Abstract: Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.
Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Abstract: Discretization of spatial derivatives is an important
issue in meshfree methods especially when the derivative terms
contain non-linear coefficients. In this paper, various methods used
for discretization of second-order spatial derivatives are investigated
in the context of Smoothed Particle Hydrodynamics. Three popular
forms (i.e. "double summation", "second-order kernel derivation",
and "difference scheme") are studied using one-dimensional unsteady
heat conduction equation. To assess these schemes, transient response
to a step function initial condition is considered. Due to parabolic
nature of the heat equation, one can expect smooth and monotone
solutions. It is shown, however in this paper, that regardless of
the type of kernel function used and the size of smoothing radius,
the double summation discretization form leads to non-physical
oscillations which persist in the solution. Also, results show that when
a second-order kernel derivative is used, a high-order kernel function
shall be employed in such a way that the distance of inflection
point from origin in the kernel function be less than the nearest
particle distance. Otherwise, solutions may exhibit oscillations near
discontinuities unlike the "difference scheme" which unconditionally
produces monotone results.
Abstract: In this paper we propose and examine an Adaptive
Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition
Autoregressive (STAR) modeling. Because STAR models follow
fuzzy logic approach, in the non-linear part fuzzy rules can be
incorporated or other training or computational methods can be
applied as the error backpropagation algorithm instead to nonlinear
squares. Furthermore, additional fuzzy membership functions can be
examined, beside the logistic and exponential, like the triangle,
Gaussian and Generalized Bell functions among others. We examine
two macroeconomic variables of US economy, the inflation rate and
the 6-monthly treasury bills interest rates.
Abstract: In this paper a comprehensive model of a fossil fueled
power plant (FFPP) is developed in order to evaluate the
performance of a newly designed turbine follower controller.
Considering the drawbacks of previous works, an overall model is
developed to minimize the error between each subsystem model
output and the experimental data obtained at the actual power plant.
The developed model is organized in two main subsystems namely;
Boiler and Turbine. Considering each FFPP subsystem
characteristics, different modeling approaches are developed. For
economizer, evaporator, superheater and reheater, first order models
are determined based on principles of mass and energy conservation.
Simulations verify the accuracy of the developed models. Due to the
nonlinear characteristics of attemperator, a new model, based on a
genetic-fuzzy systems utilizing Pittsburgh approach is developed
showing a promising performance vis-à-vis those derived with other
methods like ANFIS. The optimization constraints are handled
utilizing penalty functions. The effect of increasing the number of
rules and membership functions on the performance of the proposed
model is also studied and evaluated. The turbine model is developed
based on the equation of adiabatic expansion. Parameters of all
evaluated models are tuned by means of evolutionary algorithms.
Based on the developed model a fuzzy PI controller is developed. It
is then successfully implemented in the turbine follower control
strategy of the plant. In this control strategy instead of keeping
control parameters constant, they are adjusted on-line with regard to
the error and the error rate. It is shown that the response of the
system improves significantly. It is also shown that fuel consumption
decreases considerably.
Abstract: Aerospace vehicles are subjected to non-uniform
thermal loading that may cause thermal buckling. A study was
conducted on the thermal post-buckling of shape memory alloy
composite plates subjected to the non-uniform tent-like temperature
field. The shape memory alloy wires were embedded within the
laminated composite plates to add recovery stress to the plates. The
non-linear finite element model that considered the recovery stress of
the shape memory alloy and temperature dependent properties of the
shape memory alloy and composite matrix along with its source
codes were developed. It was found that the post-buckling paths of
the shape memory alloy composite plates subjected to various tentlike
temperature fields were stable within the studied temperature
range. The addition of shape memory alloy wires to the composite
plates was found to significantly improve the post-buckling behavior
of laminated composite plates under non-uniform temperature
distribution.
Abstract: By systematically applying different engineering
methods, difficult financial problems become approachable. Using a
combination of theory and techniques such as wavelet transform,
time series data mining, Markov chain based discrete stochastic
optimization, and evolutionary algorithms, this work formulated a
strategy to characterize and forecast non-linear time series. It
attempted to extract typical features from the volatility data sets of
S&P100 and S&P500 indices that include abrupt drops, jumps and
other non-linearity. As a result, accuracy of forecasting has reached
an average of over 75% surpassing any other publicly available
results on the forecast of any financial index.
