Abstract: A suspension bridge is the most suitable type of structure for a long-span bridge due to rational use of structural materials. Increased deformability, which is conditioned by appearance of the elastic and kinematic displacements, is the major disadvantage of suspension bridges. The problem of increased kinematic displacements under the action of non-symmetrical load can be solved by prestressing. The prestressed suspension bridge with the span of 200 m was considered as an object of investigations. The cable truss with the cross web was considered as the main load carrying structure of the prestressed suspension bridge. The considered cable truss was optimized by 47 variable factors using Genetic algorithm and FEM program ANSYS. It was stated, that the maximum total displacements are reduced up to 29.9% by using of the cable truss with the rational characteristics instead of the single cable in the case of the worst situated load.
Abstract: The necessity of solving multi dimensional
complicated scientific problems beside the necessity of several
objective functions optimization are the most motive reason of born
of artificial intelligence and heuristic methods.
In this paper, we introduce a new method for multiobjective
optimization based on learning automata. In the proposed method,
search space divides into separate hyper-cubes and each cube is
considered as an action. After gathering of all objective functions
with separate weights, the cumulative function is considered as the
fitness function. By the application of all the cubes to the cumulative
function, we calculate the amount of amplification of each action and
the algorithm continues its way to find the best solutions. In this
Method, a lateral memory is used to gather the significant points of
each iteration of the algorithm. Finally, by considering the
domination factor, pareto front is estimated. Results of several
experiments show the effectiveness of this method in comparison
with genetic algorithm based method.
Abstract: This paper presents an approach for daily optimal operation of distribution networks considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. A genetic algorithm is used to solve the optimal operation problem. The approach is tested on an IEEE34 buses distribution feeder.
Abstract: This paper mainly studies the analyses of parameters
in the intersection collision avoidance (ICA) system based on the radar
sensors. The parameters include the positioning errors, the repeat
period of the radar sensor, the conditions of potential collisions of two
cross-path vehicles, etc. The analyses of the parameters can provide
the requirements, limitations, or specifications of this ICA system. In
these analyses, the positioning errors will be increased as the measured
vehicle approach the intersection. In addition, it is not necessary to
implement the radar sensor in higher position since the positioning
sensitivities become serious as the height of the radar sensor increases.
A concept of the safety buffer distances for front and rear of the
measured vehicle is also proposed. The conditions for potential
collisions of two cross-path vehicles are also presented to facilitate the
computation algorithm.
Abstract: Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.
Abstract: In this paper we present a hybrid search algorithm for
solving constraint satisfaction and optimization problems. This
algorithm combines ideas of two basic approaches: complete and
incomplete algorithms which also known as systematic search and
local search algorithms. Different characteristics of systematic search
and local search methods are complementary. Therefore we have
tried to get the advantages of both approaches in the presented
algorithm. The major advantage of presented algorithm is finding
partial sound solution for complicated problems which their complete
solution could not be found in a reasonable time. This algorithm
results are compared with other algorithms using the well known
n-queens problem.
Abstract: Mobile Ad hoc network consists of a set of mobile
nodes. It is a dynamic network which does not have fixed topology.
This network does not have any infrastructure or central
administration, hence it is called infrastructure-less network. The
change in topology makes the route from source to destination as
dynamic fixed and changes with respect to time. The nature of
network requires the algorithm to perform route discovery, maintain
route and detect failure along the path between two nodes [1]. This
paper presents the enhancements of ARA [2] to improve the
performance of routing algorithm. ARA [2] finds route between
nodes in mobile ad-hoc network. The algorithm is on-demand source
initiated routing algorithm. This is based on the principles of swarm
intelligence. The algorithm is adaptive, scalable and favors load
balancing. The improvements suggested in this paper are handling of
loss ants and resource reservation.
Abstract: This paper examines the implementation of RC5 block cipher for digital images along with its detailed security analysis. A complete specification for the method of application of the RC5 block cipher to digital images is given. The security analysis of RC5 block cipher for digital images against entropy attack, bruteforce, statistical, and differential attacks is explored from strict cryptographic viewpoint. Experiments and results verify and prove that RC5 block cipher is highly secure for real-time image encryption from cryptographic viewpoint. Thorough experimental tests are carried out with detailed analysis, demonstrating the high security of RC5 block cipher algorithm.
