117 research outputs found
Machine Learning Interpretability of Outer Radiation Belt Enhancement \& Depletion Events
We investigate the response of outer radiation belt electron fluxes to
different solar wind and geomagnetic indices using an interpretable machine
learning method. We reconstruct the electron flux variation during 19
enhancement and 7 depletion events and demonstrate a feature attribution
analysis on the superposed epoch results for the first time. We find that the
intensity and duration of the substorm sequence following an initial dropout
determine the overall enhancement or depletion of electron fluxes, while the
solar wind pressure drives the initial dropout in both types of events. Further
statistical results from a dataset with 71 events confirm this and show a
significant correlation between the resulting flux levels and the average AL
index, indicating that the observed "depletion" event can be more accurately
described as a "non-enhancement" event. Our novel SHAP-Enhanced Superposed
Epoch Analysis (SHESEA) method can be used as an insight discovery tool in
various physical systems
Opening the Black Box of the Radiation Belt Machine Learning Model
Many Machine Learning (ML) systems, especially neural networks, are
fundamentally regarded as black boxes since it is difficult to grasp how they
function once they have been trained. Here, we tackle the issue of the
interpretability of a high-accuracy ML model created to model the flux of
Earth's radiation belt electrons. The Outer RadIation belt Electron Neural net
model (ORIENT) uses only solar wind conditions and geomagnetic indices as
input. Using the Deep SHAPley additive explanations (DeepSHAP) method, we show
that the `black box' ORIENT model can be successfully explained. Two
significant electron flux enhancement events observed by Van Allen Probes
during the storm interval of 17 to 18 March 2013 and non storm interval of 19
to 20 September 2013 are investigated using the DeepSHAP method. The results
show that the feature importances calculated from the purely data driven ORIENT
model identify physically meaningful behavior consistent with current physical
understanding.Comment: Under revie
Nonlinear Landau resonant interaction between whistler waves and electrons: Excitation of electron acoustic waves
Electron acoustic waves (EAWs), as well as electron-acoustic solitary
structures, play a crucial role in thermalization and acceleration of electron
populations in Earth's magnetosphere. These waves are often observed in
association with whistler-mode waves, but the detailed mechanism of EAW and
whistler wave coupling is not yet revealed. We investigate the excitation
mechanism of EAWs and their potential relation to whistler waves using
particle-in-cell simulations. Whistler waves are first excited by electrons
with a temperature anisotropy perpendicular to the background magnetic field.
Electrons trapped by these whistler waves through nonlinear Landau resonance
form localized field-aligned beams, which subsequently excite EAWs. By
comparing the growth rate of EAWs and the phase mixing rate of trapped electron
beams, we obtain the critical condition for EAW excitation, which is consistent
with our simulation results across a wide region in parameter space. These
results are expected to be useful in the interpretation of concurrent
observations of whistler-mode waves and nonlinear solitary structures, and may
also have important implications for investigation of cross-scale energy
transfer in the near-Earth space environment
Knot undulator to generate linearly polarized photons with low on-axis power density
Heat load on beamline optics is a serious problem to generate pure linearly
polarized photons in the third generation synchrotron radiation facilities. For
permanent magnet undulators, this problem can be overcome by a figure-8
operating mode. But there is still no good method to tackle this problem for
electromagnetic elliptical undulators. Here, a novel operating mode is
suggested, which can generate pure linearly polarized photons with very low
on-axis heat load. Also the available minimum photon energy of linearly
polarized photons can be extended much by this method
Applications of Tao General Difference in Discrete Domain
Numerical difference computation is one of the cores and indispensable in the
modern digital era. Tao general difference (TGD) is a novel theory and approach
to difference computation for discrete sequences and arrays in multidimensional
space. Built on the solid theoretical foundation of the general difference in a
finite interval, the TGD operators demonstrate exceptional signal processing
capabilities in real-world applications. A novel smoothness property of a
sequence is defined on the first- and second TGD. This property is used to
denoise one-dimensional signals, where the noise is the non-smooth points in
the sequence. Meanwhile, the center of the gradient in a finite interval can be
