Abstract: A design flow of multi-standard down-conversion
CMOS mixers for three modern standards: Global System Mobile,
Digital Enhanced Cordless Telephone and Universal Mobile
Telecommunication Systems is presented. Three active mixer-s
structures are studied. The first is based on the Gilbert cell which
gives a tolerable noise figure and linearity with a low conversion
gain. The second and third structures use the current bleeding and
charge injection techniques in order to increase the conversion gain.
An improvement of about 2 dB of the conversion gain is achieved
without a considerable degradation of the other characteristics. The
models used for noise figure, conversion gain and IIP3 used are
studied. This study describes the nature of trade-offs inherent in such
structures and gives insights that help in identifying which structure
is better for given conditions.
Abstract: This work presents a numerical model developed to
simulate the dynamics and vibrations of a multistage tractor gearbox.
The effect of time varying mesh stiffness, time varying frictional
torque on the gear teeth, lateral and torsional flexibility of the shafts
and flexibility of the bearings were included in the model. The model
was developed by using the Lagrangian method, and it was applied to
study the effect of three design variables on the vibration and stress
levels on the gears. The first design variable, module, had little effect
on the vibration levels but a higher module resulted to higher bending
stress levels. The second design variable, pressure angle, had little
effect on the vibration levels, but had a strong effect on the stress
levels on the pinion of a high reduction ratio gear pair. A pressure
angle of 25o resulted to lower stress levels for a pinion with 14 teeth
than a pressure angle of 20o. The third design variable, contact ratio,
had a very strong effect on both the vibration levels and bending
stress levels. Increasing the contact ratio to 2.0 reduced both the
vibration levels and bending stress levels significantly. For the gear
train design used in this study, a module of 2.5 and contact ratio of
2.0 for the various meshes was found to yield the best combination
of low vibration levels and low bending stresses. The model can
therefore be used as a tool for obtaining the optimum gear design
parameters for a given multistage spur gear train.
Abstract: In this paper, genetic algorithm (GA) opmization technique is applied to design Flexible AC Transmission System (FACTS)-based damping controllers. Two types of controller structures, namely a proportional-integral (PI) and a lead-lag (LL) are considered. The design problem of the proposed controllers is formulated as an optimization problem and GA is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The proposed controllers are tested on a weakly connected power system subjected to different disturbances. The non-linear simulation results are presented to show the effectiveness of the proposed controller and their ability to provide efficient damping of low frequency oscillations. It is also observed that the proposed SSSC-based controllers improve greatly the voltage profile of the system under severe disturbances. Further, the dynamic performances of both the PI and LL structured FACTS-controller are analyzed at different loading conditions and under various disturbance condition as well as under unbalanced fault conditions..
Abstract: This paper mainly proposes an efficient modified
particle swarm optimization (MPSO) method, to identify a slidercrank
mechanism driven by a field-oriented PM synchronous motor.
In system identification, we adopt the MPSO method to find
parameters of the slider-crank mechanism. This new algorithm is
added with “distance" term in the traditional PSO-s fitness function to
avoid converging to a local optimum. It is found that the comparisons
of numerical simulations and experimental results prove that the
MPSO identification method for the slider-crank mechanism is
feasible.
Abstract: Group contribution methods such as the UNIFAC are
of major interest to researchers and engineers involved synthesis,
feasibility studies, design and optimization of separation processes as
well as other applications of industrial use. Reliable knowledge of
the phase equilibrium behavior is crucial for the prediction of the fate
of the chemical in the environment and other applications. The
objective of this study was to predict the solubility of selected
volatile organic compounds (VOCs) in glycol polymers and
biodiesel. Measurements can be expensive and time consuming,
hence the need for thermodynamic models. The results obtained in
this study for the infinite dilution activity coefficients compare very
well those published in literature obtained through measurements. It
is suggested that in preliminary design or feasibility studies of
absorption systems for the abatement of volatile organic compounds,
prediction procedures should be implemented while accurate fluid
phase equilibrium data should be obtained from experiment.
Abstract: In this study, single nozzle method used for
electrospinning technique which composite polymer solution with
cellulose nanowiskers (CNW) was treated by ultrasonic sonificator
have been compared with coaxial (double) nozzle method, in terms of
mechanical, thermal and morphological properties of composite
nanofiber. The effect of water content in composite polymer solution
on properties of nanofiber has also been examined. It has been seen
that single nozzle method which polymer solution does not contain
water has better results than that of coaxial method, in terms of
mechanical, thermal and morphological properties of nanofiber.
