Power Spectrum Analysis Method Research Articles

Processing and re-interpretation of gravity bouguer map of a selected area in the W-NW of Iraq

Geophysical data interpretation is crucial in characterizing the subsurface structure. The Bouguer gravity map analysis of the W-NW region of Iraq serves as the basis for the current geophysical research. The Bouguer gravity data were processed using the Power Spectrum Analysis method. Four depth slices have been acquired after the PSA process, which are: 390 m, 1300 m, 3040 m, and 12600 m depth. The gravity anomaly depth maps show that shallow-depth anomalies are mainly related to the sedimentary cover layers and structures, while the gravity anomaly of the deeper depth slice of 12600 m is more presented to the basement rocks and mantle uplift. The 2D modeling technique was used for the quantitative interpretation of a selected Gravity profile along the study area. The model section of the gravity profile illustrated the relatively high density of subsurface basement rocks and/or upward mantle process which causes the effect of positive gravity values. Furthermore, several faults are indicated in the sedimentary sections by potential gravity methods such as several grabens, half grabens, and horst structures which are identified in Bouguer depth maps by relatively high and low gravity values, these structures were also affected by the basement rock uplift and/or Mantle upwards.

Noise spectrum analysis of superconducting kinetic inductance detectors

As a newly developed pair-breaking superconducting detector, microwave kinetic inductance detectors are simple to integrate in the frequency domain and have already been used in astronomical detection and array imaging at the (sub)millimeter and optical wavelengths. For these applications, the dark noise level of kinetic inductance detector is one of the key performance indicators. Herein a noise power spectrum analysis method is introduced in detail, which can accurately and effectively analyze the noise spectrum of kinetic inductance detector in a wide frequency range. This method can well balance the noise spectrum resolution and variance performance, by taking the noise data at the resonance frequency with two sampling rates and setting the appropriate frequency resolutions for different frequency bands. This method is used to characterize and compare the noise of aluminum (Al) kinetic inductance detectors made from two different micromachining processes. We deposite a 25-nm-thick aluminum film on high-resistivity silicon substrate for one device, while place one silicon nitride (SiN<sub><i>x</i></sub>) film on the top and one on the bottom of the aluminum film for another device. It is found that the frequency noise of the device with two silicon nitride films is about 25% to 50% of the bare aluminum device. Using this double silicon nitride film fabrication technique, we further fabricate a few groups of lumped-element aluminum kinetic inductance detectors with various inductor and interdigitated capacitor designs. We investigate the noise properties of these devices at different microwave driven power and bath temperatures, and the experimental results show typical two-level system noise behaviors. Our work provides a standard method to characterize the noise power spectrum of kinetic inductor detector, and also paves the way to developing low-noise aluminum kinetic inductance detectors for terahertz imaging, photon-counting and energy-resolving applications.

Monitoring and Analysis of the Operation Performance of Vertical Centrifugal Variable Frequency Pump in Water Supply System

The stable operation of a variable frequency pump is of great importance to the management of a water supply project. Analyzing the operation performance based on monitoring data is necessary for maintaining the stable operation of a variable frequency pump. Several sensors are installed at six monitoring points on the pump to collect signals including vibration velocity, vibration acceleration and vibration displacement. Monitoring signals are preprocessed by smoothing, adjusting waveform trend and filtering on the basis of Fast Fourier Transform (FFT). Then, the vibration features are extracted by power spectrum analysis and cepstrum analysis methods. According to the extracted features, the vibration law and actual operation performance of a variable frequency pump under different operating conditions are analyzed. Results indicate that the vibration amplitude of the pump varies sharply under the operating conditions of [15 Hz, 20 Hz] and [30 Hz, 35 Hz]. The operating condition of [0 Hz, 15 Hz] is the restricted operating area of the pump. The vibration and noise continue increasing under the operating conditions of [35 Hz, 50 Hz] and reach the maximum values at 50 Hz. Therefore, the optimal operating is within the range of [20 Hz, 30 Hz]. Finally, by analyzing the critical values of the operating conditions, the fault diagnosis and the evaluation of the operating status are conducted.

