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Journal articles on the topic 'Mechanics, data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Hwang, J. S., K. C. Chang, and G. C. Lee. "Modified Frequency‐Domain Data Processing." Journal of Engineering Mechanics 115, no. 10 (October 1989): 2333–39. http://dx.doi.org/10.1061/(asce)0733-9399(1989)115:10(2333).

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2

TAMURA, Yoshiaki, Hiroyuki FURUSAWA, Hidenori FUJII, and Yuki MIYAMOTO. "Data Processing for Large-Scale Comoutational Mechanics Results." Journal of the Visualization Society of Japan 29-1, no. 1 (2009): 129. http://dx.doi.org/10.3154/jvs.29.129.

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3

Chakrabarti, Satyananda, Donald E. Shaw, Dale E. Stephenson, and B. V. K. Vijaya Kumar. "Digital Signal Processing of Geotechnical Data." Journal of Engineering Mechanics 112, no. 1 (January 1986): 70–83. http://dx.doi.org/10.1061/(asce)0733-9399(1986)112:1(70).

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4

McConnell, K. G., D. O. Adams, and P. D. Hoist. "SUBTLE ERRORS IN DIGITAL EXPERIMENTAL DATA PROCESSING." Experimental Techniques 21, no. 6 (November 1997): 15–18. http://dx.doi.org/10.1111/j.1747-1567.1997.tb00564.x.

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5

Badalyan, V. G., A. Kh Vopilkin, S. A. Dolenko, Yu V. Orlov, and I. G. Persiantsev. "Data-processing algorithms for automatic operation of ultrasonic systems with coherent data processing." Russian Journal of Nondestructive Testing 40, no. 12 (December 2004): 791–800. http://dx.doi.org/10.1007/s11181-005-0108-7.

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6

Likhachev, Aleksey V., and Marina V. Tabanyukhova. "A new processing algorithm for photoelasticity method data." Vestnik Tomskogo gosudarstvennogo universiteta. Matematika i mekhanika, no. 79 (2022): 100–110. http://dx.doi.org/10.17223/19988621/79/9.

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The photoelasticity method is a reliable tool for studying the stress state of flat elements in building structures using the models made of optically sensitive materials. In this paper, the classical photoelasticity is considered. The experimental data obtained with the use of the method are presented as interferograms. A decoding procedure implies the obtaining of some normal and tangential stress values in the plane of the model. The polarization-projection installations that are used in optical methods are rather simple. However, the digital processing of the images obtained during the loaded model transmission requires high-intelligent software. Nowadays, national and international laboratories, working with polarization-optical methods, strive to develop digital photoelasticity. For some reasons, the authors of the presented work needed to develop their own algorithms for decoding experimental data of the photoelasticity method. This work is mainly devoted to a formulation of the problems to be solved. Some of them have already been solved, and the results obtained are presented here. The authors place special emphasis on the description of the algorithm for tracing of interference fringes based on the analysis of the image gradient.

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7

French, M., and M. Jay. "INSTRUMENTATION AND DATA PROCESSING FOR AUTOMOTIVE NVH TESTING." Experimental Techniques 22, no. 6 (November 1998): 43–44. http://dx.doi.org/10.1111/j.1747-1567.1998.tb02301.x.

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8

Zhang, He Quan. "Data Processing in Dynamic Traffic Capacity of Urban Road Based on Squatted Lane." Advanced Materials Research 910 (March 2014): 415–18. http://dx.doi.org/10.4028/www.scientific.net/amr.910.415.

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With more and more cars, rates of traffic accidents are also increasing. When there is an accident, lanes will be squatted. So the traffic capacity will be influenced. I have established a dynamic traffic capacity model based on mechanical mechanics that basic road capacity is influenced by instantaneous traffic. First of all, considering the phrase cycle of traffic lights of upstream crossing, we will set a sampling point per 60 seconds to count the number of cars that pass through the cross-section of accident in this video, and then calculate the traffic of every sampling point. Secondly, we will analyze actual traffic capacity by the line chart of traffic of cross-section and time that we evacuate queuing vehicles within 120 meters. By analyzing, we find that the actual traffic capacity at the cross-section of accident emerges fluctuation status. Based on the traditional model of road capacity and mechanical mechanics, by improving compensation factors, we can establish the model of dynamic traffic capacity at accident point.

