Abstract: The ultrasound imaging is very popular to diagnosis
the disease because of its non-invasive nature. The ultrasound
imaging slowly produces low quality images due to the presence of
spackle noise and wave interferences. There are several algorithms to
be proposed for the segmentation of ultrasound carotid artery images
but it requires a certain limit of user interaction. The pixel in an
image is highly correlated so the spatial information of surrounding
pixels may be considered in the process of image segmentation which
improves the results further. When data is highly correlated, one pixel
may belong to more than one cluster with different degree of
membership. There is an important step to computerize the evaluation
of arterial disease severity using segmentation of carotid artery lumen
in 2D and 3D ultrasonography and in finding vulnerable
atherosclerotic plaques susceptible to rupture which can cause stroke.
Abstract: Clustering involves the partitioning of n objects into k
clusters. Many clustering algorithms use hard-partitioning techniques
where each object is assigned to one cluster. In this paper we propose
an overlapping algorithm MCOKE which allows objects to belong to
one or more clusters. The algorithm is different from fuzzy clustering
techniques because objects that overlap are assigned a membership
value of 1 (one) as opposed to a fuzzy membership degree. The
algorithm is also different from other overlapping algorithms that
require a similarity threshold be defined a priori which can be
difficult to determine by novice users.
Abstract: Text mining techniques are generally applied for
classifying the text, finding fuzzy relations and structures in data
sets. This research provides plenty text mining capabilities. One
common application is text classification and event extraction,
which encompass deducing specific knowledge concerning incidents
referred to in texts. The main contribution of this paper is the
clarification of a concept graph generation mechanism, which is based
on a text classification and optimal fuzzy relationship extraction.
Furthermore, the work presented in this paper explains the application
of fuzzy relationship extraction and branch and bound (BB) method
to simplify the texts.
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: From an organizational perspective, leaders are a
variation of the same talent pool in that they all score a larger than
average value on the bell curve that maps leadership behaviors and
characteristics, namely competence, vision, communication,
confidence, cultural sensibility, stewardship, empowerment,
authenticity, reinforcement, and creativity. The question that remains
unanswered and essentially unresolved is how to explain the irony
that leaders are so much alike yet their organizations diverge so
noticeably in their ability to innovate. Leadership intersects with
innovation at the point where human interactions get exceedingly
complex and where certain paradoxical forces cohabit: conflict with
conciliation, sovereignty with interdependence, and imagination with
realism. Rather than accepting that leadership is without context, we
argue that leaders are specialists of their domain and that those
effective at leading for innovation are distinct within the broader pool
of leaders. Keeping in view the extensive literature on leadership and
innovation, we carried out a quantitative study with data collected
over a five-year period involving 240 participants from across five
dissimilar companies based in the United States. We found that while
innovation and leadership are, in general, strongly interrelated (r =
.89, p = 0.0), there are five qualities that set leaders apart on
innovation. These qualities include a large radius of trust, a restless
curiosity with a low need for acceptance, an honest sense of self and
other, a sense for knowledge and creativity as the yin and yang of
innovation, and an ability to use multiple senses in the engagement
with followers. When these particular behaviors and characteristics
are present in leaders, organizations out-innovate their rivals by a
margin of 29.3 per cent to gain an unassailable edge in a business
environment that is regularly disruptive. A strategic outcome of this
study is a psychometric scale named iLeadership, proposed with the
underlying evidence, limitations, and potential for leadership and
innovation in organizations.c
Abstract: This paper presents circular polar coordinates
transformation of periodic fuzzy membership function. The purpose
is identification of domain of periodic membership functions in
consequent part of IF-THEN rules. Proposed methods in this paper
remove complicatedness concerning domain of periodic membership
function from defuzzification in fuzzy approximate reasoning.
Defuzzification on circular polar coordinates is also proposed.
Abstract: This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.
Abstract: Events discrimination and decision maker in sport field are the subject of many interesting studies in computer vision and artificial intelligence. A large volume of research has been conducted for automatic semantic event detection and summarization of sports videos. Indeed the results of these researches have a very significant contribution, as well to television broadcasts as to the football teams, since the result of sporting event can be reflected on the economic field. In this paper, we propose a novel fuzzy sequential technique which lead to discriminate events and specify the technico-tactics on going the game, nor the fuzzy system or the sequential one, may be able to respond to the asked question, in fact fuzzy process is not sufficient, it does not respect the chronological order according the time of various events, similarly the sequential process needs flexibility about the parameters used in this study, it may affect a membership degree of each parameter on the one hand and respect the sequencing of events for each frame on the other hand. Indeed this technique describes special events such as dribbling, headings, short sprints, rapid acceleration or deceleration, turning, jumping, kicking, ball occupation, and tackling according velocity vectors of the two players and the ball direction.
