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Journal articles on the topic 'Acoustic identification'

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Published: 4 June 2021
Last updated: 2 February 2022

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1

Stearns, Scott Donaldson. "Acoustic window identification." Journal of the Acoustical Society of America 112, no. 5 (2002): 1744. http://dx.doi.org/10.1121/1.1526596.

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2

Deng, Jiang Hua, Jun Hong Dong, and Guang De Meng. "Sound Source Identification and Acoustic Contribution Analysis Using Nearfield Acoustic Holography." Advanced Materials Research 945-949 (June 2014): 717–24. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.717.

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The main goal of the present paper is to provide a method of source identification. Firstly, statistically optimal near-field acoustical holography (SONAH) techniques are applied to locate sound sources with the reflected sound field. In the presence of reflection plane parallel and perpendicular to the source plane, the incoming wave and reflected waves are separated based on the acoustic superposition principle and acoustic mirror image principle to satisfy the condition of the sound sources reconstruction using SONAH. Secondly, contribution of noise source to the special field point is analyzed and noise source ranking of interior panel groups are evaluated based the proposed three step acoustic contribution method. Finally, this method is verified experimentally.
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3

Coker, Cecil H., and David R. Fischell. "Acoustic direction identification system." Journal of the Acoustical Society of America 80, no. 5 (November 1986): 1566. http://dx.doi.org/10.1121/1.394304.

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4

Iwatsubo, Takuzo, Shozo Kawamura, and Masahito Kamada. "Identification of Acoustic-Vibratory System by Acoustic Measurement." Shock and Vibration 3, no. 1 (1996): 27–37. http://dx.doi.org/10.1155/1996/925970.

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A new method for reducing ill-conditioning in a class of identification problems is proposed. The key point of the method is that the identified vibration of the sound source is expressed as a superposition of vibration modes. The mathematical property of the coefficient matrix, the practical error expanding ratio, and the stochastic error expanding ratio are investigated in a numerical example. The mode-superposition method is shown to be an effective tool for acoustic-vibratory inverse analysis.
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5

Samet, A., M. A. Ben Souf, O. Bareille, M. N. Ichchou, T. Fakhfakh, and M. Haddar. "Structural Source Identification from Acoustic Measurements Using an Energetic Approach." Journal of Mechanics 34, no. 4 (May 15, 2017): 431–41. http://dx.doi.org/10.1017/jmech.2017.24.

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AbstractAn inverse energy method for the identification of the structural force in high frequency ranges from radiated noise measurements is presented in this paper. The radiation of acoustic energy of the structure coupled to an acoustic cavity is treated using an energetic method called the simplified energy method. The main novelty of this paper consists in using the same energy method to solve inverse structural problem. It consists of localization and quantification of the vibration source through the knowledge of acoustic energy density. Numerical test cases with different measurement points are used for validation purpose. The numerical results show that the proposed method has an excellent performance in detecting the structural force with a few acoustical measurements.
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6

Kloser, R. J., T. Ryan, P. Sakov, A. Williams, and J. A. Koslow. "Species identification in deep water using multiple acoustic frequencies." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 6 (June 1, 2002): 1065–77. http://dx.doi.org/10.1139/f02-076.

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Multifrequency 12, 38, and 120 kHz acoustics were used to identify the dominant fish groups around a deepwater (>600 m) seamount (a known spawning site for orange roughy, Hoplostethus atlanticus) by amplitude mixing of the frequencies. This method showed three distinct acoustic groupings that corresponded to three groups of fishes based on size and swimbladder type: myctophids of total length less than 10 cm, morids and macrourids with lengths >30 cm, and orange roughy with a mean standard length of 36 cm. These three groups were the dominant groups caught in the demersal and pelagic trawls in the study area. A simple model of swimbladder resonance at depth of large and small gas-filled bladder fish groups is in agreement with our experimental observations. Traditionally, demersal and pelagic trawling is used to identify fish species in acoustic records. However, orange roughy are rarely caught in mid-water owing to net avoidance. Using three frequencies, these groups could be distinguished directly over their entire vertical extent from the acoustic records. This reduces a major source of positive bias uncertainty (factor range of 2.0–6.4) in the orange roughy biomass estimates.
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7

Potapov, A. I., I. S. Pavlov, and S. A. Lisina. "Acoustic identification of nanocrystalline media." Journal of Sound and Vibration 322, no. 3 (May 2009): 564–80. http://dx.doi.org/10.1016/j.jsv.2008.09.031.

