Abstract: Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Abstract: At present, application of the extension of soft set theory in decision making problems in day to day life is progressing rapidly. The concepts of fuzzy soft set and its properties have been evolved as an area of interest for the researchers. The generalization of the concepts recently got importance and a rapid growth in the research in this area witnessed its vital-ness. In this paper, an application of the concept of generalized fuzzy soft set to make decision in a social problem is presented. Further, this paper also highlights some of the key issues of the related areas.
Abstract: Natural gas sweetening process is a controlled process that must be done at maximum efficiency and with the highest quality. In this work, due to complexity and non-linearity of the process, the H2S gas separation and the intelligent fuzzy controller, which is used to enhance the process, are simulated in MATLAB – Simulink. New design of fuzzy control for Gas Separator is discussed in this paper. The design is based on the utilization of linear state-estimation to generate the internal knowledge-base that stores input-output pairs. The obtained input/output pairs are then used to design a feedback fuzzy controller. The proposed closed-loop fuzzy control system maintains the system asymptotically-stability while it enhances the system time response to achieve better control of the concentration of the output gas from the tower. Simulation studies are carried out to illustrate the Gas Separator system performance.
Abstract: This paper describes a simple way to control the speed
of PMBLDC motor using Fuzzy logic control method. In the
conventional PI controller the performance of the motor system is
simulated and the speed is regulated by using PI controller. These
methods used to improve the performance of PMSM drives, but in
some cases at different operating conditions when the dynamics of
the system also vary over time and it can change the reference speed,
parameter variations and the load disturbance. The simulation is
powered with the MATLAB program to get a reliable and flexible
simulation. In order to highlight the effectiveness of the speed control
method the FLC method is used. The proposed method targeted in
achieving the improved dynamic performance and avoids the
variations of the motor drive. This drive has high accuracy, robust
operation from near zero to high speed. The effectiveness and
flexibility of the individual techniques of the speed control method
will be thoroughly discussed for merits and demerits and finally
verified through simulation and experimental results for comparative
analysis.
Abstract: The paper presents a method in which the expert
knowledge is applied to fuzzy inference model. Even a less
experienced person could benefit from the use of such a system, e.g.
urban planners, officials. The analysis result is obtained in a very
short time, so a large number of the proposed locations can also be
verified in a short time. The proposed method is intended for testing
of locations of car parks in a city. The paper shows selected examples
of locations of the P&R facilities in cities planning to introduce the
P&R. The analyses of existing objects are also shown in the paper
and they are confronted with the opinions of the system users, with
particular emphasis on unpopular locations. The results of the
analyses are compared to expert analysis of the P&R facilities
location that was outsourced by the city and the opinions about
existing facilities users that were expressed on social networking
sites. The obtained results are consistent with actual users’ feedback.
The proposed method proves to be good, but does not require the
involvement of a large experts team and large financial contributions
for complicated research. The method also provides an opportunity to
show the alternative location of P&R facilities. Although the results
of the method are approximate, they are not worse than results of
analysis of employed experts. The advantage of this method is ease of
use, which simplifies the professional expert analysis. The ability of
analyzing a large number of alternative locations gives a broader
view on the problem. It is valuable that the arduous analysis of the
team of people can be replaced by the model's calculation. According
to the authors, the proposed method is also suitable for
implementation on a GIS platform.
Abstract: The aim of this paper is to introduce the notion of
intuitionistic fuzzy positive implicative ideals with thresholds (λ, μ) of
BCI-algebras and to investigate its properties and characterizations.
Abstract: Brushless DC motors (BLDC) are widely used in
industrial areas. The BLDC motors are driven either by indirect ACAC
converters or by direct AC-AC converters. Direct AC-AC
converters i.e. matrix converters are used in this paper to drive the
three phase BLDC motor and it eliminates the bulky DC link energy
storage element. A matrix converter converts the AC power supply to
an AC voltage of variable amplitude and variable frequency. A
control technique is designed to generate the switching pulses for the
three phase matrix converter. For the control of speed of the BLDC
motor a separate PI controller and Fuzzy Logic Controller (FLC) are
designed and a hysteresis current controller is also designed for the
control of motor torque. The control schemes are designed and tested
separately. The simulation results of both the schemes are compared
and contrasted in this paper. The results show that the fuzzy logic
control scheme outperforms the PI control scheme in terms of
dynamic performance of the BLDC motor. Simulation results are
validated with the experimental results.
