THE APPLICATION OF STATISTICAL PARAMETERS OF PHASE RESOLVED PD DISTRIBUTION TO AGING EXTENT ASSESSMENT OF LARGE GENERATOR INSULATION

Objective To investigate the characteristic parameters employed to describe the aging extent of stator insulation of large generator and study the aging laws. Methods Multi stress aging tests of model generator stator bar specimens were performed and PD measurements were conducted using digital PD detector with frequency range from 40?kHz to 400?kHz at different aging stage. Results From the test results of model specimens it was found that the skewness of phase resolved PD distribution might be taken as the characterization parameters for aging extent assessment of generator insulation. Furthermore, the measurement results of actual generator stator bars showed that the method based on statistical parameters of PD distributions are prospective for aging extent assessment and residual lifetime estimation of large generator insulation. Conclusion Statistical parameters of phase resolved PD distribution was proposed for aging extent assessment of large generator insulation.

Prestack seismic stochastic inversion based on statistical characteristic parameters

In the conventional stochastic inversion method,the spatial structure information of underground strata is usually characterized by variograms.However,effectively characterizing the heterogeneity of complex strata is difficult.In this paper,multiple parameters are used to fully explore the underground formation information in the known seismic reflection and well log data.The spatial structure characteristics of complex underground reservoirs are described more comprehensively using multiple statistical characteristic parameters.We propose a prestack seismic stochastic inversion method based on prior information on statistical characteristic parameters.According to the random medium theory,this method obtains several statistical characteristic parameters from known seismic and logging data,constructs a prior information model that meets the spatial structure characteristics of the underground strata,and integrates multiparameter constraints into the likelihood function to construct the objective function.The very fast quantum annealing algorithm is used to optimize and update the objective function to obtain the fi nal inversion result.The model test shows that compared with the traditional prior information model construction method,the prior information model based on multiple parameters in this paper contains more detailed stratigraphic information,which can better describe complex underground reservoirs.A real data analysis shows that the stochastic inversion method proposed in this paper can effectively predict the geophysical characteristics of complex underground reservoirs and has a high resolution.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

Morphological parameters of both surfaces of coupled joint

Twenty one joints were made with Brazilian tests and each surface was scanned by the Talysurf CLI 2000. Morphological characteristics of joint surface were quantified by statistical and textural parameters. By the contrast of these parameters between both sides of each coupled joint, the following conclusions are drawn. The upper and lower surfaces of coupled joints have approximately equal values of Sp（maximum height of joint surface）, Sa（arithmetic mean height of joint surface） and Sq（root mean square height of joint surface）, but the Ssk（skewness of the height distribution of joint surface） values of the two surfaces of a coupled joint are different, one is positive while the other is negative. The Saj（auto-correlation length） parameter values of both surfaces of each coupled joint are quite close, and the S^（texture aspect ratio） values have the same situation to the Sal parameter, but the same parameters of different surfaces have big differences which illustrates its own characteristics of each joint. The two surfaces of each coupled joint have similar values of θp （mean profile angle） which can be used to deduce the value of θp each other.

A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

To overcome the large time-delay in measuring the hardness of mixed rubber, rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model, the strategy could dramat- ically save the storage cost and overcome the adverse influence of low signal-to-noise ratio samples. Moreover, it could be applied to any statistical process monitoring system without drastic changes to them, which is practical for industrial practices. As examples, the Q-based strategy was integrated with three popular algorithms （partial least squares （PIE）, recursive PIE （RPLS）, and kernel PIE （KPIE）） to form novel regression ones, QPLS, QRPIE and QKPLS, respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

On the surface roughness characteristics of the land fast sea-ice in the Bohai Sea

The surface roughness characteristics （e.g., height and slope） of sea ice are critical for determining the parameters of an electromagnetic scattering, a surface emission and a surface drag coefficients. It is also important in identifying various ice types, retrieval ice thickness, surface temperature and drag coefficients from remote sensing data. The point clouds （a set of points which are usually defined by X, Y, and Z coordinates that represents the external surface of an object on earth） of land fast ice in five in situ sites in the eastern coast Bohai Sea were measured using a laser scanner-Trimble GX during 2011-2012 winter season. Two hundred and fifty profiles selected from the point clouds of different samples have been used to calcu- late the height root mean square, height skewness, height kurtosis, slope root mean square, slope skewness and slope kurtosis of them. The root mean square of the height, the root mean square of the slope and the correlation length are about 0.090, 0.075 and 11.74 m, respectively. The heights of 150 profiles in three sites manifest the Gaussian distribution and the slopes of total 250 profiles distributed exponentially. In addition, the fractal dimension and power spectral density profiles were calculated. The results show that the fractal dimension of land fast ice in the Bohai Sea is about 1.132. The power spectral densities of 250 profiles can be expressed through an exponential autocorrelation function.

