Abstract: We have defined two suites of metrics, which cover
static and dynamic aspects of component assembly. The static
metrics measure complexity and criticality of component assembly,
wherein complexity is measured using Component Packing Density
and Component Interaction Density metrics. Further, four criticality
conditions namely, Link, Bridge, Inheritance and Size criticalities
have been identified and quantified. The complexity and criticality
metrics are combined to form a Triangular Metric, which can be used
to classify the type and nature of applications. Dynamic metrics are
collected during the runtime of a complete application. Dynamic
metrics are useful to identify super-component and to evaluate the
degree of utilisation of various components. In this paper both static
and dynamic metrics are evaluated using Weyuker-s set of properties.
The result shows that the metrics provide a valid means to measure
issues in component assembly. We relate our metrics suite with
McCall-s Quality Model and illustrate their impact on product
quality and to the management of component-based product
development.
Abstract: Faced with social and health system capacity
constraints and rising and changing demand for welfare services,
governments and welfare providers are increasingly relying on
innovation to help support and enhance services. However, the
evidence reported by several studies indicates that the realization of
that potential is not an easy task. Innovations can be deemed
inherently complex to implement and operate, because many of them
involve a combination of technological and organizational renewal
within an environment featuring a diversity of stakeholders. Many
public welfare service innovations are markedly systemic in their
nature, which means that they emerge from, and must address, the
complex interplay between political, administrative, technological,
institutional and legal issues. This paper suggests that stakeholders
dealing with systemic innovation in welfare services must deal with
ambiguous and incomplete information in circumstances of
uncertainty. Employing a literature review methodology and case
study, this paper identifies, categorizes and discusses different
aspects of the uncertainty of systemic innovation in public welfare
services, and argues that uncertainty can be classified into eight
categories: technological uncertainty, market uncertainty,
regulatory/institutional uncertainty, social/political uncertainty,
acceptance/legitimacy uncertainty, managerial uncertainty, timing
uncertainty and consequence uncertainty.
Abstract: Currently, the Malaysian construction industry is
focusing on transforming construction processes from conventional
building methods to the Industrialized Building System (IBS). Still,
research on the decision making of IBS technology adoption with the
influence of contextual factors is scarce. The purpose of this paper is
to explore how contextual factors influence the IBS decision making
in building projects which is perceived by those involved in
construction industry namely construction stakeholders and IBS
supply chain members. Theoretical background, theoretical
frameworks and literatures which identify possible contextual factors
that influence decision making towards IBS technology adoption are
presented. This paper also discusses the importance of contextual
factors in IBS decision making, highlighting some possible crossover
benefits and making some suggestions as to how these can be
utilized. Conclusions are drawn and recommendations are made with
respect to the perception of socio-economic, IBS policy and IBS
technology associated with building projects.
Abstract: Computer languages are usually lumped together
into broad -paradigms-, leaving us in want of a finer classification
of kinds of language. Theories distinguishing between -genuine
differences- in language has been called for, and we propose that
such differences can be observed through a notion of expressive mode.
We outline this concept, propose how it could be operationalized and
indicate a possible context for the development of a corresponding
theory. Finally we consider a possible application in connection
with evaluation of language revision. We illustrate this with a case,
investigating possible revisions of the relational algebra in order to
overcome weaknesses of the division operator in connection with
universal queries.
Abstract: Hydrogen is an important chemical in many industries
and it is expected to become one of the major fuels for energy
generation in the future. Unfortunately, hydrogen does not exist in its
elemental form in nature and therefore has to be produced from
hydrocarbons, hydrogen-containing compounds or water.
Above its critical point (374.8oC and 22.1MPa), water has lower
density and viscosity, and a higher heat capacity than those of
ambient water. Mass transfer in supercritical water (SCW) is
enhanced due to its increased diffusivity and transport ability. The
reduced dielectric constant makes supercritical water a better solvent
for organic compounds and gases. Hence, due to the aforementioned
desirable properties, there is a growing interest toward studies
regarding the gasification of organic matter containing biomass or
model biomass solutions in supercritical water.
