Abstract: Internet computer games turn to be more and more
attractive within the context of technology enhanced learning.
Educational games as quizzes and quests have gained significant
success in appealing and motivating learners to study in a different
way and provoke steadily increasing interest in new methods of
application. Board games are specific group of games where figures
are manipulated in competitive play mode with race conditions on a
surface according predefined rules. The article represents a new,
formalized model of traditional quizzes, puzzles and quests shown as
multimedia board games which facilitates the construction process of
such games. Authors provide different examples of quizzes and their
models in order to demonstrate the model is quite general and does
support not only quizzes, mazes and quests but also any set of
teaching activities. The execution process of such models is
explained and, as well, how they can be useful for creation and
delivery of adaptive e-learning courseware.
Abstract: Silver nano-particles have been used for antibacterial
purpose and it is also believed to have removal of odorous compounds,
oxidation capacity as a metal catalyst. In this study, silver
nano-particles in nano sizes (5-30 nm) were prepared on the surface of
NaHCO3, the supporting material, using a sputtering method that
provided high silver content and minimized conglomerating problems
observed in the common AgNO3 photo-deposition method. The silver
nano-particles were dispersed by dissolving Ag-NaHCO3 into water,
and the dispersed silver nano-particles in the aqueous phase were
applied to remove inorganic odor compounds, H2S, in a scrubbing
reactor. Hydrogen sulfide in the gas phase was rapidly removed by the
silver nano-particles, and the concentration of sulfate (SO4
2-) ion
increased with time due to the oxidation reaction by silver as a
catalyst. Consequently, the experimental results demonstrated that the
silver nano-particles in the aqueous solution can be successfully
applied to remove odorous compounds without adding additional
energy sources and producing any harmful byproducts
Abstract: Detection and tracking of the lip contour is an important
issue in speechreading. While there are solutions for lip tracking
once a good contour initialization in the first frame is available,
the problem of finding such a good initialization is not yet solved
automatically, but done manually. We have developed a new tracking
solution for lip contour detection using only few landmarks (15
to 25) and applying the well known Active Shape Models (ASM).
The proposed method is a new LMS-like adaptive scheme based on
an Auto regressive (AR) model that has been fit on the landmark
variations in successive video frames. Moreover, we propose an extra
motion compensation model to address more general cases in lip
tracking. Computer simulations demonstrate a fair match between
the true and the estimated spatial pixels. Significant improvements
related to the well known LMS approach has been obtained via a
defined Frobenius norm index.
Abstract: In this paper we present an adaptive method for image
compression that is based on complexity level of the image. The
basic compressor/de-compressor structure of this method is a multilayer
perceptron artificial neural network. In adaptive approach
different Back-Propagation artificial neural networks are used as
compressor and de-compressor and this is done by dividing the
image into blocks, computing the complexity of each block and then
selecting one network for each block according to its complexity
value. Three complexity measure methods, called Entropy, Activity
and Pattern-based are used to determine the level of complexity in
image blocks and their ability in complexity estimation are evaluated
and compared. In training and evaluation, each image block is
assigned to a network based on its complexity value. Best-SNR is
another alternative in selecting compressor network for image blocks
in evolution phase which chooses one of the trained networks such
that results best SNR in compressing the input image block. In our
evaluations, best results are obtained when overlapping the blocks is
allowed and choosing the networks in compressor is based on the
Best-SNR. In this case, the results demonstrate superiority of this
method comparing with previous similar works and JPEG standard
coding.
Abstract: In today-s era of plasma and laser cutting, machines using oxy-acetylene flame are also meritorious due to their simplicity and cost effectiveness. The objective to devise a Computer controlled Oxy-Fuel profile cutting machine arose from the increasing demand for metal cutting with respect to edge quality, circularity and lesser formation of redeposit material. The System has an 8 bit micro controller based embedded system, which assures stipulated time response. A new window based Application software was devised which takes a standard CAD file .DXF as input and converts it into numerical data required for the controller. It uses VB6 as a front end whereas MS-ACCESS and AutoCAD as back end. The system is designed around AT89C51RD2, powerful 8 bit, ISP micro controller from Atmel and is optimized to achieve cost effectiveness and also maintains the required accuracy and reliability for complex shapes. The backbone of the system is a cleverly designed mechanical assembly along with the embedded system resulting in an accuracy of about 10 microns while maintaining perfect linearity in the cut. This results in substantial increase in productivity. The observed results also indicate reduced inter laminar spacing of pearlite with an increase in the hardness of the edge region.
Abstract: This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.
