scholarly journals Modeling and Simulation of Hexapod Kinematics with Central Pattern Generator

2016 ◽  
Vol 5 (2) ◽  
pp. 72
Author(s):  
Aju M.T.
Keyword(s):  
Neural Network ◽  
Central Pattern Generator ◽  
Body Length ◽  
Network Models ◽  
Pattern Generator ◽  
Neural Network Models ◽  
Hybrid Controller ◽  
Learning Procedure ◽  
The Central Nervous System ◽  
Reflex System

<span>The revealed secrets of nature always led humans to their aspiring achievements. The fastest animal on land is Cheetah and similar robot has developed by engineers so far to attain a record speed of 20mph among legged robots. But in nature there are some insects those are far ahead of cheetah in speed with a unit of body length per second. Insects are small in their body size with legs usually countable from 4 to 12 or more. With more legs they can have more stability and can adapt to different terrain faster while walking. Six legged robot (hexapod) is generally expect to attain higher speed in terms of body length per second, since the nature has proof for it. Bio-inspired Central Pattern Generator (CPG) is in use for so far in robotic world to mimic the locomotion patterns of insects and other animals. Currently the hybrid controller of CPG and reflex is going on and this paper suggests a new architecture for the system. Neural Network modeled CPG acts as the motor neuron for each joint of the leg. In each instant a neural network models the gait of the robot by learning procedure from the reflex system. This is like the Central Nervous System (CNS) selecting gait of an animal according to the terrain that travels. CNS takes sensory feedback from eyes, force on each leg and body balance from cochlea to adapt the gait for current terrain. This paper in first place tries to simulate the gait patterns for a hexapod.</span>

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.

An evaluation of deep neural network models for music classification using spectrograms

2021 ◽  
Author(s):  
Jingxian Li ◽  
Lixin Han ◽  
Xiaoshuang Li ◽  
Jun Zhu ◽  
Baohua Yuan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Music Classification

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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