scholarly journals Evaluation of the Dreicer runaway generation rate in the presence of high- impurities using a neural network

2019 ◽  
Vol 85 (6) ◽  
Author(s):  
L. Hesslow ◽  
L. Unnerfelt ◽  
O. Vallhagen ◽  
O. Embreus ◽  
M. Hoppe ◽  
...  
Keyword(s):  
Neural Network ◽  
Runaway Electron ◽  
The Self ◽  
Generation Rate ◽  
Integrated Modelling ◽  
Plasma Parameters ◽  
The Neural Network ◽  
Wide Range ◽  
Self Consistent ◽  
Computationally Expensive

Integrated modelling of electron runaway requires computationally expensive kinetic models that are self-consistently coupled to the evolution of the background plasma parameters. The computational expense can be reduced by using parameterized runaway generation rates rather than solving the full kinetic problem. However, currently available generation rates neglect several important effects; in particular, they are not valid in the presence of partially ionized impurities. In this work, we construct a multilayer neural network for the Dreicer runaway generation rate which is trained on data obtained from kinetic simulations performed for a wide range of plasma parameters and impurities. The neural network accurately reproduces the Dreicer runaway generation rate obtained by the kinetic solver. By implementing it in a fluid runaway-electron modelling tool, we show that the improved generation rates lead to significant differences in the self-consistent runaway dynamics as compared to the results using the previously available formulas for the runaway generation rate.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

Scalar diffusion in simulated helical turbulence with molecular diffusivity

1984 ◽  
Vol 138 ◽  
pp. 75-91 ◽  
Author(s):  
I. T. Drummond ◽  
S. Duane ◽  
R. R. Horgan
Keyword(s):  
Perturbation Theory ◽  
Effective Diffusivity ◽  
The Self ◽  
Molecular Diffusivity ◽  
Simulation Techniques ◽  
Helical Turbulence ◽  
Wide Range ◽  
Self Consistent ◽  
Scalar Diffusion ◽  
Range Of Values

We extend the simulation techniques of Kraichnan (1970, 1976) to study the effective diffusivity of a scalar field in a turbulent fluid. In our model we have introduced an adjustable helicity parameter and a technique for simulating molecular diffusivity. The results show that for non-helical turbulence the self-consistent perturbation theory of Phythian & Curtis (1978) gives excellent values for the effective diffusivity over a wide range of values for both the molecular diffusivity and the parameters describing the turbulence.This ceases to be the case immediately the helicity is given a non-zero value. Wide departures are observed between the theoretical calculation and the simulation. Our conclusion is that non-perturbative effects are very important in the presence of helicity.

scholarly journals Development of a generative adversarial neural network for identification of potential HIV-1 inhibitors by deep learning methods

Informatics ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 7-17
Author(s):  
G. I. Nikolaev ◽  
N. A. Shuldov ◽  
A. I. Anishenko, ◽  
A. V. Tuzikov ◽  
A. M. Andrianov
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Rational Design ◽  
Binding Free Energy ◽  
Viral Envelope ◽  
Training Dataset ◽  
Learning Methods ◽  
The Neural Network ◽  
Wide Range ◽  
Hiv 1

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block the region of the viral envelope protein gp120 critical for the virus binding to cellular receptor CD4 was developed using deep learning methods. The research were carried out to create the  architecture of the neural network, to form  virtual compound library of potential anti-HIV-1 agents for training the neural network, to make  molecular docking of all compounds from this library with gp120, to  calculate the values of binding free energy, to generate molecular fingerprints for chemical compounds from the training dataset. The training the neural network was implemented followed by estimation of the learning outcomes and work of the autoencoder.  The validation of the neural network on a wide range of compounds from the ZINC database was carried out. The use of the neural network in combination with virtual screening of chemical databases was shown to form a productive platform for identifying the basic structures promising for the design of novel antiviral drugs that inhibit the early stages of HIV infection.

scholarly journals Runaway electron modelling in the self-consistent core European Transport Simulator

