Abstract: Existing methods of data mining cannot be applied on
spatial data because they require spatial specificity consideration, as
spatial relationships.
This paper focuses on the classification with decision trees, which
are one of the data mining techniques. We propose an extension of
the C4.5 algorithm for spatial data, based on two different approaches
Join materialization and Querying on the fly the different tables.
Similar works have been done on these two main approaches, the
first - Join materialization - favors the processing time in spite of
memory space, whereas the second - Querying on the fly different
tables- promotes memory space despite of the processing time.
The modified C4.5 algorithm requires three entries tables: a target
table, a neighbor table, and a spatial index join that contains the
possible spatial relationship among the objects in the target table and
those in the neighbor table. Thus, the proposed algorithms are applied
to a spatial data pattern in the accidentology domain.
A comparative study of our approach with other works of
classification by spatial decision trees will be detailed.
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: This paper presents a hybrid fuzzy logic control
strategy for a unicycle trajectory following robot on irregular terrains.
In literature, researchers have presented the design of path tracking
controllers of mobile robots on non-frictional surface. In this work,
the robot is simulated to drive on irregular terrains with contrasting
frictional profiles of peat and rough gravel. A hybrid fuzzy logic
controller is utilised to stabilise and drive the robot precisely with the
predefined trajectory and overcome the frictional impact. The
controller gains and scaling factors were optimised using spiral
dynamics optimisation algorithm to minimise the mean square error
of the linear and angular velocities of the unicycle robot. The robot
was simulated on various frictional surfaces and terrains and the
controller was able to stabilise the robot with a superior performance
that is shown via simulation results.
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: Medical imaging produces human body pictures in
digital form. Since these imaging techniques produce prohibitive
amounts of data, compression is necessary for storage and
communication purposes. Many current compression schemes
provide a very high compression rate but with considerable loss of
quality. On the other hand, in some areas in medicine, it may be
sufficient to maintain high image quality only in region of interest
(ROI). This paper discusses a contribution to the lossless
compression in the region of interest of Scintigraphic images based
on SPIHT algorithm and global transform thresholding using
Huffman coding.
Abstract: Time and cost are the main goals of the construction
project management. The first schedule developed may not be a
suitable schedule for beginning or completing the project to achieve
the target completion time at a minimum total cost. In general, there
are trade-offs between time and cost (TCT) to complete the activities
of a project. This research presents genetic algorithms (GAs) multiobjective
model for project scheduling considering different
scenarios such as least cost, least time, and target time.
Abstract: In this paper, we introduce a generalized Chebyshev
collocation method (GCCM) based on the generalized Chebyshev
polynomials for solving stiff systems. For employing a technique
of the embedded Runge-Kutta method used in explicit schemes, the
property of the generalized Chebyshev polynomials is used, in which
the nodes for the higher degree polynomial are overlapped with those
for the lower degree polynomial. The constructed algorithm controls
both the error and the time step size simultaneously and further
the errors at each integration step are embedded in the algorithm
itself, which provides the efficiency of the computational cost. For
the assessment of the effectiveness, numerical results obtained by the
proposed method and the Radau IIA are presented and compared.
Abstract: The world wide web network is a network with a
complex topology, the main properties of which are the distribution
of degrees in power law, A low clustering coefficient and a weak
average distance. Modeling the web as a graph allows locating the
information in little time and consequently offering a help in the
construction of the research engine. Here, we present a model based
on the already existing probabilistic graphs with all the aforesaid
characteristics. This work will consist in studying the web in order to
know its structuring thus it will enable us to modelize it more easily
and propose a possible algorithm for its exploration.
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: The cooling channels of injection mould play a crucial
role in determining the productivity of moulding process and the
product quality. It’s not a simple task to design high quality cooling
channels. In this paper, an intelligent cooling channels design system
including automatic layout of cooling channels, interference checking
and assembly of accessories is studied. Automatic layout of cooling
channels using genetic algorithm is analyzed. Through integrating
experience criteria of designing cooling channels, considering the
factors such as the mould temperature and interference checking, the
automatic layout of cooling channels is implemented. The method of
checking interference based on distance constraint algorithm and the
function of automatic and continuous assembly of accessories are
developed and integrated into the system. Case studies demonstrate the
feasibility and practicality of the intelligent design system.