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8

Korneliussen, Rolf J., Yngve Heggelund, Inge K. Eliassen, and Geir O. Johansen. "Acoustic species identification of schooling fish." ICES Journal of Marine Science 66, no. 6 (May 2, 2009): 1111–18. http://dx.doi.org/10.1093/icesjms/fsp119.

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Abstract Korneliussen, R. J., Heggelund, Y., Eliassen, I. K., and Johansen, G. O. 2009. Acoustic species identification of schooling fish. – ICES Journal of Marine Science, 66: 1111–1118. The development of methods for the acoustic identification of fish is a long-term objective aimed at reducing uncertainty in acoustic-survey estimates. The relative frequency response r(f) measured simultaneously at several frequencies is one of the main acoustic features that characterize the targets, but the relationship between nearest neighbours, school morphology, and environmental and geographical data are also important characteristics in this context. The number of acoustic categories that can be separated with a high spatial resolution is limited by the stochastic nature of the measurements. Because the acoustic categorization of larger ensembles is more reliable than for single targets, spatial smoothing of the backscattering within the school boundaries before that process allows the separation of more categories than is possible with the raw, highly resolved data. Using the mean r(f) of an entire school gives even more reliable categorization, but determining whether or not the school is monospecific sets a new challenge. This problem is evaluated here. The methods are tested and verified. Identification of acoustic categories with similar acoustic properties is done for schooling fish, although the results have limited spatial resolution. The reliability of the categorization is further improved when knowledge of school morphology and geographical distribution of the species are taken into account.
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9

Korneliussen, Rolf J. "The acoustic identification of Atlantic mackerel." ICES Journal of Marine Science 67, no. 8 (June 8, 2010): 1749–58. http://dx.doi.org/10.1093/icesjms/fsq052.

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Abstract Korneliussen, R. J. 2010. The acoustic identification of Atlantic mackerel. – ICES Journal of Marine Science, 67: 1749–1758. Calibrated, digitized data from multifrequency echosounders working simultaneously with nearly identical and overlapping acoustic beams were used to generate new, synthetic echograms which allow Atlantic mackerel (Scomber scombrus) to be identified acoustically. The raw echosounder data were processed stepwise in a modular sequence of analyses to improve categorization of the acoustic targets. The relative frequency response measured over as many as six operating frequencies, 18, 38, 70, 120, 200, and 364 kHz, was the main acoustic feature used to characterize the backscatter. Mackerel seemed to have a frequency-independent backscatter below ∼100 kHz, but significantly higher levels of backscattered energy at 200 kHz. Synthetic echograms containing targets identified acoustically as mackerel are presented and evaluated against trawl catches. Although catching fast-swimming mackerel is difficult, trawl catches from three Norwegian research vessels confirmed that the targets identified acoustically as mackerel were indeed that species. Separate experiments performed on mackerel in pens support the findings.
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10

Gomez Morales, J., R. Rodriguez, J. Durand, H. Ferdj-Allah, Z. Hadjoub, J. Attal, and A. Doghmane. "Characterization and identification of berlinite crystals by acoustic microscopy." Journal of Materials Research 6, no. 11 (November 1991): 2484–89. http://dx.doi.org/10.1557/jmr.1991.2484.

