Abstract: This research paper is based upon the simulation of
gradient of mathematical functions and scalar fields using MATLAB.
Scalar fields, their gradient, contours and mesh/surfaces are
simulated using different related MATLAB tools and commands for
convenient presentation and understanding. Different mathematical
functions and scalar fields are examined here by taking their
gradient, visualizing results in 3D with different color shadings and
using other necessary relevant commands. In this way the outputs of
required functions help us to analyze and understand in a better way
as compared to just theoretical study of gradient.
Abstract: This paper deals with a design method of a discrete
modified Internal Model Control (IMC) for a plant with an integrator
and dead time. If there is a load disturbance in the input or output side
of the plant, the proposed control system can eliminate the steady-state
error caused by it. The disturbance compensator in this method is
simple and its order is low regardless of that of a plant. The simulation
studies show that the proposed method has superior performance for a
load disturbance rejection and robustness.
Abstract: Environmental micro-organisms include a large number of taxa and some species that are generally considered nonpathogenic, but can represent a risk in certain conditions, especially for elderly people and immunocompromised individuals. Chemotaxonomic identification techniques are powerful tools for environmental micro-organisms, and cellular fatty acid methyl esters (FAME) content is a powerful fingerprinting identification technique. A system based on an unsupervised artificial neural network (ANN) was set up using the fatty acid profiles of standard bacterial strains, obtained by gas-chromatography, used as learning data. We analysed 45 certified strains belonging to Acinetobacter, Aeromonas, Alcaligenes, Aquaspirillum, Arthrobacter, Bacillus, Brevundimonas, Enterobacter, Flavobacterium, Micrococcus, Pseudomonas, Serratia, Shewanella and Vibrio genera. A set of 79 bacteria isolated from a drinking water line (AMGA, the major water supply system in Genoa) were used as an example for identification compared to standard MIDI method. The resulting ANN output map was found to be a very powerful tool to identify these fresh isolates.
Abstract: A precision CMOS chopping amplifier is adopted in this work to improve a CMOS temperature sensor high sensitive enough for intracranial temperature monitoring. An amplified temperature sensitivity of 18.8 ± 3*0.2 mV/oC is attained over the temperature range from 20 oC to 80 oC from a given 10 samples of the same wafer. The analog frontend design outputs the temperature dependent and the temperature independent signals which can be directly interfaced to a 10 bit ADC to accomplish an accurate temperature instrumentation system.
Abstract: The source voltage of high-power fuel cell shows strong load dependence at comparatively low voltage levels. In order to provide the voltage of 750V on the DC-link for feeding electrical energy into the mains via a three phase inverter a step-up converter with a large step-up ratio is required. The output voltage of this DC/DC-converter must be stabile during variations of the load current and the voltage of the fuel cell. This paper presents the methods and results of the calculation of the efficiency and the expense for the realization for the circuits of the DC/DC-converter that meet these requirements.
Abstract: The intelligent fuzzy input estimator is used to estimate
the input force of the rigid bar structural system in this study. The
fuzzy Kalman filter without the input term and the fuzzy weighting
recursive least square estimator are two main portions of this method.
The practicability and accuracy of the proposed method were verified
with numerical simulations from which the input forces of a rigid bar
structural system were estimated from the output responses. In order to
examine the accuracy of the proposed method, a rigid bar structural
system is subjected to periodic sinusoidal dynamic loading. The
excellent performance of this estimator is demonstrated by comparing
it with the use of difference weighting function and improper the
initial process noise covariance. The estimated results have a good
agreement with the true values in all cases tested.
Abstract: This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines.
Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the
dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the
effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.
Abstract: A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observations in data, and investigates the effect of dropping or misclassifying an observation on a predictor variable. The input data may consist either as a set of individual responses (yes/no) with the predictor variables or as grouped records summarizing various categories for each unique set of predictor variables' values. Graphical displays are used to output various statistical results and to assess the goodness of fit of the logistic regression model. The software recognizes possible convergence constraints when present in data, and the user is notified accordingly.
