ASSESSMENT OF NEURAL NETWORK SCHEMES TO CLASSIFY CLOUD DATA

2002 ◽  
Vol 29 (2) ◽  
pp. 151-172
Author(s):  
FAISAL HOSSAIN ◽  
TUFA DINKU ◽  
NEERAJ AGARWAL ◽  
EMMANOUIL N. ANAGNOSTOU
Keyword(s):  
Neural Network ◽  
Cloud Data

Cloud Data Set for Neural Network Classification Studies

1992 ◽  
Author(s):  
Rupert S. Hawkins ◽  
K. F. Heideman ◽  
Ira G. Smotroff
Keyword(s):  
Neural Network ◽  
Data Set ◽  
Cloud Data ◽  
Neural Network Classification

scholarly journals Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme

2020 ◽  
Vol 10 (2) ◽  
pp. 617
Author(s):  
Jo ◽  
Moon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Probability Method ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Location Data ◽  
Grid Map ◽  
Permutation Problem

In this paper, a Collision Grid Map (CGM) is proposed by using 3d point cloud data to predict the collision between the cattle and the end effector of the manipulator in the barn environment. The Generated Collision Grid Map using x-y plane and depth z data in 3D point cloud data is applied to a Convolutional Neural Network to predict a collision situation. There is an invariant of the permutation problem, which is not efficiently learned in occurring matter of different orders when 3d point cloud data is applied to Convolutional Neural Network. The Collision Grid Map is generated by point cloud data based on the probability method. The Collision Grid Map scheme is composed of a 2-channel. The first channel is constructed by location data in the x-y plane. The second channel is composed of depth data in the z-direction. 3D point cloud is measured in a barn environment and created a Collision Grid Map. Then the generated Collision Grid Map is applied to the Convolutional Neural Network to predict the collision with cattle. The experimental results show that the proposed scheme is reliable and robust in a barn environment.

scholarly journals Asphalt Pavement Friction Coefficient Prediction Method Based on Genetic-Algorithm-Improved Neural Network(GAI-NN) Model

2021 ◽  
Author(s):  
Zhaoyun Sun ◽  
Xueli Hao ◽  
Wei Li ◽  
Ju Huyan ◽  
Hongchao Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Friction Coefficient ◽  
Point Cloud ◽  
Spline Interpolation ◽  
Asphalt Pavement ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Pavement Surface ◽  
Pavement Friction

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a Genetic-Algorithm-Improved Neural Network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover missing data and perform denoising. In particular, these data were filled using cubic-spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed via modification of the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.

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