scholarly journals Inferring depth-dependent plasma motions from surface observations using the DeepVel neural network

2020 ◽  
Author(s):  
Benoit Tremblay ◽  
Jean-François Cossette ◽  
Maria D. Kazachenko ◽  
Paul Charbonneau ◽  
Alain Patrick Vincent
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Numerical Simulations ◽  
Active Regions ◽  
Transverse Component ◽  
Small Scale ◽  
Solar Photosphere ◽  
Quiet Sun ◽  
The Neural Network ◽  
Spatial Coverage

Coverage of plasma motions is limited to the line-of-sight component at the Sun's surface. Multiple tracking and inversion methods were developed to infer the transverse motions from observational data. Recently, the DeepVel neural network was trained with computations performed by numerical simulations of the solar photosphere to recover the missing transverse component at surface and at two additional optical depths simultaneously from the surface white light intensity in the Quiet Sun. We argue that deep learning could provide additional spatial coverage to existing observations in the form of depth-dependent synthetic observations, i.e. estimates generated through the emulation of numerical simulations. We trained different versions of DeepVel using slices from numerical simulations of both the Quiet Sun and Active Region at various optical and geometrical depths in the solar atmosphere, photosphere and upper convection zone to establish the upper and lower limits at which the neural network can generate reliable synthetic observations of plasma motions from surface intensitygrams. Flow fields inferred in the photosphere and low chromosphere $\tau \in [0.1, 1)$ are comparable to inversions performed at the surface ($\tau \approx 1$) and are deemed to be suitable for use as synthetic observations in data assimilation processes and data-driven simulations. This upper limit extends closer to the transition region ($\tau \approx 0.01$) in the Quiet Sun, but not for Active Regions. Subsurface flows inferred from surface intensitygrams fail to capture the small-scale features of turbulent convective motions as depth crosses a few hundred kilometers. We suggest that these reconstructions could be used as first estimates of a model's velocity vector in data assimilation processes to nowcast and forecast short term solar activity and space weather.

scholarly journals Magnetic power spectrum in the undisturbed solar photosphere

2020 ◽  
Vol 1 (1) ◽  
pp. 1-5
Author(s):  
Valentina Abramenko ◽  
Olga Kutsenko
Keyword(s):  
Power Spectrum ◽  
Power Spectra ◽  
Active Regions ◽  
Reliable Estimate ◽  
Small Scale ◽  
Solar Photosphere ◽  
Dynamo Action ◽  
Quiet Sun ◽  
Turbulent Dynamo ◽  
Wide Range

Using the magnetic field data obtained with the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO), an investigation of magnetic power spectra in the undisturbed solar photosphere was performed. The results are as follows. 1) To get a reliable estimate of a magnetic power spectrum from the uniformly distributed quiet-sun magnetic flux, a sample pattern of no less than 300 pixels length should be adopted. With smaller patterns, energy on all observable scales might be overestimated. 2) For patterns of different magnetic intensity (e.g., a coronal hole, a quiet-sun area, an area of supergranulation), the magnetic power spectra in a range of (2.5-10) Mm exhibit very close spectral indices of about -1. The observed spectrum is more shallow than the Kolmogorov-type spectrum (with a slope of -5/3) and it differs from steep spectra of active regions. Such a shallow spectrum cannot be explained by the only direct Kolmogorov’s cascade, but it can imply a small-scale turbulent dynamo action in a wide range of scales: from tens of megameters down to at least 2.5 Mm. On smaller scales, the HMI/SDO data do not allow us to reliably derive the shape of the spectrum. 3) Data make it possible to conclude that a uniform mechanism of the small-scale turbulent dynamo is at work all over the solar surface outside active regions.

scholarly journals Solar magneto-convection

2012 ◽  
Vol 8 (S294) ◽  
pp. 95-106 ◽  
Author(s):  
Manfred Schüssler
Keyword(s):  
Active Regions ◽  
Small Scale ◽  
Solar Photosphere ◽  
Surface Layers ◽  
Dynamo Action ◽  
Near Surface ◽  
Wide Range ◽  
Recent Developments ◽  
Mixed Polarity ◽  
The Magnetic Field

