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Relevant bibliographies by topics / Non-Linear modeling / Journal articles

Journal articles on the topic 'Non-Linear modeling'

To see the other types of publications on this topic, follow the link: Non-Linear modeling.

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

NAKAGAWA, Taro, Mitsuharu HIRASAWA, Katsumi KOBAYASHI, and Satoshi SASAKI. "MODELING OF NON-LINEAR HYSTERETIC SOIL." Journal of Structural and Construction Engineering (Transactions of AIJ) 76, no. 666 (2011): 1407–14. http://dx.doi.org/10.3130/aijs.76.1407.

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2

Landis, Chad M. "Non-linear constitutive modeling of ferroelectrics." Current Opinion in Solid State and Materials Science 8, no. 1 (January 2004): 59–69. http://dx.doi.org/10.1016/j.cossms.2004.03.010.

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3

Costa, Mário Rui P. F. N., and Rolando C. S. Dias. "Kinetic Modeling of Non-Linear Polymerization." Macromolecular Symposia 243, no. 1 (November 2006): 72–82. http://dx.doi.org/10.1002/masy.200651108.

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4

Rafiq, Serwan Khorshid. "Non Linear Finite Element Modeling of Reinforced Concrete Flat Plate Slabs with openings." Sulaimani Journal for Engineering Sciences 4, no. 4 (May 1, 2017): 28–40. http://dx.doi.org/10.17656/sjes.10044.

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5

Abdulkadir, Mohamed Raouf, Zana Abdalla Aziz, and Jaza Hassan Muhammad. "Non Linear Finite Element Modeling of Reinforced Concrete Flat Plate Slabs with openings." Sulaimani Journal for Engineering Sciences 4, no. 4 (May 1, 2017): 41–49. http://dx.doi.org/10.17656/sjes.10045.

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6

ISHIKAWA, Ritsuko, Hitoshi SUWA, and Matsutaro SEKI. "MODELING OF NON-LINEAR VISCO-ELASTIC DAMPER." Journal of Structural and Construction Engineering (Transactions of AIJ) 66, no. 544 (2001): 47–52. http://dx.doi.org/10.3130/aijs.66.47_2.

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7

DUGGIRALA, SHYAM K., and L. T. FAN. "STOCHASTIC MODELING OF NON-LINEAR SIEVING KINETICS." Chemical Engineering Communications 61, no. 1-6 (November 1987): 59–88. http://dx.doi.org/10.1080/00986448708912031.

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8

Post, Alvin, and Willem Stuiver. "Modeling non-linear oscillators: a new approach." International Journal of Non-Linear Mechanics 39, no. 6 (August 2004): 897–908. http://dx.doi.org/10.1016/s0020-7462(03)00073-8.

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9

Marchand, Norman J., and John C. Moosbrugger. "Non-linear structural modeling for life predictions." International Journal of Pressure Vessels and Piping 47, no. 1 (January 1991): 79–112. http://dx.doi.org/10.1016/0308-0161(91)90087-i.

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10

Paetsch, C., and A. Dorfmann. "Non-linear modeling of active biohybrid materials." International Journal of Non-Linear Mechanics 56 (November 2013): 105–14. http://dx.doi.org/10.1016/j.ijnonlinmec.2013.03.005.

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11

Chen, Pan, Yu Ogawa, Yoshiharu Nishiyama, Ahmed E. Ismail, and Karim Mazeau. "Linear, non-linear and plastic bending deformation of cellulose nanocrystals." Physical Chemistry Chemical Physics 18, no. 29 (2016): 19880–87. http://dx.doi.org/10.1039/c6cp00624h.

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Bending deformation of cellulose nanocrystal is investigated by using multi-scale modeling and transmission electron microscopy, which highlights importance of shear contribution in the deformation behavior of cellulose.

