21 research outputs found
Numeric-Symbolic Solution for Satellite Trajectory Control by Pole Placement
Control design of satellites based on pole placement method results in determined or underdetermined multivariable polynomial systems. Since only the real solutions can be considered for hardware implementation, we are looking for exclusively these solutions. In this study we suggest a numeric-symbolic approach to compute only the real solutions directly. Employing computer algebra (Dixon or reduced Gröbner basis) a condition can be formulated for the free variables as parameters of the underdetermined system, which ensures only real solutions. Numerical example illustrates the procedure and the effectivity of the control law
CLASSIFICATION OF TIME SERIES VIA WAVELET SUBBAND ANALYSIS USING SUPPORT VECTOR MACHINE CLASSIFIER
An improved feature extraction method has been developed for classification
and identification of time series, in case of the number of the experiments are
considerably less than that of the samples in time series. The method based
on the subband analysis of the wavelet transformation of the time signals,
provides lower dimension feature vectors as well as much more robust kernel-based classifier than the traditional wavelet-based feature extraction
method does. The application of this technique is illustrated by the
classification of cerebral blood flow oscillation using support vector
classifier with Gaussian kernel. The computations were carried out with
Mathematica 5.1 and its Wavelet Application
A Numeric-Symbolic Solution of GNSS Phase Ambiguity
Solution of the Global Navigation Satellite Systems (GNSS) phase ambiguity is considered as a global quadratic mixed integer programming task, which can be transformed into a pure integer problem with a given digit of accuracy. In this paper, three alter-native algorithms are suggested. Two of them are based on local and global linearization via McCormic Envelopes, respectively. These algorithms can be effective in case of simple configuration and relatively modest number of satellites. The third method is a locally nonlinear, iterative algorithm handling the problem as {-1, 0, 1} programming and also lets compute the next best integer solution easily. However, it should keep in mind that the algorithm is a heuristic one, which does not guarantee to find the global integer optimum always exactly. The procedure is very powerful utilizing the ability of the numeric-symbolic abilities of a computer algebraic system, like Wolfram Mathematica and it is properly fast for minimum 4 satellites with normal configuration, which means the Geometric Dilution of Precision (GDOP) should be between 1 and 8. Wolfram Alpha and Wolfram Clouds Apps give possibility to run the suggested code even via cell phones. All of these algorithms are illustrated with numerical examples. The result of the third one was successfully compared with the LAMBDA method, in case of ten satellites sending signals on two carrier frequencies (L1 and L2) with weighting matrix used to weight the GNSS observation and computed as the inverse of the corresponding covariance matrix
APPLICATION OF COMPUTER ALGEBRA FOR GLUCOSE-INSULIN CONTROL IN H2/Hinf SPACE USING MATHEMATICA
In this case study, an optimal control in H2/Hinf space is
presented for glucose- insulin system of diabetic patients under intensive
care. The analysis is based on a modified two-compartment Bergman model. To
design the optimal controller, the disturbance rejection LQ method based on
the minimax differential game is applied. The critical, minimax value of the
scaling parameter γcrit is determined by symbolic solution of the
modified Riccati equation. The numeric evaluation of the symbolic
computation for γ > γ crit leads to two different solutions, but the norms of
the vectors λ1, λ2 formed by the eigenvalues of the pair of
the gain matrices are the same. The numerical results are in good agreement
with that of the μ-Toolbox of MATLAB. One of the gain matrices
with increasing γ , approaches the gain matrix computed with the traditional
LQ optimal control design. The symbolic and numerical computations were
carried out with \textitMathematica 5, and with the CSPS Application 2 as well as with MATLAB
6.5
SUPPORT VECTOR CLASSIFIER VIA MATHEMATICA
In this case study a Support Vector Classifier function has been developed
in Mathematica. Starting with a brief summary of support vector classification method,
the step by step implementation of the classification algorithm in
Mathematica is presented and explained. To check our function, two test problems,
learning a chess board and classification of two intertwined spirals are
solved. In addition, an application to filtering of airborne digital land
image by pixel classification is demonstrated using a new SVM kernel family,
the KMOD, a kernel with moderate decreasing
SYMBOLIC SOLUTION OF BOUNDARY VALUE PROBLEM VIA MATHEMATICA
Symbolic computation has been applied to Runge-Kutta technique
in order to solve a two-point boundary value problem. The unknown
boundary values are considered as symbolic variables, therefore
they will appear in a system of algebraic equations, after the
integration of the ordinary differential equations. Then this
algebraic equation system can be solved for the unknown initial
values and substituted into the solution. Consequently, only
one integration pass is enough to solve the problem instead of
using an iteration technique like shooting method. This procedure
is illustrated by solving the boundary value problem of the mechanical
analysis of a liquid storage tank. Computations were carried out
by the MATHEMATICA symbolic system
FAILURE RECOGNITION IN WASTE-WATER TREATMENT PROCESS
A failure recognition method based on the inverse solution of a linear dynamical model
was applied to detect malfunctions of waste-water plant operations. Kalman filter could
be sucessfully employed to eliminate process noise as well as modelling errors
Modelling local GPS/levelling geoid undulations using Support Vector Machines
Support vector machines (SVM) with wavelet kernel has been applied to the correcting gravimetric geoid using GPS/levelling data. These data were divided into a training and a validation set in order to ensure the extendability of the approximation of the corrector surface. The optimal parameters of the SVM were considered as a trade-off between accuracy and extendability of the solution in order to avoid overlearning. Employing 194 training points and 110 validation points, SVM provided an approximation with less than 3 cm standard deviation of the error and nearly perfect extendability