Abstract: Fuzzy inference method based approach to the
forming of modular intellectual system of assessment the quality of
communication services is proposed. Developed under this approach
the basic fuzzy estimation model takes into account the
recommendations of the International Telecommunication Union in
respect of the operation of packet switching networks based on IPprotocol.
To implement the main features and functions of the fuzzy
control system of quality telecommunication services it is used
multilayer feedforward neural network.
Abstract: In this paper, Fuzzy C-Means clustering with
Expectation Maximization-Gaussian Mixture Model based hybrid
modeling algorithm is proposed for Continuous Tamil Speech
Recognition. The speech sentences from various speakers are used
for training and testing phase and objective measures are between the
proposed and existing Continuous Speech Recognition algorithms.
From the simulated results, it is observed that the proposed algorithm
improves the recognition accuracy and F-measure up to 3% as
compared to that of the existing algorithms for the speech signal from
various speakers. In addition, it reduces the Word Error Rate, Error
Rate and Error up to 4% as compared to that of the existing
algorithms. In all aspects, the proposed hybrid modeling for Tamil
speech recognition provides the significant improvements for speechto-
text conversion in various applications.
Abstract: The inspection of underneath vehicle system has been
given significant attention by governments after the threat of
terrorism become more prevalent. New technologies such as mobile
robots and computer vision are led to have more secure environment.
This paper proposed that a mobile robot like Aria robot can be used
to search and inspect the bombs under parking a lot vehicle. This
robot is using fuzzy logic and subsumption algorithms to control the
robot that movies underneath the vehicle. An OpenCV library and
laser Hokuyo are added to Aria robot to complete the experiment for
under vehicle inspection. This experiment was conducted at the
indoor environment to demonstrate the efficiency of our methods to
search objects and control the robot movements under vehicle. We
got excellent results not only by controlling the robot movement but
also inspecting object by the robot camera at same time. This success
allowed us to know the requirement to construct a new cost effective
robot with more functionality.
Abstract: The electric power supplied by a photovoltaic power
generation systems depends on the solar irradiation and temperature.
The PV system can supply the maximum power to the load at a
particular operating point which is generally called as maximum
power point (MPP), at which the entire PV system operates with
maximum efficiency and produces its maximum power. Hence, a
Maximum power point tracking (MPPT) methods are used to
maximize the PV array output power by tracking continuously the
maximum power point. The proposed MPPT controller is designed
for 10kW solar PV system installed at Cape Institute of Technology.
This paper presents the fuzzy logic based MPPT algorithm. However,
instead of one type of membership function, different structures of
fuzzy membership functions are used in the FLC design. The
proposed controller is combined with the system and the results are
obtained for each membership functions in Matlab/Simulink
environment. Simulation results are decided that which membership
function is more suitable for this system.
Abstract: The fuzzy composition of objects depicted in images
acquired through MR imaging or the use of bio-scanners has often
been a point of controversy for field experts attempting to effectively
delineate between the visualized objects. Modern approaches in
medical image segmentation tend to consider fuzziness as a
characteristic and inherent feature of the depicted object, instead of
an undesirable trait. In this paper, a novel technique for efficient
image retrieval in the context of images in which segmented objects
are either crisp or fuzzily bounded is presented. Moreover, the
proposed method is applied in the case of multiple, even conflicting,
segmentations from field experts. Experimental results demonstrate
the efficiency of the suggested method in retrieving similar objects
from the aforementioned categories while taking into account the
fuzzy nature of the depicted data.
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: 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: This paper explains about the voltage output for DC to
DC boost converter between open loop, PID controller and fuzzy
logic controller through Matlab Simulink. Simulink input voltage was
set at 12V and the voltage reference was set at 24V. The analysis on
the deviation of voltage resulted that the difference between reference
voltage setting and the output voltage is always lower. Comparison
between open loop, PID and FLC shows that, the open loop circuit
having a bit higher on the deviation of voltage. The PID circuit
boosts for FLC has a lesser deviation of voltage and proved that it is
such a better performance on control the deviation of voltage during
the boost mode.
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: 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: 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: Recently, an increasing number of researchers have
been focusing on working out realistic solutions to sustainability
problems. As sustainability issues gain higher importance for
organisations, the management of such decisions becomes critical.