Abstract: In unsupervised segmentation context, we propose a bi-dimensional hidden Markov chain model (X,Y) that we adapt to the image segmentation problem. The bi-dimensional observed process Y = (Y 1, Y 2) is such that Y 1 represents the noisy image and Y 2 represents a noisy supplementary information on the image, for example a noisy proportion of pixels of the same type in a neighborhood of the current pixel. The proposed model can be seen as a competitive alternative to the Hilbert-Peano scan. We propose a bayesian algorithm to estimate parameters of the considered model. The performance of this algorithm is globally favorable, compared to the bi-dimensional EM algorithm through numerical and visual data.
Abstract: In this paper we present a computational model for pronominal anaphora resolution in Turkish. The model is based on Hobbs’ Naїve Algorithm [4, 5, 6], which exploits only the surface syntax of sentences in a given text.
Abstract: A new secure knapsack cryptosystem based on the
Merkle-Hellman public key cryptosystem will be proposed in this
paper. Although it is common sense that when the density is low, the
knapsack cryptosystem turns vulnerable to the low-density attack. The
density d of a secure knapsack cryptosystem must be larger than
0.9408 to avoid low-density attack. In this paper, we investigate a
new Permutation Combination Algorithm. By exploiting this
algorithm, we shall propose a novel knapsack public-key cryptosystem.
Our proposed scheme can enjoy a high density to avoid the
low-density attack. The density d can also exceed 0.9408 to avoid
the low-density attack.
Abstract: Most of fuzzy clustering algorithms have some
discrepancies, e.g. they are not able to detect clusters with convex
shapes, the number of the clusters should be a priori known, they
suffer from numerical problems, like sensitiveness to the
initialization, etc. This paper studies the synergistic combination of
the hierarchical and graph theoretic minimal spanning tree based
clustering algorithm with the partitional Gath-Geva fuzzy clustering
algorithm. The aim of this hybridization is to increase the robustness
and consistency of the clustering results and to decrease the number
of the heuristically defined parameters of these algorithms to
decrease the influence of the user on the clustering results. For the
analysis of the resulted fuzzy clusters a new fuzzy similarity measure
based tool has been presented. The calculated similarities of the
clusters can be used for the hierarchical clustering of the resulted
fuzzy clusters, which information is useful for cluster merging and
for the visualization of the clustering results. As the examples used
for the illustration of the operation of the new algorithm will show,
the proposed algorithm can detect clusters from data with arbitrary
shape and does not suffer from the numerical problems of the
classical Gath-Geva fuzzy clustering algorithm.
Abstract: In this paper, a robust statistics based filter to remove salt and pepper noise in digital images is presented. The function of the algorithm is to detect the corrupted pixels first since the impulse noise only affect certain pixels in the image and the remaining pixels are uncorrupted. The corrupted pixels are replaced by an estimated value using the proposed robust statistics based filter. The proposed method perform well in removing low to medium density impulse noise with detail preservation upto a noise density of 70% compared to standard median filter, weighted median filter, recursive weighted median filter, progressive switching median filter, signal dependent rank ordered mean filter, adaptive median filter and recently proposed decision based algorithm. The visual and quantitative results show the proposed algorithm outperforms in restoring the original image with superior preservation of edges and better suppression of impulse noise
Abstract: In this research, the laminar heat transfer of natural convection on vertical surfaces has been investigated. Most of the studies on natural convection have been considered constantly whereas velocity and temperature domain, do not change with time, transient one are used a lot. Governing equations are solved using a finite volume approach. The convective terms are discretized using the power-law scheme, whereas for diffusive terms the central difference is employed. Coupling between the velocity and pressure is made with SIMPLE algorithm. The resultant system of discretized linear algebraic equations is solved with an alternating direction implicit scheme. Then a configuration of rectangular fins is put in different ways on the surface and heat transfer of natural convection on these surfaces without sliding is studied and finally optimization is done.
Abstract: Hand gesture is an active area of research in the vision
community, mainly for the purpose of sign language recognition and
Human Computer Interaction. In this paper, we propose a system to
recognize alphabet characters (A-Z) and numbers (0-9) in real-time
from stereo color image sequences using Hidden Markov Models
(HMMs). Our system is based on three main stages; automatic segmentation
and preprocessing of the hand regions, feature extraction
and classification. In automatic segmentation and preprocessing stage,
color and 3D depth map are used to detect hands where the hand
trajectory will take place in further step using Mean-shift algorithm
and Kalman filter. In the feature extraction stage, 3D combined features
of location, orientation and velocity with respected to Cartesian
systems are used. And then, k-means clustering is employed for
HMMs codeword. The final stage so-called classification, Baum-
Welch algorithm is used to do a full train for HMMs parameters.