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Berlinite crystals grown in H3PO4, HCl, H3PO4/HCl, H2SO4/HCl, or H3PO4/HCl/H2SO4 solvents are characterized by acoustic microscopy techniques. Surface and subsurface defects can be visualized via acoustical images, whereas elastic parameters of the crystal can be measured on a microscopic scale. They prove to be of great importance in the identification of not only crystal orientations but of preparation methods as well. We show, for example, that a growth in sulfuric and phosphoric mediums improves mechanical behavior of berlinite crystals. Moreover, it seems that anisotropy plays a fundamental role in this characterization technique with an appearance or a disappearance of specific modes.
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11

Bernardini, Andrea, Federica Mangiatordi, Emiliano Pallotti, and Licia Capodiferro. "Drone detection by acoustic signature identification." Electronic Imaging 2017, no. 10 (January 29, 2017): 60–64. http://dx.doi.org/10.2352/issn.2470-1173.2017.10.imawm-168.

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12

Kawabe, Hiroshi. "Inverse Acoustic Scattering for Shape Identification." Journal of the Society of Naval Architects of Japan 1993, no. 173 (1993): 247–53. http://dx.doi.org/10.2534/jjasnaoe1968.1993.247.

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13

Eberlin, Philippe. "Underwater acoustic identification of hospital ships." International Review of the Red Cross 28, no. 267 (December 1988): 505–18. http://dx.doi.org/10.1017/s0020860400071953.

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At the Twenty-fifth International Conference of the Red Cross (Geneva, 1986) the ICRC presented its report on the identification of medical transports, including the action taken to implement Resolution VIII of the previous International Conference. The report stressed that in a naval conflict the protection of medical transports at sea largely depended on the technical means of identification available.By adopting its Resolution III entitled “Identification of medical transports”, the Twenty-fifth Conference recognized the need for continuous efforts to ensure that the means used to signal and identify medical personnel, units and transports keep pace with technical advances.
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14

Wang, Ting-I. "Optical and acoustic weather identification system." Journal of the Acoustical Society of America 101, no. 4 (April 1997): 1761. http://dx.doi.org/10.1121/1.418216.

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15

Denny, Gerald, and Patrick Simpson. "A broadband acoustic fish identification system." Journal of the Acoustical Society of America 103, no. 5 (May 1998): 3069. http://dx.doi.org/10.1121/1.422851.

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16

Dwyer, Roger F. "Remote identification of moving acoustic objects." Journal of the Acoustical Society of America 96, no. 5 (November 1994): 3312. http://dx.doi.org/10.1121/1.410801.

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17

Kelly, Joseph L. "Surface acoustic wave pipe identification system." Journal of the Acoustical Society of America 83, no. 4 (April 1988): 1711. http://dx.doi.org/10.1121/1.395852.

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18

GANCHEV, TODOR, and ILYAS POTAMITIS. "AUTOMATIC ACOUSTIC IDENTIFICATION OF SINGING INSECTS." Bioacoustics 16, no. 3 (January 2007): 281–328. http://dx.doi.org/10.1080/09524622.2007.9753582.

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19

Friedel, Paul, Moritz Bürck, and J. Leo van Hemmen. "Neuronal identification of acoustic signal periodicity." Biological Cybernetics 97, no. 3 (August 24, 2007): 247–60. http://dx.doi.org/10.1007/s00422-007-0173-1.

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20

Mekarzia, M., and M. Guerti. "Measurement and Identification of Acoustic Impulse Response." Building Acoustics 15, no. 1 (January 2008): 73–78. http://dx.doi.org/10.1260/135101008784050197.

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A description is given of an implementation of a system of measurement for acoustic impulse responses. The acoustic impulse response of a room is measurable with a dynamic range of 48 dB by cross correlation of the test signals. This proved to be better than by the identification by the algorithm of the stochastic gradient with decreasing step especially in noisy environments.
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21

Zhang, Lei, Danjie Huang, Xinheng Wang, Christian Schindelhauer, and Zhi Wang. "Acoustic NLOS Identification Using Acoustic Channel Characteristics for Smartphone Indoor Localization." Sensors 17, no. 4 (March 30, 2017): 727. http://dx.doi.org/10.3390/s17040727.