Abstract: The prevalence of non organic constipation differs
from country to country and the reliability of the estimate rates is
uncertain. Moreover, the clinical relevance of subdividing the
heterogeneous functional constipation disorders into pre-defined
subgroups is largely unknown.. Aim: to estimate the prevalence of
constipation in a population-based sample and determine whether
clinical subgroups can be identified. An age and gender stratified
sample population from 5 Italian cities was evaluated using a
previously validated questionnaire. Data mining by cluster analysis
was used to determine constipation subgroups. Results: 1,500
complete interviews were obtained from 2,083 contacted households
(72%). Self-reported constipation correlated poorly with symptombased
constipation found in 496 subjects (33.1%). Cluster analysis
identified four constipation subgroups which correlated to subgroups
identified according to pre-defined symptom criteria. Significant
differences in socio-demographics and lifestyle were observed
among subgroups.
Abstract: This paper suggests a rethinking of the existing
research about Genetically Modified (GM) food. Since the first batch
of GM food was commercialised in the UK market, GM food rapidly
received and lost media attention in the UK. Disagreement on GM
food policy between the US and the EU has also drawn scholarly
attention to this issue. Much research has been carried out intending to
understand people-s views about GM food and the shaping of these
views. This paper was based on the data collected in twenty-nine
semi-structured interviews, which were examined through Erving
Goffman-s idea of self-presentation in interactions to suggest that the
existing studies investigating “consumer attitudes" towards GM food
have only considered the “front stage" in the dramaturgic metaphor.
This paper suggests that the ways in which people choose to present
themselves when participating these studies should be taken into
account during the data analysis.
Abstract: In the present work, we propose a new technique to
enhance the learning capabilities and reduce the computation
intensity of a competitive learning multi-layered neural network
using the K-means clustering algorithm. The proposed model use
multi-layered network architecture with a back propagation learning
mechanism. The K-means algorithm is first applied to the training
dataset to reduce the amount of samples to be presented to the neural
network, by automatically selecting an optimal set of samples. The
obtained results demonstrate that the proposed technique performs
exceptionally in terms of both accuracy and computation time when
applied to the KDD99 dataset compared to a standard learning
schema that use the full dataset.
Abstract: Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. As all kernel-based learning algorithms its performance depends heavily on the proper choice of the kernel parameter. This paper proposes a new approach to select kernel's parameter based on maximizing the distance between both gravity centers of normal and abnormal classes, and at the same time minimizing the variance within each class. The performance of the proposed algorithm is evaluated on several benchmarks. The experimental results demonstrate the feasibility and the effectiveness of the presented method.
Abstract: This work deals with unsupervised image deblurring.
We present a new deblurring procedure on images provided by lowresolution
synthetic aperture radar (SAR) or simply by multimedia in
presence of multiplicative (speckle) or additive noise, respectively.
The method we propose is defined as a two-step process. First, we
use an original technique for noise reduction in wavelet domain.
Then, the learning of a Kohonen self-organizing map (SOM) is
performed directly on the denoised image to take out it the blur. This
technique has been successfully applied to real SAR images, and the
simulation results are presented to demonstrate the effectiveness of
the proposed algorithms.
Abstract: Wind farms (WFs) with high level of penetration are
being established in power systems worldwide more rapidly than
other renewable resources. The Independent System Operator (ISO),
as a policy maker, should propose appropriate places for WF
installation in order to maximize the benefits for the investors. There
is also a possibility of congestion relief using the new installation of
WFs which should be taken into account by the ISO when proposing
the locations for WF installation. In this context, efficient wind farm
(WF) placement method is proposed in order to reduce burdens on
congested lines. Since the wind speed is a random variable and load
forecasts also contain uncertainties, probabilistic approaches are used
for this type of study. AC probabilistic optimal power flow (P-OPF)
is formulated and solved using Monte Carlo Simulations (MCS). In
order to reduce computation time, point estimate methods (PEM) are
introduced as efficient alternative for time-demanding MCS.
Subsequently, WF optimal placement is determined using generation
shift distribution factors (GSDF) considering a new parameter
entitled, wind availability factor (WAF). In order to obtain more
realistic results, N-1 contingency analysis is employed to find the
optimal size of WF, by means of line outage distribution factors
(LODF). The IEEE 30-bus test system is used to show and compare
the accuracy of proposed methodology.
