MRF based construction of statistical operator and its application

Based on the Markov random field (MRF) theory, a new nonlinear operator isdefined according to the statistical information in the image, and the corresponding 2Dnonlinear wavelet transform is also provided. It is proved that many detail coefficientsbeing zero (or almost zero) in the smooth gray-level variation areas can be achievedunder the conditional probability density function in MRF model, which shows that thisoperator is suitable for the task of image compression, especially for lossless codingapplications. Experimental results using several test images indicate good performancesof the proposed method with the smaller entropy for the compound and smooth medicalimages with respect to the other nonlinear transform methods based on median andmorphological operator and some well-known linear lifting wavelet transform methods(5/3, 9/7, and S+P).

Fractals Generated by Statistical Contraction Operators

In the theory of random fractal, there are two important classes of random sets, one is the class of fractals generated by the paths of stochastic processes and another one is the class of factals generated by statistical contraction operators. Now we will introduce some things about the probability basis and fractal properties of fractals in the last class. The probability basis contains (1) the convergence and measurability of a random recursive setK(ω) as a random element, (2) martingals property. The fractal properties include (3) the character of various similarity, (4) the separability property, (5) the support and zero-one law of distributionP k =P·K ?1, (6) the Hausdorff dimension and Hausdorff exact measure function.

The Construction of Statistically Recursive Sets

We have constructed a class of random sets by statistical contraction operators in this paper.When the probability space is some special product space and the statistical contraction operators are affine or similar,these statistically recursive sets are investigated by many authors.It will be very convenient for us to study their distributions and dimensions and measures using our model in this paper.

The Classification of Statistically Recursive Sets

The main aim of this paper is to make a classification of random sets K m(ω) constructed in theorem 2.1 and theorem 2.1' in . We provide five criterions for the classification. Many kinds of random sets such as Hawkes constructed in , Graf constructed in and Mauldin constructed in are the special cases of K m(ω) constructed in ,and then these random sets belong to some model respectively according to our classification.

Design of DC Partial Discharge Detecting System for Electrical Equipment

A balancing method was used to build a DC partial discharge （PD） testing circuit for electrical equipment,and a narrow-band detection system was designed using detection resistance and a filter. After signal accessed a high-speed digital acquisition （DAQ） card,the system was triggered to extract a single partial discharge （PD） signal. To eliminate the interference pulses caused by power supply ripple,etc.,the time domain and frequency domain features of pulses were extracted. Based on the features,cluster analysis was used to exclude interference pulses. Two-dimensional and three-dimensional histograms were obtained by use of the Δt method. Then,22 discharge statistical operators were calculated for the two-dimensional charts. Lastly,the defective capacitors were tested to verify the system＇s ability. The results show that the system is capable of PD detection in electrical equipment.

I.I.D.STATISTICAL CONTRACTION OPERATORS AND STATISTICALLY SELF-SIMILAR SETS

I.i.d. random sequence is the simplest but very basic one in stochastic processes, and statistically self-similar set is the simplest but very basic one in random recursive sets in the theory of random fractal. Is there any relation between i.i.d. random sequence and statistically self-similar set? This paper gives a basic theorem which tells us that the random recursive set generated by a collection of i.i.d. statistical contraction operators is always a statistically self-similar set.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

Analysis on Operation Reliability of Generating Units in 2009

This paper presents the data on operation reliability indices and relevant analyses toward China's conventional power generating units in 2009.The units brought into the statistical analysis include 100-MW or above thermal generating units,40-MW or above hydro generating units,and all nuclear generating units.The reliability indices embodied include utilization hours,times and hours of scheduled outages,times and hours of unscheduled outages,equivalent forced outage rate and equivalent availability factor.

A Comparative Analysis of Binary Logistic Regression and Analytical Hierarchy Process for Landslide Susceptibility Assessment in the Dobrovǎt River Basin,Romania

A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km^2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either the equal interval or the natural break method. Results from the study area suggest that the statistical model achieved by BLR could be successfully extrapolated to the entire area of the Moldavian Plateau.