scholarly journals Development of an adaptive mass open online course in the framework of training in artificial neural network technologies

2021 ◽  
Vol 18 (1) ◽  
pp. 100-106
Author(s):  
Dmitry V. Bordachev
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Technologies ◽  
Online Course ◽  
Artificial Neural ◽  
The Moment ◽  
High Level ◽  
Network Technologies ◽  
Neural Network Technologies

Problem and goal. The development of mass open online courses contributes to the increasing attention of students to them. At the moment, there are many large services that provide online training, but there are no clearly defined universal requirements for such courses. Also, along with this problem, there is a fairly high level of rejection of the course at various stages due to the loss of motivation to continue training. Methodology. A variant of solving these problems by using adaptive learning technologies on the example of a course on learning artificial neural network technologies was considered. Results. In the process of reviewing the issue, the topics of the online course sections were determined. As a result, a work plan was drafted and the most relevant ways to solve the identified problems were formulated. Conclusion. The developed strategy can help with further elaboration and testing of the designed course and can be applied to any mass open online course.

scholarly journals Method of identification of technical condition of equipped train equipment

2020 ◽  
pp. 48-56
Author(s):  
Y. S. Kucherov ◽  
R. V. Dopira ◽  
A. A. Shvedun ◽  
D. V. Yagolnikov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Function ◽  
Technical Condition ◽  
The Neural Network ◽  
Multilayer Architecture ◽  
Artificial Neural ◽  
Network Technologies ◽  
Neural Network Technologies ◽  
Electric Train

Due to the fact that the equipment of modern electric trains is functionally and technologically complicated, the relevance of creating airborne systems for predictive monitoring of the technical condition of trains to identify their actual and predicted technical condition is increasing. At present, it has not been possible to build automatic on-board systems for predictive monitoring of the technical condition of trains. One of the possible solutions to this problem can be considered the creation of on-board systems, the identification of the technical condition of equipment in which is carried out using neural network technologies. The article proposes a methodology for identifying the technical condition of electric train equipment using artificial neural network technologies, which allows real-time detection of the occurrence and development of malfunctions of electric train equipment with the display of information on the display in the driver’s cab. Taking into account the specifics of the problem being solved, the choice of a multilayer architecture of a direct distribution neural network is justified. All layers of the neural network are completely interconnected, while the number of neurons of the input and output layers of the network is determined, equal to the number of controlled parameters of the technical condition of the electric train and the number of its possible technical conditions, respectively. As a function of activation of network neurons, a logistic function was selected. A heuristic approach is used to train an artificial neural network.

scholarly journals Application of an Artificial Neural Network to Automate the Measurement of Kinematic Characteristics of Punches in Boxing

2021 ◽  
Vol 11 (3) ◽  
pp. 1223
Author(s):  
Ilshat Khasanshin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kinematic Parameters ◽  
Constant Control ◽  
Measurement Units ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Effective Development ◽  
High Level ◽  
Different Levels

This work aimed to study the automation of measuring the speed of punches of boxers during shadow boxing using inertial measurement units (IMUs) based on an artificial neural network (ANN). In boxing, for the effective development of an athlete, constant control of the punch speed is required. However, even when using modern means of measuring kinematic parameters, it is necessary to record the circumstances under which the punch was performed: The type of punch (jab, cross, hook, or uppercut) and the type of activity (shadow boxing, single punch, or series of punches). Therefore, to eliminate errors and accelerate the process, that is, automate measurements, the use of an ANN in the form of a multilayer perceptron (MLP) is proposed. During the experiments, IMUs were installed on the boxers’ wrists. The input parameters of the ANN were the absolute acceleration and angular velocity. The experiment was conducted for three groups of boxers with different levels of training. The developed model showed a high level of punch recognition for all groups, and it can be concluded that the use of the ANN significantly accelerates the collection of data on the kinetic characteristics of boxers’ punches and allows this process to be automated.

Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

2017 ◽  
Vol 68 (11) ◽  
pp. 2070 ◽  
Author(s):  
Manh-Ha Bui ◽  
Thanh-Luu Pham ◽  
Thanh-Son Dao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Oxygen Demand ◽  
Cyanobacterial Blooms ◽  
Cyanobacteria Bloom ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg–Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6–16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error=0.108) and high correlation coefficient values (R=0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.

scholarly journals Implementation of Artificial Neural Network to Predict the Permeability and Solubility Models of Gypseous Soil

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Imad Habeeb Obead ◽  
Hassan Ali Omran ◽  
Mohammed Yousif Fattah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Models ◽  
Soil Samples ◽  
High Rate ◽  
Soil Behavior ◽  
Coefficient Of Permeability ◽  
Artificial Neural ◽  
High Level ◽  
Gypseous Soil

The objective of the present study is to make a database that describes the leaching-permeability behavior of collapsible gypseous soil. The data will be implemented to develop ANN prediction models for predicting the saturated coefficient of permeability and percentage of solubility by weight. The complex soil behavior and tedious and time consume in soil testing have driven researchers to use Artificial Neural Network (ANN) as tool for prediction. The objectives of the study were to investigate leaching-permeability behavior of collapsible gypseous soils and to develop ANN models for estimating the saturated coefficient of permeability and solubility of the soils. The MATLAB R2015a software was used to predict the saturated coefficient of permeability and the solubility percentage by weight of gypseous soils. The dataset used in this work included (513) records of experimental measurements extracted from leaching-permeability tests conducted on gypseous soil samples taken from Baher Al-Najaf in Iraq. Four input variables were investigated to have the most important influence on the permeability and solubility percentage by weight. According to the achieved statistical analysis, the ANNs model have a reliable capability to find out the predictions with a high-level of accuracy. The gypseous soils exhibited a high rate of dissolution of soluble minerals content, which caused increase in the coefficient of permeability as the soil samples reach the state of long-term full saturation.

scholarly journals Use of an Artificial Neural Network to Assess the Degree of Training of an Operator of Selected Devices Used in Precision Agriculture

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6329
Author(s):  
Karolina Trzyniec ◽  
Adam Kowalewski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Precision Agriculture ◽  
Numerical Data ◽  
Neural Model ◽  
Operator Training ◽  
Training Time ◽  
Artificial Neural ◽  
The Moment ◽  
Parallel Strips

The article concerns the issue of automatic recognition of the moment of achieving the desired degree of training of an operator of devices used in precision agriculture. The aim of the research was to build a neural model that recognizes when an operator has acquired the skill of operating modern navigation on parallel strips used in precision agriculture. To conduct the test, a standard device to assist the operator in guiding the machine along given paths, eliminating overlaps, was selected. The thesis was proven that the moment of operator training (meaning driving along designated paths with an accuracy of up to eight centimeters) can be automatically recognized by a properly selected artificial neural network. This network was learned on the basis of data collected during the observation of the operator training process, using a criterion defined by experts. The data collected in the form of photos of the actual and designated route was converted into numerical data and entered into the network input. The output shows the binary evaluation of the trip. It has been shown that the developed neural model will allow the determining of the moment when operators acquire the skills to drive a vehicle along the indicated path and thus shorten the training time.

scholarly journals Artificial Neural Network in the CATS Training System

2019 ◽  
pp. 53-59
Author(s):  
Yu. V. Popova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Back Propagation ◽  
Main Idea ◽  
Training System ◽  
Educational Process ◽  
Academic Disciplines ◽  
Learning Trajectory ◽  
Artificial Neural

This paper presents a variant of using an artificial neural network (ANN) for adaptive learning. The main idea of using ANN is to apply it for a specific educational material, so that after completing the course or its separate topic, the student can determine, not only his level of knowledge, without the teacher’s participation, but also get some recommendations on what material needs to be studied further due to gaps in the studied issues. This approach allows you to build an individual learning trajectory, significantly reduce the time to study academic disciplines and improve the quality of the educational process. The training of an artificial neural network takes place according to the method of back propagation of an error. The developed ANN can be applied to study any academic discipline with a different number of topics and control questions. The research results are implemented and tested in the CATS adaptive training system. This system is the author's development.

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