Predictive Control of Electrical Equipment Reliability on the Basis of the Non-linear Canonical Model of a Vector Random Sequence

Atamanyuk, Igor; Shebanin, Vyacheslav; Kondratenko, Yuriy; Volosyuk, Yuriy; Sheptylevskyi, Oleksii; Atamaniuk, Valeriia

doi:10.1109/mees.2019.8896569

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Institute of New Energy Technologies was acquired by Kazatomprom to promote energy security related with electrification and mobility innovation processes in Kazakhstan through environmentally sustainable solutions including efficient use of lithium-ion batteries with superior power. This institute invests heavily in lithium-ion battery production and intends to target high value segments of home storage solutions along with EV powertrains, both fields which are expected to drive demand growth worldwide in following decades [27,33]. Furthermore, Kazatomprom has joined forces with Centrotec Sustainable AG (CEN)a Swiss firm that develops infrastructure solutions for sustainable mobilityon a subproject package regarding an adaptive charging station installed at Pitkiy Manas Village near Almaty city [14].…”

Section: Electric Vehicle Batteriesmentioning

confidence: 99%

Study of the Efficiency of Using Facilities Based on Renewable Energy Sources for Charging Electric Vehicles in Kazakhstan

Shaimurunov¹,

Ryspayev²,

Ismailov³

et al. 2023

IJSDP

View full text Add to dashboard Cite

In this study, the process of popularisation of electric vehicles in Kazakhstan is considered in more detail, since there is a small number of these transports, which negatively affects the economy and ecology of the country. The purpose of this study is to investigate the efficiency of the use of electric vehicles and the use of facilities based on renewable energy sources for charging them on the territory of Kazakhstan. The research methodology is the analysis of literature sources to investigate the efficiency of the use of electric vehicles, the correct development of models for the location of charging stations, and the efficiency of renewable energy sources as a way to boost the economy and reduce energy costs. The ways of popularising electric vehicles in the country were considered. The modelling of charging stations for electric vehicles was studied. In addition, the electric vehicle batteries were investigated. This study can be used to support decision-making for the design of charging stations for electric vehicles in the cities of Kazakhstan, which would bring economic and environmental benefits.

show abstract

Section: Electric Vehicle Batteriesmentioning

confidence: 99%

Study of the Efficiency of Using Facilities Based on Renewable Energy Sources for Charging Electric Vehicles in Kazakhstan

Shaimurunov¹,

Ryspayev²,

Ismailov³

et al. 2023

IJSDP

View full text Add to dashboard Cite

show abstract

“…To ensure high reliability of the operation of control systems, it is proposed to use the predictive control [ 124 , 125 ] module in the general structure—an estimation of the system’s state at future points in time, followed by a decision on the suitability of its use. In the general case, the parameter

characterizing the quality of the system’s functioning (time of operation, the number of operations per unit of time, the accuracy of the operation, etc.)…”

Section: Prediction Control Of Robot Sensor and Control Systems Based...mentioning

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

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.

show abstract

“…The availability of historical RS data since 1972 has also increased the potential of science to invest, design, and implement accurate and reliable methodologies by validating the methods with old yield data sets [18][19][20]. Until now, various studies have implemented different methodologies (such as interpolation [21,22], extrapolation [23,24], vegetation indices [25,26], linear regression models [27], crop growth models (CGMs) [17,28], machine learning (ML) [29][30][31], and deep learning (DL) [32,33] using the RS data and accurately predicted crop yields in almost every corner of the world. However, to adequately justify their methods' reliability, stability, and preciseness, very few studies have consistently tested their methodologies for yield prediction for more than five years.…”

Section: Introductionmentioning

confidence: 99%

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

et al. 2023

View full text Add to dashboard Cite

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.

show abstract

Predictive Control of Electrical Equipment Reliability on the Basis of the Non-linear Canonical Model of a Vector Random Sequence

Cited by 7 publications

References 12 publications

Study of the Efficiency of Using Facilities Based on Renewable Energy Sources for Charging Electric Vehicles in Kazakhstan

Study of the Efficiency of Using Facilities Based on Renewable Energy Sources for Charging Electric Vehicles in Kazakhstan

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

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Contact Info

Product

Resources

About