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12

Yamamoto, Tatsuro, Kyohei Ogawa, and Shigeo Tanaka. "GS11-5 Mathematical modeling of non-linear osteoblastic response to physical stimulation(GS11: Computational Biomechanics)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2015.8 (2015): 218. http://dx.doi.org/10.1299/jsmeapbio.2015.8.218.

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13

Rohani, S., M. Haeri, and H. C. Wood. "Modeling and control of a continuous crystallization process Part 1. Linear and non-linear modeling." Computers & Chemical Engineering 23, no. 3 (February 1999): 263–77. http://dx.doi.org/10.1016/s0098-1354(98)00271-3.

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14

Rasoolimanesh, S. Mostafa, Faizan Ali, and Mastura Jaafar. "Modeling residents’ perceptions of tourism development: Linear versus non-linear models." Journal of Destination Marketing & Management 10 (December 2018): 1–9. http://dx.doi.org/10.1016/j.jdmm.2018.05.007.

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15

Chwał, Małgorzata. "Non-Linear Modeling of Single-Walled Carbon Nanotubes." Applied Mechanics and Materials 477-478 (December 2013): 1225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.1225.

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The present paper deals with the numerical evaluation of the effective Young's modulus values for the single-walled carbon nanotubes. The analysis is focused on the definition of the interatomic interactions applying the linear and non-linear numerical procedures and their influence on the results. The interatomic potentials, the continuum mechanics, and the finite element method are used in the conducted study. The non-linear numerical procedure revealed the variation of Young's modulus values for the carbon nanotubes during deformation.

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16

Gridin, V. N., and V. I. Anisimov. "Modeling Non-Linear Systems Based on Decomposition Methods." Informacionnye Tehnologii 26, no. 3 (March 24, 2020): 131–37. http://dx.doi.org/10.17587/it.26.131-137.

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17

Lütjens, H., J.-F. Luciani, D. Leblond, F. Halpern, and P. Maget. "Non-linear modeling of core MHD in tokamaks." Plasma Physics and Controlled Fusion 51, no. 12 (November 12, 2009): 124038. http://dx.doi.org/10.1088/0741-3335/51/12/124038.

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18

LEÓN-MONTIEL, R. DE J., and H. MOYA-CESSA. "MODELING NON-LINEAR COHERENT STATES IN FIBER ARRAYS." International Journal of Quantum Information 09, supp01 (January 2011): 349–55. http://dx.doi.org/10.1142/s0219749911007319.

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A class of nonlinear coherent states related to the Susskind-Glogower (phase) operators is obtained. We call these nonlinear coherent states as Bessel states because the coefficients that expand them into number states are Bessel functions. We give a closed form for the displacement operator that produces such states.

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19

Henning, Stefan, Sebastian Linß, Philipp Gräser, René Theska, and Lena Zentner. "Non-linear analytical modeling of planar compliant mechanisms." Mechanism and Machine Theory 155 (January 2021): 104067. http://dx.doi.org/10.1016/j.mechmachtheory.2020.104067.

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20

Valencia, C., M. C. Sánchez, A. Ciruelos, A. Latorre, J. M. Madiedo, and C. Gallegos. "Non-linear viscoelasticity modeling of tomato paste products." Food Research International 36, no. 9-10 (January 2003): 911–19. http://dx.doi.org/10.1016/s0963-9969(03)00100-5.

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21

Nérneth, József G., and Johan Schoukens. "Modeling and linearization of non linear dynamic systems." IFAC Proceedings Volumes 36, no. 16 (September 2003): 339–43. http://dx.doi.org/10.1016/s1474-6670(17)34784-5.

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22

Goldenstein, Siome, Edward Large, and Dimitris Metaxas. "Non-linear dynamical system approach to behavior modeling." Visual Computer 15, no. 7-8 (November 1999): 349–64. http://dx.doi.org/10.1007/s003710050184.

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23

del-Castillo, Esteban, Luis Basañez, and Ernest Gil. "Modeling non-linear viscoelastic behavior under large deformations." International Journal of Non-Linear Mechanics 57 (December 2013): 154–62. http://dx.doi.org/10.1016/j.ijnonlinmec.2013.07.001.

