A nonuniform inverse fast Fourier transform (NU-IFFT) for nonuniformly sampled data is realised by combining the conjugate-gradient fast Fourier transform (CG-FFT) method with the newly developed nonuniform fast Fouri...
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A nonuniform inverse fast Fourier transform (NU-IFFT) for nonuniformly sampled data is realised by combining the conjugate-gradient fast Fourier transform (CG-FFT) method with the newly developed nonuniform fast Fourier transform (NUFFT) algorithms. An example application of the algorithm in computational electromagnetics is presented.
In this paper, hybrid technique proposed for tuning Time Delays System with proportional-integral-derivative (PID) controller. So, the performance and the robustness for a class of Time Delay System are improved. The ...
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In this paper, hybrid technique proposed for tuning Time Delays System with proportional-integral-derivative (PID) controller. So, the performance and the robustness for a class of Time Delay System are improved. The proposed hybrid technique is the combination of the iterative algorithm and curve fitting technique. The proposed iterative algorithm is improved, performance of the feedback tuning iterative technique;so the computational complexity of the iterative algorithm reduced. By using the iterative technique, the best polynomial coefficients of curve fitting technique is determined. Using the curve fitting technique, the Time Delay System is tuned and the stability parameters of the system is maintained. The curve fitting technique is one of the non linear programming techniques which can be constructed that approximately fits the data from the extract data. The proposed hybrid technique is implemented in MATLAB working platform and the tuning performance is evaluated. Then, the system performance of the proposed hybrid technique is compared with classical PID controller, Ziegler-Nichols tuning method.
An iterative algorithm for the decomposition of data series into trend and residual (including the seasonal effect) components is proposed. This algorithm is based on the approaches proposed by the authors in several ...
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An iterative algorithm for the decomposition of data series into trend and residual (including the seasonal effect) components is proposed. This algorithm is based on the approaches proposed by the authors in several previous studies and allows unbiased estimates for the trend and seasonal components for data with a strong trend containing different periodic (including seasonal) variations, as well as observational gaps and omissions. The main idea of the algorithm is that both the trend and the seasonal components should be estimated using a signal that is maximally cleaned of any other variations, which are considered a noise. In estimating the trend component, seasonal variation is a noise, and vice versa. The iterative approach allows a priori information to be more completely used in the optimization of models of both trend and seasonal components. The approximation procedure provides maximum flexibility and is fully controllable at all stages of the process. In addition, it allows one to naturally solve the problems in the case of missing observations and defective measurements without filling these dates with artificially simulated values. The algorithm was tested using data on changes in the concentration of CO2 in the atmosphere at four stations belonging to different latitudinal zones. The choice of these data is explained by the features that complicate the use of other methods, namely, high interannual variability, high-amplitude seasonal variations, and gaps in the series of observed data. This algorithm made it possible to obtain trend estimates (which are of particular importance for studying the characteristics and searching for the causes of global warming) for any time interval, including those that are not multiples of an integer number of years. The rate of increase in the CO2 content in the atmosphere has also been analyzed. It has been reliably established that in around 2016, the rate of CO2 accumulation in the atmosphere became stabilized and
A problem of studying parametric oscillations with damping is reduced to a spectral problem for a linear bundle of operators in a Hilbert space. This spectral problem is solved by an efficient algorithm. The boundarie...
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A problem of studying parametric oscillations with damping is reduced to a spectral problem for a linear bundle of operators in a Hilbert space. This spectral problem is solved by an efficient algorithm. The boundaries of the first stability domain for different values of a damping factor and a special form of a periodic function in the equation are calculated.
This paper is concerned with iterative solution to general Sylvester-conjugate matrix equation of the form Sigma(i) (=) (s)(1) A(i) V + Sigma(t)(j = 1) B-j W = Sigma(m)(l = 1) E-l (V) over bar F-l + C. An iterative al...
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This paper is concerned with iterative solution to general Sylvester-conjugate matrix equation of the form Sigma(i) (=) (s)(1) A(i) V + Sigma(t)(j = 1) B-j W = Sigma(m)(l = 1) E-l (V) over bar F-l + C. An iterative algorithm is established to solve this matrix equation. When this matrix equation is consistent, for any initial matrices, the solutions can be obtained within finite iterative steps in the absence of round off errors. Some lemmas and theorems are stated and proved where the iterative solutions are obtained. Finally, a numerical example is given to verify the effectiveness of the proposed algorithm.
