Abstract: Tumor is an uncontrolled growth of tissues in any part
of the body. Tumors are of different types and they have different
characteristics and treatments. Brain tumor is inherently serious and
life-threatening because of its character in the limited space of the
intracranial cavity (space formed inside the skull). Locating the tumor
within MR (magnetic resonance) image of brain is integral part of the
treatment of brain tumor. This segmentation task requires
classification of each voxel as either tumor or non-tumor, based on
the description of the voxel under consideration. Many studies are
going on in the medical field using Markov Random Fields (MRF) in
segmentation of MR images. Even though the segmentation process
is better, computing the probability and estimation of parameters is
difficult. In order to overcome the aforementioned issues, Conditional
Random Field (CRF) is used in this paper for segmentation, along
with the modified artificial bee colony optimization and modified
fuzzy possibility c-means (MFPCM) algorithm. This work is mainly
focused to reduce the computational complexities, which are found in
existing methods and aimed at getting higher accuracy. The
efficiency of this work is evaluated using the parameters such as
region non-uniformity, correlation and computation time. The
experimental results are compared with the existing methods such as
MRF with improved Genetic Algorithm (GA) and MRF-Artificial
Bee Colony (MRF-ABC) algorithm.
Abstract: The knowledge of biodiesel density over large ranges
of temperature and pressure is important for predicting the behavior
of fuel injection and combustion systems in diesel engines, and for
the optimization of such systems. In this study, cottonseed oil was
transesterified into biodiesel and its density was measured at
temperatures between 288 K and 358 K and pressures between 0.1
MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg⋅m-
3. Experimental pressure-volume-temperature (pVT) cottonseed data
was used along with literature data relative to other 18 biodiesels, in
order to build a database used to test the correlation of density with
temperarure and pressure using the Goharshadi–Morsali–Abbaspour
equation of state (GMA EoS). To our knowledge, this is the first that
density measurements are presented for cottonseed biodiesel under
such high pressures, and the GMA EoS used to model biodiesel
density. The new tested EoS allowed correlations within 0.2 kg·m-3
corresponding to average relative deviations within 0.02%. The built
database was used to develop and test a new full predictive model
derived from the observed linear relation between density and degree
of unsaturation (DU), which depended from biodiesel FAMEs
profile. The average density deviation of this method was only about
3 kg.m-3 within the temperature and pressure limits of application.
These results represent appreciable improvements in the context of
density prediction at high pressure when compared with other
equations of state.
Abstract: This paper represents performance of particle swarm
optimisation (PSO) algorithm based integral (I) controller and
proportional-integral controller (PI) for interconnected hydro-thermal
automatic generation control (AGC) with generation rate constraint
(GRC) and Thyristor controlled phase shifter (TCPS) in series with
tie line. The control strategy of TCPS provides active control of
system frequency. Conventional objective function integral square
error (ISE) and another objective function considering square of
derivative of change in frequencies of both areas and change in tie
line power are considered. The aim of designing the objective
function is to suppress oscillation in frequency deviations and change
in tie line power oscillation. The controller parameters are searched
by PSO algorithm by minimising the objective functions. The
dynamic performance of the controllers I and PI, for both the
objective functions, are compared with conventionally optimized I
controller.
Abstract: The operation of nuclear power plants involves
continuous monitoring of the environment in their area. This
monitoring is performed using a complex data acquisition system,
which collects status information about the system itself and values
of many important physical variables e.g. temperature, humidity,
dose rate etc. This paper describes a proposal and optimization of
communication that takes place in teledosimetric system between the
central control server responsible for the data processing and storing
and the decentralized measuring stations, which are measuring the
physical variables. Analyzes of ongoing communication were
performed and consequently the optimization of the system
architecture and communication was done.
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: Distributed Generation (DG) can help in reducing the
cost of electricity to the costumer, relieve network congestion and
provide environmentally friendly energy close to load centers. Its
capacity is also scalable and it provides voltage support at distribution
level. Hence, DG placement and penetration level is an important
problem for both the utility and DG owner. DG allocation and capacity
determination is a nonlinear optimization problem. The objective
function of this problem is the minimization of the total loss of the
distribution system. Also high levels of penetration of DG are a new
challenge for traditional electric power systems. This paper presents a
new methodology for the optimal placement of DG and penetration
level of DG in distribution system based on General Algebraic
Modeling System (GAMS) and Genetic Algorithm (GA).
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.
