scholarly journals Nikolaeva M. S. On application of artificial neural networks to control quality of protection environment

2019 ◽  
pp. 136-140
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Quality ◽  
Quality Of Protection ◽  
Artificial Neural

scholarly journals The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4199
Author(s):  
Anton Vladimirovich Milov ◽  
Vadim Sergeevich Tynchenko ◽  
Sergei Olegovich Kurashkin ◽  
Valeriya Valerievna Tynchenko ◽  
Vladislav Viktorovich Kukartsev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technological Process ◽  
Operating Conditions ◽  
Measurement Instruments ◽  
Soldering Process ◽  
Control Quality ◽  
Artificial Neural ◽  
Intelligent Technology

In industries that implement the technology of induction soldering, various sensors, including non-contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution that is effective in different operating conditions in terms of the accuracy of the data obtained and the reliability of the measurement equipment and duplication in case of a failure. The present article discusses the development of intelligent technology based on a collection of artificial neural networks, which allows a number of problems associated with technological process control when using pyrometric sensors to be solved: assessing the quality of measurements, correcting measurements when non-standard errors are detected, and controlling the process of induction heating in the absence of reliable readings of the measurement instruments. The collection of artificial neural networks is self-configuring with the use of multicriterion genetic algorithms. The use of the proposed intelligent technology made it possible to improve the control quality of the technological process of the induction brazing of waveguide paths of spacecraft: the overregulation was decreased from 0–20 to 0, and the difference in the heating temperatures of the elements of the brazed waveguide assembly was decreased from 20–100 to 0–10. In addition, the overall process duration decreased and became more stable. When using the classical control technology, the time varied in the range of 20–60 s; when using the proposed technology, it stabilized in the range of 30–35 s.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

scholarly journals Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations

2019 ◽  
Vol 71 ◽  
pp. 01003
Author(s):  
J. Vrbka ◽  
J. Horák ◽  
V. Machová
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
The People ◽  
Data Points ◽  
Artificial Neural ◽  
The Individual

The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.

Attempt at Resilient Modulus Modeling Using Artificial Neural Networks

1996 ◽  
Vol 1540 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Erol Tutumluer ◽  
Roger W. Meier
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Performance ◽  
Resilient Modulus ◽  
Quality Of Data ◽  
Unsuccessful Attempt ◽  
Independent Test ◽  
Artificial Neural

The pitfalls inherent in the indiscriminate application of artificial neural networks to numerical modeling problems are illustrated. An example is used of an apparently successful (but ultimately unsuccessful) attempt at training a neural network constitutive model for computing the resilient modulus of gravels as a function of stress state and various material properties. Issues such as the quantity and quality of data needed to successfully train a neural network are explored, and the importance of an independent test set to verify network performance is examined.

scholarly journals Optimal architecture for artificial neural networks as pressure estimator

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
Rui Gabriel Modesto de Souza ◽  
Bruno Melo Brentan ◽  
Gustavo Meirelles Lima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Distribution ◽  
Distribution Networks ◽  
Hydraulic Parameter ◽  
Lack Of Information ◽  
Accurate Indication ◽  
Pipe Roughness ◽  
Artificial Neural

ABSTRACT The knowledge of hydraulic parameters in water distribution networks can indicate problems in real time, such as pipe bursts, small leakages, increase in pipe roughness and illegal connections. However, an accurate indication relies on the quantity and quality of the data acquired, i.e., the number of sensors used to monitor the network and their location. It is not economic feasible have a great number of sensors, thus, the use of artificial intelligence, such as Artificial Neural Networks (ANNs) can reduce the lack of information necessary to identify problems, estimating hydraulic parameter through the few information collected. The reliability of ANNs depends on its architecture, so, in this paper, different conditions are tested for ANN training to identify which are the most relevant parameters to be adjusted when the ANN is used for pressure estimation.

scholarly journals Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

2021 ◽  
Vol 14 (2) ◽  
Author(s):  
Abdul Gani Abdul Jameel ◽  
Vincent C.O. Oudenhoven ◽  
Nimal Naser ◽  
Abdul-Hamid Emwas ◽  
Xin Gao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ignition Quality ◽  
Artificial Neural ◽  
Oxygenated Fuels
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