Time-frequency response spectrum of rotational ground motion and its application

The rotational seismic motions are estimated from one station records of the 1999 Jiji （Chi-Chi）, Taiwan, earthquake based on the theory of elastic plane wave propagation. The time-frequency response spectrum （TFRS） of the rotational motions is calculated and its characteristics are analyzed, then the TFRS is applied to analyze the damage mechanism of one twelve-storey frame concrete structure. The results show that one of the ground motion components can not reflect the characteristics of the seismic motions completely; the characteristics of each component, especially rotational motions, need to be studied. The damage line of the structure and TFRS of ground motion are important for seismic design, only the TFRS of input seismic wave is suitable, the structure design is reliable.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

AUTOREGRESSIVE MODEL AND POWER SPECTRUM CHARATERISTICS OF CURRENT SIGNAL IN HIGH FREQUENCY GROUP PULSE MICRO-ELECTROCHEMICAL MACHINING

The identification of the inter-electrode gap size in the high frequency group pulse micro-electrochemical machining （HGPECM） is mainly discussed. The auto-regressive（AR） model of group pulse current flowing across the cathode and the anode are created under different situations with different processing parameters and inter-electrode gap size. The AR model based on the current signals indicates that the order of the AR model is obviously different relating to the different processing conditions and the inter-electrode gap size; Moreover, it is different about the stability of the dynamic system, i.e. the white noise response of the Green＇s function of the dynamic system is diverse. In addition, power spectrum method is used in the analysis of the dynamic time series about the current signals with different inter-electrode gap size, the results show that there exists a strongest power spectrum peak, characteristic power spectrum（CPS）, to the current signals related to the different inter-electrode gap size in the range of 0～5 kHz. Therefore, the CPS of current signals can implement the identification of the inter-electrode gap.

Earthquake accelerogram simulation with statistical law of evolutionary power spectrum

By using the technique for evolutionary power spectrum proposed by Nakayama and with reference to the Kameda formula, an evolutionary spectrum prediction model for given earthquake magnitude and distance is established based on the 80 near-source acceleration records at rock surface with large magnitude from the ground motion database of western U.S.. Then a new iteration method is developed for generation of random accelerograms non-stationary both in amplitude and frequency which are compatible with target evolutionary spectrum. The phase spectra of those simulated accelerograms are also non-stationary in time and frequency domains since the interaction between amplitude and phase angle has been considered during the generation. Furthermore, the sign of the phase spectrum increment is identified to accelerate the iteration. With the proposed statistical model for predicting evolutionary power spectra and the new method for generating compatible time history, the artificial random earthquake accelerograms non-stationary both in amplitude and frequency for certain magnitude and distance can be provided.

Joint Power Control and Spectrum Allocation for Cognitive Radio with QoS Constraint

Spectrum sharing with quality of service (QoS) requirement and power constraint on cognitive users is studied. The objective is to maximize the system throughput. This problem is modeled as a mixed integer nonlinear programming problem and then transformed to a continuous nonlinear programming problem through eliminating integer variables. We propose the joint power control and spectrum allocation algorithm based on particle swarm optimization to obtain the global optimal solution. Simulation results show that the proposed method can achieve higher system throughput and spectrum utilization under the constraints of transmit power and QoS requirement.

Universal form of the power spectrum of the aero-optical aberration caused by the supersonic turbulent boundary layer

Based on the measurement of one-dimensional （1D） optical path difference （OPD） of the supersonic turbulent bound- ary layer, an analytical form for the power spectrum of the two-dimensional （2D） OPD is obtained with its structure function and under the locally homogeneous isotropic assumption. The universality of this spectrum is argued, and its validity is checked by the comparison with experimental result. The potential applications of this model in theoretical and numerical studies are emphasized. Another contribution of this work is around the application of correlation function to analyzing the statistics of OPD. Based on our results and other results published elsewhere, we show that the OPD is often not stationary, and one should be cautious about using this tool.

Analysis of characteristics of time-frequency correlations between runoff,tidal range and salinity in the Changjiang Estuary

Power spectrum and cross-wavelet transform analysis was adopted to study the time-frequency characteristics and multiscale correlations between runoff,tidal range and salinity in the Changjiang Estuary based on the runoff data collected at the Datong Station,the tidal range measured at the Baozhen Station,and the salinity at the Baogang Station from 2008 to 2009.The variations of the salinity showed significant periodicity at scales of 2-3,7-8,14-15 and 26-30 d.The correlation between the salinity and the runoff and the tidal range were found to be significantly related to shock at scales of 5-7,14-15,26-30 d and 0.5 a.The correlation between the runoff and the salinity was mainly in the same phase,while the correlation between the tidal range and the salinity was in the antiphase.Different frequency bands were related to different degrees,and their relevance increased as the resonance frequency decreased.In addition,changes of the seasonal runoff were obvious.Specifically,a point of discontinuity was reached in early June with a cycle of 7-8 d,which coincided with the periodicity of plum rains in the Changjiang-Huaihe region.High-frequency changes （8-16 d period） of the salinity corresponded to the time domain in January-April 2008,February-April 2009 and October-December 2009 and exhibited an approximately 0.5 a （184 d） long frequency oscillation.Short-period changes were found to be stronger than long-period changes.Cross-wavelet transforms for the salinity,the runoff and the tidal range revealed local features in the time domain,while the significant levels of different periodic oscillations were observed in the frequency domain.The correlation characteristics of the salinity and the runoff were significant in the 80-90 d frequency domain,indicating that the major impact of the runoff on the salinity was reflected in seasonal changes.The tidal range on the small scale of 14-15 and 30-32 d was more obvious than the runoff.

