Abstract: A structural study of an aqueous electrolyte whose
experimental results are available. It is a solution of LiCl-6H2O type
at glassy state (120K) contrasted with pure water at room temperature
by means of Partial Distribution Functions (PDF) issue from neutron
scattering technique. Based on these partial functions, the Reverse
Monte Carlo method (RMC) computes radial and angular correlation
functions which allow exploring a number of structural features of
the system. The obtained curves include some artifacts. To remedy
this, we propose to introduce a screened potential as an additional
constraint. Obtained results show a good matching between
experimental and computed functions and a significant improvement
in PDFs curves with potential constraint. It suggests an efficient fit of
pair distribution functions curves.
Abstract: Chua’s circuit is one of the most important electronic devices that are used for Chaos and Bifurcation studies. A central role of secure communication is devoted to it. Since the adaptive control is used vastly in the linear systems control, here we introduce a new trend of application of adaptive method in the chaos controlling field. In this paper, we try to derive a new adaptive control scheme for Chua’s circuit controlling because control of chaos is often very important in practical operations. The novelty of this approach is for sake of its robustness against the external perturbations which is simulated as an additive noise in all measured states and can be generalized to other chaotic systems. Our approach is based on Lyapunov analysis and the adaptation law is considered for the feedback gain. Because of this, we have named it NAFT (Nonlinear Adaptive Feedback Technique). At last, simulations show the capability of the presented technique for Chua’s circuit.
Abstract: Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. One such technique is Artificial Neural Network (ANN). This paper examined the application of ANN for software quality prediction using Object- Oriented (OO) metrics. Quality estimation includes estimating maintainability of software. The dependent variable in our study was maintenance effort. The independent variables were principal components of eight OO metrics. The results showed that the Mean Absolute Relative Error (MARE) was 0.265 of ANN model. Thus we found that ANN method was useful in constructing software quality model.
Abstract: We present a new method for the fully automatic 3D
reconstruction of the coronary artery centerlines, using two X-ray
angiogram projection images from a single rotating monoplane
acquisition system. During the first stage, the input images are
smoothed using curve evolution techniques. Next, a simple yet
efficient multiscale method, based on the information of the Hessian
matrix, for the enhancement of the vascular structure is introduced.
Hysteresis thresholding using different image quantiles, is used to
threshold the arteries. This stage is followed by a thinning procedure
to extract the centerlines. The resulting skeleton image is then pruned
using morphological and pattern recognition techniques to remove
non-vessel like structures. Finally, edge-based stereo correspondence
is solved using a parallel evolutionary optimization method based on
f symbiosis. The detected 2D centerlines combined with disparity
map information allow the reconstruction of the 3D vessel
centerlines. The proposed method has been evaluated on patient data
sets for evaluation purposes.
Abstract: The principle of frequency and amplitude measurement of a vibrating object in water using ultrasonic speckle technique is presented in this paper. Compared with other traditional techniques, the ultrasonic speckle technique can be applied to vibration measurement of a nonmetal object with rough surface in water in a noncontact way. The relationship between speckle movement and object movement was analyzed. Based on this study, an ultrasonic speckle measurement system was set up. With this system the frequency and amplitude of an underwater vibrating cantilever beam was detected. The result shows that the experimental data is in good agreement with the calibrating data.
Abstract: In this paper, to optimize the “Characteristic Straight Line Method" which is used in the soil displacement analysis, a “best estimate" of the geodetic leveling observations has been achieved by taking in account the concept of 'Height systems'. This concept has been discussed in detail and consequently the concept of “height". In landslides dynamic analysis, the soil is considered as a mosaic of rigid blocks. The soil displacement has been monitored and analyzed by using the “Characteristic Straight Line Method". Its characteristic components have been defined constructed from a “best estimate" of the topometric observations. In the measurement of elevation differences, we have used the most modern leveling equipment available. Observational procedures have also been designed to provide the most effective method to acquire data. In addition systematic errors which cannot be sufficiently controlled by instrumentation or observational techniques are minimized by applying appropriate corrections to the observed data: the level collimation correction minimizes the error caused by nonhorizontality of the leveling instrument's line of sight for unequal sight lengths, the refraction correction is modeled to minimize the refraction error caused by temperature (density) variation of air strata, the rod temperature correction accounts for variation in the length of the leveling rod' s Invar/LO-VAR® strip which results from temperature changes, the rod scale correction ensures a uniform scale which conforms to the international length standard and the introduction of the concept of the 'Height systems' where all types of height (orthometric, dynamic, normal, gravity correction, and equipotential surface) have been investigated. The “Characteristic Straight Line Method" is slightly more convenient than the “Characteristic Circle Method". It permits to evaluate a displacement of very small magnitude even when the displacement is of an infinitesimal quantity. The inclination of the landslide is given by the inverse of the distance reference point O to the “Characteristic Straight Line". Its direction is given by the bearing of the normal directed from point O to the Characteristic Straight Line (Fig..6). A “best estimate" of the topometric observations was used to measure the elevation of points carefully selected, before and after the deformation. Gross errors have been eliminated by statistical analyses and by comparing the heights within local neighborhoods. The results of a test using an area where very interesting land surface deformation occurs are reported. Monitoring with different options and qualitative comparison of results based on a sufficient number of check points are presented.