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24

Ciucci, Francesco, and David G. Goodwin. "Non Linear Modeling of Mixed Ionic Electronic Conductors." ECS Transactions 7, no. 1 (December 19, 2019): 2075–82. http://dx.doi.org/10.1149/1.2729321.

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25

Gonzalez, Jesús, and Wen Yu. "Non-linear system modeling using LSTM neural networks." IFAC-PapersOnLine 51, no. 13 (2018): 485–89. http://dx.doi.org/10.1016/j.ifacol.2018.07.326.

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26

Rovatti, Riccardo, Claudia D’Ambrosio, Andrea Lodi, and Silvano Martello. "Optimistic MILP modeling of non-linear optimization problems." European Journal of Operational Research 239, no. 1 (November 2014): 32–45. http://dx.doi.org/10.1016/j.ejor.2014.03.020.

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27

Novozhilov, B. V. "Non-linear SHS phenomena: Experiment, theory, numerical modeling." Pure and Applied Chemistry 64, no. 7 (January 1, 1992): 955–64. http://dx.doi.org/10.1351/pac199264070955.

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28

Alotto, Piergiorgio, Massimo Guarnieri, and Federico Moro. "Modeling non‐linear passive direct methanol fuel cells." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 28, no. 3 (May 8, 2009): 523–39. http://dx.doi.org/10.1108/03321640910940828.

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29

Berglund, Anders, Nouna Kettaneh, Lise-Lott Uppgård, Svante Wold, Nancy Bendwell, and Dave R. Cameron. "The GIFI approach to non-linear PLS modeling." Journal of Chemometrics 15, no. 4 (April 26, 2001): 321–36. http://dx.doi.org/10.1002/cem.679.

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30

Ravaud, R., G. Lemarquand, and T. Roussel. "Time-varying non linear modeling of electrodynamic loudspeakers." Applied Acoustics 70, no. 3 (March 2009): 450–58. http://dx.doi.org/10.1016/j.apacoust.2008.05.009.

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31

Sharifi, Alireza, Yagob Dinpashoh, and Rasoul Mirabbasi. "Daily runoff prediction using the linear and non-linear models." Water Science and Technology 76, no. 4 (April 28, 2017): 793–805. http://dx.doi.org/10.2166/wst.2017.234.

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Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets. In this study, the gamma test (GT), forward selection and factor analysis were used to determine the best input combination. In addition, GT was applied to determine the optimum length of training and testing data sets. Results showed the input combination based on the GT method with five variables has better performance than other combinations. For modeling, among four techniques: artificial neural networks, local linear regression, an adaptive neural-based fuzzy inference system and support vector machine (SVM), results indicated the performance of the SVM model is better than other techniques for runoff prediction in the Amameh watershed.

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32

Ette, Ene I. "Comparing non-hierarchical models: Application to non-linear mixed effects modeling." Computers in Biology and Medicine 26, no. 6 (November 1996): 505–12. http://dx.doi.org/10.1016/s0010-4825(96)00031-5.

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33

Kabutey, Abraham, David Herak, Himsar Ambarita, and Riswanti Sigalingging. "Modeling of Linear and Non-linear Compression Processes of Sunflower Bulk Oilseeds." Energies 12, no. 15 (August 3, 2019): 2999. http://dx.doi.org/10.3390/en12152999.

