Earticle

Agile development is truly the need of the hour due to its numerous advantages which are in line with the present business trends. A successful requirement engineering serves as a foundation for success for any software development project. Functional requirements point towards the product services and non-functional requirements are related to the emergent properties of the system. Correct and speedy elicitation of functional and non-functional requirements contribute a great deal towards successful requirement engineering process. Many techniques have been proposed in the past for requirement elicitation for agile development, but they do not take into consideration a holistic automatic approach concerning functional and non-functional requirements. This paper proposes a supervised learning based automated (neural network with the genetic algorithm) approach for successfully classifying functional and non-functional requirements from multiple requirements documents in an agile environment. It is implemented on two data sets and further analysis, and comparison of this model is made with an another implemented model (SVM with RBF kernel) based on precision, recall, and accuracy. This paper contributes in simplifying and automating the requirement engineering process; thus, making the life easier for many stakeholders.

In this paper, the stabilization of an inverted pendulum system is studied. Here, the PID control method is adopted to make the system stable. In order to adjust the PID gains, a three-layer neural network, which is based on the back propagation method, is used. Meanwhile, the time for training the neural network depends on the initial values of PID gains and connection weights. Hence, the genetic algorithm Is considered to shorten the time to find the desired values. Simulation results show the effectiveness of the proposed approach.

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

This paper presents the optimization of expanding velocity for tube expanding process in the manufacturing of a heat exchanger. In specific, the expanding velocity has a great influence on the performance of a heat exchanger because it is a key variable determining the quantity of tube expending at assembly stage as well as a key Parameter determining overall production rate. The simulation showed that the genetic algorithm used in this paper resulted in the optimal tube expanding velocity by performing the following series of iteration; the generation of arbitrary population for tube expanding parameters, consequently the generation of tube expanding velocities, the evaluation of tube expanding quantity using the pre-trained data of plastic deformation by means of a neural network and finally the generation of next population using a penalty faction and a Roulette wheel method.

The field of sheet metal forming using press technology has become essential in modern mass production systems. Draw bead is often used to enhance formability. However, optimal draw bead design often requires excessive time and cost due to iterative experimentation and sometimes results in some defects. Given these challenges, there is a need to enhance formability by introducing segmented blank holders without draw beads. In this paper, the feasibility of a localized holding strategy using segmented blank holders is evaluated without the use of draw beads. The possibility for improving the formability was evaluated by utilizing a combination of the forming limit diagram and the wrinkle pattern-based defect indicators. Artificial neural networks were used for predicting defect indicators corresponding to arbitrary input holding forces and the NSGA-II optimization algorithm is used to find optimum blank holder forces yielding better defect indicators than the original process with drawbeads. Using optimum holding forces obtained from the proposed procedure, the stamping process with the segmented blank holders can yield better formability than the conventional process with drawbeads.