2019 ◽  
Vol 59 (7) ◽  
pp. 076024 ◽  
Author(s):  
Gergo I. Pokol ◽  
Soma Olasz ◽  
Boglarka Erdos ◽  
Gergely Papp ◽  
Matyas Aradi ◽  
...  
Keyword(s):  
Runaway Electron ◽  
The Self ◽  
Self Consistent

scholarly journals The Advanced Algorithmic Method for Navigation System Correction of Spacecraft

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Danhe Chen ◽  
K. A. Neusypin ◽  
Xiang Zhang ◽  
Chuangge Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Navigation System ◽  
Parallel Implementation ◽  
The Self ◽  
Self Organization ◽  
Error Prediction ◽  
Measurement Systems ◽  
The Neural Network ◽  
Autonomous Mode

In this paper an advanced method for the navigation system correction of a spacecraft using an error prediction model of the system is proposed. Measuring complexes have been applied to determine the parameters of a spacecraft and the processing of signals from multiple measurement systems is carried out. Under the condition of interference in flight, when the signals of external system (such as GPS) disappear, the correction of navigation system in autonomous mode is considered to be performed using an error prediction model. A modified Volterra neural network based on the self-organization algorithm is proposed in order to build the prediction model, and the modification of algorithm indicates speeding up the neural network. Also, three approaches for accelerating the neural network have been developed; two examples of the sequential and parallel implementation speed of the system are presented by using the improved algorithm. In addition, simulation for a returning spacecraft to atmosphere is performed to verify the effectiveness of the proposed algorithm for correction of navigation system.

Friction Compensation in the Inverted Pendulum Controller by Means of a Neural Network

2020 ◽  
Vol 22 (4) ◽  
pp. 875-884
Author(s):  
Marek Balcerzak
Keyword(s):  
Neural Network ◽  
Inverted Pendulum ◽  
Friction Model ◽  
The Novel ◽  
Practical Procedure ◽  
The Neural Network ◽  
Wide Range ◽  
Novel Method ◽  
Data Acquisition And Processing ◽  
Friction Modelling

AbstractThis paper presents an experimental confirmation of the novel method of friction modelling and compensation. The method has been applied to an inverted pendulum control system. The practical procedure of data acquisition and processing has been described. Training of the neural network friction model has been covered. Application of the obtained model has been presented. The main asset of the presented model is its correctness in a wide range of relative velocities. Moreover, the model is relatively easy to build.

Vehicle Sideslip Angle Estimation Through Neural Networks: Application to Experimental Data

2006 ◽  
Author(s):  
Stefano Melzi ◽  
Edoardo Sabbioni ◽  
Alessandro Concas ◽  
Marco Pesce
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Lateral Acceleration ◽  
Slip Angle ◽  
Vehicle Speed ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Training Stage ◽  
Wide Range ◽  
Self Adaptation

This work explores the possibility of using a non-structured algorithm as a sideslip angle valuer: on the basis of a preliminary numerical analysis, a neural network was designed and trained with experimental signals of lateral acceleration, vehicle speed, yaw rate and steer angle. The network was applied to experimental data in order to verify its capability of self-adaptation to changes in friction coefficient and to provide accurate estimations for manoeuvres sensibly different from the ones used during the training stage. The simple architecture joined with an appropriate training set conferred good self-adaptation properties to the neural network which was able to provide satisfying estimation of side slip angle for a wide range of manoeuvres and different friction conditions.

scholarly journals Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part I: Theoretical and Empirical Basis

2005 ◽  
Vol 22 (11) ◽  
pp. 1621-1632 ◽  
Author(s):  
S-G. Park ◽  
V. N. Bringi ◽  
V. Chandrasekar ◽  
M. Maki ◽  
K. Iwanami
Keyword(s):  
Rain Attenuation ◽  
Mean Value ◽  
Radar Reflectivity ◽  
The Self ◽  
Band Frequency ◽  
X Band ◽  
Wide Range ◽  
Self Consistent ◽  
Consistent Method ◽  
Differential Reflectivity

