Maximization of muffler performance is important, but there is always space volume constraints.
Shape optimization of multi-segments Muffler coupled with the GA searching technique.
Derivation of Four Pole Matrices and an expression for STL
Introduction to GA and it's Implementation
A numerical case of noise elimination on pure tone
Results and Discussion
Testing is both technically and economically an important part of high quality software production. It has been estimated that testing accounts for half of the expenses in software production. Much of the testing is done manually or using other labor-intensive methods. It is thus vital for the software industry to develop efficient, cost effective, and automatic means and tools for software testing. Researchers have proposed several methods over years to generate automatically solution which have different drawbacks. This study examines automatic software testing optimization by using genetic algorithm approaches. This study will cover two approaches: a) obtain the sequence of regression tests that cover the greatest amount of code and b) once it is achieved another genetic algorithm will eliminate tests cases that cover the same section of code on the basis of still get the maximum code coverage. The overall aim of this research is to reduce the number of test cases that need to be run with the greatest amount of code covered.
By adapting a previously written percolation model in C, the threshold probabilities for square, triangular, and cubic lattice types were confirmed. An algorithm to count the distribution of cluster sizes at a variety of percolation probabilities was developed, and the expected trends towards the so called infinite cluster was achieved. An equivalent bond percolation model was adapted to the original site algorithm, and by treating occupied bonds as springs, a total compression trend for the model was constructed, which implied that structures under the boundary conditions that were imposed does not have behavior that changes the total compression constant significantly at the percolation threshold.\\
Pursuit and evasion tends to be incorporated in human nature from a very long time with a very huge range of activities. Here, we are going to create an intelligent chase algorithm, which uses two basic approaches. After which we will use the newly created equations to simulate both approaches and provide graphical results. This analysis is based upon the fact that in modern days we can estimate the speed of a moving object and then chase it down depending upon its speed. We will also be taking the accuracy of such estimation in picture and depending upon a certain accuracy and other inputs the simulation will provide the result and performance of both the approaches.