Abstract: This paper discusses two observers, which are used
for the estimation of parameters of PMSM. Former one, reduced
order observer, which is used to estimate the inaccessible parameters
of PMSM. Later one, full order observer, which is used to estimate
all the parameters of PMSM even though some of the parameters are
directly available for measurement, so as to meet with the
insensitivity to the parameter variation. However, the state space
model contains some nonlinear terms i.e. the product of different
state variables. The asymptotic state observer, which approximately
reconstructs the state vector for linear systems without uncertainties,
was presented by Luenberger. In this work, a modified form of such
an observer is used by including a non-linear term involving the
speed. So, both the observers are designed in the framework of
nonlinear control; their stability and rate of convergence is discussed.
Abstract: In this paper the modeling and analysis of Space
Vector Pulse Width Modulation (SVPWM) based Dynamic Voltage
Restorer (DVR) using PSCAD/EMTDC software will be presented in
details. The simulation includes full modeling of the SVPWM
technique used to control the DVR inverter. A test power system
composed of three phase voltage source, sag generator, DVR and
three phase resistive load is used to demonstrate restoration capability
of the DVR. The simulation results of the presented DVR proved
excellent voltage sag mitigation to protect sensitive loads.
Abstract: Primary studies are being carried out in Turkey for
expanding information and communication technologies (ICT) aided instruction activities. Subject of the present study is to identify
whether those studies achieved their goals in the application. Information technologies (IT) formative teachers in the primary
schools, and academicians in the faculties of education were interviewed to investigate the process and results of implementing
computer-aided instruction methods whose basis is strengthened in theory. Analysis of the results gained from two separate surveys
demonstrated that capability of the teachers in elementary education institutions for carrying into effect computer-aided instruction and
technical infrastructure has not been established for computer-aided instruction practices yet. Prospective teachers must be well-equipped in ICT to duly fulfill requirements of modern education and also
must be self-confident. Finally, scope and intensity of the courses given in connection with teaching of the ICT in faculties of education needs to be revised.
Abstract: This study presents an exact general solution for
steady-state conductive heat transfer in cylindrical composite
laminates. Appropriate Fourier transformation has been obtained
using Sturm-Liouville theorem. Series coefficients are achieved by
solving a set of equations that related to thermal boundary conditions
at inner and outer of the cylinder, also related to temperature
continuity and heat flux continuity between each layer. The solution
of this set of equations are obtained using Thomas algorithm. In this
paper, the effect of fibers- angle on temperature distribution of
composite laminate is investigated under general boundary
conditions. Here, we show that the temperature distribution for any
composite laminates is between temperature distribution for
laminates with θ = 0° and θ = 90° .
Abstract: Mining Sequential Patterns in large databases has become
an important data mining task with broad applications. It is
an important task in data mining field, which describes potential
sequenced relationships among items in a database. There are many
different algorithms introduced for this task. Conventional algorithms
can find the exact optimal Sequential Pattern rule but it takes a
long time, particularly when they are applied on large databases.
Nowadays, some evolutionary algorithms, such as Particle Swarm
Optimization and Genetic Algorithm, were proposed and have been
applied to solve this problem. This paper will introduce a new kind
of hybrid evolutionary algorithm that combines Genetic Algorithm
(GA) with Particle Swarm Optimization (PSO) to mine Sequential
Pattern, in order to improve the speed of evolutionary algorithms
convergence. This algorithm is referred to as SP-GAPSO.
Abstract: Urinary Tract Infections (UTI) account for an estimated 25-40% nosocomial infection, out of which 90% are associated with urinary catheter, called Catheter associated urinary tract infection (CAUTI). The microbial populations within CAUTI frequently develop as biofilms. In the present study, microbial contamination of indwelling urinary catheters was investigated. Biofilm forming ability of the isolates was determined by tissue culture plate method. Prevention of biofilm formation in the urinary catheter by Pseudomonas aeruginosa was also determined by coating the catheter with some enzymes, gentamycin and EDTA. It was found that 64% of the urinary catheters get contaminated during the course of catheterization. Of the total 6 isolates, biofilm formation was seen in 100% Pseudomonas aeruginosa and E. coli, 90% in Enterococci, 80% in Klebsiella and 66% in S. aureus. It was noted that the biofilm production by Pseudomonas was prolonged by 7 days in amylase, 8 days in protease, 6 days in lysozyme, 7days in gentamycin and 5 days in EDTA treated catheter.
