Abstract: In this paper, the position control of an electronic
throttle actuator is outlined. The dynamic behavior of the actuator is
described with the help of an uncertain plant model. This motivates
the controller design based on the ideas of higher-order slidingmodes.
As a consequence anti-chattering techniques can be omitted.
It is shown that the same concept is applicable to estimate unmeasureable
signals. The control law and the observer are implemented on
an electronic control unit. Results achieved by numerical simulations
and real world experiments are presented and discussed.
Abstract: This paper present an efficient and reliable technique of optimization which combined fuel cost economic optimization and emission dispatch using the Sigmoid Decreasing Inertia Weight Particle Swarm Optimization algorithm (PSO) to reduce the cost of fuel and pollutants resulting from fuel combustion by keeping the output of generators, bus voltages, shunt capacitors and transformer tap settings within the security boundary. The performance of the proposed algorithm has been demonstrated on IEEE 30-bus system with six generating units. The results clearly show that the proposed algorithm gives better and faster speed convergence then linearly decreasing inertia weight.
Abstract: A new low-voltage floating gate MOSFET (FGMOS)
based squarer using square law characteristic of the FGMOS is
proposed in this paper. The major advantages of the squarer are simplicity,
rail-to-rail input dynamic range, low total harmonic distortion,
and low power consumption. The proposed circuit is biased without
body effect. The circuit is designed and simulated using SPICE in
0.25μm CMOS technology. The squarer is operated at the supply
voltages of ±0.75V . The total harmonic distortion (THD) for the
input signal 0.75Vpp at 25 KHz, and maximum power consumption
were found to be less than 1% and 319μW respectively.
Abstract: This paper proposes improved delay-dependent stability conditions of the linear time-delay systems of neutral type. The proposed methods employ a suitable Lyapunov-Krasovskii’s functional and a new form of the augmented system. New delay-dependent stability criteria for the systems are established in terms of Linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Numerical examples showed that the proposed method is effective and can provide less conservative results.
Abstract: We present a system that finds road boundaries and
constructs the virtual lane based on fusion data from a laser and a
monocular sensor, and detects forward vehicle position even in no lane
markers or bad environmental conditions. When the road environment
is dark or a lot of vehicles are parked on the both sides of the road, it is
difficult to detect lane and road boundary. For this reason we use
fusion of laser and vision sensor to extract road boundary to acquire
three dimensional data. We use parabolic road model to calculate road
boundaries which is based on vehicle and sensors state parameters and
construct virtual lane. And then we distinguish vehicle position in each
lane.
Abstract: Superplastic deformation and high temperature load
relaxation behavior of coarse-grained iron aluminides with the
composition of Fe-28 at.% Al have been investigated. A series of load
relaxation and tensile tests were conducted at temperatures ranging
from 600 to 850oC. The flow curves obtained from load relaxation
tests were found to have a sigmoidal shape and to exhibit stress vs.
strain rate data in a very wide strain rate range from 10-7/s to 10-2/s.
Tensile tests have been conducted at various initial strain rates ranging
from 3×10-5/s to 1×10-2/s. Maximum elongation of ~500 % was
obtained at the initial strain rate of 3×10-5/s and the maximum strain
rate sensitivity was found to be 0.68 at 850oC in binary Fe-28Al alloy.
Microstructure observation through the optical microscopy (OM) and
the electron back-scattered diffraction (EBSD) technique has been
carried out on the deformed specimens and it has revealed the
evidences for grain boundary migration and grain refinement to occur
during superplastic deformation, suggesting the dynamic
recrystallization mechanism. The addition of Cr by the amount of 5
at.% appeared to deteriorate the superplasticity of the binary iron
aluminide. By applying the internal variable theory of structural
superplasticity, the addition of Cr has been revealed to lower the
contribution of the frictional resistance to dislocation glide during high
temperature deformation of the Fe3Al alloy.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.
Abstract: This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.