However, it is necessary to make an optimization study on setting
condition of ultrasonic treatment to get better dispersion of CNW in
composite nanofiber and to get better mechanical and thermal
properties
Abstract: This study discusses the effect of uncertainty on
production levels of a petrochemical complex. Uncertainly or
variations in some model parameters, such as prices, supply and
demand of materials, can affect the optimality or the efficiency of any
chemical process. For any petrochemical complex with many plants,
there are many sources of uncertainty and frequent variations which
require more attention. Many optimization approaches are proposed
in the literature to incorporate uncertainty within the model in order
to obtain a robust solution. In this work, a stability analysis approach
is applied to a deterministic LP model of a petrochemical complex
consists of ten plants to investigate the effect of such variations on
the obtained optimal production levels. The proposed approach can
determinate the allowable variation ranges of some parameters,
mainly objective or RHS coefficients, before the system lose its
optimality. Parameters with relatively narrow range of variations, i.e.
stability limits, are classified as sensitive parameters or constraints
that need accurate estimate or intensive monitoring. These stability
limits offer easy-to-use information to the decision maker and help in
understanding the interaction between some model parameters and
deciding when the system need to be re-optimize. The study shows
that maximum production of ethylene and the prices of intermediate
products are the most sensitive factors that affect the stability of the
optimum solution
Abstract: Most neural network (NN) models of human category learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to under predict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.
Abstract: This paper presents a comparative study of statistical methods for the multi-response surface optimization of a cryogenic freezing process. Taguchi design and analysis and steepest ascent methods based on the desirability function were conducted to ascertain the influential factors of a cryogenic freezing process and their optimal levels. The more preferable levels of the set point, exhaust fan speed, retention time and flow direction are set at -90oC, 20 Hz, 18 minutes and Counter Current, respectively. The overall desirability level is 0.7044.
Abstract: The paper addresses a problem of optimal staffing in
open shop environment. The problem is to determine the optimal
number of operators serving a given number of machines to fulfill the
number of independent operations while minimizing staff idle. Using
a Gantt chart presentation of the problem it is modeled as twodimensional
cutting stock problem. A mixed-integer programming
model is used to get minimal job processing time (makespan) for
fixed number of machines' operators. An algorithm for optimal openshop
staffing is developed based on iterative solving of the
formulated optimization task. The execution of the developed
algorithm provides optimal number of machines' operators in the
sense of minimum staff idle and optimal makespan for that number of
operators. The proposed algorithm is tested numerically for a real life
staffing problem. The testing results show the practical applicability
for similar open shop staffing problems.
Abstract: This paper presents a systematic approach for
designing Static Synchronous Series Compensator (SSSC) based
supplementary damping controllers for damping low frequency
oscillations in a single-machine infinite-bus power system. The
design problem of the proposed controller is formulated as an
optimization problem and RCGA is employed to search for optimal
controller parameters. By minimizing the time-domain based
objective function, in which the deviation in the oscillatory rotor
speed of the generator is involved; stability performance of the
system is improved. Simulation results are presented and compared
with a conventional method of tuning the damping controller
parameters to show the effectiveness and robustness of the proposed
design approach.
Abstract: In this paper, a new alignment method based on the particle swarm optimization (PSO) technique is presented. The PSO algorithm is used for locating the optimal coupling position with the highest optical power with three-degrees of freedom alignment. This algorithm gives an interesting results without a need to go thru the complex mathematical modeling of the alignment system. The proposed algorithm is validated considering practical tests considering the alignment of two Single Mode Fibers (SMF) and the alignment of SMF and PCF fibers.
Abstract: Clustering is one of an interesting data mining topics
that can be applied in many fields. Recently, the problem of cluster
analysis is formulated as a problem of nonsmooth, nonconvex optimization,
and an algorithm for solving the cluster analysis problem
based on nonsmooth optimization techniques is developed. This
optimization problem has a number of characteristics that make it
challenging: it has many local minimum, the optimization variables
can be either continuous or categorical, and there are no exact
analytical derivatives. In this study we show how to apply a particular
class of optimization methods known as pattern search methods
to address these challenges. These methods do not explicitly use
derivatives, an important feature that has not been addressed in
previous studies. Results of numerical experiments are presented
which demonstrate the effectiveness of the proposed method.