Abstract: Grid computing is growing rapidly in the distributed
heterogeneous systems for utilizing and sharing large-scale resources
to solve complex scientific problems. Scheduling is the most recent
topic used to achieve high performance in grid environments. It aims
to find a suitable allocation of resources for each job. A typical
problem which arises during this task is the decision of scheduling. It
is about an effective utilization of processor to minimize tardiness
time of a job, when it is being scheduled. This paper, therefore,
addresses the problem by developing a general framework of grid
scheduling using dynamic information and an ant colony
optimization algorithm to improve the decision of scheduling. The
performance of various dispatching rules such as First Come First
Served (FCFS), Earliest Due Date (EDD), Earliest Release Date
(ERD), and an Ant Colony Optimization (ACO) are compared.
Moreover, the benefit of using an Ant Colony Optimization for
performance improvement of the grid Scheduling is also discussed. It
is found that the scheduling system using an Ant Colony
Optimization algorithm can efficiently and effectively allocate jobs
to proper resources.
Abstract: A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise
Abstract: Extraction of edge-end-pixels is an important step for the edge linking process to achieve edge-based image segmentation. This paper presents an algorithm to extract edge-end pixels together with their directional sensitivities as an augmentation to the currently available mathematical models. The algorithm is implemented in the Java environment because of its inherent compatibility with web interfaces since its main use is envisaged to be for remote image analysis on a virtual instrumentation platform.
Abstract: Recently, fast neural networks for object/face
detection were presented in [1-3]. The speed up factor of these
networks relies on performing cross correlation in the frequency
domain between the input image and the weights of the hidden
layer. But, these equations given in [1-3] for conventional and fast
neural networks are not valid for many reasons presented here. In
this paper, correct equations for cross correlation in the spatial and
frequency domains are presented. Furthermore, correct formulas for
the number of computation steps required by conventional and fast
neural networks given in [1-3] are introduced. A new formula for
the speed up ratio is established. Also, corrections for the equations
of fast multi scale object/face detection are given. Moreover,
commutative cross correlation is achieved. Simulation results show
that sub-image detection based on cross correlation in the frequency
domain is faster than classical neural networks.
Abstract: Multi User Virtual Worlds are becoming a valuable educational tool. Learning experiences within these worlds focus on discovery and active experiences that both engage students and motivate them to explore new concepts. As educators, we need to explore these environments to determine how they can most effectively be used in our instructional practices. This paper explores the current application of virtual worlds to identify meaningful educational strategies that are being used to engage students and enhance teaching and learning.
Abstract: The belief decision tree (BDT) approach is a decision
tree in an uncertain environment where the uncertainty is represented
through the Transferable Belief Model (TBM), one interpretation
of the belief function theory. The uncertainty can appear either in
the actual class of training objects or attribute values of objects to
classify. In this paper, we develop a post-pruning method of belief
decision trees in order to reduce size and improve classification
accuracy on unseen cases. The pruning of decision tree has a
considerable intention in the areas of machine learning.
Abstract: This paper considers the autonomous navigation
problem of multiple n-link nonholonomic mobile manipulators within
an obstacle-ridden environment. We present a set of nonlinear
acceleration controllers, derived from the Lyapunov-based control
scheme, which generates collision-free trajectories of the mobile
manipulators from initial configurations to final configurations in a
constrained environment cluttered with stationary solid objects of
different shapes and sizes. We demonstrate the efficiency of the
control scheme and the resulting acceleration controllers of the
mobile manipulators with results through computer simulations of an
interesting scenario.
Abstract: A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
Abstract: There are many automotive accidents due to blind spots and driver inattentiveness. Blind spot is the area that is invisible to the driver's viewpoint without head rotation. Several methods are available for assisting the drivers. Simplest methods are — rear mirrors and wide-angle lenses. But, these methods have a disadvantage of the requirement for human assistance. So, the accuracy of these devices depends on driver. Another approach called an automated approach that makes use of sensors such as sonar or radar. These sensors are used to gather range information. The range information will be processed and used for detecting the collision. The disadvantage of this system is — low angular resolution and limited sensing volumes. This paper is a panoramic sensor based automotive vehicle monitoring..
Abstract: The mechanical properties of granular solids are
dependent on the flow of stresses from one particle to another
through inter-particle contact. Although some experimental methods
have been used to study the inter-particle contacts in the past,
preliminary work with these techniques indicated that they do not
have the necessary resolution to distinguish between those contacts
that transmit the load and those that do not, especially for systems
with a wide distribution of particle sizes. In this research, computer
simulations are used to study the nature and distribution of contacts
in a compact with wide particle size distribution, representative of
aggregate size distribution used in asphalt pavement construction.
The packing fraction, the mean number of contacts and the
distribution of contacts were studied for different scenarios. A
methodology to distinguish and compute the fraction of load-bearing
particles and the fraction of space-filling particles (particles that do
not transmit any force) is needed for further investigation.
