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Статті в журналах з теми "Linear mathematical model"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Komogortsev, Oleg, Corey Holland, Sampath Jayarathna, and Alex Karpov. "2D Linear oculomotor plant mathematical model." ACM Transactions on Applied Perception 10, no. 4 (October 2013): 1–18. http://dx.doi.org/10.1145/2536764.2536774.

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2

Neamah, Kawther Abood. "Mathematical Model for Handling Unstable Time Series by Using a Linear Approximation Technique." Webology 19, no. 1 (January 20, 2022): 2835–52. http://dx.doi.org/10.14704/web/v19i1/web19189.

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Time series are typically built on basic assumptions that include stationarity, linearity and normality. The three characteristics are crucial for estimating and building time series models. Studies on time series include these assumptions. To deal with unstable time series that are based on its basis, mathematical models that are suitable for such series are adopted in this study. A nonlinear self-regression model, called the rational model, is proposed. This model is a fraction in which the numerator is the complete sine function and the denominator is an exponential self-regression model. The fixed point and limit cycle of the model are simulated and determined, and its stability is studied using a linear approximation technique.

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3

Osipov, S. P., E. Yu Usachev, S. V. Chakhlov, S. A. Schetinkin, A. A. Manushkin, O. S. Osipov, and N. A. Sergeeva. "A Mathematical Model of Digital Linear Tomography." Russian Journal of Nondestructive Testing 55, no. 5 (May 2019): 407–17. http://dx.doi.org/10.1134/s1061830919050085.

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4

Okhrimenko, Viacheslav, and Maiia Zbіtnieva. "Mathematical Model of Tubular Linear Induction Motor." Mathematical Modelling of Engineering Problems 8, no. 1 (February 28, 2021): 103–9. http://dx.doi.org/10.18280/mmep.080113.

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Problem of calculation of distribution of magnetic field induction in clearance of tubular linear induction motor (TLIM) is considered. Mathematical model is represented by Fredholm integral equations of second kind for complexes of electric field strength and density of coupled magnetization currents at interface of environments. Algorithm of calculation of distribution of magnetic field induction in TLIM clearance has been developed. Dependence of magnetic field induction in motor clearance on value of pole division is investigated. There is area of optimum pole pitch. Reliability of results of calculations on mathematical model is confirmed by their comparison with results obtained on physical model. Calculated dependence of induction on thickness of runner's iron circuit also has extreme character. Given model can be used at design stage of TLIM. Model allows calculating its optimal geometric dimensions based on criterion of maximum induction in motor clearance, taking into account physical properties of applied materials.

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5

Parovik, Roman. "Mathematical Modeling of Linear Fractional Oscillators." Mathematics 8, no. 11 (October 29, 2020): 1879. http://dx.doi.org/10.3390/math8111879.

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In this work, based on Newton’s second law, taking into account heredity, an equation is derived for a linear hereditary oscillator (LHO). Then, by choosing a power-law memory function, the transition to a model equation with Gerasimov–Caputo fractional derivatives is carried out. For the resulting model equation, local initial conditions are set (the Cauchy problem). Numerical methods for solving the Cauchy problem using an explicit non-local finite-difference scheme (ENFDS) and the Adams–Bashforth–Moulton (ABM) method are considered. An analysis of the errors of the methods is carried out on specific test examples. It is shown that the ABM method is more accurate and converges faster to an exact solution than the ENFDS method. Forced oscillations of linear fractional oscillators (LFO) are investigated. Using the ABM method, the amplitude–frequency characteristics (AFC) were constructed, which were compared with the AFC obtained by the analytical formula. The Q-factor of the LFO is investigated. It is shown that the orders of fractional derivatives are responsible for the intensity of energy dissipation in fractional vibrational systems. Specific mathematical models of LFOs are considered: a fractional analogue of the harmonic oscillator, fractional oscillators of Mathieu and Airy. Oscillograms and phase trajectories were constructed using the ABM method for various values of the parameters included in the model equation. The interpretation of the simulation results is carried out.

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6

KANEKO, Satoshi, Ryuta SATO, and Masaomi TSUTSUMI. "Mathematical Model of Linear Motor Stage with Non-linear Friction Characteristics." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2007.4 (2007): 8D414. http://dx.doi.org/10.1299/jsmelem.2007.4.8d414.

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7

KANEKO, Satoshi, Ryuta SATO, and Masaomi TSUTSUMI. "Mathematical Model of Linear Motor Stage with Non-Linear Friction Characteristics." Journal of Advanced Mechanical Design, Systems, and Manufacturing 2, no. 4 (2008): 675–84. http://dx.doi.org/10.1299/jamdsm.2.675.

