scholarly journals Адаптивная локально-оптимальная стратегия управления эксплуатацией нейросетевой системы классификации морских целей

2020 ◽  
Author(s):  
V.A. Pyatakovich ◽  
A.P. Purdenko ◽  
V.F. Rychkova ◽  
E.G. Filippov
Keyword(s):  
Autonomous System ◽  
General Procedure ◽  
Optimal Strategies ◽  
Operation Management ◽  
Technical Object ◽  
General Technique ◽  
Generic Structure ◽  
The Neural Network ◽  
Guaranteed Control

В работе рассмотрены задачи определения последовательности, характера и величины управляющих воздействий на состояние морского технического объекта и расчета надежности его аппаратуры, используемой в общей структуре нейросетевой экспертной системы классификации морских целей (морская система мониторинга с элементами искусственного интеллекта) при мониторинге морских акваторий. Определены локально и глобально оптимальные стратегии гарантированного управления эксплуатацией интеллектуальной автономной системы классификации морских целей. Предложена общая методика гарантированного управления эксплуатацией элементной базы аппаратуры интеллектуальной автономной системы классификации морских целей. Общая методика расчета наиболее целесообразных вариантов стратегий управления объектом, выработана на основе решения частных задач, исходя из реальных возможностей управления эксплуатацией и информационного обеспечения. Представленный в работе набор алгоритмов позволяет находить математическую модель прогнозируемого процесса по критерию гарантированного времени безотказного функционирования в ситуациях, когда неизвестны стохастические характеристики возмущающих факторов. Особенность исследуемых нами задач состоит в том, что они решаются в условиях неполной и не всегда достоверной информации.The paper deals with the problems of determining the sequence, nature and magnitude of control actions influencing the state of the marine technical object as well as with the problems of calculating the reliability of its equipment used in the generic structure of the neural network expert system of classification of sea targets (marine monitoring system with elements of artificial intelligence) while monitoring sea waters. The locally and globally optimal strategies of guaranteed operation management of the intelligent autonomous system of classification of sea targets are determined. The general technique of guaranteed control over the operation of the hardware components constituting the equipment of the intelligent autonomous system for classification of sea targets is offered. The general procedure for calculating the most appropriate strategies for the object management has been developed through solving specific problems, based on the real possibilities of operation management and information systems. The set of algorithms presented in this paper can be used to calculate the mathematical model of the predicted process by considering the guaranteed time of trouble-free operation in situations where the stochastic characteristics of the disturbing factors are unknown. A distinguishing feature of the problems we study lies in the fact that they should be solved in conditions of the incomplete and not always reliable information.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

scholarly journals Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping

2021 ◽  
Vol 14 (6) ◽  
pp. 3421-3435
Author(s):  
Zhenjiao Jiang ◽  
Dirk Mallants ◽  
Lei Gao ◽  
Tim Munday ◽  
Gregoire Mariethoz ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Land Surface ◽  
Regional Scale ◽  
Learning Model ◽  
Generic Structure ◽  
Squared Error ◽  
The Neural Network ◽  
Deep Learning Model ◽  
Surface Observations

Abstract. This study introduces an efficient deep-learning model based on convolutional neural networks with joint autoencoder and adversarial structures for 3D subsurface mapping from 2D surface observations. The method was applied to delineate paleovalleys in an Australian desert landscape. The neural network was trained on a 6400 km2 domain by using a land surface topography as 2D input and an airborne electromagnetic (AEM)-derived probability map of paleovalley presence as 3D output. The trained neural network has a squared error <0.10 across 99 % of the training domain and produces a squared error <0.10 across 93 % of the validation domain, demonstrating that it is reliable in reconstructing 3D paleovalley patterns beyond the training area. Due to its generic structure, the neural network structure designed in this study and the training algorithm have broad application potential to construct 3D geological features (e.g., ore bodies, aquifer) from 2D land surface observations.

Types and Classification of "Technical Knowledge" As a First Stage of Structuring Educational Information of Electric Power Disciplines

2021 ◽  
Vol 10 (1) ◽  
pp. 29-33
Author(s):  
M. Rogulina
Keyword(s):  
Electric Power ◽  
Technological Process ◽  
Technical Knowledge ◽  
Respective Group ◽  
Technical Object ◽  
Technical Facility ◽  
Technical Description ◽  
Different Types ◽  
Educational Information

