Abstract: Purpose of this work is the development of an
automatic classification system which could be useful for radiologists
in the investigation of breast cancer. The software has been designed
in the framework of the MAGIC-5 collaboration.
In the automatic classification system the suspicious regions with
high probability to include a lesion are extracted from the image as
regions of interest (ROIs). Each ROI is characterized by some
features based on morphological lesion differences.
Some classifiers as a Feed Forward Neural Network, a K-Nearest
Neighbours and a Support Vector Machine are used to distinguish the
pathological records from the healthy ones.
The results obtained in terms of sensitivity (percentage of
pathological ROIs correctly classified) and specificity (percentage of
non-pathological ROIs correctly classified) will be presented through
the Receive Operating Characteristic curve (ROC). In particular the
best performances are 88% ± 1 of area under ROC curve obtained
with the Feed Forward Neural Network.
Abstract: Using electrical machine in conventional vehicles, also called hybrid vehicles, has become a promising control scheme that enables some manners for fuel economy and driver assist for better stability. In this paper, vehicle stability control, fuel economy and Driving/Regeneration braking for a 4WD hybrid vehicle is investigated by using an electrical machine on each non-driven wheels. In front wheels driven vehicles, fuel economy and regenerative braking can be obtained by summing torques applied on rear wheels. On the other hand, unequal torques applied to rear wheels provides enhanced safety and path correction in steering. In this paper, a model with fourteen degrees of freedom is considered for vehicle body, tires and, suspension systems. Thereafter, powertrain subsystems are modeled. Considering an electrical machine on each rear wheel, a fuzzy controller is designed for each driving, braking, and stability conditions. Another fuzzy controller recognizes the vehicle requirements between the driving/regeneration and stability modes. Intelligent vehicle control to multi objective operation and forward simulation are the paper advantages. For reaching to these aims, power management control and yaw moment control will be done by three fuzzy controllers. Also, the above mentioned goals are weighted by another fuzzy sub-controller base on vehicle dynamic. Finally, Simulations performed in MATLAB/SIMULINK environment show that the proposed structure can enhance the vehicle performance in different modes effectively.
Abstract: The low power wireless sensor devices which usually
uses the low power wireless private area network (IEEE 802.15.4)
standard are being widely deployed for various purposes and in
different scenarios. IPv6 low power wireless private area network
(6LoWPAN) was adopted as part of the IETF standard for the
wireless sensor devices so that it will become an open standard
compares to other dominated proprietary standards available in the
market. 6LoWPAN also allows the integration and communication of
sensor nodes with the Internet more viable. This paper presents a
comparative study on different available IPv6 platforms for wireless
sensor networks including open and close sources. It also discusses
about the platforms used by these stacks. Finally it evaluates and
provides appropriate suggestions which can be use for selection of
required IPv6 stack for low power devices.
Abstract: Phytophthora cinnamomi (P. c) is a plant pathogenic
oomycete that is capable of damaging plants in commercial production
systems and natural ecosystems worldwide. The most common
methods for the detection and diagnosis of P. c infection are
expensive, elaborate and time consuming. This study was carried out
to examine whether species specific and life cycle specific volatile
organic compounds (VOCs) can be absorbed by solid-phase
microextraction fibers and detected by gas chromatography that are
produced by P. c and another oomycete Pythium dissotocum. A
headspace solid-phase microextraction (HS-SPME) together with gas
chromatography (GC) method was developed and optimized for the
identification of the VOCs released by P. c. The optimized parameters
included type of fiber, exposure time, desorption temperature and
desorption time. Optimization was achieved with the analytes of P.