The frequency analysis of the gun barrel vibration based on road simulation test

Aiming at the problem that the gun barrel vibration affects the firing accuracy during tank moving, based on the road simulation test, under the excitation of the different road levels (C, D and E), the acceleration vibration response curves of the barrel under different road levels and different vehicle speeds were obtained by arranging acceleration sensors on the barrel surface. Based on the power spectrum analysis method, the frequency response characteristics of the barrel vibration were analyzed, and the main frequency of barrel vibration during moving is obtained. It has been shown that, the main frequency of the barrel response is mainly distributed in three frequency ranges: 1~3Hz, 14~15Hz and 24~26Hz. The main frequency of the barrel vibration is different with different roads. The main frequency of the barrel response is mainly in the frequency range of 1~3Hz under C level road, and in the frequency range of 1~3Hz and 24~26Hz under D lever road. And under the E level road, the main frequency of barrel response is within the three frequency ranges of 1~3Hz, 14~15Hz and 24~26Hz. And the main frequency is 25Hz as the measuring point is far away from the muzzle. The results will provide support for obtaining the vibration characteristics of barrel as tank moving and explaining the vibration response law of the barrel.

Modeling the Variation between Green Energy Stock Prices and Oil Prices Using Fourier Analysis

Green energy production is crucial to protecting the environment and meeting future energydemand. Therefore, the study aims to investigate the relationship between oil price indices and green energystock prices. In this study, the data of Nasdaq OMX Wind and Nasdaq OMX Solar indices and crude oilindex (Oil) for the period of January 2015-March 2023 are collected and investigated using Fourier analysismethod. Therefore, in this study, the correlation between Oil and other (Solar and Wind) data values isexamined. Fourier power spectrum analysis method is used and according to the results of this powerspectrum analysis; The fact that there is a correlation between the Oil data values and both the Solar andWind data values is demonstrated by the average, maximum and standard deviation values obtained as aresult of the Fourier power spectrum and power spectrum. In conclusion, the findings of this study alsoprovide important recommendations for investors, managers and policy makers.

Determination of buried active faults and earthquake potential for Izmir and its surroundings (western Turkey) using aeromagnetic anomalies and seismological data

The paper aims to delineate buried faults in Izmir city and its surroundings, western Turkey using aeromagnetic and seismological data. In this context, the geophysical data processing techniques including reduced‑to‑pole transform (RTP), power spectrum analysis, high-pass filter and second vertical derivative method (SVD) have been applied to the total field aeromagnetic data of the study area. First, to remove the undesirable effects caused by the dipolar nature of the Earth field, RTP transform has been applied to those data. Second, the average depths of the regional and residual sources in the region have been calculated as 16.84 km and 3.75 km, respectively using power spectrum analysis. After, to emphasize the effects of the residual anomalies, the high-pass filter has been applied to the RTP data. Finally, the second vertical derivative method (SVD) has applied to the filtered data for delineating the uncovering buried faults and their lineaments in the eastern part of the Aegean extension. The results from those methods show five major fault zones that could cause devastating earthquakes in the area. Especially, the study reveals for the first time that one of these faults which lies from Doganbey to the city center of Izmir in the literature actually reaches out to Manisa city in the NE direction. As a result, these lineaments can be evaluated as traces of buried faults could be an important clue in predicting earthquake potential. A comparison of seismicity map and the heat flow map shows that the region (between Cesme and Seferihisar) represented with a low b‑value has a high potential earthquake. The spatial distribution of the earthquakes, b‑values and heat flow values in the depths may be related to the existence thin lithospheric mantle. Furthermore, the region represented by strong aeromagnetic anomalies may be considered to be magmatic material arising from the magma filling inside the strike-slip faults. The fault structure observed on the SVD map are also important for the geothermal energy potential of the region as well.

Correlation Analysis of Geomagnetic Parameter with South Atlantic Anomaly Region

South Atlantic Anomaly (SAA) region is describe as a region with weak Earth magnetic field. The method apply is power spectrum analysis. By applying the power spectrum analysis method, the value of spectral exponent, β is obtained. From the spectral exponent, β, the Hurst exponent can be determined. The research is conducted by studying the SAA region where comparisons is made between the middle latitude region and high latitude region. 2 active period has been research, 11 March 2011 and 29 May 2011 and 2 normal period has been studied, 9 March 2011 and 12 May 2011. The research conduct indicate the SAA region tend to be persistent and as the vertical field intensity and total field intensity increase, the region tend to have a mixture of antipersistent and persistent characteristic. The high latitude region in this research conduct indicate a tendency to be antipersistent. By conducting research on the SAA region, it can provide knowledge of the dynamics and conditions of our Earth. The SAA region is a region where it is exposed to energetic particles. Satellites passing through the SAA region are also vulnerable to danger. Therefore, the region conducted on the SAA region, can provide knowledge on the characteristics of the SAA region. Research on SAA may increase knowledge of the Earth’s magnetic field.

Analysis of temporal structure of laser chaos by Allan variance.

Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multiscale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers- not just for conventional phase stability analysis. In the present study, we demonstrated Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics. While the detection of LFFs is difficult with the conventional power spectrum analysis method in the low-frequency regime, the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, such as LFFs. This study demonstrates that Allan variance can help in understanding and characterizing diverse laser dynamics, including LFFs, spanning a wide range of timescales.

Power spectrum analysis method of coherent Doppler lidar based on linear prediction spectrum estimation

Power spectrum analysis method of coherent Doppler lidar based on linear prediction spectrum estimation

대중가요 작사교육이 여고생의 스트레스 뇌파 및 정서지능에 미치는 효과

This study aims to validate the necessity and justification of popular song lyric education by verifying the effect of popular song lyric education on stress-related brainwaves and the emotional intelligence of female high school students. Lyrics in popular songs are language and symbols that create meaning based on their interpretation. The meaning is shared with the public in various ways (Choi Sang-jin, 2001). Adolescents, the primary consumers of popular music, also feel more familiar with pop lyrics than other reading materials. They relate to their contents (Gong Gyu-taek & Cho Woon-ah 2016). Adolescents tend to show emotional anxiety, impulsiveness, and duality due to psychological and physiological changes from rapid growth (Park So-young 2017, Kim Hyung-hee 2013). Considering that female students spend more time listening to music than male students (Lee Jung-yoon, 2014:15; Miranda & Claes, 2009:229), popular song lyric education using familiar and preferred elements might be effective for female adolescent high school students. A 10-week-lyric education program was devised based on cognitive apprenticeship theory. It was evaluated for scientific validity by examining high school female students’ emotional intelligence. Stress brainwaves were compared before and after popular song lyric education to confirm differences before and after popular song lyric education. The study were 2nd-grade female high school students at S high school in Seoul. The survey was administered by dividing them into 22 comparison groups who participated in popular song lyric education and 19 who did not. The popular song lyric education was conducted over 10 sessions once a week for 50 minutes from March to July 2021. The collected EEG data used the frequency series power spectrum analysis method by fast Fourier transform (FFT) using linear analysis and statistical processing. The IBM SPSS/WIN Version 25.0 program was used for calculations. Effectiveness differences between the groups before and after popular song lyric education was performed using Mann-Whitney and Wilcoxon Signed rank test. The results showed significant differences in stress brainwaves and emotional intelligence. First, stress brainwave analysis, after popular song lyric education, the values of ‘physical tension and stress left and right’ and psychological distraction and stress left and right’ were decreased. Second, differences in linguistic intelligence were detected between groups before and after popular song lyric education. Finally, significant improvements were shown in the fields of ‘emotional engagement,’ ‘thinking promotion,’ ‘emotional control,’ and ‘emotional utilization,’ among other sub-areas. The study clarifies the role of neuroscience’s exploration of popular music education. However, further research is required to build a meaningful scientific foundation for understanding popular music’s effects on the brain.

Application of GPR technology in moisture content detection of stored grain

How to detect grain moisture content storage inefficiently, non-destructively, and quickly is a critical task in the storage process of the modern grain industry. The influence of media with different moisture content on the propagation and attenuation of electromagnetic wave energy is the premise and basis for applying electromagnetic wave technology in detecting grain moisture content. To explore the applicability of electromagnetic wave technology in detecting grain moisture content, we used ground penetrating radar (GPR) technology and auto regressive and moving average (ARMA) power spectrum analysis method to detect and study the moisture content of the typical national grain depots and local grain depots. The results show that GPR technology could realize the moisture content of stored grains and solve the problems of detection distance, non-destructive, and detection dead ends. Compared with the actual test data, the correlation is above 90%, the error can be controlled within 0.5%, and the measurement accuracy is higher, within ±0.3%. The continuous distribution profile of stored grain moisture content was obtained using the ARMA method. The moisture content distribution range of the rice barn was 10-14%, showing the regularity of the moisture content distribution in the middle layer > upper-middle layer > lower-middle layer > bottom layer > grain surface layer. It indicates that the GPR technology has particular advantages in food safety detection and provides data support for real-time detection of food storage safety.

Air gap Eccentricity Analysis in Induction Motor using Decision tree Algorithm

This paper presents yeah the eccentricity Fault in the induction motor. the decision tree and Power spectrum Analysis methods are used to analyze the spectrum. Induction motor is very much like in the industry. It is also used a lot due to its easy control quality. All three-phase induction motors have mismatch eccentricity. Due to this eccentricity problem we got speed pulsation, vibration acoustic noise, and friction problem between stator and rotor. The proposed methodology is useful on Real-time data and achieves 90% true Value. The installation of various Sensors in order to maintain the Good condition of the induction motor is very costly. In the small industry, I would like to avoid this cost. The Status current contains unique fault spectrum components find fault with using a decision tree algorithm .We can easily analyze the air gap eccentricity faults.