In this study, hydrogen and biofuel production by the catalytic
gasification of 2-Propanol in supercritical conditions of water was
investigated. Pt/Al2O3and Ni/Al2O3were the catalysts used in the
gasification reactions. All of the experiments were performed under a
constant pressure of 25MPa. The effects of five reaction temperatures
(400, 450, 500, 550 and 600°C) and five reaction times (10, 15, 20,
25 and 30 s) on the gasification yield and flammable component
content were investigated.
Abstract: This paper solves the environmental/ economic dispatch
power system problem using the Non-dominated Sorting Genetic
Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator
Operator (CAO), called the NSGA-II/CAO. These multiobjective
evolutionary algorithms were applied to the standard IEEE 30-bus
six-generator test system. Several optimization runs were carried out
on different cases of problem complexity. Different quality measure
which compare the performance of the two solution techniques were
considered. The results demonstrated that the inclusion of the CAO
in the original NSGA-II improves its convergence while preserving
the diversity properties of the solution set.
Abstract: A prototype for audio and video capture and compression in real time on a Linux platform has been developed. It is able to visualize both the captured and the compressed video at the same time, as well as the captured and compressed audio with the goal of comparing their quality. As it is based on free code, the final goal is to run it in an embedded system running Linux. Therefore, we would implement a node to capture and compress such multimedia information. Thus, it would be possible to consider the project within a larger one aimed at live broadcast of audio and video using a streaming server which would communicate with our node. Then, we would have a very powerful and flexible system with several practical applications.
Abstract: Irradiated material is a typical example of a complex
system with nonlinear coupling between its elements. During
irradiation the radiation damage is developed and this development
has bifurcations and qualitatively different kinds of behavior.
The accumulation of primary defects in irradiated crystals is
considered in frame work of nonlinear evolution of complex system.
The thermo-concentration nonlinear feedback is carried out as a
mechanism of self-oscillation development.
It is shown that there are two ways of the defect density evolution
under stationary irradiation. The first is the accumulation of defects;
defect density monotonically grows and tends to its stationary state
for some system parameters. Another way that takes place for
opportune parameters is the development of self-oscillations of the
defect density.
The stationary state, its stability and type are found. The
bifurcation values of parameters (environment temperature, defect
generation rate, etc.) are obtained. The frequency of the selfoscillation
and the conditions of their development is found and
rated. It is shown that defect density, heat fluxes and temperature
during self-oscillations can reach much higher values than the
expected steady-state values. It can lead to a change of typical
operation and an accident, e.g. for nuclear equipment.
Abstract: LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.
Abstract: In this paper we have proposed a methodology to
develop an amperometric biosensor for the analysis of glucose
concentration using a simple microcontroller based data acquisition
system. The work involves the development of Detachable
Membrane Unit (enzyme based biomembrane) with immobilized
glucose oxidase on the membrane and interfacing the same to the
signal conditioning system. The current generated by the biosensor
for different glucose concentrations was signal conditioned, then
acquired and computed by a simple AT89C51-microcontroller. The
optimum operating parameters for the better performance were found
and reported. The detailed performance evaluation of the biosensor
has been carried out. The proposed microcontroller based biosensor
system has the sensitivity of 0.04V/g/dl, with a resolution of
50mg/dl. It has exhibited very good inter day stability observed up to
30 days. Comparing to the reference method such as HPLC, the
accuracy of the proposed biosensor system is well within ± 1.5%.
The system can be used for real time analysis of glucose
concentration in the field such as, food and fermentation and clinical
(In-Vitro) applications.
