Abstract: Physical education (PE) is still neglected in schools
despite its academic, social, psychological, and health benefits.
Based on the assumption that Information and Communication
Technologies (ICTs) can contribute to the development of PE in
schools, this study aims to design a model of the factors affecting the
adoption of ICTs for PE in schools. The proposed model is based on
a sound theoretical framework. It was designed following a literature
review of technology adoption theories and of ICT adoption factors
for physical education. The technology adoption model that fitted to
the best all ICT adoption factors was then chosen as the basis for the
proposed model. It was found that the Unified Theory of Acceptance
and Use of Technology (UTAUT) is the most adequate theoretical
framework for the modeling of ICT adoption factors for physical
education.
Abstract: The one-class support vector machine “support vector
data description” (SVDD) is an ideal approach for anomaly or outlier
detection. However, for the applicability of SVDD in real-world
applications, the ease of use is crucial. The results of SVDD are
massively determined by the choice of the regularisation parameter C
and the kernel parameter of the widely used RBF kernel. While for
two-class SVMs the parameters can be tuned using cross-validation
based on the confusion matrix, for a one-class SVM this is not
possible, because only true positives and false negatives can occur
during training. This paper proposes an approach to find the optimal
set of parameters for SVDD solely based on a training set from
one class and without any user parameterisation. Results on artificial
and real data sets are presented, underpinning the usefulness of the
approach.
Abstract: Injection molding is a very complicated process to
monitor and control. With its high complexity and many process
parameters, the optimization of these systems is a very challenging
problem. To meet the requirements and costs demanded by the
market, there has been an intense development and research with the
aim to maintain the process under control. This paper outlines the
latest advances in necessary algorithms for plastic injection process
and monitoring, and also a flexible data acquisition system that
allows rapid implementation of complex algorithms to assess their
correct performance and can be integrated in the quality control
process. This is the main topic of this paper. Finally, to demonstrate
the performance achieved by this combination, a real case of use is
presented.
Abstract: The new framework the Higher Education is
immersed in involves a complete change in the way lecturers must
teach and students must learn. Whereas the lecturer was the main
character in traditional education, the essential goal now is to
increase the students' participation in the process. Thus, one of the
main tasks of lecturers in this new context is to design activities of
different nature in order to encourage such participation. Seminars
are one of the activities included in this environment. They are active
sessions that enable going in depth into specific topics as support of
other activities. They are characterized by some features such as
favoring interaction between students and lecturers or improving
their communication skills. Hence, planning and organizing strategic
seminars is indeed a great challenge for lecturers with the aim of
acquiring knowledge and abilities. This paper proposes a method
using Artificial Intelligence techniques to obtain student profiles
from their marks and preferences. The goal of building such profiles
is twofold. First, it facilitates the task of splitting the students into
different groups, each group with similar preferences and learning
difficulties. Second, it makes it easy to select adequate topics to be a
candidate for the seminars. The results obtained can be either a
guarantee of what the lecturers could observe during the development
of the course or a clue to reconsider new methodological strategies in
certain topics.
Abstract: Long term rainfall analysis and prediction is a
challenging task especially in the modern world where the impact of
global warming is creating complications in environmental issues.
These factors which are data intensive require high performance
computational modeling for accurate prediction. This research paper
describes a prototype which is designed and developed on grid
environment using a number of coupled software infrastructural
building blocks. This grid enabled system provides the demanding
computational power, efficiency, resources, user-friendly interface,
secured job submission and high throughput. The results obtained
using sequential execution and grid enabled execution shows that
computational performance has enhanced among 36% to 75%, for
decade of climate parameters. Large variation in performance can be
attributed to varying degree of computational resources available for
job execution.
Grid Computing enables the dynamic runtime selection, sharing
and aggregation of distributed and autonomous resources which plays
an important role not only in business, but also in scientific
implications and social surroundings. This research paper attempts to
explore the grid enabled computing capabilities on weather indices
from HOAPS data for climate impact modeling and change
detection.
Abstract: this paper presents a novel neural network controller
with composite adaptation low to improve the trajectory tracking
problems of biped robots comparing with classical controller. The
biped model has 5_link and 6 degrees of freedom and actuated by
Plated Pneumatic Artificial Muscle, which have a very high power to
weight ratio and it has large stoke compared to similar actuators. The
proposed controller employ a stable neural network in to approximate
unknown nonlinear functions in the robot dynamics, thereby
overcoming some limitation of conventional controllers such as PD
or adaptive controllers and guarantee good performance. This NN
controller significantly improve the accuracy requirements by
retraining the basic PD/PID loop, but adding an inner adaptive loop
that allows the controller to learn unknown parameters such as
friction coefficient, therefore improving tracking accuracy.