Comparison of SPS and FA methods for Blind Carrier Frequency Offset Estimation in OFDM systems

In orthogonal frequency-division multiplexing (OFDM) systems, synchronization issues are of great importance since synchronization errors might destroy the orthogonality among all subcarriers and, therefore, introduce intercarrier interference (ICI) and intersymbol interference (ISI). Several schemes of frequency offset estimation in OFDM systems have been investigated. This paper compares performance and computational complexity of Smoothing Power Spectrum (SPS) and Frequency Analysis (FA) methods for blind carrier frequency offset (CFO) estimation in OFDM systems.

Effects of Electroencephalogram Biofeedback on Emotion Regulation and Brain Homeostasis of Late Adolescents in the COVID-19 Pandemic.

The purpose of this study was to examine the effects of electroencephalogram (EEG) biofeedback training for emotion regulation and brain homeostasis on anxiety about COVID-19 infection, impulsivity, anger rumination, meta-mood, and self-regulation ability of late adolescents in the prolonged COVID-19 pandemic situation. A non-equivalent control group pretest-posttest design was used. The participants included 55 late adolescents in the experimental and control groups. The variables were evaluated using quantitative EEG at pre-post time points in the experimental group. The experimental groups received 10 sessions using the three-band protocol for five weeks. The collected data were analyzed using the Shapiro-Wilk test, Wilcoxon rank sum test, Wilcoxon signed-rank test, t-test and paired t-test using the SAS 9.3 program. The collected EEG data used a frequency series power spectrum analysis method through fast Fourier transform. Significant differences in emotion regulation between the two groups were observed in the anxiety about COVID-19 infection (W = 585.50, p = .002), mood repair of meta-mood (W = 889.50, p = .024), self-regulation ability (t = -5.02, p < .001), self-regulation mode (t = -4.74, p < .001), and volitional inhibition mode (t = -2.61, p = .012). Neurofeedback training for brain homeostasis was effected on enhanced sensory-motor rhythm (S = 177.00, p < .001) and inhibited theta (S = -166.00, p < .001). The results demonstrate the potential of EEG biofeedback training as an independent nursing intervention that can markedly improve anxiety, mood-repair, and self-regulation ability for emotional distress during the COVID-19 pandemic.

Winter Persistent Extreme Cold Events in Xinjiang Region and Their Associations with the Quasi-Biweekly Oscillation of the Polar Front Jet

Winter persistent extreme cold events (WPECEs) often cause great damage to the development of economies and people’s lives. The sub-seasonal variation of the atmospheric circulation is regarded as one of important causes of extreme weather, and is key to propel the extended period prediction. In this paper, we mainly analyze the WPECEs in Xinjiang region and their relationship with the sub-seasonal variation of the East Asian polar front jet (PFJ). The results suggest the persistent extreme cold event (equal or greater than 7 days) occurs most frequently in Xinhe County of Xinjiang region, with obvious inter-annual and inter-decadal variations. Further analysis shows that the variation of the mean temperature in the key area has characteristics of intra-seasonal variation when the WPECE occurs. The result of composite analysis shows that this intra-seasonal variation is related to the sub-seasonal variation of atmospheric circulation, especially the PFJ anomalous activity near Lake Balkhash. By using the power spectrum analysis method, note that the PFJ activity has the characteristics of quasi-biweekly oscillation (QBWO) in WPECEs. On quasi-biweekly scale (10–20-day filtered), the weakening of PFJ, the intensification of the zonal easterly wind in the upper troposphere, the accumulation of the strong cold air, and the intensification of the meridional northerly wind in the lower troposphere enhance the occurrence of WPECEs in Xinjiang. Further investigation indicates that the quasi-biweekly PFJ mainly propagates eastward and southward before the WPECE occurs in Xinjiang, China.

Robust limits from upcoming neutrino telescopes and implications on minimal dark matter models

Experimental developments in neutrino telescopes are drastically improving their ability to constrain the annihilation cross-section of dark matter. In this paper, we employ an angular power spectrum analysis method to probe the galactic and extra-galactic dark matter signals. First we derive projections for a next generation of neutrino telescope that is inspired by KM3NeT. We emphasise that such analysis is much less sensitive to the choice of dark matter density profile. Remarkably, the projected sensitivity is improved by more than an order of magnitude with respect to the existing limits obtained by assuming the Burkert dark matter density profile describing the galactic halo.Second, we analyse minimal extensions to the Standard Model that will be maximally probed by the next generation of neutrino telescopes. As benchmark scenarios, we consider Dirac dark matter in s- and t-channel models with vector and scalar mediators. We follow a global approach by examining all relevant complementary experimental constraints.We find that neutrino telescopes will be able to competitively probe significant portions of parameter space. Interestingly, the anomaly-free L_μ-L_τ model can potentially be explored in regions where the relic abundance is achieved through freeze-out mechanism.

