The control system of universal platform for agriculture based on machine vision technology

The development of the agricultural industry is impossible without automation of the processes of field preparation and harvesting. One of the ways to solve this problem is the implementation of machine vision technologies supplemented with neural networks in the implementation of automated control systems for agricultural equipment. The implementation of machine vision algorithms will allow the recognition of objects in the workspace, the adjustment of the route of movement of technology, the realization of its various operating scenarios. Neural networks will allow you to analyze the surrounding objects and choose the best route to move. In this article, we consider an algorithm for determining objects based on machine vision technologies and the selection of a working area on a frame. The analysis of the intersection of the working area with recognized objects allows you to create controls that regulate the trajectory of traffic. The obtained results are experimentally verified on a laboratory prototype of a universal platform for agricultural machinery. Various approaches to the selection of object boundaries are considered and tested.

Download Full-text

INTELLECTUALIZATION OF AGROTECHNOLOGIES MANAGEMEN

Vestnik of the Russian agricultural science ◽

10.30850/vrsn/2019/2/24-28 ◽

1970 ◽

pp. 24-28

Author(s):

I. М. Mikhaylenko

Keyword(s):

Decision Making ◽

Control Systems ◽

Training Sample ◽

Knowledge Bases ◽

Automated Control ◽

Agricultural Technologies ◽

Expert Control ◽

Agricultural Industry ◽

Rural Producers ◽

High Level

The agricultural industry is one of the most important areas of digitalization of the economy. At the same time, the content basis of digitalization is the technology of precision farming (TZ), which implements the tasks of agrotechnology management. These tasks are divided into two main groups according to the type of executive control system. The first group includes organizational management tasks, embodied in control decision-making systems (DSS) and implemented by management at various levels. The second group includes the tasks of managing field agricultural technologies, embodied in automated control systems (ASUAT). These tasks are implemented by automated and robotic technological machines. The effectiveness of management systems depends on the degree of human participation in the management process, i.e. on the level of his intellectualization. The high level of intellectualization depends on how widely the achievements of modern management science are involved in the creation of control systems. Such achievements are most fully used in analytical systems DSS and ASUAT. However, their actual use is faced with the lack of the required qualifications of rural producers. This problem can be solved by moving to expert control systems that do not require complex multi-step calculations. At the same time, the breakthrough level of such systems can be provided by cloud-based information systems, when knowledge bases (BRs) in expert systems will be formed in information processing centers and transmitted through the public cloud to local DSS and MISS. In order to make optimal decisions on KBs in local DSS and ASUAT, pattern recognition algorithms or special decision-making models can be used, the parameters of which are estimated by the KB, considered as a training sample.

Download Full-text

Development and Implementation of Regulators Based on Artificial Neural Networks in the Process of Training Specialists in Automated Control Systems

2020 V International Conference on Information Technologies in Engineering Education ( Inforino ) ◽

10.1109/inforino48376.2020.9111650 ◽

2020 ◽

Author(s):

Sergey Mezin ◽

Danila Dementev ◽

Ekaterina Maximova

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Control Systems ◽

Automated Control ◽

Artificial Neural ◽

Training Specialists

Download Full-text

OPTIMIZATION OF EQUIPMENT RESERVE FOR INTELLECTUAL AUTOMATED SYSTEMS

Cybersecurity Education Science Technique ◽

10.28925/2663-4023.2021.14.8799 ◽

2021 ◽

Vol 2 (14) ◽

pp. 87-99

Author(s):

Vitaliy Chubaievskyi ◽

Valery Lakhno ◽

Berik Akhmetov ◽

Olena Kryvoruchko ◽

Dmytro Kasatkin ◽

...

Keyword(s):

Neural Network ◽

Control Systems ◽

Optimization Problem ◽

Destructive Interference ◽

Network Analyzer ◽

Calculation Methods ◽

Automated Control ◽

The Neural Network ◽

The Cost ◽

Selection Of

Algorithms for a neural network analyzer involved in the decision support system (DSS) during the selection of the composition of backup equipment (CBE) for intelligent automated control systems Smart City are proposed. A model, algorithms and software have been developed for solving the optimization problem of choosing a CBE capable of ensuring the uninterrupted operation of the IACS both in conditions of technological failures and in conditions of destructive interference in the operation of the IACS by the attackers. The proposed solutions help to reduce the cost of determining the optimal CBE for IACS by 15–17% in comparison with the results of known calculation methods. The results of computational experiments to study the degree of influence of the outputs of the neural network analyzer on the efficiency of the functioning of the CBE for IACS are presented.