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9

Xiao, Boxiang, Sheng Wu, and Xinyu Guo. "A physics-based approach to motion capture data processing for virtual plant modeling and simulation." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 03 (May 24, 2018): 1840005. http://dx.doi.org/10.1142/s1793962318400056.

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Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics. Data-driven method is an efficient way for motion analysis and animation synthesis. As a widely used tool, motion capture has been used in plant motion data acquisition and analysis. The most prominent and important problem in motion capture for plants is primary data processing such as missing markers reconstruction. This paper presents a novel physics-based approach to motion capture data processing of plants. Firstly, a physics-based mechanics model is found by Lagrangian mechanics for a motion captured plant organ such as a leaf, and then its dynamic mechanical properties are analyzed and relevant model parameters are evaluated. Further, by using the physical model with evaluated parameters, we can calculate the next positions of a maker to reconstruct the missing makers in motion capture sequence. We take an example of a maize leaf and pachira leaf to examine the proposed approach, and the results show that the physics-based method is feasible and effective for plant motion data processing.

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10

Shankar, Karthi, and Prabu Sevugan. "Spatial Data Indexing and Query Processing in GeoCloud." Journal of Testing and Evaluation 47, no. 6 (February 26, 2019): 20180502. http://dx.doi.org/10.1520/jte20180502.

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11

Guo, Huan, Zhijing Zhang, Muzheng Xiao, Xin Jin, and Heng Liu. "Measurement and data processing method of machined surface for assembly performance prediction." Journal of Mechanical Science and Technology 35, no. 4 (March 24, 2021): 1689–98. http://dx.doi.org/10.1007/s12206-021-0332-7.

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12

Lobanov, Dmitriy, and Elena Strungar. "Mathematical data processing according to digital image correlation method for polymer composites." Frattura ed Integrità Strutturale 14, no. 54 (September 23, 2020): 56–65. http://dx.doi.org/10.3221/igf-esis.54.04.

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The paper analyses the numerical algorithms for experimental data processing using a contactless video system Vic-3D, designed for three-dimension analysis of displacement and strain fields, and digital image correlation method. The authors considered methodological issues of conducting an experiment using a video system. They suggested recommendations on the choice of parameters of calculation of correlation, the size of subset and step during the analysis of non-homogeneous displacement and strain fields in polymer composite materials through laminated fiberglass composite. The efficient parameters of mathematical data processing are identified according to digital image correlation method on the basis of building fields for one frame on the surface of laminated fiberglass reinforced plastic at various subset values and at fixed step value. The paper shows the impact of step value on the strain fields detail degree. The authors have identified the relation of the chosen parameters of experimental data processing using digital image correlation method with the scaled levels of consideration of composite materials strain processes. To evaluate the strains at various scale levels, the paper uses supplementary video system instruments: “virtual extensometer”, “rectangular area” and “line”. The authors obtained a longitudinal strain profile that allows evaluating the location of strain peak areas on the composite object surface.

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13

Yuan, J., N. Takeda, and A. M. Waas. "A NOTE ON DATA PROCESSING IN THE SPLIT HOPKINSON PRESSURE BAR TESTS." Experimental Techniques 22, no. 5 (September 1998): 21–24. http://dx.doi.org/10.1111/j.1747-1567.1998.tb02317.x.

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14

Mayer, Klaus, Karl-Jörg Langenberg, Martin Krause, Boris Milmann, and Frank Mielentz. "Characterization of Reflector Types by Phase-Sensitive Ultrasonic Data Processing and Imaging." Journal of Nondestructive Evaluation 27, no. 1-3 (July 1, 2008): 35–45. http://dx.doi.org/10.1007/s10921-008-0035-3.

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15

Segal, E., P. Dickstein, Y. Segal, S. Kenig, and H. Dodiuk. "A novel method of processing pulse echo data in adhesive bond inspection." Journal of Nondestructive Evaluation 9, no. 1 (March 1990): 1–17. http://dx.doi.org/10.1007/bf00566978.

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16

Su, Xianglong, Donggang Yao, and Wenxiang Xu. "Processing of viscoelastic data via a generalized fractional model." International Journal of Engineering Science 161 (April 2021): 103465. http://dx.doi.org/10.1016/j.ijengsci.2021.103465.

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17

Storozhyk, D. V., and A. G. Protasov. "Image processing technologies based on complexing data (Reviev)." Tehničeskaâ diagnostika i nerazrušaûŝij kontrolʹ 2022, no. 4 (December 28, 2022): 17–26. http://dx.doi.org/10.37434/tdnk2022.04.03.