Abstract: One of the global combinatorial optimization
problems in machine learning is feature selection. It concerned with
removing the irrelevant, noisy, and redundant data, along with
keeping the original meaning of the original data. Attribute reduction
in rough set theory is an important feature selection method. Since
attribute reduction is an NP-hard problem, it is necessary to
investigate fast and effective approximate algorithms. In this paper,
we proposed two feature selection mechanisms based on memetic
algorithms (MAs) which combine the genetic algorithm with a fuzzy
record to record travel algorithm and a fuzzy controlled great deluge
algorithm, to identify a good balance between local search and
genetic search. In order to verify the proposed approaches, numerical
experiments are carried out on thirteen datasets. The results show that
the MAs approaches are efficient in solving attribute reduction
problems when compared with other meta-heuristic approaches.
Abstract: In this paper, for an arbitrary multiplicative functional
f from the set of all upper triangular fuzzy matrices to the fuzzy
algebra, we prove that there exist a multiplicative functional F and a
functional G from the fuzzy algebra to the fuzzy algebra such that the
image of an upper triangular fuzzy matrix under f can be represented
as the product of all the images of its main diagonal elements under
F and other elements under G.
Abstract: Distance learning systems offer useful methods of
learning and usually contain a final course test or another form of
test. The paper proposes a web application for evaluating tests using
an expert system in distance learning systems. The proposed web
application is appropriate for didactic tests or tests with results for
subsequent studying follow-up courses. The web application works
with test questions and uses an expert system and LFLC tool for test
evaluation. After test evaluation, the results are visualized and shown
to the student.
Abstract: Recent investigations have demonstrated the global
sea level rise due to climate change impacts. In this study, climate
changes study the effects of increasing water level in the strait of
Hormuz. The probable changes of sea level rise should be
investigated to employ the adaption strategies. The climatic output
data of a GCM (General Circulation Model) named CGCM3 under
climate change scenario of A1b and A2 were used. Among different
variables simulated by this model, those of maximum correlation
with sea level changes in the study region and least redundancy
among themselves were selected for sea level rise prediction by using
stepwise regression. One of models (Discrete Wavelet artificial
Neural Network) was developed to explore the relationship between
climatic variables and sea level changes. In these models, wavelet
was used to disaggregate the time series of input and output data into
different components and then ANN was used to relate the
disaggregated components of predictors and input parameters to each
other. The results showed in the Shahid Rajae Station for scenario
A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea
level rise is among 90 t0 105 cm. Furthermore, the result showed a
significant increase of sea level at the study region under climate
change impacts, which should be incorporated in coastal areas
management.
Abstract: In this paper, a novel fuzzy approach is developed
while solving the Dynamic Routing and Wavelength Assignment
(DRWA) problem in optical networks with Wavelength Division
Multiplexing (WDM). In this work, the effect of nonlinear and linear
impairments such as Four Wave Mixing (FWM) and amplifier
spontaneous emission (ASE) noise are incorporated respectively. The
novel algorithm incorporates fuzzy logic controller (FLC) to reduce
the effect of FWM noise and ASE noise on a requested lightpath
referred in this work as FWM aware fuzzy dynamic routing and
wavelength assignment algorithm. The FWM crosstalk products and
the static FWM noise power per link are pre computed in order to
reduce the set up time of a requested lightpath, and stored in an
offline database. These are retrieved during the setting up of a
lightpath and evaluated online taking the dynamic parameters like
cost of the links into consideration.
Abstract: The growth in the demand of electrical energy is
leading to load on the Power system which increases the occurrence
of frequent oscillations in the system. The reason for the oscillations
is due to the lack of damping torque which is required to dominate
the disturbances of Power system. By using FACT devices, such as
Unified Power Flow Controller (UPFC) can control power flow,
reduce sub-synchronous resonances and increase transient stability.
Hence, UPFC is used to damp the oscillations occurred in Power
system. This research focuses on adapting the neuro fuzzy controller
for the UPFC design by connecting the infinite bus (SMIB - Single
machine Infinite Bus) to a linearized model of synchronous machine
(Heffron-Phillips) in the power system. This model gains the
capability to improve the transient stability and to damp the
oscillations of the system.
