Abstract: Phonocardiography is important in appraisal of
congenital heart disease and pulmonary hypertension as it reflects the
duration of right ventricular systoles. The systolic murmur in patients
with intra-cardiac shunt decreases as pulmonary hypertension
develops and may eventually disappear completely as the pulmonary
pressure reaches systemic level. Phonocardiography and auscultation
are non-invasive, low-cost, and accurate methods to assess heart
disease. In this work an objective signal processing tool to extract
information from phonocardiography signal using Wavelet is
proposed to classify the murmur as normal or abnormal. Since the
feature vector is large, a Binary Particle Swarm Optimization (PSO)
with mutation for feature selection is proposed. The extracted
features improve the classification accuracy and were tested across
various classifiers including Naïve Bayes, kNN, C4.5, and SVM.
Abstract: Steepest descent method is a simple gradient method
for optimization. This method has a slow convergence in heading to
the optimal solution, which occurs because of the zigzag form of the
steps. Barzilai and Borwein modified this algorithm so that it
performs well for problems with large dimensions. Barzilai and
Borwein method results have sparked a lot of research on the method
of steepest descent, including alternate minimization gradient method
and Yuan method. Inspired by previous works, we modified the step
size of the steepest descent method. We then compare the
modification results against the Barzilai and Borwein method,
alternate minimization gradient method and Yuan method for
quadratic function cases in terms of the iterations number and the
running time. The average results indicate that the steepest descent
method with the new step sizes provide good results for small
dimensions and able to compete with the results of Barzilai and
Borwein method and the alternate minimization gradient method for
large dimensions. The new step sizes have faster convergence
compared to the other methods, especially for cases with large
dimensions.
Abstract: In Electric Power Steering (EPS), spoke type
Brushless AC (BLAC) motors offer distinct advantages over other
electric motor types in terms torque smoothness, reliability and
efficiency. This paper deals with the shape optimization of spoke
type BLAC motor, in order to reduce cogging torque. This paper
examines 3 steps skewing rotor angle, optimizing rotor core edge and
rotor overlap length for reducing cogging torque in spoke type BLAC
motor. The methods were applied to existing machine designs and
their performance was calculated using finite- element analysis
(FEA). Prototypes of the machine designs were constructed and
experimental results obtained. It is shown that the FEA predicted the
cogging torque to be nearly reduce using those methods.
Abstract: An Acoustic Micro-Energy Harvester (AMEH) is
developed to convert wasted acoustical energy into useful electrical
energy. AMEH is mathematically modeled using Lumped Element
Modelling (LEM) and Euler-Bernoulli beam (EBB) modelling. An
experiment is designed to validate the mathematical model and assess
the feasibility of AMEH. Comparison of theoretical and experimental
data on critical parameter value such as Mm, Cms, dm and Ceb showed
the variances are within 1% to 6%, which is reasonably acceptable.
Then, AMEH undergoes bandwidth tuning for performance
optimization. The AMEH successfully produces 0.9V/(m/s^2) and
1.79μW/(m^2/s^4) at 60Hz and 400kΩ resistive load which only
show variances about 7% compared to theoretical data. At 1g and
60Hz resonance frequency, the averaged power output is about
2.2mW which fulfilled a range of wireless sensors and
communication peripherals power requirements. Finally, the design
for AMEH is assessed, validated and deemed as a feasible design.
Abstract: Ant Colony Optimization (ACO) is a promising
modern approach to the unused combinatorial optimization. Here
ACO is applied to finding the shortest during communication link
failure. In this paper, the performances of the prim’s and ACO
algorithm are made. By comparing the time complexity and program
execution time as set of parameters, we demonstrate the pleasant
performance of ACO in finding excellent solution to finding shortest
path during communication link failure.
Abstract: This paper presents the performance of Integrated
Bacterial Foraging Optimization and Particle Swarm Optimization
(IBFO_PSO) technique in MANET routing. The BFO is a bio-inspired
algorithm, which simulates the foraging behavior of bacteria.
It is effectively applied in improving the routing performance in
MANET. In results, it is proved that the PSO integrated with BFO
reduces routing delay, energy consumption and communication
overhead.
