scholarly journals Improved real-time bio-aerosol classification using Artificial Neural Networks

2018 ◽  
Author(s):  
Maciej Leśkiewicz ◽  
Miron Kaliszewski ◽  
Maksymilian Włodarski ◽  
Jarosław Młyńczak ◽  
Zygmunt Mierczyk ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
High Performance ◽  
Spectral Characteristics ◽  
Air Contamination ◽  
Real Time Analysis ◽  
Fluorescence Characteristics ◽  
Aerosol Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract. Air contamination has had stronger and stronger impact on everyday life of humans. An increasing number of people are aware of the health problems that may result from inhaling air containing dust, bacteria, pollens or fungi. Society is awaiting anxiously for a system that could inform them in real-time about a real danger that is suspended in the air. The devices, currently available on the market, are able to detect some particles in the air, but cannot classify them by the health threats. Fortunately, a new type of technology is emerging as a really promising solution. Laser based bio-detectors are opening 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 application of Artificial Neural Network (ANN) to real-time analysis of single particle fluorescence fingerprints. We gathered a total of 114 779 spectra of 48 aerosols. We discuss an entirely new approach to data analysis using decision tree comprising 22 independent neural networks. Applying confusion matrices and ROC analysis the best sets of ANN’s for each group of similar aerosols has been determined. As a result we achieved very high performance of aerosol classification in real-time. We found that for some substances that have characteristic spectra almost each particle can be properly classified. The aerosols with similar spectral characteristics can be classified as a specific cloud with high probability.

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.

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 Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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.

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