Frequency Energy Distribution Research Articles

Transient river flow into a fjord and its control of plume energy partitioning

AbstractThe influence of variable inflows on near‐field plume dynamics and energy partitioning was examined using observations of a controlled flow into Doubtful Sound, New Zealand. The high temporal changes in flows passing through the Manapouri hydroelectric power station mimic the magnitude and variability seen in small mountainous river systems (SMRS) globally. The variable flow coupled with strong vertical density gradients akin to ambient conditions in coastal systems enabled plume behavior to be characterized for differing flow, wind, and tidal inputs in a quasi‐idealized “laboratory” system. Comparisons of the frequency distributions of energy for different forcing conditions showed that baroclinic and barotropic processes were closely intertwined for transient forcing. The periodicity of density due to tidal oscillations was initially absent; headwaters of the fjord absorbed the momentum when inflows were substantially increased from the mean of ∼420 m3 s−1. From the buoyancy frequency squared N2, six events were identified when N2 was greater than 0.07 s−2. Seven occurrences of supercritical flow (Froude number, > 1) and associated transitions to subcritical flow were observed over the duration of mooring deployment. Transient inflows induced internal hydraulic jumps in the near‐field region which lead to a rapid breakdown of vertical stratification. The horizontal length scale of an internal hydraulic jump is O (1 km). Not all transitions from > 1 to <1 are explained by transient forcing and the role of strong, up‐fjord wind stress is likely to initiate supercritical flows via setup at the head of the fjord.

Microdosimetric spread for cell-sized targets exposed to ⁶⁰Co, ¹⁹²Ir and ¹²⁵I sources.

The magnitude of the spread in specific energy deposition per cell may be a confounding factor in dose-response analysis motivating derivation of explicit data for the most common brachytherapy isotopes (125)I and (192)Ir, and for (60)Co radiation frequently used as reference in RBE studies. The aim of this study is to analyse the microdosimetric spread as given by the frequency distribution of specific energy for a range of doses imparted by (125)I, (192)Ir and (60)Co sources. An upgraded version of the Monte Carlo code PENELOPE was used for scoring energy deposition distributions in liquid water for each of the radiation qualities. Frequency distributions of specific energy were calculated according to the formalism of Kellerer and Chmelevsky. Results indicate that the magnitude of the microdosimetric spread increases with decreasing target size and decreasing energy of the radiation quality. Within the clinical relevant dose range (1 to 100 Gy), the spread does not exceed 4 % for (60)Co, 5 % for (192)Ir and 6 % for (125)I. The frequency distributions can be accurately approximated with symmetrical normal distributions at doses down to 0.2 Gy for (60)Co, 0.1 Gy for (192)Ir and 0.08 Gy for (125)I.

Fatigue Crack Detection at Gearbox Spline Component using Acoustic Emission Method

The spline section of helicopter gearbox structure is susceptible to fatigue crack, and non-redundant characteristic leads to the need for early flaw detection strategies. Acoustic Emission (AE) method relies on propagating elastic waves due to release of energy from active flaws. The initiation of damage is identified using the features of AE waveforms such as energy, amplitude and frequency centroid. The characteristics of the AE features are influenced by sensor type, sensor location and gearbox operational conditions. In this study, the AE data was collected from a helicopter gearbox with a notched spline section and realistic operational conditions using two different AE sensors located at two different positions. The data collection was conducted over one year under various operational conditions. The AE features were extracted from long duration waveforms (100 milliseconds) at every pre- defined time step (every 5 seconds). The frequency domain features of frequency centroid and energy distribution in various frequency bands were compared with gearbox operational conditions such as torque, lift, gyroscopic moment, and temperature. The influences of sensor location, sensor type and operational conditions on the AE features are presented in order to decouple their influences from the AE features due to damage. The comparison between the predicted crack growth time using the AE data and the observed crack initiation shows that the AE method using frequency domain features of streamed waveforms has great potential to identify the crack initiation when the sensor type and location are preserved.

Specific emitter identification based on Hilbert–Huang transform‐based time–frequency–energy distribution features

A novel specific emitter identification method based on transient communication signal's time–frequency–energy distribution obtained by Hilbert–Huang transform (HHT) is proposed. The transient starting point is detected using the phase-based method and the transient endpoint is detected using a self-adaptive threshold based on the HHT-based energy trajectory. Thirteen features that represent both overall and subtle transient characteristics are proposed to form a radio frequency (RF) fingerprint. The principal component analysis method is used to reduce the dimension of the feature vector and a support vector machine is used for classification. A signal acquisition system is designed to capture the signals from eight mobile phones to test the performance of the proposed method. Experimental results demonstrate that the method is effective and the proposed RF fingerprint can represent more subtle characteristics than the RF fingerprints based on instantaneous amplitude, phase, frequency and energy envelope. This method can be equally applicable for any wireless emitter to enhance the security of the wireless networks.