Abstract: This study, for its research subjects, uses patients who
had undergone total knee replacement surgery from the database of the
National Health Insurance Administration. Through the review of
literatures and the interviews with physicians, important factors are
selected after careful screening. Then using Cross Entropy Method,
Genetic Algorithm Logistic Regression, and Particle Swarm
Optimization, the weight of each factor is calculated and obtained. In
the meantime, Excel VBA and Case Based Reasoning are combined
and adopted to evaluate the system. Results show no significant
difference found through Genetic Algorithm Logistic Regression and
Particle Swarm Optimization with over 97% accuracy in both
methods. Both ROC areas are above 0.87. This study can provide
critical reference to medical personnel as clinical assessment to
effectively enhance medical care quality and efficiency, prevent
unnecessary waste, and provide practical advantages to resource
allocation to medical institutes.
Abstract: This paper reviews the model-based qualitative and
quantitative Operations Management research in the context of
Construction Supply Chain Management (CSCM). Construction
industry has been traditionally blamed for low productivity, cost and
time overruns, waste, high fragmentation and adversarial
relationships. The construction industry has been slower than other
industries to employ the Supply Chain Management (SCM) concept
and develop models that support the decision-making and planning.
However the last decade there is a distinct shift from a project-based
to a supply-based approach of construction management. CSCM
comes up as a new promising management tool of construction
operations and improves the performance of construction projects in
terms of cost, time and quality. Modeling the Construction Supply
Chain (CSC) offers the means to reap the benefits of SCM, make
informed decisions and gain competitive advantage. Different
modeling approaches and methodologies have been applied in the
multi-disciplinary and heterogeneous research field of CSCM. The
literature review reveals that a considerable percentage of the CSC
modeling research accommodates conceptual or process models
which present general management frameworks and do not relate to
acknowledged soft Operations Research methods. We particularly
focus on the model-based quantitative research and categorize the
CSCM models depending on their scope, objectives, modeling
approach, solution methods and software used. Although over the last
few years there has been clearly an increase of research papers on
quantitative CSC models, we identify that the relevant literature is
very fragmented with limited applications of simulation,
mathematical programming and simulation-based optimization. Most
applications are project-specific or study only parts of the supply
system. Thus, some complex interdependencies within construction
are neglected and the implementation of the integrated supply chain
management is hindered. We conclude this paper by giving future
research directions and emphasizing the need to develop optimization
models for integrated CSCM. We stress that CSC modeling needs a
multi-dimensional, system-wide and long-term perspective. Finally,
prior applications of SCM to other industries have to be taken into
account in order to model CSCs, but not without translating the
generic concepts to the context of construction industry.
Abstract: In this paper, the goal programming methodology for
solving multiple objective problem of the technological variants and
production plan optimization has been applied. The optimization
criteria are determined and the multiple objective linear programming
model for solving a problem of the technological variants and
production plan optimization is formed and solved. Then the obtained
results are analysed. The obtained results point out to the possibility
of efficient application of the goal programming methodology in
solving the problem of the technological variants and production plan
optimization. The paper points out on the advantages of the
application of the goal programming methodology compare to the
Surrogat Worth Trade-off method in solving this problem.
Abstract: High density electrical prospecting has been widely
used in groundwater investigation, civil engineering and
environmental survey. For efficient inversion, the forward modeling
routine, sensitivity calculation, and inversion algorithm must be
efficient. This paper attempts to provide a brief summary of the past
and ongoing developments of the method. It includes reviews of the
procedures used for data acquisition, processing and inversion of
electrical resistivity data based on compilation of academic literature.
In recent times there had been a significant evolution in field survey
designs and data inversion techniques for the resistivity method. In
general 2-D inversion for resistivity data is carried out using the
linearized least-square method with the local optimization technique
.Multi-electrode and multi-channel systems have made it possible to
conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve
complex geological structures that were not possible with traditional
1-D surveys. 3-D surveys play an increasingly important role in very
complex areas where 2-D models suffer from artifacts due to off-line
structures. Continued developments in computation technology, as
well as fast data inversion techniques and software, have made it
possible to use optimization techniques to obtain model parameters to
a higher accuracy. A brief discussion on the limitations of the
electrical resistivity method has also been presented.