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22

Xiao, Yue, Wei Gao, Changbao Chu, and Jing Sheng. "Identification of panel acoustic contribution based on patch nearfield acoustic holography." Journal of Physics: Conference Series 1965, no. 1 (July 1, 2021): 012138. http://dx.doi.org/10.1088/1742-6596/1965/1/012138.

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23

Zhang, Li Xia, Fu Zhou Feng, Peng Cheng Jiang, and Xu Chang Wang. "Application of Neural Network on Acoustic Signal Identification." Applied Mechanics and Materials 151 (January 2012): 523–26. http://dx.doi.org/10.4028/www.scientific.net/amm.151.523.

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The application based on Backpropagation (BP) Algorithm network is conducted on identifying the categories and numbers of mechanical equipments by acoustic signal in battlefield targets. Collected signal was pre-processed and extracted the power spectrum feature of acoustic signal as input vectors of neural networks, then classified by neural networks and pattern recognition theorem. We employ the acoustic signals of six kinds of normal equipments as training samples to train the network. The experiment shows that the ratio of recognition of the acoustic signal processing system based on neural networks proposed is better than the conventional methods.
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24

Koslow, J. Anthony. "The role of acoustics in ecosystem-based fishery management." ICES Journal of Marine Science 66, no. 6 (April 8, 2009): 966–73. http://dx.doi.org/10.1093/icesjms/fsp082.

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Abstract Koslow, J. A. 2009. The role of acoustics in ecosystem-based fishery management. – ICES Journal of Marine Science, 66: 966–973. For more than half a century, acoustics has been a leading tool in fishery stock assessment. Today, the need for ecosystem-based management poses new challenges for fishery scientists: the need to assess the ecological relationships of exploited species with predators and prey and to predict the potential effects of climate variability and climate change on recruitment. No research tool is likely to prove as effective as acoustics in meeting these needs, if it is properly integrated into interdisciplinary research programmes involving ecology and oceanography, as well as fisheries. Integration of data from acoustics and ocean-observation, as well as from satellites and other high-resolution oceanographic mapping tools, is likely to lead to major advances in fishery oceanography. New developments in acoustic technology, such as three-dimensional, multibeam acoustics, and shelf-scale acoustic mapping, may also lead to significant advances. Notwithstanding these developments, critical biases and shortcomings of acoustic methods that were noted 50 years ago remain with us. For example, the identification of insonified biota and single-target discrimination remains relatively primitive. Progress is urgently needed in these basic underpinnings of the acoustic method.
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25

TAROUDAKIS, MICHAEL I., and GEORGE TZAGKARAKIS. "ON THE USE OF THE REASSIGNED WAVELET TRANSFORM FOR MODE IDENTIFICATION." Journal of Computational Acoustics 12, no. 02 (June 2004): 175–96. http://dx.doi.org/10.1142/s0218396x04002237.

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This paper is concerned with the use of the reassigned wavelet transform for mode identification in shallow water acoustic propagation. Mode identification is important for inverse procedures in underwater acoustics. An efficient way to recognize the modal structure of the acoustic field when a single hydrophone is available is to refer to the time frequency analysis of the recorded signal using wavelet transform. However, the standard wavelet transform in some cases may result in an obscure representation of the dispersion curves. Thus, a reassigned process is proposed which brings important improvements in the time frequency representation of the signal. This is achieved by moving the calculation point of the scalogram in the center of gravity of the energy concentration, associated with each one of the propagating modes. This argument is supported by two illustrative examples corresponding to propagation of low frequency tomographic signals, in shallow water.
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26

Webb, Douglas P., Joan F. Power, and Eric D. Salin. "ICP Sample Matrix Identification by Acoustic Signature." Applied Spectroscopy 46, no. 9 (September 1992): 1362–69. http://dx.doi.org/10.1366/0003702924123656.

