Online, quasi-real-time analysis of high-resolution, infrared, boiling heat transfer investigations using artificial neural networks

2019 ◽  
Vol 163 ◽  
pp. 114357 ◽  
Author(s):  
Madhumitha Ravichandran ◽  
Matteo Bucci
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Real Time ◽  
Boiling Heat Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Artificial Neural

scholarly journals EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3346 ◽  
Author(s):  
Raluca Maria Aileni ◽  
Sever Pasca ◽  
Adriana Florescu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Brain Activity ◽  
Automatic Detection ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Biomedical Signals ◽  
Real Time Analysis ◽  
Artificial Neural

Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.

scholarly journals Improved real-time bio-aerosol classification using artificial neural networks

2018 ◽  
Vol 11 (11) ◽  
pp. 6259-6270 ◽  
Author(s):  
Maciej Leśkiewicz ◽  
Miron Kaliszewski ◽  
Maksymilian Włodarski ◽  
Jarosław Młyńczak ◽  
Zygmunt Mierczyk ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Spectral Characteristics ◽  
Operating Characteristics ◽  
New Era ◽  
Real Time Analysis ◽  
Fluorescence Characteristics ◽  
Aerosol Classification ◽  
Artificial Neural

Abstract. Air pollution has had an increasingly powerful impact on the everyday life of humans. More and more people are aware of the health problems that may result from inhaling air which contains dust, bacteria, pollens or fungi. There is a need for real-time information about ambient particulate matter. Devices currently available on the market can detect some particles in the air but cannot classify them according to health threats. Fortunately, a new type of technology is emerging as a promising solution. Laser-based bio-detectors are characterizing a new era in aerosol research. They are capable of characterizing a great number of individual particles in seconds by analyzing optical scattering and fluorescence characteristics. In this study we demonstrate the application of artificial neural networks (ANNs) to real-time analysis of single-particle fluorescence fingerprints acquired using BARDet (a Bio-AeRosol Detector). A total of 48 different aerosols including pollens, bacteria, fungi, spores, and nonbiological substances were characterized. An entirely new approach to data analysis using a decision tree comprising 22 independent neural networks was discussed. Applying confusion matrices and receiver operating characteristics (ROC) analysis the best sets of ANNs for each group of similar aerosols were determined. As a result, a very high accuracy of aerosol classification in real time was achieved. It was found that for some substances that have characteristic spectra, almost each particle can be properly classified. Aerosols with similar spectral characteristics can be classified as specific clouds with high probability. In both cases the system recognized aerosol type with no mistakes. In the future, it is planned that performance of the system may be determined under real environmental conditions, involving characterization of fluorescent and nonfluorescent particles.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks

2013 ◽  
Author(s):  
Jason K. Ostanek
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Neural Networks ◽  
Reynolds Number ◽  
Artificial Neural Networks ◽  
Power Law ◽  
Transfer Data ◽  
Pin Fin ◽  
The Neural Network ◽  
Artificial Neural

In much of the public literature on pin-fin heat transfer, Nusselt number is presented as a function of Reynolds number using a power-law correlation. Power-law correlations typically have an accuracy of 20% while the experimental uncertainty of such measurements is typically between 5% and 10%. Additionally, the use of power-law correlations may require many sets of empirical constants to fully characterize heat transfer for different geometrical arrangements. In the present work, artificial neural networks were used to predict heat transfer as a function of streamwise spacing, spanwise spacing, pin-fin height, Reynolds number, and row position. When predicting experimental heat transfer data, the neural network was able to predict 73% of array-averaged heat transfer data to within 10% accuracy while published power-law correlations predicted 48% of the data to within 10% accuracy. Similarly, the neural network predicted 81% of row-averaged data to within 10% accuracy while 52% of the data was predicted to within 10% accuracy using power-law correlations. The present work shows that first-order heat transfer predictions may be simplified by using a single neural network model rather than combining or interpolating between power-law correlations. Furthermore, the neural network may be expanded to include additional pin-fin features of interest such as fillets, duct rotation, pin shape, pin inclination angle, and more making neural networks expandable and adaptable models for predicting pin-fin heat transfer.

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