Abstract: A numerical study is presented on buckling and post
buckling behaviour of laminated carbon fiber reinforced plastic
(CFRP) thin-walled cylindrical shells under axial compression using
asymmetric meshing technique (AMT). Asymmetric meshing
technique is a perturbation technique to introduce disturbance without
changing geometry, boundary conditions or loading conditions.
Asymmetric meshing affects predicted buckling load, buckling mode
shape and post-buckling behaviour. Linear (eigenvalue) and nonlinear
(Riks) analyses have been performed to study the effect of
asymmetric meshing in the form of a patch on buckling behaviour.
The reduction in the buckling load using Asymmetric meshing
technique was observed to be about 15%. An isolated dimple formed
near the bifurcation point and the size of which increased to reach a
stable state in the post-buckling region. The load-displacement curve
behaviour applying asymmetric meshing is quite similar to the curve
obtained using initial geometric imperfection in the shell model.
Abstract: Few decades ago, electronic and sensor technologies
are merged into vehicles as the Advanced Driver Assistance
System(ADAS). However, sensor-based ADASs have limitations
about weather interference and a line-of-sight nature problem. In our
project, we investigate a Relative Position based ADAS(RP-ADAS).
We divide the RP-ADAS into four main research areas: GNSS,
VANET, Security/Privacy, and Application. In this paper, we research
the GNSS technologies and determine the most appropriate one. With
the performance evaluation, we figure out that the C/A code based
GPS technologies are inappropriate for 'which lane-level' application.
However, they can be used as a 'which road-level' application.
Abstract: This study was conducted published to investigate
there liability of the equation pressure-impulse (PI) reinforced
concrete column inprevious studies. Equation involves three different
levels of damage criteria known as D =0. 2, D =0. 5 and D =0. 8.The
damage criteria known as a minor when 0-0.2, 0.2-0.5is known as
moderate damage, high damage known as 0.5-0.8, and 0.8-1 of the
structure is considered a failure. In this study, two types of reliability
analyzes conducted. First, using pressure-impulse equation with
different parameters. The parameters involved are the concrete
strength, depth, width, and height column, the ratio of longitudinal
reinforcement and transverse reinforcement ratio. In the first analysis
of the reliability of this new equation is derived to improve the
previous equations. The second reliability analysis involves three
types of columns used to derive the PI curve diagram using the
derived equation to compare with the equation derived from other
researchers and graph minimum standoff versus weapon yield
Federal Emergency Management Agency (FEMA). The results
showed that the derived equation is more accurate with FEMA
standards than previous researchers.
Abstract: Type 2 diabetes mellitus (T2DM) is a complex
metabolic disorder that characterized by the presence of high glucose
in blood that cause from insulin resistance and insufficiency due to
deterioration β-cell Langerhans functions. T2DM is commonly
caused by the combination of inherited genetic variations as well as
our own lifestyle. Metallothionein (MT) is a known cysteine-rich
protein responsible in helping zinc homeostasis which is important in
insulin signaling and secretion as well as protection our body from
reactive oxygen species (ROS). MT scavenged ROS and free
radicals in our body happen to be one of the reasons of T2DM and its
complications. The objective of this study was to investigate the
association of MT1A and MT2A polymorphisms between T2DM and
control subjects among Malay populations. This study involved 150
T2DM and 120 Healthy individuals of Malay ethnic with mixed
genders. The genomic DNA was extracted from buccal cells and
amplified for MT1A and MT2A loci; the 347bp and 238bp banding
patterns were respectively produced by mean of the Polymerase
Chain Reaction (PCR). The PCR products were digested with Mlucl
and Tsp451 restriction enzymes respectively and producing
fragments lengths of (158/189/347bp) and (103/135/238bp)
respectively. The ANOVA test was conducted and it shown that there
was a significant difference between diabetic and control subjects for
age, BMI, WHR, SBP, FPG, HBA1C, LDL, TG, TC and family
history with (P0.05). The genotype
frequency for AA, AG and GG of MT1A polymorphisms was 72.7%,
22.7% and 4.7% in cases and 15%, 55% and 30% in control
respectively. As for MT2A, genotype frequency of GG, GC and CC
was 42.7%, 27.3% and 30% in case and 5%, 40% and 55% for
control respectively. Both polymorphisms show significant difference
between two investigated groups with (P=0.000). The Post hoc test
was conducted and shows a significant difference between the
genotypes within each polymorphism (P=0. 000). The MT1A and
MT2A polymorphisms were believed to be the reliable molecular
markers to distinguish the T2DM subjects from healthy individuals in
Malay populations.