Abstract: This paper is based on the bridgeless single-phase Ac–Dc Power Factor Correction (PFC) converters with Fuzzy Logic Controller. High frequency isolated Cuk converters are used as a modular dc-dc converter in Discontinuous Conduction Mode (DCM) of operation of Power Factor Correction. The aim of this paper is to simplify the program complexity of the controller by reducing the number of fuzzy sets of the Membership Functions (MFs) and to improve the efficiency and to eliminate the power quality problems. The output of Fuzzy controller is compared with High frequency triangular wave to generate PWM gating signals of Cuk converter. The proposed topologies are designed to work in Discontinuous Conduction Mode (DCM) to achieve a unity power factor and low total harmonic distortion of the input current. The Fuzzy Logic Controller gives additional advantages such as accurate result, uncertainty and imprecision and automatic control circuitry. Performance comparisons between the proposed and conventional controllers and circuits are performed based on circuit simulations.
Abstract: This paper is based on the performance of the Switched Reluctance Motor (SRM) drives using Z-Source Inverter with the simplified rule base of Fuzzy Logic Controller (FLC) with the output scaling factor (SF) self-tuning mechanism are proposed. The aim of this paper is to simplify the program complexity of the controller by reducing the number of fuzzy sets of the membership functions (MFs) without losing the system performance and stability via the adjustable controller gain. ZSI exhibits both voltage-buck and voltage-boost capability. It reduces line harmonics, improves reliability, and extends output voltage range. The output SF of the controller can be tuned continuously by a gain updating factor, whose value is derived from fuzzy logic, with the plant error and error change ratio as input variables. Then the results, carried out on a four-phase 6/8 pole SRM based on the dSPACEDS1104 platform, to show the feasibility and effectiveness of the devised methods and also performance of the proposed controllers will be compared with conventional counterpart.
Abstract: This study this is considering Boria as a conventional performance in Malaysia. Boria is a folk performance unique to Penang. This theatre style reached Penang in the mid-19th century and is believed to be derived from the Shia Islamic Passion play performed during the Muslim month of Muharram to commemorate the martyrs of Kerbela. These days in Malaysia (especially Penang) Boria mentions to a choral street performance performed annually by a number of groups composed mostly of Sunni Malaysian. Boria are performed for entertainment and often include an annual singing competition. The size, membership, themes and movements of each Boria troupe may vary from year to year. Similarly, the themes and contents of the Boria performed by the different troupes also changes each year and can have a comical, political or satirical notion. It is common to most groups during the first ten days of Muharram Boria generally is done.
Abstract: Nowadays, more engineering systems are using some
kind of Artificial Intelligence (AI) for the development of their
processes. Some well-known AI techniques include artificial neural
nets, fuzzy inference systems, and neuro-fuzzy inference systems
among others. Furthermore, many decision-making applications base
their intelligent processes on Fuzzy Logic; due to the Fuzzy
Inference Systems (FIS) capability to deal with problems that are
based on user knowledge and experience. Also, knowing that users
have a wide variety of distinctiveness, and generally, provide
uncertain data, this information can be used and properly processed
by a FIS. To properly consider uncertainty and inexact system input
values, FIS normally use Membership Functions (MF) that represent
a degree of user satisfaction on certain conditions and/or constraints.
In order to define the parameters of the MFs, the knowledge from
experts in the field is very important. This knowledge defines the MF
shape to process the user inputs and through fuzzy reasoning and
inference mechanisms, the FIS can provide an “appropriate" output.
However an important issue immediately arises: How can it be
assured that the obtained output is the optimum solution? How can it
be guaranteed that each MF has an optimum shape? A viable solution
to these questions is through the MFs parameter optimization. In this
Paper a novel parameter optimization process is presented. The
process for FIS parameter optimization consists of the five simple
steps that can be easily realized off-line. Here the proposed process
of FIS parameter optimization it is demonstrated by its
implementation on an Intelligent Interface section dealing with the
on-line customization / personalization of internet portals applied to
E-commerce.
Abstract: In data mining, the association rules are used to search
for the relations of items of the transactions database. Following the
data is collected and stored, it can find rules of value through
association rules, and assist manager to proceed marketing strategy
and plan market framework. In this paper, we attempt fuzzy partition
methods and decide membership function of quantitative values of
each transaction item. Also, by managers we can reflect the
importance of items as linguistic terms, which are transformed as
fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth
(FWFP-Growth) is used to complete the process of data mining. The
method above is expected to improve Apriori algorithm for its better
efficiency of the whole association rules. An example is given to
clearly illustrate the proposed approach.