Abstract: This paper presents the advantages of fuzzy control use in technological processes control. The paper presents a real application of the Linguistic Fuzzy-Logic Control, developed at the University of Ostrava for the control of physical models in the Intelligent Systems Laboratory. The paper presents an example of a sensitive non-linear model, such as a magnetic levitation model and obtained results which show how modern information technologies can help to solve actual technical problems. A special method based on the LFLC controller with partial components is presented in this paper followed by the method of automatic context change, which is very helpful to achieve more accurate control results. The main advantage of the used system is its robustness in changing conditions demonstrated by comparing with conventional PID controller. This technology and real models are also used as a background for problem-oriented teaching, realized at the department for master students and their collaborative as well as individual final projects.
Abstract: In order to be capable of dealing with uncertainties,
subjectivities, including vagueness arising in building construction
projects, the application of fuzzy reasoning technique based on fuzzy
set theory is proposed. This study contributes significantly to the
development of a fuzzy reasoning safety risk assessment model for
building construction projects that could be employed to assess the
risk magnitude of each hazardous event identified during
construction, and a third parameter of probability of consequence is
incorporated in the model. By using the proposed safety risk analysis
methodology, more reliable and less ambiguities, which provide the
safety risk management project team for decision-making purposes.
Abstract: Evolution strategy (ES) is a well-known instance of evolutionary algorithms, and there have been many studies on ES. In this paper, the author proposes an extended ES for solving fuzzy-valued optimization problems. In the proposed ES, genotype values are not real numbers but fuzzy numbers. Evolutionary processes in the ES are extended so that it can handle genotype instances with fuzzy numbers. In this study, the proposed method is experimentally applied to the evolution of neural networks with fuzzy weights and biases. Results reveal that fuzzy neural networks evolved using the proposed ES with fuzzy genotype values can model hidden target fuzzy functions even though no training data are explicitly provided. Next, the proposed method is evaluated in terms of variations in specifying fuzzy numbers as genotype values. One of the mostly adopted fuzzy numbers is a symmetric triangular one that can be specified by its lower and upper bounds (LU) or its center and width (CW). Experimental results revealed that the LU model contributed better to the fuzzy ES than the CW model, which indicates that the LU model should be adopted in future applications of the proposed method.
Abstract: In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.
Abstract: Ecological systems are exposed and are influenced by
various natural and anthropogenic disturbances. They produce
various effects and states seeking response symmetry to a state of
global phase coherence or stability and balance of their food webs.
This research project addresses the development of a computational
methodology for modeling plankton food webs. The use of
algorithms to establish connections, the generation of representative
fuzzy multigraphs and application of technical analysis of complex
networks provide a set of tools for defining, analyzing and evaluating
community structure of coastal aquatic ecosystems, beyond the
estimate of possible external impacts to the networks. Thus, this
study aims to develop computational systems and data models to
assess how these ecological networks are structurally and
functionally organized, to analyze the types and degree of
compartmentalization and synchronization between oscillatory and
interconnected elements network and the influence of disturbances on
the overall pattern of rhythmicity of the system.
Abstract: Mobile Ad Hoc Networks (MANETs) is a collection
of mobile devices forming a communication network without
infrastructure. MANET is vulnerable to security threats due to
network’s limited security, dynamic topology, scalability and the lack
of central management. The Quality of Service (QoS) routing in such
networks is limited by network breakage caused by node mobility or
nodes energy depletions. The impact of node mobility on trust
establishment is considered and its use to propagate trust through a
network is investigated in this paper. This work proposes an
enhanced Associativity Based Routing (ABR) with Fuzzy based
Trust (Fuzzy- ABR) routing protocol for MANET to improve QoS
and to mitigate network attacks.
Abstract: Land reallocation is one of the most important steps in
land consolidation projects. Many different models were proposed for
land reallocation in the literature such as Fuzzy Logic, block priority
based land reallocation and Spatial Decision Support Systems. A
model including four parts is considered for automatic block
reallocation with genetic algorithm method in land consolidation
projects. These stages are preparing data tables for a project land,
determining conditions and constraints of land reallocation, designing
command steps and logical flow chart of reallocation algorithm and
finally writing program codes of Genetic Algorithm respectively. In
this study, we designed the first three steps of the considered model
comprising four steps.