The gesture of alphabets and numbers is recognized using Left-Right
Banded model in conjunction with Viterbi algorithm. Experimental
results demonstrate that, our system can successfully recognize hand
gestures with 98.33% recognition rate.
Abstract: Numerous divergence measures (spectral distance, cepstral
distance, difference of the cepstral coefficients, Kullback-Leibler
divergence, distance given by the General Likelihood Ratio, distance
defined by the Recursive Bayesian Changepoint Detector and the
Mahalanobis measure) are compared in this study. The measures are
used for detection of abrupt spectral changes in synthetic AR signals
via the sliding window algorithm. Two experiments are performed;
the first is focused on detection of single boundary while the second
concentrates on detection of a couple of boundaries. Accuracy of
detection is judged for each method; the measures are compared
according to results of both experiments.
Abstract: The Continuously Adaptive Mean-Shift (CamShift)
algorithm, incorporating scene depth information is combined with
the l1-minimization sparse representation based method to form a
hybrid kernel and state space-based tracking algorithm. We take
advantage of the increased efficiency of the former with the
robustness to occlusion property of the latter. A simple interchange
scheme transfers control between algorithms based upon drift and
occlusion likelihood. It is quantified by the projection of target
candidates onto a depth map of the 2D scene obtained with a low cost
stereo vision webcam. Results are improved tracking in terms of drift
over each algorithm individually, in a challenging practical outdoor
multiple occlusion test case.
Abstract: Data compression is used operationally to reduce bandwidth and storage requirements. An efficient method for achieving lossless weather radar data compression is presented. The characteristics of the data are taken into account and the optical linear prediction is used for the PPI images in the weather radar data in the proposed method. The next PPI image is identical to the current one and a dramatic reduction in source entropy is achieved by using the prediction algorithm. Some lossless compression methods are used to compress the predicted data. Experimental results show that for the weather radar data, the method proposed in this paper outperforms the other methods.
Abstract: This paper presents a design method of self-tuning
Quantitative Feedback Theory (QFT) by using improved deadbeat
control algorithm. QFT is a technique to achieve robust control with
pre-defined specifications whereas deadbeat is an algorithm that
could bring the output to steady state with minimum step size.
Nevertheless, usually there are large peaks in the deadbeat response.
By integrating QFT specifications into deadbeat algorithm, the large
peaks could be tolerated. On the other hand, emerging QFT with
adaptive element will produce a robust controller with wider
coverage of uncertainty. By combining QFT-based deadbeat
algorithm and adaptive element, superior controller that is called selftuning
QFT-based deadbeat controller could be achieved. The output
response that is fast, robust and adaptive is expected. Using a grain
dryer plant model as a pilot case-study, the performance of the
proposed method has been evaluated and analyzed. Grain drying
process is very complex with highly nonlinear behaviour, long delay,
affected by environmental changes and affected by disturbances.
Performance comparisons have been performed between the
proposed self-tuning QFT-based deadbeat, standard QFT and
standard dead-beat controllers. The efficiency of the self-tuning QFTbased
dead-beat controller has been proven from the tests results in
terms of controller’s parameters are updated online, less percentage
of overshoot and settling time especially when there are variations in
the plant.
Abstract: This paper describes new computer vision algorithms
that have been developed to track moving objects as part of a
long-term study into the design of (semi-)autonomous vehicles. We
present the results of a study to exploit variable kernels for tracking in
video sequences. The basis of our work is the mean shift
object-tracking algorithm; for a moving target, it is usual to define a
rectangular target window in an initial frame, and then process the data
within that window to separate the tracked object from the background
by the mean shift segmentation algorithm. Rather than use the
standard, Epanechnikov kernel, we have used a kernel weighted by the
Chamfer distance transform to improve the accuracy of target
representation and localization, minimising the distance between the
two distributions in RGB color space using the Bhattacharyya
coefficient. Experimental results show the improved tracking
capability and versatility of the algorithm in comparison with results
using the standard kernel. These algorithms are incorporated as part of
a robot test-bed architecture which has been used to demonstrate their
effectiveness.