Data-driven estimation of joint roughness coefficient

Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass.Therefore,the joint roughness coefficient(JRC)estimation is of paramount importance in geomechanics engineering applications.Studies show that the application of statistical parameters alone may not produce a sufficiently reliable estimation of the JRC values.Therefore,alternative data-driven methods are proposed to assess the JRC values.In this study,Gaussian process(GP),K-star,random forest(RF),and extreme gradient boosting(XGBoost)models are employed,and their performance and accuracy are compared with those of benchmark regression formula(i.e.Z2,Rp,and SDi)for the JRC estimation.To analyze the models’performance,112 rock joint profile datasets having eight common statistical parameters(R_(ave),R_(max),SD_(h),iave,SD_(i),Z_(2),R_(p),and SF)and one output variable(JRC)are utilized,of which 89 and 23 datasets are used for training and validation of models,respectively.The interpretability of the developed XGBoost model is presented in terms of feature importance ranking,partial dependence plots(PDPs),feature interaction,and local interpretable model-agnostic explanations(LIME)techniques.Analyses of results show that machine learning models demonstrate higher accuracy and precision for estimating JRC values compared with the benchmark empirical equations,indicating the generalization ability of the data-driven models in better estimation accuracy.

A Method for Surface Roughness Parameter Estimation in Passive Microwave Remote Sensing

Surface roughness parameter is an important factor and obstacle for retrieving soil moisture in passive microwave remote sensing.Two statistical parameters,root mean square (RMS) height (s) and correlation length (l),are designed for describing the roughness of a randomly rough surface.The roughness parameter measured by traditional way is independence of frequency,soil moisture and soil heterogeneity and just the ″geometric″ roughness of random surface.This ″geometric″ roughness can not fully explain the scattered thermal radiation by the earth's surface.The relationship between ″geometric″ roughness and integrated roughness (contain both ″geometric″ roughness and ″dielectric″ roughness) is linked by empirical coefficient.In view of this problem,this paper presents a method for estimating integrated surface roughness from radiometer sampling data at different frequencies,which mainly based on the flourier relationship between power spectral density distribution and spatial autocorrelation function.We can obtain integrated surface roughness at different frequencies by this method.Besides "geometric" roughness,this integrated surface roughness not only contains "dielectric" roughness but also includes frequency dependence.Combined with Q/H model the polarization coupling coefficient can also be obtained for both H and V polarization.Meanwhile,the simulated numerical results show that radiometer with a sensitivity of 0.1 K can distinguish the different surface roughness and the change of roughness with frequency for the same rough surface.This confirms the feasibility of radiometer sampling method for estimating the surface roughness theoretically.This method overcomes the problem of ″dielectric″ roughness measurement to some extent and can achieve the integrated surface roughness within a microwave pixel which can serve soil moisture inversion better than the ″geometric″ roughness.

Wind Wave Characteristics and Engineering Environment of the South China Sea

Wave simulation was conducted for the period 1976 to 2005 in the South China Sea （SCS） using the wave model, WAVEWATCH-III. Wave characteristics and engineering environment were studied in the region. The wind input data are from the objective reanalysis wind datasets, which assimilate meteorological data from several sources. Comparisons of significant wave heights between simulation and TOPEX/Poseidon altimeter and buoy data show a good agreement in general. By statistical analysis, the wave characteristics, such as significant wave heights, dominant wave directions, and their seasonal variations, were discussed. The largest significant wave heights are found in winter and the smallest in spring. The annual mean dominant wave direction is northeast （NE） along the southwest （SW）-NE axis, east northeast in the northwest （NW） part of SCS, and north northeast in the southeast （SE） part of SCS. The joint distributions of wave heights and wave periods （directions） were studied. The results show a single peak pattern for joint significant wave heights and periods, and a double peak pattern for joint significant wave heights and mean directions. Furthermore, the main wave extreme parameters and directional extreme values, particularly for the 100-year return period, were also investigated. The main extreme values of significant wave heights are larger in the northern part of SCS than in the south- ern part, with the maximum value occurring to the southeast of Hainan Island. The direction of large directional extreme Hs values is focus in E in the northem and middle sea areas of SCS, while the direction of those is focus in N in the southeast sea areas of SCS.

Spectral classification of stars based on LAMOST spectra

In this work, we select spectra of stars with high signal-to-noise ratio from LAMOST data and map their MK classes to the spectral features. The equivalent widths of prominent spectral lines, which play a similar role as multi-color photometry, form a clean stellar locus well ordered by MK classes. The advantage of the stellar locus in line indices is that it gives a natural and continuous classification of stars consistent with either broadly used MK classes or stellar astrophysical parameters. We also employ an SVM-based classification algorithm to assign MK classes to LAMOST stellar spectra. We find that the completenesses of the classifications are up to 90% for A and G type stars, but they are down to about 50% for OB and K type stars. About 40% of the OB and K type stars are mis-classified as A and G type stars,respectively. This is likely due to the difference in the spectral features between late B type and early A type stars or between late G and early K type stars being very weak. The relatively poor performance of the automatic MK classification with SVM suggests that the direct use of line indices to classify stars is likely a more preferable choice.

