Abstract: This work describes a CACSD tool for automatic design of robust controllers for hydraulic turbines. The tool calculates the optimal controller using the MATLAB hinfopt function and it
serves as a practical and effective solution for the laborious task of
designing a different controller for each type of turbine and generator, and different parameters and conditions of the plant. Results of the simulation of a generating unit subject to parameters
variation show the accuracy and efficiency of the obtained robust
controllers.
Abstract: Linear convolutive filters are fast in calculation and in application, and thus, often used for real-time processing of continuous data streams. In the case of transient signals, a filter has not only to detect the presence of a specific waveform, but to estimate its arrival time as well. In this study, a measure is presented which indicates the performance of detectors in achieving both of these tasks simultaneously. Furthermore, a new sub-class of linear filters within the class of filters which minimize the quadratic response is proposed. The proposed filters are more flexible than the existing ones, like the adaptive matched filter or the minimum power distortionless response beamformer, and prove to be superior with respect to that measure in certain settings. Simulations of a real-time scenario confirm the advantage of these filters as well as the usefulness of the performance measure.
Abstract: In this work, we treat the problems related to chemical and petrochemical plants of a certain complex process taking the centrifugal compressor as an example, a system being very complex by its physical structure as well as its behaviour (surge phenomenon). We propose to study the application possibilities of the recent control approaches to the compressor behaviour, and consequently evaluate their contribution in the practical and theoretical fields. Facing the studied industrial process complexity, we choose to make recourse to fuzzy logic for analysis and treatment of its control problem owing to the fact that these techniques constitute the only framework in which the types of imperfect knowledge can jointly be treated (uncertainties, inaccuracies, etc..) offering suitable tools to characterise them. In the particular case of the centrifugal compressor, these imperfections are interpreted by modelling errors, the neglected dynamics, no modelisable dynamics and the parametric variations. The purpose of this paper is to produce a total robust nonlinear controller design method to stabilize the compression process at its optimum steady state by manipulating the gas rate flow. In order to cope with both the parameter uncertainty and the structured non linearity of the plant, the proposed method consists of a linear steady state regulation that ensures robust optimal control and of a nonlinear compensation that achieves the exact input/output linearization.
Abstract: In this paper, the problem of finding the optimal
topological configuration of a deregulated distribution network is
considered. The new features of this paper are proposing a multiobjective
function and its application on deregulated distribution
networks for finding the optimal configuration. The multi-objective
function will be defined for minimizing total Energy Supply Costs
(ESC) and energy losses subject to load flow constraints. The
optimal configuration will be obtained by using Binary Genetic
Algorithm (BGA).The proposed method has been tested to analyze a
sample and a practical distribution networks.
Abstract: Worldwide conventional resources of fossil fuel are depleting very fast due to large scale increase in use of transport vehicles every year, therefore consumption rate of oil in transport sector alone has gone very high. In view of this, the major thrust has now been laid upon the search of alternative energy source and also for cost effective energy conversion system. The air converted into compressed form by non conventional or conventional methods can be utilized as potential working fluid for producing shaft work in the air turbine and thus offering the capability of being a zero pollution energy source. This paper deals with the mathematical modeling and performance evaluation of a small capacity compressed air driven vaned type novel air turbine. Effect of expansion action and steady flow work in the air turbine at high admission air pressure of 6 bar, for varying injection to vane angles ratios 0.2-1.6, at the interval of 0.2 and at different vane angles such as 30o, 45o, 51.4o, 60o, 72o, 90o, and 120o for 12, 8, 7, 6, 5, 4 and 3 vanes respectively at speed of rotation 2500 rpm, has been quantified and analyzed here. Study shows that the expansion power has major contribution to total power, whereas the contribution of flow work output has been found varying only up to 19.4%. It is also concluded that for variation of injection to vane angle ratios from 0.2 to 1.2, the optimal power output is seen at vane angle 90o (4 vanes) and for 1.4 to 1.6 ratios, the optimal total power is observed at vane angle 72o (5 vanes). Thus in the vaned type novel air turbine the optimum shaft power output is developed when rotor contains 4-5 vanes for almost all situations of injection to vane angle ratios from 0.2 to 1.6.
Abstract: Asynchronous Transfer Mode (ATM) is widely used
in telecommunications systems to send data, video and voice at a
very high speed. In ATM network optimizing the bandwidth through
dynamic routing is an important consideration. Previous research
work shows that traditional optimization heuristics result in suboptimal
solution. In this paper we have explored non-traditional
optimization technique. We propose comparison of two such
algorithms - Genetic Algorithm (GA) and Tabu search (TS), based on
non-traditional Optimization approach, for solving the dynamic
routing problem in ATM networks which in return will optimize the
bandwidth. The optimized bandwidth could mean that some
attractive business applications would become feasible such as high
speed LAN interconnection, teleconferencing etc. We have also
performed a comparative study of the selection mechanisms in GA
and listed the best selection mechanism and a new initialization
technique which improves the efficiency of the GA.