Download Full-text

SYSTEM APPROACH TO THE MODELING OF AUTOMATED CONTROL SYSTEMS AND THERMAL ENERGY METERING

URBAN CONSTRUCTION AND ARCHITECTURE ◽

10.17673/vestnik.2019.03.6 ◽

2019 ◽

Vol 9 (3) ◽

pp. 40-49

Author(s):

Mikhail V. POSASHKOV ◽

Vladimir I. NEMCHENKO

Keyword(s):

Control Systems ◽

Thermal Energy ◽

System Approach ◽

Automated System ◽

Automated Control ◽

Multi Criteria Evaluation ◽

Effi Ciency ◽

Energy Metering ◽

Multi Level ◽

Selection Of

An algorithm for the formation of the structure of an automated system for monitoring and accounting for thermal energy, which is a multi-level hierarchical model, is proposed. Eight three-level linear automated systems for control and accounting of thermal energy are formed in the work. To select the best structure of the automated system for monitoring and accounting for thermal energy, with a variety of measuring and computing devices on the market that diff er in measuring and transmitt ing information, the method of multi-criteria evaluation of the system effi ciency of structures is used. The obtained results allow a comparative analysis of the structures and the selection of the best one, taking into account the priorities of the decision maker.

Download Full-text

Fuzzy and Neural Controllers for a Pneumatic Actuator

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2007.4.2368 ◽

2007 ◽

Vol 2 (4) ◽

pp. 375 ◽

Cited By ~ 21

Author(s):

Tiberiu Vesselenyi ◽

Simona Dzițac ◽

Ioan Dzițac ◽

Mișu-Jan Manolescu

Keyword(s):

Neural Networks ◽

Control Systems ◽

Process Model ◽

Neural Control ◽

Fuzzy Inference ◽

Pneumatic Actuator ◽

Automated Control ◽

Neural Controllers ◽

Robotic Grinding ◽

Definition Of

There is a great diversity of ways to use fuzzy inference in robot control systems, either in the place where it is applied in the control scheme or in the form or type of inference algorithms used. On the other hand, artificial neural networks ability to simulate nonlinear systems is used in different researches in order to develop automated control systems of industrial processes. In these applications of neural networks, there are two important steps: system identification (development of neural process model) and development of control (definition of neural control structure). In this paper we present some modelling applications, which uses fuzzy and neural controllers, developed on a pneumatic actuator containing a force and a position sensor, which can be used for robotic grinding operations. Following the simulation one of the algorithms was tested on an experimental setup. The paper also presents the development of a NARMA-L2 neural controller for a pneumatic actuator using position feedback. The structure had been trained and validated, obtaining good results.

Download Full-text

Method for Professional Selection of Human-Operator for Technical System

Safety in Technosphere ◽

10.12737/1998-071x-2019-30-35 ◽

2020 ◽

Vol 8 (1) ◽

pp. 30-35

Author(s):

Anatoliy Parahin ◽

Yulia Zotova

Keyword(s):

Control Systems ◽

Negative Impact ◽

Human Operator ◽

Working Capacity ◽

Automated Control ◽

Study Results ◽

Human Operators ◽

Time Required ◽

Initial Selection ◽

Selection Of

The human-operator occupies a key place in the uninterruptable operation of automated control systems. However, his reaction is limited in time, and his mistake can lead to both operational disorders of economics objects and a negative impact on the staff and population. To minimize such situations the professional selection of human-operators should be carefully carry out. In this study based on a computer program has been proposed a technique for the initial selection of human-operators, have been established the basic patterns, on which the time of mental activity depends, and the effect of noise on human-operators’ working capacity has been considered. The study has showed that the proposed technique is effective for initial selection of human-operators, and takes into account the information presentation nature, and, accordingly, the time required by the human-operator to solve the task in hand. The study results allow join the ranks of techniques for professional selection and expand knowledge about the noise effect on the humanoperator’s working capacity.

Download Full-text