Abstract: Water hyacinth has been used in aquatic systems for
wastewater purification in many years worldwide. The role of water
hyacinth (Eichhornia crassipes) species in polishing nitrate and
phosphorus concentration from municipal wastewater treatment plant
effluent by phytoremediation method was evaluated. The objective
of this project is to determine the removal efficiency of water
hyacinth in polishing nitrate and phosphorus, as well as chemical
oxygen demand (COD) and ammonia. Water hyacinth is considered
as the most efficient aquatic plant used in removing vast range of
pollutants such as organic matters, nutrients and heavy metals. Water
hyacinth, also referred as macrophytes, were cultivated in the
treatment house in a reactor tank of approximately 90(L) x 40(W) x
25(H) in dimension and built with three compartments. Three water
hyacinths were placed in each compartments and water sample in
each compartment were collected in every two days. The plant
observation was conducted by weight measurement, plant uptake and
new young shoot development. Water hyacinth effectively removed
approximately 49% of COD, 81% of ammonia, 67% of phosphorus
and 92% of nitrate. It also showed significant growth rate at starting
from day 6 with 0.33 shoot/day and they kept developing up to 0.38
shoot/day at the end of day 24. From the studies conducted, it was
proved that water hyacinth is capable of polishing the effluent of
municipal wastewater which contains undesirable amount of nitrate
and phosphorus concentration.
Abstract: Collaborative working environments for distance
education can be considered as a more generic form of contemporary
remote labs. At present, the majority of existing real laboratories are
not constructed to allow the involved participants to collaborate in
real time. To make this revolutionary learning environment possible
we must allow the different users to carry out an experiment
simultaneously. In recent times, multi-user environments are
successfully applied in many applications such as air traffic control
systems, team-oriented military systems, chat-text tools, multi-player
games etc. Thus, understanding the ideas and techniques behind these
systems could be of great importance in the contribution of ideas to
our e-learning environment for collaborative working. In this
investigation, collaborative working environments from theoretical
and practical perspectives are considered in order to build an
effective collaborative real laboratory, which allows two students or
more to conduct remote experiments at the same time as a team. In
order to achieve this goal, we have implemented distributed system
architecture, enabling students to obtain an automated help by either
a human tutor or a rule-based e-tutor.
Abstract: In this paper we discuss the effect of unbounded particle interaction operator on particle growth and we study how this can address the choice of appropriate time steps of the numerical simulation. We provide also rigorous mathematical proofs showing that large particles become dominating with increasing time while small particles contribute negligibly. Second, we discuss the efficiency of the algorithm by performing numerical simulations tests and by comparing the simulated solutions with some known analytic solutions to the Smoluchowski equation.
Abstract: In this paper we present a hybrid search algorithm for
solving constraint satisfaction and optimization problems. This
algorithm combines ideas of two basic approaches: complete and
incomplete algorithms which also known as systematic search and
local search algorithms. Different characteristics of systematic search
and local search methods are complementary. Therefore we have
tried to get the advantages of both approaches in the presented
algorithm. The major advantage of presented algorithm is finding
partial sound solution for complicated problems which their complete
solution could not be found in a reasonable time. This algorithm
results are compared with other algorithms using the well known
n-queens problem.
Abstract: Partial discharge (PD) detection is an important
method to evaluate the insulation condition of metal-clad apparatus.
Non-intrusive sensors which are easy to install and have no
interruptions on operation are preferred in onsite PD detection.
However, it often lacks of accuracy due to the interferences in PD
signals. In this paper a novel PD extraction method that uses frequency
analysis and entropy based time-frequency (TF) analysis is introduced.
The repetitive pulses from convertor are first removed via frequency
analysis. Then, the relative entropy and relative peak-frequency of
each pulse (i.e. time-indexed vector TF spectrum) are calculated and
all pulses with similar parameters are grouped. According to the
characteristics of non-intrusive sensor and the frequency distribution
of PDs, the pulses of PD and interferences are separated. Finally the
PD signal and interferences are recovered via inverse TF transform.
The de-noised result of noisy PD data demonstrates that the
combination of frequency and time-frequency techniques can
discriminate PDs from interferences with various frequency
distributions.