Experiment and Simulation of Hard Cutting System Dynamic Characteristics

Tool wear is an inevitable and continuously aggravated process in precision hard cutting. Interaction between tool wear and dynamic characteristics in cutting system makes machining accuracy and quality of machined surface declining, which effects dynamic characteristics of cutting system significantly. Based on this, cutter dynamic modal parameters of a specific cutting system are measured in this paper. Cutting force and change law of acceleration power spectrum are studied by experiments. Influence mechanism of tool wear on system frequency energy distribution was obtained, and tool wear effects on the system frequency distribution mechanism is obtained. The cutting system dynamic characteristics are quantified by establishing simulation model. Root locus plot of non-uniform load distribution is obtained and the minimum stiffness ratio in the root locus plot is determined. The effect of tool flank wear on cutting system stability in quantitatively stable is researched. Study results provide theoretical basis for selection of stable precision cutting conditions in different tool wear conditions.

A quantitative estimation technique for welding quality using local mean decomposition and support vector machine

The experimental nonlinear time series of welding current contain the arc feature information related to welding quality. The local mean decomposition (LMD) combining with the support vector machine (SVM) is put forward to quantitatively estimate the rationality of welding parameters and welding formation quality. The LMD is used to investigate the time---frequency distribution of arc energy, and the energy entropy is employed to quantitatively judge the welding arc characteristics related to welding quality. The collected current signal is decomposed into a number of product functions (PFs) by LMD. The energy entropy of each PF is calculated to establish the welding arc energy feature vectors, which are inputted into support vector machine classifier. The LMD combining with SVM can quantitatively estimate the time---frequency energy distribution characteristics of the arc current signal at different welding parameters and welding formation quality. Experimental results are provided to confirm the effectiveness of this approach to estimate the rationality of welding parameters and welding formation quality.

Correlation of Arc Sound and Arc-sidewall Position in Narrow Gap MAG Welding

The control of arc-sidewall distance is the key to ensure good sidewall fusion in the process of narrow gap metal active gas welding(NG-MAG) welding. Through pretreatment and analysis of arc sound in NG-MAG pulse vertical welding, it is found that there exists periodic ringing attenuation phenomenon in the time-domain waveform, and its frequency is equal to the pulse frequency of welding power. The frequency distribution of arc sound energy varies from 0.3 k Hz to 12.0 k Hz. And energy mainly concentrates in two bands, range of 0.3-3.0 k Hz and 9.0-12.0 k Hz. Spectrum distribution of arc sound is highly relevant to arc-sidewall distance. On this basis, noise reduction and time-frequency analysis of arc sound are achieved by use of wavelet transform(WT) and joint time-frequency analysis(JTFA). The results show that low-frequency energy distribution of arc sound is always present, show intermittent-continuous-intermittent, with gradually increasing of arc-sidewall distance. While high-frequency energy distribution of arc sound only present arc-sidewall distance more than 2.6 mm, show intermittent-continuous. The monitoring and early warning of arc-sidewall distance can be realized based on time-frequency distribution of arc sound in NG-MAG pulse vertical welding, which provides a new way for condition monitoring in the process of NG-MAG welding.

THREE-DIMENSIONAL MAGNETOHYDRODYNAMIC MODELING OF PROPAGATING DISTURBANCES IN FAN-LIKE CORONAL LOOPS

Quasi-periodic propagating intensity disturbances (PDs) have been observed in large coronal loops in EUV images over a decade, and are widely accepted to be slow magnetosonic waves. However, spectroscopic observations from Hinode/EIS revealed their association with persistent coronal upflows, making this interpretation debatable. Motivated by the scenario that the coronal upflows could be cumulative result of numerous individual flow pulses generated by sporadic heating events (nanoflares) at the loop base, we construct a velocity driver with repetitive tiny pulses, whose energy frequency distribution follows the flare power-law scaling. We then perform 3D MHD modeling of an idealized bipolar active region by applying this broadband velocity driver at the footpoints of large coronal loops which appear open in the computational domain. Our model successfully reproduces the PDs with similar features as the observed, and shows that any upflow pulses inevitably excite slow magnetosonic wave disturbances propagating along the loop. We find that the generated PDs are dominated by the wave signature as their propagation speeds are consistent with the wave speed in the presence of flows, and the injected flows rapidly decelerate with height. Our simulation results suggest that the observed PDs and associated persistent upflows may be produced by small-scale impulsive heating events (nanoflares) at the loop base, and that the flows and waves may both contribute to the PDs at lower heights.