Abstract: A filter is used to remove undesirable frequency information from a dynamic signal. This paper shows that the Znotch filter filtering technique can be applied to remove the noise nuisance from a machining signal. In machining, the noise components were identified from the sound produced by the operation of machine components itself such as hydraulic system, motor, machine environment and etc. By correlating the noise components with the measured machining signal, the interested components of the measured machining signal which was less interfered by the noise, can be extracted. Thus, the filtered signal is more reliable to be analysed in terms of noise content compared to the unfiltered signal. Significantly, the I-kaz method i.e. comprises of three dimensional graphical representation and I-kaz coefficient, Z∞ could differentiate between the filtered and the unfiltered signal. The bigger space of scattering and the higher value of Z∞ demonstrated that the signal was highly interrupted by noise. This method can be utilised as a proactive tool in evaluating the noise content in a signal. The evaluation of noise content is very important as well as the elimination especially for machining operation fault diagnosis purpose. The Z-notch filtering technique was reliable in extracting noise component from the measured machining signal with high efficiency. Even though the measured signal was exposed to high noise disruption, the signal generated from the interaction between cutting tool and work piece still can be acquired. Therefore, the interruption of noise that could change the original signal feature and consequently can deteriorate the useful sensory information can be eliminated.
Abstract: This paper deals with the localization of the wideband sources. We develop a new approach for estimating the wide band sources parameters. This method is based on the high order statistics of the recorded data in order to eliminate the Gaussian components from the signals received on the various hydrophones.In fact the noise of sea bottom is regarded as being Gaussian. Thanks to the coherent signal subspace algorithm based on the cumulant matrix of the received data instead of the cross-spectral matrix the wideband correlated sources are perfectly located in the very noisy environment. We demonstrate the performance of the proposed algorithm on the real data recorded during an underwater acoustics experiments.
Abstract: This paper presents a useful sub-pixel image
registration method using line segments and a sub-pixel edge detector.
In this approach, straight line segments are first extracted from gray
images at the pixel level before applying the sub-pixel edge detector.
Next, all sub-pixel line edges are mapped onto the orientation-distance
parameter space to solve for line correspondence between images.
Finally, the registration parameters with sub-pixel accuracy are
analytically solved via two linear least-square problems. The present
approach can be applied to various fields where fast registration with
sub-pixel accuracy is required. To illustrate, the present approach is
applied to the inspection of printed circuits on a flat panel. Numerical
example shows that the present approach is effective and accurate
when target images contain a sufficient number of line segments,
which is true in many industrial problems.
Abstract: In this paper, a methodology of a model based on
predicting the tool forces oblique machining are introduced by
adopting the orthogonal technique. The applied analytical calculation
is mostly based on Devries model and some parts of the methodology
are employed from Amareggo-Brown model. Model validation is
performed by comparing experimental data with the prediction results
on machining titanium alloy (Ti-6Al-4V) based on micro-cutting tool
perspective. Good agreements with the experiments are observed. A
detailed friction form that affected the tool forces also been examined
with reasonable results obtained.
Abstract: Multiprocessor task scheduling is a NP-hard problem and Genetic Algorithm (GA) has been revealed as an excellent technique for finding an optimal solution. In the past, several methods have been considered for the solution of this problem based on GAs. But, all these methods consider single criteria and in the present work, minimization of the bi-criteria multiprocessor task scheduling problem has been considered which includes weighted sum of makespan & total completion time. Efficiency and effectiveness of genetic algorithm can be achieved by optimization of its different parameters such as crossover, mutation, crossover probability, selection function etc. The effects of GA parameters on minimization of bi-criteria fitness function and subsequent setting of parameters have been accomplished by central composite design (CCD) approach of response surface methodology (RSM) of Design of Experiments. The experiments have been performed with different levels of GA parameters and analysis of variance has been performed for significant parameters for minimisation of makespan and total completion time simultaneously.
