Management System for Agricultural Enterprise on the Basis of Its Economic State Forecasting

Atamanyuk, Igor; Kondratenko, Yuriy; Sirenko, Natalya

doi:10.1007/978-3-319-69989-9_27

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…In recent years, the role of machine learning has significantly increased. Machine learning techniques have many successful applications in different areas of human activity, in particular, in medicine [ 4 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], agriculture [ 32 , 33 , 34 , 35 , 36 ], transportation [ 37 , 38 , 39 , 40 , 41 , 42 ], energy production [ 43 , 44 ], finance markets [ 45 ], investment policy [ 46 ] and research [ 47 ]. Statistical learning theory is efficiently used for processing data from sensors in real-time based on effective multi-output Gaussian processes [ 48 ] and for prognosis of the state of technical objects using canonical decomposition of a random sequence [ 49 ].…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

“…In these cases, the size of the gap may have a non-stationary character. It is necessary also, to keep and support MCRC’s high functionality [ 34 , 49 , 84 ] by controlling and predicting the technical state of all MCRC components. With the regular operation of MCRC, it is necessary to evaluate in real-time the operability of the corresponding devices for sensor and control information processing, to ensure their continuous operation and predict possible failures.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

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Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

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Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

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Section: Related Work and Problem Statementmentioning

confidence: 99%

Section: Related Work and Problem Statementmentioning

confidence: 99%

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…These intricacies require a solution to the problem of control over costs and profitability. In this light, Atamanyuk et al (2018) propose using a management system based on an enterprise's economic state forecasting, which allows managers to evaluate the results of enterprise activities in the future by taking into account its resources (land, labor, fixed assets). Skobelev et al (2019) suggest utilizing innovative technologies in enterprise management, particularly a cloud system, to boost agricultural production efficiency and benefit both large producers and small businesses.…”

Section: Cemj 131mentioning

confidence: 99%

Managing Resource-Saving Development of Agri-Food Enterprises in the Context of Food Security and Sustainability: Strategic Aspects

Markina¹,

Somych²,

Shkilniak³

et al. 2021

CEMJ

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Purpose: The study's purpose was to examine the management of agri-food enterprises' resource-saving development to minimize potential imperfections in managing material and human resources and improve data quality. Materials and methods: A combined analysis was used to display the potential relationships between the agri-food sector achievements and basic indicators of natural resources' sustainability and resilience to risks. The analysis covered Ukraine, Romania, and Poland. The analysis allowed for developing a general management scheme, including a management transformation comparative description in the economic growth context. Results:The study revealed that Ukraine is clearly not on the path to progress in manufacturing enterprises and agriculture, as most of its indicators do not meet global nutrition goals. At the same time, Ukraine remains an export-oriented country. Its agri-food enterprises' activities focus on external markets, corresponding to the priorities of the country's agricultural policy and sustainable development goals of the United Nations organization. Conclusion:The obtained results provide knowledge about and understanding of processes that occur in the agri-food enterprises' organizational structure in countries with different development levels. The awareness of these processes is crucial for effective enterprise management in the resource conservation context.

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Control of Stochastic Systems Based on the Predictive Models of Random Sequences

Atamanyuk

Kacprzyk

Kondratenko

et al. 2019

Studies in Systems, Decision and Control

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Management System for Agricultural Enterprise on the Basis of Its Economic State Forecasting

Cited by 11 publications

References 6 publications

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Managing Resource-Saving Development of Agri-Food Enterprises in the Context of Food Security and Sustainability: Strategic Aspects

Control of Stochastic Systems Based on the Predictive Models of Random Sequences

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