Abstract: Game theory could be used to analyze the conflicted
issues in the field of information hiding. In this paper, 2-phase game
can be used to build the embedder-attacker system to analyze the
limits of hiding capacity of embedding algorithms: the embedder
minimizes the expected damage and the attacker maximizes it. In the
system, the embedder first consumes its resource to build embedded
units (EU) and insert the secret information into EU. Then the attacker
distributes its resource evenly to the attacked EU. The expected
equilibrium damage, which is maximum damage in value from the
point of view of the attacker and minimum from the embedder against
the attacker, is evaluated by the case when the attacker attacks a
subset from all the EU. Furthermore, the optimal equilibrium capacity
of hiding information is calculated through the optimal number of EU
with the embedded secret information. Finally, illustrative examples
of the optimal equilibrium capacity are presented.
Abstract: This paper describes the pipeline architecture of
high-speed modified Booth multipliers. The proposed multiplier
circuits are based on the modified Booth algorithm and the pipeline
technique which are the most widely used to accelerate the
multiplication speed. In order to implement the optimally pipelined
multipliers, many kinds of experiments have been conducted. The
speed of the multipliers is greatly improved by properly deciding the
number of pipeline stages and the positions for the pipeline registers to
be inserted. We described the proposed modified Booth multiplier
circuits in Verilog HDL and synthesized the gate-level circuits using
0.13um standard cell library. The resultant multiplier circuits show
better performance than others. Since the proposed multipliers operate
at GHz ranges, they can be used in the systems requiring very high
performance.
Abstract: To improve the efficiency of parametric studies or
tests planning the method is proposed, that takes into account all input parameters, but only a few simulation runs are performed to
assess the relative importance of each input parameter. For K input
parameters with N input values the total number of possible combinations of input values equals NK. To limit the number of runs,
only some (totally N) of possible combinations are taken into account. The sampling procedure Updated Latin Hypercube
Sampling is used to choose the optimal combinations. To measure the
relative importance of each input parameter, the Spearman rank
correlation coefficient is proposed. The sensitivity and the influence
of all parameters are analyzed within one procedure and the key
parameters with the largest influence are immediately identified.
Abstract: An optimal control of Reverse Osmosis (RO) plant is
studied in this paper utilizing the auto tuning concept in conjunction
with PID controller. A control scheme composing an auto tuning
stochastic technique based on an improved Genetic Algorithm (GA) is
proposed. For better evaluation of the process in GA, objective
function defined newly in sense of root mean square error has been
used. Also in order to achieve better performance of GA, more
pureness and longer period of random number generation in operation
are sought. The main improvement is made by replacing the uniform
distribution random number generator in conventional GA technique
to newly designed hybrid random generator composed of Cauchy
distribution and linear congruential generator, which provides
independent and different random numbers at each individual steps in
Genetic operation. The performance of newly proposed GA tuned
controller is compared with those of conventional ones via simulation.
Abstract: Early Intervention Program (EIP) is required to
improve the overall development of children with Trisomy 21 (Down
syndrome). In order to help trainer and parent in the implementation
of EIP, a support system has been developed. The support system is
able to screen data automatically, store and analyze data, generate
individual EIP (curriculum) with optimal training duration and to
generate training automatically. The system consists of hardware and
software where the software has been implemented using Java
language and Linux Fedora. The software has been tested to ensure the
functionality and reliability. The prototype has been also tested in
Down syndrome centers. Test result shows that the system is reliable
to be used for generation of an individual curriculum which includes
the training program to improve the motor, cognitive, and combination
abilities of Down syndrome children under 6 years.
Abstract: In this paper we propose a new approach to constructing the Delaunay Triangulation and the optimum algorithm for the case of multidimensional spaces (d ≥ 2). Analysing the modern state, it is possible to draw a conclusion, that the ideas for the existing effective algorithms developed for the case of d ≥ 2 are not simple to generalize on a multidimensional case, without the loss of efficiency. We offer for the solving this problem an effective algorithm that satisfies all the given requirements. But theoretical complexity of the problem it is impossible to improve as the Worst - Case Optimality for algorithms of solving such a problem is proved.
Abstract: Kojic acid is an organic acid that is widely used as an ingredient for dermatological products, precursor for flavor enhancer and also as anti-inflammatory drug. The present study was undertaken to test the feasibility of pineapple residues as substrate for kojic acid production by Aspergillus flavus Link 44-1 via solid-state fermentation. The effect of initial moisture content, pH and incubation time on kojic acid fermentation was investigated. The best initial moisture content for kojic acid production from pineapple residues was observed at 70% (v/w) whereas initial culture pH 2.5 was identified to give high production of kojic acid. The optimal range of incubation time was identified between 8 and 14 days of incubation which corresponded to highest range of kojic acid produced. The results from this study pronounce the promising usability of pineapple residues as alternative substrate for kojic acid production by A. flavus Link 44-1.
Abstract: Extensive research has been devoted to economic
production quantity (EPQ) problem. However, no attention has been
paid to problems where production period length is constrained. In
this paper, we address the problem of deciding the optimal
production quantity and the number of minor setups within each
cycle, in which, production period length is constrained but a minor
setup is possible for pass the constraint. A mathematical model is
developed and Iterated Local Search (ILS) is proposed to solve this
problem. Finally, solution procedure illustrated with a numerical
example and results are analyzed.
