Dimensioning of an Electromagnetic Spindle with Interval Computation Technique

2020 ◽  
pp. 67-75
Author(s):  
A. Guizani ◽  
H. Trabelsi ◽  
M. Barkallah ◽  
M. Hammadi ◽  
J. Y. Choley ◽  
...  
Keyword(s):  
Interval Computation ◽  
Computation Technique

Study on erythrocytes by SEM photogrammetry (SEMP) technique

1990 ◽  
Vol 48 (3) ◽  
pp. 724-725
Author(s):  
Tong Wensheng ◽  
Lu Lianhuang ◽  
Zhang Zhijun
Keyword(s):  
Quantitative Analysis ◽  
Cell Membrane ◽  
Clinical Diagnosis ◽  
Human Body ◽  
Blood Cells ◽  
Three Dimensional ◽  
Normal Person ◽  
Blood Disease ◽  
Computation Technique ◽  
Important Basis

This is a combined study of two diffirent branches, photogrammetry and morphology of blood cells. The three dimensional quantitative analysis of erythrocytes using SEMP technique, electron computation technique and photogrammetry theory has made it possible to push the study of mophology of blood cells from LM, TEM, SEM to a higher stage, that of SEM P. A new path has been broken for deeply study of morphology of blood cells.In medical view, the abnormality of the quality and quantity of erythrocytes is one of the important changes of blood disease. It shows the abnormal blood—making function of the human body. Therefore, the study of the change of shape on erythrocytes is the indispensable and important basis of reference in the clinical diagnosis and research of blood disease.The erythrocytes of one normal person, three PNH Patients and one AA patient were used in this experiment. This research determines the following items: Height;Length of two axes (long and short), ratio; Crevice in depth and width of cell membrane; Circumference of erythrocytes; Isoline map of erythrocytes; Section map of erythrocytes.

New Package in Maxima for Single-Valued Interval Computation on Real Numbers

2011 ◽  
Author(s):  
Robert Ipanaque ◽  
Arnulfo Sandoval ◽  
Henry Giuseppe Sosa ◽  
Andres Iglesias Prieto
Keyword(s):  
Interval Computation ◽  
Real Numbers

scholarly journals Exploring genetic programming systems with MAP-Elites

2018 ◽  
Author(s):  
Emily Dolson ◽  
Alexander Lalejini ◽  
Charles Ofria
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Pareto Front ◽  
Underlying Structure ◽  
Phenotypic Traits ◽  
Computation Technique ◽  
Phenotype Space ◽  
Programming Systems ◽  
Insight Into ◽  
Connection Cost

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

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