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A novel technique for rapidly determining properties of the sample matrix of nebulized samples in inductively coupled plasma spectrometry via acoustic signature is discussed. Properties such as surface tension and high dissolved salts can be determined by analyzing the acoustic signal produced by the sample nebulization process. With digital signal processing equipment, determination of the presence of organic matrices or high-salt matrices should be possible before the plasma or the nebulizer is adversely affected.
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27

McKelvey, Tomas, Andrew Fleming, and S. O. Reza Moheimani. "Subspace-Based System Identification for an Acoustic Enclosure." Journal of Vibration and Acoustics 124, no. 3 (June 12, 2002): 414–19. http://dx.doi.org/10.1115/1.1467653.

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This paper is aimed at identifying a dynamical model for an acoustic enclosure, a duct with rectangular cross section, closed ends, and side-mounted speaker enclosures. Acoustic enclosures are known to be resonant systems of high order. In order to design a high performance feedback controller for an acoustic enclosure, one needs to have an accurate model of the system. Subspace-based system identification techniques have proven to be an efficient means of identifying dynamics of high order highly resonant systems. In this paper a frequency domain subspace-based method together with a second iterative optimization step minimizing a frequency domain least-squares criterion is successfully employed to identify a dynamical model for an acoustic enclosure.
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28

Huang, Jie, Ke-Yu Pan, Xue-Lei Feng, and Yong Shen. "Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers." Applied Sciences 11, no. 16 (August 21, 2021): 7713. http://dx.doi.org/10.3390/app11167713.

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Nonlinear acoustic damping is a key nonlinearity in miniature loudspeakers when the air velocity is at a high amplitude. Measurement of nonlinear acoustic damping is beneficial for predicting and analyzing the performance of miniature loudspeakers. However, the general measuring methods for acoustic impedance, such as the standing-wave tube method or the impedance tube method, are not applicable in this scenario because the nonlinear acoustic damping in miniature loudspeakers is coupled with other system nonlinearities. In this study, a measurement method based on nonlinear system identification was constructed to address this issue. The nonlinear acoustic damping was first theoretically analyzed and then coupled in an equivalent circuit model (ECM) to describe the full dynamics of miniature loudspeakers. Based on the ECM model, the nonlinear acoustic damping was identified using measured electrical data and compared with theoretical calculations. The satisfactory agreement between the identification and theoretical calculations confirms the validity of the proposed identification method.
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29

SHIMAUCHI, Suehiro. "System Identification Problems on Acoustic Echo Cancellation." IEICE ESS Fundamentals Review 6, no. 4 (2013): 265–75. http://dx.doi.org/10.1587/essfr.6.265.

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30

Kawabe, Hiroshi, and Kouji Moriguchi. "Inverse Acoustic Scattering for Shape Identification (continued)." Journal of the Society of Naval Architects of Japan 1994, no. 175 (1994): 193–204. http://dx.doi.org/10.2534/jjasnaoe1968.1994.193.

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31

Ping, Guoli, Zhigang Chu, and Yang Yang. "Compressive Spherical Beamforming for Acoustic Source Identification." Acta Acustica united with Acustica 105, no. 6 (November 1, 2019): 1000–1014. http://dx.doi.org/10.3813/aaa.919406.

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This study examines a compressive spherical beamforming (CSB) method, using a rigid spherical microphone array to localize and quantify the acoustic contribution of sources. The method relies on the array signal model in the spherical harmonics domain that can be represented as a spatially sparse problem. This makes it possible to use compressive sensing to solve an underdetermined problem via promoting sparsity. The estimation of the angular position of sources with respect to the microphone array, as well as the three-dimensional localization over a volume are investigated. Several sparse recovery algorithms [orthogonal matching pursuit (OMP), generalized OMP, ϱ1-norm minimization, and reweighted ϱ1-norm minimization] are examined for this purpose. The numerical and experimental results indicate that sparse recovery methods outperform conventional spherical harmonics beamforming. Reweighted ϱ1-norm has good adaptability to noise, improving the robustness of CSB. The method can successfully localize the angular position of sources, and quantify their relative pressure contribution. The method is promising to localize sources in a three-dimensional domain of interest. However, the three-dimensional localization is more sensitive to noise, source distance, and properties of the sensing matrix than the two-dimensional localization.
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32

Dolinšek, Slavko, and Janez Kopač. "Acoustic emission signals for tool wear identification." Wear 225-229 (April 1999): 295–303. http://dx.doi.org/10.1016/s0043-1648(98)00363-9.