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18

Ciavarella, M., G. Demelio, and C. Pappalettere. "On the post-processing of data obtained from cracked components." Journal of Strain Analysis for Engineering Design 33, no. 1 (January 1, 1998): 67–71. http://dx.doi.org/10.1243/0309324981512823.

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The use of a simple singular value decomposition (SVD) technique to post-process far-field data from cracked components is discussed. The technique employs the series expansion for stress or displacements ahead of the crack tip, which is available for a wide range of configurations. The use of higher-order terms, necessary for the post-processing, is beneficial for two reasons: (a) it permits the abstraction of the maximum usable amount of information; (b) it gives a better understanding of the fracture mechanics, especially regarding crack tip plasticity and dynamic propagation. Several numerical cases are examined and a comparison is made with analytical results, permitting an evaluation to be made of the pure numerical error in the post-processing.

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19

Petersen, DR, FC Schmitt, and JM Matthews. "Controlling, Data Acquisition, and Data Processing for Axial and Shear Creep Tests Using a Computer." Journal of Testing and Evaluation 22, no. 1 (1994): 36. http://dx.doi.org/10.1520/jte12627j.

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20

Lincy, Blessy Trencia, and Suresh Kumar Nagarajan. "An Enhanced Hybrid Feature Selection Approach for High Dimensional Data Processing." Journal of Testing and Evaluation 47, no. 6 (January 31, 2019): 20180507. http://dx.doi.org/10.1520/jte20180507.

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21

Holmes, C., B. W. Drinkwater, and P. D. Wilcox. "Post-processing of ultrasonic phased array data for optimal performance." Insight - Non-Destructive Testing and Condition Monitoring 47, no. 2 (February 1, 2005): 88–90. http://dx.doi.org/10.1784/insi.47.2.88.58978.

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22

Amin, Farhan, Omar M. Barukab, and Gyu Sang Choi. "Big Data Analytics Using Graph Signal Processing." Computers, Materials & Continua 74, no. 1 (2023): 489–502. http://dx.doi.org/10.32604/cmc.2023.030615.

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23

Zheng, Dou. "A data acquisition and real time processing system for field use-dynamic strain indicator." Strain 24, no. 3 (August 1988): 99–103. http://dx.doi.org/10.1111/j.1475-1305.1988.tb00660.x.

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24

Kupczak, Arkadiusz, Mariusz Jędrychowski, Łukasz Bratasz, Michał Łukomski, and Roman Kozłowski. "Processing relative humidity data using discrete Fourier transform to control strain in art objects." Strain 55, no. 2 (February 21, 2019): e12311. http://dx.doi.org/10.1111/str.12311.

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25

Guan, Xuefei, Jingdan Zhang, S. Kevin Zhou, El Mahjoub Rasselkorde, and Waheed A. Abbasi. "Post-Processing of Phased-Array Ultrasonic Inspection Data with Parallel Computing for Nondestructive Evaluation." Journal of Nondestructive Evaluation 33, no. 3 (December 18, 2013): 342–51. http://dx.doi.org/10.1007/s10921-013-0219-3.

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26

Hamada, Kunihiro, Shoji Takechi, M. Arif Wibisono, and Kazuhiro Aoyama. "Definitions of Data Models and Data Processing Functions for the Joint Design Supporting System." QUARTERLY JOURNAL OF THE JAPAN WELDING SOCIETY 25, no. 2 (2007): 298–308. http://dx.doi.org/10.2207/qjjws.25.298.

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27

ASHIDA, Yuzuru. "Application of Neural Network to Processing of Seismic Reflection Data." Journal of the Society of Materials Science, Japan 44, no. 502 (1995): 880–84. http://dx.doi.org/10.2472/jsms.44.880.

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28

Krivosheev, I. A. "Application of excess of fluctuation distributions to experimental data processing." Russian Journal of Nondestructive Testing 44, no. 8 (August 2008): 556–59. http://dx.doi.org/10.1134/s106183090808007x.

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29

Michallet, H., and E. Barthélemy. "Ultrasonic probes and data processing to study interfacial solitary waves." Experiments in Fluids 22, no. 5 (March 17, 1997): 380–86. http://dx.doi.org/10.1007/s003480050064.