Knowledge representation is a fundamental issue of complex
knowledge based systems. Many types of sustainability problems
would benefit from models based on experts’ knowledge. Cognitive
maps have been used for analyzing and aiding decision making. A
cognitive map can be made of almost any system or problem. A
fuzzy cognitive map (FCM) can successfully represent knowledge
and human experience, introducing concepts to represent the essential
elements and the cause and effect relationships among the concepts to
model the behaviour of any system. Integrated waste management
systems (IWMS) are complex systems that can be decomposed to
non-related and related subsystems and elements, where many factors
have to be taken into consideration that may be complementary,
contradictory, and competitive; these factors influence each other and
determine the overall decision process of the system. The goal of the
present paper is to construct an efficient IWMS which considers
various factors. The authors’ intention is to propose an expert based
system design approach for implementing expert decision support in
the area of IWMSs and introduces an appropriate methodology for
the development and analysis of group FCM. A framework for such a
methodology consisting of the development and application phases is
presented.
Abstract: The North-eastern part of India, which receives
heavier rainfall than other parts of the subcontinent, is of great
concern now-a-days with regard to climate change. High intensity
rainfall for short duration and longer dry spell, occurring due to
impact of climate change, affects river morphology too. In the present
study, an attempt is made to delineate the North-eastern region of
India into some homogeneous clusters based on the Fuzzy Clustering
concept and to compare the resulting clusters obtained by using
conventional methods and nonconventional methods of clustering.
The concept of clustering is adapted in view of the fact that, impact
of climate change can be studied in a homogeneous region without
much variation, which can be helpful in studies related to water
resources planning and management. 10 IMD (Indian Meteorological
Department) stations, situated in various regions of the North-east,
have been selected for making the clusters. The results of the Fuzzy
C-Means (FCM) analysis show different clustering patterns for
different conditions. From the analysis and comparison it can be
concluded that nonconventional method of using GCM data is
somehow giving better results than the others. However, further
analysis can be done by taking daily data instead of monthly means to
reduce the effect of standardization.
Abstract: The aim of this paper is to use matrix representation
of Fuzzy soft sets for proving some equalities connected with Fuzzy
soft sets based on set-operations.
Abstract: Diabetes is a growing health problem in worldwide.
Especially, the patients with Type 1 diabetes need strict glycemic
control because they have deficiency of insulin production. This
paper attempts to control blood glucose based on body mathematical
body model. The Bergman minimal mathematical model is used to
develop the nonlinear controller. A novel back-stepping based sliding
mode control (B-SMC) strategy is proposed as a solution that
guarantees practical tracking of a desired glucose concentration. In
order to show the performance of the proposed design, it is compared
with conventional linear and fuzzy controllers which have been done
in previous researches. The numerical simulation result shows the
advantages of sliding mode back stepping controller design to linear
and fuzzy controllers.
Abstract: The aim of this paper is to introduce the notions of
intuitionistic T-S fuzzy subalgebras and intuitionistic T-S fuzzy ideals
in BCI-algebras, and then to investigate their basic properties.
Abstract: The assessment of the risk posed by a borrower to a
lender is one of the common problems that financial institutions have
to deal with. Consumers vying for a mortgage are generally
compared to each other by the use of a number called the Credit
Score, which is generated by applying a mathematical algorithm to
information in the applicant’s credit report. The higher the credit
score, the lower the risk posed by the candidate, and the better he is
to be taken on by the lender. The objective of the present work is to
use fuzzy logic and linguistic rules to create a model that generates
Credit Scores.
Abstract: In this paper, we have proposed a numerical method
for solving fuzzy Fredholm integral equation of the second kind. In
this method a combination of orthonormal Bernstein and Block-Pulse
functions are used. In most cases, the proposed method leads to
the exact solution. The advantages of this method are shown by an
example and calculate the error analysis.
Abstract: In this paper, we propose a multi-agent intelligent
system that is used for monitoring the health conditions of elderly
people. Monitoring the health condition of elderly people is a
complex problem that involves different medical units and requires
continuous monitoring. Such expert system is highly needed in rural
areas because of inadequate number of available specialized
physicians or nurses. Such monitoring must have autonomous
interactions between these medical units in order to be effective. A
multi-agent system is formed by a community of agents that
exchange information and proactively help one another to achieve the
goal of elderly monitoring. The agents in the developed system are
equipped with intelligent decision maker that arms them with the
rule-based reasoning capability that can assist the physicians in
making decisions regarding the medical condition of elderly people.
Abstract: The system is designed to show images which are
related to the query image. Extracting color, texture, and shape
features from an image plays a vital role in content-based image
retrieval (CBIR). Initially RGB image is converted into HSV color
space due to its perceptual uniformity. From the HSV image, Color
features are extracted using block color histogram, texture features
using Haar transform and shape feature using Fuzzy C-means
Algorithm. Then, the characteristics of the global and local color
histogram, texture features through co-occurrence matrix and Haar
wavelet transform and shape are compared and analyzed for CBIR.
Finally, the best method of each feature is fused during similarity
measure to improve image retrieval effectiveness and accuracy.