Monte Carlo calculated microdosimetric spread for cell nucleus-sized targets exposed to brachytherapy 125I and 192Ir sources and 60Co cell irradiation

The stochastic nature of ionizing radiation interactions causes a microdosimetric spread in energy depositions for cell or cell nucleus-sized volumes. The magnitude of the spread may be a confounding factor in dose response analysis. The aim of this work is to give values for the microdosimetric spread for a range of doses imparted by 125I and 192Ir brachytherapy radionuclides, and for a 60Co source. An upgraded version of the Monte Carlo code PENELOPE was used to obtain frequency distributions of specific energy for each of these radiation qualities and for four different cell nucleus-sized volumes. The results demonstrate that the magnitude of the microdosimetric spread increases when the target size decreases or when the energy of the radiation quality is reduced. Frequency distributions calculated according to the formalism of Kellerer and Chmelevsky using full convolution of the Monte Carlo calculated single track frequency distributions confirm that at doses exceeding 0.08 Gy for 125I, 0.1 Gy for 192Ir, and 0.2 Gy for 60Co, the resulting distribution can be accurately approximated with a normal distribution. A parameterization of the width of the distribution as a function of dose and target volume of interest is presented as a convenient form for the use in response modelling or similar contexts.

Structure damage localization with ultrasonic guided waves based on a time–frequency method

The ultrasonic guided wave is widely used for structure health monitoring with the sparse piezoelectric actuator/transducer array in recent decades. It is based on the principle that the damage in the structure would reflect or scatter the wave pulse and thus, the damage-scattered signal could be applied as the feature signal to distinguish the damage. Precise measurement of time of the flight (TOF) of the propagating signal plays a pivotal role in structure damage localization. In this paper, a time–frequency analysis method, Wigner–Ville Distribution (WVD), is applied to calculate the TOF of signal based on its excellent time–frequency energy distribution property. The true energy distribution in the time–frequency domain is beneficial to reliably locate the position of damage. Experimental studies are demonstrated for damage localization of one-dimensional and two-dimensional structures. In comparison with traditional Hilbert envelope and Gabor wavelet transform methods, the proposed WVD-based method has better performance on the accuracy and the stability of damage localization in one-dimensional structure. In addition, the proposed scheme is validated to work effectively for damage imaging of a two-dimensional structure.

Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform

Chatter detection is an important task to improve productivity and part quality in the machining process. Since measured signals from sensors are usually contaminated by background noise and other disturbances, it is necessary to find efficient signal processing algorithms to identify the chatter as soon as possible. This paper presents an effective chatter identification method for the end milling process based on the study of two advanced signal processing techniques, i.e., wavelet package transform (WPT) and Hilbert–Huang transform (HHT). The WPT works as a preprocessor to denoise the measured signals and hence the performance of the HHT is enhanced. The proposed method consists of four steps. First, the measured signals are decomposed by the WPT, so that the chatter signals are allocated in a certain frequency band. Secondly, wavelet packets with rich chatter information are selected and are used to reconstruct new signals. Thirdly, the reconstructed signals are analyzed with HHT to obtain a Hilbert–Huang spectrum, which is a full time–frequency–energy distribution of the signals. Finally, the mean value and standard deviation of the Hilbert–Huang spectrum are calculated to detect the chatter and identify its levels as well. The proposed method is applied to the end milling process and the experimental results prove that the method can identify the chatter effectively.

Feature extraction of AC square wave SAW arc characteristics using improved Hilbert–Huang transformation and energy entropy

In order to extract the arc feature information related to welding quality in alternating current square wave submerged arc welding (AC Square Wave SAW), an improved Hilbert–Huang transform (HHT) is put forward to investigate the time–frequency distribution of arc current, and the energy entropy is employed to quantitatively judge the arc characteristics. The empirical mode decomposition (EMD) is used to decompose the collected current signal into a number of Intrinsic Mode Functions (IMFs). The method for removing the high frequency and undesirable low-frequency IMFs is proposed by using the correlation coefficient of the IMF and the original signal as criterion, and the valid IMFs are selected for the Hilbert transform and energy entropy calculation. The improved HHT combining with energy entropy can quantitatively describe the time–frequency energy distribution characteristics of the arc current signal at different duty cycle, frequency and welding speed. Experimental results are provided to confirm the effectiveness of this approach to extract the arc physical information related to welding quality.