Abstract: This paper presents a new Hybrid Fuzzy (HF) PID type controller based on Genetic Algorithms (GA-s) for solution of the Automatic generation Control (AGC) problem in a deregulated electricity environment. In order for a fuzzy rule based control system to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this method is the difficulty of accurately constructing the membership functions, because it is a computationally expensive combinatorial optimization problem. On the other hand, GAs is a technique that emulates biological evolutionary theories to solve complex optimization problems by using directed random searches to derive a set of optimal solutions. For this reason, the membership functions are tuned automatically using a modified GA-s based on the hill climbing method. The motivation for using the modified GA-s is to reduce fuzzy system effort and take large parametric uncertainties into account. The global optimum value is guaranteed using the proposed method and the speed of the algorithm-s convergence is extremely improved, too. This newly developed control strategy combines the advantage of GA-s and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed GA based HF (GAHF) controller is tested on a threearea deregulated power system under different operating conditions and contract variations. The results of the proposed GAHF controller are compared with those of Multi Stage Fuzzy (MSF) controller, robust mixed H2/H∞ and classical PID controllers through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes.
Abstract: In this study, a fuzzy similarity approach for Arabic
web pages classification is presented. The approach uses a fuzzy
term-category relation by manipulating membership degree for the
training data and the degree value for a test web page. Six measures
are used and compared in this study. These measures include:
Einstein, Algebraic, Hamacher, MinMax, Special case fuzzy and
Bounded Difference approaches. These measures are applied and
compared using 50 different Arabic web pages. Einstein measure was
gave best performance among the other measures. An analysis of
these measures and concluding remarks are drawn in this study.
Abstract: A direct adaptive controller for a class of unknown nonlinear discrete-time systems is presented in this article. The proposed controller is constructed by fuzzy rules emulated network (FREN). With its simple structure, the human knowledge about the plant is transferred to be if-then rules for setting the network. These adjustable parameters inside FREN are tuned by the learning mechanism with time varying step size or learning rate. The variation of learning rate is introduced by main theorem to improve the system performance and stabilization. Furthermore, the boundary of adjustable parameters is guaranteed through the on-line learning and membership functions properties. The validation of the theoretical findings is represented by some illustrated examples.
Abstract: One main drawback of intrusion detection system is the
inability of detecting new attacks which do not have known
signatures. In this paper we discuss an intrusion detection method
that proposes independent component analysis (ICA) based feature
selection heuristics and using rough fuzzy for clustering data. ICA is
to separate these independent components (ICs) from the monitored
variables. Rough set has to decrease the amount of data and get rid of
redundancy and Fuzzy methods allow objects to belong to several
clusters simultaneously, with different degrees of membership. Our
approach allows us to recognize not only known attacks but also to
detect activity that may be the result of a new, unknown attack. The
experimental results on Knowledge Discovery and Data Mining-
(KDDCup 1999) dataset.
Abstract: In this paper we propose and examine an Adaptive
Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition
Autoregressive (STAR) modeling. Because STAR models follow
fuzzy logic approach, in the non-linear part fuzzy rules can be
incorporated or other training or computational methods can be
applied as the error backpropagation algorithm instead to nonlinear
squares. Furthermore, additional fuzzy membership functions can be
examined, beside the logistic and exponential, like the triangle,
Gaussian and Generalized Bell functions among others. We examine
two macroeconomic variables of US economy, the inflation rate and
the 6-monthly treasury bills interest rates.
Abstract: Decision feedback equalizers are commonly employed to reduce the error caused by intersymbol interference. Here, an adaptive decision feedback equalizer is presented with a new adaptation algorithm. The algorithm follows a block-based approach of normalized least mean square (NLMS) algorithm with set-membership filtering and achieves a significantly less computational complexity over its conventional NLMS counterpart with set-membership filtering. It is shown in the results that the proposed algorithm yields similar type of bit error rate performance over a reasonable signal to noise ratio in comparison with the latter one.
Abstract: In this paper a comprehensive model of a fossil fueled
power plant (FFPP) is developed in order to evaluate the
performance of a newly designed turbine follower controller.
Considering the drawbacks of previous works, an overall model is
developed to minimize the error between each subsystem model
output and the experimental data obtained at the actual power plant.
The developed model is organized in two main subsystems namely;
Boiler and Turbine. Considering each FFPP subsystem
characteristics, different modeling approaches are developed. For
economizer, evaporator, superheater and reheater, first order models
are determined based on principles of mass and energy conservation.
Simulations verify the accuracy of the developed models. Due to the
nonlinear characteristics of attemperator, a new model, based on a
genetic-fuzzy systems utilizing Pittsburgh approach is developed
showing a promising performance vis-à-vis those derived with other
methods like ANFIS. The optimization constraints are handled
utilizing penalty functions. The effect of increasing the number of
rules and membership functions on the performance of the proposed
model is also studied and evaluated. The turbine model is developed
based on the equation of adiabatic expansion. Parameters of all
evaluated models are tuned by means of evolutionary algorithms.
Based on the developed model a fuzzy PI controller is developed. It
is then successfully implemented in the turbine follower control
strategy of the plant. In this control strategy instead of keeping
control parameters constant, they are adjusted on-line with regard to
the error and the error rate. It is shown that the response of the
system improves significantly. It is also shown that fuel consumption
decreases considerably.