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30

Beresh, Steven J. "Comparison of PIV data using multiple configurations and processing techniques." Experiments in Fluids 47, no. 6 (May 29, 2009): 883–96. http://dx.doi.org/10.1007/s00348-009-0685-7.

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31

Mateos, J., B. G. Vasallo, D. Pardo, T. Gonz lez, J. S. Galloo, Y. Roelens, S. Bollaert, and A. Cappy. "Ballistic nanodevices for terahertz data processing: Monte Carlo simulations." Nanotechnology 14, no. 2 (January 10, 2003): 117–22. http://dx.doi.org/10.1088/0957-4484/14/2/303.

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32

Elena, Vasilieva, Rajat Singh, Rajeev Sobti, Kshama Sharma, Rajat Sharma, and P. Surekha. "Leveraging Big Data Analytics for Urban Planning: A Study Using the Big Data Analytics Efficiency Test." BIO Web of Conferences 86 (2024): 01082. http://dx.doi.org/10.1051/bioconf/20248601082.

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Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing skills. Data analytics have an impact on urban development initiatives, highlighting the revolutionary potential of data-driven urban planning. The outcomes of efficiency tests demonstrated how data analytics procedures are useful for improving urban services and for making well-informed judgments. This study offers important new insights into the mechanics of data-driven urban planning and how it can influence how cities develop in the future.

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33

Krieg, F., J. Kirchhof, S. Kodera, S. Lugin, A. Ihlow, T. Schwender, G. del Galdo, F. Römer, and A. Osman. "SAFT processing for manually acquired ultrasonic measurement data with 3D smartInspect." Insight - Non-Destructive Testing and Condition Monitoring 61, no. 11 (November 1, 2019): 663–69. http://dx.doi.org/10.1784/insi.2019.61.11.663.

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The architecture and implementation of a system for synthetic aperture focusing technique (SAFT) reconstruction on ultrasonic data acquired using a hand-held device is described. The reconstruction and the measurement process are performed simultaneously, with the goal of providing instantaneous highly focused visual feedback to the engineer. The implementation is based on the 3D smartInspect system that is currently being developed at Fraunhofer Institute for Nondestructive Testing IZFP. This system assists engineers by recording, displaying and protocolling manually acquired data. In this paper, it is shown that it is possible to enhance this system by adding simultaneous SAFT processing on top of the existing functionality. This improves the imaging quality of the system. Possible limitations on the imaging quality are investigated by real-world hardware set-ups using numerical studies.

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34

Owerko, Tomasz, and Przemysław Kuras. "Effective Processing of Radar Data for Bridge Damage Detection." Shock and Vibration 2019 (April 17, 2019): 1–13. http://dx.doi.org/10.1155/2019/2621092.

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This paper presents the practical results of the evaluation of the data obtained by using ground-based radar interferometer during measurements carried out on bridge structures. Due to the nature of the objects studied, the authors proposed a comprehensive method of data analysis, which identifies whether the passage of the vehicle did not damage the bridge. The effective use of vehicles as a source of bridge excitation allowed us to first develop a method for determining the damping parameters resistant to potentially occurring beating frequencies. As a result, it is possible to determine these subsets of data registered with radar, for which it is possible to assume compliance with linear systems. This type of data, often omitted in other works, forms the basis for the second important element of the research—an algorithm based on the ARMA model supporting defect detection. The optimization of the performed calculations, in particular the proposed optimal ARMA model order, the method of fault identification based on the DSF parameter, or fault identification based on a nonmetrical Cook’s distance leads to a robust and scalable method. The method’s low computational complexity allows for implementation in real-time solutions. In addition, the distribution of errors and the sensitivity of classifiers based on the DSF parameter and Cook’s distances leaving them will enable the automation of the classification process using machine learning. The proposed method is universal; in particular, it can be used for radar interferometry methods because it is resistant to potentially variable environmental conditions.

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35

Peng, Er Bao, Hong Ge Zhang, and Hong Ying Wang. "Mechanics and Pretreatment Research of Vibration Signals for the Numerical Control Machine." Advanced Materials Research 676 (March 2013): 162–65. http://dx.doi.org/10.4028/www.scientific.net/amr.676.162.

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To study vibration signals processing mechanics and the analysis method of the numerical control machine. vibration signals analysis method include time and frequent domain analysis: the time domain analysis mainly has the statistical analysis and the correlation function analysis. frequency domain analysis include spectral analysis, cepstrum analysis, data envelopment analysis. vibration signals pretreatment is the most commonly used and effective method on the mechanical device condition monitor and the failure diagnosis.