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8

M. "Mathematical Analysis of the Non Linear Epidemic Model." Journal of Mathematics and Statistics 8, no. 2 (February 1, 2012): 258–63. http://dx.doi.org/10.3844/jmssp.2012.258.263.

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9

KARLBERG, J., I. ENGSTRÖM, P. KARLBERG, and J. G. FRYER. "Analysis of Linear Growth Using a Mathematical Model." Acta Paediatrica 76, no. 3 (May 1987): 478–88. http://dx.doi.org/10.1111/j.1651-2227.1987.tb10503.x.

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10

Zhuzhgov, Nikita, and Anatoly Klyuchnikov. "Mathematical Model for Studying a Linear Synchronous Motor." Известия высших учебных заведений. Электромеханика 64, no. 6 (2021): 22–28. http://dx.doi.org/10.17213/0136-3360-2021-6-22-28.

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In the presented article, synchronous electric motors of reciprocating motion of a linear structure and a typical rotational motion are considered. As a rule, such electric motors are two-phase, one winding is supplied with constant voltage, and the other with alternating voltage. To study transient processes in linear synchronous motors (SM), the equations of Park-Gorev SD of rotary action were taken as a basis, but they were transformed taking into account the necessary additions. As a result of the work, a mathematical model of linear SD is shown, which allows one to study the reciprocating motion of linear SD. With the help of the developed mathematical model, the study of the asynchronous start of the linear SD in the coordinates α-β and d-q was carried out. As a result of comparing the results in different coordinates, an acceptable accuracy of the calculated data was obtained.

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11

Et. al., K. Lakshmi,. "A STUDY ON MATHEMATICAL AND STATISTICAL ASPECTS OF LINEAR MODELS." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (April 10, 2021): 1328–38. http://dx.doi.org/10.17762/turcomat.v12i4.1202.

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Анотація:

The primary objective of this research article is to present the mathematical and statistical aspects of linear models and their characteristic properties. Linear model is the most common modeling used in science. Actually linear models have many different meanings depend on the context. Linear model is often preferred than other model such as quadratic model because of its ability to interpret easily. In the other hand most of the real life cases have linear relationship .Modeling the cases using linear model will able us to determine the relative influence of one or more independent variables to the dependent variable. In the present talk an attempt has been made to propose the specific forms of simple and multiple linear regression models. In this conversation mathematical aspects of linear models have been extensively depicted. Different types of mathematical models are discussed here and the methods of fitting transformed models are proposed.Furthermore specific form of linear statistical model is presented and the crucial assumptions of general linear model are extensively discussed.At the last stage of this article the method of ordinary least squares estimation of parameters of a linear model has been proposed

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12

Neetu Rani, Kiran Bamel, Abhinav Shukla, and Nandini Singh. "Analysis of Five Mathematical Models for Crop Yield Prediction." South Asian Journal of Experimental Biology 12, no. 1 (February 27, 2022): 46–54. http://dx.doi.org/10.38150/sajeb.12(1).p46-54.

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Анотація:

A review of mathematical models used for the prediction of crop yield has been presented. Though there are many other non-mathematical techniques also available for the purpose, but mathematical modeling used for any real world problem opens many perspectives and provides many possible solu-tions of the problem for the betterment of human race. Five mathematical models (remote sensing followed by mathematical modeling, fuzzy logic based model, multiple linear regression mechanistic model, linear algebra based descriptive model and growth model based on TOMGRO mechanistic model) have been extracted and analyzed critically. These models are based on different mathematical concepts and techniques (non-linear optimization, fuzzy logic, linear predictor functions, linear algebra and differential calculus) covering a wide range of mathematical modeling. The general forms of these models have been derived. Average accuracy of presented models was found to be in the range 90% - 99% that strongly favors the optimum usage of mathematical modeling for crop yield forecasting processes. The section giving gaps and future research prospects presents the comparative analysis of the models. Development of new and moderated mathematical models for more precision and better accuracy has also been suggested using new mathematical techniques and hybridization or modifying the existing models.

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13

PITCHER, ASHLEY B. "Adding police to a mathematical model of burglary." European Journal of Applied Mathematics 21, no. 4-5 (April 21, 2010): 401–19. http://dx.doi.org/10.1017/s0956792510000112.

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We review the Short model of urban residential burglary derived from taking the continuum limit of two difference equations – one of which models the attractiveness of individual houses to burglary, and the other of which models burglar movement. This leads to a system of non-linear partial differential equations. We propose a change to the Short model and also add deterrence caused by the presence of uniformed officers to the model. We solve the resulting system of non-linear partial differential equations numerically and present results both with and without deterrence.

