Abstract: In automatic manufacturing and assembling of mechanical, electrical and electronic parts one needs to reliably identify the position of components and to extract the information of these components. Data Matrix Codes (DMC) are established by these days in many areas of industrial manufacturing thanks to their concentration of information on small spaces. In today’s usually order-related industry, where increased tracing requirements prevail, they offer further advantages over other identification systems. This underlines in an impressive way the necessity of a robust code reading system for detecting DMC on the components in factories. This paper compares two methods for estimating the angle of orientation of Data Matrix Codes: one method based on the Hough Transform and the other based on the Mean Shift Algorithm. We concentrate on Data Matrix Codes in industrial environment, punched, milled, lasered or etched on different materials in arbitrary orientation.
Abstract: This paper presents Simulated Annealing based
approach to estimate solar cell model parameters. Single diode solar
cell model is used in this study to validate the proposed approach
outcomes. The developed technique is used to estimate different
model parameters such as generated photocurrent, saturation current,
series resistance, shunt resistance, and ideality factor that govern the
current-voltage relationship of a solar cell. A practical case study is
used to test and verify the consistency of accurately estimating
various parameters of single diode solar cell model. Comparative
study among different parameter estimation techniques is presented
to show the effectiveness of the developed approach.
Abstract: In this paper, we analyze and test a scheme for the
estimation of electrical fundamental frequency signals from the
harmonic load current and voltage signals.
The scheme was based on using two different Multi Layer
Artificial Neural Networks (ML-ANN) one for the current and the
other for the voltage.
This study also analyzes and tests the effect of choosing the
optimum artificial neural networks- sizes which determine the quality
and accuracy of the estimation of electrical fundamental frequency
signals.
The simulink tool box of the Matlab program for the simulation of
the test system and the test of the neural networks has been used.
Abstract: Many systems in the natural world exhibit chaos or non-linear behavior, the complexity of which is so great that they appear to be random. Identification of chaos in experimental data is essential for characterizing the system and for analyzing the predictability of the data under analysis. The Lyapunov exponents provide a quantitative measure of the sensitivity to initial conditions and are the most useful dynamical diagnostic for chaotic systems. However, it is difficult to accurately estimate the Lyapunov exponents of chaotic signals which are corrupted by a random noise. In this work, a method for estimation of Lyapunov exponents from noisy time series using unscented transformation is proposed. The proposed methodology was validated using time series obtained from known chaotic maps. In this paper, the objective of the work, the proposed methodology and validation results are discussed in detail.
Abstract: The detection of outliers is very essential because of
their responsibility for producing huge interpretative problem in
linear as well as in nonlinear regression analysis. Much work has
been accomplished on the identification of outlier in linear
regression, but not in nonlinear regression. In this article we propose
several outlier detection techniques for nonlinear regression. The
main idea is to use the linear approximation of a nonlinear model and
consider the gradient as the design matrix. Subsequently, the
detection techniques are formulated. Six detection measures are
developed that combined with three estimation techniques such as the
Least-Squares, M and MM-estimators. The study shows that among
the six measures, only the studentized residual and Cook Distance
which combined with the MM estimator, consistently capable of
identifying the correct outliers.
Abstract: This paper develops models to analyze the
relationship between leisure time and wage change. Using Thailand-s
Time Use Survey and Labor Force Survey data, the estimation of
wage changes in response to leisure time change indicates that media
receiving, personal care and social participation and volunteer
activities are the ones that significantly raise hourly wages. Thus, the
finding suggests the stimulation in time use for media access to
enhance knowledge and productivity, personal care for attractiveness
and healthiness in order to raise productivity, and social activities to
develop connections for possible future opportunities including wage
increase. These activities should be promoted for productive leisure
time and for welfare improvement.
Abstract: A new strategy for oriented immobilization of proteins was proposed. The strategy contains two steps. The first step is to search for a docking site away from the active site on the protein surface. The second step is trying to find a ligand that is able to grasp the targeted site of the protein. To avoid ligand binding to the active site of protein, the targeted docking site is selected to own opposite charges to those near the active site. To enhance the ligand-protein binding, both hydrophobic and electrostatic interactions need to be included. The targeted docking site should therefore contain hydrophobic amino acids. The ligand is then selected through the help of molecular docking simulations. The enzyme α-amylase derived from Aspergillus oryzae (TAKA) was taken as an example for oriented immobilization. The active site of TAKA is surrounded by negatively charged amino acids. All the possible hydrophobic sites on the surface of TAKA were evaluated by the free energy estimation through benzene docking. A hydrophobic site on the opposite side of TAKA-s active site was found to be positive in net charges. A possible ligand, 3,3-,4,4- – Biphenyltetra- carboxylic acid (BPTA), was found to catch TAKA by the designated docking site. Then, the BPTA molecules were grafted onto silica gels and measured the affinity of TAKA adsorption and the specific activity of thereby immobilized enzymes. It was found that TAKA had a dissociation constant as low as 7.0×10-6 M toward the ligand BPTA on silica gel. The increase in ionic strength has little effect on the adsorption of TAKA, which indicated the existence of hydrophobic interaction between ligands and proteins. The specific activity of the immobilized TAKA was compared with the randomly adsorbed TAKA on primary amine containing silica gel. It was found that the orderly immobilized TAKA owns a specific activity twice as high as the one randomly adsorbed by ionic interaction.
