Schumann Resonance Measurement Based on Nonlinear Interaction

2010 ◽  
Vol 439-440 ◽  
pp. 1294-1299
Author(s):  
Bing Xia Cao ◽  
Xiao Lin Qiao
Keyword(s):  
High Frequency ◽  
Electromagnetic Waves ◽  
Low Frequency ◽  
Middle Latitude ◽  
Schumann Resonance ◽  
Short Term ◽  
Time Service ◽  
Resonance Measurement ◽  
Modulation Model ◽  
Nonlinear Modulation

Schumann Resonance relates with global temperature variations, new geophysics phenomena in the low ionosphere and short-term earthquake prediction etc. In this paper based on the nonlinear modulation model of high frequency and extreme-low frequency electromagnetic waves in low ionosphere, the Schumann Resonance observing is researched. Taking the fair weather electric field in account, the cross modulation index was 4.2×10-4. At the first Schumann Resonance observatory of China, the first 4 peaks of Schumann Resonance respectively at 7, 14, 20, 26Hz were obtained in demodulation spectra of the high frequency time service signals. The parameter characteristics of Schumann Resonance in the low ionosphere were analyzed under the geographical condition of middle latitude area. The feasibility of Schumann Resonance measurement by demodulating the spectra of HF has been verified. The non-linearity between Schumann Resonance and very low frequency signals also was discussed.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

Scattering of electromagnetic waves by hybrid waves in a two-electron-temperature plasma

1991 ◽  
Vol 46 (1) ◽  
pp. 99-106 ◽  
Author(s):  
S. K. Sharma ◽  
A. Sudarshan
Keyword(s):  
Growth Rate ◽  
Electron Temperature ◽  
High Frequency ◽  
Electromagnetic Waves ◽  
Low Frequency ◽  
Lower Hybrid ◽  
Stimulated Scattering ◽  
Coupled Modes ◽  
Temperature Plasma ◽  
Electrostatic Perturbation

In this paper, we use the hydrodynamic approach to study the stimulated scattering of high-frequency electromagnetic waves by a low-frequency electrostatic perturbation that is either an upper- or lower-hybrid wave in a two-electron-temperature plasma. Considering the four-wave interaction between a strong high-frequency pump and the low-frequency electrostatic perturbation (LHW or UHW), we obtain the dispersion relation for the scattered wave, which is then solved to obtain an explicit expression for the growth rate of the coupled modes. For a typical Q-machine plasma, results show that in both cases the growth rate increases with noh/noc. This is in contrast with the results of Guha & Asthana (1989), who predicted that, for scattering by a UHW perturbation, the growth rate should decrease with increasing noh/noc.

scholarly journals Observation of 2nd Schumann eigenmode on Titan's surface

2013 ◽  
Vol 3 (1) ◽  
pp. 157-192 ◽  
Author(s):  
C. Béghin ◽  
G. Wattieaux ◽  
R. Grard ◽  
M. Hamelin ◽  
J. P. Lebreton
Keyword(s):  
Electromagnetic Waves ◽  
Low Frequency ◽  
Amplitude Distribution ◽  
Data Conversion ◽  
Schumann Resonance ◽  
Huygens Probe ◽  
Atmospheric Structure ◽  
The Impact ◽  
Space Wave ◽  
Expected Signal

Abstract. This works presents the results obtained from an updated data analysis of the observations of Extremely Low Frequency (ELF) electromagnetic waves performed with the HASI-PWA (Huygens Atmospheric Structure and Permittivity, Wave and Altimetry) instrumentation after Huygens Probe landing on Titan surface in January 2005. The most significant signals observed at around 36 Hz throughout the descent in the atmosphere have been extensively analyzed for several years, and subsequently interpreted as the signature of a Schumann resonance, although the latter exhibits atypical peculiarities compared with those known on Earth. The usual depicting methods of space wave data used so far could not allow retrieving the presence of weak signals when Huygens was at rest for 32 min on Titan's surface. Whereas the expected signal seems hidden within the instrumental noise, we show that a careful statistical analysis of the amplitude distribution of the 418 spectral density samples of the 36 Hz line reveals abnormal characteristics compared to other frequencies. This behavior is shown to occur under propitious circumstances due to the characteristics of the onboard data conversion processes into digital telemetry counts, namely 8-bit dynamic after logarithm compression of the DFT (Discrete Fourier Transform) of ELF waveforms. Since this phenomenon is observed only at the frequency bin around 36 Hz, we demonstrate that the Schumann resonance, seen in the atmosphere within the same band, is still present on the surface, albeit with a much smaller amplitude compared to that measured before and a few seconds after the impact, because the electric dipole is thought to have been stabilized ten seconds later almost horizontally until the end of the measurements.

Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

2017 ◽  
Vol 2634 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Yunxuan Li ◽  
Jian Lu ◽  
Lin Zhang ◽  
Yi Zhao
Keyword(s):  
High Frequency ◽  
Demand Forecasting ◽  
Low Frequency ◽  
Original Data ◽  
Mobile App ◽  
Support Vector ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Demand Prediction

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

scholarly journals Can a simple, short-term memory task help to screen dyslexia?

2019 ◽  
Author(s):  
Nick Perham ◽  
Toni Howell ◽  
Andy Watt
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Memory Task ◽  
Compulsory Education ◽  
Low Frequency ◽  
Short Term ◽  
Term Order ◽  
Significant Difference ◽  
Support Students

AbstractFunding to support students with dyslexia in post-compulsory education is under pressure and more efficient assessments may offset some of this shortfall. We tested potential tasks for screening dyslexia: recall of adjective-noun, compared to noun-adjective, pairings (syntax) and recall of high versus low frequency letter pairings (bigrams). Students who reported themselves as dyslexic failed to show a normal syntax effect (greater recall of adjective-noun compared to noun-adjective pairings) and no significant difference in recall between the two types of bigrams whereas students who were not dyslexic showed the syntax effect and a bias towards recalling high frequency bigrams. Findings are consistent with recent explanations of dyslexia suggesting that those affected find it difficult to learn and utilise sequential long-term order information (Szmalec et al. Journal of Experimental Psychology: Learning, Memory & Cognition, 37(5) ,1270-1279, 2011). Further, ROC curve analyses revealed both tasks showed acceptable diagnostic properties as they were able to discriminate between the two groups of participants.

scholarly journals An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China

2017 ◽  
Vol 63 (No. 3) ◽  
pp. 136-148 ◽  
Author(s):  
Xiong Tao ◽  
Li Chongguang ◽  
Bao Yukun
Keyword(s):  
High Frequency ◽  
Hybrid Approach ◽  
Low Frequency ◽  
Frequency Fluctuation ◽  
Price Forecasting ◽  
Short Term ◽  
Price Series ◽  
Short Term Forecasting ◽  
High Frequency Fluctuation ◽  
Improved Eemd

Short-term forecasting of hog price, which forms the basis for the decision making, is challenging and of great interest for hog producers and market participants. This study develops improved ensemble empirical mode decomposition (EEMD)-based hybrid approach for the short-term hog price forecasting. Specifically, the EEMD is first used to decompose the original hog price series into several intrinsic-mode functions (IMF) and one residue. The fine-to-coarse reconstruction algorithm is then applied to compose the obtained IMFs and residue into the high-frequency fluctuation, the low-frequency fluctuation, and the trend terms which can highlight new features of the hog price fluctuations. Afterwards, the extreme learning machine (ELM) is employed to model the low-frequency fluctuation, while the autoregressive integrated moving average (ARIMA) and the polynomial function are used to fit the high-frequency fluctuation and trend term, respectively, in a multistep-ahead fashion. The commonly used iterated prediction strategy is adopted for the implementation of the multistep-ahead forecasting. The monthly hog price series from January 2000 to May 2015 in China is employed to evaluate the forecasting performance of the proposed approach with the selected counterparts. The numerical results indicate that the improved EEMD-based hybrid approach is a promising alternative for the short-term hog price forecasting.  

ASSESSMENT OF PARASYMPATHETIC CONTROL OF BLOOD VESSEL BY PULSATION SPECTRUM AND COMPARISON WITH SPECTRAL METHOD OF RR INTERVALS

2003 ◽  
Vol 15 (01) ◽  
pp. 8-16
Author(s):  
CHANG-WEI HSIEH ◽  
CHI-WU MAO ◽  
MING-SHING YOUNG ◽  
TZUNG-LIEH YEH
Keyword(s):  
Spectral Method ◽  
High Frequency ◽  
Autonomic Function ◽  
Low Frequency ◽  
Frequency Component ◽  
Short Term ◽  
Non Invasive ◽  
Spectrum Method ◽  
Pulsation Spectrum ◽  
Parasympathetic Control

A new pulse spectrum method of assessing autonomic function was examined in a pharmacological experiment on eight healthy volunteers. The pulse pressure data is obtained under control condition and in parasympathetic blocked by atropine. Compared with the spectral method of heart rate variability (HRV), which is wide-spreading in laboratory studies and clinical diagnosis nowadays, the method of pulsation spectrum provides a new and direct view to assess parasympathetic control. As can be seen from the results, the high frequency of pulsation harmonics are reduced by the parasympathetic blocked, and on the contrary, low frequency component increased. By the analysis of linear regression, the pulsation spectrum method indicates more correlations with atropine doses. We anticipate that the non-invasive assessment of short-term autonomic function will come to be performed more reliably and conveniently by using this method.

