Abstract: Blood pulse is an important human physiological signal commonly used for the understanding of the individual physical health. Current methods of non-invasive blood pulse sensing require direct contact or access to the human skin. As such, the performances of these devices tend to vary with time and are subjective to human body fluids (e.g. blood, perspiration and skin-oil) and environmental contaminants (e.g. mud, water, etc). This paper proposes a simulation model for the novel method of non-invasive acquisition of blood pulse using the disturbance created by blood flowing through a localized magnetic field. The simulation model geometry represents a blood vessel, a permanent magnet, a magnetic sensor, surrounding tissues and air in 2-dimensional. In this model, the velocity and pressure fields in the blood stream are described based on Navier-Stroke equations and the walls of the blood vessel are assumed to have no-slip condition. The blood assumes a parabolic profile considering a laminar flow for blood in major artery near the skin. And the inlet velocity follows a sinusoidal equation. This will allow the computational software to compute the interactions between the magnetic vector potential generated by the permanent magnet and the magnetic nanoparticles in the blood. These interactions are simulated based on Maxwell equations at the location where the magnetic sensor is placed. The simulated magnetic field at the sensor location is found to assume similar sinusoidal waveform characteristics as the inlet velocity of the blood. The amplitude of the simulated waveforms at the sensor location are compared with physical measurements on human subjects and found to be highly correlated.
Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: Sharing the manufacturing facility through remote
operation and monitoring of a machining process is challenge for
effective use the production facility. Several automation tools in term
of hardware and software are necessary for successfully remote
operation of a machine. This paper presents a prototype of workpiece
holding attachment for remote operation of milling process by self
configuration the workpiece setup. The prototype is designed with
mechanism to reorient the work surface into machining spindle
direction with high positioning accuracy. Variety of parts geometry
is hold by attachment to perform single setup machining. Pin type
with array pattern additionally clamps the workpiece surface from
two opposite directions for increasing the machining rigidity.
Optimum pins configuration for conforming the workpiece geometry
with minimum deformation is determined through hybrid algorithms,
Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).
Prototype with intelligent optimization technique enables to hold
several variety of workpiece geometry which is suitable for
machining low of repetitive production in remote operation.
Abstract: Electrospinning is a broadly used technology to obtain
polymeric nanofibers ranging from several micrometers down to
several hundred nanometers for a wide range of applications. It offers
unique capabilities to produce nanofibers with controllable porous
structure. With smaller pores and higher surface area than regular
fibers, electrospun fibers have been successfully applied in various
fields, such as, nanocatalysis, tissue engineering scaffolds, protective
clothing, filtration, biomedical, pharmaceutical, optical electronics,
healthcare, biotechnology, defense and security, and environmental
engineering. In this study, polyurethane nanofibers were obtained
under different electrospinning parameters. Fiber morphology and
diameter distribution were investigated in order to understand them
as a function of process parameters.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
Abstract: An optical fiber Fabry-Perot interferometer (FFPI) is
proposed and demonstrated for dynamic measurements in a
mechanical vibrating target. A polishing metal with a low reflectance
value adhered to a mechanical vibrator was excited via a function
generator at various excitation frequencies. Output interference
fringes were generated by modulating the reference and sensing
signal at the output arm. A fringe-counting technique was used for
interpreting the displacement information on the dedicated computer.
The fiber interferometer has been found the capability of the
displacement measurements of 1.28 μm – 96.01 μm. A commercial
displacement sensor was employed as a reference sensor for
investigating the measurement errors from the fiber sensor. A
maximum percentage measurement error of approximately 1.59 %
was obtained.
Abstract: This paper reports a case study on how a conceptual
and analytical thinking approach was used in Art and Design Department at Multimedia University (Malaysia) in addressing the
issues of one nation and its impact in the society through artworks. The art project was designed for students to increase the know-how
and develop creative thinking in design and communication. Goals of the design project were: (1) to develop creative thinking in design
and communication, (2) to increase student understanding on the
process of problem solving for design work, and (3) to use design
elements and principles to generate interest, attention and emotional responses. An exhibition entitled "One Nation" was showcased to
local and international viewers consisting of the general public, professionals, academics, artists and students. Findings indicate that the project supported several visual art standards, as well as
generated awareness in the society. This project may be of interest to
current and future art educators and others interested in the potential
of utilizing global issues as content for art, community and environment studies for the purpose of educational art.
Abstract: The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations.
As a results of this, Computational Fluid Dynamic (CFD) solvers are
widely used in the aeronautical field. These solvers require the correct
selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on
the proper choice of these parameters.
In this paper we create an expert system capable of making an
accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver.
Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time
required for the convergence of a CFD solver.
Abstract: The authors present an algorithm for order reduction of linear dynamic systems using the combined advantages of stability equation method and the error minimization by Genetic algorithm. The denominator of the reduced order model is obtained by the stability equation method and the numerator terms of the lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. The proposed algorithm has also been extended for the order reduction of linear multivariable systems. Two numerical examples are solved to illustrate the superiority of the algorithm over some existing ones including one example of multivariable system.