Abstract: In this study the behavior of interlaminar fracture of
carbon-epoxy thermoplastic laminated composite is investigated
numerically and experimentally. Tests are performed with Arcan
specimens. Testing with Arcan specimen gives the opportunity of
utilizing just one kind of specimen for extracting fracture properties
for mode I, mode II and different mixed mode ratios of materials with
exerting load via different loading angles. Variation of loading angles
in range of 0-90° made possible to achieve different mixed mode
ratios. Correction factors for various conditions are obtained from
ABAQUS 2D finite element models which demonstrate the finite
shape of Arcan specimens used in this study. Finally, applying the
correction factors to critical loads obtained experimentally, critical
interlaminar fracture toughness of this type of carbon- epoxy
composite has been attained.
Abstract: This paper presents an optimization technique to economic load dispatch (ELD) problems with considering the daily load patterns and generator constraints using a particle swarm optimization (PSO). The objective is to minimize the fuel cost. The optimization problem is subject to system constraints consisting of power balance and generation output of each units. The application of a constriction factor into PSO is a useful strategy to ensure convergence of the particle swarm algorithm. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total constraint cost function is minimized. The performance of the developed methodology is demonstrated by case studies in test system of fifteen-generation units. The results show that the proposed algorithm scan give the minimum total cost of generation while satisfying all the constraints and benefiting greatly from saving in power loss reduction
Abstract: The increase in energy demand has raised concerns
over adverse impacts on the environment from energy generation. It
is important to understand the status of energy consumption for
institutions such as Curtin Sarawak to ensure the sustainability of
energy usage, and also to reduce its costs. In this study, a preliminary
audit framework was developed and was conducted around the
Malaysian campus to obtain information such as the number and
specifications of electrical appliances, built-up area and ambient
temperature to understand the relationship of these factors with
energy consumption. It was found that the number and types of
electrical appliances, population and activities in the campus
impacted the energy consumption of Curtin Sarawak directly.
However, the built-up area and ambient temperature showed no clear
correlation with energy consumption. An investigation of the diurnal
and seasonal energy consumption of the campus was also carried out.
From the data, recommendations were made to improve the energy
efficiency of the campus.
Abstract: This paper investigates the effectiveness of the use of
seismic isolation devices on the overall 3D seismic response of
curved highway viaducts with an emphasis on expansion joints.
Furthermore, an evaluation of the effectiveness of the use of cable
restrainers is presented. For this purpose, the bridge seismic
performance has been evaluated on four different radii of curvature,
considering two cases: restrained and unrestrained curved viaducts.
Depending on the radius of curvature, three-dimensional non-linear
dynamic analysis shows the vulnerability of curved viaducts to
pounding and deck unseating damage. In this study, the efficiency of
using LRB supports combined with cable restrainers on curved
viaducts is demonstrated, not only by reducing in all cases the
possible damage, but also by providing a similar behavior in the
viaducts despite of curvature radius.
Abstract: Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.
Abstract: The study and development of an innovative material
for building insulation is really important for a sustainable society in order to improve comfort and reducing energy consumption. The aim of this work is the development of insulating panels for
sustainable buildings based on an innovative material made by
cardboard and Phase Change Materials (PCMs).
The research has consisted in laboratory tests whose purpose has been the obtaining of the required properties for insulation panels: lightweight, porous structures and mechanical resistance. PCMs have been used for many years in the building industry as
smart insulation technology because of their properties of storage and release high quantity of latent heat at useful specific temperatures [1]- [2].
The integration of PCMs into cellulose matrix during the waste paper recycling process has been developed in order to obtain a
composite material.
Experiments on the productive process for the realization of insulating panels were done in order to make the new material
suitable for building application. The addition of rising agents
demonstrated the possibility to obtain a lighter structure with better
insulation properties.
Several tests were conducted to verify the new panel properties. The results obtained have shown the possibility to realize an
innovative and sustainable material suitable to replace insulating panels currently used.
Abstract: The regulatory competence of blood glucose homeostasis might determine the degree of academic performance. The aim of this study was to produce a model of students' alcohol use based on glucose homeostasis control and cognitive functions that might define the pathogenetic mechanism of alcohol's effect on academic performance. The study took six hours and thirty minutes on fasting, involving thirteen male students. Disturbances in cognitive functions, precisely a decrease in the effectiveness of active attention and a faster development of fatigue after four to six hours of mental work in alcohol users, compared to abstainers was statistically proven. These disturbances in alcohol users were retained even after seven to ten days of moderate alcohol use and might be the reason for the low academic performances among students who use alcoholic beverages.
Abstract: The problem of complex use of water resources in
Central Asia by taking into consideration the sovereignty of the states
and increasing demand on use of water for economic aspects are
considered. Complex program with appropriate mathematical
software intended for calculation of possible variants of using the
Amudarya up-stream water resources according to satisfaction of
incompatible requirements of the national economics in irrigation
and energy generation is proposed.