Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Abstract: The log periodogram regression is widely used in empirical
applications because of its simplicity, since only a least squares
regression is required to estimate the memory parameter, d, its good
asymptotic properties and its robustness to misspecification of the
short term behavior of the series. However, the asymptotic distribution
is a poor approximation of the (unknown) finite sample distribution
if the sample size is small. Here the finite sample performance of different
nonparametric residual bootstrap procedures is analyzed when
applied to construct confidence intervals. In particular, in addition to
the basic residual bootstrap, the local and block bootstrap that might
adequately replicate the structure that may arise in the errors of the
regression are considered when the series shows weak dependence in
addition to the long memory component. Bias correcting bootstrap
to adjust the bias caused by that structure is also considered. Finally,
the performance of the bootstrap in log periodogram regression based
confidence intervals is assessed in different type of models and how
its performance changes as sample size increases.
Abstract: In this paper channel estimation techniques are
considered as the support methods for OFDM transmission systems
based on Non Binary LDPC (Low Density Parity Check) codes.
Standard frequency domain pilot aided LS (Least Squares) and
LMMSE (Linear Minimum Mean Square Error) estimators are
investigated. Furthermore, an iterative algorithm is proposed as a
solution exploiting the NB-LDPC channel decoder to improve the
performance of the LMMSE estimator. Simulation results of signals
transmitted through fading mobile channels are presented to compare
the performance of the proposed channel estimators.
Abstract: In contrast to existing methods which do not take into account multiconnectivity in a broad sense of this term, we develop mathematical models and highly effective combination (BIEM and FDM) numerical methods of calculation of stationary and quasistationary temperature field of a profile part of a blade with convective cooling (from the point of view of realization on PC). The theoretical substantiation of these methods is proved by appropriate theorems. For it, converging quadrature processes have been developed and the estimations of errors in the terms of A.Ziqmound continuity modules have been received. For visualization of profiles are used: the method of the least squares with automatic conjecture, device spline, smooth replenishment and neural nets. Boundary conditions of heat exchange are determined from the solution of the corresponding integral equations and empirical relationships. The reliability of designed methods is proved by calculation and experimental investigations heat and hydraulic characteristics of the gas turbine first stage nozzle blade.
Abstract: We study the semiconvergence of Gauss-Seidel iterative
methods for the least squares solution of minimal norm of rank
deficient linear systems of equations. Necessary and sufficient conditions
for the semiconvergence of the Gauss-Seidel iterative method
are given. We also show that if the linear system of equations is
consistent, then the proposed methods with a zero vector as an initial
guess converge in one iteration. Some numerical results are given to
illustrate the theoretical results.
Abstract: This paper describes a new method for affine parameter
estimation between image sequences. Usually, the parameter
estimation techniques can be done by least squares in a quadratic
way. However, this technique can be sensitive to the presence
of outliers. Therefore, parameter estimation techniques for various
image processing applications are robust enough to withstand the
influence of outliers. Progressively, some robust estimation functions
demanding non-quadratic and perhaps non-convex potentials adopted
from statistics literature have been used for solving these. Addressing
the optimization of the error function in a factual framework for
finding a global optimal solution, the minimization can begin with
the convex estimator at the coarser level and gradually introduce nonconvexity
i.e., from soft to hard redescending non-convex estimators
when the iteration reaches finer level of multiresolution pyramid.
Comparison has been made to find the performance of the results
of proposed method with the results found individually using two
different estimators.
Abstract: This paper deals with the optimal design of two-channel recursive parallelogram quadrature mirror filter (PQMF) banks. The analysis and synthesis filters of the PQMF bank are composed of two-dimensional (2-D) recursive digital all-pass filters (DAFs) with nonsymmetric half-plane (NSHP) support region. The design problem can be facilitated by using the 2-D doubly complementary half-band (DC-HB) property possessed by the analysis and synthesis filters. For finding the coefficients of the 2-D recursive NSHP DAFs, we appropriately formulate the design problem to result in an optimization problem that can be solved by using a weighted least-squares (WLS) algorithm in the minimax (L∞) optimal sense. The designed 2-D recursive PQMF bank achieves perfect magnitude response and possesses satisfactory phase response without requiring extra phase equalizer. Simulation results are also provided for illustration and comparison.
