoustic Emission (AE) testing over other Non-Destructive Evaluation (NDE) techniques lies in its damage location capability over a wide area. The delta-T mapping technique developed by researchers has been shown to enable AE source location to a high level of accuracy in complex structures. However, the time-consuming and laborious data training process of the delta-T mapping technique has prevented this technique from large-scale application on large complex structures. In order to solve this problem, a Finite Element (FE) method was applied to model training data for localization of experimental AE events on a complex plate. Firstly, the FE model was validated through demonstrating consistency between simulated data and the experimental data in the study of Hsu-Nielsen (H-N) sources on a simple plate. Then, the FE model with the same parameters was applied to a planar location problem on a complex plate. It has been demonstrated that FE generated delta-T mapping data can achieve a reasonable degree of source location accuracy with an average error of 3.88 mm whilst decreasing the time and effort required for manually collecting and processing the training data.

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Cobos, Maximo, Fabio Antonacci, Anastasios Alexandridis, Athanasios Mouchtaris, and Bowon Lee. "A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–24. http://dx.doi.org/10.1155/2017/3956282.

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Wireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field.

50

Zhou, Zilong, Yichao Rui, Xin Cai, Ruishan Cheng, Xueming Du, and Jianyou Lu. "A Weighted Linear Least Squares Location Method of an Acoustic Emission Source without Measuring Wave Velocity." Sensors 20, no. 11 (June 4, 2020): 3191. http://dx.doi.org/10.3390/s20113191.

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The location of an acoustic emission (AE) source is crucial for predicting and controlling potential hazards. In this paper, a novel weighted linear least squares location method for AE sources without measuring wave velocity is proposed. First, the governing equations of each sensor are established according to the sensor coordinates and arrival times. Second, a mean reference equation is established by taking the mean of the squared governing equations. Third, the system of linear equations can be obtained based on the mean reference equation, and their residuals are estimated to obtain their weights. Finally, the AE source coordinate is obtained by weighting the linear equations and inserting the parameter constraint. The AE location method is verified by a pencil lead break experiment, and the results show that the locating accuracy of the proposed method is significantly higher than that of traditional methods. Furthermore, the simulation test proves that the proposed method also has a better performance (location accuracy and stability) than the traditional methods under any given scale of arrival errors.

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