Abstract: Many computational techniques were applied to
solution of heat conduction problem. Those techniques were the
finite difference (FD), finite element (FE) and recently meshless
methods. FE is commonly used in solution of equation of heat
conduction problem based on the summation of stiffness matrix of
elements and the solution of the final system of equations. Because
of summation process of finite element, convergence rate was
decreased. Hence in the present paper Cellular Automata (CA)
approach is presented for the solution of heat conduction problem.
Each cell considered as a fixed point in a regular grid lead to the
solution of a system of equations is substituted by discrete systems of
equations with small dimensions. Results show that CA can be used
for solution of heat conduction problem.
Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.
Abstract: The paper presents the modeling of nonlinear
longitudinal aerodynamics using flight data of Hansa-3 aircraft at
high angles of attack near stall. The Kirchhoff-s quasi-steady stall
model has been used to incorporate nonlinear aerodynamic effects in
the aerodynamic model used to estimate the parameters, thereby,
making the aerodynamic model nonlinear. The Maximum Likelihood
method has been applied to the flight data (at high angles of attack)
for the estimation of parameters (aerodynamic and stall
characteristics) using the nonlinear aerodynamic model. To improve
the accuracy level of the estimates, an approach of fixing the strong
parameters has also been presented.