accurately location via TGD calculation. This solves a traditional challenge in
computer vision, which is the precise localization of image edges with noise
robustness. Furthermore, the power of TGD operators extends to spatio-temporal
edge detection in three-dimensional arrays, enabling the identification of
kinetic edges in video data. These diverse applications highlight the
properties of TGD in discrete domain and the significant promise of TGD for the
computation across signal processing, image analysis, and video analytic.Comment: This paper is the application part of the paper "Tao General
Differential and Difference: Theory and Application". The theory part of the
paper is renamed as "A Theory of General Difference in Continuous and
Discrete Domain", which is Arxived in arXiv:2305.08098v
NIST 2007 Language Recognition Evaluation: From the Perspective of IIR
PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200
Evaluating the performance of empirical models of total electron density and whistler-mode wave amplitude in the Earth’s inner magnetosphere
Empirical models have been previously developed using the large dataset of satellite observations to obtain the global distributions of total electron density and whistler-mode wave power, which are important in modeling radiation belt dynamics. In this paper, we apply the empirical models to construct the total electron density and the wave amplitudes of chorus and hiss, and compare them with the observations along Van Allen Probes orbits to evaluate the model performance. The empirical models are constructed using the Hp30 and SME (or SML) indices. The total electron density model provides an overall high correlation coefficient with observations, while large deviations are found in the dynamic regions near the plasmapause or in the plumes. The chorus wave model generally agrees with observations when the plasma trough region is correctly modeled and for modest wave amplitudes of 10–100 pT. The model overestimates the wave amplitude when the chorus is not observed or weak, and underestimates the wave amplitude when a large-amplitude chorus is observed. Similarly, the hiss wave model has good performance inside the plasmasphere when modest wave amplitudes are observed. However, when the modeled plasmapause location does not agree with the observation, the model misidentifies the chorus and hiss waves compared to observations, and large modeling errors occur. In addition, strong (>200 pT) hiss waves are observed in the plumes, which are difficult to capture using the empirical model due to their transient nature and relatively poor sampling statistics. We also evaluate four metrics for different empirical models parameterized by different indices. Among the tested models, the empirical model considering a plasmapause and controlled by Hp* (the maximum Hp30 during the previous 24 h) and SME* (the maximum SME during the previous 3 h) or Hp* and SML has the best performance with low errors and high correlation coefficients. Our study indicates that the empirical models are applicable for predicting density and whistler-mode waves with modest power, but large errors could occur, especially near the highly-dynamic plasmapause or in the plumes
Aggregated demand-side response in residential distribution areas based on tiered incentive prices
The residential area refers to the power supply area from distribution transformers to the end users that contains multiple types of flexible resources, such as photovoltaics, energy storage, and power users. Focusing on the challenge of insufficient demand response incentives to multiple types of users in residential distribution areas, a tiered incentive price-based demand-side aggregated response method is proposed in this paper. Users in residential distribution areas are classified with an improved k-means clustering method for obtaining typical types of users. Thereafter, initial scores of users are calculated, and their grades are assigned based on their scores. Corresponding tiered incentive prices are designed for different grades. On this basis, a leader–follower game is proposed to obtain the demand response base price, and tiered incentives are provided to users of different grades to increase their enthusiasm for participating in demand response. In the case study, an actual urban residential distribution area is studied. The results show that the proposed user clustering method has an accuracy of 99.8% in classifying users in a residential distribution area. In addition, the proposed method has better performance in terms of improving the benefit of the load aggregator and users in the residential distribution area compared with methods such as potential game, hidden Markov, and Monte Carlo. Specifically, from the results, the benefit of load aggregators is increased by 101.96%, 76.07%, and 112.37%, and the income of the users is increased by 54.51%, 36.94%, and 64.91%
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