Abstract: The most common type of controller being used in
the industry is PI(D) controller which has been used since 1945 and
is still being widely used due to its efficiency and simplicity. In
most cases, the PI(D) controller was tuned without taking into
consideration of the effect of actuator saturation. In real processes,
the most common actuator which is valve will act as constraint and
restrict the controller output. Since the controller is not designed to
encounter saturation, the process may windup and consequently
resulted in large oscillation or may become unstable. Usually, an
antiwindup compensator is added to the feedback control loop to
reduce the deterioration effect of integral windup. This research
aims to specifically control processes with constraints. The
proposed method was applied to two different types of food
processes, which are blending and spray drying. Simulations were
done using MATLAB and the performances of the proposed
method were compared with other conventional methods. The
proposed technique was able to control the processes and avoid
saturation such that no anti windup compensator is needed.
Abstract: The experimental and theoretical results of a ZVS
(Zero Voltage Switching) isolated flyback DC-DC converter using
multilayered coreless PCB step down 2:1 transformer are presented.
The performance characteristics of the transformer are shown which
are useful for the parameters extraction. The measured energy
efficiency of the transformer is found to be more than 94% with the
sinusoidal input voltage excitation. The designed flyback converter
has been tested successfully upto the output power level of 10W,
with a switching frequency in the range of 2.7MHz-4.3MHz. The
input voltage of the converter is varied from 25V-40V DC.
Frequency modulation technique is employed by maintaining
constant off time to regulate the output voltage of the converter. The
energy efficiency of the isolated flyback converter circuit under ZVS
condition in the MHz frequency region is found to be approximately
in the range of 72-84%. This paper gives the comparative results in
terms of the energy efficiency of the hard switched and soft switched
flyback converter in the MHz frequency region.
Abstract: Alzheimer is known as the loss of mental functions
such as thinking, memory, and reasoning that is severe enough to
interfere with a person's daily functioning. The appearance of
Alzheimer Disease symptoms (AD) are resulted based on which part
of the brain has a variety of infection or damage. In this case, the
MRI is the best biomedical instrumentation can be ever used to
discover the AD existence. Therefore, this paper proposed a fusion
method to distinguish between the normal and (AD) MRIs. In this
combined method around 27 MRIs collected from Jordanian
Hospitals are analyzed based on the use of Low pass -morphological
filters to get the extracted statistical outputs through intensity
histogram to be employed by the descriptive box plot. Also, the
artificial neural network (ANN) is applied to test the performance of
this approach. Finally, the obtained result of t-test with confidence
accuracy (95%) has compared with classification accuracy of ANN
(100 %). The robust of the developed method can be considered
effectively to diagnose and determine the type of AD image.
Abstract: This paper presents a new technique for generating sets of synthetic classifiers to evaluate abstract-level combination methods. The sets differ in terms of both recognition rates of the individual classifiers and degree of similarity. For this purpose, each abstract-level classifier is considered as a random variable producing one class label as the output for an input pattern. From the initial set of classifiers, new slightly different sets are generated by applying specific operators, which are defined at the purpose. Finally, the sets of synthetic classifiers have been used to estimate the performance of combination methods for abstract-level classifiers. The experimental results demonstrate the effectiveness of the proposed approach.
Abstract: This research deals with investigations on the “Active
Generator" under rotor speed variations and output frequency
control. It runs at turbine speed and it is connected to a three phase
electrical power grid which has its own frequency different from
turbine frequency. In this regard the set composed of a four phase
synchronous generator and a natural commutated matrix converter
(NCMC) made with thyristors, is called active generator. It replaces a
classical mechanical gearbox which introduces many drawbacks. The
main idea in this article is the presentation of frequency control at
grid side when turbine runs at variable speed. Frequency control has
been done by linear and step variations of the turbine speed. Relation
between turbine speed (frequency) and main grid zero sequence
voltage frequency is presented.
Abstract: In this paper we propose a Particle Swarm heuristic
optimized Multi-Antenna (MA) system. Efficient MA systems
detection is performed using a robust stochastic evolutionary
computation algorithm based on movement and intelligence of
swarms. This iterative particle swarm optimized (PSO) detector
significantly reduces the computational complexity of conventional
Maximum Likelihood (ML) detection technique. The simulation
results achieved with this proposed MA-PSO detection algorithm
show near optimal performance when compared with ML-MA
receiver. The performance of proposed detector is convincingly
better for higher order modulation schemes and large number of
antennas where conventional ML detector becomes non-practical.