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36

Vavilov, V. P., D. A. Nesteruk, V. V. Shiryaev, and W. Swiderski. "Application of principal component analysis in dynamic thermal testing data processing." Russian Journal of Nondestructive Testing 44, no. 7 (July 2008): 509–16. http://dx.doi.org/10.1134/s1061830908070097.

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37

Slesarev, D. A., and A. A. Abakumov. "Data processing and representation in the MFL method for nondestructive testing." Russian Journal of Nondestructive Testing 49, no. 9 (September 2013): 493–98. http://dx.doi.org/10.1134/s106183091309009x.

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38

Abdelrahman, Marwa A., Mohamed K. ElBatanouny, John R. Rose, and Paul H. Ziehl. "Signal processing techniques for filtering acoustic emission data in prestressed concrete." Research in Nondestructive Evaluation 30, no. 3 (January 30, 2018): 127–48. http://dx.doi.org/10.1080/09349847.2018.1426800.

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39

Walldén, Marcus, Masao Okita, Fumihiko Ino, Dimitris Drikakis, and Ioannis Kokkinakis. "Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis." Algorithms 14, no. 5 (May 12, 2021): 154. http://dx.doi.org/10.3390/a14050154.

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Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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40

Velkavrh, Igor, Katharina Dimovski, Fevzi Kafexhiu, and Thomas Wright. "Enhanced Efficiency in Coefficient of Friction Evaluation through Automated Data Processing." Tribologie und Schmierungstechnik 70, no. 3 (August 14, 2023): 22–26. http://dx.doi.org/10.24053/tus-2023-0014.

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In this work, an approach towards automated extraction and evaluation of static and kinematic coefficients of friction is presented. By replacing manual evaluation with an automated process, this approach yields promising results, simplifies data handling, and saves significant time. The methodology shows potential for application to a wide range of experimental data and provides advanced processing capabilities using the extracted and mathematically evaluated data points. However, for data with high signal- to-noise ratios, automatic detection still requires further optimization to improve accuracy.

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41

Barone, S., and E. A. Patterson. "An alternative finite difference method for post-processing thermoelastic data using compatibility." Journal of Strain Analysis for Engineering Design 33, no. 6 (August 1, 1998): 437–47. http://dx.doi.org/10.1243/0309324981513138.

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A finite difference technique for processing thermoelastic data for biaxial problems is presented. The method enables thermoelastic data to be smoothed at interior points and boundary information to be calculated from measured non-boundary values by an iterative procedure based on the compatibility equation. The technique has been successfully applied to quantify stress concentrations and to perform stress separation.

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42

Zhong, Shan, Yongqiang Yang, and Yanlu Huang. "Data Slicing Processing Method for RE/RP System Based on Spatial Point Cloud Data." Computer-Aided Design and Applications 11, no. 1 (September 24, 2013): 20–31. http://dx.doi.org/10.1080/16864360.2013.834133.

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43

Zweifel, Nadina O., Nicholas E. Bush, Ian Abraham, Todd D. Murphey, and Mitra J. Z. Hartmann. "A dynamical model for generating synthetic data to quantify active tactile sensing behavior in the rat." Proceedings of the National Academy of Sciences 118, no. 27 (July 1, 2021): e2011905118. http://dx.doi.org/10.1073/pnas.2011905118.

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As it becomes possible to simulate increasingly complex neural networks, it becomes correspondingly important to model the sensory information that animals actively acquire: the biomechanics of sensory acquisition directly determines the sensory input and therefore neural processing. Here, we exploit the tractable mechanics of the well-studied rodent vibrissal (“whisker”) system to present a model that can simulate the signals acquired by a full sensor array actively sampling the environment. Rodents actively “whisk” ∼60 vibrissae (whiskers) to obtain tactile information, and this system is therefore ideal to study closed-loop sensorimotor processing. The simulation framework presented here, WHISKiT Physics, incorporates realistic morphology of the rat whisker array to predict the time-varying mechanical signals generated at each whisker base during sensory acquisition. Single-whisker dynamics were optimized based on experimental data and then validated against free tip oscillations and dynamic responses to collisions. The model is then extrapolated to include all whiskers in the array, incorporating each whisker’s individual geometry. Simulation examples in laboratory and natural environments demonstrate that WHISKiT Physics can predict input signals during various behaviors, currently impossible in the biological animal. In one exemplary use of the model, the results suggest that active whisking increases in-plane whisker bending compared to passive stimulation and that principal component analysis can reveal the relative contributions of whisker identity and mechanics at each whisker base to the vibrissotactile response. These results highlight how interactions between array morphology and individual whisker geometry and dynamics shape the signals that the brain must process.