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14

Leonavičiene, T., and K. Pileckas. "THE MATHEMATICAL MODEL OF COMPRESSIBLE FLUID FLOW." Mathematical Modelling and Analysis 7, no. 1 (June 30, 2002): 117–26. http://dx.doi.org/10.3846/13926292.2002.9637184.

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In this note we consider the mathematical model of the isothermal compressible fluid flow in an exterior domain O ⊂ R3. In order to solve this problem we apply a decomposition scheme and reduce the nonlinear problem to an operator equation with a contraction operator. After the decomposition the nonlinear problem splits into three linear problems: Neumann‐like problem, modified Stokes problem and transport equation. These linear problems are solved in weighted function spaces with detached asymptotics.

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15

Ammar, E. E., and A. A. Emsimir. "A mathematical model for solving integer linear programming problems." African Journal of Mathematics and Computer Science Research 13, no. 1 (February 29, 2020): 39–50. http://dx.doi.org/10.5897/ajmcsr2019.0804.

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16

Ghaderi, S. F., J. Razmi ., and B. Ostadi . "A Mathematical Model for Load Optimization: Linear Load Curves." Journal of Applied Sciences 6, no. 4 (February 1, 2006): 883–87. http://dx.doi.org/10.3923/jas.2006.883.887.

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17

Fan, Huijun, Tyler Jarvis, and Yongbin Ruan. "A mathematical theory of the gauged linear sigma model." Geometry & Topology 22, no. 1 (October 31, 2017): 235–303. http://dx.doi.org/10.2140/gt.2018.22.235.

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18

Molis, Marijanas. "THE RESEARCH RESULTS OF THE LINEAR ELECTROMAGNETIC MOTOR MATHEMATICAL MODEL." Mokslas - Lietuvos ateitis 2, no. 1 (February 28, 2010): 99–102. http://dx.doi.org/10.3846/mla.2010.022.

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Development of the mathematical model of the linear electromagnetic motor and the dependencies of the inductance and traction force on the secondary element position expressed by mathematical equations, are presented in this research article. The dependency of the inductance on the secondary element position was obtained, approximating the inductance change diagram obtained experimentally. Also, using the theory of electromechanical energy transformation, mathematical expressions of the dependency of the traction force on the secondary element position were obtained. Mathematical model of the linear electromagnetic motor is composed of the system of differential equations. The Runge – Kutta calculation method was used to solve these equations. The transitional processes of the current, speed and secondary element position obtained with the solution of the system of differential equations at different supply voltage also the transitional processes of the dynamic traction force obtained at 24 V supply voltage of the motor. All obtained results of the dependencies and transitional processes of the mathematical model are presented in the graphic form. In accordance with the obtained results of the mathematical model the conclusions were formulated, specifying electromagnetic properties of the linear electromagnetic motor.

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19

Raju, M. Naga, and M. Sandhya Rani. "Mathematical Modelling of Linear Induction Motor." International Journal of Engineering & Technology 7, no. 4.24 (November 27, 2018): 111. http://dx.doi.org/10.14419/ijet.v7i4.24.21868.

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The Linear Induction Motor is a special purpose electrical machines it produces rectilinear motion in place of rotational motion. By using D-Q axes equivalent circuit the mathematical modelling is done because to distinguish dynamic behavior of LIM, because of the time varying parameters like end effect, saturation of core, and half filled slot the dynamic modelling of LIM is difficult. For simplification hear we are using the two axes modelling because to evade inductances time varying nature it becomes complex in modelling, this also reduces number of variables in the dynamic equation. Modelling is done using MATLAB/SIMULINK. LIM can be controlled by using sliding model control, vector control, and position control.

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20

Potapenko, E. M., E. V. Dushinova, A. E. Kazurova, and S. G. Deev. "The linear mathematical model of induction drive with vector control." Electrical Engineering and Power Engineering, no. 2 (December 22, 2010): 25–36. http://dx.doi.org/10.15588/1607-6761-2010-2-5.

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21

Marciniak-Czochra, A., and M. Kimmel. "Mathematical model of tumor invasion along linear or tubular structures." Mathematical and Computer Modelling 41, no. 10 (May 2005): 1097–108. http://dx.doi.org/10.1016/j.mcm.2005.05.005.

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22

Belmonte, G., E. Caroli, M. Fabbri, L. Fasano, A. M. G. Pacilli, and G. Pallotti. "A non linear mathematical model to investigate the alveolar diffusion." Journal of Biomechanics 39 (January 2006): S600. http://dx.doi.org/10.1016/s0021-9290(06)85489-8.