Abstract: This work is the first dowel in a rather wide research
activity in collaboration with Euro Mediterranean Center for Climate
Changes, aimed at introducing scalable approaches in Ocean
Circulation Models. We discuss designing and implementation of
a parallel algorithm for solving the Variational Data Assimilation
(DA) problem on Graphics Processing Units (GPUs). The algorithm
is based on the fully scalable 3DVar DA model, previously proposed
by the authors, which uses a Domain Decomposition approach
(we refer to this model as the DD-DA model). We proceed with
an incremental porting process consisting of 3 distinct stages:
requirements and source code analysis, incremental development of
CUDA kernels, testing and optimization. Experiments confirm the
theoretic performance analysis based on the so-called scale up factor
demonstrating that the DD-DA model can be suitably mapped on
GPU architectures.
Abstract: The aim of this paper is to present the optimization
methodology developed in the frame of a Coastal Transport
Information System. The system will be used for the effective design
of coastal transportation lines and incorporates subsystems that
implement models, tools and techniques that may support the design
of improved networks. The role of the optimization and decision
subsystem is to provide the user with better and optimal scenarios
that will best fulfill any constrains, goals or requirements posed. The
complexity of the problem and the large number of parameters and
objectives involved led to the adoption of an evolutionary method
(Genetic Algorithms). The problem model and the subsystem
structure are presented in detail, and, its support for simulation is also
discussed.
Abstract: Significant quota of Municipal Electrical Energy
consumption is related to Decentralized Air Conditioning which is
mostly provided by evaporative coolers. So the aim is to optimize
design of air conditioners to increase their efficiencies. To achieve
this goal, results of practical standardized tests for 40 evaporative
coolers in different types collected and simultaneously results for
same coolers based on one of EER (Energy Efficiency Ratio)
modeling styles are figured out. By comparing experimental results
of different coolers standardized tests with modeling results,
preciseness of used model is assessed and after comparing gained
preciseness with international standards based on EER for cooling
capacity, aeration, and also electrical energy consumption, energy
label from A (most effective) to G (less effective) is classified; finally
needed methods to optimize energy consumption and coolers’
classification are provided.
Abstract: The thermal behavior of a large-scale, phase change material (PCM) enhanced building envelope system was studied in regard to the need for pre-fabricated construction in subtropical regions. The proposed large-scale envelope consists of a reinforced aluminum skin, insulation core, phase change material and reinforced gypsum board. The PCM impact on an energy efficiency of an enveloped room was resolved by validation of the EnergyPlus numerical scheme and optimization of a smart material location in the core. The PCM location was optimized by a minimization method of a cooling energy demand. It has been shown that there is good agreement between the test and simulation results. The optimal location of the PCM layer in Hong Kong summer conditions has been then recomputed for core thicknesses of 40, 60 and 80 mm. A non-dimensional value of the optimal PCM location was obtained to be same for all the studied cases and the considered external and internal conditions.
Abstract: Power systems are operating under stressed condition
due to continuous increase in demand of load. This can lead to
voltage instability problem when face additional load increase or
contingency. In order to avoid voltage instability suitable size of
reactive power compensation at optimal location in the system is
required which improves the load margin. This work aims at
obtaining optimal size as well as location of compensation in the 39-
bus New England system with the help of Bacteria Foraging and
Genetic algorithms. To reduce the computational time the work
identifies weak candidate buses in the system, and then picks only
two of them to take part in the optimization. The objective function is
based on a recently proposed voltage stability index which takes into
account the weighted average sensitivity index is a simpler and faster
approach than the conventional CPF algorithm. BFOA has been
found to give better results compared to GA.
Abstract: Maize constitutes a major agrarian production for use
by the vast population but despite its economic importance; it has not
been produced to meet the economic needs of the country. Achieving
optimum yield in maize can meaningfully be supported by land
suitability analysis in order to guarantee self-sufficiency for future
production optimization. This study examines land suitability for
maize production through the analysis of the physicochemical
variations in soil properties and other land attributes over space using
a Geographic Information System (GIS) framework.
