Abstract: Distance protection of transmission lines including advanced flexible AC transmission system (FACTS) devices has been a very challenging task. FACTS devices of interest in this paper are static synchronous series compensators (SSSC) and unified power flow controller (UPFC). In this paper, a new algorithm is proposed to detect and classify the fault and identify the fault position in a transmission line with respect to a FACTS device placed in the midpoint of the transmission line. Discrete wavelet transformation and wavelet entropy calculations are used to analyze during fault current and voltage signals of the compensated transmission line. The proposed algorithm is very simple and accurate in fault detection and classification. A variety of fault cases and simulation results are introduced to show the effectiveness of such algorithm.
Abstract: The effects of equilibrium time, solution pH, and
sorption temperature of cationic methylene blue (MB) adsorption on nanoporous metallosilicoaluminophosphate ZnAPSO-34 was studied
using a batch equilibration method. UV–VIS spectroscopy was used
to obtain the adsorption isotherms at 20° C. The optimum period for
adsorption was 300 min. However, MB removal increased from
81,82 % to 94,81 %. The equilibrium adsorption data was analyzed
by using Langmuir, Freundlich and Temkin isotherm models.
Langmuir isotherm was found to be the better-fitting model and the process followed pseudo second–order kinetics. The results showed
that ZnAPSO-34 could be employed as an effective material and could be an attractive alternative for the removal of dyes and colors
from aqueous solutions.
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: 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: In this paper, we present the video quality measure
estimation via a neural network. This latter predicts MOS (mean
opinion score) by providing height parameters extracted from
original and coded videos. The eight parameters that are used are: the
average of DFT differences, the standard deviation of DFT
differences, the average of DCT differences, the standard deviation
of DCT differences, the variance of energy of color, the luminance
Y, the chrominance U and the chrominance V. We chose Euclidean
Distance to make comparison between the calculated and estimated
output.
Abstract: In the present work, a study has been made on the combination of the electrical discharge machining (EDM) with ultrasonic vibrations to improve the machining efficiency. In experiments the graphite used as tool electrode and material of workpiece was AISIH13 tool steel. The parameters such as discharge peak current and pulse duration were changed to explore their effect on the material removal rate (MRR), relative tool wear ratio (TWR) and surface roughness. From the experimental result it can be seen that ultrasonic vibration of the workpiece can significantly reduces the inactive pulses and improves the stability of process. It was found that ultrasonic assisted EDM (US-EDM) is effective in attaining a high material removal rate (MRR) in finishing regime.
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: Detection, feature extraction and pose estimation of
people in images and video is made challenging by the variability of
human appearance, the complexity of natural scenes and the high
dimensionality of articulated body models and also the important
field in Image, Signal and Vision Computing in recent years. In this
paper, four types of people in 2D dimension image will be tested and
proposed. The system will extract the size and the advantage of them
(such as: tall fat, short fat, tall thin and short thin) from image. Fat
and thin, according to their result from the human body that has been
extract from image, will be obtained. Also the system extract every
size of human body such as length, width and shown them in output.
Abstract: Un-doped GaN film of thickness 1.90 mm, grown on
sapphire substrate were uniformly implanted with 325 keV Mn+ ions
for various fluences varying from 1.75 x 1015 - 2.0 x 1016 ions cm-2 at
3500 C substrate temperature. The structural, morphological and
magnetic properties of Mn ion implanted gallium nitride samples
were studied using XRD, AFM and SQUID techniques. XRD of the
sample implanted with various ion fluences showed the presence of
different magnetic phases of Ga3Mn, Ga0.6Mn0.4 and Mn4N.
However, the compositions of these phases were found to be
depended on the ion fluence. AFM images of non-implanted sample
showed micrograph with rms surface roughness 2.17 nm. Whereas
samples implanted with the various fluences showed the presence of
nano clusters on the surface of GaN. The shape, size and density of
the clusters were found to vary with respect to ion fluence. Magnetic
moment versus applied field curves of the samples implanted with
various fluences exhibit the hysteresis loops. The Curie temperature
estimated from zero field cooled and field cooled curves for the
samples implanted with the fluence of 1.75 x 1015, 1.5 x 1016 and 2.0
x 1016 ions cm-2 was found to be 309 K, 342 K and 350 K
respectively.
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: There are only limited studies that directly correlate
the increase in reinforced concrete (RC) panel structural capacities in
resisting the blast loads with different RC panel structural properties
in terms of blast loading characteristics, RC panel dimensions, steel
reinforcement ratio and concrete material strength. In this paper,
numerical analyses of dynamic response and damage of the one-way
RC panel to blast loads are carried out using the commercial software
LS-DYNA. A series of simulations are performed to predict the blast
response and damage of columns with different level and magnitude
of blast loads. The numerical results are used to develop pressureimpulse
(P-I) diagrams of one-way RC panels. Based on the
numerical results, the empirical formulae are derived to calculate the
pressure and impulse asymptotes of the P-I diagrams of RC panels.
The results presented in this paper can be used to construct P-I
diagrams of RC panels with different concrete and reinforcement
properties. The P-I diagrams are very useful to assess panel capacities
in resisting different blast loads.
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.
Abstract: Cameras are often mounted on platforms that canmove like rovers, booms, gantries and aircraft. People operate suchplatforms to capture desired views of scene or target. To avoidcollisions with the environment and occlusions, such platforms oftenpossess redundant degrees-of-freedom. As a result, manipulatingsuch platforms demands much skill. Visual-servoing some degrees-of-freedom may reduce operator burden and improve tracking per-formance. This concept, which we call human-in-the-loop visual-servoing, is demonstrated in this paper and applies a Α-β-γ filter and feedforward controller to a broadcast camera boom.
Abstract: It has been established that microRNAs (miRNAs) play
an important role in gene expression by post-transcriptional regulation
of messengerRNAs (mRNAs). However, the precise relationships
between microRNAs and their target genes in sense of numbers,
types and biological relevance remain largely unclear. Dissecting the
miRNA-target relationships will render more insights for miRNA
targets identification and validation therefore promote the understanding
of miRNA function. In miRBase, miRanda is the key
algorithm used for target prediction for Zebrafish. This algorithm
is high-throughput but brings lots of false positives (noise). Since
validation of a large scale of targets through laboratory experiments
is very time consuming, several computational methods for miRNA
targets validation should be developed. In this paper, we present an
integrative method to investigate several aspects of the relationships
between miRNAs and their targets with the final purpose of extracting
high confident targets from miRanda predicted targets pool. This is
achieved by using the techniques ranging from statistical tests to
clustering and association rules. Our research focuses on Zebrafish.
It was found that validated targets do not necessarily associate with
the highest sequence matching. Besides, for some miRNA families,
the frequency of their predicted targets is significantly higher in the
genomic region nearby their own physical location. Finally, in a case
study of dre-miR-10 and dre-miR-196, it was found that the predicted
target genes hoxd13a, hoxd11a, hoxd10a and hoxc4a of dre-miR-
10 while hoxa9a, hoxc8a and hoxa13a of dre-miR-196 have similar
characteristics as validated target genes and therefore represent high
confidence target candidates.