Daily geomagnetic field prediction of INTERMAGNET observatories data using the multilayer perceptron neural network

Ouadfeul, Sid‐Ali; Tourtchine, Victor; Aliouane, Leila

doi:10.1007/s12517-014-1308-z

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…In the process of data transmission, the active state of the mobile node creates a perceptron based on the network state and service request, as well as the initialization transfer function and learning function. A perceptron network could be trained and created according to formula (11).…”

Section: The Perceptron Transfer Function Can Only Acceptmentioning

confidence: 99%

“…The use of multilayer perceptron neural networks to invert dispersion curves obtained via multichannel analysis of surface waves (MASW) for shear S-wave velocity profile was proposed by Caylak and Kaftan [10]. Ouadfeul et al [11] implanted a tentative prediction of daily geomagnetic field and storms by analyzing the International Real-Time Magnetic Observatory Network data using the artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

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Cloud-assisted QoE guarantee scheme based on adaptive cross-layer perceptron of artificial neural network for mobile Internet

Silin

2016

J Embedded Systems

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For improving the system performance of mobile Internet, how to provide the Quality of Experience (QoE) guarantee is an important factor. First, based on artificial neural network and adaptive cross-layer perceptron, we studied the cloudassisted QoE guarantee mechanism. Then, according to the power, we divided the distance and perceptron layers of mobile Internet and cloud into three levels. We showed the state information definition of the mobile node on the basis of the adaptive adjustment perceptron layers. Thirdly, the perceptron network topology would be updated according to the customer service, which would be updated based on the perceptron learning rule for improving the training practice efficiency. The above scheme would guarantee the QoE effectively. The experimental results show that the proposed QoE guarantee mechanism has obvious advantages in terms of throughput, efficiency, and reliability.

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Section: The Perceptron Transfer Function Can Only Acceptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud-assisted QoE guarantee scheme based on adaptive cross-layer perceptron of artificial neural network for mobile Internet

Silin

2016

J Embedded Systems

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The Embrace Magnetometer Network for South America: First Scientific Results

et al. 2018

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The present work is the second of a two‐part paper on the Embrace Magnetometer Network. In this part, we provide some of the first scientific findings that we have already achieved with this network. We identified the diurnal and the seasonal natural variations of the H component. We provided the precise determination of sudden storm commencements and sudden impulse. We showed that the ΔH amplitudes derived from the Embrace MagNet during intense magnetic storm are in very good agreement with the Dst index. We showed that it is possible to investigate the effects on the solar quiet ionospheric current systems as a response to the X‐class solar flares occurring during daytime under magnetically quiet conditions.

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Automated recognition of jumps in GOES satellite magnetic data

Soloviev¹,

Богоутдинов²,

Agayan³

et al. 2018

Russ. J. Earth Sci.

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As a part of the space environment monitor instrument suite, Geostationary Operational Environmental Satellite carries two boom-mounted magnetometers that measure the local magnetic field vector with a 0.5 second sampling rate. These data contain occasional baseline perturbations not of geophysical origin. One source of contamination is due to switching heaters that are installed along with each magnetometer and used to stabilize the temperature of the instrument. Detection of the heater induced field is complicated by the fact that in most cases these jumps are so small that they are hard to distinguish visually. In the present work we have developed the algorithm JM (from JUMP) aimed at automated and uniform recognition of jumps in GOES 2 Hz vector magnetic measurements. We present the performance of the JM algorithm to a full day of measurements on 3 April 2010. On this date, almost all jumps were recognized by the JM algorithm. The results demonstrate that the algorithm might be used to improve the existing data set from GOES 13, 14 and 15 series, and perhaps find use with the next generation of GOES satellites, beginning with GOES 16 launched on

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Daily geomagnetic field prediction of INTERMAGNET observatories data using the multilayer perceptron neural network

Cited by 3 publications

References 9 publications

Cloud-assisted QoE guarantee scheme based on adaptive cross-layer perceptron of artificial neural network for mobile Internet

Cloud-assisted QoE guarantee scheme based on adaptive cross-layer perceptron of artificial neural network for mobile Internet

The Embrace Magnetometer Network for South America: First Scientific Results

Automated recognition of jumps in GOES satellite magnetic data

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