Abstract: Simulation model is an easy way to build up models
to represent real life scenarios, to identify bottlenecks and to enhance
system performance. Using a valid simulation model may give
several advantages in creating better manufacturing design in order to
improve the system performances. This paper presents result of
implementing a simulation model to design hard disk drive
manufacturing process by applying line balancing to improve both
productivity and quality of hard disk drive process. The line balance
efficiency showed 86% decrease in work in process, output was
increased by an average of 80%, average time in the system was
decreased 86% and waiting time was decreased 90%.
Abstract: In this study we focus on improvement performance
of a cue based Motor Imagery Brain Computer Interface (BCI). For
this purpose, data fusion approach is used on results of different
classifiers to make the best decision. At first step Distinction
Sensitive Learning Vector Quantization method is used as a feature
selection method to determine most informative frequencies in
recorded signals and its performance is evaluated by frequency
search method. Then informative features are extracted by packet
wavelet transform. In next step 5 different types of classification
methods are applied. The methodologies are tested on BCI
Competition II dataset III, the best obtained accuracy is 85% and the
best kappa value is 0.8. At final step ordered weighted averaging
(OWA) method is used to provide a proper aggregation classifiers
outputs. Using OWA enhanced system accuracy to 95% and kappa
value to 0.9. Applying OWA just uses 50 milliseconds for
performing calculation.
Abstract: From the perspective of industrial structure
coordination and based on an explicit definition for the connotation of
industrial structure coordination, the synergetic coefficients are used
to measure the coordination degree between three industries' input
structure and output structure, and then the efficacy function method is
employed to comprehensively evaluate the level of China-s industrial
structure optimization. It is showed that Chinese industrial structure
presented a "v-shaped" variation tendency between 1996 and 2008,
and its industrial structure adjustment got obvious achievements after
2003, with the industrial structure optimization level increasing
continuously. However in 2009, the level of China-s industrial
structure optimization declined sharply due to the decreasing
contribution degree of value added structure and energy structure
coordination and the lower coordination degree of value added
structure and capital structure.
Abstract: In this paper a new cost function for blind equalization
is proposed. The proposed cost function, referred to as the modified
maximum normalized cumulant criterion (MMNC), is an extension
of the previously proposed maximum normalized cumulant criterion
(MNC). While the MNC requires a separate phase recovery system
after blind equalization, the MMNC performs joint blind equalization
and phase recovery. To achieve this, the proposed algorithm
maximizes a cost function that considers both amplitude and phase of
the equalizer output. The simulation results show that the proposed
algorithm has an improved channel equalization effect than the MNC
algorithm and simultaneously can correct the phase error that the
MNC algorithm is unable to do. The simulation results also show that
the MMNC algorithm has lower complexity than the MNC algorithm.
Moreover, the MMNC algorithm outperforms the MNC algorithm
particularly when the symbols block size is small.
Abstract: In this paper the optimal control strategy for
Permanent Magnet Synchronous Motor (PMSM) based drive system
is presented. The designed full optimal control is available for speed
operating range up to base speed. The optimal voltage space-vector
assures input energy reduction and stator loss minimization,
maintaining the output energy in the same limits with the
conventional PMSM electrical drive. The optimal control with three
components is based on the energetically criteria and it is applicable
in numerical version, being a nonrecursive solution. The simulation
results confirm the increased efficiency of the optimal PMSM drive.
The properties of the optimal voltage space vector are shown.
Abstract: This paper proposes a new technique based on nonlinear Minmax Detector Based (MDB) filter for image restoration. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Image degradation can be due to the addition of different types of noise in the original image. Image noise can be modeled of many types and impulse noise is one of them. Impulse noise generates pixels with gray value not consistent with their local neighborhood. It appears as a sprinkle of both light and dark or only light spots in the image. Filtering is a technique for enhancing the image. Linear filter is the filtering in which the value of an output pixel is a linear combination of neighborhood values, which can produce blur in the image. Thus a variety of smoothing techniques have been developed that are non linear. Median filter is the one of the most popular non-linear filter. When considering a small neighborhood it is highly efficient but for large window and in case of high noise it gives rise to more blurring to image. The Centre Weighted Mean (CWM) filter has got a better average performance over the median filter. However the original pixel corrupted and noise reduction is substantial under high noise condition. Hence this technique has also blurring affect on the image. To illustrate the superiority of the proposed approach, the proposed new scheme has been simulated along with the standard ones and various restored performance measures have been compared.