c+V8A and V8A alone. To perform the HS-SPME, six types of fiber
were assayed and compared: 7μm Polydimethylsiloxane (PDMS),
100μm Polydimethylsiloxane (PDMS), 50/30μm
Divinylbenzene/CarboxenTM/Polydimethylsiloxane
DVB/CAR/PDMS), 65μm Polydimethylsiloxane/Divinylbenzene
(PDMS/DVB), 85μm Polyacrylate (PA) fibre and 85μm CarboxenTM/
Polydimethylsiloxane (Carboxen™/PDMS). In a comparison of the
efficacy of the fibers, the bipolar fiber DVB/CAR/PDMS had a higher
extraction efficiency than the other fibers. An exposure time of 16h
with DVB/CAR/PDMS fiber in the sample headspace was enough to
reach the maximum extraction efficiency. A desorption time of 3min
in the GC injector with the desorption temperature of 250°C was
enough for the fiber to desorb the compounds of interest. The chromatograms and morphology study confirmed that the VOCs from
P. c+V8A had distinct differences from V8A alone, as did different
life cycle stages of P. c and different taxa such as Pythium dissotocum.
The study proved that P. c has species and life cycle specific VOCs,
which in turn demonstrated the feasibility of this method as means of
Abstract: Work ethic and labour productivity issues are
extremely important for any society. It has been long proven by the
global practice and various scholars that the country promoting the
labour has always been way forward from the other countries. This
paper studies the thoughts suggested by M.Weber, Confucius, Lee
Kuan Yew, Mahathir Mohammad and other prominent thinkers
concerning the issues of work ethics and labour productivity. The
article analyzes why developed nations are way more advanced in
their development compared to other nations.
Abstract: Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.
Abstract: This paper focuses on systematic analysis and
controller design of the two-inertia STABILIZATION system,
considering the angular motion on a base body. This approach is
essential to the stabilization system to aim at a target under three or six
degrees of freedom base motion. Four controllers, such as
conventional PDF(Pseudo-Derivative Feedback) controller with
motor speed feedback, PDF controller with load speed feedback,
modified PDF controller with motor-load speed feedback and
feedforward controller added to modified PDF controller, are
suggested to improve reference tracking and disturbance rejection
performance. Characteristics and performance of each controller are
analyzed and validated by simulation in the case of the modified PDF
controller with and without a feedforward controller.
Abstract: Periodicities in the environmetric time series can be
idyllically assessed by utilizing periodic models. In this
communication fugitive emission of gases from open sewer channel
Lyari which follows periodic behaviour are approximated by
employing periodic autoregressive model of order p. The orders of
periodic model for each season are selected through the examination
of periodic partial autocorrelation or information criteria. The
parameters for the selected order of season are estimated individually
for each emitted air toxin. Subsequently, adequacies of fitted models
are established by examining the properties of the residual for each
season. These models are beneficial for schemer and administrative
bodies for the improvement of implemented policies to surmount
future environmental problems.
Abstract: Business Process Management (BPM) helps in optimizing the business processes inside an enterprise. But BPM architecture does not provide any help for extending the enterprise. Modern business environments and rapidly changing technologies are asking for brisk changes in the business processes. Service Oriented Architecture (SOA) can help in enabling the success of enterprise-wide BPM. SOA supports agility in software development that is directly related to achieve loose coupling of interacting software agents. Agility is a premium concern of the current software designing architectures. Together, BPM and SOA provide a perfect combination for enterprise computing. SOA provides the capabilities for services to be combined together and to support and create an agile, flexible enterprise. But there are still many questions to answer; BPM is better or SOA? and what is the future track of BPM and SOA? This paper tries to answer some of these important questions.
Abstract: The back-propagation algorithm calculates the weight
changes of an artificial neural network, and a two-term algorithm
with a dynamically optimal learning rate and a momentum factor
is commonly used. Recently the addition of an extra term, called a
proportional factor (PF), to the two-term BP algorithm was proposed.
The third term increases the speed of the BP algorithm. However,
the PF term also reduces the convergence of the BP algorithm, and
optimization approaches for evaluating the learning parameters are
required to facilitate the application of the three terms BP algorithm.
