The Effect of Inducer Polarity and Contrast on the Perception of Illusory Figures

Perception ◽  
1997 ◽  
Vol 26 (11) ◽  
pp. 1431-1443 ◽  
Author(s):  
Nestor Matthews ◽  
Leslie Welch
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Form Perception ◽  
Illusory Figure ◽  
Contrast Level ◽  
Neural Network Models ◽  
Boundary Formation ◽  
Black And White ◽  
Mixed Polarity ◽  
Surface Lightness

A study designed to determine how inducer–surround contrast and inducer polarity affect the contour clarity and the lightness of illusory figures is reported. Using magnitude estimation procedures, ten naive subjects rated both the contour clarity and the lightness of Kanizsa squares. The magnitude of the inducer–surround contrast and the inducer polarity (all-black, all-white, or black-and-white) were varied randomly on each trial. The data indicate that contour clarity increases with contrast at the same rate across polarity conditions but that contour clarity at any given contrast level depends significantly on polarity. Contour clarity judgments were significantly lower when the inducers were all-white than when the inducers were all-black or black-and-white, and significantly greater in the ‘mixed’ polarity case (black-and-white inducers) than in the ‘same’ polarity case (the average of the all-black and all-white inducer conditions). Inducer contrast and polarity significantly affected the lightness of the illusory figure in a manner consistent with simultaneous spatial contrast. Also, for a given increment in contrast, contour clarity altered significantly more than surface lightness, regardless of inducer polarity. The findings suggest that the mechanism which mediates boundary formation is sensitive to the direction of contrast, and that the boundary formation mechanism is more sensitive than the surface lightness mechanism to changes in contrast magnitude. The results are considered within the context of neural network models of form perception.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

scholarly journals Epoch interrogation for skin cancer detection using convolutional neural network models

2021 ◽  
Vol 1074 (1) ◽  
pp. 012025
Author(s):  
A Poornima ◽  
M Shyamala Devi ◽  
M Sumithra ◽  
Mullaguri Venkata Bharath ◽  
Swathi ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Network Models ◽  
Neural Network Models ◽  
Skin Cancer Detection
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