Abstract: A pilot project was carried out in 2007 by the senior
students of Cyprus International University, aiming to minimize the
total cost of waste collection in Northern Cyprus. Many developed
and developing countries have cut their transportation costs – which
lies between 30-40% – down at a rate of 40% percent, by
implementing network models for their route assignments.
Accordingly, a network model was implemented at Göçmenköy
district, to optimize and standardize waste collection works. The
work environment of the employees were also redesigned to provide
maximum ergonomy and to increase productivity, efficiency and
safety. Following the collection of the required data including waste
densities, lengths of roads and population, a model was constructed
to allocate the optimal route assignment for the waste collection
trucks at Göçmenköy district.
Abstract: The proper design of RF pulses in magnetic resonance imaging (MRI) has a direct impact on the quality of acquired images, and is needed for many applications. Several techniques have been proposed to obtain the RF pulse envelope given the desired slice profile. Unfortunately, these techniques do not take into account the limitations of practical implementation such as limited amplitude resolution. Moreover, implementing constraints for special RF pulses on most techniques is not possible. In this work, we propose to develop an approach for designing optimal RF pulses under theoretically any constraints. The new technique will pose the RF pulse design problem as a combinatorial optimization problem and uses efficient techniques from this area such as genetic algorithms (GA) to solve this problem. In particular, an objective function will be proposed as the norm of the difference between the desired profile and the one obtained from solving the Bloch equations for the current RF pulse design values. The proposed approach will be verified using analytical solution based RF simulations and compared to previous methods such as Shinnar-Le Roux (SLR) method, and analysis, selected, and tested the options and parameters that control the Genetic Algorithm (GA) can significantly affect its performance to get the best improved results and compared to previous works in this field. The results show a significant improvement over conventional design techniques, select the best options and parameters for GA to get most improvement over the previous works, and suggest the practicality of using of the new technique for most important applications as slice selection for large flip angles, in the area of unconventional spatial encoding, and another clinical use.
Abstract: The rotation of starting pitchers is a strategic issue
which has a significant impact on the performance of a professional
team. Choosing an optimal starting pitcher from among many
alternatives is a multi-criteria decision-making (MCDM) problem. In
this study, a model using the Analytic Hierarchy Process (AHP) and
Technique for Order Performance by Similarity to the Ideal Solution
(TOPSIS) is proposed with which to arrange the starting pitcher
rotation for teams of the Chinese Professional Baseball League. The
AHP is used to analyze the structure of the starting pitcher selection
problem and to determine the weights of the criteria, while the
TOPSIS method is used to make the final ranking. An empirical
analysis is conducted to illustrate the utilization of the model for the
starting pitcher rotation problem. The results demonstrate the
effectiveness and feasibility of the proposed model.
Abstract: The current trend of increasing quality and demands
of the final product is affected by time analysis of the entire
manufacturing process. The primary requirement of manufacturing is
to produce as many products as soon as possible, at the lowest
possible cost, but of course with the highest quality. Such
requirements may be satisfied only if all the elements entering and
affecting the production cycle are in a fully functional condition.
These elements consist of sensory equipment and intelligent control
elements that are essential for building intelligent manufacturing
systems. The intelligent manufacturing paradigm includes a new
approach to production system structure design. Intelligent behaviors
are based on the monitoring of important parameters of system and
its environment. The flexible reaction to changes. The realization and
utilization of this design paradigm as an "intelligent manufacturing
system" enables the flexible system reaction to production
requirement as soon as environmental changes too. Results of these
flexible reactions are a smaller layout space, be decreasing of
production and investment costs and be increasing of productivity.
Intelligent manufacturing system itself should be a system that can
flexibly respond to changes in entering and exiting the process in
interaction with the surroundings.
Abstract: The Neuro-Fuzzy hybridization scheme has become
of research interest in pattern classification over the past decade. The
present paper proposes a novel Modified Adaptive Fuzzy Inference
Engine (MAFIE) for pattern classification. A modified Apriori
algorithm technique is utilized to reduce a minimal set of decision
rules based on input output data sets. A TSK type fuzzy inference
system is constructed by the automatic generation of membership
functions and rules by the fuzzy c-means clustering and Apriori
algorithm technique, respectively. The generated adaptive fuzzy
inference engine is adjusted by the least-squares fit and a conjugate
gradient descent algorithm towards better performance with a
minimal set of rules. The proposed MAFIE is able to reduce the
number of rules which increases exponentially when more input
variables are involved. The performance of the proposed MAFIE is
compared with other existing applications of pattern classification
schemes using Fisher-s Iris and Wisconsin breast cancer data sets and
shown to be very competitive.