Abstract: In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. In this context, most of the studies in the literature can be located in two possible general classes of regression models: models that study the effect the covariates have on the hazard function; and models that study the effect the covariates have on the censored response variable. Proposals in this paper are in the second class of models and, more specifically, on least squares based model approach. Thus, using the bootstrap estimate of the bias, we try to improve the estimation of the regression parameters by reducing their bias, for small sample sizes. Simulation results presented in the paper show that, for reasonable sample sizes and censoring levels, the bias is always smaller for the new proposals.
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: In this article, an adaptive least-squares mixed finite element method is studied for pseudo-parabolic integro-differential equations. The solutions of least-squares mixed weak formulation and mixed finite element are proved. A posteriori error estimator is constructed based on the least-squares functional and the posteriori errors are obtained.
Abstract: There is a world-wide need for the development of sustainable management strategies to control pest infestation and the development of phosphine (PH3) resistance in lesser grain borer (Rhyzopertha dominica). Computer simulation models can provide a relatively fast, safe and inexpensive way to weigh the merits of various management options. However, the usefulness of simulation models relies on the accurate estimation of important model parameters, such as mortality. Concentration and time of exposure are both important in determining mortality in response to a toxic agent. Recent research indicated the existence of two resistance phenotypes in R. dominica in Australia, weak and strong, and revealed that the presence of resistance alleles at two loci confers strong resistance, thus motivating the construction of a two-locus model of resistance. Experimental data sets on purified pest strains, each corresponding to a single genotype of our two-locus model, were also available. Hence it became possible to explicitly include mortalities of the different genotypes in the model. In this paper we described how we used two generalized linear models (GLM), probit and logistic models, to fit the available experimental data sets. We used a direct algebraic approach generalized inverse matrix technique, rather than the traditional maximum likelihood estimation, to estimate the model parameters. The results show that both probit and logistic models fit the data sets well but the former is much better in terms of small least squares (numerical) errors. Meanwhile, the generalized inverse matrix technique achieved similar accuracy results to those from the maximum likelihood estimation, but is less time consuming and computationally demanding.
Abstract: The scroll pump belongs to the category of positive
displacement pump can be used for continuous pumping of gases at
low pressure apart from general vacuum application. The shape of
volume occupied by the gas moves and deforms continuously as the
spiral orbits. To capture flow features in such domain where mesh
deformation varies with time in a complicated manner, mesh less
solver was found to be very useful. Least Squares Kinetic Upwind
Method (LSKUM) is a kinetic theory based mesh free Euler solver
working on arbitrary distribution of points. Here upwind is enforced
in molecular level based on kinetic flux vector splitting scheme
(KFVS). In the present study we extended the LSKUM to moving
node viscous flow application. This new code LSKUM-NS-MN for
moving node viscous flow is validated for standard airfoil pitching
test case. Simulation performed for flow through scroll pump using
LSKUM-NS-MN code agrees well with the experimental pumping
speed data.
Abstract: Variable digital filters are useful for various signal processing and communication applications where the frequency characteristics, such as fractional delays and cutoff frequencies, can be varied. In this paper, we propose a design method of variable FIR digital filters with an approximate linear phase characteristic in the passband. The proposed variable FIR filters have some large attenuation in stopband and their large attenuation can be varied by spectrum parameters. In the proposed design method, a quasi-equiripple characteristic can be obtained by using an iterative weighted least square method. The usefulness of the proposed design method is verified through some examples.
Abstract: The survival of publicly listed companies largely
depends on their stocks being liquidly traded. This goal can be
achieved when new investors are attracted to invest on companies-
stocks. Among different groups of investors, individual investors are
generally less able to objectively evaluate companies- risks and
returns, and tend to be emotionally biased in their investing
decisions. Therefore their decisions may be formed as a result of
perceived risks and returns, and influenced by companies- images.
This study finds that perceived risk, perceived returns and trust
directly affect individual investors- trading decisions while attitude
towards brand partially mediates the relationships. This finding
suggests that, in courting individual investors, companies still need to
perform financially while building a good image can result in their
stocks being accepted quicker than the stocks of good performing
companies with hidden images.
