Project Estimation and Scheduling Using Computational Intelligence

Author(s):  
Vikram Bali ◽  
Shivani Bali ◽  
Gaurav Singhania
2019 ◽  
pp. 57-63
Author(s):  
M. A. Artyukhova ◽  
S. N. Polesskiy

Human activity is often accompanied by exposure of ionizing radiation: the exploitation of space systems and power plants, research using isotopic sources, medicine. The development of electronic equipment is regulated by carrying out activities to ensure the required reliability and radiation resistance. However, the effect of ionizing radiation on reliability indicators is not taken into account properly, or is not taken into account at all, that sometimes leads to the loss of expensive equipment and even to human victims. The article discusses the methodology for carrying out an adequate estimate of the reliability considering the influence of external influencing factors, including ionizing radiation. The timeliness of decisions making to ensure the required reliability indicators is determined by the completeness of the reliability estimation at the design stage. Effort to ensure the reliability and durability of devices after the design stage is not economically viable. The completeness and adequacy of the estimation always depends on the interaction of specialists in different fields: designers, programmers, experts in the field of circuit design, electrical engineering and experts in the field of reliability and radiation resistance.


Engevista ◽  
2014 ◽  
Vol 17 (2) ◽  
pp. 152
Author(s):  
Radael De Souza Parolin ◽  
Pedro Paulo Gomes Watts Rodrigues ◽  
Antônio J. Silva Neto

The quality of a given water body can be assessed through the analysis of a number of indicators. Mathematical and computational models can be built to simulate the behavior of these indicators (observable variables), in such a way that different scenarios can be generated, supporting decisions regarding water resources management. In this study, the transport of a conservative contaminant in an estuarine environment is simulated in order to identify the position and intensity of the contaminant source. For this, it was formulated an inverse problem, which was solved through computational intelligence methods. This approach required adaptations to these methods, which had to be modified to relate the source position to the discrete mesh points of the domain. In this context, two adaptive techniques were developed. In one, the estimated points are projected to the grid points, and in the other, points are randomly selected in the iterative search spaces of the methods. The results showed that the methodology here developed has a strong potential in water bodies’ management and simulation.


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