Abstract: Re-entrant scheduling is an important search problem
with many constraints in the flow shop. In the literature, a number of
approaches have been investigated from exact methods to
meta-heuristics. This paper presents a genetic algorithm that encodes
the problem as multi-level chromosomes to reflect the dependent
relationship of the re-entrant possibility and resource consumption.
The novel encoding way conserves the intact information of the data
and fastens the convergence to the near optimal solutions. To test the
effectiveness of the method, it has been applied to the
resource-constrained re-entrant flow shop scheduling problem.
Computational results show that the proposed GA performs better than
the simulated annealing algorithm in the measure of the makespan
Abstract: Due to the excess of a vehicle operation through its life, some elements may face failure and deteriorate with time. This leads us to carry out maintenance, repair, tune up or full overhaul. After a certain period, the vehicle elements deteriorations increase with time which causes a very high increase of doing the maintenance operations and their costs. However, the logic decision at this point is to replace the current vehicle by a new one with minimum failure and maximum income. The importance of studying vehicle replacement problems come from the increase of stopping days due to many deteriorations in the vehicle parts. These deteriorations increase year after year causing an increase of operating costs and decrease the vehicle income. Vehicle replacement aims to determine the optimum time to keep, maintain, overhaul, renew and replace vehicles. This leads to an improvement in vehicle income, total operating costs, maintenance cost, fuel and oil costs, ton-kilometers, vehicle and engine performance, vehicle noise, vibration, and pollution. The aim of this paper is to find the optimum replacement policies of Kuwait Passenger Transport Company (KPTCP) fleet of busses. The objective of these policies is to maximize the busses pure profits. The dynamic programming (D.P.) technique is used to generate the busses optimal replacement policies
Abstract: In this paper an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.
Abstract: This Paper presents a particle swarm optimization (PSO) method for determining the optimal proportional-integral-derivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The brushless DC motor is modelled in Simulink and the PSO algorithm is implemented in MATLAB. Comparing with Genetic Algorithm (GA) and Linear quadratic regulator (LQR) method, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a linear brushless DC motor.
Abstract: A considerable amount of energy is consumed during
transmission and reception of messages in a wireless mesh network
(WMN). Reducing per-node transmission power would greatly
increase the network lifetime via power conservation in addition to
increasing the network capacity via better spatial bandwidth reuse. In
this work, the problem of topology control in a hybrid WMN of
heterogeneous wireless devices with varying maximum transmission
ranges is considered. A localized distributed topology control
algorithm is presented which calculates the optimal transmission
power so that (1) network connectivity is maintained (2) node
transmission power is reduced to cover only the nearest neighbours
(3) networks lifetime is extended. Simulations and analysis of results
are carried out in the NS-2 environment to demonstrate the
correctness and effectiveness of the proposed algorithm.
Abstract: Asphalt surfaces are exposed to various weather
conditions and dynamic loading caused by passing trucks and
vehicles. In such situations, asphalt cement shows so different
rheological-mechanical behavior. If asphalt cement isn-t compatible
enough, asphalt layer will be damaged immediately and expensive
repairing procedures should be performed then. To overcome this
problem, researchers study on mechanical improved asphalt cement.
In this study, bentonite was used in order to modify bitumen
characteristics and the modified bitumen's characteristics were
investigated by asphalt cement tests. Then, the optimal bitumen
content in various compounds was determined and asphalt samples
with different contents of additives were prepared and tested. Results
show using this kind of additive not only has caused improvement in
bitumen mechanical properties, but also improvement in Marshall
Parameters was achieved.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: This article deals with the numerical simulation of the
floor heating convector in 3D. Presented convector can operate in
two modes – cooling mode and heating mode. This initial numerical
simulation is focused on cooling mode of the convector. Models with
different temperature of the fins are compared and three various
shapes of the fins are examined as well. The objective of the work is
to predict air flow and heat transfer inside convector for further
optimalization of these devices. For the numerical simulation was
used commercial software Ansys Fluent.
Abstract: Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.
Abstract: In this paper, we propose a fuzzy aggregate
production planning (APP) model for blending problem in a brass
factory which is the problem of computing optimal amounts of raw
materials for the total production of several types of brass in a
period. The model has deterministic and imprecise parameters
which follows triangular possibility distributions. The brass casting
APP model can not always be solved by using common approaches
used in the literature. Therefore a mathematical model is presented
for solving this problem. In the proposed model, the Lai and
Hwang-s fuzzy ranking concept is relaxed by using one constraint
instead of three constraints. An application of the brass casting
APP model in a brass factory shows that the proposed model
successfully solves the multi-blend problem in casting process and
determines the optimal raw material purchasing policies.
Abstract: An alternative approach to the use of Discrete Fourier
Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction
is the use of parametric modeling technique. This method
is suitable for problems in which the image can be modeled by
explicit known source functions with a few adjustable parameters.
Despite the success reported in the use of modeling technique as an
alternative MRI reconstruction technique, two important problems
constitutes challenges to the applicability of this method, these are
estimation of Model order and model coefficient determination. In
this paper, five of the suggested method of evaluating the model
order have been evaluated, these are: The Final Prediction Error
(FPE), Akaike Information Criterion (AIC), Residual Variance (RV),
Minimum Description Length (MDL) and Hannan and Quinn (HNQ)
criterion. These criteria were evaluated on MRI data sets based on the
method of Transient Error Reconstruction Algorithm (TERA). The
result for each criterion is compared to result obtained by the use of a
fixed order technique and three measures of similarity were evaluated.
Result obtained shows that the use of MDL gives the highest measure
of similarity to that use by a fixed order technique.