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Obtaining the sample data of fault feature, then the data is divided into training data and test sample data.
Equipment running status data is high-dimensional information obtained by lifting wavelet packet transform method.
In low-dimensional manifold failure feature extracting method, the vibration data is picked from the low-dimensional manifold of high-dimensional euclidean space.
Then divided the data in to training data and test sample data. 3) Training neural network learning algorithm. 4) Solving state trend prediction problem using the trained network.
This paper proposed data-based multi-transform domains non-linear fault trend forecasting method.

The volume fraction of these small equi-axed grains increased from 19.9% after 75%reduction at 800°C to 67.8% after 75% reduction at 1.100°C.
Dynamic recrystallization (DRX), though, has not yet been reported for these alloys although the present data suggest that a DRX mechanism might be responsible for the remarkable grain refinement after relatively low amounts of strain applied at high temperatures.
Texture and orientation contrast map after PSC at 800°C and a reduction of 0.75.
Dynamic recrystallization (DRX), though, has not yet been reported for these alloys although the present data suggest that a type of dynamic recrystallization may be responsible for the remarkable grain refinement after relatively low amounts of strain applied at high temperatures.
The data of Fig. 5 do not reveal an obvious difference in texture between the recrystallised and the deformed fraction of the microstructure, although there seems to be a tendency that γfibre grains preferentially disappear from the deformed matrix.

Research of Noise Reduction Algorithm Based on Real Time Guoxiang Tong1, a, Fangfang Lv1, b* , Jian Tan 1, c 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology,Shanghai 200093,China aemail: guoxiangtong@163.com, bemail:keykeylvjian@163.com,cemail:Taozhe58@163.com Keywords:Noise reduction; Modified LMS; Step size; SNR; MSE; Misalignment; RNS; Abstract.
Structure of de-noising In order to better illustrate the noise reduction effect of each modified LMS algorithm.
Then the outputted bit-stream was processed by the DSP processing system based on RNS [12] which included three parts: (1) the front part of the data conversion system that changed bit-stream to RNS; (2) the FIR filter processing module based on RNS; (3) the last part of the data conversion system that changed RNS to bit-stream.
Then the signal processed by the DSP system was operated by the D/A converter based on Delta - sigma, the data flow was converted to analog signal.
The combined system not only increased the processing speed of data, but also expanded the processing width of data, which may be achieved three times faster than the original system.

In particular we have found the processing of the ultrasonic signals using wavelet transform extremely useful for noise reduction.
In particular we have found the processing of the ultrasonic signals using wavelet transform extremely useful for noise reduction.
It has been shown that the thresholding of wavelet coefficients has near optimal noise reduction property for many classes of signals.
EVOC EIC-2406 industrial computer and three ultrasonic plate cards, including data acquisition card PR401, 8 bits 100M A / D converter card, integrated timing control and filter card TCF6401, also products of the U.S.
Experimental data obtained from a steel plate with a crack produced by an electrical-discharge-machine have been used to highlight the excellent performance of the proposed method and, thereby validate it.

Rough set theory Rough set is primarily applied to process and analyze various data of incongruity and inaccuracy to discover intrinsic knowledge and reveal potential rules.
In the grey relational analysis, the data which contain the same features are regarded as series.
(2) We possibly use the existing historical data for analysis.
If there is no existing data, we can by means of questionnaire survey and other methods to collect data
(3) As different data generally have different dimensions,data is required for appropriate transformationin order to make different dimensions compared

Energy conservation, consumption reduction and emission reduction are inevitable choice for an enterprise on technology improvement and sustainable development.
It suffers a heavy pressure on the energy conservation and emission reduction.
This suggests that oxygen-enriched combustion indeed have effect on energy saving and consumption reduction. 3.3.2 The influence caused by enriched oxygen on energy consumption of producing clinkers In the normal production, Jiahua cement clinker sintering energy consumption can meet the design index and close to the domestic advanced level during the Oxygen enriched test(generally production line of about 750 kCal / t, the advanced level of 650 kCal / t, the production line design value of 740 kCal /t); (1)Oxygen-enriched during the trial make average clinker claiming reduce energy consumption by 2.07%,every day is equivalent to the 15 tons of coal (if it is a high-quality coal, the coal saving rate will be higher); (2)Test data analysis shows that, oxygen-enriched combustion mainly in the kiln head quarter in coal is obvious, the average of 8.38% firing fuel consumption is decreased , the kiln outlet coal is decreased by 1.49% on average (according to the data in table 13 can be disassembled
The daily amount of CO2 emission reduction is 24.2t / d, according to 320 days per year in calculation.
Every year the CO2 emission reduction is 7744 t/a, which is beneficial for society. .

This was attributed to difficulty in estimating the mean rolling temperature from the available data.
The required input data are rolling speed, roll radius, furnace temperature, initial and final height of the specimen, and specimen width.
From the output of the program, the temperature distribution, yield stress validation, rolling load and torque distribution across the thickness of the rolled specimen at different pass reduction are evident. 2.5 Experimental Data used in validating the new hot rolling simulation was obtained through preliminary metallographic, hot torsion tests, and hot rolling experiments performed on the as-received wrought AISI316 with inclusions of Nb, V and Ti in the temperature range (600 – 1200) 0C and strain rate range of 3.6 X 10-3s-1 to 1.4s-1.
The program made use of experimental and theoretical data from a study of loads and torques for light reduction in hot flat rolling at strain rates carried out by [2] and the results from the reverse sandwich effects in HCSS316 hot flat rolled at low strain rates and low reductions investigated by [13] as input data.
(c) The ratio of experimental to calculated Torque based on RMS mean rolling temperature on mill A was high about 1.17 at low reductions (5%) and low about 0.91 at higher reduction

The experiment results show that the proposed FFT processor is suitable for PAPR reduction in OFDM communication systems. 1.
Hence, design of variable-Length the FFT processor is key module of PTS technique for PAPR Reduction in OFDM Systems.
SDF architecture for radix-2/4/8 FFT is shown as Fig.1[3].To multiply -j, design can take advantage of the exchange of the real part situation and imaginary part situation, at the same time change the data symbols.
One mode is that input data directly stored in the FIFO shift register, while not action, until the FIFO shift register is full stored.
Wall: Architectures for Dynamic Data Scaling in 2/4/8K Pipeline FFT Cores.

It is needed to reduce the input data set by using the data preprocessing method in order to reduce the complexity of the BP neural network computing and speed power load forecasting.
Then for any period i, the influencing factors of power load data and historical load data constitute an object xi={A1,i, A2,i, … , Am,i, yi} on the field U={x1,x2,…,xn}.
Discretization of the Attribute Values of Training Samples Since the rough sets theory can only deals with discrete data, we discretize the data using method of the fuzzy membership function [4].
Data from July 1 to July 15 are the training samples, and data from July 16 to July 22 are the test sample.
Chen: Warehouse and Data Mining (In Chinese).

Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples.
So it has increasingly become a difficult problem in LIBS analysis to extract useful information from the raw spectral data and reduce the dimensions of the original data.
Data sets: Two data sets were used to verify the classification ability of the model.
PCA data reduction method is a good analytical approach considering this method does not need human intervention and can be developed as an automatic solution.
Conclusions We acquired spectra of 27 steel samples by LIBS and used two methods to reduce the dimensions of the data.