AbstractAn overview is given about recent developments and results of comprehensive simulations of magneto-convective processes in the near-surface layers and photosphere of the Sun. Simulations now cover a wide range of phenomena, from whole active regions, over individual sunspots and pores, magnetic flux concentrations and vortices in intergranular lanes, down to the intricate mixed-polarity structure of the magnetic field generated by small-scale dynamo action. The simulations in concert with high-resolution observations have provided breakthroughs in our understanding of the structure and dynamics of the magnetic fields in the solar photosphere.

scholarly journals Sequential Low-Thrust Orbit-Raising of All-Electric Satellites

Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 74
Author(s):  
Pardhasai Chadalavada ◽  
Tanzimul Farabi ◽  
Atri Dutta
Keyword(s):  
Neural Network ◽  
Numerical Simulations ◽  
Power Loss ◽  
Average Power ◽  
Radiation Belts ◽  
Solar Array ◽  
Low Thrust ◽  
Computational Framework ◽  
Transfer Orbits ◽  
The Neural Network

In this paper, we consider a recently developed formulation of the electric orbit-raising problem that utilizes a novel dynamic model and a sequence of optimal control sub-problems to yield fast and robust computations of low-thrust trajectories. This paper proposes two enhancements of the computational framework. First, we use thruster efficiency in order to determine the trajectory segments over which the spacecraft coasts. Second, we propose the use of a neural network to compute the solar array degradation in the Van Allen radiation belts. The neural network is trained on AP-9 data and SPENVIS in order to compute the associated power loss. The proposed methodology is demonstrated by considering transfers from different geosynchronous transfer orbits. Numerical simulations analyzing the effect of thruster efficiency and average power degradation indicate the suitability of starting the maneuver from super-geosynchronous transfer orbits in order to limit fuel expenditure and radiation damage. Furthermore, numerical simulations demonstrate that proposed enhancements are achieved with only marginal increase in computational runtime, thereby still facilitating rapid exploration of all-electric mission scenarios.

scholarly journals Investigation of a Multitasking System for Automatic Ship Berthing in Marine Practice Based on an Integrated Neural Controller

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1167
Author(s):  
Van Suong Nguyen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coordinate System ◽  
Numerical Simulations ◽  
Neural Controller ◽  
The Neural Network ◽  
Artificial Neural ◽  
Ship Berthing

In this article, a multitasking system is investigated for automatic ship berthing in marine practices, based on artificial neural networks (ANNs). First, a neural network with separate structures in hidden layers is developed, based on a head-up coordinate system. This network is trained once with the berthing data of a ship in an original port to conduct berthing tasks in different ports. Then, on the basis of the developed network, an integrated mechanism including three negative signs is linked to achieve an integrated neural controller. This controller can bring the ship to a berth on each side of the ship in different ports. The whole system has the ability to berth for different tasks without retraining the neural network. Finally, to validate the effectiveness of the proposed system for automatic ship berthing, numerical simulations were performed for berthing tasks, such as different ports, and berthing each side of the ship. The results indicate that the proposed system shows a good performance in automatic ship berthing.

scholarly journals Neural Network Based Hurdle Avoidance System for Smart Vehicles

2019 ◽  
Vol 8 (04) ◽  
pp. 01-07
Author(s):  
Dr. B. Maruthi Shankar
Keyword(s):  
Neural Network ◽  
Small Scale ◽  
Direct Estimate ◽  
Feed Forward ◽  
Smart Vehicles ◽  
Ir Sensor ◽  
The Neural Network ◽  
Feed Forward Network ◽  
Positive Direct ◽  
Matlab Programming