Abstract: Global Positioning System (GPS) technology is widely used today in the areas of geodesy and topography as well as in aeronautics mainly for military purposes. Due to the military usage of GPS, full access and use of this technology is being denied to the civilian user who must then work with a less accurate version. In this paper we focus on the estimation of the receiver coordinates ( X, Y, Z ) and its clock bias ( δtr ) of a fixed point based on pseudorange measurements of a single GPS receiver. Utilizing the instantaneous coordinates of just 4 satellites and their clock offsets, by taking into account the atmospheric delays, we are able to derive a set of pseudorange equations. The estimation of the four unknowns ( X, Y, Z , δtr ) is achieved by introducing an extended Kalman filter that processes, off-line, all the data collected from the receiver. Higher performance of position accuracy is attained by appropriate tuning of the filter noise parameters and by including other forms of biases.
Abstract: In power systems, protective relays must filter their
inputs to remove undesirable quantities and retain signal quantities of
interest. This job must be performed accurate and fast. A new
method for filtering the undesirable components such as DC and
harmonic components associated with the fundamental system
signals. The method is s based on a dynamic filtering algorithm. The
filtering algorithm has many advantages over some other classical
methods. It can be used as dynamic on-line filter without the need of
parameters readjusting as in the case of classic filters. The proposed
filter is tested using different signals. Effects of number of samples
and sampling window size are discussed. Results obtained are
presented and discussed to show the algorithm capabilities.
Abstract: Text data mining is a process of exploratory data
analysis. Classification maps data into predefined groups or classes.
It is often referred to as supervised learning because the classes are
determined before examining the data. This paper describes proposed
radial basis function Classifier that performs comparative crossvalidation
for existing radial basis function Classifier. The feasibility
and the benefits of the proposed approach are demonstrated by means
of data mining problem: direct Marketing. Direct marketing has
become an important application field of data mining. Comparative
Cross-validation involves estimation of accuracy by either stratified
k-fold cross-validation or equivalent repeated random subsampling.
While the proposed method may have high bias; its performance
(accuracy estimation in our case) may be poor due to high variance.
Thus the accuracy with proposed radial basis function Classifier was
less than with the existing radial basis function Classifier. However
there is smaller the improvement in runtime and larger improvement
in precision and recall. In the proposed method Classification
accuracy and prediction accuracy are determined where the
prediction accuracy is comparatively high.
Abstract: This paper presents an improvement method of
the multiple pitch estimation algorithm using comb filters.
Conventionally the pitch was estimated by using parallel
-connected comb filters method (PCF). However, PCF has
problems which often fail in the pitch estimation when there is
the fundamental frequency of higher tone near harmonics of
lower tone. Therefore the estimation is assigned to a wrong
note when shared frequencies happen. This issue often occurs
in estimating octave 3 or more. Proposed method, for solving
the problem, estimates the pitch with every harmonic instead of
every octave. As a result, our method reaches the accuracy of
more than 80%.
Abstract: The accuracy of estimated stability and control
derivatives of a light aircraft from flight test data were evaluated. The light aircraft, named ChangGong-91, is the first certified aircraft from
the Korean government. The output error method, which is a maximum likelihood estimation technique and considers measurement
noise only, was used to analyze the aircraft responses measures. The
multi-step control inputs were applied in order to excite the short period mode for the longitudinal and Dutch-roll mode for the lateral-directional motion. The estimated stability/control derivatives of Chan Gong-91 were analyzed for the assessment of handling
qualities comparing them with those of similar aircraft. The accuracy of the flight derivative estimates derived from flight test measurement
was examined in engineering judgment, scatter and Cramer-Rao bound, which turned out to be satisfactory with minor defects..
Abstract: This paper presents an adaptive feedback linearization approach to derive helicopter. Ideal feedback linearization is defined for the cases when the system model is known. Adaptive feedback linearization is employed to get asymptotically exact cancellation for the inherent uncertainty in the knowledge of the given parameters of system. The control algorithm is implemented using the feedback linearization technique and adaptive method. The controller parameters are unknown where an adaptive control law aims to drive them towards their ideal values for providing perfect model matching between the reference model and the closed-loop plant model. The converged parameters of controller would then provide good estimates for the unknown plant parameters.
Abstract: This paper includes two novel techniques for skew
estimation of binary document images. These algorithms are based on
connected component analysis and Hough transform. Both these
methods focus on reducing the amount of input data provided to
Hough transform. In the first method, referred as word centroid
approach, the centroids of selected words are used for skew detection.
In the second method, referred as dilate & thin approach, the selected
characters are blocked and dilated to get word blocks and later
thinning is applied. The final image fed to Hough transform has the
thinned coordinates of word blocks in the image. The methods have
been successful in reducing the computational complexity of Hough
transform based skew estimation algorithms. Promising experimental
results are also provided to prove the effectiveness of the proposed
methods.