Effect of increased and maintained frequency of speed endurance training on performance and muscle adaptations in runners

2017 ◽  
Vol 122 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Casper Skovgaard ◽  
Nicki Winfield Almquist ◽  
Jens Bangsbo
Keyword(s):  
Endurance Training ◽  
Exercise Capacity ◽  
High Frequency ◽  
Citrate Synthase ◽  
Low Frequency ◽  
Maximal Activity ◽  
Running Economy ◽  
Time To Exhaustion ◽  
Training Set ◽  
Short Term

The aim of the study was, in runners accustomed to speed endurance training (SET), to examine the effect of increased and maintained frequency of SET on performance and muscular adaptations. After familiarization (FAM) to SET, 18 male ( n = 14) and female ( n = 4) runners (V̇o2max: 57.3 ± 3.4 ml/min; means ± SD) completed 20 sessions of maintained low-frequency (LF; every fourth day; n = 7) or high-frequency (HF; every second day; n = 11) SET. Before FAM as well as before and after an intervention period (INT), subjects completed a series of running tests and a biopsy from m. vastus lateralis was collected. Ten-kilometer performance improved ( P < 0.05) ~3.5% during FAM with no further change during INT. Time to exhaustion at 90% vV̇o2max was 15 and 22% longer ( P < 0.05) during FAM and a further 12 and 16% longer ( P < 0.05) during INT in HF and LF, respectively. During FAM, muscle expression of NHE1 and maximal activity of citrate synthase (CS) and phosphofructokinase (PFK) increased ( P < 0.05), running economy (RE) improved ( P < 0.05), and V̇o2max was unchanged. During INT, both HF and LF increased ( P < 0.05) muscle expression of NKAβ1, whereas maximal activity of CS and PFK, RE, and V̇o2max were unchanged. Furthermore, during INT, muscle expression of FXYD1 and SERCA1, and FXYD1 activity increased ( P < 0.05) in HF, while muscle expression of SERCA2 decreased ( P < 0.05) in LF. Thus increased or maintained frequency of SET leads to further improvements in short-term exercise capacity, but not in 10-km running performance. The better short-term exercise capacity may be associated with elevated expression of muscle proteins related to Na+/K+ transportation and Ca2+ reuptake. NEW & NOTEWORTHY Ten speed endurance training (SET) sessions improved short-term exercise capacity and 10-km performance, which was followed by further improved short-term exercise capacity, but unchanged 10-km performance after 20 SET sessions performed with either high frequency (4 per 8 days) or continued low frequency (2 per 8 days) in trained runners. The further gain in short-term exercise capacity was associated with changes in muscle expression of proteins of importance for the development of fatigue.

Continued Word Associations and Free Recall

1966 ◽  
Vol 18 (1) ◽  
pp. 31-38 ◽  
Author(s):  
W. A. Matthews
Keyword(s):  
Free Recall ◽  
Word Frequency ◽  
High Frequency ◽  
Low Frequency ◽  
Short Term ◽  
Word Associations ◽  
Word Lists ◽  
Degree Of Association ◽  
High Frequency Words

Two experiments on the short-term free recall of 12-word associated and non-associated lists are reported. Degree of association (derived from norms obtained by continuous controlled association) and word frequency were varied. Significant facilitation as a result of the associative manipulations was obtained and clustering of the responses was positively related to this. Clustering was also affected by the method of presentation of the associated words; this occurred more often when they were grouped in presentation than when they were presented randomly arranged among other words in the list. Low frequency associated word lists were generally found to be more efficiently recalled than those of comparable association values but consisting of high frequency words.

Forecast of short-term wind power based on a novel hybrid method

2019 ◽  
Vol 234 (8) ◽  
pp. 937-947
Author(s):  
Dinghui Wu ◽  
Haibo Huang ◽  
Ren Xiao ◽  
Cong Gao
Keyword(s):  
Wind Power ◽  
High Frequency ◽  
Low Frequency ◽  
Prediction Method ◽  
Gray Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Four Seasons ◽  
Frequency Curves ◽  
Power Forecasting

Short-term wind power forecasting plays an important role in power generation, because it prevents the power system operation from its uncertain and intermittent nature. This article proposes a novel method for short-term wind power forecasting, which combines the wavelet transform, particle swarm optimization dynamic gray model and Lyapunov exponent prediction method. First, the approach decomposes the wind power curve into the high-frequency and low-frequency curves by wavelet transform, which represent the detail and tendency signals, respectively. Then, we use the proposed particle swarm optimization dynamic gray model to forecast the low-frequency curve with its smooth and periodic outline. Moreover, Lyapunov exponent prediction method is used to predict high-frequency curves, which possess the chaos characteristics. Finally, we obtain the wind power forecasting result from the combination of the predicted low and high frequencies. The experiment of four seasons in an US wind farm validates that the proposed method is effective in solving the short-term wind power forecasting problem. The obtained results, discussed comprehensively, show that the hybrid method has better prediction accuracy than the other methods, such as artificial neural network, persistence, and autoregressive integrated moving average model, with the lowest average mean absolute percentage error is 8.07% and the average root mean square error is 0.8164 over four seasons.

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