The structure of a self-ruling vehicle dependent on neural sophisticated network for route in obscure condition is proposed. The vehicle is equipped with an IR sensor for obstacle separation estimation, a GPS collector for goal data and heading position, L298 H-connect for driving the engines which runs the wheels; all interfaced to a controller unit. The smaller scale controller forms the data gained from the sensor and GPS to produce robot movement through neural based network. The neural network running inside the small scale controller is a multi-layer feed-forward network with back-engendering blunder calculation. The network is prepared disconnected with tangent-sigmoid and positive direct estimate as enactment work for neurons and is executed progressively with piecewise straight guess of tangent-sigmoid capacity. The programming of the miniaturized scale controller is finished by PIC C Compiler and the neural network is actualized utilizing MATLAB programming. Results have shown that up to twenty neurons can be actualized in shrouded layer with this method. The vehicle is tried with differing goal places in open air situations containing fixed as well as moving obstructions and is found to arrive at the set targets effectively and its yield exactness is about equivalent to that of the normal precision.

scholarly journals Nanoscale neural network using non-linear spin-wave interference

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ádám Papp ◽  
Wolfgang Porod ◽  
Gyorgy Csaba
Keyword(s):  
Neural Network ◽  
Spin Wave ◽  
Spin Waves ◽  
Field Pattern ◽  
Small Scale ◽  
Learning Framework ◽  
Wave Interference ◽  
The Neural Network ◽  
Neural Network Hardware ◽  
Signal Routing

AbstractWe demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.

Artificial Neural Network for Data Assimilation by WRF Model in Rio de Janeiro, Brazil

2020 ◽  
Vol 38 (2) ◽  
Author(s):  
Vinícius Albuquerque de Almeida Albuquerque de Almeida ◽  
Gutemberg Borges França ◽  
Haroldo Fraga Campos Velho ◽  
Nelson F. Favilla Ebecken
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Data Assimilation ◽  
Air Temperature ◽  
Rio De Janeiro ◽  
Mean Squared Error ◽  
Grid Point ◽  
Wrf Model ◽  
Particle Collision ◽  
The Neural Network

ABSTRACTThis study investigates the use of neural networks for data assimilation of local data in the WRF model in Rio de Janeiro, Brazil. Surface and upper-air data (air temperature, relative humidity and wind speed and direction) from airport stations and 6-hour forecast from WRF are used as input for the model and the 3D-Var analysis for each grid point is used as target variable. Periods of 168h from 2014 and 2015 are used with 6h and 12h assimilation cycles for surface and upper-air data, respectively. The neural network model was built using the Multi-Particle Collision Algorithm (MPCA) where different topologies are tested until the optimum solution is found. Results show that the neural network is able to emulate the 3D-Var with root mean squared error (standard deviation), respectively, of 0.31 K (0.37 K), 3.10% (4.04%), 0.63 ms-1 (1.05 ms-1), 1.10 ms-1 (1.56 ms-1) for air temperature, relative humidity, u-component of the wind and v-component of the wind. Also, the results show the neural network method is able to run 71 times faster than the conventional method under similar hardware configurations.RESUMOEste estudo investiga o uso de redes neurais para assimilação de dados locais no modelo WRF no Rio de Janeiro. Dados de superfície e do ar superior (temperatura do ar, umidade relativa e velocidade e direção do vento) das estações do aeroporto e previsão de 6 horas do WRF são usados como entrada para o modelo, e a análise 3D-Var para cada ponto da grade é usada como variável destino. Períodos de 168h de 2014 e 2015 são utilizados com ciclos de assimilação de 6h e 12h para dados de superfície e do ar superior, respectivamente. O modelo de rede neural foi construído usando o algoritmo de colisão de partículas múltiplas (MPCA), onde diferentes topologias são testadas até que a solução ideal seja encontrada. Os resultados mostram que a rede neural é capaz de emular o 3D-Var com raiz do erro quadrático médio (desvio padrão) de 0,31 K (0,37 K), 3,10% (4,04%), 0,63 ms -1 (1,05 ms-1), 1,10 ms-1 (1,56 ms-1) para temperatura do ar, umidade relativa, componente u do vento e componente v do vento. Além disso, os resultados mostram que o método de rede neural é capaz de rodar 71 vezes mais rápido que o método convencional em configurações de hardware semelhantes.

