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The problem can be solved by the data fusion technology.
Some data are shown in Table 2.
Corresponding output values of the input data are shown in Table 3.
There are 40 groups of known failure mode of fault symptoms data.
The result is shown in Table 6 with 20 simulation data.

An Optimized Particle Filter for Signal De-noising Processing Hanxin Chen1,a, Ling Tu1,b , Kui Sun1,cand Cen Liu1,d 1School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, China apg01074075@163.com, bkarlingtu1989@163.com, c59182355@qq.com, d104742579@qq.com Keywords: Particle filter; signal noise reduction processing; radial-basis function network; signal to noise ratio Abstract: The traditional particle filter (PF) algorithm is well known for signal noise reduction processing, but it exists problems of particle impoverishment and cumulation of estimation errors.
To solve these problems, a RBF-PF algorithm for noisy signal reduction processing is proposed.
Signal noise reduction processing results.
Using the above two algorithms in signal noise reduction processing, to further validate RBF - PF algorithm has better de-noising effect than traditional particle filter algorithm by comparison.
Acknowledgement The experimental data is provided by the Reliability Research Lab in the Department of Mechanical Engineering at the University of Alberta in Canada.

By data-mining through large amounts of datasets, we showed that CNTs with small diameter, large number of walls, and crosslinks between walls can have high nominal tensile strength.
Data Mining with SOM With the ability of visualizing and categorizing information of materials, SOM is an integrated tool for material research [20].
The data mining approach with SOM aims to decrease the complexity of high-dimensional data, and reduce the high-dimension data to two-dimensional data.
To further understand the overall relation between geometrical properties of chirality, diameter, wall, crosslink and geometrical properties, we use SOM to dig on our data shown as Fig. 7.
Summary The relationship between mechanical properties and geometrical properties of CNTs were investigated by high-throughput molecular simulation and data mining technique.

Decode circuit in chip decompresses the compressed data from the ATE and applies them to ICs during testing.
Let counter 1 decrease by 1 and the circuit latch data on the bit_in.
This indicates that the proposed test-bit rearrangement algorithm is successful and effective in improving compression effect of test data.
This indicates that the run-length assignment strategy is effective in improving compression effect of test data.
Reduction in average power consumption is .

A projection based model order reduction method is then utilized to achieve a compact macro-model.
The scheme and computational methodology of a developed calculation tool are introduced, together with some practical calibration data which indicates the effectiveness of the proposed methods.
The content is organized as follows, the overview of ECSM is given firstly, with intention of method disadvantage representation; then the scheme and computational methodology of a developed calculation tool is given in detail, together with some practical antenna calibration data which can indicate the effectiveness of the proposed methods; finally, it ends with conclusions.
Model Order Reduction.
Fig.7 (a) shows the monopole model after discretization; while Fig.7 (b) gives the calculation results, together with data by the analytical formula (1).

Currently on the stability of hinge joint concrete block revetment research data is still rare, mainly in Europe and America and other countries abroad[3-5], only the Nanjing Hydraulic Research Institute of China study wave action on the stability of the slope experiment[6].
Fig.1 Map of Bird Island Fig.2 Hollow and solid block size dimensions Fig.3 Foot and the top of the hill form paving Strengthen reduction method of the basic principles and FLAC calculation principle Strengthen reduction method of the basic principles.Strength reduction in slope stability safety factor is defined as the slope just to reach the critical damage state of the soil shear strength reduction of the extent that the safety factor defined as the soil shear strength and critical damage to the actual time after the reduction ratio of shear strength.
as a strength reduction factor, but from the style that F is the strength reserve coefficient, or the actual degree of strength to play a factor.
Strength reduction finite element points is to use the formula (3) and (4) to adjust the soil strength index c and ( c is the cohesion after the reduction, is the reduction after friction angle, F is the discount factor), then the numerical analysis of slope stability, and constantly increasing reduction factor, repeated the calculation until it reaches the critical damage, then get the discount factor is the safety factor Fs.
Table2 Safety factor of slope protection in different angel Slope angel/(°) 20 30 35 40 45 48 49 50 Stability safety factor 2.42 1.69 1.69 1.47 1.10 1.03 1.01 0.98 From Table 2 data are available, with the slope gradient increases, the stability of the lower slope gradient of 40° or less, the difference between stability safety factor of greater magnitude of changes; at 45° or more, the difference between stability safety factor magnitude of changes small.

All reported data of kinetics tests are averages of two parallel bottles.
The identification of the crystalline phases was accomplished using standard cards from the International Centre for Diffraction Data (ICDD).
Growth kinetic of dissimilatory Fe(III) reducing mixed cultures (Each data represents means of two sampled vials ± standard deviation) Bacterial growth on Fe(III) oxyhydroxides.
The two solids tested were dissolved under dissimilatory Fe(III) reduction conditions (Fig. 2).
Kinetic of dissimilatory dissolution of Fe(III) oxyhydroxides (Each data represents means of two sampled vials ± standard deviation) References [1] A.

With the great volume of operation data, an fuzzy rough set (FRS) –based big data analytics were introduced to build the intelligent energy-saving decision-making model.
Big data-driven energy-saving decision-making model of thermal power units 3.1.
Energy-saving decision making model The proposed intelligent energy-saving decision making model are based on the great volume of practical operation data with big data analytics.
Such a method emphasizes the huge volume of data and implies that the collected data set covers almost the whole population as well.
Remote data center monitoring and management.

The x-ray diffraction (XRD) data show that the reduction annealing process decreases c-axis length indicating successful removal of the excess oxygen.
Moreover, some studies claimed that the O(3) can be eliminated through the “modified” reduction annealing process [11–13].
Fig. 1 also describes that there are peaks that shift to a higher diffraction angle after the reduction annealing.
Similarly, the reduction annealing also provides an effect on the R distance.
Baqiya et al., ‘Ce-Doping and Reduction Annealing Effects on Magnetic Properties of Pr2-xCexCuO4 Nanoparticles’, J.

Introduction With the development of computer, network and communication of information technology and data storage technology, especially the appearance of data warehouse, an increasing number of data is stored.
For these massive data, not only do the traditional data analysis methods and data retrieval mechanism consume a lot of time, but also they completely depend on the advance of the data on the relationship between assumptions and estimates of data, which has been unable to satisfy people's eager on implicit knowledge in these data.
Data Mining Theory and Power System Load Forecasting Closely related areas to Data Mining include inductive learning, machine learning and Statistics analysis.
Data Collection and Pretreatment The first collection is historical date related with all load forecasting, including weather data, historical load data, holiday type data, transforming the data format, removing the noise in the data, and cleaning incomplete and inconsistent data, after the full exploration of load forecasting related with history data, selecting strong prediction capacity variable establishing load forecasting mining model.
This paper describes Data Mining technology and its application in power system, and applies the similarity of Data Mining to analyze load forecasting.