The article presents classification features different types of technical knowledge of the technical object and technological process. Selected elements of the three structures for technical facility and the technological process, which are the basis to facilitate actions by the technical description of the object or of the technological process of the respective group.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

scholarly journals Classification of δ-invariant amalgamation classes

1999 ◽  
Vol 64 (4) ◽  
pp. 1743-1750 ◽  
Author(s):  
Roman D. Aref'ev ◽  
John T. Baldwin ◽  
Marco Mazzucco
Keyword(s):  
Finite Graph ◽  
Theory Model ◽  
Dimension Function ◽  
Model Completeness ◽  
Minimal Set ◽  
Generic Structure ◽  
Finite Graphs ◽  
Generic Models ◽  
Random Graph Theory

Hrushovski's generalization of the Fraisse construction has provided a rich source of examples in model theory, model theoretic algebra and random graph theory. The construction assigns to a dimension function δ and a class K of finite (finitely generated) models a countable ‘generic’ structure. We investigate here some of the simplest possible cases of this construction. The class K will be a class of finite graphs; the dimension, δ(A), of a finite graph A will be the cardinality of A minus the number of edges of A. Finally and significantly we restrict to classes which are δ-invariant. A class of finite graphs is δ-invariant if membership of a graph in the class is determined (as specified below) by the dimension and cardinality of the graph, and dimension and cardinality of all its subgraphs. Note that a generic graph constructed as in Hrushovski's example of a new strongly minimal set does not arise from a δ-invariant class.We show there are countably many δ-invariant (strong) amalgamation classes of finite graphs which are closed under subgraph and describe the countable generic models for these classes. This analysis provides ω-stable generic graphs with an array of saturation and model completeness properties which belies the similarity of their construction. In particular, we answer a question of Baizhanov (unpublished) and Baldwin [5] and show that this construction can yield an ω-stable generic which is not saturated. Further, we exhibit some ω-stable generic graphs that are not model complete.

scholarly journals Classification of EMG signal for multiple hand gestures based on neural network

2020 ◽  
Vol 17 (1) ◽  
pp. 256 ◽  
Author(s):  
Mohd Azlan Abu ◽  
Syazwani Rosleesham ◽  
Mohd Zubir Suboh ◽  
Mohd Syazwan Md Yid ◽  
Zainudin Kornain ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Invasive Technique ◽  
Hand Gestures ◽  
Emg Signal ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>

scholarly journals Solving the problem of classification of material properties using a neural network

2021 ◽  
Vol 2131 (3) ◽  
pp. 032084
Author(s):  
N E Babushkina ◽  
A A Lyapin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Recurrent Neural Network ◽  
Material Properties ◽  
Practical Significance ◽  
Experimental Measurements ◽  
Statistical Parameters ◽  
The Neural Network ◽  
Main Components

Abstract The article sets the task of classifying various materials and determining their belonging to a specified group using a recurrent neural network. The practical significance of the article is to obtain the results of the neural network, confirming the possibility of classifying materials by the hardness parameter using a neural network. As part of the study, a number of experimental measurements were carried out. The structure of the neural network and its main components are described. The statistical parameters of the experimental data are estimated.

scholarly journals Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francisco J. Bravo Sanchez ◽  
Md Rahat Hossain ◽  
Nathan B. English ◽  
Steven T. Moore
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Limited Data ◽  
Extraction Techniques ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Current Classification ◽  
Efficient Learning ◽  
Sound Features

AbstractThe use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Here we test SincNet, an efficient neural network architecture that learns from the raw waveform using sinc-based filters. Results using an off-the-shelf implementation of SincNet on a publicly available bird sound dataset (NIPS4Bplus) show that the neural network rapidly converged reaching accuracies of over 65% with limited data. Their performance is comparable with traditional methods after hyperparameter tuning but they are more efficient. Learning directly from the raw waveform allows the algorithm to select automatically those elements of the sound that are best suited for the task, bypassing the onerous task of selecting feature extraction techniques and reducing possible biases. We use publicly released code and datasets to encourage others to replicate our results and to apply SincNet to their own datasets; and we review possible enhancements in the hope that algorithms that learn from the raw waveform will become useful bioacoustic tools.

scholarly journals NEURAL NETWORK CLASSIFICATION METHOD FOR AIRCRAFT IN ISAR IMAGES

2019 ◽  
Vol 2 ◽  
pp. 141
Author(s):  
Krasimir Ognyanov Slavyanov
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Synthetic Aperture ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Full Explanation ◽  
Network Method ◽  
High Level ◽  
And Training

This article offers a neural network method for automatic classification of Inverse Synthetic Aperture Radar objects represented in images with high level of post-receive optimization. A full explanation of the procedures of two-layer neural network architecture creating and training is described. The classification in the recognition stage is proposed, based on several main classes or sets of flying objects. The classification sets are designed according to distinctive specifications in the structural models of the aircrafts. The neural network is experimentally simulated in MATLAB environment. Numerical results of the experiments carried, prove the correct classification of the objects in ISAR optimized images.

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