scholarly journals A neural network model of three-dimensional dynamic electron density in the inner magnetosphere

2017 ◽  
Vol 122 (9) ◽  
pp. 9183-9197 ◽  
Author(s):  
X. Chu ◽  
J. Bortnik ◽  
W. Li ◽  
Q. Ma ◽  
R. Denton ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Density ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Inner Magnetosphere

A Neural Network Model for Simulating Cloth Texture Deformation

2012 ◽  
Vol 157-158 ◽  
pp. 1608-1613
Author(s):  
Zhi Hong Zhao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Experimental Results ◽  
Curved Surface ◽  
Topological Structures ◽  
The Relationship ◽  
Smooth Texture

This study describes a method to simulate cloth texture deformation using a neural network model. The cloth texture may be represented by its texture colors, positions and its topological structures. In addition, the relationship between the texture colors can be deduced based on the smooth texture and the two and three dimensional texture deformation are correspondingly concerned. A multilayered single direction neural network model is adopted to numerically represent the cloth texture for the purpose of speeding up the simulation. The color values of the points on the cloth deformed curved surface can be calculated with such neural network model. The experimental results show that such method is efficient and executable for the regularized texture deformation.

A Neural Network Model for the Acquisition of a Spatial Body Scheme Through Sensorimotor Interaction

2011 ◽  
Vol 23 (7) ◽  
pp. 1821-1834 ◽  
Author(s):  
Vadim Y. Roschin ◽  
Alexander A. Frolov ◽  
Yves Burnod ◽  
Marc A. Maier
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Degrees Of Freedom ◽  
Three Dimensional ◽  
Internal Representation ◽  
Spherical Body ◽  
The Body ◽  
Body Scheme ◽  
Sensorimotor Interaction

This letter presents a novel unsupervised sensory matching learning technique for the development of an internal representation of three-dimensional information. The representation is invariant with respect to the sensory modalities involved. Acquisition of the internal representation is demonstrated with a neural network model of a sensorimotor system of a simple model creature, consisting of a tactile-sensitive body and a multiple-degrees-of-freedom arm with proprioceptive sensitivity. Acquisition of the 3D representation as well as a distributed representation of the body scheme, occurs through sensorimotor interactions (i.e., the sensory-motor experience of the creature). Convergence of the learning is demonstrated through computer simulations for the model creature with a 7-DoF arm and a spherical body covered by 20 tactile fields.

scholarly journals Prediction and Evaluation of Rockburst Based on Depth Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jin Zhang ◽  
Mengxue Wang ◽  
Chuanhao Xi
Keyword(s):  
Neural Network ◽  
Principal Stress ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Maximum Principal Stress ◽  
Difficult Problem ◽  
Uniaxial Tensile ◽  
Uniaxial Tensile Strength ◽  
The Neural Network

The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.

Intelligent system modelling of a three-dimensional ultrasonic positioning system using neural networks and genetic algorithms

2003 ◽  
Vol 217 (5) ◽  
pp. 367-377
Author(s):  
N Unnikrishnan ◽  
A Mahajan ◽  
T Chu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Analytical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Position Estimation ◽  
System Modelling ◽  
Dimensional System ◽  
The Neural Network

This paper presents a neural network model for a three-dimensional ultrasonic position estimation system that uses the difference in the time of arrivals of waves from a transmitter to various receivers. Even though a linearized analytical model for the three-dimensional system exists and is currently being used to estimate the position of the transmitter, its accuracy is highly dependent on complex and time consuming signal conditioning. A neural network approach is developed to train the system based on unconditioned training sets obtained directly from the receivers. It is proposed to use the final trained system to estimate the three-dimensional position in real time using these raw signals, thereby simplifying the hardware and the computational software as well as increasing the update rate. The weights of the neural network are obtained from a traditional back-progation method and by using genetic algorithms. Results for one-, two- and three-dimensional systems are presented as proof of concept. The performance of the neural network model using the raw signals is shown to be comparable to the analytical model using conditioned signals. Further, it is shown that the neural network model is extremely robust in terms of providing accurate position estimates, even after loss of information from multiple receivers. This work has significant applications in robotics, autonomous systems, virtual reality and image-guided surgery.

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