Abstract: The winding hot-spot temperature is one of the most
critical parameters that affect the useful life of the power
transformers. The winding hot-spot temperature can be calculated as
function of the top-oil temperature that can estimated by using the
ambient temperature and transformer loading measured data. This
paper proposes the estimation of the top-oil temperature by using a
method based on Least Squares Support Vector Machines approach.
The estimated top-oil temperature is compared with measured data of
a power transformer in operation. The results are also compared with
methods based on the IEEE Standard C57.91-1995/2000 and
Artificial Neural Networks. It is shown that the Least Squares
Support Vector Machines approach presents better performance than
the methods based in the IEEE Standard C57.91-1995/2000 and
artificial neural networks.
Abstract: Power System Security is a major concern in real time
operation. Conventional method of security evaluation consists of
performing continuous load flow and transient stability studies by
simulation program. This is highly time consuming and infeasible
for on-line application. Pattern Recognition (PR) is a promising
tool for on-line security evaluation. This paper proposes a Support
Vector Machine (SVM) based binary classification for static and
transient security evaluation. The proposed SVM based PR approach
is implemented on New England 39 Bus and IEEE 57 Bus systems.
The simulation results of SVM classifier is compared with the other
classifier algorithms like Method of Least Squares (MLS), Multi-
Layer Perceptron (MLP) and Linear Discriminant Analysis (LDA)
classifiers.
Abstract: Manufacturing tolerancing is intended to determine
the intermediate geometrical and dimensional states of the part
during its manufacturing process. These manufacturing dimensions
also serve to satisfy not only the functional requirements given in
the definition drawing, but also the manufacturing constraints, for
example geometrical defects of the machine, vibration and the
wear of the cutting tool. In this paper, an experimental study on the
influence of the wear of the cutting tool (systematic dispersions) is
explored. This study was carried out on three stages .The first stage
allows machining without elimination of dispersions (random,
systematic) so the tolerances of manufacture according to total
dispersions. In the second stage, the results of the first stage are
filtered in such way to obtain the tolerances according to random
dispersions. Finally, from the two previous stages, the systematic
dispersions are generated. The objective of this study is to model
by the least squares method the error of manufacture based on
systematic dispersion. Finally, an approach of optimization of the
manufacturing tolerances was developed for machining on a CNC
machine tool
Abstract: Power system state estimation is the process of
calculating a reliable estimate of the power system state vector
composed of bus voltages' angles and magnitudes from telemetered
measurements on the system. This estimate of the state vector
provides the description of the system necessary for the operation
and security monitoring. Many methods are described in the
literature for solving the state estimation problem, the most important
of which are the classical weighted least squares method and the nondeterministic
genetic based method; however both showed
drawbacks. In this paper a modified version of the genetic
algorithm power system state estimation is introduced, Sensitivity of
the proposed algorithm to genetic operators is discussed, the
algorithm is applied to case studies and finally it is compared with
the classical weighted least squares method formulation.
Abstract: In this paper, estimation of the linear regression
model is made by ordinary least squares method and the
partially linear regression model is estimated by penalized
least squares method using smoothing spline. Then, it is
investigated that differences and similarity in the sum of
squares related for linear regression and partial linear
regression models (semi-parametric regression models). It is
denoted that the sum of squares in linear regression is reduced
to sum of squares in partial linear regression models.
Furthermore, we indicated that various sums of squares in the
linear regression are similar to different deviance statements in
partial linear regression. In addition to, coefficient of the
determination derived in linear regression model is easily
generalized to coefficient of the determination of the partial
linear regression model. For this aim, it is made two different
applications. A simulated and a real data set are considered to
prove the claim mentioned here. In this way, this study is
supported with a simulation and a real data example.
Abstract: A thin layer on the component surface can be found
with high tensile residual stresses, due to turning operations, which
can dangerously affect the fatigue performance of the component. In
this paper an analytical approach is presented to reconstruct the
residual stress field from a limited incomplete set of measurements.
Airy stress function is used as the primary unknown to directly solve
the equilibrium equations and satisfying the boundary conditions. In
this new method there exists the flexibility to impose the physical
conditions that govern the behavior of residual stress to achieve a
meaningful complete stress field. The analysis is also coupled to a
least squares approximation and a regularization method to provide
stability of the inverse problem. The power of this new method is
then demonstrated by analyzing some experimental measurements
and achieving a good agreement between the model prediction and
the results obtained from residual stress measurement.