scholarly journals Convolutional Neural Networks Training for Autonomous Robotics

2021 ◽  
Vol 29 (1) ◽  
pp. 75-79
Author(s):  
Alexander Lozhkin ◽  
Konstantin Maiorov ◽  
Pavol Bozek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semiotic Analysis ◽  
Autonomous Robotics ◽  
New Approach ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Gradient Descent Algorithm ◽  
Modern Mathematics

AbstractThe article discusses methods for accelerating the operation of convolutional neural networks for autonomous robotics learning. The analysis of the theoretical possibility of modifying the neural network learning mechanism is carried out. Classic semiotic analysis and the theory of neural networks is proposed to union. An assumption is made about the possibility of using the symmetry mechanism to accelerate the training of convolutional neural networks. A multilayer neural network to represent how space is an attempt has been made. The conclusion was based on the laws on the plane obtained earlier. The derivation of formulas turned out to be impossible due to the problems of modern mathematics. A new approach is proposed, which involves combining the gradient descent algorithm and the stochastic completion of convolutional filters by the principles of symmetries. The identified algorithms allow increasing the learning rate from 5% to 15%, depending on the problem that the neural network solves.

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

scholarly journals Baby Cry Detection in Domestic Environment using Convolutional Neural Networks

2020 ◽  
Vol 9 (7) ◽  
pp. 793-795
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Web Application ◽  
Well Being ◽  
Emotion Detection ◽  
Neural Network Architecture ◽  
Domestic Environment ◽  
The Neural Network

In this paper we will identify a cry signals of infants and the explanation behind the screams below 0-6 months of segment age. Detection of baby cry signals is essential for the pre-processing of various applications involving crial analysis for baby caregivers, such as emotion detection. Since cry signals hold baby well-being information and can be understood to an extent by experienced parents and experts. We train and validate the neural network architecture for baby cry detection and also test the fastAI with the neural network. Trained neural networks will provide a model and this model can predict the reason behind the cry sound. Only the cry sounds are recognized, and alert the user automatically. Created a web application by responding and detecting different emotions including hunger, tired, discomfort, bellypain.

scholarly journals Neural networks application to determine the types and magnitude of aberrations from the pattern of the point spread function out of the focal plane

2021 ◽  
Vol 2086 (1) ◽  
pp. 012148
Author(s):  
P A Khorin ◽  
A P Dzyuba ◽  
P G Serafimovich ◽  
S N Khonina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Point Spread Function ◽  
Focal Plane ◽  
Prediction Errors ◽  
Point Spread ◽  
The Neural Network ◽  
Spread Function ◽  
The Mean

Abstract Recognition of the types of aberrations corresponding to individual Zernike functions were carried out from the pattern of the intensity of the point spread function (PSF) outside the focal plane using convolutional neural networks. The PSF intensity patterns outside the focal plane are more informative in comparison with the focal plane even for small values/magnitudes of aberrations. The mean prediction errors of the neural network for each type of aberration were obtained for a set of 8 Zernike functions from a dataset of 2 thousand pictures of out-of-focal PSFs. As a result of training, for the considered types of aberrations, the obtained averaged absolute errors do not exceed 0.0053, which corresponds to an almost threefold decrease in the error in comparison with the same result for focal PSFs.

scholarly journals Robot Manipulators Control with Guaranteed Stability Using Feedback Error Learning Neural Networks

1996 ◽  
Vol 8 (4) ◽  
pp. 383-391
Author(s):  
Ju-Jang Lee ◽  
◽  
Sung-Woo Kim ◽  
Kang-Bark Park
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Rule ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network ◽  
Control Schemes ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Among various neural network learning control schemes, feedback error learning(FEL)8),9) has been known that it has advantages over other schemes. However, such advantages are founded on the assumption that the systems is linearly parameterized and stable. Thus, FEL has difficulties in coping with uncertain and unstable systems. Furthermore, it is not clear how the learning rule of FEL is obtained in the minimization sense. Therefore, to overcome such problems, we propose neural network control schemes using FEL with guaranteed performance. The proposed strategy is to use multi-layer neural networks, to design a stabilityguaranteeing controller(SGC), and to derive a learning rule to obtain the tracking performance. Using multilayer neural networks we can fully utilize the learning capability no matter how the system is linearly parameterized or not. The SGC makes it possible for the neural network to learn without fear of instability. As a result, the more the neural network learning proceeds, the better the tracking performance becomes.

scholarly journals Driver Drowsiness Detection Using Convolutional Neural Networks

2021 ◽  
Vol 9 (VII) ◽  
pp. 2652-2658
Author(s):  
Md Gouse Pasha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Emotional State ◽  
Steering Wheel ◽  
Road Accidents ◽  
Driver Drowsiness ◽  
The Neural Network ◽  
Segmentation Methods ◽  
Neural Network Technology

Accidents are now increasingly increasing as more cases are caused by driver drowsiness. To reduce these situations we were working on something that could reduce numbers and get accidents early. Seeing a drowsy driver behind the steering wheel once and warning him could reduce road accidents. In this case drowsiness is detected using an automatic camera, where, based on the captured image, the neural network detects whether the driver is awake or tired. Convolutional Neural Network Technology (CNN) has been used as part of a neural network, where each framework is examined separately and the average of the last 20 frames are tested, corresponding for about one second to a set of training and test data. We analyse image segmentation methods, construct a model based on convolutional neural networks. Using a detailed database of more than 2000 image fragments we are training and analysing the segmentation network to extract the emotional state of the driver in images.