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33

Ng, DanielWingChong, Shamachary Sathish, Aameera Khan, Parakrama Chandrasoma, William Wijns, and P. A. N. Chandraratna. "Identification of hibernating myocardium by acoustic microscopy." Ultrasound in Medicine & Biology 30, no. 5 (May 2004): 693–96. http://dx.doi.org/10.1016/j.ultrasmedbio.2004.03.012.

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34

Higuchi, Norio, and Makoto Hashimoto. "Analysis of acoustic features affecting speaker identification." Journal of the Acoustical Society of Japan (E) 17, no. 1 (1996): 33–35. http://dx.doi.org/10.1250/ast.17.33.

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35

Carbó, Rafael, and Adriana C. Molero. "Improved acoustic detection and identification of gillnets." Applied Acoustics 59, no. 4 (April 2000): 373–83. http://dx.doi.org/10.1016/s0003-682x(99)00019-5.

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36

Dreyer, Uilian Jose, Guilherme Dutra, Guilherme Heim Weber, Rafael Jose Daciuk, Manoel Feliciano da Silva, Ricardo Munoz Freitas, Daniel Rodrigues Pipa, Jean Carlos Cardozo da Silva, Marco Jose da Silva, and Cicero Martelli. "Horse Gait Identification Using Distributed Acoustic Sensing." IEEE Sensors Journal 21, no. 3 (February 1, 2021): 3058–65. http://dx.doi.org/10.1109/jsen.2020.3027922.

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37

Campbell, Gregory S., Robert Gisner, and David A. Helweg. "Acoustic identification of female Steller sea lions." Journal of the Acoustical Society of America 108, no. 5 (November 2000): 2541. http://dx.doi.org/10.1121/1.4743416.

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38

Mauuary, D. "Ray identification theory in ocean acoustic tomography." Journal of the Acoustical Society of America 97, no. 5 (May 1995): 3235. http://dx.doi.org/10.1121/1.412968.

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39

Ioup, George E., Juliette W. Ioup, Lisa A. Pflug, Arslan M. Tashmukhambetov, and Natalia A. Sidorovskaia. "Acoustic identification of beaked and sperm whales." Journal of the Acoustical Society of America 122, no. 5 (2007): 3003. http://dx.doi.org/10.1121/1.2942722.

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40

Martin, Vincent, and Frédéric Cohen-Tenoudji. "Identification of acoustic sources with uncertain data." Journal of the Acoustical Society of America 133, no. 5 (May 2013): 3576. http://dx.doi.org/10.1121/1.4806568.

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41

Zilio, Angelo, and Damiano G. Preatoni. "A system for acoustic identification of bats." Italian Journal of Zoology 63, no. 1 (January 1996): 53–56. http://dx.doi.org/10.1080/11250009609356107.

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42

Eller, Matthias, and Nicolas P. Valdivia. "Acoustic source identification using multiple frequency information." Inverse Problems 25, no. 11 (October 5, 2009): 115005. http://dx.doi.org/10.1088/0266-5611/25/11/115005.

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43

Sun, Qiu Ming, Feng Tian, and Yan Jun Zhang. "Surface Acoustic Wave Based Radio Frequency Identification." Advanced Materials Research 490-495 (March 2012): 1802–6. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1802.

