Abstract: Application of flexible structures has been
significantly, increased in industry and aerospace missions due to
their contributions and unique advantages over the rigid counterparts.
In this paper, vibration analysis of a flexible structure i.e., automobile
wiper blade is investigated and controlled. The wiper generates
unwanted noise and vibration during the wiping the rain and other
particles on windshield which may cause annoying noise in different
ranges of frequency. A two dimensional analytical modeled wiper
blade whose model accuracy is verified by numerical studies in
literature is considered in this study. Particle swarm optimization
(PSO) is employed in alliance with input shaping (IS) technique in
order to control or to attenuate the amplitude level of unwanted
noise/vibration of the wiper blade.
Abstract: The recognition of human faces, especially those with
different orientations is a challenging and important problem in image
analysis and classification. This paper proposes an effective scheme
for rotation invariant face recognition using Log-Polar Transform and
Discrete Cosine Transform combined features. The rotation invariant
feature extraction for a given face image involves applying the logpolar
transform to eliminate the rotation effect and to produce a row
shifted log-polar image. The discrete cosine transform is then applied
to eliminate the row shift effect and to generate the low-dimensional
feature vector. A PSO-based feature selection algorithm is utilized to
search the feature vector space for the optimal feature subset.
Evolution is driven by a fitness function defined in terms of
maximizing the between-class separation (scatter index).
Experimental results, based on the ORL face database using testing
data sets for images with different orientations; show that the
proposed system outperforms other face recognition methods. The
overall recognition rate for the rotated test images being 97%,
demonstrating that the extracted feature vector is an effective rotation
invariant feature set with minimal set of selected features.
Abstract: In the last decades to supply the various and different
demands of clients, a lot of manufacturers trend to use the mixedmodel
assembly line (MMAL) in their production lines, since this
policy make possible to assemble various and different models of the
equivalent goods on the same line with the MTO approach.
In this article, we determine the sequence of (MMAL) line, with
applying the kitting approach and planning of rest time for general
workers to reduce the wastages, increase the workers effectiveness
and apply the sector of lean production approach.
This Multi-objective sequencing problem solved in small size with
GAMS22.2 and PSO meta heuristic in 10 test problems and compare
their results together and conclude that their results are very similar
together, next we determine the important factors in computing the
cost, which improving them cost reduced. Since this problem, is NPhard
in large size, we use the particle swarm optimization (PSO)
meta-heuristic for solving it. In large size we define some test
problems to survey it-s performance and determine the important
factors in calculating the cost, that by change or improved them
production in minimum cost will be possible.
Abstract: A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise
Abstract: Particle Swarm Optimization (PSO) with elite PSO
parameters has been developed for power flow analysis under
practical constrained situations. Multiple solutions of the power flow
problem are useful in voltage stability assessment of power system.
A method of determination of multiple power flow solutions is
presented using a hybrid of Particle Swarm Optimization (PSO) and
local search technique. The unique and innovative learning factors of
the PSO algorithm are formulated depending upon the node power
mismatch values to be highly adaptive with the power flow problems.
The local search is applied on the pbest solution obtained by the PSO
algorithm in each iteration. The proposed algorithm performs reliably
and provides multiple solutions when applied on standard and illconditioned
systems. The test results show that the performances of
the proposed algorithm under critical conditions are better than the
conventional methods.
Abstract: Fuzzy logic control (FLC) systems have been tested in
many technical and industrial applications as a useful modeling tool
that can handle the uncertainties and nonlinearities of modern control
systems. The main drawback of the FLC methodologies in the
industrial environment is challenging for selecting the number of
optimum tuning parameters.
In this paper, a method has been proposed for finding the optimum
membership functions of a fuzzy system using particle swarm
optimization (PSO) algorithm. A synthetic algorithm combined from
fuzzy logic control and PSO algorithm is used to design a controller
for a continuous stirred tank reactor (CSTR) with the aim of
achieving the accurate and acceptable desired results. To exhibit the
effectiveness of proposed algorithm, it is used to optimize the
Gaussian membership functions of the fuzzy model of a nonlinear
CSTR system as a case study. It is clearly proved that the optimized
membership functions (MFs) provided better performance than a
fuzzy model for the same system, when the MFs were heuristically
defined.