Abstract: This paper explores the scalability issues associated
with solving the Named Entity Recognition (NER) problem using
Support Vector Machines (SVM) and high-dimensional features. The
performance results of a set of experiments conducted using binary
and multi-class SVM with increasing training data sizes are
examined. The NER domain chosen for these experiments is the
biomedical publications domain, especially selected due to its
importance and inherent challenges. A simple machine learning
approach is used that eliminates prior language knowledge such as
part-of-speech or noun phrase tagging thereby allowing for its
applicability across languages. No domain-specific knowledge is
included. The accuracy measures achieved are comparable to those
obtained using more complex approaches, which constitutes a
motivation to investigate ways to improve the scalability of multiclass
SVM in order to make the solution more practical and useable.
Improving training time of multi-class SVM would make support
vector machines a more viable and practical machine learning
solution for real-world problems with large datasets. An initial
prototype results in great improvement of the training time at the
expense of memory requirements.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
Abstract: There have been many variations of technologies that helped educators in teaching & learning. From the past research it is evident that Information Technology significantly increases student participation and interactivity in the classrooms. This research started with a aim to find whether adoption of Wi-Fi environment by Malaysian Higher Educational Institutions (HEI) can benefit students and staff equally. The study was carried out in HEI-s of Klang Valley, Malaysia and the data is gathered through paper based surveys. A sample size of 237 units were randomly selected from 5 higher educational institutions in the Klang Valley using the Stratified Random sampling method and from the analysis of the data, it was found that the implementation of wireless technologies in HEIs have created lot of opportunities and also challenges.
Abstract: In this paper, the application of sliding-mode control to a permanent-magnet synchronous motor (PMSM) is presented. The control design is based on a generic mathematical model of the motor. Some dynamics of the motor and of the power amplification stage remain unmodelled. This model uncertainty is estimated in realtime. The estimation is based on the differentiation of measured signals using the ideas of robust exact differentiator (RED). The control law is implemented on an industrial servo drive. Simulations and experimental results are presented and compared to the same control strategy without uncertainty estimation. It turns out that the proposed concept is superior to the same control strategy without uncertainty estimation especially in the case of non-smooth reference signals.
Abstract: GSM has undoubtedly become the most widespread
cellular technology and has established itself as one of the most
promising technology in wireless communication. The next
generation of mobile telephones had also become more powerful and
innovative in a way that new services related to the user-s location
will arise. Other than the 911 requirements for emergency location
initiated by the Federal Communication Commission (FCC) of the
United States, GSM positioning can be highly integrated in cellular
communication technology for commercial use. However, GSM
positioning is facing many challenges. Issues like accuracy,
availability, reliability and suitable cost render the development and
implementation of GSM positioning a challenging task. In this paper,
we investigate the optimal mobile position tracking means. We
employ an innovative scheme by integrating the Kalman filter in the
localization process especially that it has great tracking
characteristics. When tracking in two dimensions, Kalman filter is
very powerful due to its reliable performance as it supports
estimation of past, present, and future states, even when performing
in unknown environments. We show that enhanced position tracking
results is achieved when implementing the Kalman filter for GSM
tracking.
Abstract: This paper is focused on issues of process modeling
and two model based control strategies of a fed-batch sugar
crystallization process applying the concept of artificial neural
networks (ANNs). The control objective is to force the operation into
following optimal supersaturation trajectory. It is achieved by
manipulating the feed flow rate of sugar liquor/syrup, considered as
the control input. The control task is rather challenging due to the
strong nonlinearity of the process dynamics and variations in the
crystallization kinetics. Two control alternatives are considered –
model predictive control (MPC) and feedback linearizing control
(FLC). Adequate ANN process models are first built as part of the
controller structures. MPC algorithm outperforms the FLC approach
with respect to satisfactory reference tracking and smooth control
action. However, the MPC is computationally much more involved
since it requires an online numerical optimization, while for the FLC
an analytical control solution was determined.