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23

Ying Wang, Jun Li, Zanji Wang, Min Gao, and Yanjie Cao. "A mathematical model of the reusable linear magnetic flux compressor." IEEE Transactions on Magnetics 37, no. 1 (2001): 147–51. http://dx.doi.org/10.1109/20.911809.

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24

Shekhovtsov, A. N. "Non-linear Mathematical Model of the Dolphin Tail Fin Motion." International Journal of Fluid Mechanics Research 28, no. 1-2 (2001): 90–115. http://dx.doi.org/10.1615/interjfluidmechres.v28.i1-2.80.

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25

Bliokh, Yu P., M. G. Lyubarsky, V. O. Podobinsky, and Ya B. Fainberg. "Unsteady beam-plasma discharge: I. Mathematical model and linear theory." Plasma Physics Reports 29, no. 9 (September 2003): 740–47. http://dx.doi.org/10.1134/1.1609576.

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26

M�ller, H. J., M. Leitner, and T. Dietzfelbinger. "A linear mathematical model for computerized analyses of mood curves." European Archives of Psychiatry and Neurological Sciences 236, no. 5 (July 1987): 260–68. http://dx.doi.org/10.1007/bf00380950.

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27

Fujioka, J. "A mathematical model for electrocrystallization under linear sweep voltammetry conditions." Journal of Crystal Growth 91, no. 1-2 (August 1988): 147–54. http://dx.doi.org/10.1016/0022-0248(88)90380-6.

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28

Klimasara, Anthony J. "A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications." Advances in X-ray Analysis 36 (1992): 1–10. http://dx.doi.org/10.1154/s0376030800018577.

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Анотація:

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

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29

Lupenko, Sergiy, Nadiia Lutsyk, and Yuri Lapusta. "Cyclic Linear Random Process As A Mathematical Model Of Cyclic Signals." Acta Mechanica et Automatica 9, no. 4 (December 1, 2015): 219–24. http://dx.doi.org/10.1515/ama-2015-0035.

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Abstract In this study the cyclic linear random process is defined, that combines the properties of linear random process and cyclic random process. This expands the possibility describing cyclic signals and processes within the framework of linear random processes theory and generalizes their known mathematical model as a linear periodic random process. The conditions for the kernel are given and the probabilistic characteristics of generated process of linear random process in order to be a cyclic random process. The advantages of the cyclic linear random process are presented. It can be used as the mathematical model of the cyclic stochastic signals and processes in various fields of science and technology.

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30

Banerjee, Sumita. "Mathematical Model of Relativistic Anisotropic Compact Stellar Model Admitting Linear Equation of State." Communications in Theoretical Physics 70, no. 5 (November 2018): 585. http://dx.doi.org/10.1088/0253-6102/70/5/585.

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31

Vorokova, Nodira Kh, Alina E. Sennikova, Vladimir V. Abrosimov, Valeria E. Vasilchenko, and Pavel N. Priymak. "MATHEMATICAL MODEL OF FINANCIAL INVESTMENT RISK." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 5/3, no. 125 (2022): 89–95. http://dx.doi.org/10.36871/ek.up.p.r.2022.05.03.012.

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Анотація:

This article measures the amount of income and the degree of risk of financial investments. Analysis of the relationship between income and risks in financial investments is based on the theory of multipurpose linear programming. In the process of using study data obtained using MATLAB software. This approach requires an analysis of the investor's profitability assessment at a constant level of risk and minimized risk with the search for a profitable one. The results of the study are the basis for the formation of a diverse portfolio in the presence of various risks in financial investment. The monitor calculations presented in the article are generated by the application model to define a generic portfolio.

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32

El-Ashmawy, Khalid L. A. "USING DIRECT LINEAR TRANSFORMATION (DLT) METHOD FOR AERIAL PHOTOGRAMMETRY APPLICATIONS." Geodesy and cartography 44, no. 3 (October 16, 2018): 71–79. http://dx.doi.org/10.3846/gac.2018.1629.

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DLT has gained a wide popularity in close range photogrammetry, computer vision, robotics, and biomechanics. The wide popularity of the DLT is due to the linear formulation of the relationship between image and object space coordinates. This paper aims to develop a simple mathematical model in the form of self calibration direct linear transformation for aerial photogrammetry applications. Software based on the derived mathematical model has been developed and tested using mathematical photogrammetric data. The effects of block size, number and location of control points, and random and lens distortion errors on self calibration block adjustments using the derived mathematical model and collinearity equations have been studied. It was found that the accuracy of the results of self calibration block adjustment using the derived mathematical model is, to some extent, comparable to the results with collinearity model. The developed mathematical model widens the application areas of DLT method to include aerial photogrammetry applications especially when the camera interior and exterior orientations are unknown.