Abstract: MinC plays an important role in bacterial cell division
system by inhibiting FtsZ assembly. However, the molecular
mechanism of the action is poorly understood. E. coli MinC Nterminus
domain was purified and crystallized using 1.4 M sodium
citrate pH 6.5 as a precipitant. X-ray diffraction data was collected
and processed to 2.3 Å from a native crystal. The crystal belonged to
space group P212121, with the unit cell parameters a = 52.7, b = 54.0,
c = 64.7 Å. Assuming the presence of two molecules in the
asymmetric unit, the Matthews coefficient value is 1.94 Å3 Da-1,
which corresponds to a solvent content of 36.5%. The overall
structure of MinCN is observed as a dimer form through anti-parallel
ß-strand interaction.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: Analytical solution of the first-order and third-order
shear deformation theories are developed to study the free vibration
behavior of simply supported functionally graded plates. The
material properties of plate are assumed to be graded in the thickness
direction as a power law distribution of volume fraction of the
constituents. The governing equations of functionally graded plates
are established by applying the Hamilton's principle and are solved
by using the Navier solution method. The influence of side-tothickness
ratio and constituent of volume fraction on the natural
frequencies are studied. The results are validated with the known
data in the literature.
Abstract: The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.
Abstract: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
Abstract: In this paper, we proposed a new routing protocol for
Unmanned Aerial Vehicles (UAVs) that equipped with directional
antenna. We named this protocol Directional Optimized Link State
Routing Protocol (DOLSR). This protocol is based on the well
known protocol that is called Optimized Link State Routing Protocol
(OLSR). We focused in our protocol on the multipoint relay (MPR)
concept which is the most important feature of this protocol. We
developed a heuristic that allows DOLSR protocol to minimize
the number of the multipoint relays. With this new protocol the
number of overhead packets will be reduced and the End-to-End
delay of the network will also be minimized. We showed through
simulation that our protocol outperformed Optimized Link State
Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad-
Hoc On demand Distance Vector (AODV) routing protocol in
reducing the End-to-End delay and enhancing the overall
throughput. Our evaluation of the previous protocols was based
on the OPNET network simulation tool.
Abstract: Phishing scheme is a new emerged security issue of
E-Commerce Crime in globalization. In this paper, the legal scaffold
of Malaysia, United States and United Kingdom are analyzed and
followed by discussion on critical issues that rose due to phishing
activities. The result revealed that inadequacy of current legal
framework is the main challenge to govern this epidemic. However,
lack of awareness among consumers, crisis on merchant-s
responsibility and lack of intrusion reports and incentive arrangement
contributes to phishing proliferating. Prevention is always better than
curb. By the end of this paper, some best practices for consumers and
corporations are suggested.
Abstract: Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.
Abstract: In this paper we designed and implemented a new
ensemble of classifiers based on a sequence of classifiers which were
specialized in regions of the training dataset where errors of its
trained homologous are concentrated. In order to separate this
regions, and to determine the aptitude of each classifier to properly
respond to a new case, it was used another set of classifiers built
hierarchically. We explored a selection based variant to combine the
base classifiers. We validated this model with different base
classifiers using 37 training datasets. It was carried out a statistical
comparison of these models with the well known Bagging and
Boosting, obtaining significantly superior results with the
hierarchical ensemble using Multilayer Perceptron as base classifier.
Therefore, we demonstrated the efficacy of the proposed ensemble,
as well as its applicability to general problems.
Abstract: The inherent iterative nature of product design and development poses significant challenge to reduce the product design and development time (PD). In order to shorten the time to market, organizations have adopted concurrent development where multiple specialized tasks and design activities are carried out in parallel. Iterative nature of work coupled with the overlap of activities can result in unpredictable time to completion and significant rework. Many of the products have missed the time to market window due to unanticipated or rather unplanned iteration and rework. The iterative and often overlapped processes introduce greater amounts of ambiguity in design and development, where the traditional methods and tools of project management provide less value. In this context, identifying critical metrics to understand the iteration probability is an open research area where significant contribution can be made given that iteration has been the key driver of cost and schedule risk in PD projects. Two important questions that the proposed study attempts to address are: Can we predict and identify the number of iterations in a product development flow? Can we provide managerial insights for a better control over iteration? The proposal introduces the concept of decision points and using this concept intends to develop metrics that can provide managerial insights into iteration predictability. By characterizing the product development flow as a network of decision points, the proposed research intends to delve further into iteration probability and attempts to provide more clarity.
Abstract: Abstract–Indoor air (VOCs) samples were collected
simultaneously from variety of indoors (e.g. living rooms, baby-s
rooms) and outdoor environments which were voluntarily selected
from the houses in which pregnant residents live throughout Ankara.
This is the first comprehensive study done in Turkey starting from
prenatal period and continued till the babies had one year old. VOCs
levels were measured over 76 homes. Air samples were collected in
Tenax TA sorbent filled tubes with active sampling method and
analyzed with Thermal Desorber and Gas Chromatography/Mass
spectrometry (TD-GC/MS). At the first sampling period in the baby-s
rooms maximum concentration of toluene was measured about
240.77μg.m-3 and in the living rooms maximum concentration of
naphthalene was 180.24μg.m-3. At the second sampling period in the
baby-s rooms maximum concentration of toluene was measured
about 144.97μg.m-3 and in the living rooms maximum concentration
of naphthalene was 247.89μg.m-3. Concentration of TVOCs in the
first period was generally higher than the second period.