Thermal variability of the Indo-Pacific warm pool

The Indo-Pacific warm pool (IPWP) over the Indian Ocean and the Pacific Ocean is the largest warm pool in the global oceans. Its thermal variability and trend between 1982 and 2011 are extracted from the Ensemble Empirical Mode Decomposition method. Time–frequency energy distributions using the Hilbert–Huang transform are computed to evaluate the thermal spectrum-energy. From the SST variability and the heat storage anomaly, we find that the thermal variability is strongly influenced by the Pacific Decadal Oscillation (PDO). The size, increasing trend in the IPWP, calculated from the SST data is (0.18±0.01)×106km2/decade. Besides, the magnitude of increasing rate during the positive phase of the PDO is couple times higher than that during the negative phase. Furthermore, the SST trend is also associated with 1999/2000 climate shift. The heat increases in the IPWP from 1993 to 2011 implied an average warming rate of 1.41Wm−2. The thermal variations in the IPWP are controlled by interannual to decadal variabilities mostly related to the PDO in the Pacific Ocean sector and by annual variability in the Indian Ocean sector.

Estimating the frequency of extremely energetic solar events, based on solar, stellar, lunar, and terrestrial records

The most powerful explosions on the Sun – in the form of bright flares, intense storms of solar energetic particles (SEPs), and fast coronal mass ejections (CMEs) – drive the most severe space‐weather storms. Proxy records of flare energies based on SEPs in principle may offer the longest time base to study infrequent large events. We conclude that one suggested proxy, nitrate concentrations in polar ice cores, does not map reliably to SEP events. Concentrations of select radionuclides measured in natural archives may prove useful in extending the time interval of direct observations up to ten millennia, but as their calibration to solar flare fluences depends on multiple poorly known properties and processes, these proxies cannot presently be used to help determine the flare energy frequency distribution. Being thus limited to the use of direct flare observations, we evaluate the probabilities of large‐energy solar events by combining solar flare observations with an ensemble of stellar flare observations. We conclude that solar flare energies form a relatively smooth distribution from small events to large flares, while flares on magnetically active, young Sun‐like stars have energies and frequencies markedly in excess of strong solar flares, even after an empirical scaling with the mean coronal activity level of these stars. In order to empirically quantify the frequency of uncommonly large solar flares extensive surveys of stars of near‐solar age need to be obtained, such as is feasible with the Kepler satellite. Because the likelihood of flares larger than approximately X30 remains empirically unconstrained, we present indirect arguments, based on records of sunspots and on statistical arguments, that solar flares in the past four centuries have likely not substantially exceeded the level of the largest flares observed in the space era, and that there is at most about a 10% chance of a flare larger than about X30 in the next 30 years.

STATISTICAL ANALYSES ON THERMAL ASPECTS OF SOLAR FLARES

The frequency distribution of flare energies provides a crucial diagnostic to calculate the overall energy residing in flares and to estimate the role of flares in coronal heating. It often takes a power law as its functional form. We have analyzed various variables, including the thermal energies E-th of 1843 flares at their peak time. They were recorded by both Geostationary Operational Environmental Satellites and Reuven Ramaty High-Energy Solar Spectroscopic Imager during the time period from 2002 to 2009 and are classified as flares greater than C 1.0. The relationship between different flare parameters is investigated. It is found that fitting the frequency distribution of E-th to a power law results in an index of -2.38. We also investigate the corrected thermal energy E-c_th, which represents the flare total thermal energy including the energy loss in the rising phase. Its corresponding power-law slope is -2.35. Compilation of the frequency distributions of the thermal energies from nanoflares, microflares, and flares in the present work and from other authors shows that power-law indices below -2.0 have covered the range from 10(24) to 10(32) erg. Whether this frequency distribution can provide sufficient energy to coronal heatings in active regions and the quiet Sun is discussed.

Analysis on Grinding Force of SiCp/Al Composites Based on MATLAB

SiCp/Al composites were grinded with Diamond Wheel on high-precision machine. Acquiring grinding force signals of the experiment with High Speed Advantech PCI－1712 board. Acquired grinding force signals are analyzed in MATLAB which has mighty function disposing signals. Through analyzing the grinding force in time field, frequency field and the signals wavelet analysis for different grinding parameters, found that the feed velocity has much more influence on the grinding force, the grinding force ratio and the standard variance of the grinding force than the depth of grinding when the velocity of the Diamond Wheel is fixed. The frequency structure and energy distribution of grinding force are showed in PSD analysis. The signals are de-noised in wavelet tools. The result of the experiment indicates the grinding force increases, the grinding force ratio decreases and the standard variance of the grinding force varies complex for different material with the increasing of the velocity of the workpiece and the grinding depth.

