Neural network reconstruction of longitudinal beam phase space from the synchrotron radiation spectrum

1995 ◽  
Vol 363 (3) ◽  
pp. 580-590 ◽  
Author(s):  
J. Koga ◽  
T. Takeda
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Synchrotron Radiation ◽  
Radiation Spectrum ◽  
Network Reconstruction

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

scholarly journals Accelerating Quantitative Susceptibility and R2* Mapping using Incoherent Undersampling and Deep Neural Network Reconstruction

NeuroImage ◽  
2021 ◽  
pp. 118404
Author(s):  
Yang Gao ◽  
Martijn Cloos ◽  
Feng Liu ◽  
Stuart Crozier ◽  
G. Bruce Pike ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Reconstruction

scholarly journals Thermalization of synchrotron radiation from field-aligned currents

1988 ◽  
Vol 6 (3) ◽  
pp. 493-501 ◽  
Author(s):  
William Peter ◽  
Anthony L. Peratt
Keyword(s):  
Magnetic Field ◽  
Synchrotron Radiation ◽  
Energy Density ◽  
Radiation Spectrum ◽  
Axial Magnetic Field ◽  
Three Dimensional ◽  
Synchrotron Emission ◽  
Microwave Background ◽  
Strong Function ◽  
Current Filaments

Three-dimensional plasma simulations of interacting galactic-dimensioned current filaments show bursts of synchroton radiation of energy density 1·2 ×10−13 erg/cm3 which can be compared with the measured cosmic microwave background energy density of 1·5 × 10−13 erg/cm3. However, the synchrotron emission observed in the simulations is not blackbody. In this paper, we analyze the absorption of the synchrotron emission by the current filaments themselves (i.e., self-absorption) in order to investigate the thermalization of the emitted radiation. It is found that a large number of current filaments (>1031) are needed to make the radiation spectrum blackbody up to the observed measured frequency of 100 GHz. The radiation spectrum and the required number of current filaments is a strong function of the axial magnetic field in the filaments.

scholarly journals A phase-space beam position monitor for synchrotron radiation

2015 ◽  
Vol 22 (4) ◽  
pp. 946-955 ◽  
Author(s):  
Nazanin Samadi ◽  
Bassey Bassey ◽  
Mercedes Martinson ◽  
George Belev ◽  
Les Dallin ◽  
...  
Keyword(s):  
Phase Space ◽  
Synchrotron Radiation ◽  
Incident Angle ◽  
Operating Conditions ◽  
Photon Beam ◽  
Profile Measurement ◽  
Beam Position Monitor ◽  
Position Measurement ◽  
Beam Position ◽  
X Ray

The stability of the photon beam position on synchrotron beamlines is critical for most if not all synchrotron radiation experiments. The position of the beam at the experiment or optical element location is set by the position and angle of the electron beam source as it traverses the magnetic field of the bend-magnet or insertion device. Thus an ideal photon beam monitor would be able to simultaneously measure the photon beam's position and angle, and thus infer the electron beam's position in phase space. X-ray diffraction is commonly used to prepare monochromatic beams on X-ray beamlines usually in the form of a double-crystal monochromator. Diffraction couples the photon wavelength or energy to the incident angle on the lattice planes within the crystal. The beam from such a monochromator will contain a spread of energies due to the vertical divergence of the photon beam from the source. This range of energies can easily cover the absorption edge of a filter element such as iodine at 33.17 keV. A vertical profile measurement of the photon beam footprint with and without the filter can be used to determine the vertical centroid position and angle of the photon beam. In the measurements described here an imaging detector is used to measure these vertical profiles with an iodine filter that horizontally covers part of the monochromatic beam. The goal was to investigate the use of a combined monochromator, filter and detector as a phase-space beam position monitor. The system was tested for sensitivity to position and angle under a number of synchrotron operating conditions, such as normal operations and special operating modes where the photon beam is intentionally altered in position and angle at the source point. The results are comparable with other methods of beam position measurement and indicate that such a system is feasible in situations where part of the synchrotron beam can be used for the phase-space measurement.

scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

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