Abstract: Several optimization algorithms specifically applied to
the problem of Operation Planning of Hydrothermal Power Systems
have been developed and are used. Although providing solutions to
various problems encountered, these algorithms have some
weaknesses, difficulties in convergence, simplification of the original
formulation of the problem, or owing to the complexity of the
objective function. Thus, this paper presents the development of a
computational tool for solving optimization problem identified and to
provide the User an easy handling. Adopted as intelligent
optimization technique, Genetic Algorithms and programming
language Java. First made the modeling of the chromosomes, then
implemented the function assessment of the problem and the
operators involved, and finally the drafting of the graphical interfaces
for access to the User. The program has managed to relate a coherent
performance in problem resolution without the need for
simplification of the calculations together with the ease of
manipulating the parameters of simulation and visualization of output
results.
Abstract: The Resource-Constrained Project Scheduling
Problem (RCPSP) is concerned with single-item or small batch
production where limited resources have to be allocated to dependent
activities over time. Over the past few decades, a lot of work has
been made with the use of optimal solution procedures for this basic
problem type and its extensions. Brucker and Knust[1] discuss, how
timetabling problems can be modeled as a RCPSP. Authors discuss
high school timetabling and university course timetabling problem as
an example. We have formulated two mathematical formulations of
course timetabling problem in a new way which are the prototype of
single-mode RCPSP. Our focus is to show, how course timetabling
problem can be transformed into RCPSP. We solve this
transformation model with genetic algorithm.
Abstract: Supply chain consists of all stages involved, directly
or indirectly, includes all functions involved in fulfilling a customer
demand. In two stage transportation supply chain problem,
transportation costs are of a significant proportion of final product
costs. It is often crucial for successful decisions making approaches
in two stage supply chain to explicit account for non-linear
transportation costs. In this paper, deterministic demand and finite
supply of products was considered. The optimized distribution level
and the routing structure from the manufacturing plants to the
distribution centres and to the end customers is determined using
developed mathematical model and solved by proposed particle
swarm optimization based genetic algorithm. Numerical analysis of
the case study is carried out to validate the model.
Abstract: In intensity modulated radiation therapy (IMRT)
treatment planning, beam angles are usually preselected on the basis of
experience and intuition. Therefore, getting an appropriate beam
configuration needs a very long time. Based on the present situation,
the paper puts forward beam orientation optimization using ant colony
optimization (ACO). We use ant colony optimization to select the
beam configurations, after getting the beam configuration using
Conjugate Gradient (CG) algorithm to optimize the intensity profiles.
Combining with the information of the effect of pencil beam, we can
get the global optimal solution accelerating. In order to verify the
feasibility of the presented method, a simulated and clinical case was
tested, compared with dose-volume histogram and isodose line
between target area and organ at risk. The results showed that the
effect was improved after optimizing beam configurations. The
optimization approach could make treatment planning meet clinical
requirements more efficiently, so it had extensive application
perspective.
Abstract: Ant Colony Algorithms have been applied to difficult
combinatorial optimization problems such as the travelling salesman
problem and the quadratic assignment problem. In this paper gridbased
and random-based ant colony algorithms are proposed for
automatic 3D hose routing and their pros and cons are discussed. The
algorithm uses the tessellated format for the obstacles and the
generated hoses in order to detect collisions. The representation of
obstacles and hoses in the tessellated format greatly helps the
algorithm towards handling free-form objects and speeds up
computation. The performance of algorithm has been tested on a
number of 3D models.
Abstract: A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.
Abstract: This paper describes the optimization of a complex
dairy farm simulation model using two quite different methods of
optimization, the Genetic algorithm (GA) and the Lipschitz
Branch-and-Bound (LBB) algorithm. These techniques have been
used to improve an agricultural system model developed by Dexcel
Limited, New Zealand, which describes a detailed representation of
pastoral dairying scenarios and contains an 8-dimensional parameter
space. The model incorporates the sub-models of pasture growth and
animal metabolism, which are themselves complex in many cases.
Each evaluation of the objective function, a composite 'Farm
Performance Index (FPI)', requires simulation of at least a one-year
period of farm operation with a daily time-step, and is therefore
computationally expensive. The problem of visualization of the
objective function (response surface) in high-dimensional spaces is
also considered in the context of the farm optimization problem.
Adaptations of the sammon mapping and parallel coordinates
visualization are described which help visualize some important
properties of the model-s output topography. From this study, it is
found that GA requires fewer function evaluations in optimization
than the LBB algorithm.