scholarly journals Using the neural network in clinical systems

2021 ◽  
Vol 2131 (4) ◽  
pp. 042008
Author(s):  
Yu S Gusynina ◽  
T A Shornikova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Local Level ◽  
Probabilistic Method ◽  
Complex Function ◽  
Two Dimensions ◽  
Image Size ◽  
Network Training ◽  
The Neural Network

Abstract The article examines the identification of human bone fractures using convoluted neural networks. The method of recognition of photographs of patients is intended for automated systems of identification and video recording of images. Convolutional neural networks have a number of advantages, such as invariability when reducing or increasing image size, immunity to photo movements and deviations, changes in image perspective, and many other image errors. In addition, convolutional neural networks allow you to combine neurons at a local level in two dimensions, connect photographic elements in any place, and also reduce the total number of weights. The work describes a multi-layer convolutional network. The layers of which it consists are divided into two types: convolutional and sub-selective. Of interest is the use of the principle of weighting in the work. This principle allows you to reduce the number of characteristics of the neural network that can be trained. Network training is based on the rule of minimizing empirical error. This rule is based on the algorithm of inverse error propagation. This algorithm provides an instant calculation of the gradient of a complex function of several variables in case the function itself is predefined. Neural network training is based on probabilistic method. This method leads to more optimal results due to interference in the restructuring of network weights. The work confirms the axiomatics of the applied neural network, its architecture and its learning algorithm.

scholarly journals Identification of Earth’s surface objects using ensembles of convolutional neural networks.

2021 ◽  
pp. 114-123
Author(s):  
Evgenii E. Marushko ◽  
Alexander A. Doudkin ◽  
Xiangtao Zheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Machine Learning Methods ◽  
The Neural Network ◽  
Vector Machines ◽  
Identification Technique ◽  
Optimal Values ◽  
Fold Cross Validation

The paper proposes an identification technique of objects on the Earth’s surface images based on combination of machine learning methods. Different variants of multi-layer convolutional neural networks and support vector machines are considered as original models. A hybrid convolutional neural network that combines features extracted by the neural network and experts is proposed. Optimal values of hyperparameters of the models are calculated by grid search methods using k-fold cross-validation. The possibility of improving the accuracy of identification based on the ensembles of these models is shown. Effectiveness of the proposed technique is demonstrated by the example of images obtained by synthetic aperture radar.

scholarly journals Study of Convolutional Neural Networks applied to Image Stereo Matching

2020 ◽  
Author(s):  
João Pedro Poloni Ponce ◽  
Ricardo Suyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Error Rate ◽  
Stereo Matching ◽  
Structural Parameters ◽  
Neural Model ◽  
Temporal Difference ◽  
Training Time ◽  
The Neural Network

Stereo images are images formed from two or more sources that capture the same scene so that it is possible to infer the depth of the scene under analysis. The use of convolutional neural networks to compute these images has been shown to be a viable alternative due to its speed in finding the correspondence between the images. This raises questions related to the influence of structural parameters, such as size of kernel, stride and pooling policy on the performance of the neural network. To this end, this work sought to reproduce an article that deals with the topic and to explore the influence of the parameters mentioned above in function of the results of error rate and losses of the neural model. The results obtained reveal improvements. The influence of the parameters on the training time of the models was also notable, even using the GPU, the temporal difference in the training period between the maximum and minimum limits reached a ratio of six times.

scholarly journals Neuroevolutionary reinforcing learning of neural networks

2022 ◽  
pp. 16-24
Author(s):  
Y. A. Bury ◽  
D. I. Samal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Evolutionary Process ◽  
Control Device ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Living Nature ◽  
Developmental Features ◽  
Self Learning

The article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. The last two are used as the primary method for reducing the dimension of the input signal of the system and simplifying the process of its training in terms of computational complexity.In the presented work, the neural network structure of the control device of the modeled system is formed in the course of the evolutionary process, taking into account the currently known structural and developmental features of self-learning systems that take place in living nature. This method of constructing it makes it possible to bypass the specific limitations of models created on the basis of recombination of already known topologies of neural networks.

scholarly journals Training algorithms for convolutional neural networks

2019 ◽  
Vol 15 (1) ◽  
pp. 41-54
Author(s):  
Arsentiy Igorevich Bredikhin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Learning Algorithm ◽  
Signal Propagation ◽  
Training Algorithms ◽  
Classification Problems ◽  
The Neural Network ◽  
Matlab Programming ◽  
Implementation In Matlab

In this article we consider one of the most used classes of neural networks convolutional neural networks (hereinafter CNN). In particular, the areas of their application, algorithms of signal propagation by CNN and CNN training are described and the methods of CNN functioning algorithms implementation in MATLAB programming language are given. The article presents the results of research on the effectiveness of the CNN learning algorithm in solving classification problems with its help. In the course of these studies, such a characteristic of the neural network as the dynamics of the network error values depending on the learning rate is considered, and the correctness of the algorithm of learning convolutional neural network is checked. In this case, the problem of handwritten digits recognition on the MNIST sample is used as a classification task.

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