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33

Dexter, F., Y. Rudy, and G. M. Saidel. "Mathematical model of acetylcholine kinetics in neuroeffector junctions." American Journal of Physiology-Heart and Circulatory Physiology 266, no. 1 (January 1, 1994): H298—H309. http://dx.doi.org/10.1152/ajpheart.1994.266.1.h298.

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Анотація:

Acetylcholine (ACh) kinetics in neuroeffector junctions (NEJ) of the sinus node plays a key role in vagal control of heart rate. Prior studies have shown that the concentration of ACh ([ACh]) in NEJ appears to follow first-order linear kinetics. To find out the reason why, we examine mathematically diffusion, degradation, and receptor binding of ACh in NEJ. We identify seven conditions that potentially influence ACh kinetics. Because these conditions are satisfied for NEJ in the sinus node, 1) the nonlinearity of ACh binding to muscarinic receptors has little effect on [ACh]; 2) [ACh] does not depend on the distribution of acetylcholinesterase between the interstitial space and the pacemaker cells; 3) the interval from trough to subsequent peak [ACh] at the pacemaker cells is negligible; 4) the mean [ACh] at the pacemaker cells is proportional to the frequency of vagal activity multiplied by the amount of ACh released per vagal stimulus and divided by the rate coefficient of ACh degradation; and 5) [ACh] at pacemaker cells nearly follows first-order linear kinetics but does not at other sites in the NEJ. We conclude that earlier studies showed that [ACh] follows first-order linear kinetics, because they predicted [ACh] only at pacemaker cells. ACh kinetics at other sites in the NEJ, such as at nerve endings, is different.

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34

Scott, Gary M., and W. Harmon Ray. "Neural Network Process Models Based on Linear Model Structures." Neural Computation 6, no. 4 (July 1994): 718–38. http://dx.doi.org/10.1162/neco.1994.6.4.718.

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Анотація:

The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. This idea is extended by presenting the MANNIDENT (Multivariable Artificial Neural Network Identification) algorithm by which the mathematical equations of linear dynamic process models determine the topology and initial weights of a network, which is further trained using backpropagation. This method is applied to the task of modeling a nonisothermal chemical reactor in which a first-order exothermic reaction is occurring. This method produces statistically significant gains in accuracy over both a standard neural network approach and a linear model. Furthermore, using the approximate linear model to initialize the weights of the network produces statistically less variation in model fidelity. By structuring the neural network according to the approximate linear model, the model can be readily interpreted.

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35

Skrobian, Milan, and Rudolf Pernis. "MATHEMATICAL EQUATION FOR IMPURITY DISTRIBUTION AFTER SECOND PASS OF ZONE REFINING." Acta Metallurgica Slovaca 27, no. 1 (February 25, 2021): 32–35. http://dx.doi.org/10.36547/ams.27.1.808.

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Анотація:

A mathematical equation has been derived that describes impurity distribution in ingot after second pass of zone refining. While an exponential impurity distribution is calculated by a simplified model after first pass, second pass is described by mixed linear - exponential model. Relationship of transformed impurity concentration is constant over whole length of semi-infinite ingot for first pass. However, it has linear trend for second pass. Last part of molten zone at infinity solidifies differently and can be described mathematically as directional crystallization. A mathematical tool devised for second pass of zone refining can be tried to be used for derivation of functions of more complex models that would describe impurity distribution in more realistic way compared to simplified approach. Such models could include non-constant distribution coefficient and/or shrinking or widening molten zone over a length of ingot.

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36

Leszczyński, Maciej, Urszula Ledzewicz, and Heinz Schättler. "Optimal control for a mathematical model for chemotherapy with pharmacometrics." Mathematical Modelling of Natural Phenomena 15 (2020): 69. http://dx.doi.org/10.1051/mmnp/2020008.

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Анотація:

An optimal control problem for an abstract mathematical model for cancer chemotherapy is considered. The dynamics is for a single drug and includes pharmacodynamic (PD) and pharmacokinetic (PK) models. The aim is to point out qualitative changes in the structures of optimal controls that occur as these pharmacometric models are varied. This concerns (i) changes in the PD-model for the effectiveness of the drug (e.g., between a linear log-kill term and a non-linear Michaelis-Menten type Emax-model) and (ii) the question how the incorporation of a mathematical model for the pharmacokinetics of the drug effects optimal controls. The general results will be illustrated and discussed in the framework of a mathematical model for anti-angiogenic therapy.