Abstract: Microarray technology is universally used in the study
of disease diagnosis using gene expression levels. The main
shortcoming of gene expression data is that it includes thousands of
genes and a small number of samples. Abundant methods and
techniques have been proposed for tumor classification using
microarray gene expression data. Feature or gene selection methods
can be used to mine the genes that directly involve in the
classification and to eliminate irrelevant genes. In this paper
statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR)
and F-Statistics are used to rank the genes. The ranked genes are used
for further classification. Particle Swarm Optimization (PSO)
algorithm and Shuffled Frog Leaping (SFL) algorithm are used to
find the significant genes from the top-m ranked genes. The Naïve
Bayes Classifier (NBC) is used to classify the samples based on the
significant genes. The proposed work is applied on Lung and Ovarian
datasets. The experimental results show that the proposed method
achieves 100% accuracy in all the three datasets and the results are
compared with previous works.
Abstract: Natural gas, as one of the most important sources of
energy for many of the industrial and domestic users all over the
world, has a complex, huge supply chain which is in need of heavy
investments in all the phases of exploration, extraction, production,
transportation, storage and distribution. The main purpose of supply
chain is to meet customers’ need efficiently and with minimum cost.
In this study, with the aim of minimizing economic costs, different
levels of natural gas supply chain in the form of a multi-echelon,
multi-period fuzzy linear programming have been modeled. In this
model, different constraints including constraints on demand
satisfaction, capacity, input/output balance and presence/absence of a
path have been defined. The obtained results suggest efficiency of the
recommended model in optimal allocation and reduction of supply
chain costs.
Abstract: This paper presents a real-time visualization technique
and filtering of classified LiDAR point clouds. The visualization is
capable of displaying filtered information organized in layers by the
classification attribute saved within LiDAR datasets. We explain the
used data structure and data management, which enables real-time
presentation of layered LiDAR data. Real-time visualization is
achieved with LOD optimization based on the distance from the
observer without loss of quality. The filtering process is done in two
steps and is entirely executed on the GPU and implemented using
programmable shaders.
Abstract: Ancillary services are support services which are
essential for humanizing and enhancing the reliability and security of
the electric power system. Reactive power ancillary service is one of
the important ancillary services in a restructured electricity market
which determines the cost of supplying ancillary services and finding
of how this cost would change with respect to operating decisions.
This paper presents a new formation that can be used to minimize the
Independent System Operator (ISO)’s total payment for reactive
power ancillary service. The modified power flow tracing algorithm
estimates the availability of reserve reactive power for ancillary
service. In order to find optimum reactive power dispatch,
Biogeography based optimization method (BPO) is proposed. Market
Reactive Clearing Price (MRCP) is then estimated and it encourages
generator companies (GENCOs) to participate in an ancillary service.
Finally, optimal weighting factor and real time utilization factor of
reactive power give the minimum ISO’s total payment. The
effectiveness of proposed design is verified using IEEE 30 bus
system.
Abstract: Nonstandard tests are necessary for analyses and
verification of new developed structural and technological solutions
with application of composite materials. One of the most critical
primary structural parts of a typical aerospace structure is T-joint.
This structural element is loaded mainly in shear, bending, peel and
tension. The paper is focused on the shear loading simulations. The
aim of the work is to obtain a representative uniform distribution of
shear loads along T-joint during the mechanical testing. A new
design of T-joint test procedure, numerical simulation and
optimization of representative boundary conditions are presented.
The different conditions and inaccuracies both in simulations and
experiments are discussed. The influence of different parameters on
stress and strain distributions is demonstrated on T-joint made of
CFRP (carbon fibre reinforced plastic). A special test rig designed by
VZLU (Aerospace Research and Test Establishment) for T-shear test
procedure is presented.
Abstract: Characterization of the engineering behavior of
unsaturated soil is dependent on the soil-water characteristic curve
(SWCC), a graphical representation of the relationship between water
content or degree of saturation and soil suction. A reasonable
description of the SWCC is thus important for the accurate prediction
of unsaturated soil parameters. The measurement procedures for
determining the SWCC, however, are difficult, expensive, and timeconsuming.
During the past few decades, researchers have laid a
major focus on developing empirical equations for predicting the
SWCC, with a large number of empirical models suggested. One of
the most crucial questions is how precisely existing equations can
represent the SWCC. As different models have different ranges of
capability, it is essential to evaluate the precision of the SWCC
models used for each particular soil type for better SWCC estimation.