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The present study aimed at describing the experimental and theoretical force-deformation curves of sunflower bulk oilseeds at varying initial pressing heights and vessel diameters as well as determining the theoretical pressure and energy along the screw press FL 200 pressing chambers. The design of efficient oil expression systems for industry and small-scale application remains a major challenge to engineers and researchers. In attempting to solve the problem, it is important to understand the linear compression process and to transfer the knowledge to the industry involving mechanical screw presses. The universal compression testing machine at a preset load of 200 kN and a speed of 5 mm·min−1, tangent curve model and the screw press FL 200 geometry parameters were applied. The obtained results of pressure and energy along the screw pressing chambers (1–7) ranged from 0.31 to 101.653 MPa and 12.616 to 1231.228 J. Applying the tangent model at n = 1 and n = 2, the cumulative pressure decreased with increasing vessel diameters while energy increased. The study provides useful information for the analysis of other bulk oilseeds and optimizing the processing parameters of screw press FL 200 and the design and development of new oil presses.

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34

Chen, Q., and V. Hans. "A new procedure for structural modeling of linear and non-linear systems." European Journal of Mechanics - A/Solids 17, no. 1 (January 1998): 139–51. http://dx.doi.org/10.1016/s0997-7538(98)80068-5.

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35

Hui, S. Y. R., and C. Christopoulos. "Modeling non-linear power electronic circuits with the transmission-line modeling technique." IEEE Transactions on Power Electronics 10, no. 1 (January 1995): 48–54. http://dx.doi.org/10.1109/63.368461.

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36

Pantò, Bartolomeo, Marialaura Malena, and Gianmarco de Felice. "Non-Linear Modeling of Masonry Arches Strengthened with FRCM." Key Engineering Materials 747 (July 2017): 93–100. http://dx.doi.org/10.4028/www.scientific.net/kem.747.93.

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Recent seismic events, such as the Central Italy (2016), the Emilia (2012) and L’Aquila (2009) earthquake, have demonstrated the high vulnerability of cultural heritage represented by historical and monumental buildings. These structures are often characterized by the presence of elements with a curved geometry such as arches and vaults, which interact with the vertical elements (walls or columns) during the earthquake motion, producing a significant effect on the seismic response of the entire structure. Aiming at the reduction of the seismic vulnerability of curved masonry elements, several techniques of reinforcing based on composite fiber materials, have been recently developed and widely investigated by means of experimental tests and numerical simulations. The using of fiber reinforced systems, applied through cementitious mortar (FRCM), is becoming a very common technique of retrofitting for historical and monumental masonry buildings. This technique, if compared to the using of fiber polymeric materials (FRP), is more compatible with the mechanical properties of the masonry and more appropriate with the preservation needs of cultural heritage, associated to the historical constructions. A discrete macro-modeling approach, already available in the literature for modeling masonry structures with plane and curved geometry, is here employed to predict the non-linear behaviour of masonry arches strengthened with FRCM. In that approach the reinforcement is explicitly modeled by using a rigid plate, while the interaction between the reinforcement and the masonry support is governed by a discrete zero thickness interface. In this paper the interfacial behavior is updated with a more sophisticated bond-slip constitutive law specifically conceived for FRCM reinforcement within the framework of fracture mechanics; in particular the proposed calibration takes into account both the pure opening mode (mode I) and the in plane shear mode (mode II). The obtained numerical results are compared with an analytical closed form solution of the problem and validated by mean of experimental tests on prototypes, available in the literature.

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37

Susilo, Edy, Suripin, and Suharyanto. "Perforated Horizontal Recharge Pipe Modeling with Non-Linear Regress." International Journal of Engineering Research and Technology 13, no. 7 (July 31, 2020): 1724. http://dx.doi.org/10.37624/ijert/13.7.2020.1724-1734.

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38

Speck, Thomas. "Modeling non-linear dielectric susceptibilities of supercooled molecular liquids." Journal of Chemical Physics 155, no. 1 (July 7, 2021): 014506. http://dx.doi.org/10.1063/5.0056657.

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39

Sharma, Bhupendra, and Subhash Chandra Sikdar. "NON-LINEAR MODELING AND ALGORITHM DEVELOPMENT FOR POULTRY HENS." Journal of Harmonized Research in Applied Science 7, no. 2 (June 30, 2019): 30. http://dx.doi.org/10.30876/johr.7.2.2019.30-33.