Abstract: In our paper we describe the security capabilities of
data collection. Data are collected with probes located in the near and
distant surroundings of the company. Considering the numerous
obstacles e.g. forests, hills, urban areas, the data collection is realized
in several ways. The collection of data uses connection via wireless
communication, LAN network, GSM network and in certain areas
data are collected by using vehicles. In order to ensure the connection
to the server most of the probes have ability to communicate in
several ways. Collected data are archived and subsequently used in
supervisory applications.
To ensure the collection of the required data, it is necessary to
propose algorithms that will allow the probes to select suitable
communication channel.
Abstract: The aim of this paper is to propose a novel technique
to guarantee Quality of Service (QoS) in a highly dynamic
environment. The MANET changes its topology dynamically as the
nodes are moved frequently. This will cause link failure between
mobile nodes. MANET cannot ensure reliability without delay. The
relay node is selected based on achieving QoS in previous
transmission. It considers one more factor Connection Existence
Period (CEP) to ensure reliability. CEP is to find out the period
during that connection exists between the nodes. The node with
highest CEP becomes a next relay node. The relay node is selected
dynamically to avoid frequent failure. The bandwidth of each link
changed dynamically based on service rate and request rate. This
paper proposes Active bandwidth setting up algorithm to guarantee
the QoS. The series of results obtained by using the Network
Simulator (NS-2) demonstrate the viability of our proposed
techniques.
Abstract: Fuzzy systems have been successfully used for
exchange rate forecasting. However, fuzzy system is very confusing
and complex to be designed by an expert, as there is a large set of
parameters (fuzzy knowledge base) that must be selected, it is not a
simple task to select the appropriate fuzzy knowledge base for an
exchange rate forecasting. The researchers often look the effect of
fuzzy knowledge base on the performances of fuzzy system
forecasting. This paper proposes a genetic fuzzy predictor to forecast
the future value of daily US Dollar/Euro exchange rate time’s series.
A range of methodologies based on a set of fuzzy predictor’s which
allow the forecasting of the same time series, but with a different
fuzzy partition. Each fuzzy predictor is built from two stages, where
each stage is performed by a real genetic algorithm.
Abstract: The parameters of a two-layer soil can be determined by
processing resistivity data obtained from resistivity measurements
carried out on the soil of interest. The processing usually entails
applying the resistivity data as inputs to an optimisation function.
This paper proposes an algorithm which utilises the square error as an
optimisation function. Resistivity data from previous works were
applied to test the accuracy of the new algorithm developed and the
result obtained conforms significantly to results from previous works.
Abstract: A novel simulation method to determine the
displacements of machine tools due to thermal factors is presented.
The specific characteristic of this method is the employment of
original CAD data from the design process chain, which is
interpreted by an algorithm in terms of geometry-based allocation of
convection and radiation parameters. Furthermore analogous models
relating to the thermal behaviour of machine elements are
automatically implemented, which were gained by extensive
experimental testing with thermography imaging. With this a
transient simulation of the thermal field and in series of the
displacement of the machine tool is possible simultaneously during
the design phase. This method was implemented and is already used
industrially in the design of machining centres in order to improve
the quality of herewith manufactured workpieces.
Abstract: This study proposes the transformation of nonlinear
Magnetic Levitation System into linear one, via state and feedback
transformations using explicit algorithm. This algorithm allows
computing explicitly the linearizing state coordinates and feedback
for any nonlinear control system, which is feedback linearizable,
without solving the Partial Differential Equations. The algorithm is
performed using a maximum of N-1 steps where N being the
dimension of the system.
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: 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: An inversion-free iterative algorithm is presented for
solving nonlinear matrix equation with a stepsize parameter t. The
existence of the maximal solution is discussed in detail, and the
method for finding it is proposed. Finally, two numerical examples
are reported that show the efficiency of the method.