Abstract: Modeling the behavior of the dialogue management in
the design of a spoken dialogue system using statistical methodologies
is currently a growing research area. This paper presents a work
on developing an adaptive learning approach to optimize dialogue
strategy. At the core of our system is a method formalizing dialogue
management as a sequential decision making under uncertainty whose
underlying probabilistic structure has a Markov Chain. Researchers
have mostly focused on model-free algorithms for automating the
design of dialogue management using machine learning techniques
such as reinforcement learning. But in model-free algorithms there
exist a dilemma in engaging the type of exploration versus exploitation.
Hence we present a model-based online policy learning
algorithm using interconnected learning automata for optimizing
dialogue strategy. The proposed algorithm is capable of deriving
an optimal policy that prescribes what action should be taken in
various states of conversation so as to maximize the expected total
reward to attain the goal and incorporates good exploration and
exploitation in its updates to improve the naturalness of humancomputer
interaction. We test the proposed approach using the most
sophisticated evaluation framework PARADISE for accessing to the
railway information system.
Abstract: The purpose of this study is to derive optimal shapes of
a body located in viscous flows by the finite element method using the
acoustic velocity and the four-step explicit scheme. The formulation
is based on an optimal control theory in which a performance function
of the fluid force is introduced. The performance function should be
minimized satisfying the state equation. This problem can be transformed
into the minimization problem without constraint conditions
by using the adjoint equation with adjoint variables corresponding to
the state equation. The performance function is defined by the drag
and lift forces acting on the body. The weighted gradient method
is applied as a minimization technique, the Galerkin finite element
method is used as a spatial discretization and the four-step explicit
scheme is used as a temporal discretization to solve the state equation
and the adjoint equation. As the interpolation, the orthogonal basis
bubble function for velocity and the linear function for pressure
are employed. In case that the orthogonal basis bubble function is
used, the mass matrix can be diagonalized without any artificial
centralization. The shape optimization is performed by the presented
method.
Abstract: Cosmic showers, from their places of origin in space,
after entering earth generate secondary particles called Extensive Air
Shower (EAS). Detection and analysis of EAS and similar High
Energy Particle Showers involve a plethora of experimental setups
with certain constraints for which soft-computational tools like
Artificial Neural Network (ANN)s can be adopted. The optimality
of ANN classifiers can be enhanced further by the use of Multiple
Classifier System (MCS) and certain data - dimension reduction
techniques. This work describes the performance of certain data
dimension reduction techniques like Principal Component Analysis
(PCA), Independent Component Analysis (ICA) and Self Organizing
Map (SOM) approximators for application with an MCS formed
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN). The data inputs are
obtained from an array of detectors placed in a circular arrangement
resembling a practical detector grid which have a higher dimension
and greater correlation among themselves. The PCA, ICA and SOM
blocks reduce the correlation and generate a form suitable for real
time practical applications for prediction of primary energy and
location of EAS from density values captured using detectors in a
circular grid.
Abstract: Both the minimum energy consumption and
smoothness, which is quantified as a function of jerk, are generally
needed in many dynamic systems such as the automobile and the
pick-and-place robot manipulator that handles fragile equipments.
Nevertheless, many researchers come up with either solely
concerning on the minimum energy consumption or minimum jerk
trajectory. This research paper proposes a simple yet very interesting
relationship between the minimum direct and indirect jerks
approaches in designing the time-dependent system yielding an
alternative optimal solution. Extremal solutions for the cost functions
of direct and indirect jerks are found using the dynamic optimization
methods together with the numerical approximation. This is to allow
us to simulate and compare visually and statistically the time history
of control inputs employed by minimum direct and indirect jerk
designs. By considering minimum indirect jerk problem, the
numerical solution becomes much easier and yields to the similar
results as minimum direct jerk problem.
Abstract: In this paper, we present a maintenance model of a
two-unit series system with economic dependence. Unit#1 which is
considered to be more expensive and more important, is subject to
condition monitoring (CM) at equidistant, discrete time epochs and
unit#2, which is not subject to CM has a general lifetime distribution.
The multivariate observation vectors obtained through condition
monitoring carry partial information about the hidden state of unit#1,
which can be in a healthy or a warning state while operating. Only the
failure state is assumed to be observable for both units. The objective
is to find an optimal opportunistic maintenance policy minimizing
the long-run expected average cost per unit time. The problem
is formulated and solved in the partially observable semi-Markov
decision process framework. An effective computational algorithm
for finding the optimal policy and the minimum average cost is
developed, illustrated by a numerical example.
Abstract: In this paper, we study the application of Extreme
Learning Machine (ELM) algorithm for single layered feedforward
neural networks to non-linear chaotic time series problems. In this
algorithm the input weights and the hidden layer bias are randomly
chosen. The ELM formulation leads to solving a system of linear
equations in terms of the unknown weights connecting the hidden
layer to the output layer. The solution of this general system of
linear equations will be obtained using Moore-Penrose generalized
pseudo inverse. For the study of the application of the method we
consider the time series generated by the Mackey Glass delay
differential equation with different time delays, Santa Fe A and
UCR heart beat rate ECG time series. For the choice of sigmoid,
sin and hardlim activation functions the optimal values for the
memory order and the number of hidden neurons which give the
best prediction performance in terms of root mean square error are
determined. It is observed that the results obtained are in close
agreement with the exact solution of the problems considered
which clearly shows that ELM is a very promising alternative
method for time series prediction.
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 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: 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: 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.