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44

Kontomaris, Stylianos Vasileios, Andreas Stylianou, and Anna Malamou. "Atomic Force Microscopy Nanoindentation Method on Collagen Fibrils." Materials 15, no. 7 (March 27, 2022): 2477. http://dx.doi.org/10.3390/ma15072477.

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Atomic Force Microscopy nanoindentation method is a powerful technique that can be used for the nano-mechanical characterization of bio-samples. Significant scientific efforts have been performed during the last two decades to accurately determine the Young’s modulus of collagen fibrils at the nanoscale, as it has been proven that mechanical alterations of collagen are related to various pathological conditions. Different contact mechanics models have been proposed for processing the force–indentation data based on assumptions regarding the shape of the indenter and collagen fibrils and on the elastic or elastic–plastic contact assumption. However, the results reported in the literature do not always agree; for example, the Young’s modulus values for dry collagen fibrils expand from 0.9 to 11.5 GPa. The most significant parameters for the broad range of values are related to the heterogeneous structure of the fibrils, the water content within the fibrils, the data processing errors, and the uncertainties in the calibration of the probe. An extensive discussion regarding the models arising from contact mechanics and the results provided in the literature is presented, while new approaches with respect to future research are proposed.

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45

Maciel, Y., and C. Gleyzes. "Survey of multi-wire probe data processing techniques and efficient processing of four-wire probe velocity measurements in turbulent flows." Experiments in Fluids 29, no. 1 (July 5, 2000): 66–78. http://dx.doi.org/10.1007/s003480050428.

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46

Li, Jun, and Zong Lin Wang. "Force Analysis of the Middle Wall in Multi-Arch Tunnel under Central Hole + CRD Method." Applied Mechanics and Materials 580-583 (July 2014): 1188–91. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.1188.

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arch tunnel construction in the presence of construction mechanics often unclear, the lack of systematic design and construction of the theory can learn. Harbin Clinic Road thesis double-arch tunnel project as the basis, the construction process through its hole + CRD using the finite element simulation analysis and on-site monitoring measurement data processing, engineering arch tunnel song used in the wall study the mechanical properties.

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47

Gao, Yan, Jian Hu Lin, and Chong Li. "The Pretreatment and Time Domain Analysis of the Track Long-Wave Irregularity Testing Data in Maglev Railway Based on Magnetic Mechanics." Advanced Materials Research 460 (February 2012): 19–24. http://dx.doi.org/10.4028/www.scientific.net/amr.460.19.

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In digital signal analysis, the signal needs to be pretreated first. Because there are many uncertain factors affecting the signal in the engineering projects and the signal analysis methods are different too, the signal can be characterized through pretreatment so that the processing methods will be selected accordingly. This belongs to signal processing qualitative part. On the other hand, through pretreatment the abnormal signals caused by the machines or people error can be removed. This belongs to signal processing quantitative part. After the pretreatment of the track long-wave irregularity testing data in the maglev railway, the signal is determined as non-stationary stochastic data. According to property of the data, modern signal time-frequency analysis method was applied in this article for further study.

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Brunton, Steven L., Bernd R. Noack, and Petros Koumoutsakos. "Machine Learning for Fluid Mechanics." Annual Review of Fluid Mechanics 52, no. 1 (January 5, 2020): 477–508. http://dx.doi.org/10.1146/annurev-fluid-010719-060214.

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Abstract:

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experiments, and simulations. ML provides a powerful information-processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

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Buxbaum, S. R., R. B. Pond, and A. J. Willetts. "Application of an ultrasonic data recording and processing system to reactor pressure vessel examination." International Journal of Pressure Vessels and Piping 35, no. 1-4 (January 1988): 307–20. http://dx.doi.org/10.1016/0308-0161(88)90107-x.

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Nishioka, Kouki, Takeaki Murayama, and Yoichi Ono. "Estimation of Effective Thermal Diffusivity of Porous Solid Using Data for Image Processing." ISIJ International 36, no. 2 (1996): 150–55. http://dx.doi.org/10.2355/isijinternational.36.150.

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