The work concerns the study of the possibility of using an artificial neural network to determine the gas pressure or liquid, in the flow system. The basis for determining the pressure is the view of the membrane, which is obtained discreetly from the vision sensor. The essence of the method operation consists of associating the fuzzy image of the marker placed on the membrane with the corresponding reference pressure value, which in the network learning process, is read from the standard pressure gauge. The test used a device allowing the measuring of gas pressure with an accuracy no lower than 2%. The operation of the artificial neural network is based on identifying the degree of blurring the marker on the examined views of the membranes and associating them with the pressure values. In the case when the membrane views cannot be uniquely qualified for the training set, the network acts as an interpolator and predicts the pressure value.
The paper investigates the influence of selection and operators of evolutionary strategy on the reconstruction of the shape of the pneumatic membrane surface of the extracorporeal cardiac support pump. Sets consisting of selection, mutation and crossing were assessed. The study was conducted in the context of optimizing the distribution of markers on the surface of the flaccid membrane. The arrangement of the markers is important from the point of view of modeling the shape of the membrane surface and ultimately determining the stroke volume. The experiments were carried out for a convex membrane with a known mathematical description. The value of the error of mapping the determined shape of the membrane with respect to the shape of the reference surface was assumed as the criterion of the assessment.
The publication concerns the reconstruction of the flaccid membrane surface shape based on information in an image obtained from a camera. The article includes results of the research, which aimed at optimizing the position of markers located on the surface of the flaccid membrane. The experiment used a membrane used in a model of an extracorporeal pneumatic heart assist pump. It was expected that the optimization of the position of the markers would increase the accuracy of modeling the shape of the membrane surface. The basis for modeling is the knowledge of the position of markers located in the R3 space. The coordinates of the markers were determined using a visual technique with the help of a camera. Coordinates determined in such a way were subjected to interpolation in three-dimensional space, and then were oversampled. The result is a grid representing the shape of the surface of the flaccid membrane. Evolutionary strategy was used to optimize the position of the markers. For this optimization, a unique design, selection method, a stopping condition method and an assessment function were proposed. The study was carried out for a convex membrane with a known mathematical description. Due to this, it was possible to determine the mapping error of the obtained membrane surface shape in relation to the shape of the reference surface (model), determined from a formula.
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