Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is the coat that covers the biodigester used to control the migration of fluids. Therefore, the selection of the proper geomembrane, in terms of thickness, resistance, flexibility, etc., is fundamental. Unfortunately, there are no studies for the selection of geomembranes, and usually, it is an empirical process performed by workers based on their own experience. Such empirical selection might be inaccurate, limited, inconvenient, and even dangerous. In order to assist workers during the building process of a biodigester, this study proposes the use of an Artificial Neural Network (ANN) to classify a geomembrane as appropriate or not appropriate for the manufacture of a biodigester. The ANN is trained with a database built from qualitative and quantitative evaluations of different characteristics of geomembranes. The results indicate that the proposed ANN classifies the most suitable geomembranes with a 99.9% success rate. The proposed ANN becomes a reliable tool that contributes to the quality and safety of a biodigester.

RNA Identification Technique and RST Control of a Hybrid Indirect Matrix Converter with a Flying Capacitor Three Level Inverter in Power Active Filtering Application

The present paper deals with RST controller using implementation of neural architectures to control power electronics systems dedicated to power quality improvement in a distribution grid. We present a technique for designing a robust RST controller, The computation of the controller is used to enhance the control a Hybrid Indirect Matrix Converter with a flying capacitor three level inverter (HIMC) coupled to the power grid (PG) by an RL filter, the synthesizing of this RST controller is formulated through open and close loop. Artificial neuronal network (ANN) identification technique is used to define/extract this disturbance. Simulation results by MATLAB, Simscap/sim power system code shows the effectiveness of the proposed method.

Automatic Detection of Microsleep Episodes With Deep Learning

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30 s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We performed a visualization of the internal representation of the data by the artificial neuronal network performing best using t-distributed stochastic neighbor embedding (t-SNE). Visualization revealed that MSEs and wakefulness were mostly separable, though not entirely, and MSEc and ED largely intersected with the two main classes. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. The code of the algorithms (https://github.com/alexander-malafeev/microsleep-detection) and data (https://zenodo.org/record/3251716) are available.

Determination of the rock mass resistance index (GSI) based on image processing

More and more often, and on an increasingly large scale, the geological resistance index (GSI) system is used for the design and practice of the mining process. The GSI, is a unique system for classifying the mass of rocks, linked to the parameters of rock strength and mass distortion, based on the generalized criteria of Hoek-Brown and MohrCoulomb. The GSI can be estimated using standard and in situ tables by direct surface observations in underground or surface mining. The GSI value provides a numerical representation of the overall Geotechnical quality of the rock mass. The method for determining GSI using photographic images of the in situ rock mass, with image processing technology, fractal theory and artificial neuronal network (ANN), is already known and successfully applied in several projects.

A Parametric Study of Trailing Edge Flap Implementation on Three Different Airfoils Through an Artificial Neuronal Network

Trailing edge flaps (TEFs) are high-lift devices that generate changes in the lift and drag coefficients of an airfoil. A large number of 2D simulations are performed in this study, in order to measure these changes in aerodynamic coefficients and to analyze them for a given Reynolds number. Three different airfoils, namely NACA 0012, NACA 64(3)-618, and S810, are studied in relation to three combinations of the following parameters: angle of attack, flap angle (deflection), and flaplength. Results are in concordance with the aerodynamic results expected when studying a TEF on an airfoil, showing the effect exerted by the three parameters on both aerodynamic coefficients lift and drag. Depending on whether the airfoil flap is deployed on either the pressure zone or the suction zone, the lift-to-drag ratio, CL/CD, will increase or decrease, respectively. Besides, the use of a larger flap length will increase the higher values and decrease the lower values of the CL/CD ratio. In addition, an artificial neural network (ANN) based prediction model for aerodynamic forces was built through the results obtained from the research.