We introduce and study a system of variational inclusions involving H-accretive operators in Banach spaces. By using the resolvent operator technique associated with an H-accretive operator, we prove the existence and...
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We introduce and study a system of variational inclusions involving H-accretive operators in Banach spaces. By using the resolvent operator technique associated with an H-accretive operator, we prove the existence and uniqueness of solution for the system of variational inclusions involving H-accretive operators and construct a new iterative algorithm to approximate the unique solution.
The character of the spectrum is investigated and eigen frequencies are calculated with the use of a new iterative algorithm by an example of a spectral problem on eigen frequencies of a variable-section beam loaded w...
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The character of the spectrum is investigated and eigen frequencies are calculated with the use of a new iterative algorithm by an example of a spectral problem on eigen frequencies of a variable-section beam loaded with the moment of torsion.
The conventional method for converting flight-strain gauge data to aerodynamic loading is to draw a cut on the structure at some station, make a free-body diagram, and require the internal loads of all structural elem...
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The conventional method for converting flight-strain gauge data to aerodynamic loading is to draw a cut on the structure at some station, make a free-body diagram, and require the internal loads of all structural elements at the cut to balance the external aerodynamic load distributed over the free body. A significant disadvantage with this approach arises if the wing is inadequately strain gauged or if the distribution of the aerodynamic loads is poorly understood. The proposed approach presents a linear combination technique, which assumes the aerodynamic load on the structure can be represented by using a set of polynomial shape functions over the wing surface, including lift, drag, and side forces, as well as inertial force, even thermal load, because the measured strain gauge data reflect the influence of all forces. The linear combination technique is an attempt to simulate these forces by distinguishing each individual contribution to the measured strain gauge data, so that the shape functions used in the linear combination should include all possible forces. A finite element model of the wing structure is used to calibrate the shape functions in terms of unit strain at given gauge locations. From measured gauge data, weight factors for the shape functions are computed, thereby determining the pressure distribution on the structure.
The purpose of this paper is to investigate the iterative algorithm for finding approximate solutions of a class of mixed variational-like inequalities in a real Hilbert space, where the iterative algorithm is present...
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The purpose of this paper is to investigate the iterative algorithm for finding approximate solutions of a class of mixed variational-like inequalities in a real Hilbert space, where the iterative algorithm is presented by virtue of the auxiliary principle technique. On one hand, the existence of approximate solutions of this class of mixed variational-like inequalities is proven. On the other hand, it is shown that the approximate solutions converge strongly to the exact solution of this class of mixed variational-like inequalities.
The paper proposes a new algorithm for solving one class of the tool path problems for CNC sheet cutting machines (the generalized segmental continuous cutting problem, GSCCP) with an additional parameter limited the ...
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ISBN:
(纸本)9783031671944;9783031671951
The paper proposes a new algorithm for solving one class of the tool path problems for CNC sheet cutting machines (the generalized segmental continuous cutting problem, GSCCP) with an additional parameter limited the calculation time for finding an optimal solution. As input data in the GSCCP, 2D layout of nested parts and a finite set of subtasks for optimizing the cutting path are used. Each subtask contains a set of so-called basic cutting segments, which determine the trajectories of tool movement between the points for material piercing and the points for switching tool off. Each subtask can be solved independently within the specified calculation time. The best solution found for the all subtasks is a solution to the GSCCP problem. The proposed iterative algorithm involves quantizing the total computation time. Moreover, within each time quant, all subtasks are also solved sequentially by calculation the upper and lower bounds. Initial upper bounds can, for example, be obtained using any fast heuristic based on effective combinatorial optimization methods. Next, for each of them, the procedure for searching for the lower boundary is launched. The next iteration of the solution process is performed taking into account the adjusted bounds. The solution procedures are interrupted when the specified time quant is reached, or upon obtaining a guaranteed exact solution. The effectiveness of the algorithm is illustrated by practical examples. It is also shown that for generate a set of finite set of subtasks for GSCCP, it is advisable to use neural networks.
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