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37

Long, Benjamin L., A. Isabella Gillespie, and Martin L. Tanaka. "Mathematical Model to Predict Drivers’ Reaction Speeds." Journal of Applied Biomechanics 28, no. 1 (February 2012): 48–56. http://dx.doi.org/10.1123/jab.28.1.48.

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Mental distractions and physical impairments can increase the risk of accidents by affecting a driver’s ability to control the vehicle. In this article, we developed a linear mathematical model that can be used to quantitatively predict drivers’ performance over a variety of possible driving conditions. Predictions were not limited only to conditions tested, but also included linear combinations of these tests conditions. Two groups of 12 participants were evaluated using a custom drivers’ reaction speed testing device to evaluate the effect of cell phone talking, texting, and a fixed knee brace on the components of drivers’ reaction speed. Cognitive reaction time was found to increase by 24% for cell phone talking and 74% for texting. The fixed knee brace increased musculoskeletal reaction time by 24%. These experimental data were used to develop a mathematical model to predict reaction speed for an untested condition, talking on a cell phone with a fixed knee brace. The model was verified by comparing the predicted reaction speed to measured experimental values from an independent test. The model predicted full braking time within 3% of the measured value. Although only a few influential conditions were evaluated, we present a general approach that can be expanded to include other types of distractions, impairments, and environmental conditions.

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38

Jagathesan, Dr T. "Application of Mathematical Models in Agriculture- A Review." JOURNAL OF DEVELOPMENT ECONOMICS AND MANAGEMENT RESEARCH STUDIES 05, no. 05 (2020): 66–82. http://dx.doi.org/10.53422/jdms.2020.5501.

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Application of mathematical models are for solving problems in agriculture for a scientific understanding, quantitative expression and to take strategic decisions. Mathematical models include mechanistic, empirical, deterministic, and stochastic approaches. It has dynamic models with differential equations, static models with algebraic for a specific set of conditions, deterministic models suggest solutions, stochastic model deals with defined by probability functions, mechanistic model deals with theory or hypothesis, and empirical models uses existing data to explain the relationship between one or two variables. Mathematical models have been developed to investigate specific issues limited to mathematical formulation and the added complexity inherent of integrated models. Mathematical methods of resource utilization optimization have been used in practice and the first mathematical programming approaches include the method of linear programming (simplex method). Linear approach to modeling establishes the relationship between a dependent variable and one or more independent variables. In the linear equation, dependent and independent variables, coefficients, intercept or the bias coefficient and degree of freedom have been used. The application of mathematical models in agriculture portrays the main methods of various mathematical tools like analytical, simulation and empirical. This paper aims at application of Mathematical Models in agriculture.

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39

Jimenez-Fernandez, Victor, Luis Hernandez-Martinez, and Arturo Sarmiento-Reyes. "An Iterative Decomposed Piecewise-Linear Model Description." Active and Passive Electronic Components 2009 (2009): 1–5. http://dx.doi.org/10.1155/2009/824531.

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A model description for the representation of one-dimensional piecewise-linear characteristics is presented. The model can be denoted as a decomposed one, because the independent and dependent variables of the PWL characteristic are treated separately. It is also called iterative, because the particular representation of each segment of the PWL characteristic depends on the value of only one parameter included in the mathematical formulation, it gives the possibility of modeling both, univalued and multivalued PWL characteristics.

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40

Sun, Minghe. "Linear Programming Approaches for Multiple-Class Discriminant and Classification Analysis." International Journal of Strategic Decision Sciences 1, no. 1 (January 2010): 57–80. http://dx.doi.org/10.4018/jsds.2010103004.

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New linear programming approaches are proposed as nonparametric procedures for multiple-class discriminant and classification analysis. A new MSD model minimizing the sum of the classification errors is formulated to construct discriminant functions. This model has desirable properties because it is versatile and is immune to the pathologies of some of the earlier mathematical programming models for two-class classification. It is also purely systematic and algorithmic and no user ad hoc and trial judgment is required. Furthermore, it can be used as the basis to develop other models, such as a multiple-class support vector machine and a mixed integer programming model, for discrimination and classification. A MMD model minimizing the maximum of the classification errors, although with very limited use, is also studied. These models may also be considered as generalizations of mathematical programming formulations for two-class classification. By the same approach, other mathematical programming formulations for two-class classification can be easily generalized to multiple-class formulations. Results on standard as well as randomly generated test datasets show that the MSD model is very effective in generating powerful discriminant functions.