Abstract: Cantilever L-shaped walls are known to be relatively economical as retaining solution. The design starts by proportioning the wall dimensions for which the stability is checked for. A ratio between the lengths of the base and the stem, falling between 0.5 to 0.7 ensure in most case the stability requirements, however, the displacement pattern of the wall in terms of rotations and translations, and the lateral pressure profile, do not have the same figure for all wall’s proportioning, as it is usually assumed. In the present work the results of a numerical analysis are presented, different wall geometries were considered. The results show that the proportioning governs the equilibrium between the instantaneous rotation and the translation of the wall-toe, also, the lateral pressure estimation based on the average value between the at-rest and the active pressure, recommended by most design standards, is found to be not applicable for all walls.
Abstract: Fuzzy Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here (long term load forecasting). Among the various techniques used in forecasting load, artificial intelligence techniques provide greater accuracy to the forecasts as compared to conventional techniques. Fuzzy Logic, a very robust artificial intelligent technique, is described in this paper to forecast load on long term basis. The paper gives a general algorithm to forecast long term load. The algorithm is an Extension of Short term load forecasting method to Long term load forecasting and concentrates not only on the forecast values of load but also on the errors incorporated into the forecast. Hence, by correcting the errors in the forecast, forecasts with very high accuracy have been achieved. The algorithm, in the paper, is demonstrated with the help of data collected for residential sector (LT2 (a) type load: Domestic consumers). Load, is determined for three consecutive years (from April-06 to March-09) in order to demonstrate the efficiency of the algorithm and to forecast for the next two years (from April-09 to March-11).
Abstract: The quantum mechanics simulation was applied for
calculating the interaction force between 2 molecules based on atomic level. For the simple extractive distillation system, it is ternary
components consisting of 2 closed boiling point components (A,lower boiling point and B, higher boiling point) and solvent (S). The
quantum mechanics simulation was used to calculate the intermolecular force (interaction force) between the closed boiling
point components and solvents consisting of intermolecular between
A-S and B-S.
The requirement of the promising solvent for extractive distillation
is that solvent (S) has to form stronger intermolecular force with only
one component than the other component (A or B). In this study, the
systems of aromatic-aromatic, aromatic-cycloparaffin, and paraffindiolefin
systems were selected as the demonstration for solvent
selection. This study defined new term using for screening the solvents called relative interaction force which is calculated from the
quantum mechanics simulation. The results showed that relative
interaction force gave the good agreement with the literature data
(relative volatilities from the experiment). The reasons are discussed. Finally, this study suggests that quantum mechanics results can improve the relative volatility estimation for screening the solvents leading to reduce time and money consuming
Abstract: Among neural models the Support Vector Machine
(SVM) solutions are attracting increasing attention, mostly because
they eliminate certain crucial questions involved by neural network
construction. The main drawback of standard SVM is its high
computational complexity, therefore recently a new technique, the
Least Squares SVM (LS–SVM) has been introduced. In this paper we
present an extended view of the Least Squares Support Vector
Regression (LS–SVR), which enables us to develop new
formulations and algorithms to this regression technique. Based on
manipulating the linear equation set -which embodies all information
about the regression in the learning process- some new methods are
introduced to simplify the formulations, speed up the calculations
and/or provide better results.
Abstract: Revolutions Applications such as telecommunications, hands-free communications, recording, etc. which need at least one microphone, the signal is usually infected by noise and echo. The important application is the speech enhancement, which is done to remove suppressed noises and echoes taken by a microphone, beside preferred speech. Accordingly, the microphone signal has to be cleaned using digital signal processing DSP tools before it is played out, transmitted, or stored. Engineers have so far tried different approaches to improving the speech by get back the desired speech signal from the noisy observations. Especially Mobile communication, so in this paper will do reconstruction of the speech signal, observed in additive background noise, using the Kalman filter technique to estimate the parameters of the Autoregressive Process (AR) in the state space model and the output speech signal obtained by the MATLAB. The accurate estimation by Kalman filter on speech would enhance and reduce the noise then compare and discuss the results between actual values and estimated values which produce the reconstructed signals.
Abstract: In this paper, a frequency-variation based method has
been proposed for transistor parameter estimation in a commonemitter
transistor amplifier circuit. We design an algorithm to estimate
the transistor parameters, based on noisy measurements of the output
voltage when the input voltage is a sine wave of variable frequency
and constant amplitude. The common emitter amplifier circuit has
been modelled using the transistor Ebers-Moll equations and the
perturbation technique has been used for separating the linear and
nonlinear parts of the Ebers-Moll equations. This model of the amplifier
has been used to determine the amplitude of the output sinusoid as
a function of the frequency and the parameter vector. Then, applying
the proposed method to the frequency components, the transistor
parameters have been estimated. As compared to the conventional
time-domain least squares method, the proposed method requires
much less data storage and it results in more accurate parameter
estimation, as it exploits the information in the time and frequency
domain, simultaneously. The proposed method can be utilized for
parameter estimation of an analog device in its operating range of
frequencies, as it uses data collected from different frequencies output
signals for parameter estimation.