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In this paper, a novel SAW RFID tag based on UWB chirp spread spectrum theory was proposed. The tag designation is in accordance with FCC standards of UWB wireless communications. The combination of UWB for SAW RFID has the characteristic of short delay due to large bandwidth and low energy required for the reader. Moreover, the encoding and detection for UWB SAW tag, which replaced the universally employed pulse position and on/off encoding methods in SAW tags with its central frequency of 2.45GHz and bandwidth of 82.5MHz, offers an effective way for multi-access SAW RFID tag system. Implementation of UWB CSS SAW RFID tag was demonstrated.
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44

Shreedharan, Srisharan, Chiranth Hegde, Sunil Sharma, and Harsha Vardhan. "Acoustic fingerprinting for rock identification during drilling." International Journal of Mining and Mineral Engineering 5, no. 2 (2014): 89. http://dx.doi.org/10.1504/ijmme.2014.060193.

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45

Friesel, M. A. "Acoustic emission source identification using longwaveguide sensors." NDT International 19, no. 3 (June 1986): 203–6. http://dx.doi.org/10.1016/0308-9126(86)90110-0.

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46

Bedoya, Carol, Claudia Isaza, Juan M. Daza, and José D. López. "Automatic identification of rainfall in acoustic recordings." Ecological Indicators 75 (April 2017): 95–100. http://dx.doi.org/10.1016/j.ecolind.2016.12.018.

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47

Arias-Aguilar, Adriana, Frederico Hintze, Ludmilla M. S. Aguiar, Vincent Rufray, Enrico Bernard, and Maria João Ramos Pereira. "Who’s calling? Acoustic identification of Brazilian bats." Mammal Research 63, no. 3 (April 23, 2018): 231–53. http://dx.doi.org/10.1007/s13364-018-0367-z.

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48

Delgado-Gutiérrez, G., F. Rodríguez-Santos, O. Jiménez-Ramírez, and R. Vázquez-Medina. "Acoustic environment identification by Kullback–Leibler divergence." Forensic Science International 281 (December 2017): 134–40. http://dx.doi.org/10.1016/j.forsciint.2017.10.031.

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49

Scalabrin, C. "Narrowband acoustic identification of monospecific fish shoals." ICES Journal of Marine Science 53, no. 2 (April 1996): 181–88. http://dx.doi.org/10.1006/jmsc.1996.0020.

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50

Vinogradov, Aleksandr V., and Aleksey V. Bukreev. "Microcontroller Device for Conductor Identification Using Acoustic Signal." Elektrotekhnologii i elektrooborudovanie v APK 67, no. 1 (March 28, 2020): 28–34. http://dx.doi.org/10.22314/2658-4859-2020-67-1-28-34.

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Abstract:
When repairing and replacing electrical wiring in enterprises, the main difficulty is the lack or poor quality of documentation, plans for conductors laying. Distinguishing wires (cables) and their cores by the color of the shells or using tags attached to the ends is difficult if the shells have the same color and there are no tags. Devices and technical solutions used to identify wires and cables do not allow recognizing conductors without breaking the electrical circuit, removing insulation, and de-energizing the network. Searching for the right conductor is a time-consuming operation. (Research purpose) The research purpose is developing a new microcontroller device for identifying wires using an acoustic signal. (Materials and methods) Literature sources has been searched for devices for conductors identifying. (Results and discussion) The article proposes a method that involves feeding an acoustic signal to a wire at one point and capturing it at another, in order to recognize the desired wire. The article presents results of comparison of the developed microcontroller device for identifying conductors using an acoustic signal with known devices and methods for conductors recognizing. (Conclusions) The article reveals the shortcomings of existing methods and means of identifying wires and cables. Authors performed a theoretical calculation of the sound pressure in the conductor at a given distance. The article presents the calculation of speed of acoustic waves in conductors with different types of insulation. Authors designed a microcontroller device for identifying conductors using an acoustic signal and tested it. It was determined that the device increases the safety of work, reduces the cost of operating internal wiring and identification time; eliminates the violation of wire insulation, the need to disable electrical receivers. The convergence of theoretical calculations and experimental data was shown.
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