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41

Baliuta, S. M., P. O. Chernenko, Iu V. Kuievda, and V. P. Kuevda. "IDENTIFICATION OF MATHEMATICAL MODEL OF TURBINE GENERATOR UNIT IN PRESENCE OF UNCERTAINTY." Tekhnichna Elektrodynamika 2021, no. 1 (January 14, 2021): 32–39. http://dx.doi.org/10.15407/techned2021.01.032.

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An identification procedure of mathematical model of turbine generator unit in the presence of uncertainty is studied for using in the interconnected robust control automated system. The procedure is based on “worst-case” identification approach. The controlled object is modelled by the matrix transfer function with additive uncertainty. The identification consists of two stages: first is to identify transfer function with nominal parameters with the use of prediction error minimization algorithm, second – to determine weight function in additive uncertainty model using finding the worst-case log-magnitude curve of uncertainties. Identification is performed in active way, determining datasets for each control channel from individual experiments. A linear frequency-modulated signal is selected as the input test disturbance. A simulation model of the controlled object is constructed and the numerical experiment is conducted using the identification procedure. References 11, figures 7.

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42

Mańko, Robert, and Norbert Laskowski. "Comparative analysis of the effectiveness of the conceptual rainfall-runoff hydrological models on the selected rivers in Odra and Vistula basins." ITM Web of Conferences 23 (2018): 00025. http://dx.doi.org/10.1051/itmconf/20182300025.

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Identification of physical processes occurred in the watershed is one of the main tasks in hydrology. Currently the most efficient hydrological processes describing and forecasting tool are mathematical models. They can be defined as a mathematical description of relations between specified attributes of analysed object. It can be presented by: graphs, arrays, equations describing functioning of the object etc. With reference to watershed a mathematical model is commonly defined as a mathematical and logical relations, which evaluate quantitative dependencies between runoff characteristics and factors, which create it. Many rainfall-runoff linear reservoirs conceptual models have been developed over the years. The comparison of effectiveness of Single Linear Reservoir model, Nash model, Diskin model and Wackermann model is presented in this article.

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43

Khramov, B. A., and A. V. Gusev. "Investigation of dynamic characteristics of three-linear flow regulator." Journal of «Almaz – Antey» Air and Space Defence Corporation, no. 1 (March 30, 2019): 91–97. http://dx.doi.org/10.38013/2542-0542-2019-1-91-97.

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The study focuses on the dynamic model of the three-linear spool flow regulator for various solutions of the spool geometry, and for two variants of mathematical description of hydraulic damping devices. The paper describes the process of small deflection linearization of the obtained mathematical models. As a result of Laplace transformation of the mathematical models, we obtained a block diagram of the spool flow regulator operation. By using Nyquist criterion, we analyzed the spool flow regulator stability. As a result, we draw conclusions on the spool flow regulator stability, and on the various types of damping devices affecting it

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44

Morón, Carlos, Alfonso Garcia, and Jose Andrés Somolinos. "Mathematical Model Switched Reluctance Motor." Key Engineering Materials 644 (May 2015): 87–91. http://dx.doi.org/10.4028/www.scientific.net/kem.644.87.

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This paper describes a mathematical model of switched reluctance motor (SRM). The mathematical model of the SR motor is nonparametric and can only be established with experimental data, instead of an analytical representation. Because the reluctance varies with rotor position and magnetic saturation is part of the normal operation of SR motors, there is no simple analytical expression for the magnetic field produced by the phase windings. The shape of phase current before commutation is of interest because it varies widely depending on when the phase winding is excited and what the rotor speed is. To illustrate this effect, two step response simulations were done here in Matlab/Simulink. The SR motor model used in these two simulations is a 6/4 linear magnetics model, the same structure as the experimental SR motor. For the first simulation, a step voltage is fed into phase A and the initial rotor position is set to be 1o instead of 0o so that the rotor will move in the positive direction. The results show that the rotor stops at 45o after some oscillation which is the aligned position of this phase A. For the second simulation, a step voltage is fed into phase C. The initial position is 0o. According to this, the rotor will move towards the aligned position of phase C, i.e. 15o.

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45

Wang, J. H., and H. Y. Huang. "Model and Parameters Identification of Non-Linear Joint by Force-State Mapping in Frequency Domain." Journal of Mechanics 23, no. 4 (December 2007): 367–80. http://dx.doi.org/10.1017/s1727719100001428.