scholarly journals Small-Scale Magnetic Features Observed in the Photosphere

1990 ◽  
Vol 138 ◽  
pp. 129-146 ◽  
Author(s):  
Sara F. Martin
Keyword(s):  
Magnetic Fields ◽  
Magnetic Flux ◽  
Active Regions ◽  
Line Of Sight ◽  
Small Scale ◽  
Quiet Sun ◽  
Magnetic Features

Small-scale solar features identifiable on the quiet sun in magnetograms of the line-of-sight component consist of network, intranetwork, ephemeral region magnetic fields, and the elementary bipoles of ephemeral active regions. Network fields are frequently observed to split into smaller fragments and equally often, small fragments are observed to merge or coalesce into larger clumps; this splitting and merging is generally confined to the borders and vertices of the convection cells known as supergranules. Intranetwork magnetic fields originate near the centers of the supergranule convection cells and appear to increase in magnetic flux as they flow in approximate radial patterns towards the boundaries of the cells.

scholarly journals Early Mass Diagnosis of Fusarium Wilt in Banana Cultivations using an E-Nose Integrated Autonomous Rover System

2017 ◽  
Vol 5 (2) ◽  
pp. 261-266 ◽  
Author(s):  
M. Sanjay ◽  
B. Kalpana
Keyword(s):  
Neural Network ◽  
Fusarium Wilt ◽  
Infected Plant ◽  
Disease Outbreaks ◽  
Training Model ◽  
Early Warning Systems ◽  
Small Scale ◽  
The Neural Network ◽  
Similar Disease ◽  
And Control

Nucleic acid based diagnostics are the standard means for diagnosis of infected plant material. However, these methods are expensive and time-consuming, but they are accurate. On the contrary, disease prediction methods based on Volatile organic compound (VOC) emission from plants are less accurate but, allow for screening of large volumes of samples. This work reports the methodology for development of an inexpensive electronic nose for implementation as early warning systems intended to prevent plant disease outbreaks using VOC pattern analysis. It is proven that plants emit VOCs in response to pathogenic attacks. In this project, efforts were made to register the pattern of VOCs released by the diseased plants. The disease taken for this purpose was Fusarium wilt disease of banana. The E-Nose was successfully fabricated using five MOS sensors connected to a microcontroller, which along with a microSD card module was able to store the acquired VOC data. The VOC data analysis was done in MS-Excel, using NeuroXL Predictor, a neural networking add-in. A small scale banana field containing 35 plants, divided into disease, test and control groups, was established. The disease and test sets were subjected to similar disease induction protocols and VOC data was collected over a period of 40 days. NeuroXL Predictor was trained to recognize odours corresponding to diseases by feeding the neural network with the disease set VOC data. Finally, the training model was validated by providing the test set VOC data to the neural network and the results were found to be accurate. Efforts were made to automate the VOC data acquisition from the plants, as it will be impractical to carry around, a device, through several hectares of plantation. Therefore, a simple autonomous rover was fabricated using DC motors connected to a microcontroller. A DC motor placed on top was used to move the E-nose towards the plants in left and right of the rover. The microcontroller was programmed to stop, move forward and turn the E-nose towards left or right as per the measurements of the field.Int. J. Appl. Sci. Biotechnol. Vol 5(2): 261-266

scholarly journals Bearing Defect Classification Algorithm Based on Autoencoder Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Manhuai Lu ◽  
Yuanxiang Mou
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Defect Detection ◽  
Classification Accuracy ◽  
Large Scale ◽  
Small Scale ◽  
Defect Classification ◽  
The Neural Network ◽  
Bearing Defect

The postproduction defect classification and detection of bearings still relies on manual detection, which is time-consuming and tedious. To address this, we propose a bearing defect classification network based on an autoencoder to enhance the efficiency and accuracy of bearing defect detection. An improved autoencoder is used to reduce dimension feature extraction and reduce large-scale images to small-scale images through encoder dimensional reduction. Defect classification is completed by feeding the extracted features into a convolutional classification network. Comparative experiments show that the neural network can effectively complete feature selection and substantially improve classification accuracy while avoiding the laborious algorithm of the conventional method.

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