Abstract: The emergence of blended learning has been
influenced by the rapid changes in Higher Education within the last
few years. However, there is a lack of studies that look into the future
of blended learning in the Saudi context. The most likely explanation
is that blended learning is relatively new and, with respect to learning
in general, under-researched. This study addresses this gap and
explores the views of lecturers and students towards the future of
blended learning in Saudi Arabia. This study was informed by the
interpretive paradigm that appears to be most appropriate to
understand and interpret the perceptions of students and instructors
towards a new learning environment. While globally there has been
considerable research on the perceptions of e-learning and blended
learning with its different models, there is plenty of space for further
research specifically in the Arab region, and in Saudi Arabia where
blended learning is now being introduced.
Abstract: When cars are released from the factory, strut noises are very small and therefore it is difficult to perceive them. As the use time and travel distance increase, however, strut noises get larger so as to cause users much uneasiness. The noises generated at the field include engine noises and flow noises and therefore it is difficult to clearly discern the noises generated from struts. This study developed a test method which can reproduce field strut noises in the lab. Using the newly developed noise evaluation test, this study analyzed the effects that insulator performance degradation and failure can have on car noises. The study also confirmed that the insulator durability test by the simple back-and-forth motion cannot completely reflect the state of the parts failure in the field. Based on this, the study also confirmed that field noises can be reproduced through a durability test that considers heat aging.
Abstract: As the Internet continues to grow at a rapid pace as
the primary medium for communications and commerce and as
telecommunication networks and systems continue to expand their
global reach, digital information has become the most popular and
important information resource and our dependence upon the
underlying cyber infrastructure has been increasing significantly.
Unfortunately, as our dependency has grown, so has the threat to the
cyber infrastructure from spammers, attackers and criminal
enterprises. In this paper, we propose a new machine learning based
network intrusion detection framework for cyber security. The
detection process of the framework consists of two stages: model
construction and intrusion detection. In the model construction stage,
a semi-supervised machine learning algorithm is applied to a
collected set of network audit data to generate a profile of normal
network behavior and in the intrusion detection stage, input network
events are analyzed and compared with the patterns gathered in the
profile, and some of them are then flagged as anomalies should these
events are sufficiently far from the expected normal behavior. The
proposed framework is particularly applicable to the situations where
there is only a small amount of labeled network training data
available, which is very typical in real world network environments.
Abstract: In digital signal processing it is important to
approximate multi-dimensional data by the method called rank
reduction, in which we reduce the rank of multi-dimensional data from
higher to lower. For 2-dimennsional data, singular value
decomposition (SVD) is one of the most known rank reduction
techniques. Additional, outer product expansion expanded from SVD
was proposed and implemented for multi-dimensional data, which has
been widely applied to image processing and pattern recognition.
However, the multi-dimensional outer product expansion has behavior
of great computation complex and has not orthogonally between the
expansion terms. Therefore we have proposed an alterative method,
Third-order Orthogonal Tensor Product Expansion short for 3-OTPE.
3-OTPE uses the power method instead of nonlinear optimization
method for decreasing at computing time. At the same time the group
of B. D. Lathauwer proposed Higher-Order SVD (HOSVD) that is
also developed with SVD extensions for multi-dimensional data.
3-OTPE and HOSVD are similarly on the rank reduction of
multi-dimensional data. Using these two methods we can obtain
computation results respectively, some ones are the same while some
ones are slight different. In this paper, we compare 3-OTPE to
HOSVD in accuracy of calculation and computing time of resolution,
and clarify the difference between these two methods.
Abstract: As the enormous amount of on-line text grows on the
World-Wide Web, the development of methods for automatically
summarizing this text becomes more important. The primary goal of
this research is to create an efficient tool that is able to summarize
large documents automatically. We propose an Evolving
connectionist System that is adaptive, incremental learning and
knowledge representation system that evolves its structure and
functionality. In this paper, we propose a novel approach for Part of
Speech disambiguation using a recurrent neural network, a paradigm
capable of dealing with sequential data. We observed that
connectionist approach to text summarization has a natural way of
learning grammatical structures through experience. Experimental
results show that our approach achieves acceptable performance.