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AbstractGenerally, the Force-State Mapping (FSM) is an effective method to identify the parameters of nonlinear joints provided that the joint model is exactly known in advance. However, the variation of the non-linear joints is so large that the mathematical models of non-linear joints generally are not known in advance. Therefore, the model and the parameters of a non-linear joint should be identified simultaneously in practice. In this work, a new identification procedure which was based on the FSM method in frequency domain was proposed to identify the mathematical model and parameters of a non-linear joint simultaneously. Generally, there are many feasible combinations of models and parameters which can satisfy the measurement data within an allowable range of error. In this work, an iteration procedure was used to update the feasible models to result in an optimal model with its parameters. The simulation results show that a proper mathematical model and accurate parameters can be identified simultaneously by the new procedure even that the measurement data are contaminated by noise.

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46

Bunge, H. J. "Some Remarks on Modelling and Simulation of Physical Phenomena." Textures and Microstructures 28, no. 3-4 (January 1, 1997): 151–65. http://dx.doi.org/10.1155/tsm.28.151.

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Mathematical modelling and computer simulation of physical phenomena is a rapidly growing field of work in all areas of pure and applied sciences. In principle, mathematical modelling of physical phenomena has been the field of theoretical physics from the very beginning of physics although the computer has increased the potentials of this method by many orders of magnitude. Modelling and simulation are often used as synonyms. It may, however, be meaningfull to distinguish the development of a mathematical model from its use in computer simulation. Also, a mathematical model in this sense must be distinguished from mathematical expressions interpolating experimental data. In the field of textures, models of texture formation, models of materials properties, as well as the combination of the two are being used. In this connection it is important whether a texture formation model is linear or non-linear. In the first case the texture formation operator can be reduced to the orientation space whereas a non-linear operator operates in the full texture space.

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47

MORDESON, JOHN N., HILARY C. WETHING, and TERRY D. CLARK. "A FUZZY MATHEMATICAL MODEL OF NUCLEAR STABILITY." New Mathematics and Natural Computation 06, no. 02 (July 2010): 119–40. http://dx.doi.org/10.1142/s1793005710001669.

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We use techniques from fuzzy mathematics to develop metrics for measuring how well the US is achieving its overarching national security goal: to protect itself, its allies and its friends from both nuclear attack and coercive pressures by states possessing nuclear weapons. The metrics are linear equations assigning weights to each of the six components of the overarching goal. These weights are based on expert opinions. We determine the degree to which the experts consider certain goals as the most important. We conclude by examining the degree of agreement among experts.

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48

Sahid, Sahid, Fitriana Yuli S, and Dwi Lestari. "MATHEMATIC MODEL FOR SITY BUS SCHEDULING IN YOGYAKARTA." Jurnal Sains Dasar 4, no. 2 (May 20, 2016): 109. http://dx.doi.org/10.21831/jsd.v4i2.9085.

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Various methods can be used to construct a mathematical model of the transportation problems. One model that can be used is a linear model. Several studies have used a linear model to get the schedule and the optimal route of bus trips. This research will build a mathematical model of a city bus transportation problems in DIY using linear models. Linear model is built to get the condition density city bus passengers on shifts respectively that morning, noon, and evening. After finding a suitable model, applied to the bus passengers data in Yogyakarta. From these results it can be seen the optimum conditions in terms of density, because the condition of the city bus at this time that quiet enthusiasts. Besides, the optimum density at each shift in the morning is 11 passengers, 10 passengers during the day, and evening 9 passengers. Keywords: transportation problems, the linear model, the optimal route, density

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49

Narayana, C., B. Mahaboob, B. Venkateswarlu, J. Ravi sankar, and P. Balasiddamuni. "A Treatise on Testing General Linear Hypothesis in Stochastic Linear Regression Model." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 539. http://dx.doi.org/10.14419/ijet.v7i4.10.21223.

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The main objective of this research article is to propose test statistics for testing general linear hypothesis about parameters in stochastics linear regression model using studentized residuals, RLS estimates and unrestricted internally studentized residuals. In 1998, M. Celia Rodriguez -Campos et.al [1] introduced a new test statistics to test the hypothesis of a generalized linear model in a regression context with random design. Li Cai et.al [2] provide a new test statistic for testing linear hypothesis in an OLS regression model that not assume homoscedasticity. P. Balasiddamuni et.al [3] proposed some advanced tools for mathematical and stochastical modelling.

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50

KANEKO, Satoshi, Ryuta SATO, and Masaomi TSUTSUMI. "Mathematical Model of Linear Motor Stage with Non-Linear Friction Characteristics(Machine Elements and Manufacturing)." Transactions of the Japan Society of Mechanical Engineers Series C 75, no. 750 (2009): 470–75. http://dx.doi.org/10.1299/kikaic.75.470.

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