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Propagation of Flood Wave in River and Construction Riverbanks – Example of River Velika Morava
Issue:
Volume 8, Issue 2, April 2020
Pages:
46-50
Received:
26 December 2019
Accepted:
3 February 2020
Published:
13 February 2020
Abstract: In hydraulics area there is an open space for apply of numerical methods, instead of frequently used empirical formulas. At first place numerical methods may be used in river flow hydraulics and ground water flow. Application of numerical methods is given enough correct results and prediction for all parameters in river flow, that is presented in this paper. Natural phenomenon of flood wave is researched and original numerical method is shown, having calculation of water depth as primary goal. There are different amount of flooding in river in correspondence with event frequency which are monitored through numerical calculation and measuring. Numerical calculation of water depth in huge amount of flooding is important for defined defense areas. In this paper is researched flood wave in river Velika Morava, propagated from city Naissus to measuring station Bagrdan, continuing to city Smederevo where is merged with Danube. Flooding is unsteady flow with dominant part of kinetic energy. Propagation of flood wave is analyzed as plane problem and through numerical procedure are calculated values of water depth. Calculation results of propagated flood wave is treated with measured data water depth H and discharge Q. Presented calculated and measured data are compared and they have small differences, therefor results are acceptable as correct. Whole volume of flooded water in river is analyzed for calculation results correction and average cross section of flow is got.
Abstract: In hydraulics area there is an open space for apply of numerical methods, instead of frequently used empirical formulas. At first place numerical methods may be used in river flow hydraulics and ground water flow. Application of numerical methods is given enough correct results and prediction for all parameters in river flow, that is presented in t...
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Medical Image Intelligent Recognition Predicts the Recessivity Variation of Human Tissue
Issue:
Volume 8, Issue 2, April 2020
Pages:
51-63
Received:
7 January 2020
Accepted:
10 February 2020
Published:
24 February 2020
Abstract: By analyzing and predicting the latent variation of human tissues, the concept of iterative programming of heavy kernel clustering is introduced to solve the problem of intelligent recognition of medical images of inflammation and cancer. Inflammatory cells modify the accumulation of cancer cells and leap to the early stage of cancer, which is called image entropy. The hypercomplex symmetric structure of the edge sliding kernel of the entropy kernel of high-dimensional s≥ 6 image. As well as the fusion of image entropy nucleus dumbbell double sphere complex sphere, the exchangeability of the central source extreme compression line sink; the central source superstring sink compresses to the critical point, and the unconstrained 2N + 1 laminated incision will cause the high-dimensional superstring sink to break up and release the exfoliated cells. Non analytic exploitation is the inverse kernel factor of aidicom that can judge the entropy of latent tissue variation image from inflammation to early cancer. It is a foundation of revealing (predicting) system recognition data array, and can carry the first-order and second-order partial differential carriers of kernel core area. In the medical image, the identification of inflammation and cancer often troubles doctors. Based on the inherent logic between cell modification fluctuation and image, aidicom system gives the concept of image entropy, and uses the dieg algorithm to complete the classification of focus detection and recognition, as well as the prediction of future development.
Abstract: By analyzing and predicting the latent variation of human tissues, the concept of iterative programming of heavy kernel clustering is introduced to solve the problem of intelligent recognition of medical images of inflammation and cancer. Inflammatory cells modify the accumulation of cancer cells and leap to the early stage of cancer, which is call...
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Development of a Finite-difference Regularized Solution of the One-Dimensional Inverse Problem of the Wave Process
Abdugany Dzhunusovich Satybaev,
Yuliya Vladimirovna Anishchenko,
Ainagul Zhylkychyevna Kokozova,
Aliyma Torozhanovna Mamatkasymova,
Guljamal Abdazovna Kaldybaeva
Issue:
Volume 8, Issue 2, April 2020
Pages:
64-73
Received:
3 March 2020
Accepted:
23 March 2020
Published:
13 April 2020
Abstract: We consider a one-dimensional inverse problem for a partial differential equation of hyperbolic type with sources - the Dirac delta-function and the Heaviside theta-function. The generalized inverse problem is reduced to the inverse problem with data on the characteristics using the method of characteristics and the method of isolation of singularities. At the beginning, the inverse problem of the wave process with data on the characteristics with additional information for the inverse problem without small perturbations is solved by the finite-difference method. Then, for the inverse problem of the wave process with data on the characteristics with additional information with small perturbations, that is, with small changes is used by the finite-difference regularized method, which developed by one of the authors of this article. The convergence of the finite-difference regularized solution to the exact solution of the one-dimensional inverse problem of the wave process on the characteristics is shown, and the theorem on the convergence of the approximate solution to the exact solution is proved. An estimate is obtained for the convergence of the numerical regularized solution to the exact solution, which depends on the grid step, on the perturbations parameter, and on the norm of known functions. From the equivalence of the problems, the one-dimensional inverse problem of the wave process with sources - the Dirac delta-function and the Heaviside theta-function and the one-dimensional inverse problem of the wave process with data on the characteristics, it follows that the solution of the last problem will be the solution of the posed initial problem. An algorithm for solving a finite-difference regularized solution of a generalized one-dimensional inverse problem is constructed.
Abstract: We consider a one-dimensional inverse problem for a partial differential equation of hyperbolic type with sources - the Dirac delta-function and the Heaviside theta-function. The generalized inverse problem is reduced to the inverse problem with data on the characteristics using the method of characteristics and the method of isolation of singulari...
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