This paper considers the optimization of the new back-propagation
algorithm by using derivative information. A family of approaches
exploiting the derivatives with respect to the learning rate, momentum
factor and proportional factor is presented. These autonomously
compute the derivatives in the weight space, by using information
gathered from the forward and backward procedures. The three-term
BP algorithm and the optimization approaches are evaluated using
the benchmark XOR problem.
Abstract: Biodiversity crisis is one of the many crises that
started at the turn of the millennia. Concrete form of expression is
still disputed, but there is a relatively high consensus regarding the
high rate of degradation and the urgent need for action. The strategy
of action outlines a strong economic component, together with the
recognition of market mechanisms as the most effective policies to
protect biodiversity. In this context, biodiversity and ecosystem
services are natural assets that play a key role in economic strategies
and technological development to promote development and
prosperity. Developing and strengthening policies for transition to an
economy based on efficient use of resources is the way forward.
To emphasize the co-viability specific to the connection economyecosystem
services, scientific approach aimed on one hand how to
implement policies for nature conservation and on the other hand, the
concepts underlying the economic expression of ecosystem services-
value, in the context of current technology. Following the analysis of
business opportunities associated with changes in ecosystem services
was concluded that development of market mechanisms for nature
conservation is a trend that is increasingly stronger individualized
within recent years. Although there are still many controversial issues
that have already given rise to an obvious bias, international
organizations and national governments have initiated and
implemented in cooperation or independently such mechanisms.
Consequently, they created the conditions for convergence between
private interests and social interests of nature conservation, so there
are opportunities for ongoing business development which leads,
among other things, the positive effects on biodiversity. Finally,
points out that markets fail to quantify the value of most ecosystem
services. Existing price signals reflect at best, only a proportion of the
total amount corresponding provision of food, water or fuel.
Abstract: This paper addresses the problem of trajectory
tracking control of an underactuated autonomous underwater vehicle
(AUV) in the horizontal plane. The underwater vehicle under
consideration is not actuated in the sway direction, and the system
matrices are not assumed to be diagonal and linear, as often found in
the literature. In addition, the effect of constant bias of environmental
disturbances is considered. Using backstepping techniques and the
tracking error dynamics, the system states are stabilized by forcing
the tracking errors to an arbitrarily small neighborhood of zero. The
effectiveness of the proposed control method is demonstrated through
numerical simulations. Simulations are carried out for an
experimental vehicle for smooth, inertial, two dimensional (2D)
reference trajectories such as constant velocity trajectory (a circle
maneuver – constant yaw rate), and time varying velocity trajectory
(a sinusoidal path – sinusoidal yaw rate).
Abstract: High level synthesis (HLS) is a process which
generates register-transfer level design for digital systems from
behavioral description. There are many HLS algorithms and
commercial tools. However, most of these algorithms consider a
behavioral description for the system when a single token is
presented to the system. This approach does not exploit extra
hardware efficiently, especially in the design of digital filters where
common operations may exist between successive tokens. In this
paper, we modify the behavioral description to process multiple
tokens in parallel. However, this approach is unlike the full
processing that requires full hardware replication. It exploits the
presence of common operations between successive tokens. The
performance of the proposed approach is better than sequential
processing and approaches that of full parallel processing as the
hardware resources are increased.
Abstract: A new Feed-Forward/Feedback Generalized
Minimum Variance Pole-placement Controller to incorporate the
robustness of classical pole-placement into the flexibility of
generalized minimum variance self-tuning controller for Single-Input
Single-Output (SISO) has been proposed in this paper. The design,
which provides the user with an adaptive mechanism, which ensures
that the closed loop poles are, located at their pre-specified positions.
In addition, the controller design which has a feed-forward/feedback
structure overcomes the certain limitations existing in similar poleplacement
control designs whilst retaining the simplicity of
adaptation mechanisms used in other designs. It tracks set-point
changes with the desired speed of response, penalizes excessive
control action, and can be applied to non-minimum phase systems.