Abstract In this two-part paper, a correction for rain attenuation of radar reflectivity (ZH) and differential reflectivity (ZDR) at the X-band wavelength is presented. The correction algorithm that is used is based on the self-consistent method with constraints proposed by Bringi et al., which was originally developed and evaluated for C-band polarimetric radar data. The self-consistent method is modified for the X-band frequency and is applied to radar measurements made with the multiparameter radar at the X-band wavelength (MP-X) operated by the National Research Institute for Earth Science and Disaster Prevention (NIED) in Japan. In this paper, characteristic properties of relations among polarimetric variables, such as AH–KDP, ADP–AH, AH–ZH, and ZDR–ZH, that are required in the correction methodology are presented for the frequency of the MP-X radar (9.375 GHz), based on scattering simulations using drop spectra measured by disdrometers at the surface. The scattering simulations were performed under conditions of three different temperatures and three different relations for drop shapes, in order to consider variability of polarimetric variables for these conditions. For the X-band wavelength, the AH–KDP and ADP–AH relations can be assumed to be nearly linear. The coefficient α of the AH–KDP relation varies over a wide range from 0.139 to 0.335 dB (°)−1 with a mean value of 0.254 dB (°)−1. The coefficient γ of the ADP–AH relation varies from 0.114 to 0.174, with a mean value of 0.139. The exponent b of the AH–ZH relation does not depend on drop shapes and is almost constant for a given temperature (about 0.78 at the temperature of 15°C). The ZDR–ZH relation depends primarily on drop shape, and does not vary with temperature.

scholarly journals Térdporc szegmentálása MR-felvételekből mesterséges intelligencia segítségével

2021 ◽  
Vol 162 (9) ◽  
pp. 352-360
Author(s):  
Péter Szoldán ◽  
Zsófia Egyed ◽  
Endre Szabó ◽  
János Somogyi ◽  
György Hangody ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Knee Joint ◽  
Cartilage Tissue ◽  
Osteochondral Allograft ◽  
Mr Images ◽  
Training Set ◽  
Mr Image ◽  
The Neural Network ◽  
Wide Range

Összefoglaló. Bevezetés: A térdízületnek ultrafriss osteochondralis allograft segítségével történő részleges ortopédiai rekonstrukciója képalkotó vizsgálatokon alapuló pontos tervezést igényel, mely folyamatban a morfológia felismerésére képes mesterséges intelligencia nagy segítséget jelenthet. Célkitűzés: Jelen kutatásunk célja a porc morfológiájának MR-felvételen történő felismerésére alkalmas mesterséges intelligencia kifejlesztése volt. Módszer: A feladatra legalkalmasabb MR-szekvencia meghatározása és 180 térd-MR-felvétel elkészítése után a mesterséges intelligencia tanításához manuálisan és félautomata szegmentálási módszerrel bejelölt porckontúrokkal tréninghalmazt hoztunk létre. A mély convolutiós neuralis hálózaton alapuló mesterséges intelligenciát ezekkel az adatokkal tanítottuk be. Eredmények: Munkánk eredménye, hogy a mesterséges intelligencia képes a meghatározott szekvenciájú MR-felvételen a porcnak a műtéti tervezéshez szükséges pontosságú bejelölésére, mely az első lépés a gép által végzett műtéti tervezés felé. Következtetés: A választott technológia – a mesterséges intelligencia – alkalmasnak tűnik a porc geometriájával kapcsolatos feladatok megoldására, ami széles körű alkalmazási lehetőséget teremt az ízületi terápiában. Orv Hetil. 2021; 162(9): 352–360. Summary. Introduction: The partial orthopedic reconstruction of the knee joint with an osteochondral allograft requires precise planning based on medical imaging reliant; an artificial intelligence capable of determining the morphology of the cartilage tissue can be of great help in such a planning. Objective: We aimed to develop and train an artificial intelligence capable of determining the cartilage morphology in a knee joint based on an MR image. Method: After having determined the most appropriate MR sequence to use for this project and having acquired 180 knee MR images, we created the training set for the artificial intelligence by manually and semi-automatically segmenting the contours of the cartilage in the images. We then trained the neural network with this dataset. Results: As a result of our work, the artificial intelligence is capable to determine the morphology of the cartilage tissue in the MR image to a level of accuracy that is sufficient for surgery planning, therefore we have made the first step towards machine-planned surgeries. Conclusion: The selected technology – artificial intelligence – seems capable of solving tasks related to cartilage geometry, creating a wide range of application opportunities in joint therapy. Orv Hetil. 2021; 162(9): 352–360.

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