Simulation results plus graphical simulation in virtual reality show
that NN controller tracking performance is considerably better than
PD controller tracking performance.
Abstract: An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.
Abstract: Data envelopment analysis (DEA) has gained great popularity in environmental performance measurement because it can provide a synthetic standardized environmental performance index when pollutants are suitably incorporated into the traditional DEA framework. Since some of the environmental performance indicators cannot be controlled by companies managers, it is necessary to develop the model in a way that it could be applied when discretionary and/or non-discretionary factors were involved. In this paper, we present a semi-radial DEA approach to measuring environmental performance, which consists of non-discretionary factors. The model, then, has been applied on a real case.
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: Knowledge sharing in general and the contextual
access to knowledge in particular, still represent a key challenge in
the knowledge management framework. Researchers on semantic
web and human machine interface study techniques to enhance this
access. For instance, in semantic web, the information retrieval is
based on domain ontology. In human machine interface, keeping
track of user's activity provides some elements of the context that can
guide the access to information. We suggest an approach based on
these two key guidelines, whilst avoiding some of their weaknesses.
The approach permits a representation of both the context and the
design rationale of a project for an efficient access to knowledge. In
fact, the method consists of an information retrieval environment
that, in the one hand, can infer knowledge, modeled as a semantic
network, and on the other hand, is based on the context and the
objectives of a specific activity (the design). The environment we
defined can also be used to gather similar project elements in order to
build classifications of tasks, problems, arguments, etc. produced in a
company. These classifications can show the evolution of design
strategies in the company.
Abstract: This Classifying Bird Sounds (chip notes) project-s
purpose is to reduce the unwanted noise from recorded bird sound
chip notes, design a scheme to detect differences and similarities
between recorded chip notes, and classify bird sound chip notes. The
technologies of determining the similarities of sound waves have
been used in communication, sound engineering and wireless sound
applications for many years. Our research is focused on the similarity
of chip notes, which are the sounds from different birds. The program
we use is generated by Microsoft Cµ.
Abstract: This research work is aimed at speech recognition
using scaly neural networks. A small vocabulary of 11 words were
established first, these words are “word, file, open, print, exit, edit,
cut, copy, paste, doc1, doc2". These chosen words involved with
executing some computer functions such as opening a file, print
certain text document, cutting, copying, pasting, editing and exit.
It introduced to the computer then subjected to feature extraction
process using LPC (linear prediction coefficients). These features are
used as input to an artificial neural network in speaker dependent
mode. Half of the words are used for training the artificial neural
network and the other half are used for testing the system; those are
used for information retrieval.
The system components are consist of three parts, speech
processing and feature extraction, training and testing by using neural
networks and information retrieval.
The retrieve process proved to be 79.5-88% successful, which is
quite acceptable, considering the variation to surrounding, state of
the person, and the microphone type.
Abstract: In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.
Abstract: This paper considers the effect of heat generation
proportional l to (T - T∞ )p , where T is the local temperature and T∞
is the ambient temperature, in unsteady free convection flow near the
stagnation point region of a three-dimensional body. The fluid is
considered in an ambient fluid under the assumption of a step change
in the surface temperature of the body. The non-linear coupled partial
differential equations governing the free convection flow are solved
numerically using an implicit finite-difference method for different
values of the governing parameters entering these equations. The
results for the flow and heat characteristics when p ≤ 2 show that
the transition from the initial unsteady-state flow to the final steadystate
flow takes place smoothly. The behavior of the flow is seen
strongly depend on the exponent p.
Abstract: In this paper, the dynamics of a system of two van der Pol oscillators with delayed position and velocity is studied. We provide an approximate solution for this system using parameterexpansion method. Also, we obtain approximate values for frequencies of the system. The parameter-expansion method is more efficient than the perturbation method for this system because the method is independent of perturbation parameter assumption.