Degeneracy and discreteness in cosmological model fitting

We explore the problems of degeneracy and discreteness in the standard cosmological model（ΛCDM）. We use the Observational Hubble Data（OHD） and the type Ia supernovae（SNe Ia） data to study this issue. In order to describe the discreteness in fitting of data, we define a factor G to test the influence from each single data point and analyze the goodness of G. Our results indicate that a higher absolute value of G shows a better capability of distinguishing models, which means the parameters are restricted into smaller confidence intervals with a larger figure of merit evaluation. Consequently, we claim that the factor G is an effective way of model differentiation when using different models to fit the observational data.

A tool for the morphological classification of galaxies: the concentration index

Using galaxy data from the Sloan Digital Sky Survey Data Release 8, I ex- plore whether the concentration index is a good morphological classification tool and find that a reasonable sample of pure late-type galaxies can be constructed with the choice of the r-band concentration index ci=2.85. The opposite is not true, however, due to the fairly high contamination of an early-type sample by late-type galaxies. In such an analysis, the influence of selection effects is less important. To disentangle correlations of the morphology and concentration index with stellar mass, star forma- tion rate （SFR）, specific star formation rate （SSFR） and active galactic nucleus （AGN） activity, I investigate correlations of the concentration index with these properties at a fixed morphology and correlations of the morphology with these properties at a fixed concentration index. It is found that at a fixed morphology, high-concentration galaxies are preferentially more massive and have a lower SFR and SSFR than low- concentration galaxies, whereas at a fixed concentration index, elliptical galaxies are preferentially more massive and have a lower SFR and SSFR than spiral galaxies. This result shows that the stellar mass, SFR and SSFR of a galaxy are correlated with its concentration index as well as its morphology. In addition, I note that AGNs are pref- erentially found in more concentrated galaxies only in the sample of spiral galaxies.

DWT Power Spectrum of the Two-Degree Field Galaxy Redshift Survey

The power spectrum of the two-degree Field Galaxy Redshift Survey （2dFGRS） sample is estimated with the discrete wavelet transform （DWT） method. The DWT power spectra within 0.035 〈 k 〈 2.2 h Mpc^-1 are measured for three volume-limited samples defined in consecutive absolute magnitude bins - 19 - - 18, - 20 - - 19 and - 21 - - 20. We show that the DWT power spectrum can effectively distinguish ACDM models of σ8 = 0.84 and σ8 = 0.74. We adopt maximum likelihood method to perform three-parameter fitting of the bias parameter b, pairwise velocity dispersion σpv and redshift distortion parameterβ = Ωm^0.6/b to the measured DWT power spectrum. The fitting results state that in a σ8 = 0.84 universe the best-fit values of Ωm given by the three samples are mutually consistent within the range 0.28 - 0.36, and the best fitted values of Opv are 398-27^＋35, 475-29^37 and 550 ± 20 km s^-1 for the three samples, respectively. In the model of σ8 = 0.74, our three samples give very different values of Ωm. We repeated the fitting using the empirical formula of redshift distortion. The result of the model of low σ8 is still poor, especially, one of the best-fit values of σpv is as large as 10^3 km s^-1. We also repeated our fitting by incorporating a scale-dependent galaxy bias. This gave a slightly lower value of Ωm. Differences between the models of σ8 = 0.84 and σ8 = 0.74 still exist in the fitting results. The power spectrum of 2dFGRS seems to disfavor models with low amplitude of density fluctuations if the bias parameter is assumed to be scale independent. For the fitting value of Ωm to be consistent with that given by WMAP3, strong scale dependence of the bias parameters is needed.

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit(ITSC)faults in induction motors(IMs)is proposed.Determining the incipient ITSC fault and its severity is challenging for several reasons.The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault.Moreover,the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity.The objective of this study is the early detection of incipient ITSC faults.Furthermore,it aims to determine the percentage of shorted turns in the faulty phase,which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform.A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet.The statistical parameters L1 and L2 norms are computed for the detailed coefficients.These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault.Combining L1 and L2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim.An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process.The classifier output defines the percentage of defective turns with a high level of accuracy.The competency of the adopted methodology is validated via simulations and experiments.The results confirm the merits of the proposed method,with a classification test correctness of 95.29%.

Probabilistic model for vessel-bridge collisions in the Three Gorges Reservoir

Based on a field observation on vessel transit path of three bridges over the Yangtze River in the Three Gorges Reservoir,and an analysis of the geometric probabilistic model of transiting vessels in collision probability calculation,the aberrancy angle and vessel velocity probabilistic model related with impact force,a probabilistic model is established and also verified by goodness-of-fit test.The vessel transit path distribution can be expressed by the normal distribution model.For the Three Gorges Reservoir,the mean and standard deviation adopt 0.2w and 0.1w,respectively(w is the channel width).The aberrancy angle distribution of vessels accepts maximum I distribution model,and its distribution parameters can be taken as 0.314 and 4.354.The velocity distribution of up-bound and down-bound vessels can also be expressed by the normal distribution model.

Generalized Coherent States for Position-Dependent Effective Mass Systems

A generalized scheme for the construction of coherent states in the context of position-dependent effective mass systems has been presented. This formalism is based on the ladder operators and associated algebra of the system which are obtained using the concepts of supersymmetric quantum mechanics and the property of shape invariance. In order to exemplify the general results and to analyze the properties of the coherent states, several examples have been considered.