Abstract: Protein-protein interactions (PPI) play a crucial role in many biological processes such as cell signalling, transcription, translation, replication, signal transduction, and drug targeting, etc. Structural information about protein-protein interaction is essential for understanding the molecular mechanisms of these processes. Structures of protein-protein complexes are still difficult to obtain by biophysical methods such as NMR and X-ray crystallography, and therefore protein-protein docking computation is considered an important approach for understanding protein-protein interactions. However, reliable prediction of the protein-protein complexes is still under way. In the past decades, several grid-based docking algorithms based on the Katchalski-Katzir scoring scheme were developed, e.g., FTDock, ZDOCK, HADDOCK, RosettaDock, HEX, etc. However, the success rate of protein-protein docking prediction is still far from ideal. In this work, we first propose a more practical measure for evaluating the success of protein-protein docking predictions,the rate of first success (RFS), which is similar to the concept of mean first passage time (MFPT). Accordingly, we have assessed the ZDOCK bound and unbound benchmarks 2.0 and 3.0. We also createda new benchmark set for protein-protein docking predictions, in which the complexes have experimentally determined binding affinity data. We performed free energy calculation based on the solution of non-linear Poisson-Boltzmann equation (nlPBE) to improve the binding mode prediction. We used the well-studied thebarnase-barstarsystem to validate the parameters for free energy calculations. Besides,thenlPBE-based free energy calculations were conducted for the badly predicted cases by ZDOCK and ZRANK. We found that direct molecular mechanics energetics cannot be used to discriminate the native binding pose from the decoys.Our results indicate that nlPBE-based calculations appeared to be one of the promising approaches for improving the success rate of binding pose predictions.
Abstract: In Blind Source Separation (BSS) processing, taking
advantage of scaling factor indetermination and based on the floatingpoint
representation, we propose a scaling technique applied to the
separation matrix, to avoid the saturation or the weakness in the
recovered source signals. This technique performs an Automatic Gain
Control (AGC) in an on-line BSS environment. We demonstrate
the effectiveness of this technique by using the implementation of
a division free BSS algorithm with two input, two output. This
technique is computationally cheaper and efficient for a hardware
implementation.
Abstract: Leprosy is an infectious disease caused by
Mycobacterium Leprae, this disease, generally, compromises
the neural fibers, leading to the development of disability.
Disabilities are changes that limit daily activities or social life
of a normal individual. When comes to leprosy, the study of
disability considered the functional limitation (physical
disabilities), the limitation of activity and social participation,
which are measured respectively by the scales: EHF, SALSA
and PARTICIPATION SCALE. The objective of this work is
to propose an on-line monitoring of leprosy patients, which is
based on information scales EHF, SALSA and
PARTICIPATION SCALE. It is expected that the proposed
system is applied in monitoring the patient during treatment
and after healing therapy of the disease. The correlations that
the system is between the scales create a variety of
information, presented the state of the patient and full of
changes or reductions in disability. The system provides
reports with information from each of the scales and the
relationships that exist between them. This way, health
professionals, with access to patient information, can
intervene with techniques for the Prevention of Disability.
Through the automated scale, the system shows the level of
the patient and allows the patient, or the responsible, to take a
preventive measure. With an online system, it is possible take
the assessments and monitor patients from anywhere.
Abstract: Occurrences of spurious crests on the troughs of large,
relatively steep second-order Stokes waves are anomalous and not an
inherent characteristic of real waves. Here, the effects of such
occurrences on the statistics described by the standard second-order
stochastic model are examined theoretically and by way of
simulations. Theoretical results and simulations indicate that when
spurious occurrences are sufficiently large, the standard model leads
to physically unrealistic surface features and inaccuracies in the
statistics of various surface features, in particular, the troughs and
thus zero-crossing heights of large waves. Whereas inaccuracies can
be fairly noticeable for long-crested waves in both deep and
shallower depths, they tend to become relatively insignificant in
directional waves.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: This work addresses the problem of optimizing
completely batch water-using network with multiple contaminants
where the flow change caused by mass transfer is taken into
consideration for the first time. A mathematical technique for
optimizing water-using network is proposed based on
source-tank-sink superstructure. The task is to obtain the freshwater
usage, recycle assignments among water-using units, wastewater
discharge and a steady water-using network configuration by
following steps. Firstly, operating sequences of water-using units are
determined by time constraints. Next, superstructure is simplified by
eliminating the reuse and recycle from water-using units with
maximum concentration of key contaminants. Then, the non-linear
programming model is solved by GAMS (General Algebra Model
System) for minimum freshwater usage, maximum water recycle and
minimum wastewater discharge. Finally, numbers of operating periods
are calculated to acquire the steady network configuration. A case
study is solved to illustrate the applicability of the proposed approach.