Besides, at steady state, the controller has the ability to regulate the
constant load disturbance to zero. Example simulation results using
both simulated and real plant models demonstrate the effectiveness of
the proposed controller.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: This paper addresses the problem of asymptotic tracking
control of a linear parabolic partial differential equation with indomain
point actuation. As the considered model is a non-standard
partial differential equation, we firstly developed a map that allows
transforming this problem into a standard boundary control problem
to which existing infinite-dimensional system control methods can
be applied. Then, a combination of energy multiplier and differential
flatness methods is used to design an asymptotic tracking controller.
This control scheme consists of stabilizing state-feedback derived
from the energy multiplier method and feed-forward control based
on the flatness property of the system. This approach represents
a systematic procedure to design tracking control laws for a class
of partial differential equations with in-domain point actuation. The
applicability and system performance are assessed by simulation
studies.
Abstract: Scale defects are common surface defects in hot steel rolling. The modelling of such defects is problematic and their causes are not straightforward. In this study, we investigated genetic algorithms in search for a mathematical solution to scale formation. For this research, a high-dimensional data set from hot steel rolling process was gathered. The synchronisation of the variables as well as the allocation of the measurements made on the steel strip were solved before the modelling phase.
Abstract: This paper presents a simple approach for load
flow analysis of a radial distribution network. The proposed
approach utilizes forward and backward sweep algorithm
based on Kirchoff-s current law (KCL) and Kirchoff-s voltage
law (KVL) for evaluating the node voltages iteratively. In this
approach, computation of branch current depends only on the
current injected at the neighbouring node and the current in
the adjacent branch. This approach starts from the end nodes
of sub lateral line, lateral line and main line and moves
towards the root node during branch current computation. The
node voltage evaluation begins from the root node and moves
towards the nodes located at the far end of the main, lateral
and sub lateral lines. The proposed approach has been tested
using four radial distribution systems of different size and
configuration and found to be computationally efficient.
Abstract: An effective approach for unbalanced three-phase
distribution power flow solutions is proposed in this paper. The
special topological characteristics of distribution networks have been
fully utilized to make the direct solution possible. Two matrices–the
bus-injection to branch-current matrix and the branch-current to busvoltage
matrix– and a simple matrix multiplication are used to
obtain power flow solutions. Due to the distinctive solution
techniques of the proposed method, the time-consuming LU
decomposition and forward/backward substitution of the Jacobian
matrix or admittance matrix required in the traditional power flow
methods are no longer necessary. Therefore, the proposed method is
robust and time-efficient. Test results demonstrate the validity of the
proposed method. The proposed method shows great potential to be
used in distribution automation applications.
Abstract: The decision to recruit manpower in an organization
requires clear identification of the criteria (attributes) that distinguish
successful from unsuccessful performance. The choice of appropriate
attributes or criteria in different levels of hierarchy in an organization
is a multi-criteria decision problem and therefore multi-criteria
decision making (MCDM) techniques can be used for prioritization
of such attributes. Analytic Hierarchy Process (AHP) is one such
technique that is widely used for deciding among the complex criteria
structure in different levels. In real applications, conventional AHP
still cannot reflect the human thinking style as precise data
concerning human attributes are quite hard to be extracted. Fuzzy
logic offers a systematic base in dealing with situations, which are
ambiguous or not well defined. This study aims at defining a
methodology to improve the quality of prioritization of an
employee-s performance measurement attributes under fuzziness. To
do so, a methodology based on the Extent Fuzzy Analytic Hierarchy
Process is proposed. Within the model, four main attributes such as
Subject knowledge and achievements, Research aptitude, Personal
qualities and strengths and Management skills with their subattributes
are defined. The two approaches conventional AHP
approach and the Extent Fuzzy Analytic Hierarchy Process approach
have been compared on the same hierarchy structure and criteria set.