Abstract: Concrete strength evaluated from compression tests
on cores is affected by several factors causing differences from the
in-situ strength at the location from which the core specimen was
extracted. Among the factors, there is the damage possibly occurring
during the drilling phase that generally leads to underestimate the
actual in-situ strength. In order to quantify this effect, in this study
two wide datasets have been examined, including: (i) about 500 core
specimens extracted from Reinforced Concrete existing structures,
and (ii) about 600 cube specimens taken during the construction of
new structures in the framework of routine acceptance control. The
two experimental datasets have been compared in terms of
compression strength and specific weight values, accounting for the
main factors affecting a concrete property, that is type and amount of
cement, aggregates' grading, type and maximum size of aggregates,
water/cement ratio, placing and curing modality, concrete age. The
results show that the magnitude of the strength reduction due to
drilling damage is strongly affected by the actual properties of
concrete, being inversely proportional to its strength. Therefore, the
application of a single value of the correction coefficient, as generally
suggested in the technical literature and in structural codes, appears
inappropriate. A set of values of the drilling damage coefficient is
suggested as a function of the strength obtained from compressive
tests on cores.
Abstract: The highly nonlinear characteristics of drying
processes have prompted researchers to seek new nonlinear control
solutions. However, the relation between the implementation
complexity, on-line processing complexity, reliability control
structure and controller-s performance is not well established. The
present paper proposes high performance nonlinear fuzzy controllers
for a real-time operation of a drying machine, being developed under
a consistent match between those issues. A PCI-6025E data
acquisition device from National Instruments® was used, and the
control system was fully designed with MATLAB® / SIMULINK
language. Drying parameters, namely relative humidity and
temperature, were controlled through MIMOs Hybrid Bang-bang+PI
(BPI) and Four-dimensional Fuzzy Logic (FLC) real-time-based
controllers to perform drying tests on biological materials. The
performance of the drying strategies was compared through several
criteria, which are reported without controllers- retuning. Controllers-
performance analysis has showed much better performance of FLC
than BPI controller. The absolute errors were lower than 8,85 % for
Fuzzy Logic Controller, about three times lower than the
experimental results with BPI control.
Abstract: Secure electronic payment system is presented in this
paper. This electronic payment system is to be secure for clients such
as customers and shop owners. The security architecture of the
system is designed by RC5 encryption / decryption algorithm. This
eliminates the fraud that occurs today with stolen credit card
numbers. The symmetric key cryptosystem RC5 can protect
conventional transaction data such as account numbers, amount and
other information. This process can be done electronically using RC5
encryption / decryption program written by Microsoft Visual Basic
6.0. There is no danger of any data sent within the system being
intercepted, and replaced. The alternative is to use the existing
network, and to encrypt all data transmissions. The system with
encryption is acceptably secure, but that the level of encryption has
to be stepped up, as computing power increases. Results In order to
be secure the system the communication between modules is
encrypted using symmetric key cryptosystem RC5. The system will
use simple user name, password, user ID, user type and cipher
authentication mechanism for identification, when the user first
enters the system. It is the most common method of authentication in
most computer system.
Abstract: One major issue that is regularly cited as a block to
the widespread use of online assessments in eLearning, is that of the
authentication of the student and the level of confidence that an
assessor can have that the assessment was actually completed by that
student. Currently, this issue is either ignored, in which case
confidence in the assessment and any ensuing qualification is
damaged, or else assessments are conducted at central, controlled
locations at specified times, losing the benefits of the distributed
nature of the learning programme. Particularly as we move towards
constructivist models of learning, with intentions towards achieving
heutagogic learning environments, the benefits of a properly
managed online assessment system are clear. Here we discuss some
of the approaches that could be adopted to address these issues,
looking at the use of existing security and biometric techniques,
combined with some novel behavioural elements. These approaches
offer the opportunity to validate the student on accessing an
assessment, on submission, and also during the actual production of
the assessment. These techniques are currently under development in
the DECADE project, and future work will evaluate and report their
use..
Abstract: We present a method for fast volume rendering using
graphics hardware (GPU). To our knowledge, it is the first implementation
on the GPU. Based on the Shear-Warp algorithm, our
GPU-based method provides real-time frame rates and outperforms
the CPU-based implementation. When the number of slices is not
sufficient, we add in-between slices computed by interpolation. This
improves then the quality of the rendered images. We have also
implemented the ray marching algorithm on the GPU. The results
generated by the three algorithms (CPU-based and GPU-based Shear-
Warp, GPU-based Ray Marching) for two test models has proved that
the ray marching algorithm outperforms the shear-warp methods in
terms of speed up and image quality.