Abstract: MANEMO is the integration of Network Mobility
(NEMO) and Mobile Ad Hoc Network (MANET). A MANEMO
node has an interface to both a MANET and NEMO network, and
therefore should choose the optimal interface for packet delivery,
however such a handover between interfaces will introduce packet
loss. We define the steps necessary for a MANEMO handover,
using Mobile IP and NEMO to signal the new binding to the
relevant Home Agent(s). The handover steps aim to minimize the
packet loss by avoiding waiting for Duplicate Address Detection
and Neighbour Unreachability Detection. We present expressions for
handover delay and packet loss, and then use numerical examples to
evaluate a MANEMO handover. The analysis shows how the packet
loss depends on level of nesting within NEMO, the delay between
Home Agents and the load on the MANET, and hence can be used
to developing optimal MANEMO handover algorithms.
Abstract: For Seismic design, it is important to estimate,
maximum lateral displacement (inelastic displacement) of the
structures due to sever earthquakes for several reasons. Seismic
design provisions estimate the maximum roof and storey drifts
occurring in major earthquakes by amplifying the drifts of the
structures obtained by elastic analysis subjected to seismic design
load, with a coefficient named “displacement amplification factor"
which is greater than one. Here, this coefficient depends on various
parameters, such as ductility and overstrength factors. The present
research aims to evaluate the value of the displacement amplification
factor in seismic design codes and then tries to propose a value to
estimate the maximum lateral structural displacement from sever
earthquakes, without using non-linear analysis. In seismic codes,
since the displacement amplification is related to “force reduction
factor" hence; this aspect has been accepted in the current study.
Meanwhile, two methodologies are applied to evaluate the value of
displacement amplification factor and its relation with the force
reduction factor. In the first methodology, which is applied for all
structures, the ratio of displacement amplification and force reduction
factors is determined directly. Whereas, in the second methodology
that is applicable just for R/C moment resisting frame, the ratio is
obtained by calculating both factors, separately. The acquired results
of these methodologies are alike and estimate the ratio of two factors
from 1 to 1.2. The results indicate that the ratio of the displacement
amplification factor and the force reduction factor differs to those
proposed by seismic provisions such as NEHRP, IBC and Iranian
seismic code (standard no. 2800).
Abstract: This paper proposes a balance control scheme for a biped robot to trace an arbitrary path using image information. While moving, it estimates the zero moment point(ZMP) of the biped robot in the next step using a Kalman filter and renders an appropriate balanced pose of the robot. The ZMP can be calculated from the robot's pose, which is measured from the reference object image acquired by a CCD camera on the robot's head. For simplifying the kinematical model, the coordinates systems of individual joints of each leg are aligned and the robot motion is approximated as an inverted pendulum so that a simple linear dynamics, 3D-LIPM(3D-Linear Inverted Pendulum Mode) can be applied. The efficiency of the proposed algorithm has been proven by the experiments performed on unknown trajectory.
Abstract: A Picard-Newton iteration method is studied to accelerate the numerical solution procedure of a class of two-dimensional nonlinear coupled parabolic-hyperbolic system. The Picard-Newton iteration is designed by adding higher-order terms of small quantity to an existing Picard iteration. The discrete functional analysis and inductive hypothesis reasoning techniques are used to overcome difficulties coming from nonlinearity and coupling, and theoretical analysis is made for the convergence and approximation properties of the iteration scheme. The Picard-Newton iteration has a quadratic convergent ratio, and its solution has second order spatial approximation and first order temporal approximation to the exact solution of the original problem. Numerical tests verify the results of the theoretical analysis, and show the Picard-Newton iteration is more efficient than the Picard iteration.
Abstract: Three similar negative differential resistance (NDR)
profiles with both high peak to valley current density ratio (PVCDR)
value and high peak current density (PCD) value in unity resonant
tunneling electronic circuit (RTEC) element is developed in this paper.