The Study on Automobile Engine Noise based on Sound Intensity Method

Automobile noise contains a variety of different noise. It can be divided into the engine noise and chassis of noise. The engine noise is main noise sources of automobile noise, and can be roughly divided into pneumatic noise, combustion noise, and mechanical noise. These are caused by the recruit vibration of the students in air, when a car engine exhausts fan rotation, these voices, the light into the air directly. No, the largest into exhaust muffler for automobile engine exhaust noise, another kind is the source of the intake noise. Fan noise, usually main noise sources especially air-cooled engine. Combustion noise, mechanical noise is difficult to disaggregate. Study on emission of vehicle noise frequency characteristics and energy distribution characteristics, with the optical image automatic overlapping, obtained the vehicle noise source frequency, spatial location and origin. The test results show that, the microphone array technology is an effective way for noise diagnosis and sound localization, for noise control of vehicle to provide a scientific basis.

Identification Technique for the Intrusion of Airport Enclosure Based on Double Mach-Zehnder Interferometer

In view of the high rate of false alarm problems on the distributed optical fiber sensing technique applied in enclosure, a sensor is proposed based on double Mach-Zehnder interferometer and a method is needed urgently to identify intrusion accurately. This involves eliminating non-intrusion events caused by operating environments from intrusion events. To address the issue, this paper states the tensile strain principle of optical fiber and combines autocorrelation with frequency-energy distribution of signals to extract vibration characteristics. Finally, an identification technique is presented based on vibration theory to distinguish events. The results of simulation show that the algorithm can eliminate vibration characteristic of non-intrusion events so as to minimize the nuisance alarm rate and make prepare for the following recognition of intrusion.

CAN A NANOFLARE MODEL OF EXTREME-ULTRAVIOLET IRRADIANCES DESCRIBE THE HEATING OF THE SOLAR CORONA?

Nanoflares, the basic unit of impulsive energy release may produce much of the solar background emission. Extrapolation of the energy frequency distribution of observed microflares, which follows a power law to lower energies can give an estimation of the importance of nanoflares for heating the solar corona. If the power law index is greater than 2, then the nanoflare contribution is dominant. We model time series of extreme ultraviolet emission radiance, as random flares with a power law exponent of the flare event distribution. The model is based on three key parameters, the flare rate, the flare duration and the power law exponent of the flare intensity frequency distribution. We use this model to simulate emission line radiance detected in 171 \AA, observed by STEREO/EUVI and SDO/AIA. The Observed light curves are matched with simulated light curves using an Artificial Neural Network and parameter values are determined across regions of active region, quiet sun, and coronal hole. The damping rate of nanoflares is compared with radiative losses cooling time. The effect of background emission, data cadence, and network sensitivity on the key parameters of model is studied. Most of the observed light curves have a power law exponent, $\alpha$, greater than the critical value 2. At these sites nanoflare heating could be significant.

Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm

Problem statement: The time frequency analysis of non-stationary signals has been the considerable research effort in recent years. Wavelet transform is one of the favored tool for the analyzing the biomedical signals. Approach: We describe the identification of Electro-Oculograph (EOG) signals of eye movement potentials by using wavelet algorithm which gives a lot of information than FFT. The capability of wavelet transform was to distribute the signal energy with the change of time in different frequency bands. This will showed the characteristic of the signals since energy was an important physical variable in signal analysis. The EOG signals were captured using electrodes placed on the forehead around the eyes to record the eye movements. The wavelet features used to determine the characteristic of eye movement waveform. This technique adopted because it was a non-invasive, inexpensive and accurate. The new technology enhancement has allowed the EOG signals captured using the Neuronal EEG-9200. The recorded data was composed of an eye movement toward four directions, i.e., downward, upward, leftward and rightward. The proposed analysis for each eyes signal is analyzed by using Wavelet Transform (WT) with energy algorithm and by comparing the energy distribution with the change of time and frequency of each signal. Results: A wavelet Scalogram was plotted to display the different percentages of energy for each wavelet coefficient towards different movement. Conclusion: From the result, it is proved that the different EOG signals exhibit differences in signals energy with their corresponding scale such as leftward with scale 6 (8- 16Hz), rightward with scale 8 (2-4Hz), downward with scale 9 (1-2Hz) and upward with level 7 (4-8Hz). Statistically, the results in this study indicate that there are 93% (averages) significance differences in the extracted features of wavelet Scalogram analysis.