scholarly journals 3D Simulation and Artificial Neural Networks Application in the Diffusion Study of Champignon Mushroom/Solution Interface During the Salting

2022 ◽  
Author(s):  
Mírian Bordin ◽  
Hágata Silva ◽  
Diego Galvan ◽  
Ana Mantovani ◽  
Karina Angilelli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Finite Elements ◽  
Surface Morphology ◽  
Film Formation ◽  
Direct Relation ◽  
Diffusion Study ◽  
Self Organizing Maps ◽  
Diffusion Behavior ◽  
Solution Interface

The influence of the film formed during the salting of champignon mushrooms with brine containing NaCl and KCl was modeled using the finite elements method (FEM). It was verified that the film formed on the mushroom surface had a greater influence in the static salting since the diffusion of the ions was 7.5-fold smaller in this system than in the stirred salting. The application of self-organizing maps showed that the ions diffusion along the surface of the solid presented a heterogeneous occurrence and depended on the region for both static and stirred salting. A direct relation was observed among the mushroom surface morphology, the salts diffusion behavior, and the film formation. In addition, the film was not completely extinguished in the stirred system, although it has a minimal influence as the film formation is also dependent on the biosolid surface.

Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks

2006 ◽  
Vol 514-516 ◽  
pp. 789-793 ◽  
Author(s):  
Rui de Oliveira ◽  
António Torres Marques
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Tensile Test ◽  
A Priori ◽  
The Self ◽  
A Priori Knowledge ◽  
Self Organizing Maps ◽  
Damage Mechanisms ◽  
Artificial Neural

In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.

scholarly journals Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

2015 ◽  
Vol 29 (2) ◽  
pp. 221-229 ◽  
Author(s):  
Izabela Świetlicka ◽  
Siemowit Muszyński ◽  
Agata Marzec
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Radial Basis Function ◽  
Water Activity ◽  
Basis Function ◽  
Acoustic Emission Signal ◽  
Self Organizing Maps ◽  
Emission Signal ◽  
Artificial Neural

Abstract The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

scholarly journals Investigation of trends in synoptic patterns over Europe with artificial neural networks

2010 ◽  
Vol 23 ◽  
pp. 107-112 ◽  
Author(s):  
S. Michaelides ◽  
F. Tymvios ◽  
D. Charalambous
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Frequency Distribution ◽  
Reanalysis Data ◽  
Self Organizing Maps ◽  
Synoptic Patterns ◽  
Weather Forecasts ◽  
The Neural Network ◽  
Artificial Neural

Abstract. The present study is a comprehensive application of a methodology developed for the classification of synoptic situations using artificial neural networks. In this respect, the 500 hPa geopotential height patterns at 12:00 UTC (Universal Time Coordinated) determined from the reanalysis data (ERA-40 dataset) of the European Centre for Medium range Weather Forecasts (ECMWF) over Europe were used. The dataset covers a period of 45 years (1957–2002) and the neural network methodology applied is the SOM architecture (Self Organizing Maps). The classification of the synoptic scale systems was conducted by considering 9, 18, 27 and 36 synoptic patterns. The statistical analysis of the frequency distribution of the classification results for the 36 clusters over the entire 44-year period revealed significant tendencies in the frequency distribution of certain clusters, thus substantiating a possible climatic change. In the following, the database was split into two periods, the "reference" period that includes the first 30 years and the "test" period comprising the remaining 14 years.

scholarly journals Estimation of critical force of the buckling composite structures using modelling methods

2019 ◽  
Vol 252 ◽  
pp. 03004 ◽  
Author(s):  
Karol Szklarek ◽  
Jakub Gajewski ◽  
David Valis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Finite Elements ◽  
Composite Structures ◽  
Fem Simulation ◽  
Critical Force ◽  
Stacking Sequence ◽  
Ann Model ◽  
Input Variables ◽  
Modelling Methods

The study reported in this paper employed Artificial Neural Networks (ANN) to predict the critical force of the buckling composite structures. The critical force depends upon various factors such as thickness, stacking sequence, etc. These factors have been identified in earlier studies by means of the Finite Elements Method (FEM). The critical force is affected by the above-mentioned factors. Various approaches have been applied in the course of the presented study. Apart from our FEM simulation, the ANN approach has been applied and the results were compared. The main contribution of these two approaches is the estimation of the critical force. The ANN model is trained to predict the critical force for different configurations of input variables.

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