Abstract: The kinetics of palm oil catalytic cracking over
aluminum containing mesoporous silica Al-MCM-41 (5% Al) was
investigated in a batch autoclave reactor at the temperatures range of
573 – 673 K. The catalyst was prepared by using sol-gel technique
and has been characterized by nitrogen adsorption and x-ray
diffraction methods. Surface area of 1276 m2/g with average pore
diameter of 2.54 nm and pore volume of 0.811 cm3/g was obtained.
The experimental catalytic cracking runs were conducted using 50 g
of oil and 1 g of catalyst. The reaction pressure was recorded at
different time intervals and the data were analyzed using Levenberg-
Marquardt (LM) algorithm using polymath software. The results
show that the reaction order was found to be -1.5 and activation
energy of 3200 J/gmol.
Abstract: In this study a neural network (NN) was proposed to
predict the sorption of binary mixture of copper-cobalt ions into
clinoptilolite as ion-exchanger. The configuration of the
backpropagation neural network giving the smallest mean square
error was three-layer NN with tangent sigmoid transfer function at
hidden layer with 10 neurons, linear transfer function at output layer
and Levenberg-Marquardt backpropagation training algorithm.
Experiments have been carried out in the batch reactor to obtain
equilibrium data of the individual sorption and the mixture of coppercobalt
ions. The obtained modeling results have shown that the used
of neural network has better adjusted the equilibrium data of the
binary system when compared with the conventional sorption
isotherm models.
Abstract: Introduction: Obesity is a major health risk issue in
the present day of life for one and all globally. Obesity is one of the
major concerns for public health according to recent increasing trends
in obesity-related diseases such as Type 2 diabetes. ( Kazuya,
1994).and hyperlipidemia, (Sakata,1990) .which are more prevalent
in Japanese adults with body mass index (BMI) values Z25 kg/m2.(
Japanese Ministry of Health and Welfare,1997). The purpose of the
study was to assess the effect of twelve weeks of brisk walking on
blood pressure and body mass index, anthropometric measurements
of obese males. Method: Thirty obese (BMI= above 30) males, aged
18 to 22 years, were selected from King Fahd University of
Petroleum & Minerals, Saudi Arabia. The subject-s height (cm) was
measured using a stadiometer and body mass (kg) was measured with
a electronic weighing machine. BMI was subsequently calculated
(kg/m2). The blood pressure was measured with standardized
sphygmomanometer in mm of Hg. All the measurements were taken
twice before and twice after the experimental period. The pre and
post anthropometric measurements of waist and hip circumference
were measured with the steel tape in cm. The subjects underwent
walking schedule two times in a week for 12 weeks. The 45 minute
sessions of brisk walking were undertaken at an average intensity of
65% to 85% of maximum HR (HRmax; calculated as 220-age).
Results & Discussion: Statistical findings revealed significant
changes from pre test to post test in case of both systolic blood
pressure and diastolic blood pressure in the walking group. Results
also showed significant decrease in their body mass index and
anthropometric measurements i.e. (waist & hip circumference).
Conclusion: It was concluded that twelve weeks brisk walking is
beneficial for lowering of blood pressure, body mass index, and
anthropometric circumference of obese males.
Abstract: Drying characteristics of rough rice (variety of lenjan) with an initial moisture content of 25% dry basis (db) was studied in a hot air dryer assisted by infrared heating. Three arrival air temperatures (30, 40 and 500C) and four infrared radiation intensities (0, 0.2 , 0.4 and 0.6 W/cm2) and three arrival air speeds (0.1, 0.15 and 0.2 m.s-1) were studied. Bending strength of brown rice kernel, percentage of cracked kernels and time of drying were measured and evaluated. The results showed that increasing the drying arrival air temperature and radiation intensity of infrared resulted decrease in drying time. High bending strength and low percentage of cracked kernel was obtained when paddy was dried by hot air assisted infrared dryer. Between this factors and their interactive effect were a significant difference (p
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.