Spectrum Efficiency Maximization for Cooperative Power Beacon-Enabled Wireless Powered Communication Networks

We consider a spectrum efficiency(SE)maximization problem for cooperative power beacon-enabled wireless powered communication networks(CPB-WPCNs),where each transmitter harvests en-ergy from multi-antenna power beacons(PBs)and transmits data to the corresponding receiver.For data transmission,both orthogonal transmission,i.e.,the time splitting(TS)mode,and non-orthogonal trans-mission,i.e.,the interference channel(IC)mode,are considered.Aiming to improve the system SE,the energy beamformers of PBs,the transmit power,and the transmit time duration of transmitters are jointly optimized.For the TS mode,the original non-convex problem is transformed into a convex opti-mization problem by means of variable substitution and semidefinite relaxation(SDR).The rank-one na-ture of this SDR is proved,and then a Lagrange-dual based fast algorithm is proposed to obtain the opti-mal solution with much lower complexity.For the IC mode,to conquer the strong non-convexity of the problem,a branch-reduce-and-bound(BRB)mono-tonic optimization algorithm is designed as a bench-mark.Furthermore,a low-complexity distributed suc-cessive convex approximation(SCA)algorithm is pre-sented.Finally,simulation results validate the perfor-mance of the proposed algorithms,achieving optimal-ity within only 1%∼2%computation time compared to the CVX solver in the TS mode and achieving 98%of the optimal performance in the IC mode.

Research and Implementation of Text Similarity System Based on Power Spectrum Analysis

The paper proposed the research and implement of text similarity system based on power spectrum analysis. It is not difficult to imagine that the signals of brain are closely linked with writing process. So we build text modeling and set pulse signal function to get the power spectrum of the text. The specific detail is getting power spectrum from economic field to build spectral library, and then using the method of power spectrum matching algorithm to judge whether the test text belonged to the economic field. The method made text similarity system finish the function of text intelligent classification efficiently and accurately.

MODAL PARAMETERS EXTRACTION WITH CROSS CORRELATION FUNCTION AND CROSS POWER SPECTRUM UNDER UNKNOWN EXCITATION

In most of real operational conditions only response data are measurable while the actual excitations are unknown, so modal parameter must be extracted only from responses. This paper gives a theoretical formulation for the cross correlation functions and cross power spectra between the outputs under the assumption of white noise excitation. It widens the field of modal analysis under ambient excitation because many classical methods by impulse response functions or frequency response functions can be used easily for modal analysis under unknown excitation. The Polyreference Complex Exponential method and Eigensystem Realization Algorithm using cross correlation functions in time domain and Orthogonal Polynomial method using cross power spectra in frequency domain are applied to a steel frame to extract modal parameters under operational conditions. The modal properties of the steel frame from these three methods are compared with those from frequency response functions analysis. The results show that the modal analysis method using cross correlation functions or cross power spectra presented in this paper can extract modal parameters efficiently under unknown excitation.

Matched Filter Based Spectrum Sensing When Primary User Has Multiple Power Levels

In this paper,we investigate the matched filter based spectrum sensing in a more reasonable cognitive radio(CR) scenario when the primary user(PU) has more than one transmit power levels,as regulated in most standards,i.e.,IEEE 802.11 Series,GSM,LTE,LTE-A,etc.This new multiple primary transmit power(MPTP) scenario is specialized by two different targets:detecting the presence of PU and identifying the power level.Compared to the traditional binary sensing where only the presence of PU is checked,SU may attain more information about the primary network(making CR more "intelligent") and design the subsequent optimization strategy.The key technology is the multiple hypothesis testing as opposed to the traditional binary hypothesis testing.We discuss two situations under whether the channel phase is known or not,and we derive the closed form solutions for decision regions and several performance metrics,from which some interesting phenomenons are observed and the related discussions are presented.Numerical examples are provided to corroborate the proposed studies.

