Using classification techniques for informal requirements in the requirements analysis-supporting system

2007 ◽  
Vol 49 (11-12) ◽  
pp. 1128-1140 ◽  
Author(s):  
Youngjoong Ko ◽  
Sooyong Park ◽  
Jungyun Seo ◽  
Soonhwang Choi
Author(s):  
Takashi Niwa ◽  
Kazuhiro Aoyama

Various process models have been proposed for system development, which represent all stages from requirements analysis to implementations and tests. However, these process models focus not on improvement of the model of the system itself but on the development of processes to adapt to the properties of the requirements in system development. First, we arranged the features of system development. Then, we modeled the requirements and a system, and propose here a design method by which the customer and designer can always check requirements by introducing not plural design methods using various conventional design model figures but a unified multiphase design method, even if the designer does not implement the system. The customer and designer can check the requirements using the animations of the scenarios described as requirements. Finally, we present an example of execution, which designs a system using the proposed model, and describe avenues for future work.


1950 ◽  
Author(s):  
Jay L. Otis ◽  
Milton H. Iredell ◽  
Theodore Kunin ◽  
William A. Gorham

2012 ◽  
Author(s):  
Alasdair Matthew Goodwill ◽  
Skye Stephens ◽  
Sandra Oziel ◽  
Nicola Bowes

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Solomon Roach

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying system’s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.


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