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40

Yilmaz, Atila, and Birsen Saka. "Non-Linear Modeling of Parabolic Reflector: Neural Network Approach." Electromagnetics 19, no. 2 (March 1999): 187–200. http://dx.doi.org/10.1080/02726349908908634.

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41

Sri Harsha, C., C. S. R. Prasanth, and Barun Pratiher. "Modeling and non-linear responses of MEMS capacitive accelerometer." MATEC Web of Conferences 16 (2014): 04003. http://dx.doi.org/10.1051/matecconf/20141604003.

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42

Grznar, John, Sameer Prasad, and Jasmine Tata. "Neural networks and organizational systems: Modeling non-linear relationships." European Journal of Operational Research 181, no. 2 (September 2007): 939–55. http://dx.doi.org/10.1016/j.ejor.2005.12.051.

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43

Tomac, Ingrid, and Marte Gutierrez. "Discrete element modeling of non-linear submerged particle collisions." Granular Matter 15, no. 6 (August 27, 2013): 759–69. http://dx.doi.org/10.1007/s10035-013-0442-8.

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44

Bickel, Bernd, Moritz Bächer, Miguel A. Otaduy, Wojciech Matusik, Hanspeter Pfister, and Markus Gross. "Capture and modeling of non-linear heterogeneous soft tissue." ACM Transactions on Graphics 28, no. 3 (July 27, 2009): 1–9. http://dx.doi.org/10.1145/1531326.1531395.

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45

Baraldi, Daniele, Antonella Cecchi, and Paolo Foraboschi. "Broken tempered laminated glass: Non-linear discrete element modeling." Composite Structures 140 (April 2016): 278–95. http://dx.doi.org/10.1016/j.compstruct.2015.12.050.

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46

Swami, A., G. B. Giannakis, and J. M. Mendel. "Linear modeling of multidimensional non-gaussian processes using cumulants." Multidimensional Systems and Signal Processing 1, no. 1 (March 1990): 11–37. http://dx.doi.org/10.1007/bf01812204.

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47

Torrecilla, José S., Regina Aroca-Santos, John C. Cancilla, and Gemma Matute. "Linear and non-linear modeling to identify vinegars in blends through spectroscopic data." LWT - Food Science and Technology 65 (January 2016): 565–71. http://dx.doi.org/10.1016/j.lwt.2015.08.027.

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48

Fiori, S. "A two-dimensional Poisson equation formulation of non-parametric statistical non-linear modeling." Computers & Mathematics with Applications 67, no. 5 (March 2014): 1171–85. http://dx.doi.org/10.1016/j.camwa.2013.12.002.

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49

Yoon, Young-Ran. "Overview of the Pharmacokinetic/Pharmacodynamic Modeling: Non-Linear Mixed Effects Modelling using NONMEM®." Journal of Korean Society for Clinical Pharmacology and Therapeutics 14, no. 2 (2006): 99. http://dx.doi.org/10.12793/jkscpt.2006.14.2.99.

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50

Bajorski, Peter. "Non-Gaussian Linear Mixing Models for Hyperspectral Images." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/818175.

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Modeling of hyperspectral data with non-Gaussian distributions is gaining popularity in recent years. Such modeling mostly concentrates on attempts to describe a distribution, or its tails, of all image spectra. In this paper, we recognize that the presence of major materials in the image scene is likely to exhibit nonrandomness and only the remaining variability due to noise, or other factors, would exhibit random behavior. Hence, we assume a linear mixing model with a structured background, and we investigate various distributional models for the error term in that model. We propose one model based on the multivariatet-distribution and another one based on independent components following an exponential power distribution. The former model does not perform well in the context of the two images investigated in this paper, one AVIRIS and one HyMap image. On the other hand, the latter model works reasonably well with the AVIRIS image and very well with the HyMap image. This paper provides the tools that researchers can use for verifying a given model to be used with a given image.

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