Physicochemical parameters of importance selected include slope,
landuse, physical and chemical properties of the soil, and climatic
variables. Landsat imagery was used to categorize the landuse,
Shuttle Radar Topographic Mapping (SRTM) generated the slope and
soil samples were analyzed for its physical and chemical components.
Suitability was categorized into highly, moderately and marginally
suitable based on Food and Agricultural Organisation (FAO)
classification, using the Analytical Hierarchy Process (AHP)
technique of GIS. This result can be used by small scale farmers for
efficient decision making in the allocation of land for maize
production.
Abstract: Axial flow fans, while incapable of developing high
pressures, they are well suitable for handling large volumes of air at
relatively low pressures. In general, they are low in cost and possess
good efficiency, and can have blades of airfoil shape. Axial flow fans
show good efficiencies, and can operate at high static pressures if
such operation is necessary. Our objective is to model and analyze
the flow through AXIAL FANS using CFD Software and draw
inference from the obtained results, so as to get maximum efficiency.
The performance of an axial fan was simulated using CFD and the
effect of variation of different parameters such as the blade number,
noise level, velocity, temperature and pressure distribution on the
blade surface was studied. This paper aims to present a final 3D CAD
model of axial flow fan. Adapting this model to the available
components in the market, the first optimization was done. After this
step, CFX flow solver is used to do the necessary numerical analyses
on the aerodynamic performance of this model. This analysis results
in a final optimization of the proposed 3D model which is presented
in this article.
Abstract: The aim of the present work was to statistically design
an autotrophic medium for maximum biomass production by
Chlorella pyrenoidosa using response surface methodology. After
evaluating one factor at a time approach, K2HPO4, KNO3,
MgSO4.7H2O and NaHCO3 were preferred over the other
components of the fog’s medium as most critical autotrophic medium
components. The study showed that the maximum biomass yield was
achieved while the concentrations of MgSO4.7H2O, K2HPO4, KNO3
and NaHCO3 were 0.409 g/L, 0.24 g/L, 1.033 g/L, and 3.265 g/L,
respectively. The study reported that the biomass productivity of C.
pyrenoidosa improved from 0.14 g/L in defined fog’s medium to 1.40
g/L in modified fog’s medium resulting 10 fold increase. The
biochemical composition biosynthesis of C. pyrenoidosa was altered
using nitrogen limiting stress bringing about 5.23 fold increase in
lipid content than control (cell without stress), as analyzed by FTIR
integration method.
Abstract: The objective of the Economic Dispatch(ED) Problems
of electric power generation is to schedule the committed generating
units outputs so as to meet the required load demand at minimum
operating cost while satisfying all units and system equality and
inequality constraints. This paper presents a new method of ED
problems utilizing the Max-Min Ant System Optimization.
Historically, traditional optimizations techniques have been used,
such as linear and non-linear programming, but within the past
decade the focus has shifted on the utilization of Evolutionary
Algorithms, as an example Genetic Algorithms, Simulated Annealing
and recently Ant Colony Optimization (ACO). In this paper we
introduce the Max-Min Ant System based version of the Ant System.
This algorithm encourages local searching around the best solution
found in each iteration. To show its efficiency and effectiveness, the
proposed Max-Min Ant System is applied to sample ED problems
composed of 4 generators. Comparison to conventional genetic
algorithms is presented.
Abstract: This paper deals with the problem of automatic rule
generation for fuzzy systems design. The proposed approach is based
on hybrid artificial bee colony (ABC) optimization and weighted least
squares (LS) method and aims to find the structure and parameters of
fuzzy systems simultaneously. More precisely, two ABC based fuzzy
modeling strategies are presented and compared. The first strategy
uses global optimization to learn fuzzy models, the second one
hybridizes ABC and weighted least squares estimate method. The
performances of the proposed ABC and ABC-LS fuzzy modeling
strategies are evaluated on complex modeling problems and compared
to other advanced modeling methods.