Abstract: The motion of a sphere moving along the axis of a
rotating viscous fluid is studied at high Reynolds numbers and
moderate values of Taylor number. The Higher Order Compact
Scheme is used to solve the governing Navier-Stokes equations. The
equations are written in the form of Stream function, Vorticity
function and angular velocity which are highly non-linear, coupled
and elliptic partial differential equations. The flow is governed by
two parameters Reynolds number (Re) and Taylor number (T). For
very low values of Re and T, the results agree with the available
experimental and theoretical results in the literature. The results are
obtained at higher values of Re and moderate values of T and
compared with the experimental results. The results are fourth order
accurate.
Abstract: Iran is one of the greatest producers of date in the
world. However due to lack of information about its viscoelastic
properties, much of the production downgraded during harvesting
and postharvesting processes. In this study the effect of temperature
and moisture content of product were investigated on stress
relaxation characteristics. Therefore, the freshly harvested date
(kabkab) at tamar stage were put in controlled environment chamber
to obtain different temperature levels (25, 35, 45, and 55 0C) and
moisture contents (8.5, 8.7, 9.2, 15.3, 20, 32.2 %d.b.). A texture
analyzer TAXT2 (Stable Microsystems, UK) was used to apply
uniaxial compression tests. A chamber capable to control temperature
was designed and fabricated around the plunger of texture analyzer to
control the temperature during the experiment. As a new approach a
CCD camera (A4tech, 30 fps) was mounted on a cylindrical glass
probe to scan and record contact area between date and disk.
Afterwards, pictures were analyzed using image processing toolbox
of Matlab software. Individual date fruit was uniaxially compressed
at speed of 1 mm/s. The constant strain of 30% of thickness of date
was applied to the horizontally oriented fruit. To select a suitable
model for describing stress relaxation of date, experimental data were
fitted with three famous stress relaxation models including the
generalized Maxwell, Nussinovitch, and Pelege. The constant in
mentioned model were determined and correlated with temperature
and moisture content of product using non-linear regression analysis.
It was found that Generalized Maxwell and Nussinovitch models
appropriately describe viscoelastic characteristics of date fruits as
compared to Peleg mode.
Abstract: The protection issues in distribution systems with open and closed-loop are studied, and a generalized protection setting scheme based on the traditional over current protection theories is proposed to meet the new requirements. The setting method is expected to be easier realized using computer program, so that the on-line adaptive setting for coordination in distribution system can be implemented. An automatic setting program is created and several cases are taken into practice. The setting results are verified by the coordination curves of the protective devices which are plotted using MATLAB.
Abstract: In 2011, Debiao et al. pointed out that S-3PAKE protocol proposed by Lu and Cao for password-authenticated key exchange in the three-party setting is vulnerable to an off-line dictionary attack. Then, they proposed some countermeasures to eliminate the security vulnerability of the S-3PAKE. Nevertheless, this paper points out their enhanced S-3PAKE protocol is still vulnerable to undetectable on-line dictionary attacks unlike their claim.
Abstract: In this paper a sliding-mode torque and flux control is
designed for encoderless synchronous reluctance motor drive. The
sliding-mode plus PI controllers are designed in the stator-flux field
oriented reference frame which is able to track the mentioned
reference signals with a minimum pulsations in the state condition. In
addition, with these controllers a fast dynamic response is also
achieved for the drive system. The proposed control scheme is robust
subject to parameters variation except to stator resistance. To solve
this problem a simple estimator is used for on-line detecting of this
parameter. Moreover, the rotor position and speed are estimated by
on-line obtaining of the stator-flux-space vector. The effectiveness
and capability of the proposed control approach is verified by both
the simulation and experimental results.
Abstract: Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for Thyristor Controlled Series Compensator (TCSC)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a TCSC-based controller, to enhance power system stability.
Abstract: This paper presents a new function expansion method for finding traveling wave solution of a non-linear equation and calls it the (G'/G)-expansion method. The shallow water wave equation is reduced to a non linear ordinary differential equation by using a simple transformation. As a result the traveling wave solutions of shallow water wave equation are expressed in three forms: hyperbolic solutions, trigonometric solutions and rational solutions.