PEMETAAN MAAR DAN CINDER CONE WILAYAH GUNUNG LAMONGAN MENGGUNAKAN ANALISIS POWER SPECTRUMDAN UPWARD CONTINUATION

The gravity method is a method often used in geophysical exploration. According to recent technological developments, data acquisition has evolved by utilizing satellite data and modeling, one of which is GGMplus gravity data. In this study, GGMplus gravity data will be used to map the distribution of maar and cinder cones in the Gunung Lamongan Region. To obtain the optimal local anomaly results of gravity data, a combination of power spectrum analysis and upward continuation filtering methods is carried out.ABL data contours are sliced, gravity anomaly data from each slicing path is processed using FFT so that the wave number and power spectrum values are obtained as well as the relationship graph between the two.The power spectrum analysis method provides window width data that is used as information on determining the height of the upward continuation method.The ABL contour map in the Gunung Lamongan region shows a range of gravitational field values between -20 mGal to 50 mGal. High anomaly values indicate the existence of Mount Lamongan which is dominated by intrusive rocks. The subsurface structure of the Gunung Lamongan region is thought to consist of tuff, lava, volcanic breccia, and lava. The existence of maar and cinder cones in the Gunung Lamongan area can be mapped from the contours of the local anomaly. Local anomaly contours are obtained from ABL data by connecting the two methods of filtering i.e power spectrum analysis and upward continuation. There are 10 cinder cones and 8 maar visible on the local anomaly contour map.

Detection of Target Frequency from SSVEP Signal Using Empirical Mode Decomposition for SSVEP Based BCI Inference System

This paper describes the effectiveness of feature obtained by power spectrum analysis (PSA) as well as the combined method of empirical mode decomposition (EMD) and PSA for the development of brain–computer interface (BCI) system using steady-state visual evoked potential (SSVEP). Accurate detection of SSVEP response from the recorded EEG signal is a difficult task for a new development of the BCI inference system. The EMD technique is a non-linear method of signal decomposition, which generates several intrinsic mode functions (IMFs) of different flickering frequencies. Prominent IMF signal of SSVEP plays a vital role in the accurate detection of frequency. The proposed method achieves the average detection accuracy of 81.45% over four subjects; in contrast, the conventional method of PSA achieves average detection accuracy of 80.43%. The achieved result indicates that the proposed method out performs state of the art by more than 1.02% over four subjects.

Optimal allocation of a hybrid energy storage system considering its dynamic operation characteristics for wind power applications in active distribution networks

This paper aims at specifying the optimal allocation of a hybrid supercapacitor-vanadium redox flow battery (VRB) energy storage system (ESS) for maintaining power balance of active distribution networks (ADNs) for wind power applications. Correspondingly, an optimal allocation approach for the hybrid ESS considering the application scenarios of supercapacitor ESS and VRB ESS was proposed. The supercapacitor ESS was used for restraining the high-frequency components of net load power and a net load power spectrum analysis method based on dividing the variations of net load power was used to obtain the optimal capacity of supercapacitor ESS. For the allocation of hybrid ESS, a mathematical framework was proposed considering both the economy and reliability of ADNs associated with VRB′ dynamic operation characteristics. Finally, the proposed optimal allocation approach was validated and tested by a modified IEEE 33-bus system. Test results have demonstrated the effectiveness of the proposed optimal allocation approach associated with the power flow operation characteristics of ADNs.

EEG-based multi-feature fusion assessment for autism

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder which affects the developmental trajectory in several behavioral domains, including impairments of social communication, cognitive and language abilities. In this paper, multi-feature fusion method based on EEG signal is used to extract as many as possible features including power spectrum analysis, bicoherence, entropy and coherence methods, then we use minimum redundancy maximum correlation (mRMR) algorithm to choose the features, which are applied to input to three classifiers to obtain accuracy classification results. We try to find some key biomarkers of ASD by examining the accuracy of classifier, using different models which use the combination of multiplex features. The results show when nine features are selected by SVM-linear classifier, the accuracy is up to 91.38%. This method might provide objective basis for clinical diagnosis of autism.