Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target

1996 ◽  
Vol 74 (4) ◽  
pp. 331-338
Author(s):  
Itiel E. Dror ◽  
Faith L. Florer ◽  
Damien Rios ◽  
Mark Zagaeski
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Complex Pattern ◽  
Moving Target ◽  
Bat Sonar

Using artificial bat sonar neural networks for complex pattern recognition: Recognizing faces and the speed of a moving target

1996 ◽  
Vol 74 (4) ◽  
pp. 331-338 ◽  
Author(s):  
Itiel E. Dror ◽  
Faith L. Florer ◽  
Damien Rios ◽  
Mark Zagaeski
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Complex Pattern ◽  
Moving Target ◽  
Bat Sonar

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Methods and tools of ensuring the operational reliability of complex industrial facilities.

2020 ◽  
Vol 14 (1) ◽  
pp. 34-42
Author(s):  
A. VAZHYNSKYI ◽  
◽  
S. ZHUKOV ◽  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Residual Life ◽  
Full Potential ◽  
Scientific Publications ◽  
Specific Element ◽  
Industrial Facilities ◽  
Artificial Neural

Approaches and algorithms for processing experimental data and data obtained as a result of using modern means of measuring equipment, selecting diagnostic parameters, pattern recognition, which constitute the methodological basis for developing methods and designing tools for creating a service system for complex industrial facilities based on predicting their performance and residual life are described in submitted article. Along with classical methods, methods based on using the full potential of the modern elemental base of microprocessor technology and the use of artificial neural networks, machine learning, and "big data" are discovered. The given examples can serve as the basis for constructing a methodology for the application of the considered approaches for organizing predictive maintenance of complex industrial equipment. An analytical review of a number of scientific publications showed that the creation of new automated diagnostic systems that can increase fault tolerance and extend the life of sophisticated modern power equipment is extremely relevant. For this, various approaches are applied, based on mathematical models, expert systems, artificial neural networks and other algorithms. Summarizing the results of scientific publications, it can be argued that the implementation of a systematic approach to the organization of repair service at the enterprise requires a comprehensive solution to the following urgent problems: • monitoring is formulated as the task of interrogating sensors and collecting information necessary for further analysis; • diagnostics, it is solved as tasks of identifying informative signs with further detection and classification of failures and anomalies in data sets; • improving the accuracy of algorithms aimed at pattern recognition; • condition forecasting is the task of assessing the current and accumulated readings of monitoring systems for making decisions regarding either a specific element of the complex or the facilities. Thus, modern technology make it possible to arrange arbitrarily complex algorithms. However, to use the full potential that artificial neural networks, expert systems, and classical methods for identifying and diagnosing equipment it is necessary to have a conceptual development of the foundations of building systems for organizing maintenance and repair of complex energy equipment

TRANSITION TO CONVOLUTIONAL NEURAL NETWORKS IN RADAR PROBLEMS

2019 ◽  
pp. 96-99
Author(s):  
K. Maystrenko ◽  
A. Budilov ◽  
D. Afanasev
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Target Detection ◽  
Convolutional Neural Networks ◽  
Network Topology ◽  
Subject Areas ◽  
The Subject ◽  
Printed Materials ◽  
Hardware Costs

Goal. Identify trends and prospects for the development of radar in terms of the use of convolutional neural networks for target detection. Materials and methods. Analysis of relevant printed materials related to the subject areas of radar and convolutional neural networks. Results. The transition to convolutional neural networks in the field of radar is considered. A review of papers on the use of convolutional neural networks in pattern recognition problems, in particular, in the radar problem, is carried out. Hardware costs for the implementation of convolutional neural networks are analyzed. Conclusion. The conclusion is made about the need to create a methodology for selecting a network topology depending on the parameters of the radar task.

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