It is expected that better estimation of SWCC would be achieved via
a thorough statistical analysis of its distribution within a particular
soil class. With this in view, a statistical analysis was conducted in
order to evaluate the reliability of the SWCC prediction models
against laboratory measurement. Optimization techniques were used
to obtain the best-fit of the model parameters in four forms of SWCC
equation, using laboratory data for relatively coarse-textured (i.e.,
sandy) soil. The four most prominent SWCCs were evaluated and
computed for each sample. The result shows that the Brooks and
Corey model is the most consistent in describing the SWCC for sand
soil type. The Brooks and Corey model prediction also exhibit
compatibility with samples ranging from low to high soil water
content in which subjected to the samples that evaluated in this study.
Abstract: Segmentation is one of the essential tasks in image
processing. Thresholding is one of the simplest techniques for
performing image segmentation. Multilevel thresholding is a simple
and effective technique. The primary objective of bi-level or
multilevel thresholding for image segmentation is to determine a best
thresholding value. To achieve multilevel thresholding various
techniques has been proposed. A study of some nature inspired
metaheuristic algorithms for multilevel thresholding for image
segmentation is conducted. Here, we study about Particle swarm
optimization (PSO) algorithm, artificial bee colony optimization
(ABC), Ant colony optimization (ACO) algorithm and Cuckoo
search (CS) algorithm.
Abstract: This research focuses on the optimization of glazed
surfaces and the assessment of possible solar gains in industrial
buildings. Existing window rating methods for single windows were
evaluated and a new method for a simple analysis of energy gains and
losses by single windows was introduced. Furthermore extensive
transient building simulations were carried out to appraise the
performance of low cost polycarbonate multi-cell sheets in
interaction with typical buildings for industrial applications. Mainly
energy saving potential was determined by optimizing the orientation
and area of such glazing systems in dependency on their thermal
qualities. Moreover the impact on critical aspects such as summer
overheating and daylight illumination was considered to ensure the
user comfort and avoid additional energy demand for lighting or
cooling. Hereby the simulated heating demand could be reduced by
up to 1/3 compared to traditional architecture of industrial halls using
mainly skylights.
Abstract: Theoretical optimization of a copper-water negative
inclination heat pipe with internal composite wick structure had been
performed, regarding a new introduced parameter: the ratio between
the coarse mesh wraps and the fine mesh wraps of the composite
wick. Since in many cases, the design of a heat pipe matches specific
thermal requirements and physical limitations, this work
demonstrates the optimization of a 1m length, 8mm internal diameter
heat pipe without an adiabatic section, at a negative inclination angle
of -10º. The optimization is based on a new introduced parameter, LR:
the ratio between the coarse mesh wraps and the fine mesh wraps.
Abstract: In order to efficiently solve the problems created by the deepening energy crisis affecting Europe and the world,
governments cannot neglect the opportunities of using the energy
produced by sun collectors. In many of the EU countries there are sun
collectors producing heat energy, e.g. in 2011 in the area of EU27
(countries which belong to European Union) + Switzerland altogether
37519126 m2 were operated, which are capable of producing 26.3
GWh heat energy. The energy produced by these sun collectors is
utilized at the place of production. In the near future governments
will have to focus more on spreading and using sun collectors.
Among the complex problems of operating sun collectors, this article
deals with determining the optimal tilt angle, directions of sun
collectors. We evaluate the contamination of glass surface of sun
collector to the produced energy. Our theoretically results are confirmed by laboratory measurements. The purpose of our work is to help users and engineers in determination of optimal operation
parameters of sun collectors.
Abstract: This paper presents general results on the Java source
code snippet detection problem. We propose the tool which uses
graph and subgraph isomorphism detection. A number of solutions
for all of these tasks have been proposed in the literature. However,
although that all these solutions are really fast, they compare just the
constant static trees. Our solution offers to enter an input sample
dynamically with the Scripthon language while preserving an
acceptable speed. We used several optimizations to achieve very low
number of comparisons during the matching algorithm.
Abstract: In addition to the production, which is already
frequently optimized, improving the distribution logistics also opens
up tremendous potential for increasing an enterprise’s
competitiveness. Here too though, numerous interactions need to be
taken into account, enterprises thus need to be able to identify and
weigh between different potentials for economically efficient
optimizations. In order to be able to assess potentials, enterprises
require a suitable method. This paper first briefly presents the need
for this research before introducing the procedure that will be used to
develop an appropriate method that not only considers interactions
but is also quickly and easily implemented.