Abstract: Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.
Abstract: This paper presents image compression with wavelet based method. The wavelet transformation divides image to low- and high pass filtered parts. The traditional JPEG compression technique requires lower computation power with feasible losses, when only compression is needed. However, there is obvious need for wavelet based methods in certain circumstances. The methods are intended to the applications in which the image analyzing is done parallel with compression. Furthermore, high frequency bands can be used to detect changes or edges. Wavelets enable hierarchical analysis for low pass filtered sub-images. The first analysis can be done for a small image, and only if any interesting is found, the whole image is processed or reconstructed.
Abstract: In this research, we propose to use the discrete cosine
transform to approximate the cumulative distributions of data cube
cells- values. The cosine transform is known to have a good energy
compaction property and thus can approximate data distribution
functions easily with small number of coefficients. The derived
estimator is accurate and easy to update. We perform experiments to
compare its performance with a well-known technique - the (Haar)
wavelet. The experimental results show that the cosine transform
performs much better than the wavelet in estimation accuracy, speed,
space efficiency, and update easiness.
Abstract: Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for preprocessing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based preprocessing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.
Abstract: The paper focuses on the enhanced stiffness modeling
of robotic manipulators by taking into account influence of the external force/torque acting upon the end point. It implements the
virtual joint technique that describes the compliance of manipulator elements by a set of localized six-dimensional springs separated by
rigid links and perfect joints. In contrast to the conventional
formulation, which is valid for the unloaded mode and small
displacements, the proposed approach implicitly assumes that the loading leads to the non-negligible changes of the manipulator posture and corresponding amendment of the Jacobian. The
developed numerical technique allows computing the static
equilibrium and relevant force/torque reaction of the manipulator for
any given displacement of the end-effector. This enables designer
detecting essentially nonlinear effects in elastic behavior of
manipulator, similar to the buckling of beam elements. It is also proposed the linearization procedure that is based on the inversion of
the dedicated matrix composed of the stiffness parameters of the
virtual springs and the Jacobians/Hessians of the active and passive
joints. The developed technique is illustrated by an application example that deals with the stiffness analysis of a parallel
manipulator of the Orthoglide family
Abstract: Cooperative communication scheme can be substituted
for multiple-input multiple-output (MIMO) technique when it may
not be able to support multiple antennas due to size, cost or
hardware limitations. In other words, cooperative communication
scheme is an efficient method to achieve spatial diversity without
multiple antennas. For satisfaction of rising QoS, we propose a
reliable cooperative communication scheme with M-QAM based Dual
Carrier Modulation (M-DCM), which can increase diversity gain.
Although our proposed scheme is very simple method, it gives us
frequency and spatial diversity. Simulation result shows our proposed
scheme obtains diversity gain more than the conventional cooperative
communication scheme.
Abstract: The objective of this paper is to analyze the
performance of a double-sided axial flux permanent magnet
brushless DC (AFPM BLDC) motor with two-phase winding.
To study the motor operation, a mathematical dynamic model
has been proposed for motor, which became the basis for
simulations that were performed using MATLAB/SIMULINK
software package. The results of simulations were presented
in form of the waveforms of selected quantities and the
electromechanical characteristics performed by the motor. The
calculation results show that the two-phase motor version
develops smooth torque and reaches high efficiency. The twophase
motor can be applied where more smooth torque is
required. Finally a study on the influence of switching angle
on motor performance shows that when advance switching
technique is used, the motor operates with the highest
efficiency.
Abstract: In this paper, novel techniques in increasing the accuracy
and speed of convergence of a Feed forward Back propagation
Artificial Neural Network (FFBPNN) with polynomial activation
function reported in literature is presented. These technique was
subsequently used to determine the coefficients of Autoregressive
Moving Average (ARMA) and Autoregressive (AR) system. The
results obtained by introducing sequential and batch method of weight
initialization, batch method of weight and coefficient update, adaptive
momentum and learning rate technique gives more accurate result
and significant reduction in convergence time when compared t the
traditional method of back propagation algorithm, thereby making
FFBPNN an appropriate technique for online ARMA coefficient
determination.
Abstract: This research’s objective is to select the model with
most accurate value by using Neural Network Technique as a way to
filter potential students who enroll in IT course by Electronic learning
at Suan Suanadha Rajabhat University. It is designed to help students
selecting the appropriate courses by themselves. The result showed
that the most accurate model was 100 Folds Cross-validation which
had 73.58% points of accuracy.