BP-Neural-Network-Based Tool Wear Monitoring by Using Wav elet Decomposition of the Power Spectrum

In a drilling process, the power spectrum of the drilling force is related tothe tool wear and is widely applied in the monitoring of tool wear. But the feature extraction andidentification of the power spectrum have always been an unresolved difficult problem. This papersolves it through decomposition of the power spectrum in multilayers using wavelet transform andextraction of the low frequency decomposition coefficient as the envelope information of the powerspectrum. Intelligent identification of the tool wear status is achieved in the drilling processthrough fusing the wavelet decomposition coefficient of the power spectrum by using a BP (BackPropagation) neural network. The experimental results show that the features of the power spectrumcan be extracted efficiently through this method, and the trained neural networks show highidentification precision and the ability of extension.

A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density（PSD）, a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method（PSC）, is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform,the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman–Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty,and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.

The power spectrum and correlation of flow noise for an axisymmetric body in water

Understanding the physical features of the flow noise for an axisymmetric body is important for improving the performance of a sonar mounted on an underwater platform. Analytical calculation and numerical analysis of the physical features of the flow noise for an axisymmetric body are presented and a simulation scheme for the noise correlation on the hydrophones is given. It is shown that the numerical values of the flow noise coincide well with the analytical values. The main physical features of flow noise are obtained. The flow noises of two different models are compared and a model with a rather optimal fore-body shape is given. The flow noise in horizontal symmetry profile of the axisymmetric body is non-uniform, but it is omni-directional and has little difference in the cross section of the body. The loss of noise diffraction has a great effect on the flow noise from boundary layer transition. Meanwhile, based on the simulation, the noise power level increases with velocity to approximately the fifth power at high frequencies, which is consistent with the experiment data reported in the literature. Furthermore, the flow noise received by the acoustic array has lower correlation at a designed central frequency, which is important for sonar system design.

Improved significance testing of wavelet power spectrum near data boundaries as applied to polar research

When one applies the wavelet transform to analyze finite-length time series, discontinuities at the data boundaries will distort its wavelet power spectrum in some regions which are defined as a wavelength-dependent cone of influence （COI）. In the COI, significance tests are unreliable. At the same time, as many time series are short and noisy, the COI is a serious limitation in wavelet analysis of time series. In this paper, we will give a method to reduce boundary effects and discover significant frequencies in the COI. After that, we will apply our method to analyze Greenland winter temperature and Baltic sea ice. The new method makes use of line removal and odd extension of the time series. This causes the derivative of the series to be continuous （unlike the case for other padding methods）. This will give the most reasonable padding methodology if the time series being analyzed has red noise characteristics.

Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection.In this paper,a new method is presented to capture the contour edge,texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm.More abundant texture information is extracted.Cloud images can be obtained a multiscale and multidirection decomposition.The coefficient matrix from Contourlet transform of ground nephogram is calculated.The energy,mean and variance characteristics calculated from coefficient matrix are composed of the feature information.The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis.Then Support Vector Machines(SVM)classifier is used to classify according to the frequency information of the trend graph of data series.It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.

Novel Power Law of Turbulent Spectrum

This paper is concerned with novel power law of turbulent energy spectrum and the relevant experiment in tidal current. The power law in the inertial sub-range has been proposed in such a way that the power of the one-dimensional turbulent energy spectrum varies from 0 to -2 approximately, but it is accompanying the small oscillation with increasing the wave number. The well-known Kolmogorov -5/3 power law is merely one facet, to appear within the present proposed novel power law. The turbulent energy spectra (Su, Sv, Sw) in x-, y-and z-directions, respectively oscillate with the wave number. It is found that the turbulent in the tidal currents is three dimensional, and the intermittence of momentum transport is a predominant and characteristic feature in tidal current.

Hydrodynamic Response and Power Performance of A Heave and Pitch Buoy Wave Energy Converter Under Bimodal Ochi-Hubble Wave Spectrum

In coastal sea areas with the bimodal Ochi-Hubble wave spectrum, such as parts of the China Sea and Indian Ocean,wave energy is the superposition of wind wave and swell. Traditional heaving buoy wave energy converters developed with narrowband wave spectrums suffer from big energy loss in these areas, leading to lower power absorption efficiency and higher generating costs. In contrast, multi-freedom buoy has different resonant frequencies and maximal power capture wave frequencies in different degrees of freedom(DOFs). Therefore, this study proposed using two DOFs to capture the energy of wind wave and swell correspondingly. A heave and pitch buoy model was established by potential flow theory and validated by experimental data. Coupling effect on the motion and power absorption, power capture frequency distribution and power absorption with different linear power takeoff system damping coefficients were analyzed to reveal the hydrodynamic response and the power performance of the two DOFs. The results indicate that by using heave and pitch DOFs, the wave energy components of wind wave and swell were captured in a targeted manner. It demonstrates that the 2-DOF buoy is an effective tool to avoid the energy loss and realize the efficient power absorption in coastal sea areas with bimodal Ochi-Hubble waves.