The PCD values and valley current density (VCD) values of the three
NDR curves are all about 3.5 A and 0.8 A, respectively. All PV values
of NDR curves are 0.40 V, 0.82 V, and 1.35 V, respectively. The VV
values are 0.61 V, 1.07 V, and 1.69 V, respectively. All PVCDR
values reach about 4.4 in three NDR curves. The PCD value of 3.5 A
in triple PVCDR RTEC element is better than other resonant
tunneling devices (RTD) elements. The high PVCDR value is
concluded the lower VCD value about 0.8 A. The low VCD value is
achieved by suitable selection of resistors in triple PVCDR RTEC
element. The low PV value less than 1.35 V possesses low power
dispersion in triple PVCDR RTEC element. The designed multiple
value logical level (MVLL) system using triple PVCDR RTEC
element provides equidistant logical level. The logical levels of
MVLL system are about 0.2 V, 0.8 V, 1.5 V, and 2.2 V from low
voltage to high voltage and then 2.2 V, 1.3 V, 0.8 V, and 0.2 V from
high voltage back to low voltage in half cycle of sinusoid wave. The
output level of four levels MVLL system is represented in 0.3 V, 1.1 V,
1.7 V, and 2.6 V, which satisfies the NMP condition of traditional
two-bit system. The remarkable logical characteristic of improved
MVLL system with paralleled capacitor are with four significant
stable logical levels about 220 mV, 223 mV, 228 mV, and 230 mV.
The stability and articulation of logical levels of improved MVLL
system are outstanding. The average holding time of improved MVLL
system is approximately 0.14 μs. The holding time of improved
MVLL system is fourfold than of basic MVLL system. The function of
additional capacitor in the improved MVLL system is successfully
discovered.
Abstract: Ethanol has become more attractive in fuel industry
either as fuel itself or an additive that helps enhancing the octane
number and combustibility of gasoline. This research studied a
pressure swing adsorption using cassava-based adsorbent prepared
from mixture of cassava starch and cassava pulp for dehydration of
ethanol vapor. The apparatus used in the experiments consisted of
double adsorption columns, an evaporator, and a vacuum pump. The
feed solution contained 90-92 %wt of ethanol. Three process
variables: adsorption temperatures (110, 120 and 130°C), adsorption
pressures (1 and 2 bar gauge) and feed vapor flow rate (25, 50 and 75
% valve opening of the evaporator) were investigated. According to
the experimental results, the optimal operating condition for this
system was found to be at 2 bar gauge for adsorption pressure, 120°C
for adsorption temperature and 25% valve opening of the evaporator.
Production of 1.48 grams of ethanol with concentration higher than
99.5 wt% per gram of adsorbent was obtained. PSA with cassavabased
adsorbent reported in this study could be an alternative method
for production of nearly anhydrous ethanol. Dehydration of ethanol
vapor achieved in this study is due to an interaction between free
hydroxyl group on the glucose units of the starch and the water
molecules.
Abstract: This paper quantifies the impact of providing a shortterm
excess active power support of a variable speed wind turbine
(VSWT) and effect of super magnetic energy storage (SMES) unit on
frequency control, particularly temporary minimum frequency (TMF)
term. To demonstrate the effect of these factors on the power system
frequency, a three-area power system is considered as a test system.
Abstract: In most cases, it is considerably difficult to directly measure structural vibration with a lot of sensors because of complex
geometry, time and equipment cost. For this reason, this paper deals
with the problem of locating sensors on a plate model by four advanced sensor placement optimization (S.P.O) techniques. It also
suggests the evaluation index representing the characteristic of orthogonal between each of natural modes. The index value provides the assistance to selecting of proper S.P.O technique and optimal
positions for monitoring of dynamic systems without the experiment.
Abstract: In this paper optimal capacitor placement problem has
been formulated in a restructured distribution network. In this
scenario the distribution network operator can consider reactive
energy also as a service that can be sold to transmission system. Thus
search for optimal location, size and number of capacitor banks with
the objective of loss reduction, maximum income from selling
reactive energy to transmission system and return on investment for
capacitors, has been performed. Results is influenced with economic
value of reactive energy, therefore problem has been solved for
various amounts of it. The implemented optimization technique is
genetic algorithm. For any value of reactive power economic value,
when reverse of investment index increase and change from zero or
negative values to positive values, the threshold value of selling
reactive power has been obtained. This increasing price of economic
parameter is reasonable until the network losses is less than loss
before compensation.